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| author | dschult <dschult@colgate.edu> | 2023-01-04 17:46:14 +0000 |
|---|---|---|
| committer | dschult <dschult@colgate.edu> | 2023-01-04 17:46:14 +0000 |
| commit | 9d17431d8d530159a2c9176b54ae9717f0152907 (patch) | |
| tree | a1eef458e7095b1110b367990b5a758dc52637b0 | |
| parent | 7373c258b5ac70011f7b681b557ada5ed0ee6886 (diff) | |
| download | networkx-9d17431d8d530159a2c9176b54ae9717f0152907.tar.gz | |
Deploying to gh-pages from @ networkx/networkx@59ed0cfdc5262219da5aa120a11c9df3d6fab879 🚀
155 files changed, 4170 insertions, 585 deletions
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href="https://networkx.org/documentation/latest/index.html" + >devel (latest)</a + > + </li> + <li> + <a href="https://networkx.org/documentation/stable/index.html" + >current (stable)</a + > + </li> + </ul> + </li> +</ul> + </div> + + </div> + + </div> + + + <div class="sidebar-start-items sidebar-primary__section"> + <div class="sidebar-start-items__item"> + </div> + </div> + + + + <div class="sidebar-end-items sidebar-primary__section"> + <div class="sidebar-end-items__item"> + </div> + </div> + + + <div id="rtd-footer-container"></div> + + </div> + <main id="main-content" class="bd-main"> + + + <div class="bd-content"> + <div class="bd-article-container"> + + <div class="bd-header-article"> + + </div> + + + <article class="bd-article" role="main"> + + <h1>Source code for networkx.drawing.nx_latex</h1><div class="highlight"><pre> +<span></span><span class="sa">r</span><span class="sd">"""</span> +<span class="sd">*****</span> +<span class="sd">LaTeX</span> +<span class="sd">*****</span> + +<span class="sd">Export NetworkX graphs in LaTeX format using the TikZ library within TeX/LaTeX.</span> +<span class="sd">Usually, you will want the drawing to appear in a figure environment so</span> +<span class="sd">you use ``to_latex(G, caption="A caption")``. If you want the raw</span> +<span class="sd">drawing commands without a figure environment use :func:`to_latex_raw`.</span> +<span class="sd">And if you want to write to a file instead of just returning the latex</span> +<span class="sd">code as a string, use ``write_latex(G, "filname.tex", caption="A caption")``.</span> + +<span class="sd">To construct a figure with subfigures for each graph to be shown, provide</span> +<span class="sd">``to_latex`` or ``write_latex`` a list of graphs, a list of subcaptions,</span> +<span class="sd">and a number of rows of subfigures inside the figure.</span> + +<span class="sd">To be able to refer to the figures or subfigures in latex using ``\\ref``,</span> +<span class="sd">the keyword ``latex_label`` is available for figures and `sub_labels` for</span> +<span class="sd">a list of labels, one for each subfigure.</span> + +<span class="sd">We intend to eventually provide an interface to the TikZ Graph</span> +<span class="sd">features which include e.g. layout algorithms.</span> + +<span class="sd">Let us know via github what you'd like to see available, or better yet</span> +<span class="sd">give us some code to do it, or even better make a github pull request</span> +<span class="sd">to add the feature.</span> + +<span class="sd">The TikZ approach</span> +<span class="sd">=================</span> +<span class="sd">Drawing options can be stored on the graph as node/edge attributes, or</span> +<span class="sd">can be provided as dicts keyed by node/edge to a string of the options</span> +<span class="sd">for that node/edge. Similarly a label can be shown for each node/edge</span> +<span class="sd">by specifying the labels as graph node/edge attributes or by providing</span> +<span class="sd">a dict keyed by node/edge to the text to be written for that node/edge.</span> + +<span class="sd">Options for the tikzpicture environment (e.g. "[scale=2]") can be provided</span> +<span class="sd">via a keyword argument. Similarly default node and edge options can be</span> +<span class="sd">provided through keywords arguments. The default node options are applied</span> +<span class="sd">to the single TikZ "path" that draws all nodes (and no edges). The default edge</span> +<span class="sd">options are applied to a TikZ "scope" which contains a path for each edge.</span> + +<span class="sd">Examples</span> +<span class="sd">========</span> +<span class="sd">>>> G = nx.path_graph(3)</span> +<span class="sd">>>> nx.write_latex(G, "just_my_figure.tex", as_document=True)</span> +<span class="sd">>>> nx.write_latex(G, "my_figure.tex", caption="A path graph", latex_label="fig1")</span> +<span class="sd">>>> latex_code = nx.to_latex(G) # a string rather than a file</span> + +<span class="sd">You can change many features of the nodes and edges.</span> + +<span class="sd">>>> G = nx.path_graph(4, create_using=nx.DiGraph)</span> +<span class="sd">>>> pos = {n: (n, n) for n in G} # nodes set on a line</span> + +<span class="sd">>>> G.nodes[0]["style"] = "blue"</span> +<span class="sd">>>> G.nodes[2]["style"] = "line width=3,draw"</span> +<span class="sd">>>> G.nodes[3]["label"] = "Stop"</span> +<span class="sd">>>> G.edges[(0, 1)]["label"] = "1st Step"</span> +<span class="sd">>>> G.edges[(0, 1)]["label_opts"] = "near start"</span> +<span class="sd">>>> G.edges[(1, 2)]["style"] = "line width=3"</span> +<span class="sd">>>> G.edges[(1, 2)]["label"] = "2nd Step"</span> +<span class="sd">>>> G.edges[(2, 3)]["style"] = "green"</span> +<span class="sd">>>> G.edges[(2, 3)]["label"] = "3rd Step"</span> +<span class="sd">>>> G.edges[(2, 3)]["label_opts"] = "near end"</span> + +<span class="sd">>>> nx.write_latex(G, "latex_graph.tex", pos=pos, as_document=True)</span> + +<span class="sd">Then compile the LaTeX using something like ``pdflatex latex_graph.tex``</span> +<span class="sd">and view the pdf file created: ``latex_graph.pdf``.</span> + +<span class="sd">If you want **subfigures** each containing one graph, you can input a list of graphs.</span> + +<span class="sd">>>> H1 = nx.path_graph(4)</span> +<span class="sd">>>> H2 = nx.complete_graph(4)</span> +<span class="sd">>>> H3 = nx.path_graph(8)</span> +<span class="sd">>>> H4 = nx.complete_graph(8)</span> +<span class="sd">>>> graphs = [H1, H2, H3, H4]</span> +<span class="sd">>>> caps = ["Path 4", "Complete graph 4", "Path 8", "Complete graph 8"]</span> +<span class="sd">>>> lbls = ["fig2a", "fig2b", "fig2c", "fig2d"]</span> +<span class="sd">>>> nx.write_latex(graphs, "subfigs.tex", n_rows=2, sub_captions=caps, sub_labels=lbls)</span> +<span class="sd">>>> latex_code = nx.to_latex(graphs, n_rows=2, sub_captions=caps, sub_labels=lbls)</span> + +<span class="sd">>>> node_color = {0: "red", 1: "orange", 2: "blue", 3: "gray!90"}</span> +<span class="sd">>>> edge_width = {e: "line width=1.5" for e in H3.edges}</span> +<span class="sd">>>> pos = nx.circular_layout(H3)</span> +<span class="sd">>>> latex_code = nx.to_latex(H3, pos, node_options=node_color, edge_options=edge_width)</span> +<span class="sd">>>> print(latex_code)</span> +<span class="sd">\documentclass{report}</span> +<span class="sd">\usepackage{tikz}</span> +<span class="sd">\usepackage{subcaption}</span> +<span class="sd"><BLANKLINE></span> +<span class="sd">\begin{document}</span> +<span class="sd">\begin{figure}</span> +<span class="sd"> \begin{tikzpicture}</span> +<span class="sd"> \draw</span> +<span class="sd"> (1.0, 0.0) node[red] (0){0}</span> +<span class="sd"> (0.707, 0.707) node[orange] (1){1}</span> +<span class="sd"> (-0.0, 1.0) node[blue] (2){2}</span> +<span class="sd"> (-0.707, 0.707) node[gray!90] (3){3}</span> +<span class="sd"> (-1.0, -0.0) node (4){4}</span> +<span class="sd"> (-0.707, -0.707) node (5){5}</span> +<span class="sd"> (0.0, -1.0) node (6){6}</span> +<span class="sd"> (0.707, -0.707) node (7){7};</span> +<span class="sd"> \begin{scope}[-]</span> +<span class="sd"> \draw[line width=1.5] (0) to (1);</span> +<span class="sd"> \draw[line width=1.5] (1) to (2);</span> +<span class="sd"> \draw[line width=1.5] (2) to (3);</span> +<span class="sd"> \draw[line width=1.5] (3) to (4);</span> +<span class="sd"> \draw[line width=1.5] (4) to (5);</span> +<span class="sd"> \draw[line width=1.5] (5) to (6);</span> +<span class="sd"> \draw[line width=1.5] (6) to (7);</span> +<span class="sd"> \end{scope}</span> +<span class="sd"> \end{tikzpicture}</span> +<span class="sd">\end{figure}</span> +<span class="sd">\end{document}</span> + +<span class="sd">Notes</span> +<span class="sd">-----</span> +<span class="sd">If you want to change the preamble/postamble of the figure/document/subfigure</span> +<span class="sd">environment, use the keyword arguments: `figure_wrapper`, `document_wrapper`,</span> +<span class="sd">`subfigure_wrapper`. The default values are stored in private variables</span> +<span class="sd">e.g. ``nx.nx_layout._DOCUMENT_WRAPPER``</span> + +<span class="sd">References</span> +<span class="sd">----------</span> +<span class="sd">TikZ: https://tikz.dev/</span> + +<span class="sd">TikZ options details: https://tikz.dev/tikz-actions</span> +<span class="sd">"""</span> +<span class="kn">import</span> <span class="nn">numbers</span> +<span class="kn">import</span> <span class="nn">os</span> + +<span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span> + +<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span> + <span class="s2">"to_latex_raw"</span><span class="p">,</span> + <span class="s2">"to_latex"</span><span class="p">,</span> + <span class="s2">"write_latex"</span><span class="p">,</span> +<span class="p">]</span> + + +<div class="viewcode-block" id="to_latex_raw"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_latex.to_latex_raw.html#networkx.drawing.nx_latex.to_latex_raw">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">not_implemented_for</span><span class="p">(</span><span class="s2">"multigraph"</span><span class="p">)</span> +<span class="k">def</span> <span class="nf">to_latex_raw</span><span class="p">(</span> + <span class="n">G</span><span class="p">,</span> + <span class="n">pos</span><span class="o">=</span><span class="s2">"pos"</span><span class="p">,</span> + <span class="n">tikz_options</span><span class="o">=</span><span class="s2">""</span><span class="p">,</span> + <span class="n">default_node_options</span><span class="o">=</span><span class="s2">""</span><span class="p">,</span> + <span class="n">node_options</span><span class="o">=</span><span class="s2">"node_options"</span><span class="p">,</span> + <span class="n">node_label</span><span class="o">=</span><span class="s2">"label"</span><span class="p">,</span> + <span class="n">default_edge_options</span><span class="o">=</span><span class="s2">""</span><span class="p">,</span> + <span class="n">edge_options</span><span class="o">=</span><span class="s2">"edge_options"</span><span class="p">,</span> + <span class="n">edge_label</span><span class="o">=</span><span class="s2">"label"</span><span class="p">,</span> + <span class="n">edge_label_options</span><span class="o">=</span><span class="s2">"edge_label_options"</span><span class="p">,</span> +<span class="p">):</span> +<span class="w"> </span><span class="sd">"""Return a string of the LaTeX/TikZ code to draw `G`</span> + +<span class="sd"> This function produces just the code for the tikzpicture</span> +<span class="sd"> without any enclosing environment.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ==========</span> +<span class="sd"> G : NetworkX graph</span> +<span class="sd"> The NetworkX graph to be drawn</span> +<span class="sd"> pos : string or dict (default "pos")</span> +<span class="sd"> The name of the node attribute on `G` that holds the position of each node.</span> +<span class="sd"> Positions can be sequences of length 2 with numbers for (x,y) coordinates.</span> +<span class="sd"> They can also be strings to denote positions in TikZ style, such as (x, y)</span> +<span class="sd"> or (angle:radius).</span> +<span class="sd"> If a dict, it should be keyed by node to a position.</span> +<span class="sd"> If an empty dict, a circular layout is computed by TikZ.</span> +<span class="sd"> tikz_options : string</span> +<span class="sd"> The tikzpicture options description defining the options for the picture.</span> +<span class="sd"> Often large scale options like `[scale=2]`.</span> +<span class="sd"> default_node_options : string</span> +<span class="sd"> The draw options for a path of nodes. Individual node options override these.</span> +<span class="sd"> node_options : string or dict</span> +<span class="sd"> The name of the node attribute on `G` that holds the options for each node.</span> +<span class="sd"> Or a dict keyed by node to a string holding the options for that node.</span> +<span class="sd"> node_label : string or dict</span> +<span class="sd"> The name of the node attribute on `G` that holds the node label (text)</span> +<span class="sd"> displayed for each node. If the attribute is "" or not present, the node</span> +<span class="sd"> itself is drawn as a string. LaTeX processing such as ``"$A_1$"`` is allowed.</span> +<span class="sd"> Or a dict keyed by node to a string holding the label for that node.</span> +<span class="sd"> default_edge_options : string</span> +<span class="sd"> The options for the scope drawing all edges. The default is "[-]" for</span> +<span class="sd"> undirected graphs and "[->]" for directed graphs.</span> +<span class="sd"> edge_options : string or dict</span> +<span class="sd"> The name of the edge attribute on `G` that holds the options for each edge.</span> +<span class="sd"> If the edge is a self-loop and ``"loop" not in edge_options`` the option</span> +<span class="sd"> "loop," is added to the options for the self-loop edge. Hence you can</span> +<span class="sd"> use "[loop above]" explicitly, but the default is "[loop]".</span> +<span class="sd"> Or a dict keyed by edge to a string holding the options for that edge.</span> +<span class="sd"> edge_label : string or dict</span> +<span class="sd"> The name of the edge attribute on `G` that holds the edge label (text)</span> +<span class="sd"> displayed for each edge. If the attribute is "" or not present, no edge</span> +<span class="sd"> label is drawn.</span> +<span class="sd"> Or a dict keyed by edge to a string holding the label for that edge.</span> +<span class="sd"> edge_label_options : string or dict</span> +<span class="sd"> The name of the edge attribute on `G` that holds the label options for</span> +<span class="sd"> each edge. For example, "[sloped,above,blue]". The default is no options.</span> +<span class="sd"> Or a dict keyed by edge to a string holding the label options for that edge.</span> + +<span class="sd"> Returns</span> +<span class="sd"> =======</span> +<span class="sd"> latex_code : string</span> +<span class="sd"> The text string which draws the desired graph(s) when compiled by LaTeX.</span> + +<span class="sd"> See Also</span> +<span class="sd"> ========</span> +<span class="sd"> to_latex</span> +<span class="sd"> write_latex</span> +<span class="sd"> """</span> + <span class="n">i4</span> <span class="o">=</span> <span class="s2">"</span><span class="se">\n</span><span class="s2"> "</span> + <span class="n">i8</span> <span class="o">=</span> <span class="s2">"</span><span class="se">\n</span><span class="s2"> "</span> + + <span class="c1"># set up position dict</span> + <span class="c1"># TODO allow pos to be None and use a nice TikZ default</span> + <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pos</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> + <span class="n">pos</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">get_node_attributes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">pos</span><span class="p">)</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">pos</span><span class="p">:</span> + <span class="c1"># circular layout with radius 1</span> + <span class="n">pos</span> <span class="o">=</span> <span class="p">{</span><span class="n">n</span><span class="p">:</span> <span class="sa">f</span><span class="s2">"(</span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="w"> </span><span class="mf">3.1415</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="nb">len</span><span class="p">(</span><span class="n">G</span><span class="p">),</span><span class="w"> </span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s2">:10)"</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">G</span><span class="p">)}</span> + <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">G</span><span class="p">:</span> + <span class="k">if</span> <span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">pos</span><span class="p">:</span> + <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"node </span><span class="si">{</span><span class="n">node</span><span class="si">}</span><span class="s2"> has no specified pos </span><span class="si">{</span><span class="n">pos</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> + <span class="n">posnode</span> <span class="o">=</span> <span class="n">pos</span><span class="p">[</span><span class="n">node</span><span class="p">]</span> + <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">posnode</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span> + <span class="k">try</span><span class="p">:</span> + <span class="n">posx</span><span class="p">,</span> <span class="n">posy</span> <span class="o">=</span> <span class="n">posnode</span> + <span class="n">pos</span><span class="p">[</span><span class="n">node</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"(</span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="n">posx</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="n">posy</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s2">)"</span> + <span class="k">except</span> <span class="p">(</span><span class="ne">TypeError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">):</span> + <span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"position pos[</span><span class="si">{</span><span class="n">node</span><span class="si">}</span><span class="s2">] is not 2-tuple or a string: </span><span class="si">{</span><span class="n">posnode</span><span class="si">}</span><span class="s2">"</span> + <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span> + + <span class="c1"># set up all the dicts</span> + <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">node_options</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> + <span class="n">node_options</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">get_node_attributes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">node_options</span><span class="p">)</span> + <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">node_label</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> + <span class="n">node_label</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">get_node_attributes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">node_label</span><span class="p">)</span> + <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">edge_options</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> + <span class="n">edge_options</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">get_edge_attributes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">edge_options</span><span class="p">)</span> + <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">edge_label</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> + <span class="n">edge_label</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">get_edge_attributes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">edge_label</span><span class="p">)</span> + <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">edge_label_options</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> + <span class="n">edge_label_options</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">get_edge_attributes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">edge_label_options</span><span class="p">)</span> + + <span class="c1"># process default options (add brackets or not)</span> + <span class="n">topts</span> <span class="o">=</span> <span class="s2">""</span> <span class="k">if</span> <span class="n">tikz_options</span> <span class="o">==</span> <span class="s2">""</span> <span class="k">else</span> <span class="sa">f</span><span class="s2">"[</span><span class="si">{</span><span class="n">tikz_options</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s1">'[]'</span><span class="p">)</span><span class="si">}</span><span class="s2">]"</span> + <span class="n">defn</span> <span class="o">=</span> <span class="s2">""</span> <span class="k">if</span> <span class="n">default_node_options</span> <span class="o">==</span> <span class="s2">""</span> <span class="k">else</span> <span class="sa">f</span><span class="s2">"[</span><span class="si">{</span><span class="n">default_node_options</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s1">'[]'</span><span class="p">)</span><span class="si">}</span><span class="s2">]"</span> + <span class="n">linestyle</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="s1">'->'</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="n">G</span><span class="o">.</span><span class="n">is_directed</span><span class="p">()</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="s1">'-'</span><span class="si">}</span><span class="s2">"</span> + <span class="k">if</span> <span class="n">default_edge_options</span> <span class="o">==</span> <span class="s2">""</span><span class="p">:</span> + <span class="n">defe</span> <span class="o">=</span> <span class="s2">"["</span> <span class="o">+</span> <span class="n">linestyle</span> <span class="o">+</span> <span class="s2">"]"</span> + <span class="k">elif</span> <span class="s2">"-"</span> <span class="ow">in</span> <span class="n">default_edge_options</span><span class="p">:</span> + <span class="n">defe</span> <span class="o">=</span> <span class="n">default_edge_options</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">defe</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"[</span><span class="si">{</span><span class="n">linestyle</span><span class="si">}</span><span class="s2">,</span><span class="si">{</span><span class="n">default_edge_options</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s1">'[]'</span><span class="p">)</span><span class="si">}</span><span class="s2">]"</span> + + <span class="c1"># Construct the string line by line</span> + <span class="n">result</span> <span class="o">=</span> <span class="s2">" </span><span class="se">\\</span><span class="s2">begin</span><span class="si">{tikzpicture}</span><span class="s2">"</span> <span class="o">+</span> <span class="n">topts</span> + <span class="n">result</span> <span class="o">+=</span> <span class="n">i4</span> <span class="o">+</span> <span class="s2">" </span><span class="se">\\</span><span class="s2">draw"</span> <span class="o">+</span> <span class="n">defn</span> + <span class="c1"># load the nodes</span> + <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">G</span><span class="p">:</span> + <span class="c1"># node options goes inside square brackets</span> + <span class="n">nopts</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"[</span><span class="si">{</span><span class="n">node_options</span><span class="p">[</span><span class="n">n</span><span class="p">]</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s1">'[]'</span><span class="p">)</span><span class="si">}</span><span class="s2">]"</span> <span class="k">if</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">node_options</span> <span class="k">else</span> <span class="s2">""</span> + <span class="c1"># node text goes inside curly brackets {}</span> + <span class="n">ntext</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="se">{{</span><span class="si">{</span><span class="n">node_label</span><span class="p">[</span><span class="n">n</span><span class="p">]</span><span class="si">}</span><span class="se">}}</span><span class="s2">"</span> <span class="k">if</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">node_label</span> <span class="k">else</span> <span class="sa">f</span><span class="s2">"</span><span class="se">{{</span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="se">}}</span><span class="s2">"</span> + + <span class="n">result</span> <span class="o">+=</span> <span class="n">i8</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">pos</span><span class="p">[</span><span class="n">n</span><span class="p">]</span><span class="si">}</span><span class="s2"> node</span><span class="si">{</span><span class="n">nopts</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s2">)</span><span class="si">{</span><span class="n">ntext</span><span class="si">}</span><span class="s2">"</span> + <span class="n">result</span> <span class="o">+=</span> <span class="s2">";</span><span class="se">\n</span><span class="s2">"</span> + + <span class="c1"># load the edges</span> + <span class="n">result</span> <span class="o">+=</span> <span class="s2">" </span><span class="se">\\</span><span class="s2">begin</span><span class="si">{scope}</span><span class="s2">"</span> <span class="o">+</span> <span class="n">defe</span> + <span class="k">for</span> <span class="n">edge</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">:</span> + <span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">edge</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span> + <span class="n">e_opts</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">edge_options</span><span class="p">[</span><span class="n">edge</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s2">"[]"</span><span class="p">)</span> <span class="k">if</span> <span class="n">edge</span> <span class="ow">in</span> <span class="n">edge_options</span> <span class="k">else</span> <span class="s2">""</span> + <span class="c1"># add loop options for selfloops if not present</span> + <span class="k">if</span> <span class="n">u</span> <span class="o">==</span> <span class="n">v</span> <span class="ow">and</span> <span class="s2">"loop"</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">e_opts</span><span class="p">:</span> + <span class="n">e_opts</span> <span class="o">=</span> <span class="s2">"loop,"</span> <span class="o">+</span> <span class="n">e_opts</span> + <span class="n">e_opts</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"[</span><span class="si">{</span><span class="n">e_opts</span><span class="si">}</span><span class="s2">]"</span> <span class="k">if</span> <span class="n">e_opts</span> <span class="o">!=</span> <span class="s2">""</span> <span class="k">else</span> <span class="s2">""</span> + <span class="c1"># TODO -- handle bending of multiedges</span> + + <span class="n">els</span> <span class="o">=</span> <span class="n">edge_label_options</span><span class="p">[</span><span class="n">edge</span><span class="p">]</span> <span class="k">if</span> <span class="n">edge</span> <span class="ow">in</span> <span class="n">edge_label_options</span> <span class="k">else</span> <span class="s2">""</span> + <span class="c1"># edge label options goes inside square brackets []</span> + <span class="n">els</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"[</span><span class="si">{</span><span class="n">els</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s1">'[]'</span><span class="p">)</span><span class="si">}</span><span class="s2">]"</span> + <span class="c1"># edge text is drawn using the TikZ node command inside curly brackets {}</span> + <span class="n">e_label</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">" node</span><span class="si">{</span><span class="n">els</span><span class="si">}</span><span class="s2"> </span><span class="se">{{</span><span class="si">{</span><span class="n">edge_label</span><span class="p">[</span><span class="n">edge</span><span class="p">]</span><span class="si">}</span><span class="se">}}</span><span class="s2">"</span> <span class="k">if</span> <span class="n">edge</span> <span class="ow">in</span> <span class="n">edge_label</span> <span class="k">else</span> <span class="s2">""</span> + + <span class="n">result</span> <span class="o">+=</span> <span class="n">i8</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">"</span><span class="se">\\</span><span class="s2">draw</span><span class="si">{</span><span class="n">e_opts</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2">) to</span><span class="si">{</span><span class="n">e_label</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">);"</span> + + <span class="n">result</span> <span class="o">+=</span> <span class="s2">"</span><span class="se">\n</span><span class="s2"> </span><span class="se">\\</span><span class="s2">end</span><span class="si">{scope}</span><span class="se">\n</span><span class="s2"> </span><span class="se">\\</span><span class="s2">end</span><span class="si">{tikzpicture}</span><span class="se">\n</span><span class="s2">"</span> + <span class="k">return</span> <span class="n">result</span></div> + + +<span class="n">_DOC_WRAPPER_TIKZ</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">"""\documentclass{{report}}</span> +<span class="s2">\usepackage{{tikz}}</span> +<span class="s2">\usepackage{{subcaption}}</span> + +<span class="s2">\begin{{document}}</span> +<span class="si">{content}</span> +<span class="s2">\end{{document}}"""</span> + + +<span class="n">_FIG_WRAPPER</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">"""\begin{{figure}}</span> +<span class="si">{content}{caption}{label}</span> +<span class="s2">\end{{figure}}"""</span> + + +<span class="n">_SUBFIG_WRAPPER</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">""" \begin{{subfigure}}{{</span><span class="si">{size}</span><span class="s2">\textwidth}}</span> +<span class="si">{content}{caption}{label}</span> +<span class="s2"> \end{{subfigure}}"""</span> + + +<div class="viewcode-block" id="to_latex"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_latex.to_latex.html#networkx.drawing.nx_latex.to_latex">[docs]</a><span class="k">def</span> <span class="nf">to_latex</span><span class="p">(</span> + <span class="n">Gbunch</span><span class="p">,</span> + <span class="n">pos</span><span class="o">=</span><span class="s2">"pos"</span><span class="p">,</span> + <span class="n">tikz_options</span><span class="o">=</span><span class="s2">""</span><span class="p">,</span> + <span class="n">default_node_options</span><span class="o">=</span><span class="s2">""</span><span class="p">,</span> + <span class="n">node_options</span><span class="o">=</span><span class="s2">"node_options"</span><span class="p">,</span> + <span class="n">node_label</span><span class="o">=</span><span class="s2">"node_label"</span><span class="p">,</span> + <span class="n">default_edge_options</span><span class="o">=</span><span class="s2">""</span><span class="p">,</span> + <span class="n">edge_options</span><span class="o">=</span><span class="s2">"edge_options"</span><span class="p">,</span> + <span class="n">edge_label</span><span class="o">=</span><span class="s2">"edge_label"</span><span class="p">,</span> + <span class="n">edge_label_options</span><span class="o">=</span><span class="s2">"edge_label_options"</span><span class="p">,</span> + <span class="n">caption</span><span class="o">=</span><span class="s2">""</span><span class="p">,</span> + <span class="n">latex_label</span><span class="o">=</span><span class="s2">""</span><span class="p">,</span> + <span class="n">sub_captions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">sub_labels</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">n_rows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> + <span class="n">as_document</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">document_wrapper</span><span class="o">=</span><span class="n">_DOC_WRAPPER_TIKZ</span><span class="p">,</span> + <span class="n">figure_wrapper</span><span class="o">=</span><span class="n">_FIG_WRAPPER</span><span class="p">,</span> + <span class="n">subfigure_wrapper</span><span class="o">=</span><span class="n">_SUBFIG_WRAPPER</span><span class="p">,</span> +<span class="p">):</span> +<span class="w"> </span><span class="sd">"""Return latex code to draw the graph(s) in `Gbunch`</span> + +<span class="sd"> The TikZ drawing utility in LaTeX is used to draw the graph(s).</span> +<span class="sd"> If `Gbunch` is a graph, it is drawn in a figure environment.</span> +<span class="sd"> If `Gbunch` is an iterable of graphs, each is drawn in a subfigure envionment</span> +<span class="sd"> within a single figure environment.</span> + +<span class="sd"> If `as_document` is True, the figure is wrapped inside a document environment</span> +<span class="sd"> so that the resulting string is ready to be compiled by LaTeX. Otherwise,</span> +<span class="sd"> the string is ready for inclusion in a larger tex document using ``\\include``</span> +<span class="sd"> or ``\\input`` statements.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ==========</span> +<span class="sd"> Gbunch : NetworkX graph or iterable of NetworkX graphs</span> +<span class="sd"> The NetworkX graph to be drawn or an iterable of graphs</span> +<span class="sd"> to be drawn inside subfigures of a single figure.</span> +<span class="sd"> pos : string or list of strings</span> +<span class="sd"> The name of the node attribute on `G` that holds the position of each node.</span> +<span class="sd"> Positions can be sequences of length 2 with numbers for (x,y) coordinates.</span> +<span class="sd"> They can also be strings to denote positions in TikZ style, such as (x, y)</span> +<span class="sd"> or (angle:radius).</span> +<span class="sd"> If a dict, it should be keyed by node to a position.</span> +<span class="sd"> If an empty dict, a circular layout is computed by TikZ.</span> +<span class="sd"> If you are drawing many graphs in subfigures, use a list of position dicts.</span> +<span class="sd"> tikz_options : string</span> +<span class="sd"> The tikzpicture options description defining the options for the picture.</span> +<span class="sd"> Often large scale options like `[scale=2]`.</span> +<span class="sd"> default_node_options : string</span> +<span class="sd"> The draw options for a path of nodes. Individual node options override these.</span> +<span class="sd"> node_options : string or dict</span> +<span class="sd"> The name of the node attribute on `G` that holds the options for each node.</span> +<span class="sd"> Or a dict keyed by node to a string holding the options for that node.</span> +<span class="sd"> node_label : string or dict</span> +<span class="sd"> The name of the node attribute on `G` that holds the node label (text)</span> +<span class="sd"> displayed for each node. If the attribute is "" or not present, the node</span> +<span class="sd"> itself is drawn as a string. LaTeX processing such as ``"$A_1$"`` is allowed.</span> +<span class="sd"> Or a dict keyed by node to a string holding the label for that node.</span> +<span class="sd"> default_edge_options : string</span> +<span class="sd"> The options for the scope drawing all edges. The default is "[-]" for</span> +<span class="sd"> undirected graphs and "[->]" for directed graphs.</span> +<span class="sd"> edge_options : string or dict</span> +<span class="sd"> The name of the edge attribute on `G` that holds the options for each edge.</span> +<span class="sd"> If the edge is a self-loop and ``"loop" not in edge_options`` the option</span> +<span class="sd"> "loop," is added to the options for the self-loop edge. Hence you can</span> +<span class="sd"> use "[loop above]" explicitly, but the default is "[loop]".</span> +<span class="sd"> Or a dict keyed by edge to a string holding the options for that edge.</span> +<span class="sd"> edge_label : string or dict</span> +<span class="sd"> The name of the edge attribute on `G` that holds the edge label (text)</span> +<span class="sd"> displayed for each edge. If the attribute is "" or not present, no edge</span> +<span class="sd"> label is drawn.</span> +<span class="sd"> Or a dict keyed by edge to a string holding the label for that edge.</span> +<span class="sd"> edge_label_options : string or dict</span> +<span class="sd"> The name of the edge attribute on `G` that holds the label options for</span> +<span class="sd"> each edge. For example, "[sloped,above,blue]". The default is no options.</span> +<span class="sd"> Or a dict keyed by edge to a string holding the label options for that edge.</span> +<span class="sd"> caption : string</span> +<span class="sd"> The caption string for the figure environment</span> +<span class="sd"> latex_label : string</span> +<span class="sd"> The latex label used for the figure for easy referral from the main text</span> +<span class="sd"> sub_captions : list of strings</span> +<span class="sd"> The sub_caption string for each subfigure in the figure</span> +<span class="sd"> sub_latex_labels : list of strings</span> +<span class="sd"> The latex label for each subfigure in the figure</span> +<span class="sd"> n_rows : int</span> +<span class="sd"> The number of rows of subfigures to arrange for multiple graphs</span> +<span class="sd"> as_document : bool</span> +<span class="sd"> Whether to wrap the latex code in a document envionment for compiling</span> +<span class="sd"> document_wrapper : formatted text string with variable ``content``.</span> +<span class="sd"> This text is called to evaluate the content embedded in a document</span> +<span class="sd"> environment with a preamble setting up TikZ.</span> +<span class="sd"> figure_wrapper : formatted text string</span> +<span class="sd"> This text is evaluated with variables ``content``, ``caption`` and ``label``.</span> +<span class="sd"> It wraps the content and if a caption is provided, adds the latex code for</span> +<span class="sd"> that caption, and if a label is provided, adds the latex code for a label.</span> +<span class="sd"> subfigure_wrapper : formatted text string</span> +<span class="sd"> This text evaluate variables ``size``, ``content``, ``caption`` and ``label``.</span> +<span class="sd"> It wraps the content and if a caption is provided, adds the latex code for</span> +<span class="sd"> that caption, and if a label is provided, adds the latex code for a label.</span> +<span class="sd"> The size is the vertical size of each row of subfigures as a fraction.</span> + +<span class="sd"> Returns</span> +<span class="sd"> =======</span> +<span class="sd"> latex_code : string</span> +<span class="sd"> The text string which draws the desired graph(s) when compiled by LaTeX.</span> + +<span class="sd"> See Also</span> +<span class="sd"> ========</span> +<span class="sd"> write_latex</span> +<span class="sd"> to_latex_raw</span> +<span class="sd"> """</span> + <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">Gbunch</span><span class="p">,</span> <span class="s2">"adj"</span><span class="p">):</span> + <span class="n">raw</span> <span class="o">=</span> <span class="n">to_latex_raw</span><span class="p">(</span> + <span class="n">Gbunch</span><span class="p">,</span> + <span class="n">pos</span><span class="p">,</span> + <span class="n">tikz_options</span><span class="p">,</span> + <span class="n">default_node_options</span><span class="p">,</span> + <span class="n">node_options</span><span class="p">,</span> + <span class="n">node_label</span><span class="p">,</span> + <span class="n">default_edge_options</span><span class="p">,</span> + <span class="n">edge_options</span><span class="p">,</span> + <span class="n">edge_label</span><span class="p">,</span> + <span class="n">edge_label_options</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> <span class="c1"># iterator of graphs</span> + <span class="n">sbf</span> <span class="o">=</span> <span class="n">subfigure_wrapper</span> + <span class="n">size</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">n_rows</span> + + <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">Gbunch</span><span class="p">)</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pos</span><span class="p">,</span> <span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)):</span> + <span class="n">pos</span> <span class="o">=</span> <span class="p">[</span><span class="n">pos</span><span class="p">]</span> <span class="o">*</span> <span class="n">N</span> + <span class="k">if</span> <span class="n">sub_captions</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">sub_captions</span> <span class="o">=</span> <span class="p">[</span><span class="s2">""</span><span class="p">]</span> <span class="o">*</span> <span class="n">N</span> + <span class="k">if</span> <span class="n">sub_labels</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">sub_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s2">""</span><span class="p">]</span> <span class="o">*</span> <span class="n">N</span> + <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Gbunch</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">pos</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">sub_captions</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">sub_labels</span><span class="p">)):</span> + <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXError</span><span class="p">(</span> + <span class="s2">"length of Gbunch, sub_captions and sub_figures must agree"</span> + <span class="p">)</span> + + <span class="n">raw</span> <span class="o">=</span> <span class="s2">""</span> + <span class="k">for</span> <span class="n">G</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">subcap</span><span class="p">,</span> <span class="n">sublbl</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">Gbunch</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">sub_captions</span><span class="p">,</span> <span class="n">sub_labels</span><span class="p">):</span> + <span class="n">subraw</span> <span class="o">=</span> <span class="n">to_latex_raw</span><span class="p">(</span> + <span class="n">G</span><span class="p">,</span> + <span class="n">pos</span><span class="p">,</span> + <span class="n">tikz_options</span><span class="p">,</span> + <span class="n">default_node_options</span><span class="p">,</span> + <span class="n">node_options</span><span class="p">,</span> + <span class="n">node_label</span><span class="p">,</span> + <span class="n">default_edge_options</span><span class="p">,</span> + <span class="n">edge_options</span><span class="p">,</span> + <span class="n">edge_label</span><span class="p">,</span> + <span class="n">edge_label_options</span><span class="p">,</span> + <span class="p">)</span> + <span class="n">cap</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">" </span><span class="se">\\</span><span class="s2">caption</span><span class="se">{{</span><span class="si">{</span><span class="n">subcap</span><span class="si">}</span><span class="se">}}</span><span class="s2">"</span> <span class="k">if</span> <span class="n">subcap</span> <span class="k">else</span> <span class="s2">""</span> + <span class="n">lbl</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="se">\\</span><span class="s2">label</span><span class="se">{{</span><span class="si">{</span><span class="n">sublbl</span><span class="si">}</span><span class="se">}}</span><span class="s2">"</span> <span class="k">if</span> <span class="n">sublbl</span> <span class="k">else</span> <span class="s2">""</span> + <span class="n">raw</span> <span class="o">+=</span> <span class="n">sbf</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> <span class="n">content</span><span class="o">=</span><span class="n">subraw</span><span class="p">,</span> <span class="n">caption</span><span class="o">=</span><span class="n">cap</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">lbl</span><span class="p">)</span> + <span class="n">raw</span> <span class="o">+=</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">"</span> + + <span class="c1"># put raw latex code into a figure environment and optionally into a document</span> + <span class="n">raw</span> <span class="o">=</span> <span class="n">raw</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> + <span class="n">cap</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2"> </span><span class="se">\\</span><span class="s2">caption</span><span class="se">{{</span><span class="si">{</span><span class="n">caption</span><span class="si">}</span><span class="se">}}</span><span class="s2">"</span> <span class="k">if</span> <span class="n">caption</span> <span class="k">else</span> <span class="s2">""</span> + <span class="n">lbl</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="se">\\</span><span class="s2">label</span><span class="se">{{</span><span class="si">{</span><span class="n">latex_label</span><span class="si">}</span><span class="se">}}</span><span class="s2">"</span> <span class="k">if</span> <span class="n">latex_label</span> <span class="k">else</span> <span class="s2">""</span> + <span class="n">fig</span> <span class="o">=</span> <span class="n">figure_wrapper</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">content</span><span class="o">=</span><span class="n">raw</span><span class="p">,</span> <span class="n">caption</span><span class="o">=</span><span class="n">cap</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">lbl</span><span class="p">)</span> + <span class="k">if</span> <span class="n">as_document</span><span class="p">:</span> + <span class="k">return</span> <span class="n">document_wrapper</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">content</span><span class="o">=</span><span class="n">fig</span><span class="p">)</span> + <span class="k">return</span> <span class="n">fig</span></div> + + +<div class="viewcode-block" id="write_latex"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_latex.write_latex.html#networkx.drawing.nx_latex.write_latex">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">open_file</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"w"</span><span class="p">)</span> +<span class="k">def</span> <span class="nf">write_latex</span><span class="p">(</span><span class="n">Gbunch</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="o">**</span><span class="n">options</span><span class="p">):</span> +<span class="w"> </span><span class="sd">"""Write the latex code to draw the graph(s) onto `path`.</span> + +<span class="sd"> This convenience function creates the latex drawing code as a string</span> +<span class="sd"> and writes that to a file ready to be compiled when `as_document` is True</span> +<span class="sd"> or ready to be ``\\import``ed or `\\include``ed into your main LaTeX document.</span> + +<span class="sd"> The `path` argument can be a string filename or a file handle to write to.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> Gbunch : NetworkX graph or iterable of NetworkX graphs</span> +<span class="sd"> If Gbunch is a graph, it is drawn in a figure environment.</span> +<span class="sd"> If Gbunch is an iterable of graphs, each is drawn in a subfigure</span> +<span class="sd"> envionment within a single figure environment.</span> +<span class="sd"> path : filename</span> +<span class="sd"> Filename or file handle to write to</span> +<span class="sd"> options : dict</span> +<span class="sd"> By default, TikZ is used with options: (others are ignored):</span> + +<span class="sd"> pos : string or dict or list</span> +<span class="sd"> The name of the node attribute on `G` that holds the position of each node.</span> +<span class="sd"> Positions can be sequences of length 2 with numbers for (x,y) coordinates.</span> +<span class="sd"> They can also be strings to denote positions in TikZ style, such as (x, y)</span> +<span class="sd"> or (angle:radius).</span> +<span class="sd"> If a dict, it should be keyed by node to a position.</span> +<span class="sd"> If an empty dict, a circular layout is computed by TikZ.</span> +<span class="sd"> If you are drawing many graphs in subfigures, use a list of position dicts.</span> +<span class="sd"> tikz_options : string</span> +<span class="sd"> The tikzpicture options description defining the options for the picture.</span> +<span class="sd"> Often large scale options like `[scale=2]`.</span> +<span class="sd"> default_node_options : string</span> +<span class="sd"> The draw options for a path of nodes. Individual node options override these.</span> +<span class="sd"> node_options : string or dict</span> +<span class="sd"> The name of the node attribute on `G` that holds the options for each node.</span> +<span class="sd"> Or a dict keyed by node to a string holding the options for that node.</span> +<span class="sd"> node_label : string or dict</span> +<span class="sd"> The name of the node attribute on `G` that holds the node label (text)</span> +<span class="sd"> displayed for each node. If the attribute is "" or not present, the node</span> +<span class="sd"> itself is drawn as a string. LaTeX processing such as ``"$A_1$"`` is allowed.</span> +<span class="sd"> Or a dict keyed by node to a string holding the label for that node.</span> +<span class="sd"> default_edge_options : string</span> +<span class="sd"> The options for the scope drawing all edges. The default is "[-]" for</span> +<span class="sd"> undirected graphs and "[->]" for directed graphs.</span> +<span class="sd"> edge_options : string or dict</span> +<span class="sd"> The name of the edge attribute on `G` that holds the options for each edge.</span> +<span class="sd"> If the edge is a self-loop and ``"loop" not in edge_options`` the option</span> +<span class="sd"> "loop," is added to the options for the self-loop edge. Hence you can</span> +<span class="sd"> use "[loop above]" explicitly, but the default is "[loop]".</span> +<span class="sd"> Or a dict keyed by edge to a string holding the options for that edge.</span> +<span class="sd"> edge_label : string or dict</span> +<span class="sd"> The name of the edge attribute on `G` that holds the edge label (text)</span> +<span class="sd"> displayed for each edge. If the attribute is "" or not present, no edge</span> +<span class="sd"> label is drawn.</span> +<span class="sd"> Or a dict keyed by edge to a string holding the label for that edge.</span> +<span class="sd"> edge_label_options : string or dict</span> +<span class="sd"> The name of the edge attribute on `G` that holds the label options for</span> +<span class="sd"> each edge. For example, "[sloped,above,blue]". The default is no options.</span> +<span class="sd"> Or a dict keyed by edge to a string holding the label options for that edge.</span> +<span class="sd"> caption : string</span> +<span class="sd"> The caption string for the figure environment</span> +<span class="sd"> latex_label : string</span> +<span class="sd"> The latex label used for the figure for easy referral from the main text</span> +<span class="sd"> sub_captions : list of strings</span> +<span class="sd"> The sub_caption string for each subfigure in the figure</span> +<span class="sd"> sub_latex_labels : list of strings</span> +<span class="sd"> The latex label for each subfigure in the figure</span> +<span class="sd"> n_rows : int</span> +<span class="sd"> The number of rows of subfigures to arrange for multiple graphs</span> +<span class="sd"> as_document : bool</span> +<span class="sd"> Whether to wrap the latex code in a document envionment for compiling</span> +<span class="sd"> document_wrapper : formatted text string with variable ``content``.</span> +<span class="sd"> This text is called to evaluate the content embedded in a document</span> +<span class="sd"> environment with a preamble setting up the TikZ syntax.</span> +<span class="sd"> figure_wrapper : formatted text string</span> +<span class="sd"> This text is evaluated with variables ``content``, ``caption`` and ``label``.</span> +<span class="sd"> It wraps the content and if a caption is provided, adds the latex code for</span> +<span class="sd"> that caption, and if a label is provided, adds the latex code for a label.</span> +<span class="sd"> subfigure_wrapper : formatted text string</span> +<span class="sd"> This text evaluate variables ``size``, ``content``, ``caption`` and ``label``.</span> +<span class="sd"> It wraps the content and if a caption is provided, adds the latex code for</span> +<span class="sd"> that caption, and if a label is provided, adds the latex code for a label.</span> +<span class="sd"> The size is the vertical size of each row of subfigures as a fraction.</span> + +<span class="sd"> See Also</span> +<span class="sd"> ========</span> +<span class="sd"> to_latex</span> +<span class="sd"> """</span> + <span class="n">path</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">to_latex</span><span class="p">(</span><span class="n">Gbunch</span><span class="p">,</span> <span class="o">**</span><span class="n">options</span><span class="p">))</span></div> +</pre></div> + + </article> + + + + </div> + + + + <div class="bd-sidebar-secondary bd-toc"> + +<div class="toc-item"> + +<div id="searchbox"></div> +</div> + +<div class="toc-item"> + +</div> + +<div class="toc-item"> + +</div> + + </div> + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"> + + </div> + </footer> + + </main> + </div> + </div> + + + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../_static/scripts/bootstrap.js?digest=796348d33e8b1d947c94"></script> +<script src="../../../_static/scripts/pydata-sphinx-theme.js?digest=796348d33e8b1d947c94"></script> + + <footer class="bd-footer"><div class="bd-footer__inner container"> + + <div class="footer-item"> + +<p class="copyright"> + + © Copyright 2004-2023, NetworkX Developers.<br> + +</p> + + </div> + + <div class="footer-item"> + <p class="theme-version"> + Built with the + <a href="https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html"> + PyData Sphinx Theme + </a> + 0.12.0. +</p> + </div> + + <div class="footer-item"> + +<p class="sphinx-version"> +Created using <a href="http://sphinx-doc.org/">Sphinx</a> 5.2.3.<br> +</p> + + </div> + +</div> + </footer> + </body> +</html>
\ No newline at end of file diff --git a/auto_examples/3d_drawing/plot_basic.html b/auto_examples/3d_drawing/plot_basic.html index 25a3d173..c5e9834c 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 0.089 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-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 cc78e7af..a19c07a3 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>00:00.089</strong> total execution time for <strong>auto_examples_3d_drawing</strong> files:</p> +<p><strong>00:00.090</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>00:00.089</p></td> +<td><p>00:00.090</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="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 654b397b..4b5b42b1 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 0.232 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.250 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 b43001ad..b397270d 100644 --- a/auto_examples/algorithms/plot_betweenness_centrality.html +++ b/auto_examples/algorithms/plot_betweenness_centrality.html @@ -582,7 +582,7 @@ using WormNet v.3-GS.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.685 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 4.096 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 1546a67d..7f577dbb 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 0.467 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.410 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 a7c00777..5d30436b 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 0.119 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.122 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 0ef7985b..0193d4d6 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 0.080 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.083 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-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 eb55893f..35509c1c 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 0.274 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.277 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 cc86ce9f..54be3ab3 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 0.106 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.108 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 8eb94f55..2c83cd35 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 0.070 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.069 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-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 90f4f694..2c5312a8 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: 2.2248 seconds - Betweenness centrality for node 0: 0.06085 + Time: 2.1512 seconds + Betweenness centrality for node 0: 0.07223 Non-Parallel version - Time: 3.3705 seconds - Betweenness centrality for node 0: 0.06085 + Time: 3.4002 seconds + Betweenness centrality for node 0: 0.07223 Computing betweenness centrality for: -Graph with 1000 nodes and 4973 edges +Graph with 1000 nodes and 5070 edges Parallel version - Time: 2.5278 seconds - Betweenness centrality for node 0: 0.00191 + Time: 2.6347 seconds + Betweenness centrality for node 0: 0.00053 Non-Parallel version - Time: 4.3282 seconds - Betweenness centrality for node 0: 0.00191 + Time: 4.4795 seconds + Betweenness centrality for node 0: 0.00053 Computing betweenness centrality for: Graph with 1000 nodes and 2000 edges Parallel version - Time: 1.7547 seconds - Betweenness centrality for node 0: 0.00094 + Time: 1.7567 seconds + Betweenness centrality for node 0: 0.00362 Non-Parallel version - Time: 2.9823 seconds - Betweenness centrality for node 0: 0.00094 + Time: 3.0709 seconds + Betweenness centrality for node 0: 0.00362 </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 23.104 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 23.868 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 b8dc64e7..620641e4 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 1.317 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.270 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 03eb00d7..2d533fe6 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 0.187 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.199 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 f4069f8a..65c88137 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 0.770 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 0.793 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 ea08b9d5..94b2659a 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>00:30.410</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p> +<p><strong>00:31.544</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>00:23.104</p></td> +<td><p>00:23.868</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_betweenness_centrality.html#sphx-glr-auto-examples-algorithms-plot-betweenness-centrality-py"><span class="std std-ref">Betweeness Centrality</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_betweenness_centrality.py</span></code>)</p></td> -<td><p>00:03.685</p></td> +<td><p>00:04.096</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>00:01.317</p></td> +<td><p>00:01.270</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>00:00.770</p></td> +<td><p>00:00.793</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>00:00.467</p></td> +<td><p>00:00.410</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_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>00:00.274</p></td> +<td><p>00:00.277</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>00:00.232</p></td> +<td><p>00:00.250</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>00:00.187</p></td> +<td><p>00:00.199</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>00:00.119</p></td> +<td><p>00:00.122</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>00:00.106</p></td> +<td><p>00:00.108</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>00:00.080</p></td> +<td><p>00:00.083</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>00:00.070</p></td> +<td><p>00:00.069</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 d6e7ee50..517753d1 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 0.099 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-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 55e1a791..a9be9b8c 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 0.069 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.071 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 6cd077c6..85f42b9d 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 0.433 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 0.400 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 ca2fdd7c..e8508f89 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>00:00.601</strong> total execution time for <strong>auto_examples_basic</strong> files:</p> +<p><strong>00:00.577</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>00:00.433</p></td> +<td><p>00:00.400</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>00:00.099</p></td> +<td><p>00:00.105</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>00:00.069</p></td> +<td><p>00:00.071</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/drawing/plot_chess_masters.html b/auto_examples/drawing/plot_chess_masters.html index 7aa7db9c..7f8698b9 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: -['Kasparov, Gary', 'Karpov, Anatoly', 'Korchnoi, Viktor L'] +['Karpov, Anatoly', 'Korchnoi, Viktor L', '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 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b/auto_examples/drawing/plot_custom_node_icons.html index 9a6ec175..381d9125 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 0.319 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.322 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 59a18962..71a87735 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 0.296 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.300 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 1434a714..299a41d4 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 0.245 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.251 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-directed-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/6c2f9c3544cb695b31867eecc0f7fb1e/plot_directed.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_directed.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_ego_graph.html b/auto_examples/drawing/plot_ego_graph.html index 52df8f20..7ffc13f2 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 0.108 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.117 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 f1257184..29a89eac 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 0.715 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.747 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 78fa6dc6..12208f91 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 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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 0.083 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.087 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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_labels_and_colors.html b/auto_examples/drawing/plot_labels_and_colors.html index 8121bbe3..b05e61a7 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> 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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 0.085 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.088 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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 89273df8..89262b51 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 0.056 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.058 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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_random_geometric_graph.html b/auto_examples/drawing/plot_random_geometric_graph.html index dda6e51a..74b92ad9 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 0.114 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.121 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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 3a120450..b0beb9f0 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 0.274 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.282 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 d5b2aa4d..e9ac38bd 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 0.089 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.093 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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 d2fd5be2..ad8590e3 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 0.064 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-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 fdf389a9..49b2de1a 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 0.256 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.268 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 a29a6637..2df99b52 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 0.096 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.102 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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 d5dcb15d..13c1497c 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 0.126 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.121 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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 2bde1672..458922e6 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 0.098 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.095 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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 db471575..f4c9efe6 100644 --- a/auto_examples/drawing/sg_execution_times.html +++ b/auto_examples/drawing/sg_execution_times.html @@ -463,43 +463,43 @@ <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>00:04.449</strong> total execution time for <strong>auto_examples_drawing</strong> files:</p> +<p><strong>00:04.573</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>00:00.715</p></td> +<td><p>00:00.747</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>00:00.434</p></td> +<td><p>00:00.445</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>00:00.371</p></td> +<td><p>00:00.385</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>00:00.319</p></td> +<td><p>00:00.322</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>00:00.296</p></td> +<td><p>00:00.300</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>00:00.274</p></td> +<td><p>00:00.282</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_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>00:00.256</p></td> +<td><p>00:00.268</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>00:00.245</p></td> +<td><p>00:00.251</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>00:00.210</p></td> +<td><p>00:00.215</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_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> @@ -507,39 +507,39 @@ <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>00:00.126</p></td> +<td><p>00:00.121</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" 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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 0.134 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.133 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-dag-layout-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/317508b452046ab7944bed07a87a11a5/plot_dag_layout.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_dag_layout.py</span></code></a></p> diff --git a/auto_examples/graph/plot_degree_sequence.html b/auto_examples/graph/plot_degree_sequence.html index a5ca79ba..4adc9f32 100644 --- a/auto_examples/graph/plot_degree_sequence.html +++ b/auto_examples/graph/plot_degree_sequence.html @@ -548,7 +548,7 @@ degree #nodes <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.066 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.068 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-degree-sequence-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/27102b9986eea2f742603c5d8496d2f8/plot_degree_sequence.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_degree_sequence.py</span></code></a></p> diff --git a/auto_examples/graph/plot_erdos_renyi.html b/auto_examples/graph/plot_erdos_renyi.html index 5d5410b9..00964089 100644 --- a/auto_examples/graph/plot_erdos_renyi.html +++ b/auto_examples/graph/plot_erdos_renyi.html @@ -562,7 +562,7 @@ the adjacency list <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.066 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.069 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 202fd8da..e6556539 100644 --- a/auto_examples/graph/plot_expected_degree_sequence.html +++ b/auto_examples/graph/plot_expected_degree_sequence.html @@ -537,47 +537,51 @@ degree (#nodes) **** 27 ( 0) 28 ( 0) 29 ( 0) -30 ( 1) * -31 ( 0) -32 ( 1) * -33 ( 3) *** +30 ( 0) +31 ( 1) * +32 ( 0) +33 ( 0) 34 ( 2) ** -35 ( 3) *** -36 ( 3) *** -37 ( 5) ***** -38 ( 4) **** -39 ( 9) ********* -40 ( 6) ****** -41 (12) ************ -42 (15) *************** -43 (19) ******************* -44 (23) *********************** -45 (27) *************************** -46 (30) ****************************** -47 (26) ************************** -48 (25) ************************* -49 (24) ************************ -50 (35) *********************************** -51 (25) ************************* -52 (29) ***************************** -53 (28) **************************** -54 (22) ********************** -55 (17) ***************** -56 (22) ********************** -57 (16) **************** -58 (13) ************* -59 (13) ************* -60 (13) ************* -61 (11) *********** -62 ( 7) ******* -63 ( 1) * +35 ( 0) +36 ( 4) **** +37 ( 4) **** +38 (12) ************ +39 ( 6) ****** +40 ( 9) ********* +41 (16) **************** +42 (20) ******************** +43 (20) ******************** +44 (19) ******************* +45 (22) ********************** +46 (33) ********************************* +47 (21) ********************* +48 (33) ********************************* +49 (31) ******************************* +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) ***** -65 ( 0) -66 ( 3) *** -67 ( 1) * +65 ( 1) * +66 ( 1) * +67 ( 3) *** 68 ( 0) 69 ( 0) -70 ( 1) * +70 ( 0) +71 ( 0) +72 ( 0) +73 ( 0) +74 ( 1) * </pre></div> </div> <div class="line-block"> @@ -597,7 +601,7 @@ 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 0.035 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.036 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 b47788a4..3aedc39f 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 0.426 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.435 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 e90d7f2e..8cbe205e 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 0.101 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.103 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 18296f95..93d2f414 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 0.199 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.219 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 75306537..1f2deb0a 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 0.144 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.147 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 fc0d781f..f1148b07 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 0.263 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.268 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 f68eb1b8..fc1519fe 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 1.166 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.223 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 4e0f19e8..336e5df3 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 0.437 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.443 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 b45b4b0f..659f54fb 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>00:03.036</strong> total execution time for <strong>auto_examples_graph</strong> files:</p> +<p><strong>00:03.143</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>00:01.166</p></td> +<td><p>00:01.223</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>00:00.437</p></td> +<td><p>00:00.443</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_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>00:00.426</p></td> +<td><p>00:00.435</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>00:00.263</p></td> +<td><p>00:00.268</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>00:00.199</p></td> +<td><p>00:00.219</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>00:00.144</p></td> +<td><p>00:00.147</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>00:00.134</p></td> +<td><p>00:00.133</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>00:00.101</p></td> +<td><p>00:00.103</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>00:00.066</p></td> +<td><p>00:00.069</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_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>00:00.066</p></td> +<td><p>00:00.068</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_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>00:00.035</p></td> +<td><p>00:00.036</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 841d7132..2e9da75a 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 0.033 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.119 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 e2260744..31482f29 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 0.030 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.031 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-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 9de7c58b..bac3f2c2 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 0.076 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-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 51247777..77bf7674 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 0.093 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-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 089b9da1..4e7f4f0c 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>00:00.232</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p> +<p><strong>00:00.324</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p> <table class="table"> <tbody> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_mini_atlas.html#sphx-glr-auto-examples-graphviz-drawing-plot-mini-atlas-py"><span class="std std-ref">Atlas</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_mini_atlas.py</span></code>)</p></td> -<td><p>00:00.093</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>00:00.119</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_grid.html#sphx-glr-auto-examples-graphviz-drawing-plot-grid-py"><span class="std std-ref">2D Grid</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_grid.py</span></code>)</p></td> -<td><p>00:00.076</p></td> +<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> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_attributes.html#sphx-glr-auto-examples-graphviz-drawing-plot-attributes-py"><span class="std std-ref">Attributes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_attributes.py</span></code>)</p></td> -<td><p>00:00.033</p></td> +<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> <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>00:00.030</p></td> +<td><p>00:00.031</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 853b691a..92bac9b7 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 4.109 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 4.433 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 61e431f9..d9969e9d 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 0.169 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.176 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 004200a4..907a75a9 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 0.341 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.349 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 4a094f61..d3b972a6 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 0.960 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.942 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 258b1091..e819247b 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 0.377 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.393 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 7f655dc9..2edf268b 100644 --- a/auto_examples/graphviz_layout/sg_execution_times.html +++ b/auto_examples/graphviz_layout/sg_execution_times.html @@ -463,27 +463,27 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-graphviz-layout-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:05.956</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p> +<p><strong>00:06.294</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_atlas.html#sphx-glr-auto-examples-graphviz-layout-plot-atlas-py"><span class="std std-ref">Atlas</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_atlas.py</span></code>)</p></td> -<td><p>00:04.109</p></td> +<td><p>00:04.433</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>00:00.960</p></td> +<td><p>00:00.942</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>00:00.377</p></td> +<td><p>00:00.393</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_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>00:00.341</p></td> +<td><p>00:00.349</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>00:00.169</p></td> +<td><p>00:00.176</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 b8a8ba6a..a9c85611 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 0.102 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.103 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/sg_execution_times.html b/auto_examples/subclass/sg_execution_times.html index 867d6d74..30936be8 100644 --- a/auto_examples/subclass/sg_execution_times.html +++ b/auto_examples/subclass/sg_execution_times.html @@ -463,11 +463,11 @@ <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>00:00.167</strong> total execution time for <strong>auto_examples_subclass</strong> files:</p> +<p><strong>00:00.168</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>00:00.102</p></td> +<td><p>00:00.103</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> diff --git a/developer/about_us.html b/developer/about_us.html index cefffffb..286b625f 100644 --- a/developer/about_us.html +++ b/developer/about_us.html @@ -867,6 +867,7 @@ to add your name to the bottom of the list.</p> <li><p>Philip Boalch</p></li> <li><p>Matt Schwennesen, Github: <a class="reference external" href="https://github.com/mjschwenne">mjschwenne</a></p></li> <li><p>Andrew Knyazev, Github: <a class="reference external" href="https://github.com/lobpcg">lobpcg</a>, LinkedIn: <a class="reference external" href="https://www.linkedin.com/in/andrew-knyazev">andrew-knyazev</a></p></li> +<li><p>Luca Cappelletti, GitHub: <a class="reference external" href="https://github.com/LucaCappelletti94">LucaCappelletti94</a></p></li> <li><p>Sultan Orazbayev, GitHub: <a class="reference external" href="https://github.com/SultanOrazbayev">SultanOrazbayev</a>, LinkedIn: <a class="reference external" href="https://www.linkedin.com/in/sultan-orazbayev/">Sultan Orazbayev</a></p></li> </ul> <p>A supplementary (but still incomplete) list of contributors is given by the diff --git a/genindex.html b/genindex.html index 13904a36..160ac70c 100644 --- a/genindex.html +++ b/genindex.html @@ -2504,6 +2504,8 @@ </li> <li><a href="reference/drawing.html#module-networkx.drawing.nx_agraph">networkx.drawing.nx_agraph</a> </li> + <li><a href="reference/drawing.html#module-networkx.drawing.nx_latex">networkx.drawing.nx_latex</a> +</li> <li><a href="reference/drawing.html#module-networkx.drawing.nx_pydot">networkx.drawing.nx_pydot</a> </li> <li><a href="reference/drawing.html#module-networkx.drawing.nx_pylab">networkx.drawing.nx_pylab</a> @@ -3679,6 +3681,13 @@ </li> </ul></li> <li> + networkx.drawing.nx_latex + + <ul> + <li><a href="reference/drawing.html#module-networkx.drawing.nx_latex">module</a> +</li> + </ul></li> + <li> networkx.drawing.nx_pydot <ul> @@ -4849,6 +4858,10 @@ </li> <li><a href="reference/readwrite/generated/networkx.readwrite.graph6.to_graph6_bytes.html#networkx.readwrite.graph6.to_graph6_bytes">to_graph6_bytes() (in module networkx.readwrite.graph6)</a> </li> + <li><a href="reference/generated/networkx.drawing.nx_latex.to_latex.html#networkx.drawing.nx_latex.to_latex">to_latex() (in module networkx.drawing.nx_latex)</a> +</li> + <li><a href="reference/generated/networkx.drawing.nx_latex.to_latex_raw.html#networkx.drawing.nx_latex.to_latex_raw">to_latex_raw() (in module networkx.drawing.nx_latex)</a> +</li> <li><a href="reference/algorithms/generated/networkx.algorithms.tree.coding.to_nested_tuple.html#networkx.algorithms.tree.coding.to_nested_tuple">to_nested_tuple() (in module networkx.algorithms.tree.coding)</a> </li> <li><a href="reference/generated/networkx.convert.to_networkx_graph.html#networkx.convert.to_networkx_graph">to_networkx_graph() (in module networkx.convert)</a> @@ -5081,6 +5094,8 @@ </li> <li><a href="reference/readwrite/generated/networkx.readwrite.graphml.write_graphml.html#networkx.readwrite.graphml.write_graphml">write_graphml() (in module networkx.readwrite.graphml)</a> </li> + <li><a href="reference/generated/networkx.drawing.nx_latex.write_latex.html#networkx.drawing.nx_latex.write_latex">write_latex() (in module networkx.drawing.nx_latex)</a> +</li> <li><a href="reference/readwrite/generated/networkx.readwrite.multiline_adjlist.write_multiline_adjlist.html#networkx.readwrite.multiline_adjlist.write_multiline_adjlist">write_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)</a> </li> <li><a href="reference/readwrite/generated/networkx.readwrite.pajek.write_pajek.html#networkx.readwrite.pajek.write_pajek">write_pajek() (in module networkx.readwrite.pajek)</a> diff --git a/objects.inv b/objects.inv Binary files differindex d6f4358e..7adc0732 100644 --- a/objects.inv +++ b/objects.inv diff --git a/py-modindex.html b/py-modindex.html index 68e3fbf3..c2074e64 100644 --- a/py-modindex.html +++ b/py-modindex.html @@ -1213,6 +1213,11 @@ <tr class="cg-3"> <td></td> <td>    + <a href="reference/drawing.html#module-networkx.drawing.nx_latex"><code class="xref">networkx.drawing.nx_latex</code></a></td><td> + <em></em></td></tr> + <tr class="cg-3"> + <td></td> + <td>    <a href="reference/drawing.html#module-networkx.drawing.nx_pydot"><code class="xref">networkx.drawing.nx_pydot</code></a></td><td> <em></em></td></tr> <tr class="cg-3"> diff --git a/reference/drawing.html b/reference/drawing.html index 5e7a168f..e270fe22 100644 --- a/reference/drawing.html +++ b/reference/drawing.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="generated/networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="generated/networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="generated/networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="generated/networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="generated/networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="generated/networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="randomness.html">Randomness</a></li> @@ -755,6 +758,142 @@ Changing <code class="xref py py-obj docutils literal notranslate"><span class=" </tbody> </table> </section> +<section id="module-networkx.drawing.nx_latex"> +<span id="latex-code"></span><h2>LaTeX Code<a class="headerlink" href="#module-networkx.drawing.nx_latex" title="Permalink to this heading">#</a></h2> +<p>Export NetworkX graphs in LaTeX format using the TikZ library within TeX/LaTeX. +Usually, you will want the drawing to appear in a figure environment so +you use <code class="docutils literal notranslate"><span class="pre">to_latex(G,</span> <span class="pre">caption="A</span> <span class="pre">caption")</span></code>. If you want the raw +drawing commands without a figure environment use <a class="reference internal" href="generated/networkx.drawing.nx_latex.to_latex_raw.html#networkx.drawing.nx_latex.to_latex_raw" title="networkx.drawing.nx_latex.to_latex_raw"><code class="xref py py-func docutils literal notranslate"><span class="pre">to_latex_raw()</span></code></a>. +And if you want to write to a file instead of just returning the latex +code as a string, use <code class="docutils literal notranslate"><span class="pre">write_latex(G,</span> <span class="pre">"filname.tex",</span> <span class="pre">caption="A</span> <span class="pre">caption")</span></code>.</p> +<p>To construct a figure with subfigures for each graph to be shown, provide +<code class="docutils literal notranslate"><span class="pre">to_latex</span></code> or <code class="docutils literal notranslate"><span class="pre">write_latex</span></code> a list of graphs, a list of subcaptions, +and a number of rows of subfigures inside the figure.</p> +<p>To be able to refer to the figures or subfigures in latex using <code class="docutils literal notranslate"><span class="pre">\\ref</span></code>, +the keyword <code class="docutils literal notranslate"><span class="pre">latex_label</span></code> is available for figures and <code class="xref py py-obj docutils literal notranslate"><span class="pre">sub_labels</span></code> for +a list of labels, one for each subfigure.</p> +<p>We intend to eventually provide an interface to the TikZ Graph +features which include e.g. layout algorithms.</p> +<p>Let us know via github what you’d like to see available, or better yet +give us some code to do it, or even better make a github pull request +to add the feature.</p> +<section id="the-tikz-approach"> +<h3>The TikZ approach<a class="headerlink" href="#the-tikz-approach" title="Permalink to this heading">#</a></h3> +<p>Drawing options can be stored on the graph as node/edge attributes, or +can be provided as dicts keyed by node/edge to a string of the options +for that node/edge. Similarly a label can be shown for each node/edge +by specifying the labels as graph node/edge attributes or by providing +a dict keyed by node/edge to the text to be written for that node/edge.</p> +<p>Options for the tikzpicture environment (e.g. “[scale=2]”) can be provided +via a keyword argument. Similarly default node and edge options can be +provided through keywords arguments. The default node options are applied +to the single TikZ “path” that draws all nodes (and no edges). The default edge +options are applied to a TikZ “scope” which contains a path for each edge.</p> +</section> +<section id="id6"> +<h3>Examples<a class="headerlink" href="#id6" title="Permalink to this heading">#</a></h3> +<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">path_graph</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> +<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">write_latex</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="s2">"just_my_figure.tex"</span><span class="p">,</span> <span class="n">as_document</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> +<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">write_latex</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="s2">"my_figure.tex"</span><span class="p">,</span> <span class="n">caption</span><span class="o">=</span><span class="s2">"A path graph"</span><span class="p">,</span> <span class="n">latex_label</span><span class="o">=</span><span class="s2">"fig1"</span><span class="p">)</span> +<span class="gp">>>> </span><span class="n">latex_code</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">to_latex</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="c1"># a string rather than a file</span> +</pre></div> +</div> +<p>You can change many features of the nodes and edges.</p> +<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">path_graph</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">)</span> +<span class="gp">>>> </span><span class="n">pos</span> <span class="o">=</span> <span class="p">{</span><span class="n">n</span><span class="p">:</span> <span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">G</span><span class="p">}</span> <span class="c1"># nodes set on a line</span> +</pre></div> +</div> +<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"style"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"blue"</span> +<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="s2">"style"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"line width=3,draw"</span> +<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="s2">"label"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"Stop"</span> +<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)][</span><span class="s2">"label"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"1st Step"</span> +<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)][</span><span class="s2">"label_opts"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"near start"</span> +<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)][</span><span class="s2">"style"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"line width=3"</span> +<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)][</span><span class="s2">"label"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"2nd Step"</span> +<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">[(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)][</span><span class="s2">"style"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"green"</span> +<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">[(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)][</span><span class="s2">"label"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"3rd Step"</span> +<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">[(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)][</span><span class="s2">"label_opts"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"near end"</span> +</pre></div> +</div> +<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">write_latex</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="s2">"latex_graph.tex"</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">,</span> <span class="n">as_document</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> +</pre></div> +</div> +<p>Then compile the LaTeX using something like <code class="docutils literal notranslate"><span class="pre">pdflatex</span> <span class="pre">latex_graph.tex</span></code> +and view the pdf file created: <code class="docutils literal notranslate"><span class="pre">latex_graph.pdf</span></code>.</p> +<p>If you want <strong>subfigures</strong> each containing one graph, you can input a list of graphs.</p> +<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">H1</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">path_graph</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> +<span class="gp">>>> </span><span class="n">H2</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">complete_graph</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> +<span class="gp">>>> </span><span class="n">H3</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">path_graph</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span> +<span class="gp">>>> </span><span class="n">H4</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">complete_graph</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span> +<span class="gp">>>> </span><span class="n">graphs</span> <span class="o">=</span> <span class="p">[</span><span class="n">H1</span><span class="p">,</span> <span class="n">H2</span><span class="p">,</span> <span class="n">H3</span><span class="p">,</span> <span class="n">H4</span><span class="p">]</span> +<span class="gp">>>> </span><span class="n">caps</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Path 4"</span><span class="p">,</span> <span class="s2">"Complete graph 4"</span><span class="p">,</span> <span class="s2">"Path 8"</span><span class="p">,</span> <span class="s2">"Complete graph 8"</span><span class="p">]</span> +<span class="gp">>>> </span><span class="n">lbls</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"fig2a"</span><span class="p">,</span> <span class="s2">"fig2b"</span><span class="p">,</span> <span class="s2">"fig2c"</span><span class="p">,</span> <span class="s2">"fig2d"</span><span class="p">]</span> +<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">write_latex</span><span class="p">(</span><span class="n">graphs</span><span class="p">,</span> <span class="s2">"subfigs.tex"</span><span class="p">,</span> <span class="n">n_rows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sub_captions</span><span class="o">=</span><span class="n">caps</span><span class="p">,</span> <span class="n">sub_labels</span><span class="o">=</span><span class="n">lbls</span><span class="p">)</span> +<span class="gp">>>> </span><span class="n">latex_code</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">to_latex</span><span class="p">(</span><span class="n">graphs</span><span class="p">,</span> <span class="n">n_rows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sub_captions</span><span class="o">=</span><span class="n">caps</span><span class="p">,</span> <span class="n">sub_labels</span><span class="o">=</span><span class="n">lbls</span><span class="p">)</span> +</pre></div> +</div> +<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">node_color</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="s2">"red"</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span> <span class="s2">"orange"</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="s2">"blue"</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span> <span class="s2">"gray!90"</span><span class="p">}</span> +<span class="gp">>>> </span><span class="n">edge_width</span> <span class="o">=</span> <span class="p">{</span><span class="n">e</span><span class="p">:</span> <span class="s2">"line width=1.5"</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">H3</span><span class="o">.</span><span class="n">edges</span><span class="p">}</span> +<span class="gp">>>> </span><span class="n">pos</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">circular_layout</span><span class="p">(</span><span class="n">H3</span><span class="p">)</span> +<span class="gp">>>> </span><span class="n">latex_code</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">to_latex</span><span class="p">(</span><span class="n">H3</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">node_options</span><span class="o">=</span><span class="n">node_color</span><span class="p">,</span> <span class="n">edge_options</span><span class="o">=</span><span class="n">edge_width</span><span class="p">)</span> +<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">latex_code</span><span class="p">)</span> +<span class="go">\documentclass{report}</span> +<span class="go">\usepackage{tikz}</span> +<span class="go">\usepackage{subcaption}</span> + +<span class="go">\begin{document}</span> +<span class="go">\begin{figure}</span> +<span class="go"> \begin{tikzpicture}</span> +<span class="go"> \draw</span> +<span class="go"> (1.0, 0.0) node[red] (0){0}</span> +<span class="go"> (0.707, 0.707) node[orange] (1){1}</span> +<span class="go"> (-0.0, 1.0) node[blue] (2){2}</span> +<span class="go"> (-0.707, 0.707) node[gray!90] (3){3}</span> +<span class="go"> (-1.0, -0.0) node (4){4}</span> +<span class="go"> (-0.707, -0.707) node (5){5}</span> +<span class="go"> (0.0, -1.0) node (6){6}</span> +<span class="go"> (0.707, -0.707) node (7){7};</span> +<span class="go"> \begin{scope}[-]</span> +<span class="go"> \draw[line width=1.5] (0) to (1);</span> +<span class="go"> \draw[line width=1.5] (1) to (2);</span> +<span class="go"> \draw[line width=1.5] (2) to (3);</span> +<span class="go"> \draw[line width=1.5] (3) to (4);</span> +<span class="go"> \draw[line width=1.5] (4) to (5);</span> +<span class="go"> \draw[line width=1.5] (5) to (6);</span> +<span class="go"> \draw[line width=1.5] (6) to (7);</span> +<span class="go"> \end{scope}</span> +<span class="go"> \end{tikzpicture}</span> +<span class="go">\end{figure}</span> +<span class="go">\end{document}</span> +</pre></div> +</div> +<section id="notes"> +<h4>Notes<a class="headerlink" href="#notes" title="Permalink to this heading">#</a></h4> +<p>If you want to change the preamble/postamble of the figure/document/subfigure +environment, use the keyword arguments: <code class="xref py py-obj docutils literal notranslate"><span class="pre">figure_wrapper</span></code>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">document_wrapper</span></code>, +<code class="xref py py-obj docutils literal notranslate"><span class="pre">subfigure_wrapper</span></code>. The default values are stored in private variables +e.g. <code class="docutils literal notranslate"><span class="pre">nx.nx_layout._DOCUMENT_WRAPPER</span></code></p> +</section> +<section id="references"> +<h4>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h4> +<p>TikZ: <a class="reference external" href="https://tikz.dev/">https://tikz.dev/</a></p> +<p>TikZ options details: <a class="reference external" href="https://tikz.dev/tikz-actions">https://tikz.dev/tikz-actions</a></p> +</section> +</section> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.drawing.nx_latex.to_latex_raw.html#networkx.drawing.nx_latex.to_latex_raw" title="networkx.drawing.nx_latex.to_latex_raw"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_latex_raw</span></code></a>(G[, pos, tikz_options, ...])</p></td> +<td><p>Return a string of the LaTeX/TikZ code to draw <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="generated/networkx.drawing.nx_latex.to_latex.html#networkx.drawing.nx_latex.to_latex" title="networkx.drawing.nx_latex.to_latex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_latex</span></code></a>(Gbunch[, pos, tikz_options, ...])</p></td> +<td><p>Return latex code to draw the graph(s) in <code class="xref py py-obj docutils literal notranslate"><span class="pre">Gbunch</span></code></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.drawing.nx_latex.write_latex.html#networkx.drawing.nx_latex.write_latex" title="networkx.drawing.nx_latex.write_latex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">write_latex</span></code></a>(Gbunch, path, **options)</p></td> +<td><p>Write the latex code to draw the graph(s) onto <code class="xref py py-obj docutils literal notranslate"><span class="pre">path</span></code>.</p></td> +</tr> +</tbody> +</table> +</section> </section> @@ -831,6 +970,35 @@ Changing <code class="xref py py-obj docutils literal notranslate"><span class=" Graph Layout </a> </li> + <li class="toc-h2 nav-item toc-entry"> + <a class="reference internal nav-link" href="#module-networkx.drawing.nx_latex"> + LaTeX Code + </a> + <ul class="nav section-nav flex-column"> + <li class="toc-h3 nav-item toc-entry"> + <a class="reference internal nav-link" href="#the-tikz-approach"> + The TikZ approach + </a> + </li> + <li class="toc-h3 nav-item toc-entry"> + <a class="reference internal nav-link" href="#id6"> + Examples + </a> + <ul class="nav section-nav flex-column"> + <li class="toc-h4 nav-item toc-entry"> + <a class="reference internal nav-link" href="#notes"> + Notes + </a> + </li> + <li class="toc-h4 nav-item toc-entry"> + <a class="reference internal nav-link" href="#references"> + References + </a> + </li> + </ul> + </li> + </ul> + </li> </ul> </nav> diff --git a/reference/generated/networkx.drawing.layout.bipartite_layout.html b/reference/generated/networkx.drawing.layout.bipartite_layout.html index d18e004d..f54c5b24 100644 --- a/reference/generated/networkx.drawing.layout.bipartite_layout.html +++ b/reference/generated/networkx.drawing.layout.bipartite_layout.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.layout.circular_layout.html b/reference/generated/networkx.drawing.layout.circular_layout.html index 926e3b10..c81bc201 100644 --- a/reference/generated/networkx.drawing.layout.circular_layout.html +++ b/reference/generated/networkx.drawing.layout.circular_layout.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.layout.kamada_kawai_layout.html b/reference/generated/networkx.drawing.layout.kamada_kawai_layout.html index bbbffa21..36a8c56e 100644 --- a/reference/generated/networkx.drawing.layout.kamada_kawai_layout.html +++ b/reference/generated/networkx.drawing.layout.kamada_kawai_layout.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.layout.multipartite_layout.html b/reference/generated/networkx.drawing.layout.multipartite_layout.html index f9a6c4a8..52914ec3 100644 --- a/reference/generated/networkx.drawing.layout.multipartite_layout.html +++ b/reference/generated/networkx.drawing.layout.multipartite_layout.html @@ -51,7 +51,7 @@ href="../../_static/opensearch.xml"/> <link rel="index" title="Index" href="../../genindex.html" /> <link rel="search" title="Search" href="../../search.html" /> - <link rel="next" title="Randomness" href="../randomness.html" /> + <link rel="next" title="to_latex_raw" href="networkx.drawing.nx_latex.to_latex_raw.html" /> <link rel="prev" title="spiral_layout" href="networkx.drawing.layout.spiral_layout.html" /> <meta name="viewport" content="width=device-width, initial-scale=1" /> <meta name="docsearch:language" content="en"> @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2 current active"><a class="current reference internal" href="#">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.layout.planar_layout.html b/reference/generated/networkx.drawing.layout.planar_layout.html index 53b374d5..f2c47db5 100644 --- a/reference/generated/networkx.drawing.layout.planar_layout.html +++ b/reference/generated/networkx.drawing.layout.planar_layout.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.layout.random_layout.html b/reference/generated/networkx.drawing.layout.random_layout.html index 096b624f..1a0722c8 100644 --- a/reference/generated/networkx.drawing.layout.random_layout.html +++ b/reference/generated/networkx.drawing.layout.random_layout.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.layout.rescale_layout.html b/reference/generated/networkx.drawing.layout.rescale_layout.html index 7641974c..8278afc4 100644 --- a/reference/generated/networkx.drawing.layout.rescale_layout.html +++ b/reference/generated/networkx.drawing.layout.rescale_layout.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.layout.rescale_layout_dict.html b/reference/generated/networkx.drawing.layout.rescale_layout_dict.html index 981e670e..dbf01649 100644 --- a/reference/generated/networkx.drawing.layout.rescale_layout_dict.html +++ b/reference/generated/networkx.drawing.layout.rescale_layout_dict.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.layout.shell_layout.html b/reference/generated/networkx.drawing.layout.shell_layout.html index 2d0bb7c9..21056eec 100644 --- a/reference/generated/networkx.drawing.layout.shell_layout.html +++ b/reference/generated/networkx.drawing.layout.shell_layout.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.layout.spectral_layout.html b/reference/generated/networkx.drawing.layout.spectral_layout.html index 1f167871..9ed200bd 100644 --- a/reference/generated/networkx.drawing.layout.spectral_layout.html +++ b/reference/generated/networkx.drawing.layout.spectral_layout.html @@ -488,6 +488,9 @@ <li class="toctree-l2 current active"><a class="current reference internal" href="#">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.layout.spiral_layout.html b/reference/generated/networkx.drawing.layout.spiral_layout.html index a6050fa5..58b63b51 100644 --- a/reference/generated/networkx.drawing.layout.spiral_layout.html +++ b/reference/generated/networkx.drawing.layout.spiral_layout.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2 current active"><a class="current reference internal" href="#">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.layout.spring_layout.html b/reference/generated/networkx.drawing.layout.spring_layout.html index a5286176..6e9bd873 100644 --- a/reference/generated/networkx.drawing.layout.spring_layout.html +++ b/reference/generated/networkx.drawing.layout.spring_layout.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_agraph.from_agraph.html b/reference/generated/networkx.drawing.nx_agraph.from_agraph.html index db5bd3d1..757a0b15 100644 --- a/reference/generated/networkx.drawing.nx_agraph.from_agraph.html +++ b/reference/generated/networkx.drawing.nx_agraph.from_agraph.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_agraph.graphviz_layout.html b/reference/generated/networkx.drawing.nx_agraph.graphviz_layout.html index dcbcd441..849f21f4 100644 --- a/reference/generated/networkx.drawing.nx_agraph.graphviz_layout.html +++ b/reference/generated/networkx.drawing.nx_agraph.graphviz_layout.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_agraph.pygraphviz_layout.html b/reference/generated/networkx.drawing.nx_agraph.pygraphviz_layout.html index 963e9733..d279afb0 100644 --- a/reference/generated/networkx.drawing.nx_agraph.pygraphviz_layout.html +++ b/reference/generated/networkx.drawing.nx_agraph.pygraphviz_layout.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_agraph.read_dot.html b/reference/generated/networkx.drawing.nx_agraph.read_dot.html index c1d7d161..8cb5f12f 100644 --- a/reference/generated/networkx.drawing.nx_agraph.read_dot.html +++ b/reference/generated/networkx.drawing.nx_agraph.read_dot.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_agraph.to_agraph.html b/reference/generated/networkx.drawing.nx_agraph.to_agraph.html index 425eac45..9a75c9af 100644 --- a/reference/generated/networkx.drawing.nx_agraph.to_agraph.html +++ b/reference/generated/networkx.drawing.nx_agraph.to_agraph.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_agraph.write_dot.html b/reference/generated/networkx.drawing.nx_agraph.write_dot.html index ec873c58..6bcb7ac5 100644 --- a/reference/generated/networkx.drawing.nx_agraph.write_dot.html +++ b/reference/generated/networkx.drawing.nx_agraph.write_dot.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_latex.to_latex.html 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Navigation + </p> + <div class="bd-toc-item navbar-nav"> + <ul class="current nav bd-sidenav"> +<li class="toctree-l1"><a class="reference internal" href="../introduction.html">Introduction</a></li> +<li class="toctree-l1"><a class="reference internal" href="../classes/index.html">Graph types</a></li> +<li class="toctree-l1"><a class="reference internal" href="../algorithms/index.html">Algorithms</a></li> +<li class="toctree-l1"><a class="reference internal" href="../functions.html">Functions</a></li> +<li class="toctree-l1"><a class="reference internal" href="../generators.html">Graph generators</a></li> +<li class="toctree-l1"><a class="reference internal" href="../linalg.html">Linear algebra</a></li> +<li class="toctree-l1"><a class="reference internal" href="../convert.html">Converting to and from other data formats</a></li> +<li class="toctree-l1"><a class="reference internal" href="../relabel.html">Relabeling nodes</a></li> +<li class="toctree-l1"><a class="reference internal" href="../readwrite/index.html">Reading and writing graphs</a></li> +<li class="toctree-l1 current active has-children"><a class="reference internal" href="../drawing.html">Drawing</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul class="current"> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_pylab.draw.html">draw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_pylab.draw_networkx.html">draw_networkx</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_pylab.draw_networkx_nodes.html">draw_networkx_nodes</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_pylab.draw_networkx_edges.html">draw_networkx_edges</a></li> +<li class="toctree-l2"><a class="reference internal" 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href="networkx.drawing.nx_pylab.draw_spring.html">draw_spring</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_pylab.draw_shell.html">draw_shell</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_agraph.from_agraph.html">from_agraph</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_agraph.to_agraph.html">to_agraph</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_agraph.write_dot.html">write_dot</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_agraph.read_dot.html">read_dot</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_agraph.graphviz_layout.html">graphviz_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_agraph.pygraphviz_layout.html">pygraphviz_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_pydot.from_pydot.html">from_pydot</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_pydot.to_pydot.html">to_pydot</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_pydot.write_dot.html">write_dot</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_pydot.read_dot.html">read_dot</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_pydot.graphviz_layout.html">graphviz_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_pydot.pydot_layout.html">pydot_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.bipartite_layout.html">bipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.circular_layout.html">circular_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.kamada_kawai_layout.html">kamada_kawai_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.planar_layout.html">planar_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.random_layout.html">random_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.rescale_layout.html">rescale_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.rescale_layout_dict.html">rescale_layout_dict</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.shell_layout.html">shell_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spring_layout.html">spring_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2 current active"><a class="current reference internal" href="#">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> +</ul> +</li> +<li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> +<li class="toctree-l1"><a class="reference internal" href="../exceptions.html">Exceptions</a></li> +<li class="toctree-l1"><a class="reference internal" href="../utils.html">Utilities</a></li> +<li class="toctree-l1"><a class="reference internal" href="../glossary.html">Glossary</a></li> +</ul> + + </div> +</nav> + </div> + <div class="sidebar-start-items__item"> + </div> + </div> + + + + <div class="sidebar-end-items sidebar-primary__section"> + <div class="sidebar-end-items__item"> + </div> + </div> + + + <div id="rtd-footer-container"></div> + + </div> + <main id="main-content" class="bd-main"> + + + <div class="bd-content"> + <div class="bd-article-container"> + + <div class="bd-header-article"> + + </div> + + + <article class="bd-article" role="main"> + + <section id="to-latex"> +<h1>to_latex<a class="headerlink" href="#to-latex" title="Permalink to this heading">#</a></h1> +<dl class="py function"> +<dt class="sig sig-object py" id="networkx.drawing.nx_latex.to_latex"> +<span class="sig-name descname"><span class="pre">to_latex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Gbunch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pos'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tikz_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_node_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'node_options'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'node_label'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_edge_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'edge_options'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'edge_label'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_label_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'edge_label_options'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">caption</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">latex_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sub_captions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sub_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_document</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">document_wrapper</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'\\documentclass{{report}}\n\\usepackage{{tikz}}\n\\usepackage{{subcaption}}\n\n\\begin{{document}}\n{content}\n\\end{{document}}'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figure_wrapper</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'\\begin{{figure}}\n{content}{caption}{label}\n\\end{{figure}}'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subfigure_wrapper</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'</span>  <span class="pre">\\begin{{subfigure}}{{{size}\\textwidth}}\n{content}{caption}{label}\n</span>  <span class="pre">\\end{{subfigure}}'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/networkx/drawing/nx_latex.html#to_latex"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#networkx.drawing.nx_latex.to_latex" title="Permalink to this definition">#</a></dt> +<dd><p>Return latex code to draw the graph(s) in <code class="xref py py-obj docutils literal notranslate"><span class="pre">Gbunch</span></code></p> +<p>The TikZ drawing utility in LaTeX is used to draw the graph(s). +If <code class="xref py py-obj docutils literal notranslate"><span class="pre">Gbunch</span></code> is a graph, it is drawn in a figure environment. +If <code class="xref py py-obj docutils literal notranslate"><span class="pre">Gbunch</span></code> is an iterable of graphs, each is drawn in a subfigure envionment +within a single figure environment.</p> +<p>If <code class="xref py py-obj docutils literal notranslate"><span class="pre">as_document</span></code> is True, the figure is wrapped inside a document environment +so that the resulting string is ready to be compiled by LaTeX. Otherwise, +the string is ready for inclusion in a larger tex document using <code class="docutils literal notranslate"><span class="pre">\include</span></code> +or <code class="docutils literal notranslate"><span class="pre">\input</span></code> statements.</p> +<dl class="field-list"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl> +<dt><strong>Gbunch</strong><span class="classifier">NetworkX graph or iterable of NetworkX graphs</span></dt><dd><p>The NetworkX graph to be drawn or an iterable of graphs +to be drawn inside subfigures of a single figure.</p> +</dd> +<dt><strong>pos</strong><span class="classifier">string or list of strings</span></dt><dd><p>The name of the node attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the position of each node. +Positions can be sequences of length 2 with numbers for (x,y) coordinates. +They can also be strings to denote positions in TikZ style, such as (x, y) +or (angle:radius). +If a dict, it should be keyed by node to a position. +If an empty dict, a circular layout is computed by TikZ. +If you are drawing many graphs in subfigures, use a list of position dicts.</p> +</dd> +<dt><strong>tikz_options</strong><span class="classifier">string</span></dt><dd><p>The tikzpicture options description defining the options for the picture. +Often large scale options like <code class="xref py py-obj docutils literal notranslate"><span class="pre">[scale=2]</span></code>.</p> +</dd> +<dt><strong>default_node_options</strong><span class="classifier">string</span></dt><dd><p>The draw options for a path of nodes. Individual node options override these.</p> +</dd> +<dt><strong>node_options</strong><span class="classifier">string or dict</span></dt><dd><p>The name of the node attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the options for each node. +Or a dict keyed by node to a string holding the options for that node.</p> +</dd> +<dt><strong>node_label</strong><span class="classifier">string or dict</span></dt><dd><p>The name of the node attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the node label (text) +displayed for each node. If the attribute is “” or not present, the node +itself is drawn as a string. LaTeX processing such as <code class="docutils literal notranslate"><span class="pre">"$A_1$"</span></code> is allowed. +Or a dict keyed by node to a string holding the label for that node.</p> +</dd> +<dt><strong>default_edge_options</strong><span class="classifier">string</span></dt><dd><p>The options for the scope drawing all edges. The default is “[-]” for +undirected graphs and “[->]” for directed graphs.</p> +</dd> +<dt><strong>edge_options</strong><span class="classifier">string or dict</span></dt><dd><p>The name of the edge attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the options for each edge. +If the edge is a self-loop and <code class="docutils literal notranslate"><span class="pre">"loop"</span> <span class="pre">not</span> <span class="pre">in</span> <span class="pre">edge_options</span></code> the option +“loop,” is added to the options for the self-loop edge. Hence you can +use “[loop above]” explicitly, but the default is “[loop]”. +Or a dict keyed by edge to a string holding the options for that edge.</p> +</dd> +<dt><strong>edge_label</strong><span class="classifier">string or dict</span></dt><dd><p>The name of the edge attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the edge label (text) +displayed for each edge. If the attribute is “” or not present, no edge +label is drawn. +Or a dict keyed by edge to a string holding the label for that edge.</p> +</dd> +<dt><strong>edge_label_options</strong><span class="classifier">string or dict</span></dt><dd><p>The name of the edge attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the label options for +each edge. For example, “[sloped,above,blue]”. The default is no options. +Or a dict keyed by edge to a string holding the label options for that edge.</p> +</dd> +<dt><strong>caption</strong><span class="classifier">string</span></dt><dd><p>The caption string for the figure environment</p> +</dd> +<dt><strong>latex_label</strong><span class="classifier">string</span></dt><dd><p>The latex label used for the figure for easy referral from the main text</p> +</dd> +<dt><strong>sub_captions</strong><span class="classifier">list of strings</span></dt><dd><p>The sub_caption string for each subfigure in the figure</p> +</dd> +<dt><strong>sub_latex_labels</strong><span class="classifier">list of strings</span></dt><dd><p>The latex label for each subfigure in the figure</p> +</dd> +<dt><strong>n_rows</strong><span class="classifier">int</span></dt><dd><p>The number of rows of subfigures to arrange for multiple graphs</p> +</dd> +<dt><strong>as_document</strong><span class="classifier">bool</span></dt><dd><p>Whether to wrap the latex code in a document envionment for compiling</p> +</dd> +<dt><strong>document_wrapper</strong><span class="classifier">formatted text string with variable <code class="docutils literal notranslate"><span class="pre">content</span></code>.</span></dt><dd><p>This text is called to evaluate the content embedded in a document +environment with a preamble setting up TikZ.</p> +</dd> +<dt><strong>figure_wrapper</strong><span class="classifier">formatted text string</span></dt><dd><p>This text is evaluated with variables <code class="docutils literal notranslate"><span class="pre">content</span></code>, <code class="docutils literal notranslate"><span class="pre">caption</span></code> and <code class="docutils literal notranslate"><span class="pre">label</span></code>. +It wraps the content and if a caption is provided, adds the latex code for +that caption, and if a label is provided, adds the latex code for a label.</p> +</dd> +<dt><strong>subfigure_wrapper</strong><span class="classifier">formatted text string</span></dt><dd><p>This text evaluate variables <code class="docutils literal notranslate"><span class="pre">size</span></code>, <code class="docutils literal notranslate"><span class="pre">content</span></code>, <code class="docutils literal notranslate"><span class="pre">caption</span></code> and <code class="docutils literal notranslate"><span class="pre">label</span></code>. +It wraps the content and if a caption is provided, adds the latex code for +that caption, and if a label is provided, adds the latex code for a label. +The size is the vertical size of each row of subfigures as a fraction.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt><strong>latex_code</strong><span class="classifier">string</span></dt><dd><p>The text string which draws the desired graph(s) when compiled by LaTeX.</p> +</dd> +</dl> +</dd> +</dl> +<div class="admonition seealso"> +<p class="admonition-title">See also</p> +<dl class="simple"> +<dt><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html#networkx.drawing.nx_latex.write_latex" title="networkx.drawing.nx_latex.write_latex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">write_latex</span></code></a></dt><dd></dd> +<dt><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html#networkx.drawing.nx_latex.to_latex_raw" title="networkx.drawing.nx_latex.to_latex_raw"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_latex_raw</span></code></a></dt><dd></dd> +</dl> +</div> +</dd></dl> + +</section> + + + </article> + + + + </div> + + + + <div class="bd-sidebar-secondary bd-toc"> + +<div class="toc-item"> + +<div class="tocsection onthispage"> + <i class="fa-solid fa-list"></i> On this page +</div> +<nav id="bd-toc-nav" class="page-toc"> + <ul class="visible nav section-nav flex-column"> + <li class="toc-h2 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href="../exceptions.html">Exceptions</a></li> +<li class="toctree-l1"><a class="reference internal" href="../utils.html">Utilities</a></li> +<li class="toctree-l1"><a class="reference internal" href="../glossary.html">Glossary</a></li> +</ul> + + </div> +</nav> + </div> + <div class="sidebar-start-items__item"> + </div> + </div> + + + + <div class="sidebar-end-items sidebar-primary__section"> + <div class="sidebar-end-items__item"> + </div> + </div> + + + <div id="rtd-footer-container"></div> + + </div> + <main id="main-content" class="bd-main"> + + + <div class="bd-content"> + <div class="bd-article-container"> + + <div class="bd-header-article"> + + </div> + + + <article class="bd-article" role="main"> + + <section id="to-latex-raw"> +<h1>to_latex_raw<a class="headerlink" href="#to-latex-raw" title="Permalink to this heading">#</a></h1> +<dl class="py function"> +<dt class="sig sig-object py" id="networkx.drawing.nx_latex.to_latex_raw"> +<span class="sig-name descname"><span class="pre">to_latex_raw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">G</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pos'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tikz_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_node_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'node_options'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'label'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_edge_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'edge_options'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'label'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_label_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'edge_label_options'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/networkx/drawing/nx_latex.html#to_latex_raw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#networkx.drawing.nx_latex.to_latex_raw" title="Permalink to this definition">#</a></dt> +<dd><p>Return a string of the LaTeX/TikZ code to draw <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code></p> +<p>This function produces just the code for the tikzpicture +without any enclosing environment.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>G</strong><span class="classifier">NetworkX graph</span></dt><dd><p>The NetworkX graph to be drawn</p> +</dd> +<dt><strong>pos</strong><span class="classifier">string or dict (default “pos”)</span></dt><dd><p>The name of the node attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the position of each node. +Positions can be sequences of length 2 with numbers for (x,y) coordinates. +They can also be strings to denote positions in TikZ style, such as (x, y) +or (angle:radius). +If a dict, it should be keyed by node to a position. +If an empty dict, a circular layout is computed by TikZ.</p> +</dd> +<dt><strong>tikz_options</strong><span class="classifier">string</span></dt><dd><p>The tikzpicture options description defining the options for the picture. +Often large scale options like <code class="xref py py-obj docutils literal notranslate"><span class="pre">[scale=2]</span></code>.</p> +</dd> +<dt><strong>default_node_options</strong><span class="classifier">string</span></dt><dd><p>The draw options for a path of nodes. Individual node options override these.</p> +</dd> +<dt><strong>node_options</strong><span class="classifier">string or dict</span></dt><dd><p>The name of the node attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the options for each node. +Or a dict keyed by node to a string holding the options for that node.</p> +</dd> +<dt><strong>node_label</strong><span class="classifier">string or dict</span></dt><dd><p>The name of the node attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the node label (text) +displayed for each node. If the attribute is “” or not present, the node +itself is drawn as a string. LaTeX processing such as <code class="docutils literal notranslate"><span class="pre">"$A_1$"</span></code> is allowed. +Or a dict keyed by node to a string holding the label for that node.</p> +</dd> +<dt><strong>default_edge_options</strong><span class="classifier">string</span></dt><dd><p>The options for the scope drawing all edges. The default is “[-]” for +undirected graphs and “[->]” for directed graphs.</p> +</dd> +<dt><strong>edge_options</strong><span class="classifier">string or dict</span></dt><dd><p>The name of the edge attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the options for each edge. +If the edge is a self-loop and <code class="docutils literal notranslate"><span class="pre">"loop"</span> <span class="pre">not</span> <span class="pre">in</span> <span class="pre">edge_options</span></code> the option +“loop,” is added to the options for the self-loop edge. Hence you can +use “[loop above]” explicitly, but the default is “[loop]”. +Or a dict keyed by edge to a string holding the options for that edge.</p> +</dd> +<dt><strong>edge_label</strong><span class="classifier">string or dict</span></dt><dd><p>The name of the edge attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the edge label (text) +displayed for each edge. If the attribute is “” or not present, no edge +label is drawn. +Or a dict keyed by edge to a string holding the label for that edge.</p> +</dd> +<dt><strong>edge_label_options</strong><span class="classifier">string or dict</span></dt><dd><p>The name of the edge attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the label options for +each edge. For example, “[sloped,above,blue]”. The default is no options. +Or a dict keyed by edge to a string holding the label options for that edge.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt><strong>latex_code</strong><span class="classifier">string</span></dt><dd><p>The text string which draws the desired graph(s) when compiled by LaTeX.</p> +</dd> +</dl> +</dd> +</dl> +<div class="admonition seealso"> +<p class="admonition-title">See also</p> +<dl class="simple"> +<dt><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html#networkx.drawing.nx_latex.to_latex" title="networkx.drawing.nx_latex.to_latex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_latex</span></code></a></dt><dd></dd> +<dt><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html#networkx.drawing.nx_latex.write_latex" title="networkx.drawing.nx_latex.write_latex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">write_latex</span></code></a></dt><dd></dd> +</dl> +</div> +</dd></dl> + +</section> + + + </article> + + + + </div> + + + + <div class="bd-sidebar-secondary bd-toc"> + +<div class="toc-item"> + +<div class="tocsection onthispage"> + <i class="fa-solid fa-list"></i> On this page +</div> +<nav id="bd-toc-nav" class="page-toc"> + <ul class="visible nav section-nav flex-column"> + <li class="toc-h2 nav-item toc-entry"> + <a class="reference internal nav-link" href="#networkx.drawing.nx_latex.to_latex_raw"> + <code class="docutils literal notranslate"> + <span class="pre"> + to_latex_raw() + </span> + </code> + </a> + </li> +</ul> + +</nav> +</div> + +<div class="toc-item"> + +<div id="searchbox"></div> +</div> + +<div class="toc-item"> + +</div> + +<div class="toc-item"> + +</div> + + </div> + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"> + + </div> + </footer> + + </main> + </div> + </div> + + + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../_static/scripts/bootstrap.js?digest=796348d33e8b1d947c94"></script> +<script src="../../_static/scripts/pydata-sphinx-theme.js?digest=796348d33e8b1d947c94"></script> + + <footer class="bd-footer"><div class="bd-footer__inner container"> + + <div class="footer-item"> + +<p class="copyright"> + + © Copyright 2004-2023, NetworkX Developers.<br> + +</p> + + </div> + + <div class="footer-item"> + <p class="theme-version"> + Built with the + <a href="https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html"> + PyData Sphinx Theme + </a> + 0.12.0. +</p> + </div> + + <div class="footer-item"> + +<p class="sphinx-version"> +Created using <a href="http://sphinx-doc.org/">Sphinx</a> 5.2.3.<br> +</p> + + </div> + +</div> + </footer> + </body> +</html>
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href="../utils.html">Utilities</a></li> +<li class="toctree-l1"><a class="reference internal" href="../glossary.html">Glossary</a></li> +</ul> + + </div> +</nav> + </div> + <div class="sidebar-start-items__item"> + </div> + </div> + + + + <div class="sidebar-end-items sidebar-primary__section"> + <div class="sidebar-end-items__item"> + </div> + </div> + + + <div id="rtd-footer-container"></div> + + </div> + <main id="main-content" class="bd-main"> + + + <div class="bd-content"> + <div class="bd-article-container"> + + <div class="bd-header-article"> + + </div> + + + <article class="bd-article" role="main"> + + <section id="write-latex"> +<h1>write_latex<a class="headerlink" href="#write-latex" title="Permalink to this heading">#</a></h1> +<dl class="py function"> +<dt class="sig sig-object py" id="networkx.drawing.nx_latex.write_latex"> +<span class="sig-name descname"><span class="pre">write_latex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Gbunch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">options</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/networkx/drawing/nx_latex.html#write_latex"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#networkx.drawing.nx_latex.write_latex" title="Permalink to this definition">#</a></dt> +<dd><p>Write the latex code to draw the graph(s) onto <code class="xref py py-obj docutils literal notranslate"><span class="pre">path</span></code>.</p> +<p>This convenience function creates the latex drawing code as a string +and writes that to a file ready to be compiled when <code class="xref py py-obj docutils literal notranslate"><span class="pre">as_document</span></code> is True +or ready to be <a href="#id1"><span class="problematic" id="id2">``</span></a>import``ed or <a href="#id3"><span class="problematic" id="id4">`</span></a>include``ed into your main LaTeX document.</p> +<p>The <code class="xref py py-obj docutils literal notranslate"><span class="pre">path</span></code> argument can be a string filename or a file handle to write to.</p> +<dl class="field-list"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl> +<dt><strong>Gbunch</strong><span class="classifier">NetworkX graph or iterable of NetworkX graphs</span></dt><dd><p>If Gbunch is a graph, it is drawn in a figure environment. +If Gbunch is an iterable of graphs, each is drawn in a subfigure +envionment within a single figure environment.</p> +</dd> +<dt><strong>path</strong><span class="classifier">filename</span></dt><dd><p>Filename or file handle to write to</p> +</dd> +<dt><strong>options</strong><span class="classifier">dict</span></dt><dd><p>By default, TikZ is used with options: (others are ignored):</p> +<dl> +<dt>pos<span class="classifier">string or dict or list</span></dt><dd><p>The name of the node attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the position of each node. +Positions can be sequences of length 2 with numbers for (x,y) coordinates. +They can also be strings to denote positions in TikZ style, such as (x, y) +or (angle:radius). +If a dict, it should be keyed by node to a position. +If an empty dict, a circular layout is computed by TikZ. +If you are drawing many graphs in subfigures, use a list of position dicts.</p> +</dd> +<dt>tikz_options<span class="classifier">string</span></dt><dd><p>The tikzpicture options description defining the options for the picture. +Often large scale options like <code class="xref py py-obj docutils literal notranslate"><span class="pre">[scale=2]</span></code>.</p> +</dd> +<dt>default_node_options<span class="classifier">string</span></dt><dd><p>The draw options for a path of nodes. Individual node options override these.</p> +</dd> +<dt>node_options<span class="classifier">string or dict</span></dt><dd><p>The name of the node attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the options for each node. +Or a dict keyed by node to a string holding the options for that node.</p> +</dd> +<dt>node_label<span class="classifier">string or dict</span></dt><dd><p>The name of the node attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the node label (text) +displayed for each node. If the attribute is “” or not present, the node +itself is drawn as a string. LaTeX processing such as <code class="docutils literal notranslate"><span class="pre">"$A_1$"</span></code> is allowed. +Or a dict keyed by node to a string holding the label for that node.</p> +</dd> +<dt>default_edge_options<span class="classifier">string</span></dt><dd><p>The options for the scope drawing all edges. The default is “[-]” for +undirected graphs and “[->]” for directed graphs.</p> +</dd> +<dt>edge_options<span class="classifier">string or dict</span></dt><dd><p>The name of the edge attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the options for each edge. +If the edge is a self-loop and <code class="docutils literal notranslate"><span class="pre">"loop"</span> <span class="pre">not</span> <span class="pre">in</span> <span class="pre">edge_options</span></code> the option +“loop,” is added to the options for the self-loop edge. Hence you can +use “[loop above]” explicitly, but the default is “[loop]”. +Or a dict keyed by edge to a string holding the options for that edge.</p> +</dd> +<dt>edge_label<span class="classifier">string or dict</span></dt><dd><p>The name of the edge attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the edge label (text) +displayed for each edge. If the attribute is “” or not present, no edge +label is drawn. +Or a dict keyed by edge to a string holding the label for that edge.</p> +</dd> +<dt>edge_label_options<span class="classifier">string or dict</span></dt><dd><p>The name of the edge attribute on <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code> that holds the label options for +each edge. For example, “[sloped,above,blue]”. The default is no options. +Or a dict keyed by edge to a string holding the label options for that edge.</p> +</dd> +<dt>caption<span class="classifier">string</span></dt><dd><p>The caption string for the figure environment</p> +</dd> +<dt>latex_label<span class="classifier">string</span></dt><dd><p>The latex label used for the figure for easy referral from the main text</p> +</dd> +<dt>sub_captions<span class="classifier">list of strings</span></dt><dd><p>The sub_caption string for each subfigure in the figure</p> +</dd> +<dt>sub_latex_labels<span class="classifier">list of strings</span></dt><dd><p>The latex label for each subfigure in the figure</p> +</dd> +<dt>n_rows<span class="classifier">int</span></dt><dd><p>The number of rows of subfigures to arrange for multiple graphs</p> +</dd> +<dt>as_document<span class="classifier">bool</span></dt><dd><p>Whether to wrap the latex code in a document envionment for compiling</p> +</dd> +<dt>document_wrapper<span class="classifier">formatted text string with variable <code class="docutils literal notranslate"><span class="pre">content</span></code>.</span></dt><dd><p>This text is called to evaluate the content embedded in a document +environment with a preamble setting up the TikZ syntax.</p> +</dd> +<dt>figure_wrapper<span class="classifier">formatted text string</span></dt><dd><p>This text is evaluated with variables <code class="docutils literal notranslate"><span class="pre">content</span></code>, <code class="docutils literal notranslate"><span class="pre">caption</span></code> and <code class="docutils literal notranslate"><span class="pre">label</span></code>. +It wraps the content and if a caption is provided, adds the latex code for +that caption, and if a label is provided, adds the latex code for a label.</p> +</dd> +<dt>subfigure_wrapper<span class="classifier">formatted text string</span></dt><dd><p>This text evaluate variables <code class="docutils literal notranslate"><span class="pre">size</span></code>, <code class="docutils literal notranslate"><span class="pre">content</span></code>, <code class="docutils literal notranslate"><span class="pre">caption</span></code> and <code class="docutils literal notranslate"><span class="pre">label</span></code>. +It wraps the content and if a caption is provided, adds the latex code for +that caption, and if a label is provided, adds the latex code for a label. +The size is the vertical size of each row of subfigures as a fraction.</p> +</dd> +</dl> +</dd> +</dl> +</dd> +</dl> +<div class="admonition seealso"> +<p class="admonition-title">See also</p> +<dl class="simple"> +<dt><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html#networkx.drawing.nx_latex.to_latex" title="networkx.drawing.nx_latex.to_latex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_latex</span></code></a></dt><dd></dd> +</dl> +</div> +</dd></dl> + +</section> + + + </article> + + + + </div> + + + + <div class="bd-sidebar-secondary bd-toc"> + +<div class="toc-item"> + +<div class="tocsection onthispage"> + <i class="fa-solid fa-list"></i> On this page +</div> +<nav id="bd-toc-nav" class="page-toc"> + <ul class="visible nav section-nav flex-column"> + <li class="toc-h2 nav-item toc-entry"> + <a class="reference internal nav-link" href="#networkx.drawing.nx_latex.write_latex"> + <code class="docutils literal notranslate"> + <span class="pre"> + write_latex() + </span> + </code> + </a> + </li> +</ul> + +</nav> +</div> + +<div class="toc-item"> + +<div id="searchbox"></div> +</div> + +<div class="toc-item"> + +</div> + +<div class="toc-item"> + +</div> + + </div> + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"> + + </div> + </footer> + + </main> + </div> + </div> + + + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../_static/scripts/bootstrap.js?digest=796348d33e8b1d947c94"></script> +<script src="../../_static/scripts/pydata-sphinx-theme.js?digest=796348d33e8b1d947c94"></script> + + <footer class="bd-footer"><div class="bd-footer__inner container"> + + <div class="footer-item"> + +<p class="copyright"> + + © Copyright 2004-2023, NetworkX Developers.<br> + +</p> + + </div> + + <div class="footer-item"> + <p class="theme-version"> + Built with the + <a href="https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html"> + PyData Sphinx Theme + </a> + 0.12.0. +</p> + </div> + + <div class="footer-item"> + +<p class="sphinx-version"> +Created using <a href="http://sphinx-doc.org/">Sphinx</a> 5.2.3.<br> +</p> + + </div> + +</div> + </footer> + </body> +</html>
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class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pydot.graphviz_layout.html b/reference/generated/networkx.drawing.nx_pydot.graphviz_layout.html index e0cccf07..443e07a7 100644 --- a/reference/generated/networkx.drawing.nx_pydot.graphviz_layout.html +++ b/reference/generated/networkx.drawing.nx_pydot.graphviz_layout.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pydot.pydot_layout.html b/reference/generated/networkx.drawing.nx_pydot.pydot_layout.html index 8024464c..0dd78346 100644 --- a/reference/generated/networkx.drawing.nx_pydot.pydot_layout.html +++ b/reference/generated/networkx.drawing.nx_pydot.pydot_layout.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pydot.read_dot.html b/reference/generated/networkx.drawing.nx_pydot.read_dot.html index ca3119b2..0b4078d3 100644 --- a/reference/generated/networkx.drawing.nx_pydot.read_dot.html +++ b/reference/generated/networkx.drawing.nx_pydot.read_dot.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pydot.to_pydot.html b/reference/generated/networkx.drawing.nx_pydot.to_pydot.html index 8ca0d06e..a8f00797 100644 --- a/reference/generated/networkx.drawing.nx_pydot.to_pydot.html +++ b/reference/generated/networkx.drawing.nx_pydot.to_pydot.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pydot.write_dot.html b/reference/generated/networkx.drawing.nx_pydot.write_dot.html index d5cde93b..14bd7771 100644 --- a/reference/generated/networkx.drawing.nx_pydot.write_dot.html +++ b/reference/generated/networkx.drawing.nx_pydot.write_dot.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pylab.draw.html b/reference/generated/networkx.drawing.nx_pylab.draw.html index ee8e4008..18fd0582 100644 --- a/reference/generated/networkx.drawing.nx_pylab.draw.html +++ b/reference/generated/networkx.drawing.nx_pylab.draw.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pylab.draw_circular.html b/reference/generated/networkx.drawing.nx_pylab.draw_circular.html index e14530c9..393655da 100644 --- a/reference/generated/networkx.drawing.nx_pylab.draw_circular.html +++ b/reference/generated/networkx.drawing.nx_pylab.draw_circular.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pylab.draw_kamada_kawai.html b/reference/generated/networkx.drawing.nx_pylab.draw_kamada_kawai.html index 1195fcfc..923f0f39 100644 --- a/reference/generated/networkx.drawing.nx_pylab.draw_kamada_kawai.html +++ b/reference/generated/networkx.drawing.nx_pylab.draw_kamada_kawai.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html b/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html index 9d4f80a1..70d0b1c7 100644 --- a/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html +++ b/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pylab.draw_networkx_edge_labels.html b/reference/generated/networkx.drawing.nx_pylab.draw_networkx_edge_labels.html index 25b43591..f2f53e01 100644 --- a/reference/generated/networkx.drawing.nx_pylab.draw_networkx_edge_labels.html +++ b/reference/generated/networkx.drawing.nx_pylab.draw_networkx_edge_labels.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pylab.draw_networkx_edges.html b/reference/generated/networkx.drawing.nx_pylab.draw_networkx_edges.html index b9be333f..f41458d2 100644 --- a/reference/generated/networkx.drawing.nx_pylab.draw_networkx_edges.html +++ b/reference/generated/networkx.drawing.nx_pylab.draw_networkx_edges.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pylab.draw_networkx_labels.html b/reference/generated/networkx.drawing.nx_pylab.draw_networkx_labels.html index 99ee1521..dd90d1db 100644 --- a/reference/generated/networkx.drawing.nx_pylab.draw_networkx_labels.html +++ b/reference/generated/networkx.drawing.nx_pylab.draw_networkx_labels.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pylab.draw_networkx_nodes.html b/reference/generated/networkx.drawing.nx_pylab.draw_networkx_nodes.html index fa336a19..38c3140b 100644 --- a/reference/generated/networkx.drawing.nx_pylab.draw_networkx_nodes.html +++ b/reference/generated/networkx.drawing.nx_pylab.draw_networkx_nodes.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pylab.draw_planar.html b/reference/generated/networkx.drawing.nx_pylab.draw_planar.html index 1a11fff1..fea11de7 100644 --- a/reference/generated/networkx.drawing.nx_pylab.draw_planar.html +++ b/reference/generated/networkx.drawing.nx_pylab.draw_planar.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pylab.draw_random.html b/reference/generated/networkx.drawing.nx_pylab.draw_random.html index a1d0e8f5..a73d7f6d 100644 --- a/reference/generated/networkx.drawing.nx_pylab.draw_random.html +++ b/reference/generated/networkx.drawing.nx_pylab.draw_random.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pylab.draw_shell.html b/reference/generated/networkx.drawing.nx_pylab.draw_shell.html index fabdebd7..ca8d5458 100644 --- a/reference/generated/networkx.drawing.nx_pylab.draw_shell.html +++ b/reference/generated/networkx.drawing.nx_pylab.draw_shell.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pylab.draw_spectral.html b/reference/generated/networkx.drawing.nx_pylab.draw_spectral.html index 54c3fb0a..f5d133f8 100644 --- a/reference/generated/networkx.drawing.nx_pylab.draw_spectral.html +++ b/reference/generated/networkx.drawing.nx_pylab.draw_spectral.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/generated/networkx.drawing.nx_pylab.draw_spring.html b/reference/generated/networkx.drawing.nx_pylab.draw_spring.html index c8d557d4..a5d6210d 100644 --- a/reference/generated/networkx.drawing.nx_pylab.draw_spring.html +++ b/reference/generated/networkx.drawing.nx_pylab.draw_spring.html @@ -488,6 +488,9 @@ <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spectral_layout.html">spectral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.spiral_layout.html">spiral_layout</a></li> <li class="toctree-l2"><a class="reference internal" href="networkx.drawing.layout.multipartite_layout.html">multipartite_layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex_raw.html">to_latex_raw</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.to_latex.html">to_latex</a></li> +<li class="toctree-l2"><a class="reference internal" href="networkx.drawing.nx_latex.write_latex.html">write_latex</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="../randomness.html">Randomness</a></li> diff --git a/reference/index.html b/reference/index.html index 590e3f06..2367a55c 100644 --- a/reference/index.html +++ b/reference/index.html @@ -675,6 +675,7 @@ <li class="toctree-l2"><a class="reference internal" href="drawing.html#module-networkx.drawing.nx_agraph">Graphviz AGraph (dot)</a></li> <li class="toctree-l2"><a class="reference internal" href="drawing.html#module-networkx.drawing.nx_pydot">Graphviz with pydot</a></li> <li class="toctree-l2"><a class="reference internal" href="drawing.html#module-networkx.drawing.layout">Graph Layout</a></li> +<li class="toctree-l2"><a class="reference internal" href="drawing.html#module-networkx.drawing.nx_latex">LaTeX Code</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="randomness.html">Randomness</a></li> diff --git a/reference/introduction-7.hires.png b/reference/introduction-7.hires.png Binary files differindex ebc417ad..f1171717 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 1a075032..b1354389 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 f59bb09a..8417b6a9 100644 --- a/reference/introduction-7.png +++ b/reference/introduction-7.png diff --git a/reference/introduction.ipynb b/reference/introduction.ipynb index 3cdab788..5c2d176b 100644 --- a/reference/introduction.ipynb +++ b/reference/introduction.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "4b02aa4e", + "id": "7a8960fd", "metadata": {}, "source": [ "## Introduction\n", @@ -34,7 +34,7 @@ { "cell_type": "code", "execution_count": null, - "id": "33a0bce8", + "id": "e3b56c58", "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "484e02b8", + "id": "f82971c9", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -82,7 +82,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5e546053", + "id": "ee8cf743", "metadata": {}, "outputs": [], "source": [ @@ -94,7 +94,7 @@ }, { "cell_type": "markdown", - "id": "74c45c46", + "id": "ffd8b3d9", "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": "3377f258", + "id": "9905e01f", "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "956ea5e2", + "id": "8aa73c16", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -214,7 +214,7 @@ { "cell_type": "code", "execution_count": null, - "id": "76e5fdf4", + "id": "fb4483bc", "metadata": {}, "outputs": [], "source": [ @@ -225,7 +225,7 @@ }, { "cell_type": "markdown", - "id": "09743c25", + "id": "025b8142", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -234,7 +234,7 @@ { "cell_type": "code", "execution_count": null, - "id": "64ee3a79", + "id": "3321272a", "metadata": {}, "outputs": [], "source": [ @@ -246,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "2b898aa9", + "id": "a614e3a5", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -311,7 +311,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f0a9eb6a", + "id": "f4931113", "metadata": {}, "outputs": [], "source": [ @@ -323,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "c31e2ef8", + "id": "21505cdc", "metadata": {}, "source": [ "# Drawing\n", @@ -344,7 +344,7 @@ { "cell_type": "code", "execution_count": null, - "id": "242bcc6c", + "id": "58ec1ac9", "metadata": {}, "outputs": [], "source": [ @@ -358,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "8e6a5b97", + "id": "d7434695", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -398,7 +398,7 @@ { "cell_type": "code", "execution_count": null, - "id": "96fa3f07", + "id": "cab35b17", "metadata": {}, "outputs": [], "source": [ @@ -410,7 +410,7 @@ }, { "cell_type": "markdown", - "id": "c21afb7d", + "id": "5608f047", "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": "49f71f35", + "id": "2320ac72", "metadata": {}, "outputs": [], "source": [ diff --git a/reference/introduction_full.ipynb b/reference/introduction_full.ipynb index cfe83970..2f685085 100644 --- a/reference/introduction_full.ipynb +++ b/reference/introduction_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "4b02aa4e", + "id": "7a8960fd", "metadata": {}, "source": [ "## Introduction\n", @@ -34,13 +34,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "33a0bce8", + "id": "e3b56c58", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:22.982341Z", - "iopub.status.busy": "2023-01-04T14:37:22.981914Z", - "iopub.status.idle": "2023-01-04T14:37:23.059258Z", - "shell.execute_reply": "2023-01-04T14:37:23.058052Z" + "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" } }, "outputs": [], @@ -50,7 +50,7 @@ }, { "cell_type": "markdown", - "id": "484e02b8", + "id": "f82971c9", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -89,13 +89,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "5e546053", + "id": "ee8cf743", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:23.064662Z", - "iopub.status.busy": "2023-01-04T14:37:23.064410Z", - "iopub.status.idle": "2023-01-04T14:37:23.068092Z", - "shell.execute_reply": "2023-01-04T14:37:23.067377Z" + "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" } }, "outputs": [], @@ -108,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "74c45c46", + "id": "ffd8b3d9", "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": "3377f258", + "id": "9905e01f", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:23.071684Z", - "iopub.status.busy": "2023-01-04T14:37:23.071458Z", - "iopub.status.idle": "2023-01-04T14:37:23.074660Z", - "shell.execute_reply": "2023-01-04T14:37:23.074071Z" + "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" } }, "outputs": [], @@ -226,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "956ea5e2", + "id": "8aa73c16", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -235,13 +235,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "76e5fdf4", + "id": "fb4483bc", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:23.077623Z", - "iopub.status.busy": "2023-01-04T14:37:23.077425Z", - "iopub.status.idle": "2023-01-04T14:37:23.080531Z", - "shell.execute_reply": "2023-01-04T14:37:23.079810Z" + "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" } }, "outputs": [], @@ -253,7 +253,7 @@ }, { "cell_type": "markdown", - "id": "09743c25", + "id": "025b8142", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -262,13 +262,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "64ee3a79", + "id": "3321272a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:23.083661Z", - "iopub.status.busy": "2023-01-04T14:37:23.083439Z", - "iopub.status.idle": "2023-01-04T14:37:23.087568Z", - "shell.execute_reply": "2023-01-04T14:37:23.086914Z" + "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" } }, "outputs": [], @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "2b898aa9", + "id": "a614e3a5", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -346,13 +346,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "f0a9eb6a", + "id": "f4931113", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:23.091173Z", - "iopub.status.busy": "2023-01-04T14:37:23.090924Z", - "iopub.status.idle": "2023-01-04T14:37:23.095366Z", - "shell.execute_reply": "2023-01-04T14:37:23.094698Z" + "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" } }, "outputs": [ @@ -373,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "c31e2ef8", + "id": "21505cdc", "metadata": {}, "source": [ "# Drawing\n", @@ -394,19 +394,19 @@ { "cell_type": "code", "execution_count": 7, - "id": "242bcc6c", + "id": "58ec1ac9", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:23.099934Z", - "iopub.status.busy": "2023-01-04T14:37:23.099683Z", - "iopub.status.idle": "2023-01-04T14:37:23.718241Z", - "shell.execute_reply": "2023-01-04T14:37:23.717617Z" + "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" } }, "outputs": [ { "data": { - "image/png": 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\n", 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"text/plain": [ "<Figure size 640x480 with 2 Axes>" ] @@ -426,7 +426,7 @@ }, { "cell_type": "markdown", - "id": "8e6a5b97", + "id": "d7434695", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -466,13 +466,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "96fa3f07", + "id": "cab35b17", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:23.722108Z", - "iopub.status.busy": "2023-01-04T14:37:23.721537Z", - "iopub.status.idle": "2023-01-04T14:37:23.725821Z", - "shell.execute_reply": "2023-01-04T14:37:23.725244Z" + "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" } }, "outputs": [ @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "c21afb7d", + "id": "5608f047", "metadata": {}, "source": [ "The data structure gets morphed slightly for each base graph class.\n", @@ 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href="generated/networkx.drawing.layout.multipartite_layout.html" /> + <link rel="prev" title="write_latex" href="generated/networkx.drawing.nx_latex.write_latex.html" /> <meta name="viewport" content="width=device-width, initial-scale=1" /> <meta name="docsearch:language" content="en"> </head> diff --git a/searchindex.js b/searchindex.js index 665efcee..f3fcb0ce 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", "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", 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[8, 94, 298, 307], "mislead": 8, "That": [8, 97, 132, 165, 212, 221, 227, 295, 385, 436, 465, 525, 535, 555, 588, 657, 671, 672, 673, 674, 691, 704, 717, 791, 862, 907, 943, 988, 1046, 1162, 1210, 1296, 1388, 1404, 1409], "okai": 8, "becaus": [8, 11, 54, 69, 94, 99, 101, 102, 103, 112, 132, 161, 215, 216, 220, 255, 311, 378, 387, 389, 390, 394, 411, 412, 427, 494, 498, 499, 500, 510, 569, 585, 587, 615, 616, 632, 652, 934, 979, 1038, 1236, 1273, 1296, 1303, 1326, 1345, 1350, 1404, 1407, 1416], "AND": [8, 110, 598, 748, 762], "OR": [8, 110, 157, 175, 188, 856, 869, 877, 901, 937, 947, 950, 959, 982, 992], "symmetr": [8, 145, 148, 237, 545, 586, 593, 761, 1173, 1192, 1235, 1246, 1250, 1251, 1256, 1258, 1269, 1320, 1321, 1387], "It": [8, 52, 56, 58, 92, 93, 94, 97, 99, 101, 102, 104, 107, 110, 112, 115, 132, 172, 184, 207, 214, 215, 216, 229, 230, 231, 249, 260, 261, 262, 264, 278, 310, 316, 324, 325, 326, 343, 346, 347, 351, 353, 412, 414, 415, 416, 417, 418, 419, 429, 438, 440, 452, 457, 464, 480, 496, 500, 508, 530, 540, 545, 559, 560, 565, 566, 567, 582, 588, 594, 595, 598, 600, 601, 615, 619, 628, 629, 630, 652, 658, 659, 663, 671, 674, 692, 717, 718, 719, 760, 761, 762, 791, 796, 868, 873, 892, 913, 916, 928, 949, 955, 973, 994, 998, 1010, 1012, 1013, 1018, 1037, 1038, 1039, 1040, 1054, 1117, 1170, 1174, 1200, 1201, 1206, 1207, 1210, 1217, 1223, 1227, 1234, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1253, 1254, 1258, 1261, 1263, 1264, 1269, 1275, 1276, 1277, 1280, 1296, 1297, 1323, 1324, 1326, 1328, 1343, 1382, 1383, 1393, 1395, 1398, 1402, 1404, 1407, 1408, 1409, 1411, 1412, 1413, 1426], "just": [8, 99, 102, 103, 104, 184, 199, 338, 374, 439, 464, 559, 560, 577, 660, 661, 662, 692, 791, 873, 887, 916, 925, 946, 955, 960, 968, 991, 998, 1007, 1120, 1126, 1229, 1278, 1279, 1296, 1328, 1393, 1404, 1406], "operand": 8, "predict": [8, 567, 568, 569, 570, 571, 572, 573, 574, 591, 592, 758, 1325, 1402, 1406, 1412], "henc": [8, 168, 189, 521, 865, 878, 910, 946, 960, 991, 1059, 1202, 1383], "doe": [8, 77, 93, 94, 99, 101, 102, 103, 104, 114, 115, 132, 147, 153, 154, 165, 168, 189, 207, 208, 227, 228, 229, 230, 231, 232, 293, 308, 339, 340, 342, 343, 352, 357, 373, 382, 385, 410, 414, 426, 450, 469, 494, 495, 496, 497, 498, 499, 500, 502, 503, 506, 507, 509, 510, 511, 512, 534, 544, 549, 550, 551, 564, 566, 583, 584, 586, 589, 601, 612, 626, 627, 678, 691, 693, 694, 698, 699, 717, 718, 721, 722, 723, 724, 725, 726, 762, 862, 865, 878, 892, 907, 910, 928, 943, 946, 960, 973, 988, 991, 1010, 1038, 1043, 1066, 1070, 1072, 1081, 1102, 1103, 1105, 1106, 1107, 1109, 1114, 1173, 1175, 1177, 1192, 1207, 1222, 1223, 1227, 1229, 1234, 1241, 1296, 1300, 1303, 1326, 1333, 1334, 1341, 1342, 1344, 1351, 1353, 1354, 1355, 1356, 1357, 1358, 1371, 1379, 1380, 1381, 1383, 1393, 1404, 1405, 1406, 1410, 1417, 1426], "necessarili": [8, 99, 341, 451, 483, 559, 560, 641, 643, 1038, 1219], "behav": [8, 88, 103, 159, 190, 200, 220, 351, 858, 879, 888, 903, 939, 969, 984, 1229, 1296, 1395, 1404], "everi": [8, 11, 57, 88, 93, 109, 112, 120, 144, 157, 161, 177, 211, 212, 220, 221, 229, 230, 231, 235, 243, 264, 287, 295, 300, 324, 325, 343, 352, 380, 397, 437, 439, 440, 450, 462, 471, 472, 473, 474, 475, 477, 483, 484, 491, 512, 516, 565, 606, 614, 615, 619, 632, 633, 635, 636, 663, 685, 687, 688, 717, 718, 791, 856, 901, 937, 982, 1052, 1053, 1054, 1070, 1071, 1072, 1085, 1086, 1102, 1103, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1117, 1148, 1162, 1195, 1216, 1217, 1257, 1264, 1278, 1279, 1296, 1407], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 166, 188, 491, 678, 863, 877, 944, 959, 1177, 1207, 1208, 1404, 1406, 1407, 1426], "ha": [8, 11, 16, 44, 67, 88, 91, 93, 94, 95, 97, 99, 100, 101, 102, 103, 105, 107, 110, 112, 116, 120, 127, 152, 161, 165, 166, 173, 174, 175, 184, 188, 198, 207, 212, 214, 215, 219, 220, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 241, 242, 243, 244, 247, 249, 252, 269, 271, 272, 273, 274, 275, 276, 282, 289, 291, 293, 294, 295, 300, 305, 310, 324, 331, 343, 352, 355, 356, 363, 364, 365, 373, 378, 380, 381, 383, 384, 385, 386, 391, 393, 394, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 424, 427, 428, 429, 439, 450, 458, 460, 466, 467, 468, 471, 472, 473, 474, 475, 476, 477, 480, 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 522, 564, 566, 577, 578, 581, 590, 593, 605, 607, 610, 611, 622, 623, 624, 628, 629, 630, 632, 633, 634, 635, 636, 638, 646, 647, 649, 652, 657, 658, 682, 688, 690, 692, 697, 711, 717, 718, 729, 730, 731, 739, 749, 786, 791, 854, 862, 863, 869, 873, 877, 886, 892, 899, 907, 908, 916, 924, 928, 935, 943, 944, 948, 950, 955, 959, 967, 973, 980, 988, 989, 993, 998, 1006, 1010, 1040, 1043, 1045, 1066, 1068, 1070, 1072, 1075, 1080, 1084, 1098, 1099, 1101, 1102, 1103, 1105, 1122, 1130, 1145, 1154, 1160, 1162, 1165, 1176, 1180, 1185, 1193, 1195, 1196, 1197, 1198, 1199, 1207, 1210, 1211, 1215, 1217, 1222, 1234, 1239, 1243, 1244, 1248, 1249, 1254, 1259, 1261, 1264, 1267, 1269, 1270, 1272, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1283, 1284, 1285, 1286, 1289, 1291, 1293, 1296, 1300, 1326, 1328, 1330, 1333, 1334, 1353, 1354, 1371, 1372, 1379, 1382, 1393, 1394, 1395, 1398, 1403, 1404, 1405, 1406, 1407, 1409, 1413, 1414, 1416, 1423, 1425], "output": [8, 13, 16, 89, 93, 101, 102, 103, 109, 197, 287, 288, 345, 374, 380, 494, 498, 499, 509, 510, 575, 588, 677, 678, 691, 722, 1045, 1193, 1197, 1199, 1269, 1296, 1326, 1334, 1341, 1344, 1355, 1358, 1399, 1402, 1404, 1406, 1411, 1413, 1414, 1426], "two": [8, 11, 16, 27, 34, 38, 43, 54, 55, 57, 58, 65, 67, 71, 88, 93, 95, 99, 100, 102, 109, 112, 114, 115, 120, 132, 151, 171, 175, 184, 185, 188, 202, 207, 211, 212, 213, 214, 215, 216, 217, 220, 221, 226, 227, 230, 231, 232, 245, 249, 251, 252, 253, 257, 258, 260, 261, 262, 265, 269, 270, 271, 272, 273, 274, 275, 276, 282, 285, 286, 287, 289, 305, 311, 315, 316, 322, 326, 329, 330, 337, 341, 343, 345, 351, 352, 358, 359, 377, 380, 381, 383, 391, 411, 412, 419, 423, 428, 429, 430, 431, 442, 443, 444, 445, 447, 452, 453, 454, 457, 462, 471, 472, 473, 474, 475, 476, 480, 491, 494, 498, 499, 500, 502, 503, 506, 508, 509, 510, 511, 521, 545, 549, 550, 551, 555, 559, 560, 561, 562, 563, 564, 565, 566, 568, 569, 572, 574, 578, 584, 585, 586, 587, 588, 593, 598, 605, 607, 608, 610, 611, 615, 619, 626, 627, 629, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 675, 676, 680, 692, 694, 731, 732, 738, 739, 760, 761, 762, 780, 786, 791, 796, 853, 867, 869, 873, 874, 877, 890, 892, 898, 912, 916, 917, 926, 928, 934, 946, 948, 950, 955, 956, 959, 960, 971, 973, 979, 991, 993, 998, 999, 1008, 1010, 1019, 1020, 1021, 1022, 1036, 1037, 1039, 1040, 1056, 1084, 1088, 1098, 1100, 1101, 1106, 1107, 1108, 1109, 1114, 1116, 1134, 1146, 1147, 1149, 1151, 1152, 1156, 1174, 1185, 1186, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1204, 1207, 1210, 1211, 1215, 1217, 1218, 1243, 1244, 1253, 1271, 1272, 1275, 1276, 1294, 1295, 1296, 1323, 1324, 1326, 1328, 1359, 1360, 1363, 1393, 1394, 1395, 1397, 1402, 1404, 1405, 1406, 1407, 1410, 1411, 1413, 1425], "layer": [8, 36, 55, 61, 67, 103, 438, 705, 1038, 1109, 1420], "third": [8, 102, 114, 249, 422, 467, 585, 587, 734, 736, 1217, 1226, 1262, 1263, 1326, 1407], "appear": [8, 83, 93, 95, 99, 100, 102, 179, 204, 230, 231, 238, 243, 246, 247, 277, 363, 364, 365, 378, 451, 452, 453, 455, 466, 470, 584, 585, 587, 588, 675, 679, 707, 730, 734, 736, 891, 972, 1036, 1088, 1102, 1136, 1150, 1152, 1154, 1157, 1159, 1187, 1188, 1277, 1282, 1323, 1324, 1345, 1348, 1349, 1350, 1382, 1407, 1413, 1414], "both": [8, 52, 55, 92, 93, 94, 100, 101, 102, 103, 115, 161, 164, 204, 214, 215, 216, 217, 240, 257, 258, 259, 264, 282, 286, 287, 289, 337, 358, 379, 383, 415, 417, 418, 419, 423, 427, 440, 470, 502, 506, 545, 575, 581, 598, 600, 601, 602, 603, 604, 605, 606, 607, 610, 611, 615, 621, 635, 636, 653, 654, 655, 676, 711, 720, 760, 761, 762, 782, 891, 972, 1020, 1036, 1066, 1075, 1080, 1084, 1088, 1097, 1120, 1126, 1144, 1165, 1189, 1192, 1199, 1207, 1210, 1211, 1213, 1215, 1282, 1296, 1326, 1328, 1358, 1363, 1364, 1387, 1393, 1395, 1402, 1413, 1416, 1417, 1425, 1426], "negat": 8, "sole": [8, 786, 1278, 1279, 1326], "fourth": [8, 230, 231, 1326, 1404], "digraph": [8, 10, 11, 16, 21, 25, 41, 45, 56, 61, 67, 69, 70, 82, 88, 101, 102, 115, 132, 151, 152, 156, 157, 158, 160, 162, 163, 165, 166, 168, 170, 171, 172, 175, 176, 185, 186, 187, 188, 189, 192, 193, 194, 195, 196, 198, 199, 202, 204, 207, 208, 216, 227, 229, 230, 231, 240, 246, 247, 299, 308, 314, 318, 319, 321, 327, 328, 334, 335, 336, 337, 339, 340, 342, 343, 388, 391, 393, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 430, 431, 437, 450, 452, 453, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 481, 482, 492, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 513, 514, 518, 519, 523, 555, 566, 575, 576, 577, 588, 590, 613, 615, 623, 630, 636, 643, 644, 652, 656, 657, 658, 659, 663, 678, 688, 690, 693, 696, 697, 698, 699, 700, 701, 702, 706, 707, 708, 709, 711, 716, 717, 718, 719, 721, 722, 723, 724, 725, 726, 740, 741, 744, 745, 746, 747, 748, 749, 750, 752, 760, 789, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 905, 906, 907, 908, 911, 912, 913, 915, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 935, 936, 937, 938, 940, 941, 942, 943, 949, 957, 958, 963, 964, 965, 966, 967, 968, 972, 973, 974, 975, 977, 978, 980, 981, 982, 983, 985, 986, 987, 988, 989, 994, 996, 1000, 1001, 1003, 1004, 1005, 1006, 1007, 1010, 1035, 1037, 1038, 1039, 1040, 1041, 1052, 1062, 1066, 1070, 1072, 1075, 1080, 1083, 1084, 1098, 1099, 1101, 1118, 1135, 1150, 1154, 1168, 1169, 1170, 1173, 1177, 1178, 1180, 1182, 1183, 1184, 1185, 1189, 1217, 1270, 1272, 1273, 1274, 1283, 1284, 1287, 1290, 1292, 1298, 1323, 1326, 1333, 1337, 1342, 1356, 1357, 1362, 1365, 1366, 1371, 1393, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1424, 1425, 1426], "add_nod": [8, 11, 26, 34, 69, 74, 89, 102, 157, 184, 246, 339, 340, 398, 422, 491, 492, 496, 504, 505, 508, 522, 523, 605, 607, 610, 611, 691, 796, 856, 873, 901, 916, 937, 955, 982, 998, 1037, 1039, 1040, 1086, 1275, 1326, 1345, 1407, 1408, 1417, 1426], "get_node_attribut": [8, 39, 44, 71, 1213, 1404], "600": [8, 10, 12], "font_siz": [8, 16, 21, 25, 32, 35, 38, 45, 46, 1133, 1134, 1136], "22": [8, 35, 64, 66, 383, 384, 1271, 1323, 1403, 1408, 1412, 1422], "multipartite_layout": [8, 36, 61, 67, 1412, 1414, 1420], "subset_kei": [8, 36, 61, 67, 1109], "equal": [8, 36, 81, 144, 214, 215, 216, 230, 231, 238, 269, 271, 273, 276, 288, 297, 298, 300, 303, 306, 307, 310, 311, 312, 315, 316, 320, 323, 324, 325, 329, 330, 331, 373, 410, 411, 412, 413, 418, 419, 428, 471, 474, 476, 491, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 525, 535, 545, 552, 553, 554, 555, 568, 572, 605, 623, 657, 671, 672, 673, 674, 687, 688, 689, 690, 721, 722, 740, 741, 753, 761, 791, 1112, 1116, 1162, 1165, 1198, 1204, 1230, 1239, 1271, 1280, 1291, 1307, 1309, 1312, 1398, 1399], "119": [8, 17], "plot_circuit": [8, 17], "southern": [9, 1265], "women": [9, 1265, 1398, 1406], "unipartit": [9, 115, 258, 259, 358], "properti": [9, 11, 18, 22, 33, 63, 86, 101, 102, 103, 112, 134, 159, 161, 166, 168, 175, 176, 179, 184, 188, 189, 190, 200, 284, 285, 286, 287, 288, 363, 364, 365, 388, 476, 500, 545, 569, 619, 685, 858, 863, 865, 869, 870, 873, 877, 878, 879, 888, 903, 908, 910, 916, 939, 944, 946, 950, 951, 955, 959, 960, 969, 984, 989, 991, 998, 1085, 1086, 1122, 1134, 1136, 1193, 1202, 1217, 1219, 1269, 1283, 1284, 1326, 1328, 1383, 1398, 1405, 1406, 1407, 1408, 1413, 1417, 1426], "These": [9, 52, 58, 73, 79, 86, 93, 94, 105, 112, 336, 385, 494, 512, 559, 671, 673, 732, 748, 779, 786, 1038, 1045, 1047, 1323, 1326, 1385, 1387, 1392, 1394, 1395, 1397, 1399, 1404, 1405, 1411, 1426], "were": [9, 65, 88, 99, 101, 104, 215, 216, 220, 289, 305, 410, 437, 460, 588, 962, 1002, 1199, 1393, 1395, 1399, 1402, 1405, 1406, 1407, 1413, 1416], "et": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202], "al": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202, 1407, 1413], "1930": [9, 1396], "thei": [9, 54, 58, 65, 71, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 112, 132, 151, 165, 207, 213, 220, 249, 285, 287, 288, 296, 297, 298, 301, 302, 306, 307, 308, 309, 351, 362, 374, 391, 396, 427, 451, 452, 453, 454, 464, 465, 471, 472, 473, 474, 475, 496, 504, 505, 508, 512, 546, 547, 548, 559, 560, 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1279, 1323, 1324, 1326, 1329, 1330, 1346, 1347, 1388, 1393, 1406], "observ": [9, 13, 132, 223, 1414, 1426], "attend": 9, "14": [9, 11, 16, 19, 25, 38, 44, 64, 66, 71, 229, 230, 231, 383, 384, 405, 406, 501, 619, 690, 1150, 1242, 1250, 1262, 1406, 1408, 1426], "event": [9, 25, 99, 100, 110, 1165, 1229, 1300], "18": [9, 44, 64, 66, 93, 324, 325, 345, 383, 384, 618, 1169, 1249, 1255, 1258, 1260, 1263, 1269, 1393, 1406, 1416, 1417, 1421, 1426], "bipartit": [9, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 350, 351, 358, 377, 439, 440, 443, 581, 588, 758, 1043, 1106, 1151, 1203, 1204, 1205, 1265, 1325, 1395, 1398, 1399, 1400, 1401, 1406, 1407, 1411, 1413, 1417, 1421, 1425], "biadjac": [9, 282, 283, 1400, 1406], "7": [9, 12, 14, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 89, 99, 101, 102, 115, 125, 151, 158, 170, 171, 192, 207, 232, 268, 297, 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58], "libpys": [54, 55, 57, 58], "cg": [54, 102, 296, 301, 302, 303, 308, 309, 323, 588], "voronoi_fram": 54, "contextili": [54, 55, 57], "add_basemap": [54, 55, 57], "geopackag": [54, 55, 56, 57], "sqlite": [54, 57], "reli": [54, 57, 99, 103, 362, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 502, 503, 506, 507, 1393, 1407, 1411, 1425], "fiona": [54, 57], "level": [54, 57, 101, 103, 104, 106, 111, 112, 115, 125, 165, 220, 322, 334, 336, 374, 380, 381, 387, 389, 390, 394, 423, 427, 640, 691, 770, 786, 862, 907, 943, 988, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1094, 1108, 1155, 1202, 1207, 1208, 1236, 1296, 1323, 1328, 1396, 1399, 1407, 1412, 1413, 1414], "interfac": [54, 57, 58, 75, 76, 96, 98, 99, 101, 102, 107, 109, 110, 184, 429, 496, 673, 758, 761, 762, 780, 873, 916, 955, 998, 1042, 1044, 1326, 1328, 1393, 1396, 1398, 1402, 1404, 1405, 1406, 1409, 1413, 1414, 1426], "kind": [54, 57, 58, 92, 93, 94, 99, 208, 466, 722, 1202, 1326, 1383], "read_fil": [54, 55, 57, 58], "cholera_cas": [54, 57], "gpkg": [54, 56, 57], "correctli": [54, 164, 324, 325, 1393, 1404, 1406, 1411, 1412, 1419], "construct": [54, 55, 56, 57, 58, 67, 94, 102, 227, 229, 230, 231, 232, 269, 273, 276, 352, 423, 450, 460, 513, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 609, 685, 695, 708, 716, 732, 1046, 1047, 1052, 1053, 1101, 1102, 1103, 1104, 1105, 1153, 1154, 1175, 1177, 1178, 1180, 1186, 1190, 1191, 1192, 1195, 1203, 1207, 1208, 1209, 1210, 1217, 1219, 1222, 1229, 1236, 1251, 1259, 1263, 1269, 1272, 1278, 1279, 1296, 1323, 1327, 1395, 1399, 1406, 1409, 1415], "column_stack": [54, 57, 58], "could": [54, 93, 101, 102, 103, 165, 215, 216, 224, 581, 679, 862, 907, 943, 988, 1066, 1094, 1102, 1103, 1120, 1126, 1174, 1296, 1300, 1326, 1393, 1404, 1414, 1426], "present": [54, 58, 93, 107, 110, 132, 184, 220, 226, 315, 316, 330, 357, 359, 429, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 567, 581, 594, 595, 597, 600, 601, 604, 632, 633, 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991, 1328, 1402, 1408, 1409, 1413], "cell": [54, 58, 752, 758, 1271, 1323, 1325, 1407], "convex": 54, "hull": 54, "contigu": [54, 58, 438, 1102, 1277, 1278], "being": [54, 92, 94, 95, 99, 101, 102, 109, 217, 227, 464, 465, 466, 559, 560, 711, 1038, 1045, 1144, 1175, 1236, 1296, 1393, 1394, 1407, 1412, 1413, 1416, 1425], "face": [54, 101, 102, 115, 183, 206, 615, 1043, 1262, 1263], "analogu": [54, 58, 230], "von": 54, "neuman": 54, "neighborhood": [54, 58, 114, 213, 240, 249, 285, 286, 324, 325, 512, 690, 786, 1189], "cardin": [54, 115, 218, 221, 264, 277, 278, 279, 280, 339, 341, 343, 345, 414, 415, 416, 417, 428, 440, 441, 444, 446, 581, 583, 611, 691, 1395], "regular": [54, 58, 65, 88, 99, 477, 478, 479, 480, 622, 623, 624, 758, 1038, 1185, 1190, 1191, 1192, 1239, 1245, 1250, 1251, 1254, 1258, 1261, 1262, 1263, 1264, 1280, 1290, 1323, 1325, 1394, 1395, 1398, 1406, 1412, 1413], "come": [54, 93, 100, 101, 102, 517, 577, 588, 598, 608, 677, 698, 699, 1046, 1243, 1326, 1402, 1413], 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1424, 1425], "barnowski": [91, 103, 108, 1412, 1413, 1414, 1416, 1417, 1419, 1420, 1421, 1422, 1424, 1425], "rossbar": [91, 103, 105, 108, 1412], "stefan": [91, 108, 1411, 1412, 1413, 1415, 1417], "van": [91, 108, 380, 511, 512, 1239, 1407, 1411, 1412, 1413, 1414, 1415, 1417, 1425], "der": [91, 108, 1411, 1412, 1413, 1415, 1417], "walt": [91, 108, 1411, 1412, 1413, 1415, 1417], "stefanv": [91, 108, 1411], "vadim": [91, 108, 1414], "abzalov": [91, 108], "vdshk": [91, 105, 108, 1414], "dimitrio": [91, 108, 128, 1413, 1414, 1421], "papageorgi": [91, 108, 1413, 1414, 1421], "z3y50n": [91, 105, 108, 1414], "benjamin": [91, 108, 1409, 1410], "edward": [91, 108, 1409, 1410], "bjedward": [91, 108], "chebee7i": [91, 108, 1407, 1409], "jfinkel": [91, 108, 1407], "jordi": [91, 108, 1407, 1408], "torrent": [91, 108, 220, 427, 1407, 1408], "jtorrent": [91, 108], "lo\u00efc": [91, 108], "s\u00e9guin": [91, 108], "charbonneau": [91, 108], "loicseguin": [91, 108], "ysitu": [91, 108, 1402], "feel": 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"brodka": 91, "gutfraind": 91, "alessandro": [91, 1407], "luongo": [91, 1407], "huston": [91, 1408], "heding": [91, 1408], "olegu": 91, "sagarra": 91, "kazimierz": [91, 1412], "wojciechowski": [91, 1412], "gaetano": [91, 1412], "pietro": 91, "paolo": [91, 320, 1412], "carpinato": [91, 1412], "carghaez": 91, "gaetanocarpinato": 91, "arun": 91, "nampal": 91, "arunwis": [91, 1412], "b57845b7": 91, "duve": [91, 1412], "shashi": [91, 1412], "prakash": 91, "tripathi": [91, 517, 1412], "itsshavar": 91, "itsshashitripathi": 91, "danni": [91, 1412], "niquett": [91, 1412], "trimbl": [91, 1412, 1414], "jamestrimbl": 91, "matthia": [91, 1412, 1413, 1416, 1422], "bruhn": [91, 1412], "mbruhn": 91, "philip": 91, "boalch": 91, "knyazev": [91, 1414], "sultan": [91, 1414, 1416, 1422, 1425], "orazbayev": [91, 1414, 1416, 1422, 1425], "sultanorazbayev": 91, "supplementari": 91, "incomplet": [91, 112, 1406, 1408], "commit": [91, 92, 93, 94, 99, 100, 105, 106, 1407, 1409, 1411, 1412, 1413, 1414, 1415, 1417, 1419, 1425], "git": [91, 93, 94, 97, 99, 106, 111, 1416, 1419], "repositori": [91, 93, 99, 106, 1406], "grep": [91, 97], "uniq": 91, "histor": [91, 99, 101, 1217], "earlier": [91, 299, 363, 364, 365, 739, 1199, 1393, 1402, 1408, 1413], "acknowledg": [91, 92, 96], "nonlinear": [91, 1213, 1215, 1222], "lo": 91, "alamo": 91, "nation": [91, 92, 457, 720], "laboratori": 91, "pi": [91, 653, 1114], "program": [91, 105, 110, 362, 455, 488, 490, 678, 1119, 1120, 1125, 1226, 1302, 1324, 1326, 1328, 1414], "offic": [91, 1267], "complex": [91, 94, 101, 105, 210, 217, 229, 230, 231, 239, 240, 274, 290, 293, 294, 300, 314, 327, 330, 331, 332, 333, 337, 346, 347, 355, 356, 371, 372, 376, 385, 386, 423, 434, 438, 452, 453, 494, 500, 519, 520, 521, 574, 616, 619, 625, 659, 692, 698, 699, 749, 1120, 1126, 1175, 1179, 1196, 1197, 1198, 1341, 1342, 1344, 1381, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "depart": [91, 494], "physic": [91, 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1099, 1100, 1103, 1412], "stat": [93, 244, 380, 381, 748, 750, 1193, 1197, 1224, 1228, 1232], "optim": [93, 107, 112, 125, 208, 212, 226, 230, 231, 330, 353, 362, 380, 381, 382, 385, 422, 429, 496, 508, 672, 692, 720, 722, 723, 724, 725, 726, 729, 731, 732, 760, 780, 1108, 1117, 1235, 1320, 1321, 1402, 1411, 1412, 1416], "subpackag": [93, 767, 1326, 1413, 1425], "particular": [93, 97, 110, 115, 357, 374, 517, 618, 750, 1175, 1278, 1279, 1328, 1350, 1409], "decor": [93, 102, 103, 1045, 1046, 1047, 1297, 1298, 1299, 1300, 1301, 1325, 1405, 1407, 1411, 1413, 1414, 1417], "not_implemented_for": [93, 1296, 1407, 1417], "doesn": [93, 94, 97, 101, 102, 156, 170, 561, 562, 563, 761, 796, 855, 866, 900, 911, 936, 947, 981, 992, 1037, 1039, 1040, 1117, 1175, 1177, 1179, 1216, 1222, 1296, 1326, 1404, 1406, 1407, 1412, 1414, 1425], "function_not_for_multidigraph": 93, "function_only_for_graph": 93, "framework": [93, 102, 1358], "submodul": [93, 1413], "specif": [93, 96, 99, 101, 107, 110, 111, 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"machin": [93, 312, 331, 494, 511, 512, 762, 1396, 1406, 1413], "snapshot": 93, "unreach": 93, "pyarg": [93, 111, 1038], "tell": [93, 99, 102, 760, 1275, 1278, 1279, 1296, 1328, 1412], "compar": [93, 464, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 559, 560, 561, 562, 563, 615, 760, 782, 1165, 1302, 1414], "baselin": [93, 1134, 1136], "ones": [93, 99, 107, 109, 282, 680, 1038, 1395, 1402, 1404], "savefig": [93, 1426], "mpl_image_compar": 93, "test_barbel": 93, "barbel": [93, 293, 294, 391, 424, 1146, 1157, 1276, 1426], "conduct": [93, 96, 100, 109, 447, 448, 758], "contributor": [94, 96, 99, 105, 106, 110, 1271, 1323, 1403], "shepherd": [94, 99], "mission": [94, 96, 97, 100, 107], "approv": [94, 100], "nuclear": 94, "launch": 94, "carefulli": 94, "clean": [94, 106, 530, 540, 1300, 1406, 1407, 1411, 1413, 1420], "nearli": 94, "volunt": [94, 107, 1413], "tremend": 94, "felt": 94, "evalu": [94, 130, 152, 157, 158, 195, 330, 618, 619, 626, 627, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1296, 1417], "novic": 94, "strongli": [94, 217, 232, 388, 391, 397, 398, 399, 403, 405, 406, 423, 480, 491, 492, 519, 588, 633, 697, 699, 751, 753, 1185, 1402, 1406, 1411, 1414, 1417, 1425], "mentorship": [94, 1413], "handhold": 94, "liber": 94, "workflow": [94, 96, 97, 100, 106, 1413, 1420], "realiz": [94, 513, 514, 515, 516, 517, 518, 693, 1175, 1177, 1180, 1207, 1208, 1209, 1210, 1222, 1264], "gentl": 94, "abandon": 94, "difficult": [94, 1405], "carri": [94, 100, 508], "polici": [94, 96, 99, 1412, 1414], "readabl": [94, 107, 109, 169, 172, 460, 868, 913, 949, 994, 1393, 1414], "effici": [94, 102, 112, 212, 275, 290, 377, 387, 389, 390, 392, 394, 399, 405, 406, 407, 422, 425, 426, 486, 487, 508, 512, 581, 614, 680, 688, 691, 698, 699, 758, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1179, 1203, 1230, 1325, 1386, 1390, 1398, 1399, 1406, 1407, 1408, 1411, 1413], "explor": [94, 105, 107, 110, 704, 711, 717], "corner": [94, 1407, 1414], "tempt": 94, "nitpicki": 94, "spell": [94, 1406, 1412, 1413], "suggest": [94, 102, 105, 632, 635, 636, 1165, 1326, 1402, 1406, 1412, 1414, 1425], "latter": [94, 100, 102, 440, 729, 731, 791, 1299], "choic": [94, 102, 204, 385, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 479, 502, 503, 506, 507, 734, 735, 736, 737, 780, 891, 972, 1038, 1042, 1225, 1241, 1280, 1326, 1426], "wish": [94, 619, 1066, 1393], "bring": [94, 101, 566], "advis": [94, 110, 1414], "aris": [94, 110, 238, 243, 1217, 1245], "experienc": 94, "credit": [94, 105], "send": [94, 99, 496, 497, 501, 504, 505, 508, 1393, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "notif": 94, "maintain": [94, 95, 99, 100, 103, 105, 107, 109, 230, 231, 614, 796, 1037, 1039, 1040, 1406, 1425], "concern": [94, 101, 103, 132, 789, 791, 1382], "mere": [94, 1146, 1157], "understood": 94, "made": [94, 99, 100, 102, 222, 282, 284, 285, 286, 287, 288, 324, 325, 331, 693, 694, 1122, 1210, 1326, 1393, 1403, 1404, 1407, 1412, 1425], "freeli": 94, "consult": [94, 111], "extern": [94, 107, 619, 1326, 1383, 1407], "insight": 94, "opportun": [94, 99], "patch": [94, 99, 102, 1042, 1133, 1135, 1412, 1413], "vouch": 94, "fulli": [94, 761, 1042, 1188], "behind": [94, 105], "clarif": [94, 299, 322], "deem": 94, "nich": 94, "devot": 94, "sustain": [94, 96], "effort": [94, 107, 1326], "priorit": 94, "similarli": [94, 103, 115, 207, 356, 598, 621, 796, 892, 928, 973, 1010, 1037, 1039, 1040, 1148, 1175, 1177, 1193, 1198, 1207, 1296, 1394, 1404, 1426], "worth": [94, 761, 1426], "mainten": 94, "burden": 94, "necessari": [94, 95, 100, 104, 527, 537, 954, 997, 1135, 1137, 1296, 1406, 1412], "valid": [94, 101, 161, 177, 256, 277, 278, 281, 282, 377, 386, 439, 458, 464, 466, 497, 513, 514, 515, 516, 517, 518, 559, 560, 578, 579, 580, 588, 614, 615, 734, 735, 736, 737, 746, 758, 1038, 1043, 1071, 1087, 1100, 1104, 1105, 1165, 1187, 1193, 1237, 1238, 1274, 1278, 1279, 1296, 1331, 1334, 1407, 1412, 1413, 1414, 1417, 1419, 1422, 1425], "wari": 94, "alien": 94, "visibl": [94, 97], "thread": [94, 97, 99, 103, 104, 1413], "appeal": [94, 100], "empow": 94, "regardless": [94, 99, 1135, 1191, 1404], "outcom": [94, 105, 1036, 1088, 1382, 1417], "past": [94, 106, 1405], "pep8": [94, 1407, 1412, 1416], "pep257": 94, "superset": [94, 582], "stackoverflow": 94, "monitor": [94, 101], "signatur": [95, 97, 103, 109, 545, 1045, 1296, 1399, 1404, 1407, 1413, 1419, 1422, 1425], "buggi": 95, "usual": [95, 101, 168, 176, 189, 291, 292, 329, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 438, 440, 467, 615, 753, 762, 796, 865, 870, 878, 910, 946, 951, 960, 991, 1039, 1040, 1045, 1094, 1174, 1199, 1217, 1272, 1296, 1326, 1403], "minor": [95, 100, 106, 584, 758, 1325, 1394, 1395, 1403, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "strict": [95, 110, 214, 215, 216, 619, 1408, 1413], "rule": [95, 100, 199, 508, 760, 887, 925, 968, 1007, 1061, 1082, 1144, 1298], "procedur": [95, 97, 99, 217, 220, 281, 305, 377, 508, 680, 1188, 1417], "upon": [95, 102, 580, 1296, 1413, 1416], "justif": [95, 104], "literal_string": [95, 1345, 1350, 1384, 1412], "literal_destring": [95, 1347, 1349, 1384, 1412], "coreview": [95, 1413], "filter": [95, 322, 453, 1036, 1061, 1082, 1088, 1269, 1324, 1325, 1413], "link_analysi": [95, 1405], "pagerank_alg": [95, 1405], "replac": [95, 99, 102, 103, 202, 232, 270, 385, 411, 412, 430, 431, 512, 583, 796, 890, 926, 934, 971, 979, 1008, 1037, 1039, 1040, 1051, 1094, 1225, 1241, 1295, 1296, 1297, 1311, 1317, 1326, 1347, 1363, 1364, 1393, 1394, 1396, 1399, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1417, 1422, 1424, 1425], "pagerank": [95, 311, 312, 324, 325, 565, 758, 1283, 1284, 1394, 1398, 1405, 1406, 1407, 1413], "pagerank_scipi": [95, 1405, 1411, 1413], "renam": [95, 102, 106, 597, 601, 604, 609, 1295, 1348, 1349, 1357, 1394, 1407, 1412, 1421, 1424, 1425], "pagerank_numpi": 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1405, 1406, 1411], "algebra": [96, 110, 313, 380, 385, 1266, 1275, 1286, 1325, 1395, 1402, 1405, 1406], "nxep": [96, 107, 109, 1403, 1412, 1416], "govern": [96, 98, 109, 1412], "slice": [96, 98, 107, 1413], "builder": [96, 98, 1151, 1323, 1413], "frequent": [97, 378, 675], "newcom": [97, 109, 1326], "few": [97, 100, 101, 103, 362, 1402, 1404, 1411, 1412, 1413, 1414], "known": [97, 227, 280, 293, 301, 302, 303, 308, 309, 323, 369, 424, 450, 468, 618, 740, 741, 742, 743, 762, 791, 1068, 1097, 1145, 1148, 1200, 1201, 1224, 1228, 1230, 1232, 1247, 1272, 1324, 1412], "Of": [97, 1426], "sprint": [97, 1425], "permiss": [97, 110, 111, 457], "forget": 97, "sai": [97, 99, 101, 211, 512, 517, 518, 675, 676, 762, 1206, 1411], "rememb": [97, 101], "stick": [97, 1394], "plot_circular_layout": 97, "perhap": [97, 99, 102, 107], "deal": [97, 102], "worri": [97, 583, 1296, 1326], "ipython": 97, "field": [97, 99, 591, 593, 770, 1098, 1099, 1102, 1192], "breviti": 97, "offici": [97, 99, 1402], "inclus": 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330, 496, 608, 692, 1180, 1242, 1426], "tend": [99, 593, 1175, 1326], "doubt": [99, 1426], "champion": 99, "attempt": [99, 101, 194, 202, 204, 282, 284, 285, 286, 287, 288, 361, 362, 377, 425, 426, 584, 692, 693, 694, 786, 883, 890, 891, 922, 926, 927, 964, 971, 972, 1004, 1008, 1009, 1041, 1122, 1225, 1237, 1238, 1302, 1333, 1347, 1371, 1393, 1394, 1406, 1411, 1412, 1421, 1425], "ascertain": 99, "suitabl": [99, 110, 659, 693, 694, 1165, 1359, 1363, 1365, 1385, 1390], "0000": 99, "backward": [99, 217, 1199, 1402, 1404, 1406], "compat": [99, 429, 496, 691, 1302, 1404, 1405, 1406, 1412, 1414], "impact": [99, 100, 107, 329, 796, 1037, 1039, 1040], "broader": 99, "scope": [99, 107, 1045, 1413], "earliest": [99, 465], "conveni": [99, 101, 152, 497, 501, 504, 505, 508, 615, 796, 854, 899, 935, 980, 1037, 1038, 1039, 1040, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1270, 1296, 1326, 1394, 1405, 1409, 1426], "expand": [99, 101, 373, 653, 1038, 1190, 1325, 1395, 1406, 1407, 1408, 1413, 1424, 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268, 1333, 1334, 1337, 1338, 1339, 1340, 1341, 1344, 1355, 1358, 1368, 1371, 1372, 1375, 1376, 1387, 1406], "restructuredtext": 99, "restructuredtextprim": 99, "dd": [99, 104, 1094], "mmm": 99, "yyyi": [99, 104], "dom": 99, "ain": 99, "separ": [99, 102, 106, 152, 157, 158, 195, 214, 215, 258, 265, 266, 267, 268, 299, 322, 343, 427, 428, 454, 464, 758, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1045, 1112, 1116, 1193, 1195, 1325, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1369, 1370, 1371, 1372, 1395, 1406, 1407, 1412, 1413, 1425, 1426], "older": 99, "brows": 99, "colgat": [100, 110], "deadlock": 100, "websit": [100, 106, 1165, 1382, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "ongo": [100, 1405], "trust": [100, 1381, 1383], "cast": [100, 101, 1412, 1422, 1425], "vote": [100, 337, 1412], "therebi": 100, "adher": 100, "nomin": 100, "lazi": [100, 1283, 1284], "unanim": 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1419, 1421], "land": 100, "outlin": [100, 249, 336, 462, 1407], "templat": [100, 1413], "taken": [100, 101, 145, 148, 207, 443, 450, 717, 718, 749, 761, 892, 928, 973, 1010, 1117, 1409], "suffici": [100, 101, 1326], "scikit": [100, 103, 109], "expos": [101, 374, 1405], "nodeview": [101, 184, 391, 598, 599, 601, 602, 603, 604, 695, 873, 916, 955, 998, 1036, 1088, 1349, 1362, 1404, 1407], "nodedataview": [101, 184, 391, 591, 592, 600, 873, 916, 955, 998, 1217, 1426], "edgeview": [101, 590, 591, 592, 598, 599, 600, 601, 602, 603, 604, 612, 624, 770, 910, 1036, 1088, 1098, 1404, 1413], "edgedataview": [101, 168, 189, 865, 878, 910, 946, 960, 991, 1098, 1217, 1362, 1412, 1426], "semant": [101, 531, 541, 762, 1403, 1405], "inher": [101, 220, 427], "impli": [101, 110, 132, 220, 312, 314, 327, 455, 466, 511, 512, 545, 1296], "element": [101, 102, 230, 231, 270, 291, 292, 311, 350, 371, 391, 457, 464, 518, 559, 560, 578, 579, 580, 586, 640, 656, 671, 673, 675, 677, 728, 730, 739, 749, 752, 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342, 356, 398, 435, 437, 464, 467, 549, 550, 551, 608, 618, 619, 620, 625, 676, 685, 687, 700, 735, 737, 791, 1192, 1193, 1197, 1217, 1235, 1287, 1326, 1406, 1413, 1426], "coupl": [101, 102, 132, 1257, 1402, 1404], "realis": 101, "But": [101, 102, 107, 143, 170, 238, 243, 256, 277, 278, 281, 297, 298, 583, 796, 866, 911, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1094, 1328, 1393, 1425], "seem": [101, 102, 298, 307, 791, 1234], "eas": [101, 107, 1409], "idiom": [101, 159, 190, 200, 858, 879, 888, 903, 939, 969, 984, 1296, 1394, 1404, 1411], "subscript": [101, 151, 159, 200, 796, 853, 858, 888, 898, 903, 934, 939, 969, 979, 984, 1037, 1039, 1040, 1394, 1426], "repr": [101, 1347, 1413], "4950": [101, 1414], "traceback": [101, 450, 464, 584, 652, 658, 1302, 1303], "recent": [101, 437, 450, 464, 584, 652, 658, 963, 1003, 1302, 1303, 1411], "typeerror": [101, 382, 464, 1206, 1302, 1404], "opaqu": 101, "ambigu": [101, 103, 115, 252, 253, 464, 762, 1043, 1406], "ambigi": 101, 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464, 465, 466, 467, 468, 469, 892, 928, 973, 1010, 1041, 1404, 1426], "mdg": [102, 207, 892, 928, 973, 1010, 1420], "customgraph": 102, "elist": [102, 1326], "isol": [102, 355, 380, 435, 491, 492, 522, 524, 621, 735, 737, 758, 1218, 1325, 1330, 1398, 1401, 1406, 1407, 1417], "ekei": [102, 207, 892, 928, 934, 973, 979, 1010, 1084, 1104], "protocol": [102, 1404], "hashabl": [102, 144, 151, 156, 171, 180, 267, 545, 546, 547, 548, 761, 796, 853, 855, 867, 871, 898, 900, 912, 914, 934, 936, 947, 948, 952, 962, 979, 981, 992, 993, 995, 1002, 1037, 1038, 1039, 1040, 1087, 1207, 1278, 1279, 1295, 1310, 1324, 1326, 1333, 1337, 1338, 1426], "logic": [102, 103, 220, 760, 762, 1298, 1406, 1407, 1419, 1425], "denot": [102, 114, 212, 219, 299, 300, 322, 567, 568, 569, 570, 571, 572, 573, 608, 619, 687, 688, 689, 690, 691, 1174], "multiedg": [102, 553, 934, 979, 1039, 1040, 1085, 1326, 1356, 1357, 1393, 1406, 1412, 1414], "attrdict": [102, 157, 856, 901, 937, 982, 1406], "edge_kei": [102, 489, 1039, 1040, 1100, 1104, 1413], "networkxinvalidedgelist": 102, "flexibl": [102, 110, 467, 1326, 1382, 1383, 1395, 1401, 1406, 1407, 1411, 1426], "wheel": [102, 106, 1163, 1261, 1411, 1421, 1425], "spoke": 102, "wheel_graph": [102, 341, 671, 672, 674], "star": [102, 260, 300, 615, 626, 627, 779, 1054, 1151, 1160, 1223, 1227, 1394, 1404, 1406, 1407, 1411], "mycustomgraph": 102, "configuration_model_graph": 102, "deg_sequ": [102, 515, 517, 518, 1175, 1176, 1177, 1178, 1180, 1222], "graph_build": 102, "py_random_st": [102, 103, 1296, 1299, 1405], "extended_barabasi_albert_graph": 102, "node_and_edge_build": 102, "ladder_graph": 102, "incompat": [102, 1199, 1402, 1403, 1406], "thrust": 102, "incept": 102, "attach": [102, 214, 274, 357, 569, 571, 621, 1036, 1088, 1122, 1182, 1185, 1223, 1227, 1229, 1326, 1426], "presum": [102, 1297], "rewritten": [102, 1395, 1402, 1406], "gradual": 102, "accomplish": [102, 109, 1165], "wrap": [102, 1045, 1047, 1296, 1301, 1304], "custom_graph": 102, "ichain": 102, "tripl": [102, 114, 249, 250, 711, 1411], "overli": 102, "empty_graph": [102, 753, 1057, 1158, 1297, 1323, 1406, 1409, 1410], "3036": 102, "1393": 102, "canon": [102, 684, 730, 1412], "huge": 102, "path_edgelist": 102, "disallow": [102, 796, 1037, 1039, 1040, 1187, 1417], "2022": [103, 105, 693, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424], "pseudo": [103, 104, 676, 1320, 1321, 1405, 1407], "nep19": 103, "legaci": [103, 1395, 1402, 1408], "randomst": [103, 1100, 1111, 1117, 1299, 1301, 1304, 1305, 1328, 1405, 1409], "statist": [103, 110, 128, 274, 358, 383, 385, 438, 1222, 1328, 1405], "strategi": [103, 123, 222, 362, 366, 370, 453], "engin": [103, 107, 729, 731, 1412], "modern": [103, 110, 1405], "prng": 103, "np_random_st": [103, 1301, 1405, 1414], "random_st": [103, 208, 213, 217, 222, 223, 227, 230, 231, 271, 272, 274, 275, 296, 297, 306, 368, 373, 377, 378, 380, 381, 589, 625, 681, 682, 683, 684, 686, 692, 693, 694, 701, 722, 738, 747, 1164, 1165, 1168, 1169, 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1219, 1243, 1244, 1245, 1246, 1248, 1249, 1250, 1251, 1256, 1257, 1258, 1259, 1261, 1262, 1263, 1264, 1271, 1323], "greedi": [112, 222, 229, 230, 231, 232, 330, 362, 366, 383, 384, 722, 1395, 1407], "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, 1148], "notion": [112, 125, 128, 260, 261, 262, 289, 791], "partli": 112, "intract": 112, "solvabl": [112, 114], "constant": [112, 497, 501, 504, 505, 508, 675, 1175, 1195, 1215], "treewidth_min_degre": 112, "treewidth_min_fill_in": 112, "han": [112, 358, 1181, 1239, 1412, 1413], "bodlaend": 112, "ari": [112, 1145, 1155, 1397, 1406], "koster": 112, "2010": [112, 241, 244, 311, 312, 324, 325, 361, 379, 693, 1171, 1202, 1269, 1394, 1406, 1407], "inf": [112, 274, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 629, 753, 1411, 1413], "march": [112, 1286, 1406, 1415], "259": 112, "275": 112, "dx": [112, 257, 258, 259, 297, 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1414], "unblock": 132, "commonli": [132, 280, 454, 684, 782], "probabilist": [132, 378], "causal": 132, "markov": [132, 462, 565, 692, 1188], "hmm": 132, "s1": [132, 1242, 1313, 1363], "s2": [132, 1242, 1313], "s3": [132, 1313], "s4": 132, "s5": 132, "o1": 132, "o2": 132, "o3": 132, "o4": 132, "o5": 132, "ob": 132, "d_separ": [132, 758, 1412], "darwich": 132, "shachter": 132, "1998": [132, 1143, 1144, 1225, 1241, 1407], "bay": 132, "ball": 132, "ration": 132, "pastim": 132, "irrelev": [132, 1407], "requisit": 132, "influenc": [132, 324, 325, 512, 786], "fourteenth": [132, 1186], "uncertainti": [132, 590, 732], "artifici": [132, 574, 590, 732], "480": [132, 426, 514, 518, 1398, 1406], "487": 132, "francisco": [132, 732], "morgan": [132, 732], "kaufmann": [132, 732], "koller": 132, "friedman": 132, "mit": [132, 342, 519, 618], "causal_markov_condit": 132, "ness": [133, 684, 782], "classmethod": [141, 1047], "auxiliari": [141, 142, 143, 220, 411, 412, 413, 415, 416, 417, 418, 419, 423, 430, 431, 1402], "sink": [141, 302, 309, 416, 418, 494, 495, 498, 499, 501, 502, 503, 506, 507, 509, 510, 565], "pick": [141, 217, 331, 657, 1188, 1207, 1210, 1407], "st": [141, 415, 417], "cut": [141, 222, 223, 293, 377, 382, 387, 389, 390, 394, 411, 412, 414, 415, 416, 417, 419, 427, 428, 429, 442, 443, 444, 445, 447, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 619, 758, 760, 1038, 1066, 1115, 1262, 1325, 1395, 1402, 1406, 1413], "refin": [143, 215, 423, 438], "auxgraph": [143, 423], "node_partit": 144, "permut": [144, 368, 452, 453, 455, 466, 748, 1285, 1320, 1321], "containin": 144, "frozenset": [144, 267, 339, 383, 586, 588, 752, 1165, 1333, 1337, 1338, 1412], "abc": [144, 545, 1154, 1206, 1303, 1412, 1413], "interchang": [144, 362], "bool": [145, 146, 148, 149, 165, 168, 171, 176, 184, 189, 196, 204, 208, 232, 237, 238, 242, 243, 245, 249, 250, 258, 265, 266, 267, 268, 272, 275, 286, 287, 288, 291, 294, 295, 296, 297, 298, 299, 301, 302, 305, 306, 307, 308, 309, 310, 314, 315, 322, 324, 325, 326, 327, 330, 343, 350, 355, 362, 393, 394, 395, 396, 397, 398, 439, 454, 462, 463, 467, 479, 480, 488, 489, 491, 494, 498, 499, 509, 510, 513, 514, 515, 516, 517, 518, 520, 521, 522, 545, 562, 564, 578, 579, 580, 581, 588, 613, 614, 616, 617, 622, 623, 625, 640, 652, 663, 673, 679, 685, 690, 696, 698, 699, 700, 704, 708, 719, 723, 724, 725, 726, 728, 730, 733, 734, 735, 736, 737, 738, 740, 741, 742, 743, 862, 865, 867, 870, 873, 878, 885, 891, 907, 910, 912, 916, 927, 931, 943, 946, 948, 951, 955, 960, 966, 972, 976, 988, 991, 993, 998, 1039, 1040, 1045, 1057, 1068, 1070, 1071, 1072, 1084, 1091, 1097, 1116, 1133, 1134, 1135, 1136, 1169, 1179, 1185, 1189, 1209, 1211, 1212, 1213, 1215, 1224, 1228, 1230, 1231, 1232, 1275, 1276, 1277, 1278, 1279, 1282, 1295, 1296, 1307, 1309, 1312, 1335, 1336, 1337, 1339, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1360, 1364, 1379, 1380], "account": [145, 148, 398, 448, 749, 761, 1270, 1393, 1413], "graph_nod": [145, 148], "subgraph_nod": [145, 148], "find_isomorph": [147, 150], "induc": [148, 167, 199, 211, 226, 342, 388, 392, 406, 427, 436, 437, 470, 487, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 512, 586, 589, 752, 761, 762, 864, 887, 909, 925, 945, 968, 990, 1007, 1038, 1061, 1066, 1087, 1102, 1103, 1105, 1189, 1283, 1284, 1393], "u_of_edg": [151, 853, 898], "v_of_edg": [151, 853, 898], "capac": [151, 265, 296, 301, 302, 303, 308, 309, 323, 411, 412, 415, 416, 417, 418, 419, 430, 431, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 758, 853, 898, 934, 979, 1335, 1402], "342": [151, 853, 898, 934, 979, 1255], "ebunch_to_add": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "add_weighted_edges_from": [152, 229, 230, 231, 508, 581, 630, 657, 659, 721, 854, 899, 935, 980, 1070, 1326, 1404, 1407, 1426], "runtimeerror": [152, 157, 158, 195, 464, 465, 466, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005], "happen": [152, 157, 158, 195, 380, 584, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1403, 1404, 1425], "iterator_of_edg": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "wn2898": [152, 854, 899, 935, 980], "wrong": [152, 157, 158, 722, 854, 856, 857, 899, 901, 902, 935, 937, 938, 980, 982, 983, 1406, 1411, 1416, 1425], "start_nod": [153, 154, 155], "end_nod": [153, 154, 155], "reference_neighbor": [153, 154], "half": [153, 154, 155, 164, 177, 183, 206, 297, 298, 615, 653], "clockwis": [153, 154, 169, 182, 197, 615], "networkxexcept": [153, 154, 161, 331, 588, 593, 724, 726, 1043, 1110, 1138, 1180, 1325], "add_half_edge_cw": [153, 155, 164, 615], "connect_compon": [153, 154, 155, 615], "add_half_edge_first": [153, 154, 164, 615], "add_half_edge_ccw": [154, 155, 164, 615], "node_for_ad": [156, 855, 900, 936, 981], "mutabl": [156, 855, 900, 936, 981, 1061, 1066, 1082, 1085, 1086], "hash": [156, 511, 512, 758, 855, 900, 936, 981, 1324, 1325, 1414, 1426], "hello": 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926, 927, 943, 971, 972, 988, 1008, 1009, 1394], "deepcopi": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1409], "__class__": [165, 199, 862, 887, 907, 925, 943, 968, 988, 1007, 1404, 1407, 1409, 1410, 1411], "fresh": [165, 862, 907, 943, 988, 1404], "inspir": [165, 230, 231, 342, 681, 862, 907, 943, 988, 1226, 1323, 1404], "deep": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1265, 1394], "degreeview": [166, 863, 908, 944, 950, 989, 1404, 1426], "didegreeview": [166, 863], "outedgeview": [168, 189, 467, 468, 613, 747, 750, 865, 878, 1035, 1083, 1404, 1418], "ddict": [168, 176, 184, 189, 865, 870, 873, 878, 910, 916, 946, 951, 955, 960, 991, 998], "in_edg": [168, 189, 865, 878, 946, 960, 1404, 1406, 1407], "out_edg": [168, 865, 946, 1062, 1404, 1406, 1407, 1426], "quietli": [168, 189, 865, 878, 910, 946, 960, 991, 1087, 1426], "outedgedataview": [168, 189, 865, 878, 1404, 1411], "set_data": 169, "edge_dict": [170, 866, 911, 947, 992], "safe": [170, 866, 911, 1404, 1412], "edge_ind": [171, 867, 912, 948, 993], "data_dictionari": [171, 867, 912], "simpler": [172, 184, 868, 873, 913, 916, 949, 955, 994, 998, 1406, 1407, 1417], "indegreeview": [175, 869, 1404], "deg": [175, 188, 243, 259, 356, 361, 685, 869, 877, 950, 959, 1165, 1179, 1222, 1404], "inedgeview": [176, 870, 1404], "inedgedataview": [176, 870], "silent": [180, 193, 195, 320, 871, 882, 884, 914, 921, 923, 952, 963, 965, 995, 1003, 1005, 1085, 1086, 1127, 1353, 1354, 1359, 1363, 1406, 1413], "niter": [180, 681, 682, 683, 684, 851, 871, 896, 914, 932, 952, 977, 995, 1414], "__iter__": [180, 871, 914, 952, 995, 1303], "nodedata": [184, 873, 916, 955, 998], "5pm": [184, 796, 873, 916, 955, 998, 1037, 1039, 1040, 1394, 1426], "Not": [184, 379, 432, 433, 434, 435, 436, 437, 438, 476, 873, 916, 955, 998, 1117, 1216], "nedg": [185, 588, 874, 917, 956, 999], "__len__": [186, 187, 875, 876, 918, 919, 957, 958, 1000, 1001], "outdegreeview": [188, 877], "Will": [193, 362, 605, 607, 610, 882, 921, 963, 1003, 1404, 1414], "get_data": [197, 616], "inplac": [199, 690, 887, 925, 968, 1007, 1066, 1393], "reduct": [199, 469, 618, 786, 887, 925, 968, 1007, 1066, 1320, 1321, 1413, 1414], "sg": [199, 887, 925, 968, 1007], "largest_wcc": [199, 887, 925, 968, 1007], "is_multigraph": [199, 758, 887, 925, 968, 1007, 1154, 1412], "keydict": [199, 207, 887, 892, 925, 928, 968, 973, 1007, 1010, 1039, 1040], "contrast": [202, 204, 301, 302, 308, 309, 890, 891, 926, 927, 971, 972, 1008, 1009, 1066, 1233, 1241, 1426], "reciproc": [204, 299, 320, 322, 356, 411, 430, 447, 476, 620, 758, 891, 972, 1325, 1416, 1425], "mark_half_edg": 206, "li": [206, 619, 670, 675, 685, 775, 1207, 1210, 1425], "straightforward": [207, 892, 928, 973, 1010], "slightli": [207, 326, 437, 520, 521, 581, 892, 928, 973, 1010, 1165, 1326, 1404, 1407, 1412, 1414, 1425], "singleton": [207, 588, 892, 928, 973, 1010, 1218, 1251, 1407], "preserve_attr": [208, 723, 724, 725, 726], "optimum": [208, 231, 583, 720, 722, 791, 1395, 1406], "arboresc": [208, 460, 719, 720, 722, 724, 726, 740, 743, 758, 1272, 1395, 1406], "span": [208, 226, 227, 228, 295, 508, 618, 619, 624, 719, 720, 722, 724, 726, 732, 733, 734, 735, 736, 737, 738, 758, 1394, 1397, 1406, 1407, 1420], "max_ind_cliqu": 209, "networkxnotimpl": [209, 210, 211, 212, 220, 224, 227, 293, 294, 295, 318, 319, 321, 328, 343, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 403, 404, 405, 406, 407, 422, 424, 425, 426, 427, 429, 455, 457, 458, 459, 460, 468, 481, 482, 500, 589, 590, 608, 680, 732, 1043, 1216, 1275, 1276, 1298, 1325, 1353, 1354, 1379, 1407, 1408], "boppana": [209, 211, 212], "halld\u00f3rsson": [209, 211, 212], "1992": [209, 211, 212, 517, 518, 1407], "exclud": [209, 211, 212, 215, 216, 261, 262, 453, 688, 719, 723, 724, 725, 726, 733, 751, 1036, 1038, 1088, 1217, 1412], "180": [209, 211, 212, 238], "196": [209, 211, 212], "heurist": [210, 220, 228, 233, 234, 377, 380, 381, 427, 494, 509, 626, 627, 652, 663, 703, 758, 1173, 1320, 1321, 1325, 1395, 1408, 1412, 1413], "max_cliqu": 210, "rigor": 210, "pattabiraman": 210, "bharath": 210, "massiv": [210, 217], "421": 210, "448": 210, "1080": [210, 297, 298, 306, 307, 329], "15427951": 210, "986778": 210, "apx": [211, 212], "subseteq": [211, 280, 289, 618, 675], "omega": [211, 758, 782, 1414], "maximum_cliqu": 211, "1007": [211, 226, 296, 301, 302, 303, 308, 309, 323, 324, 325, 341, 431, 451, 498, 574, 1144, 1181], "bf01994876": 211, "iset": 212, "trial": [213, 230, 231, 1195, 1237, 1238], "estim": [213, 224, 297, 306, 313, 564, 625, 626, 627, 782, 1280, 1407], "coeffici": [213, 248, 260, 261, 262, 263, 289, 355, 356, 358, 570, 618, 619, 625, 682, 684, 778, 782, 1397, 1398, 1399, 1406, 1413], "fraction": [213, 257, 259, 286, 289, 297, 299, 304, 306, 315, 317, 318, 319, 321, 322, 326, 328, 330, 356, 358, 359, 519, 1165, 1234], "schank": 213, "thoma": [213, 751, 1407, 1409, 1413], "dorothea": [213, 1168], "wagner": [213, 429, 758, 1168, 1402, 1406], "universit\u00e4t": 213, "karlsruh": 213, "fakult\u00e4t": 213, "f\u00fcr": 213, "informatik": [213, 412], "5445": 213, "ir": [213, 606], "1000001239": 213, "erdos_renyi_graph": [213, 1224, 1232, 1326, 1406, 1426], "214": 213, "cutoff": [214, 215, 310, 326, 383, 410, 411, 412, 418, 419, 494, 495, 498, 499, 510, 637, 638, 640, 641, 642, 643, 644, 647, 648, 649, 656, 660, 661, 662, 667, 668, 669, 677, 678, 1234, 1398, 1402, 1406, 1413, 1416, 1424, 1425], "distinct": [214, 215, 255, 281, 288, 352, 391, 452, 453, 460, 578, 595, 608, 618, 700, 701, 734, 735, 736, 737, 789, 1150, 1244, 1271, 1323, 1326, 1328, 1395, 1417], "nonadjac": [214, 215, 480, 584, 585, 587], "cutset": [214, 215, 414, 415, 416, 417, 427, 428, 500, 506, 758], "menger": [214, 215, 216], "theorem": [214, 215, 216, 220, 235, 281, 311, 312, 322, 411, 506, 507, 514, 517, 518, 618, 1190, 1205], "local_node_connect": [214, 216, 408, 409, 410, 411, 413], "node_connect": [214, 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How do I find it in the source code?": [[97, "q-i-want-to-work-on-a-specific-function-how-do-i-find-it-in-the-source-code"]], "Q: What is the policy for deciding whether to include a new algorithm?": [[97, "q-what-is-the-policy-for-deciding-whether-to-include-a-new-algorithm"]], "NXEPs": [[98, "nxeps"], [1413, "nxeps"]], "NXEP 0 \u2014 Purpose and Process": [[99, "nxep-0-purpose-and-process"]], "What is a NXEP?": [[99, "what-is-a-nxep"]], "Types": [[99, "types"]], "NXEP Workflow": [[99, "nxep-workflow"]], "Review and Resolution": [[99, "review-and-resolution"]], "How a NXEP becomes Accepted": [[99, "how-a-nxep-becomes-accepted"]], "Maintenance": [[99, "maintenance"]], "Format and Template": [[99, "format-and-template"]], "Header Preamble": [[99, "header-preamble"]], "References and Footnotes": [[99, "references-and-footnotes"]], "NXEP 1 \u2014 Governance and Decision Making": [[100, "nxep-1-governance-and-decision-making"]], "Abstract": [[100, "abstract"], [101, "abstract"], [102, 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"single-source-dijkstra-path"]], "single_source_dijkstra_path_length": [[669, "single-source-dijkstra-path-length"]], "generate_random_paths": [[670, "generate-random-paths"]], "graph_edit_distance": [[671, "graph-edit-distance"]], "optimal_edit_paths": [[672, "optimal-edit-paths"]], "optimize_edit_paths": [[673, "optimize-edit-paths"]], "optimize_graph_edit_distance": [[674, "optimize-graph-edit-distance"]], "panther_similarity": [[675, "panther-similarity"]], "simrank_similarity": [[676, "simrank-similarity"]], "all_simple_edge_paths": [[677, "all-simple-edge-paths"]], "all_simple_paths": [[678, "all-simple-paths"]], "is_simple_path": [[679, "is-simple-path"]], "shortest_simple_paths": [[680, "shortest-simple-paths"]], "lattice_reference": [[681, "lattice-reference"]], "omega": [[682, "omega"]], "random_reference": [[683, "random-reference"]], "sigma": [[684, "sigma"]], "s_metric": [[685, "s-metric"]], "spanner": [[686, "spanner"]], "constraint": [[687, "constraint"]], "effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "Recognition Tests": [[791, "recognition-tests"]], "Branchings and Spanning Arborescences": [[791, "module-networkx.algorithms.tree.branchings"]], "Encoding and decoding": [[791, "module-networkx.algorithms.tree.coding"]], "Operations": [[791, "module-networkx.algorithms.tree.operations"]], "Spanning Trees": [[791, "module-networkx.algorithms.tree.mst"]], "Exceptions": [[791, "exceptions"], [1043, "module-networkx.exception"]], "Vitality": [[793, "module-networkx.algorithms.vitality"]], "Voronoi cells": [[794, "module-networkx.algorithms.voronoi"]], "Wiener index": [[795, "module-networkx.algorithms.wiener"]], "DiGraph\u2014Directed graphs with self loops": [[796, "digraph-directed-graphs-with-self-loops"]], "Overview": [[796, "overview"], [1037, "overview"], [1039, "overview"], [1040, "overview"]], "Methods": [[796, "methods"], [1037, "methods"], [1039, "methods"], [1040, "methods"]], "Adding and removing nodes and edges": [[796, "adding-and-removing-nodes-and-edges"], [1037, "adding-and-removing-nodes-and-edges"], [1040, "adding-and-removing-nodes-and-edges"]], "Reporting nodes edges and neighbors": [[796, "reporting-nodes-edges-and-neighbors"], [1037, "reporting-nodes-edges-and-neighbors"], [1039, "reporting-nodes-edges-and-neighbors"], [1040, "reporting-nodes-edges-and-neighbors"]], "Counting nodes edges and neighbors": [[796, "counting-nodes-edges-and-neighbors"], [1037, "counting-nodes-edges-and-neighbors"], [1039, "counting-nodes-edges-and-neighbors"], [1040, "counting-nodes-edges-and-neighbors"]], "Making copies and subgraphs": [[796, "making-copies-and-subgraphs"], [1037, "making-copies-and-subgraphs"], [1039, "making-copies-and-subgraphs"], [1040, "making-copies-and-subgraphs"]], "AdjacencyView.copy": [[797, "adjacencyview-copy"]], "AdjacencyView.get": [[798, "adjacencyview-get"]], "AdjacencyView.items": [[799, "adjacencyview-items"]], "AdjacencyView.keys": [[800, "adjacencyview-keys"]], "AdjacencyView.values": [[801, "adjacencyview-values"]], "AtlasView.copy": [[802, "atlasview-copy"]], "AtlasView.get": [[803, "atlasview-get"]], "AtlasView.items": [[804, "atlasview-items"]], "AtlasView.keys": [[805, "atlasview-keys"]], "AtlasView.values": [[806, "atlasview-values"]], "FilterAdjacency.get": [[807, "filteradjacency-get"]], "FilterAdjacency.items": [[808, "filteradjacency-items"]], "FilterAdjacency.keys": [[809, "filteradjacency-keys"]], "FilterAdjacency.values": [[810, "filteradjacency-values"]], "FilterAtlas.get": [[811, "filteratlas-get"]], "FilterAtlas.items": [[812, "filteratlas-items"]], "FilterAtlas.keys": [[813, "filteratlas-keys"]], "FilterAtlas.values": [[814, "filteratlas-values"]], "FilterMultiAdjacency.get": [[815, "filtermultiadjacency-get"]], "FilterMultiAdjacency.items": [[816, "filtermultiadjacency-items"]], "FilterMultiAdjacency.keys": [[817, "filtermultiadjacency-keys"]], "FilterMultiAdjacency.values": [[818, "filtermultiadjacency-values"]], "FilterMultiInner.get": [[819, "filtermultiinner-get"]], "FilterMultiInner.items": [[820, "filtermultiinner-items"]], "FilterMultiInner.keys": [[821, "filtermultiinner-keys"]], "FilterMultiInner.values": [[822, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[823, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[824, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[825, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[826, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[827, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[828, "unionadjacency-copy"]], "UnionAdjacency.get": [[829, "unionadjacency-get"]], "UnionAdjacency.items": [[830, "unionadjacency-items"]], "UnionAdjacency.keys": [[831, "unionadjacency-keys"]], "UnionAdjacency.values": [[832, "unionadjacency-values"]], "UnionAtlas.copy": [[833, "unionatlas-copy"]], "UnionAtlas.get": [[834, "unionatlas-get"]], "UnionAtlas.items": [[835, "unionatlas-items"]], "UnionAtlas.keys": [[836, "unionatlas-keys"]], "UnionAtlas.values": [[837, "unionatlas-values"]], "UnionMultiAdjacency.copy": [[838, "unionmultiadjacency-copy"]], "UnionMultiAdjacency.get": [[839, "unionmultiadjacency-get"]], "UnionMultiAdjacency.items": [[840, "unionmultiadjacency-items"]], "UnionMultiAdjacency.keys": [[841, "unionmultiadjacency-keys"]], "UnionMultiAdjacency.values": [[842, "unionmultiadjacency-values"]], "UnionMultiInner.copy": [[843, "unionmultiinner-copy"]], "UnionMultiInner.get": [[844, "unionmultiinner-get"]], "UnionMultiInner.items": [[845, "unionmultiinner-items"]], "UnionMultiInner.keys": [[846, "unionmultiinner-keys"]], "UnionMultiInner.values": [[847, "unionmultiinner-values"]], "DiGraph.__contains__": [[848, "digraph-contains"]], "DiGraph.__getitem__": [[849, "digraph-getitem"]], "DiGraph.__init__": [[850, "digraph-init"]], "DiGraph.__iter__": [[851, "digraph-iter"]], "DiGraph.__len__": [[852, "digraph-len"]], "DiGraph.add_edge": [[853, "digraph-add-edge"]], "DiGraph.add_edges_from": [[854, "digraph-add-edges-from"]], "DiGraph.add_node": [[855, "digraph-add-node"]], "DiGraph.add_nodes_from": [[856, "digraph-add-nodes-from"]], "DiGraph.add_weighted_edges_from": [[857, "digraph-add-weighted-edges-from"]], "DiGraph.adj": [[858, "digraph-adj"]], "DiGraph.adjacency": [[859, "digraph-adjacency"]], "DiGraph.clear": [[860, "digraph-clear"]], "DiGraph.clear_edges": [[861, "digraph-clear-edges"]], "DiGraph.copy": [[862, "digraph-copy"]], "DiGraph.degree": [[863, "digraph-degree"]], "DiGraph.edge_subgraph": [[864, "digraph-edge-subgraph"]], "DiGraph.edges": [[865, "digraph-edges"]], "DiGraph.get_edge_data": [[866, "digraph-get-edge-data"]], "DiGraph.has_edge": [[867, "digraph-has-edge"]], "DiGraph.has_node": [[868, "digraph-has-node"]], "DiGraph.in_degree": [[869, "digraph-in-degree"]], "DiGraph.in_edges": [[870, "digraph-in-edges"]], "DiGraph.nbunch_iter": [[871, "digraph-nbunch-iter"]], "DiGraph.neighbors": [[872, "digraph-neighbors"]], "DiGraph.nodes": [[873, "digraph-nodes"]], "DiGraph.number_of_edges": [[874, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[875, "digraph-number-of-nodes"]], "DiGraph.order": [[876, "digraph-order"]], "DiGraph.out_degree": [[877, "digraph-out-degree"]], "DiGraph.out_edges": [[878, "digraph-out-edges"]], "DiGraph.pred": [[879, "digraph-pred"]], "DiGraph.predecessors": [[880, "digraph-predecessors"]], "DiGraph.remove_edge": [[881, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[882, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[883, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[884, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[885, "digraph-reverse"]], "DiGraph.size": [[886, "digraph-size"]], "DiGraph.subgraph": [[887, "digraph-subgraph"]], "DiGraph.succ": [[888, "digraph-succ"]], "DiGraph.successors": [[889, "digraph-successors"]], "DiGraph.to_directed": [[890, "digraph-to-directed"]], "DiGraph.to_undirected": [[891, "digraph-to-undirected"]], "DiGraph.update": [[892, "digraph-update"]], "Graph.__contains__": [[893, "graph-contains"]], "Graph.__getitem__": [[894, "graph-getitem"]], "Graph.__init__": [[895, "graph-init"]], "Graph.__iter__": [[896, "graph-iter"]], "Graph.__len__": [[897, "graph-len"]], "Graph.add_edge": [[898, "graph-add-edge"]], "Graph.add_edges_from": [[899, "graph-add-edges-from"]], "Graph.add_node": [[900, "graph-add-node"]], "Graph.add_nodes_from": [[901, "graph-add-nodes-from"]], "Graph.add_weighted_edges_from": [[902, "graph-add-weighted-edges-from"]], "Graph.adj": [[903, "graph-adj"]], "Graph.adjacency": [[904, "graph-adjacency"]], "Graph.clear": [[905, "graph-clear"]], "Graph.clear_edges": [[906, "graph-clear-edges"]], "Graph.copy": [[907, "graph-copy"]], "Graph.degree": [[908, "graph-degree"]], "Graph.edge_subgraph": [[909, "graph-edge-subgraph"]], "Graph.edges": [[910, "graph-edges"]], "Graph.get_edge_data": [[911, "graph-get-edge-data"]], "Graph.has_edge": [[912, "graph-has-edge"]], "Graph.has_node": [[913, "graph-has-node"]], "Graph.nbunch_iter": [[914, "graph-nbunch-iter"]], "Graph.neighbors": [[915, "graph-neighbors"]], "Graph.nodes": [[916, "graph-nodes"]], "Graph.number_of_edges": [[917, "graph-number-of-edges"]], "Graph.number_of_nodes": [[918, "graph-number-of-nodes"]], "Graph.order": [[919, "graph-order"]], "Graph.remove_edge": [[920, "graph-remove-edge"]], "Graph.remove_edges_from": [[921, "graph-remove-edges-from"]], "Graph.remove_node": [[922, "graph-remove-node"]], "Graph.remove_nodes_from": [[923, "graph-remove-nodes-from"]], "Graph.size": [[924, "graph-size"]], "Graph.subgraph": [[925, "graph-subgraph"]], "Graph.to_directed": [[926, "graph-to-directed"]], "Graph.to_undirected": [[927, "graph-to-undirected"]], "Graph.update": [[928, "graph-update"]], "MultiDiGraph.__contains__": [[929, "multidigraph-contains"]], "MultiDiGraph.__getitem__": [[930, "multidigraph-getitem"]], "MultiDiGraph.__init__": [[931, "multidigraph-init"]], "MultiDiGraph.__iter__": [[932, "multidigraph-iter"]], "MultiDiGraph.__len__": [[933, "multidigraph-len"]], "MultiDiGraph.add_edge": [[934, "multidigraph-add-edge"]], "MultiDiGraph.add_edges_from": [[935, "multidigraph-add-edges-from"]], "MultiDiGraph.add_node": [[936, "multidigraph-add-node"]], "MultiDiGraph.add_nodes_from": [[937, "multidigraph-add-nodes-from"]], "MultiDiGraph.add_weighted_edges_from": [[938, "multidigraph-add-weighted-edges-from"]], "MultiDiGraph.adj": [[939, "multidigraph-adj"]], "MultiDiGraph.adjacency": [[940, "multidigraph-adjacency"]], "MultiDiGraph.clear": [[941, "multidigraph-clear"]], "MultiDiGraph.clear_edges": [[942, "multidigraph-clear-edges"]], "MultiDiGraph.copy": [[943, "multidigraph-copy"]], "MultiDiGraph.degree": [[944, "multidigraph-degree"]], "MultiDiGraph.edge_subgraph": [[945, "multidigraph-edge-subgraph"]], "MultiDiGraph.edges": [[946, "multidigraph-edges"]], "MultiDiGraph.get_edge_data": [[947, "multidigraph-get-edge-data"]], "MultiDiGraph.has_edge": [[948, "multidigraph-has-edge"]], "MultiDiGraph.has_node": [[949, "multidigraph-has-node"]], "MultiDiGraph.in_degree": [[950, "multidigraph-in-degree"]], "MultiDiGraph.in_edges": [[951, "multidigraph-in-edges"]], "MultiDiGraph.nbunch_iter": [[952, "multidigraph-nbunch-iter"]], "MultiDiGraph.neighbors": [[953, "multidigraph-neighbors"]], "MultiDiGraph.new_edge_key": [[954, "multidigraph-new-edge-key"]], "MultiDiGraph.nodes": [[955, "multidigraph-nodes"]], "MultiDiGraph.number_of_edges": [[956, "multidigraph-number-of-edges"]], "MultiDiGraph.number_of_nodes": [[957, "multidigraph-number-of-nodes"]], "MultiDiGraph.order": [[958, "multidigraph-order"]], "MultiDiGraph.out_degree": [[959, "multidigraph-out-degree"]], "MultiDiGraph.out_edges": [[960, "multidigraph-out-edges"]], "MultiDiGraph.predecessors": [[961, "multidigraph-predecessors"]], "MultiDiGraph.remove_edge": [[962, "multidigraph-remove-edge"]], "MultiDiGraph.remove_edges_from": [[963, 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Applying classic graph operations, such as:": [[1426, "applying-classic-graph-operations-such-as"]], "2. Using a call to one of the classic small graphs, e.g.,": [[1426, "using-a-call-to-one-of-the-classic-small-graphs-e-g"]], "3. Using a (constructive) generator for a classic graph, e.g.,": [[1426, "using-a-constructive-generator-for-a-classic-graph-e-g"]], "4. Using a stochastic graph generator, e.g,": [[1426, "using-a-stochastic-graph-generator-e-g"]], "5. 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property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[739, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[740, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[741, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[744, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[745, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.is_triad"]], "random_triad() (in module networkx.algorithms.triads)": [[747, "networkx.algorithms.triads.random_triad"]], "triad_type() (in module networkx.algorithms.triads)": [[748, "networkx.algorithms.triads.triad_type"]], "triadic_census() (in module networkx.algorithms.triads)": [[749, "networkx.algorithms.triads.triadic_census"]], "triads_by_type() (in module networkx.algorithms.triads)": [[750, "networkx.algorithms.triads.triads_by_type"]], "closeness_vitality() (in module networkx.algorithms.vitality)": [[751, "networkx.algorithms.vitality.closeness_vitality"]], "voronoi_cells() (in module networkx.algorithms.voronoi)": [[752, "networkx.algorithms.voronoi.voronoi_cells"]], "wiener_index() (in module networkx.algorithms.wiener)": [[753, "networkx.algorithms.wiener.wiener_index"]], "networkx.algorithms.graph_hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "networkx.algorithms.graphical": [[755, "module-networkx.algorithms.graphical"]], "networkx.algorithms.hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "networkx.algorithms.hybrid": [[757, "module-networkx.algorithms.hybrid"]], "networkx.algorithms.isolate": [[759, "module-networkx.algorithms.isolate"]], "networkx.algorithms.isomorphism": [[760, "module-networkx.algorithms.isomorphism"]], "networkx.algorithms.isomorphism.tree_isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "networkx.algorithms.isomorphism.vf2pp": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "networkx.algorithms.isomorphism.ismags": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "networkx.algorithms.isomorphism.isomorphvf2": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "networkx.algorithms.link_analysis.hits_alg": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "networkx.algorithms.link_analysis.pagerank_alg": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "networkx.algorithms.link_prediction": [[764, "module-networkx.algorithms.link_prediction"]], "networkx.algorithms.lowest_common_ancestors": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "networkx.algorithms.matching": [[766, "module-networkx.algorithms.matching"]], "networkx.algorithms.minors": [[767, "module-networkx.algorithms.minors"]], "networkx.algorithms.mis": [[768, "module-networkx.algorithms.mis"]], "networkx.algorithms.moral": [[769, "module-networkx.algorithms.moral"]], "networkx.algorithms.node_classification": [[770, "module-networkx.algorithms.node_classification"]], "networkx.algorithms.non_randomness": [[771, "module-networkx.algorithms.non_randomness"]], "networkx.algorithms.operators.all": [[772, "module-networkx.algorithms.operators.all"]], "networkx.algorithms.operators.binary": [[772, "module-networkx.algorithms.operators.binary"]], "networkx.algorithms.operators.product": [[772, "module-networkx.algorithms.operators.product"]], "networkx.algorithms.operators.unary": [[772, "module-networkx.algorithms.operators.unary"]], "networkx.algorithms.planar_drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "networkx.algorithms.planarity": [[774, "module-networkx.algorithms.planarity"]], "networkx.algorithms.polynomials": [[775, "module-networkx.algorithms.polynomials"]], "networkx.algorithms.reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "networkx.algorithms.regular": [[777, "module-networkx.algorithms.regular"]], "networkx.algorithms.richclub": [[778, "module-networkx.algorithms.richclub"]], "networkx.algorithms.shortest_paths.astar": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "networkx.algorithms.shortest_paths.dense": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "networkx.algorithms.shortest_paths.generic": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "networkx.algorithms.shortest_paths.unweighted": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "networkx.algorithms.shortest_paths.weighted": [[779, "module-networkx.algorithms.shortest_paths.weighted"]], "networkx.algorithms.similarity": [[780, "module-networkx.algorithms.similarity"]], "networkx.algorithms.simple_paths": [[781, "module-networkx.algorithms.simple_paths"]], "networkx.algorithms.smallworld": [[782, "module-networkx.algorithms.smallworld"]], "networkx.algorithms.smetric": [[783, "module-networkx.algorithms.smetric"]], "networkx.algorithms.sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "networkx.algorithms.structuralholes": [[785, "module-networkx.algorithms.structuralholes"]], "networkx.algorithms.summarization": [[786, "module-networkx.algorithms.summarization"]], "networkx.algorithms.swap": [[787, "module-networkx.algorithms.swap"]], "networkx.algorithms.threshold": [[788, "module-networkx.algorithms.threshold"]], "networkx.algorithms.tournament": [[789, "module-networkx.algorithms.tournament"]], "networkx.algorithms.traversal.beamsearch": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "networkx.algorithms.traversal.breadth_first_search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "networkx.algorithms.traversal.depth_first_search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "networkx.algorithms.traversal.edgebfs": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "networkx.algorithms.traversal.edgedfs": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "networkx.algorithms.tree.branchings": [[791, "module-networkx.algorithms.tree.branchings"]], "networkx.algorithms.tree.coding": [[791, "module-networkx.algorithms.tree.coding"]], "networkx.algorithms.tree.decomposition": [[791, "module-networkx.algorithms.tree.decomposition"]], "networkx.algorithms.tree.mst": [[791, "module-networkx.algorithms.tree.mst"]], "networkx.algorithms.tree.operations": [[791, "module-networkx.algorithms.tree.operations"]], "networkx.algorithms.tree.recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "networkx.algorithms.triads": [[792, "module-networkx.algorithms.triads"]], "networkx.algorithms.vitality": [[793, "module-networkx.algorithms.vitality"]], "networkx.algorithms.voronoi": [[794, "module-networkx.algorithms.voronoi"]], "networkx.algorithms.wiener": [[795, "module-networkx.algorithms.wiener"]], "digraph (class in networkx)": [[796, "networkx.DiGraph"]], "copy() (adjacencyview method)": [[797, "networkx.classes.coreviews.AdjacencyView.copy"]], "get() (adjacencyview method)": [[798, "networkx.classes.coreviews.AdjacencyView.get"]], "items() (adjacencyview method)": [[799, "networkx.classes.coreviews.AdjacencyView.items"]], "keys() (adjacencyview method)": [[800, "networkx.classes.coreviews.AdjacencyView.keys"]], "values() (adjacencyview method)": [[801, "networkx.classes.coreviews.AdjacencyView.values"]], "copy() (atlasview method)": [[802, "networkx.classes.coreviews.AtlasView.copy"]], "get() (atlasview method)": [[803, 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method)": [[824, "networkx.classes.coreviews.MultiAdjacencyView.get"]], "items() (multiadjacencyview method)": [[825, "networkx.classes.coreviews.MultiAdjacencyView.items"]], "keys() (multiadjacencyview method)": [[826, "networkx.classes.coreviews.MultiAdjacencyView.keys"]], "values() (multiadjacencyview method)": [[827, "networkx.classes.coreviews.MultiAdjacencyView.values"]], "copy() (unionadjacency method)": [[828, "networkx.classes.coreviews.UnionAdjacency.copy"]], "get() (unionadjacency method)": [[829, "networkx.classes.coreviews.UnionAdjacency.get"]], "items() (unionadjacency method)": [[830, "networkx.classes.coreviews.UnionAdjacency.items"]], "keys() (unionadjacency method)": [[831, "networkx.classes.coreviews.UnionAdjacency.keys"]], "values() (unionadjacency method)": [[832, "networkx.classes.coreviews.UnionAdjacency.values"]], "copy() (unionatlas method)": [[833, "networkx.classes.coreviews.UnionAtlas.copy"]], "get() (unionatlas method)": [[834, "networkx.classes.coreviews.UnionAtlas.get"]], "items() (unionatlas method)": [[835, "networkx.classes.coreviews.UnionAtlas.items"]], "keys() (unionatlas method)": [[836, "networkx.classes.coreviews.UnionAtlas.keys"]], "values() (unionatlas method)": [[837, "networkx.classes.coreviews.UnionAtlas.values"]], "copy() (unionmultiadjacency method)": [[838, "networkx.classes.coreviews.UnionMultiAdjacency.copy"]], "get() (unionmultiadjacency method)": [[839, "networkx.classes.coreviews.UnionMultiAdjacency.get"]], "items() (unionmultiadjacency method)": [[840, "networkx.classes.coreviews.UnionMultiAdjacency.items"]], "keys() (unionmultiadjacency method)": [[841, "networkx.classes.coreviews.UnionMultiAdjacency.keys"]], "values() (unionmultiadjacency method)": [[842, "networkx.classes.coreviews.UnionMultiAdjacency.values"]], "copy() (unionmultiinner method)": [[843, "networkx.classes.coreviews.UnionMultiInner.copy"]], "get() (unionmultiinner method)": [[844, "networkx.classes.coreviews.UnionMultiInner.get"]], "items() (unionmultiinner method)": [[845, "networkx.classes.coreviews.UnionMultiInner.items"]], "keys() (unionmultiinner method)": [[846, "networkx.classes.coreviews.UnionMultiInner.keys"]], "values() (unionmultiinner method)": [[847, "networkx.classes.coreviews.UnionMultiInner.values"]], "__contains__() (digraph method)": [[848, "networkx.DiGraph.__contains__"]], "__getitem__() (digraph method)": [[849, "networkx.DiGraph.__getitem__"]], "__init__() (digraph method)": [[850, "networkx.DiGraph.__init__"]], "__iter__() (digraph method)": [[851, "networkx.DiGraph.__iter__"]], "__len__() (digraph method)": [[852, "networkx.DiGraph.__len__"]], "add_edge() (digraph method)": [[853, "networkx.DiGraph.add_edge"]], "add_edges_from() (digraph method)": [[854, "networkx.DiGraph.add_edges_from"]], "add_node() (digraph method)": [[855, "networkx.DiGraph.add_node"]], "add_nodes_from() (digraph method)": [[856, "networkx.DiGraph.add_nodes_from"]], "add_weighted_edges_from() (digraph method)": [[857, "networkx.DiGraph.add_weighted_edges_from"]], "adj (digraph property)": [[858, "networkx.DiGraph.adj"]], "adjacency() (digraph method)": [[859, "networkx.DiGraph.adjacency"]], "clear() (digraph method)": [[860, "networkx.DiGraph.clear"]], "clear_edges() (digraph method)": [[861, "networkx.DiGraph.clear_edges"]], "copy() (digraph method)": [[862, "networkx.DiGraph.copy"]], "degree (digraph property)": [[863, "networkx.DiGraph.degree"]], "edge_subgraph() (digraph method)": [[864, "networkx.DiGraph.edge_subgraph"]], "edges (digraph property)": [[865, "networkx.DiGraph.edges"]], "get_edge_data() (digraph method)": [[866, "networkx.DiGraph.get_edge_data"]], "has_edge() (digraph method)": [[867, "networkx.DiGraph.has_edge"]], "has_node() (digraph method)": [[868, "networkx.DiGraph.has_node"]], "in_degree (digraph property)": [[869, "networkx.DiGraph.in_degree"]], "in_edges (digraph property)": [[870, "networkx.DiGraph.in_edges"]], 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"add_edges_from() (graph method)": [[899, "networkx.Graph.add_edges_from"]], "add_node() (graph method)": [[900, "networkx.Graph.add_node"]], "add_nodes_from() (graph method)": [[901, "networkx.Graph.add_nodes_from"]], "add_weighted_edges_from() (graph method)": [[902, "networkx.Graph.add_weighted_edges_from"]], "adj (graph property)": [[903, "networkx.Graph.adj"]], "adjacency() (graph method)": [[904, "networkx.Graph.adjacency"]], "clear() (graph method)": [[905, "networkx.Graph.clear"]], "clear_edges() (graph method)": [[906, "networkx.Graph.clear_edges"]], "copy() (graph method)": [[907, "networkx.Graph.copy"]], "degree (graph property)": [[908, "networkx.Graph.degree"]], "edge_subgraph() (graph method)": [[909, "networkx.Graph.edge_subgraph"]], "edges (graph property)": [[910, "networkx.Graph.edges"]], "get_edge_data() (graph method)": [[911, "networkx.Graph.get_edge_data"]], "has_edge() (graph method)": [[912, "networkx.Graph.has_edge"]], "has_node() (graph method)": [[913, "networkx.Graph.has_node"]], "nbunch_iter() (graph method)": [[914, "networkx.Graph.nbunch_iter"]], "neighbors() (graph method)": [[915, "networkx.Graph.neighbors"]], "nodes (graph property)": [[916, "networkx.Graph.nodes"]], "number_of_edges() (graph method)": [[917, "networkx.Graph.number_of_edges"]], "number_of_nodes() (graph method)": [[918, "networkx.Graph.number_of_nodes"]], "order() (graph method)": [[919, "networkx.Graph.order"]], "remove_edge() (graph method)": [[920, "networkx.Graph.remove_edge"]], "remove_edges_from() (graph method)": [[921, "networkx.Graph.remove_edges_from"]], "remove_node() (graph method)": [[922, "networkx.Graph.remove_node"]], "remove_nodes_from() (graph method)": [[923, "networkx.Graph.remove_nodes_from"]], "size() (graph method)": [[924, "networkx.Graph.size"]], "subgraph() (graph method)": [[925, "networkx.Graph.subgraph"]], "to_directed() (graph method)": [[926, "networkx.Graph.to_directed"]], "to_undirected() (graph method)": [[927, "networkx.Graph.to_undirected"]], "update() (graph method)": [[928, "networkx.Graph.update"]], "__contains__() (multidigraph method)": [[929, "networkx.MultiDiGraph.__contains__"]], "__getitem__() (multidigraph method)": [[930, "networkx.MultiDiGraph.__getitem__"]], "__init__() (multidigraph method)": [[931, "networkx.MultiDiGraph.__init__"]], "__iter__() (multidigraph method)": [[932, "networkx.MultiDiGraph.__iter__"]], "__len__() (multidigraph method)": [[933, "networkx.MultiDiGraph.__len__"]], "add_edge() (multidigraph method)": [[934, "networkx.MultiDiGraph.add_edge"]], "add_edges_from() (multidigraph method)": [[935, "networkx.MultiDiGraph.add_edges_from"]], "add_node() (multidigraph method)": [[936, "networkx.MultiDiGraph.add_node"]], "add_nodes_from() (multidigraph method)": [[937, "networkx.MultiDiGraph.add_nodes_from"]], "add_weighted_edges_from() (multidigraph method)": [[938, "networkx.MultiDiGraph.add_weighted_edges_from"]], "adj (multidigraph property)": [[939, 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"networkx.MultiDiGraph.remove_edges_from"]], "remove_node() (multidigraph method)": [[964, "networkx.MultiDiGraph.remove_node"]], "remove_nodes_from() (multidigraph method)": [[965, "networkx.MultiDiGraph.remove_nodes_from"]], "reverse() (multidigraph method)": [[966, "networkx.MultiDiGraph.reverse"]], "size() (multidigraph method)": [[967, "networkx.MultiDiGraph.size"]], "subgraph() (multidigraph method)": [[968, "networkx.MultiDiGraph.subgraph"]], "succ (multidigraph property)": [[969, "networkx.MultiDiGraph.succ"]], "successors() (multidigraph method)": [[970, "networkx.MultiDiGraph.successors"]], "to_directed() (multidigraph method)": [[971, "networkx.MultiDiGraph.to_directed"]], "to_undirected() (multidigraph method)": [[972, "networkx.MultiDiGraph.to_undirected"]], "update() (multidigraph method)": [[973, "networkx.MultiDiGraph.update"]], "__contains__() (multigraph method)": [[974, "networkx.MultiGraph.__contains__"]], "__getitem__() (multigraph method)": [[975, "networkx.MultiGraph.__getitem__"]], "__init__() (multigraph method)": [[976, "networkx.MultiGraph.__init__"]], "__iter__() (multigraph method)": [[977, "networkx.MultiGraph.__iter__"]], "__len__() (multigraph method)": [[978, "networkx.MultiGraph.__len__"]], "add_edge() (multigraph method)": [[979, "networkx.MultiGraph.add_edge"]], "add_edges_from() (multigraph method)": [[980, "networkx.MultiGraph.add_edges_from"]], "add_node() (multigraph method)": [[981, "networkx.MultiGraph.add_node"]], "add_nodes_from() (multigraph method)": [[982, "networkx.MultiGraph.add_nodes_from"]], "add_weighted_edges_from() (multigraph method)": [[983, "networkx.MultiGraph.add_weighted_edges_from"]], "adj (multigraph property)": [[984, "networkx.MultiGraph.adj"]], "adjacency() (multigraph method)": [[985, "networkx.MultiGraph.adjacency"]], "clear() (multigraph method)": [[986, "networkx.MultiGraph.clear"]], "clear_edges() (multigraph method)": [[987, "networkx.MultiGraph.clear_edges"]], "copy() (multigraph method)": [[988, "networkx.MultiGraph.copy"]], "degree (multigraph property)": [[989, "networkx.MultiGraph.degree"]], "edge_subgraph() (multigraph method)": [[990, "networkx.MultiGraph.edge_subgraph"]], "edges (multigraph property)": [[991, "networkx.MultiGraph.edges"]], "get_edge_data() (multigraph method)": [[992, "networkx.MultiGraph.get_edge_data"]], "has_edge() (multigraph method)": [[993, "networkx.MultiGraph.has_edge"]], "has_node() (multigraph method)": [[994, "networkx.MultiGraph.has_node"]], "nbunch_iter() (multigraph method)": [[995, "networkx.MultiGraph.nbunch_iter"]], "neighbors() (multigraph method)": [[996, "networkx.MultiGraph.neighbors"]], "new_edge_key() (multigraph method)": [[997, "networkx.MultiGraph.new_edge_key"]], "nodes (multigraph property)": [[998, "networkx.MultiGraph.nodes"]], "number_of_edges() (multigraph method)": [[999, "networkx.MultiGraph.number_of_edges"]], "number_of_nodes() (multigraph method)": [[1000, "networkx.MultiGraph.number_of_nodes"]], "order() (multigraph method)": [[1001, "networkx.MultiGraph.order"]], "remove_edge() (multigraph method)": [[1002, "networkx.MultiGraph.remove_edge"]], "remove_edges_from() (multigraph method)": [[1003, "networkx.MultiGraph.remove_edges_from"]], "remove_node() (multigraph method)": [[1004, "networkx.MultiGraph.remove_node"]], "remove_nodes_from() (multigraph method)": [[1005, "networkx.MultiGraph.remove_nodes_from"]], "size() (multigraph method)": [[1006, "networkx.MultiGraph.size"]], "subgraph() (multigraph method)": [[1007, "networkx.MultiGraph.subgraph"]], "to_directed() (multigraph method)": [[1008, "networkx.MultiGraph.to_directed"]], "to_undirected() (multigraph method)": [[1009, "networkx.MultiGraph.to_undirected"]], "update() (multigraph method)": [[1010, "networkx.MultiGraph.update"]], "_dispatch() (in module networkx.classes.backends)": [[1011, "networkx.classes.backends._dispatch"]], "adjacencyview (class in networkx.classes.coreviews)": [[1012, "networkx.classes.coreviews.AdjacencyView"]], "__init__() (adjacencyview method)": [[1012, "networkx.classes.coreviews.AdjacencyView.__init__"]], "atlasview (class in networkx.classes.coreviews)": [[1013, "networkx.classes.coreviews.AtlasView"]], "__init__() (atlasview method)": [[1013, "networkx.classes.coreviews.AtlasView.__init__"]], "filteradjacency (class in networkx.classes.coreviews)": [[1014, "networkx.classes.coreviews.FilterAdjacency"]], "__init__() (filteradjacency method)": [[1014, "networkx.classes.coreviews.FilterAdjacency.__init__"]], "filteratlas (class in networkx.classes.coreviews)": [[1015, "networkx.classes.coreviews.FilterAtlas"]], "__init__() (filteratlas method)": [[1015, "networkx.classes.coreviews.FilterAtlas.__init__"]], "filtermultiadjacency (class in networkx.classes.coreviews)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency"]], "__init__() (filtermultiadjacency method)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency.__init__"]], "filtermultiinner (class in networkx.classes.coreviews)": [[1017, "networkx.classes.coreviews.FilterMultiInner"]], "__init__() (filtermultiinner method)": [[1017, "networkx.classes.coreviews.FilterMultiInner.__init__"]], "multiadjacencyview (class in networkx.classes.coreviews)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView"]], "__init__() (multiadjacencyview method)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView.__init__"]], "unionadjacency (class in networkx.classes.coreviews)": [[1019, "networkx.classes.coreviews.UnionAdjacency"]], "__init__() (unionadjacency method)": [[1019, "networkx.classes.coreviews.UnionAdjacency.__init__"]], "unionatlas (class in networkx.classes.coreviews)": [[1020, "networkx.classes.coreviews.UnionAtlas"]], "__init__() (unionatlas method)": [[1020, "networkx.classes.coreviews.UnionAtlas.__init__"]], "unionmultiadjacency (class in networkx.classes.coreviews)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency"]], "__init__() (unionmultiadjacency method)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency.__init__"]], "unionmultiinner (class in networkx.classes.coreviews)": [[1022, "networkx.classes.coreviews.UnionMultiInner"]], "__init__() (unionmultiinner method)": [[1022, "networkx.classes.coreviews.UnionMultiInner.__init__"]], "hide_diedges() (in module networkx.classes.filters)": [[1023, "networkx.classes.filters.hide_diedges"]], "hide_edges() (in module networkx.classes.filters)": [[1024, "networkx.classes.filters.hide_edges"]], "hide_multidiedges() (in module networkx.classes.filters)": [[1025, "networkx.classes.filters.hide_multidiedges"]], "hide_multiedges() (in module networkx.classes.filters)": [[1026, "networkx.classes.filters.hide_multiedges"]], "hide_nodes() (in module networkx.classes.filters)": [[1027, "networkx.classes.filters.hide_nodes"]], "no_filter() (in module networkx.classes.filters)": [[1028, "networkx.classes.filters.no_filter"]], "show_diedges() (in module networkx.classes.filters)": [[1029, "networkx.classes.filters.show_diedges"]], "show_edges() (in module networkx.classes.filters)": [[1030, "networkx.classes.filters.show_edges"]], "show_multidiedges() (in module networkx.classes.filters)": [[1031, "networkx.classes.filters.show_multidiedges"]], "show_multiedges() (in module networkx.classes.filters)": [[1032, "networkx.classes.filters.show_multiedges"]], "__init__() (show_nodes method)": [[1033, "networkx.classes.filters.show_nodes.__init__"]], "show_nodes (class in networkx.classes.filters)": [[1033, "networkx.classes.filters.show_nodes"]], "generic_graph_view() (in module networkx.classes.graphviews)": [[1034, "networkx.classes.graphviews.generic_graph_view"]], "reverse_view() (in module networkx.classes.graphviews)": [[1035, "networkx.classes.graphviews.reverse_view"]], "subgraph_view() (in module networkx.classes.graphviews)": [[1036, "networkx.classes.graphviews.subgraph_view"]], "graph (class in networkx)": [[1037, "networkx.Graph"]], "networkx.classes.backends": [[1038, "module-networkx.classes.backends"]], "networkx.classes.coreviews": [[1038, "module-networkx.classes.coreviews"]], "networkx.classes.filters": [[1038, "module-networkx.classes.filters"]], "networkx.classes.graphviews": [[1038, "module-networkx.classes.graphviews"]], "multidigraph (class in networkx)": [[1039, "networkx.MultiDiGraph"]], "multigraph (class in networkx)": [[1040, "networkx.MultiGraph"]], "networkx.convert": [[1041, "module-networkx.convert"]], "networkx.convert_matrix": [[1041, "module-networkx.convert_matrix"]], "networkx.drawing.layout": [[1042, "module-networkx.drawing.layout"]], "networkx.drawing.nx_agraph": [[1042, "module-networkx.drawing.nx_agraph"]], "networkx.drawing.nx_pydot": [[1042, "module-networkx.drawing.nx_pydot"]], "networkx.drawing.nx_pylab": [[1042, "module-networkx.drawing.nx_pylab"]], "ambiguoussolution (class in networkx)": [[1043, "networkx.AmbiguousSolution"]], "exceededmaxiterations (class in networkx)": [[1043, "networkx.ExceededMaxIterations"]], "hasacycle (class in networkx)": [[1043, "networkx.HasACycle"]], "networkxalgorithmerror (class in networkx)": [[1043, "networkx.NetworkXAlgorithmError"]], "networkxerror (class in networkx)": [[1043, "networkx.NetworkXError"]], "networkxexception (class in networkx)": [[1043, "networkx.NetworkXException"]], "networkxnocycle (class in networkx)": [[1043, "networkx.NetworkXNoCycle"]], "networkxnopath (class in networkx)": [[1043, "networkx.NetworkXNoPath"]], "networkxnotimplemented (class in networkx)": [[1043, "networkx.NetworkXNotImplemented"]], "networkxpointlessconcept (class in networkx)": [[1043, "networkx.NetworkXPointlessConcept"]], "networkxunbounded (class in networkx)": [[1043, "networkx.NetworkXUnbounded"]], "networkxunfeasible (class in networkx)": [[1043, "networkx.NetworkXUnfeasible"]], "nodenotfound (class in networkx)": [[1043, "networkx.NodeNotFound"]], "poweriterationfailedconvergence (class in networkx)": [[1043, "networkx.PowerIterationFailedConvergence"]], "networkx.exception": [[1043, "module-networkx.exception"]], "networkx.classes.function": [[1044, "module-networkx.classes.function"]], "assemble() (argmap method)": [[1045, "networkx.utils.decorators.argmap.assemble"]], "compile() (argmap method)": [[1046, "networkx.utils.decorators.argmap.compile"]], "signature() (argmap class method)": [[1047, "networkx.utils.decorators.argmap.signature"]], "pop() (mappedqueue method)": [[1048, "networkx.utils.mapped_queue.MappedQueue.pop"]], "push() (mappedqueue method)": [[1049, "networkx.utils.mapped_queue.MappedQueue.push"]], "remove() (mappedqueue method)": [[1050, "networkx.utils.mapped_queue.MappedQueue.remove"]], "update() (mappedqueue method)": [[1051, "networkx.utils.mapped_queue.MappedQueue.update"]], "add_cycle() (in module networkx.classes.function)": [[1052, "networkx.classes.function.add_cycle"]], "add_path() (in module networkx.classes.function)": [[1053, "networkx.classes.function.add_path"]], "add_star() (in module networkx.classes.function)": [[1054, "networkx.classes.function.add_star"]], "all_neighbors() (in module networkx.classes.function)": [[1055, "networkx.classes.function.all_neighbors"]], "common_neighbors() (in module networkx.classes.function)": [[1056, "networkx.classes.function.common_neighbors"]], "create_empty_copy() (in module networkx.classes.function)": [[1057, "networkx.classes.function.create_empty_copy"]], "degree() (in module networkx.classes.function)": [[1058, "networkx.classes.function.degree"]], "degree_histogram() (in module networkx.classes.function)": [[1059, "networkx.classes.function.degree_histogram"]], "density() (in module networkx.classes.function)": [[1060, "networkx.classes.function.density"]], "edge_subgraph() (in module networkx.classes.function)": [[1061, "networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1062, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1063, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1064, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1065, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1066, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1067, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1068, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1069, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1073, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1074, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1075, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1076, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1077, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1078, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1079, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1080, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1081, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1082, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1083, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1084, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1085, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1086, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1087, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1088, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1089, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1090, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1091, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1092, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1093, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1097, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1098, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1099, "networkx.convert_matrix.from_pandas_adjacency"]], "from_pandas_edgelist() (in module networkx.convert_matrix)": [[1100, "networkx.convert_matrix.from_pandas_edgelist"]], "from_scipy_sparse_array() (in module networkx.convert_matrix)": [[1101, "networkx.convert_matrix.from_scipy_sparse_array"]], "to_numpy_array() (in module networkx.convert_matrix)": [[1102, "networkx.convert_matrix.to_numpy_array"]], "to_pandas_adjacency() (in module networkx.convert_matrix)": [[1103, "networkx.convert_matrix.to_pandas_adjacency"]], "to_pandas_edgelist() (in module networkx.convert_matrix)": [[1104, "networkx.convert_matrix.to_pandas_edgelist"]], "to_scipy_sparse_array() (in module networkx.convert_matrix)": [[1105, "networkx.convert_matrix.to_scipy_sparse_array"]], "bipartite_layout() (in module networkx.drawing.layout)": [[1106, "networkx.drawing.layout.bipartite_layout"]], "circular_layout() (in module networkx.drawing.layout)": [[1107, "networkx.drawing.layout.circular_layout"]], 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module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, 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module networkx.generators.classic)": [[1161, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1164, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1165, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1166, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1167, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() 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networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, 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networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, 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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, 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, "083": [9, 17], "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, "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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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, "277": [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, 17, 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, 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1112, 1216, 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], "400": [21, 22, 69, 1305], 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66, 77, 93, 300, 362, 485, 486, 487, 500, 501, 1409, 1412, 1429], "approxim": [44, 93, 209, 210, 211, 212, 213, 214, 215, 216, 218, 219, 220, 221, 226, 227, 228, 229, 230, 231, 232, 235, 296, 297, 306, 422, 673, 674, 675, 681, 682, 683, 684, 758, 1043, 1115, 1168, 1237, 1272, 1328, 1398, 1402, 1403, 1409, 1410, 1416, 1425, 1428], "nx_app": 44, "depot": 44, "hypot": [44, 1417], "edge_list": 44, "closest": [44, 57, 226], "plot_tsp": [44, 47], "allow": [45, 49, 52, 55, 69, 88, 92, 99, 100, 101, 102, 103, 105, 107, 110, 111, 112, 164, 168, 184, 189, 231, 232, 280, 287, 373, 423, 464, 467, 491, 492, 534, 544, 591, 592, 659, 671, 673, 680, 693, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 796, 865, 873, 878, 910, 916, 946, 955, 960, 991, 998, 1037, 1038, 1039, 1040, 1045, 1046, 1066, 1104, 1117, 1124, 1125, 1126, 1133, 1173, 1178, 1180, 1183, 1188, 1191, 1196, 1218, 1225, 1232, 1272, 1278, 1279, 1280, 1298, 1299, 1300, 1305, 1329, 1353, 1396, 1397, 1398, 1399, 1401, 1402, 1407, 1409, 1410, 1412, 1413, 1414, 1415, 1416, 1417, 1420, 1425, 1428, 1429], "mailbox": 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, 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1366, 1367, 1396, 1407, 1408, 1409, 1410, 1416, 1417, 1427, 1428], "unix_email": 45, "mbox": [45, 258, 259], "alic": 45, "To": [45, 52, 54, 57, 58, 93, 94, 97, 99, 101, 102, 103, 110, 111, 152, 157, 158, 167, 180, 184, 195, 199, 207, 232, 238, 269, 270, 271, 272, 273, 274, 275, 276, 282, 285, 297, 298, 299, 316, 345, 346, 347, 357, 374, 380, 383, 388, 390, 392, 406, 453, 455, 460, 466, 469, 488, 508, 511, 512, 523, 586, 597, 600, 604, 636, 678, 679, 703, 704, 707, 711, 752, 762, 789, 796, 854, 856, 857, 864, 871, 873, 884, 887, 892, 899, 901, 902, 909, 914, 916, 923, 925, 928, 934, 935, 937, 938, 945, 952, 955, 965, 968, 973, 979, 980, 982, 983, 990, 995, 998, 1005, 1007, 1010, 1037, 1038, 1039, 1040, 1042, 1061, 1063, 1066, 1082, 1112, 1114, 1123, 1178, 1180, 1185, 1187, 1196, 1201, 1215, 1225, 1270, 1275, 1298, 1305, 1327, 1328, 1329, 1331, 1334, 1336, 1337, 1339, 1340, 1362, 1366, 1367, 1368, 1374, 1378, 1396, 1402, 1404, 1405, 1407, 1408, 1411, 1429], "bob": 45, "gov": [45, 110, 1396, 1397, 1400, 1401, 1402, 1403, 1409], "ted": 45, "packag": [45, 50, 53, 54, 56, 57, 58, 86, 93, 103, 106, 107, 110, 115, 127, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 494, 498, 499, 509, 510, 615, 850, 895, 931, 976, 1038, 1042, 1196, 1200, 1301, 1304, 1305, 1307, 1329, 1331, 1396, 1398, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429], "togeth": [45, 92, 102, 211, 289, 512, 678, 786, 1149, 1326, 1329, 1344, 1345, 1347, 1358, 1359, 1360, 1361, 1384, 1386, 1410, 1429], "lunch": 45, "discuss": [45, 92, 97, 99, 100, 105, 106, 107, 310, 311, 315, 330, 346, 347, 616, 618, 619, 1220, 1326, 1385, 1396, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429], "carol": [45, 1258], "getaddress": 45, "parseaddr": 45, "recip": [45, 660, 667], "doc": [45, 93, 99, 101, 106, 165, 202, 204, 282, 566, 620, 749, 862, 890, 891, 907, 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1180, 1225, 1290, 1291, 1409, 1416], "tool": [52, 99, 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, 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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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1426, 1427, 1428, 1429], "nonplanar": [52, 1253], "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, 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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, 336, 374, 380, 381, 387, 389, 390, 394, 423, 427, 640, 691, 770, 786, 862, 907, 943, 988, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1094, 1108, 1158, 1205, 1210, 1211, 1239, 1299, 1326, 1331, 1399, 1402, 1410, 1415, 1416, 1417], "interfac": [54, 57, 58, 75, 76, 96, 98, 99, 101, 102, 107, 109, 110, 184, 429, 496, 673, 758, 761, 762, 780, 873, 916, 955, 998, 1042, 1044, 1329, 1331, 1396, 1399, 1401, 1405, 1407, 1408, 1409, 1412, 1416, 1417, 1429], "kind": [54, 57, 58, 92, 93, 94, 99, 208, 466, 722, 1205, 1329, 1386], "read_fil": [54, 55, 57, 58], "cholera_cas": [54, 57], "gpkg": [54, 56, 57], "correctli": [54, 164, 324, 325, 1396, 1407, 1409, 1414, 1415, 1422], "construct": [54, 55, 56, 57, 58, 67, 94, 102, 227, 229, 230, 231, 232, 269, 273, 276, 352, 423, 450, 460, 513, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 609, 685, 695, 708, 716, 732, 1042, 1046, 1047, 1052, 1053, 1101, 1102, 1103, 1104, 1105, 1156, 1157, 1178, 1180, 1181, 1183, 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1415], "gaetano": [91, 1415], "pietro": 91, "paolo": [91, 320, 1415], "carpinato": [91, 1415], "carghaez": 91, "gaetanocarpinato": 91, "arun": 91, "nampal": 91, "arunwis": [91, 1415], "b57845b7": 91, "duve": [91, 1415], "shashi": [91, 1415], "prakash": 91, "tripathi": [91, 517, 1415], "itsshavar": 91, "itsshashitripathi": 91, "danni": [91, 1415], "niquett": [91, 1415], "trimbl": [91, 1415, 1417], "jamestrimbl": 91, "matthia": [91, 1415, 1416, 1419, 1425], "bruhn": [91, 1415], "mbruhn": 91, "philip": 91, "boalch": 91, "knyazev": [91, 1417], "cappelletti": 91, "lucacappelletti94": 91, "sultan": [91, 1417, 1419, 1425, 1428], "orazbayev": [91, 1417, 1419, 1425, 1428], "sultanorazbayev": 91, "supplementari": 91, "incomplet": [91, 112, 1409, 1411], "commit": [91, 92, 93, 94, 99, 100, 105, 106, 1410, 1412, 1414, 1415, 1416, 1417, 1418, 1420, 1422, 1428], "git": [91, 93, 94, 97, 99, 106, 111, 1419, 1422], "repositori": [91, 93, 99, 106, 1409], "grep": [91, 97], "uniq": 91, "histor": [91, 99, 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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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, 1196, 1199, 1200, 1201, 1202, 1242, 1299, 1329, 1334, 1337, 1393, 1401, 1409, 1415, 1420], "manag": [92, 93, 100, 111, 228, 680, 691, 1405, 1414, 1415], "privat": [92, 100, 1042, 1415, 1416, 1424, 1428], "tracker": [92, 97, 100, 107], "wiki": [92, 112, 120, 121, 132, 211, 226, 230, 282, 283, 293, 340, 341, 425, 454, 469, 476, 483, 484, 488, 490, 590, 676, 695, 696, 704, 710, 732, 761, 767, 782, 1209, 1222, 1246, 1247, 1248, 1249, 1251, 1252, 1253, 1254, 1259, 1260, 1261, 1262, 1264, 1265, 1266, 1267], "channel": 92, "honor": 92, "particip": [92, 100, 357, 519, 569], "formal": [92, 100, 114, 132, 220, 289, 342, 621, 687, 688, 689], "claim": [92, 94, 1262], "affili": [92, 257, 258, 259, 286, 288, 1168], "role": [92, 103, 355, 1202, 1205, 1269, 1410], "exhaust": [92, 180, 375, 871, 914, 952, 995, 1138, 1299], "distil": 92, "understand": [92, 100, 101, 109, 132, 384, 760, 1299, 1408], "collabor": [92, 110, 128, 284, 326], "environ": [92, 93, 97, 99, 110, 111, 373, 564, 1038, 1042, 1124, 1125, 1126, 1410, 1414], "spirit": 92, "much": [92, 94, 102, 110, 384, 494, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 653, 682, 698, 699, 1038, 1046, 1102, 1134, 1135, 1141, 1142, 1143, 1144, 1145, 1216, 1218, 1397, 1408, 1409, 1412, 1429], "friendli": [92, 93, 102, 1329, 1413, 1428], "enrich": 92, "strive": 92, "invit": [92, 100], "anyon": [92, 94, 99, 100, 102], "prefer": [92, 93, 94, 99, 102, 103, 109, 491, 492, 598, 615, 762, 1041, 1097, 1102, 1103, 1329, 1331, 1396, 1397, 1409, 1412, 1429], "unless": [92, 94, 100, 109, 127, 207, 270, 422, 488, 892, 928, 973, 1010, 1117, 1333, 1397, 1429], "someth": [92, 94, 101, 103, 107, 527, 537, 796, 1037, 1039, 1040, 1042, 1046, 1120, 1129, 1303, 1359, 1360, 1407], "sensit": [92, 100, 1272], "too": [92, 94, 691, 780, 1043, 1168, 1237, 1298, 1329, 1331, 1407, 1428, 1429], "answer": [92, 97, 761, 1410], "question": [92, 97, 693, 1329, 1396, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428], "inadvert": 92, "mistak": [92, 94, 1427, 1428], "easili": [92, 100, 115, 380, 494, 688, 691, 1331, 1402, 1407, 1429], "detect": [92, 95, 105, 128, 210, 322, 373, 374, 378, 379, 380, 381, 383, 385, 386, 438, 519, 593, 652, 658, 663, 758, 786, 1168, 1172, 1173, 1174, 1329, 1410, 1411, 1412, 1415, 1417], "empathet": 92, "welcom": [92, 94, 109], "patient": 92, "resolv": [92, 93, 94, 97, 99, 100, 101, 464, 1414, 1415, 1428], "assum": [92, 93, 94, 97, 101, 106, 111, 132, 184, 219, 235, 265, 291, 292, 314, 316, 327, 378, 429, 471, 472, 473, 474, 475, 577, 581, 588, 600, 626, 627, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 688, 689, 691, 753, 761, 873, 916, 931, 955, 976, 998, 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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], "mentorship": [94, 1416], "handhold": 94, "liber": 94, "workflow": [94, 96, 97, 100, 106, 1416, 1423], "realiz": [94, 513, 514, 515, 516, 517, 518, 693, 1178, 1180, 1183, 1210, 1211, 1212, 1213, 1225, 1267], "gentl": 94, "abandon": 94, "difficult": [94, 1408], "carri": [94, 100, 508], "polici": [94, 96, 99, 1415, 1417], "readabl": [94, 107, 109, 169, 172, 460, 868, 913, 949, 994, 1396, 1417], "effici": [94, 102, 112, 212, 275, 290, 377, 387, 389, 390, 392, 394, 399, 405, 406, 407, 422, 425, 426, 486, 487, 508, 512, 581, 614, 680, 688, 691, 698, 699, 758, 1134, 1135, 1141, 1142, 1143, 1144, 1145, 1182, 1206, 1233, 1328, 1389, 1393, 1401, 1402, 1409, 1410, 1411, 1414, 1416], "explor": [94, 105, 107, 110, 704, 711, 717], "corner": [94, 1410, 1417], "tempt": 94, "nitpicki": 94, "spell": [94, 1409, 1415, 1416], "suggest": [94, 102, 105, 632, 635, 636, 1168, 1329, 1405, 1409, 1415, 1417, 1428], "latter": [94, 100, 102, 440, 729, 731, 791, 1302], "choic": [94, 102, 204, 385, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 479, 502, 503, 506, 507, 734, 735, 736, 737, 780, 891, 972, 1038, 1042, 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": [94, 105, 1036, 1088, 1385, 1420], "past": [94, 106, 1408], "pep8": [94, 1410, 1415, 1419], "pep257": 94, "superset": [94, 582], "stackoverflow": 94, "monitor": [94, 101], "signatur": [95, 97, 103, 109, 545, 1045, 1299, 1402, 1407, 1410, 1416, 1422, 1425, 1428], "buggi": 95, "usual": [95, 101, 168, 176, 189, 291, 292, 329, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 438, 440, 467, 615, 753, 762, 796, 865, 870, 878, 910, 946, 951, 960, 991, 1039, 1040, 1042, 1045, 1094, 1177, 1202, 1220, 1275, 1299, 1329, 1406], "minor": [95, 100, 106, 584, 758, 1328, 1397, 1398, 1406, 1409, 1410, 1411, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428], "strict": [95, 110, 214, 215, 216, 619, 1411, 1416], 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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, 426, 584, 692, 693, 694, 786, 883, 890, 891, 922, 926, 927, 964, 971, 972, 1004, 1008, 1009, 1041, 1122, 1228, 1240, 1241, 1305, 1336, 1350, 1374, 1396, 1397, 1409, 1414, 1415, 1424, 1428], "ascertain": 99, "suitabl": [99, 110, 659, 693, 694, 1168, 1362, 1366, 1368, 1388, 1393], "0000": 99, "backward": [99, 217, 1202, 1405, 1407, 1409], "compat": [99, 429, 496, 691, 1305, 1407, 1408, 1409, 1415, 1417], "impact": [99, 100, 107, 329, 796, 1037, 1039, 1040], "broader": 99, "scope": [99, 107, 1042, 1045, 1124, 1125, 1126, 1416], "earliest": [99, 465], "conveni": [99, 101, 152, 497, 501, 504, 505, 508, 615, 796, 854, 899, 935, 980, 1037, 1038, 1039, 1040, 1126, 1134, 1135, 1141, 1142, 1143, 1144, 1145, 1273, 1299, 1329, 1397, 1408, 1412, 1429], "expand": [99, 101, 373, 653, 1038, 1193, 1328, 1398, 1409, 1410, 1411, 1416, 1427, 1428], "prototyp": 99, "sound": 99, "principl": [99, 100, 103, 132], "impract": 99, "wip": [99, 1410, 1411, 1415], "incorpor": [99, 1402, 1429], "stabil": [99, 334, 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, 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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, 850, 895, 931, 976, 1037, 1039, 1040, 1102, 1105, 1108, 1117, 1188, 1189, 1190, 1191, 1226, 1230, 1237, 1281, 1282, 1299, 1305, 1326, 1397, 1398, 1409, 1414, 1415, 1416, 1417], "voic": 100, "smooth": 100, "strateg": 100, "plan": [100, 1397, 1408, 1410, 1416], "fund": [100, 1417, 1428], "theirs": 100, "pursu": 100, "pictur": [100, 1124, 1125, 1126], "perspect": [100, 104, 1198, 1329], "timefram": 100, "entiti": [100, 1348, 1351, 1352, 1353, 1385, 1429], "occasion": [100, 230], "seek": [100, 762, 1355, 1357, 1381, 1383, 1390], "tri": [100, 112, 343, 380, 931, 976, 1039, 1040, 1178, 1184, 1228, 1240, 1241, 1407], "distinguish": [100, 934, 962, 979, 1002, 1040], "fundament": [100, 107, 110, 338, 449, 618, 619, 1220, 1416], "flaw": 100, "forward": [100, 217, 450, 711, 717, 718], "typo": [100, 1399, 1409, 1410, 1411, 1414, 1415, 1416, 1417, 1419, 1420, 1422, 1424], "land": 100, "outlin": [100, 249, 336, 462, 1410], "templat": [100, 1416], "taken": [100, 101, 145, 148, 207, 443, 450, 717, 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, 1285, 1290, 1291, 1299, 1305, 1306, 1314, 1321, 1326, 1358, 1361, 1364, 1365, 1408], "intend": [101, 104, 107, 111, 327, 567, 1038, 1042, 1272, 1299, 1396], "impos": [101, 103, 545, 791], "due": [101, 102, 109, 231, 264, 440, 581, 583, 626, 627, 1220, 1408, 1415, 1417, 1426, 1428], "bit": [101, 209, 211, 212, 453, 511, 512, 786, 1348, 1351, 1352, 1353, 1385, 1414], "lot": [101, 452, 1329, 1408], "screen": 101, "instinct": 101, "error": [101, 102, 152, 157, 158, 195, 280, 288, 296, 311, 324, 414, 422, 471, 472, 473, 474, 475, 489, 497, 501, 504, 505, 508, 556, 557, 558, 564, 566, 581, 584, 653, 660, 667, 675, 676, 796, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1037, 1043, 1117, 1147, 1399, 1404, 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], "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": [101, 102, 171, 796, 867, 912, 948, 993, 1037, 1039, 1040, 1126, 1299, 1385, 1386, 1413, 1415], "introspect": 101, "neither": [101, 110, 305, 427, 625, 635, 636, 671, 672, 673, 674, 676, 700, 748], "downsid": 101, "drawback": 101, "discover": 101, "complic": [101, 1299, 1329], "nix": 101, "background": 101, "pertain": 101, "arguabl": [101, 102], "overrid": [101, 671, 672, 673, 674, 1124, 1125, 1126, 1414], "mix": [101, 236, 237, 238, 241, 242, 243, 244, 245, 248, 445, 758, 1100, 1344, 1345, 1347, 1358, 1359, 1360, 1361, 1384, 1386, 1396, 1409, 1410, 1414], "pervas": 101, "unforeseen": 101, "preced": [101, 152, 157, 464, 598, 703, 854, 856, 899, 901, 935, 937, 980, 982, 1045, 1366, 1367], "un": [101, 464, 732, 1410, 1416], "sliceabl": 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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, 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"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, "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": 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"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, "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"]], 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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. Reading a graph stored in a file using common graph formats": [[1429, "reading-a-graph-stored-in-a-file-using-common-graph-formats"]], "Analyzing graphs": [[1429, "analyzing-graphs"]], "Drawing graphs": [[1429, "drawing-graphs"]]}, "indexentries": {"module": [[112, "module-networkx.algorithms.approximation"], [112, "module-networkx.algorithms.approximation.clique"], [112, "module-networkx.algorithms.approximation.clustering_coefficient"], [112, "module-networkx.algorithms.approximation.connectivity"], [112, "module-networkx.algorithms.approximation.distance_measures"], [112, "module-networkx.algorithms.approximation.dominating_set"], [112, "module-networkx.algorithms.approximation.kcomponents"], [112, "module-networkx.algorithms.approximation.matching"], [112, "module-networkx.algorithms.approximation.maxcut"], [112, "module-networkx.algorithms.approximation.ramsey"], [112, "module-networkx.algorithms.approximation.steinertree"], [112, 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"networkx.algorithms.chains": [[119, "module-networkx.algorithms.chains"]], "networkx.algorithms.chordal": [[120, "module-networkx.algorithms.chordal"]], "networkx.algorithms.clique": [[121, "module-networkx.algorithms.clique"]], "networkx.algorithms.cluster": [[122, "module-networkx.algorithms.cluster"]], "networkx.algorithms.coloring": [[123, "module-networkx.algorithms.coloring"]], "networkx.algorithms.communicability_alg": [[124, "module-networkx.algorithms.communicability_alg"]], "networkx.algorithms.community": [[125, "module-networkx.algorithms.community"]], "networkx.algorithms.community.asyn_fluid": [[125, "module-networkx.algorithms.community.asyn_fluid"]], "networkx.algorithms.community.centrality": [[125, "module-networkx.algorithms.community.centrality"]], "networkx.algorithms.community.community_utils": [[125, "module-networkx.algorithms.community.community_utils"]], "networkx.algorithms.community.kclique": [[125, "module-networkx.algorithms.community.kclique"]], "networkx.algorithms.community.kernighan_lin": [[125, "module-networkx.algorithms.community.kernighan_lin"]], "networkx.algorithms.community.label_propagation": [[125, "module-networkx.algorithms.community.label_propagation"]], "networkx.algorithms.community.louvain": [[125, "module-networkx.algorithms.community.louvain"]], "networkx.algorithms.community.lukes": [[125, "module-networkx.algorithms.community.lukes"]], "networkx.algorithms.community.modularity_max": [[125, "module-networkx.algorithms.community.modularity_max"]], "networkx.algorithms.community.quality": [[125, "module-networkx.algorithms.community.quality"]], "networkx.algorithms.components": [[126, "module-networkx.algorithms.components"]], "networkx.algorithms.connectivity": [[127, "module-networkx.algorithms.connectivity"]], "networkx.algorithms.connectivity.connectivity": [[127, "module-networkx.algorithms.connectivity.connectivity"]], "networkx.algorithms.connectivity.cuts": [[127, "module-networkx.algorithms.connectivity.cuts"]], "networkx.algorithms.connectivity.disjoint_paths": [[127, "module-networkx.algorithms.connectivity.disjoint_paths"]], "networkx.algorithms.connectivity.edge_augmentation": [[127, "module-networkx.algorithms.connectivity.edge_augmentation"]], "networkx.algorithms.connectivity.edge_kcomponents": [[127, "module-networkx.algorithms.connectivity.edge_kcomponents"]], "networkx.algorithms.connectivity.kcomponents": [[127, "module-networkx.algorithms.connectivity.kcomponents"]], "networkx.algorithms.connectivity.kcutsets": [[127, "module-networkx.algorithms.connectivity.kcutsets"]], "networkx.algorithms.connectivity.stoerwagner": [[127, "module-networkx.algorithms.connectivity.stoerwagner"]], "networkx.algorithms.connectivity.utils": [[127, "module-networkx.algorithms.connectivity.utils"]], "networkx.algorithms.core": [[128, "module-networkx.algorithms.core"]], "networkx.algorithms.covering": [[129, "module-networkx.algorithms.covering"]], "networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct"]], "k_edge_components() (edgecomponentauxgraph method)": [[142, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components"]], "k_edge_subgraphs() (edgecomponentauxgraph method)": [[143, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs"]], "analyze_symmetry() (ismags method)": [[144, "networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry"]], "find_isomorphisms() (ismags method)": [[145, "networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms"]], "is_isomorphic() (ismags method)": [[146, "networkx.algorithms.isomorphism.ISMAGS.is_isomorphic"]], "isomorphisms_iter() (ismags method)": [[147, "networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter"]], "largest_common_subgraph() (ismags method)": [[148, "networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph"]], "subgraph_is_isomorphic() (ismags method)": [[149, "networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (ismags method)": [[150, "networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter"]], "add_edge() (planarembedding method)": [[151, "networkx.algorithms.planarity.PlanarEmbedding.add_edge"]], "add_edges_from() (planarembedding method)": [[152, "networkx.algorithms.planarity.PlanarEmbedding.add_edges_from"]], "add_half_edge_ccw() (planarembedding method)": [[153, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw"]], "add_half_edge_cw() (planarembedding method)": [[154, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw"]], "add_half_edge_first() (planarembedding method)": [[155, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first"]], "add_node() (planarembedding method)": [[156, "networkx.algorithms.planarity.PlanarEmbedding.add_node"]], "add_nodes_from() (planarembedding method)": [[157, "networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": [[166, "networkx.algorithms.planarity.PlanarEmbedding.degree"]], "edge_subgraph() (planarembedding method)": [[167, "networkx.algorithms.planarity.PlanarEmbedding.edge_subgraph"]], "edges (planarembedding property)": [[168, "networkx.algorithms.planarity.PlanarEmbedding.edges"]], "get_data() (planarembedding method)": [[169, "networkx.algorithms.planarity.PlanarEmbedding.get_data"]], "get_edge_data() (planarembedding method)": [[170, "networkx.algorithms.planarity.PlanarEmbedding.get_edge_data"]], "has_edge() (planarembedding method)": [[171, "networkx.algorithms.planarity.PlanarEmbedding.has_edge"]], "has_node() (planarembedding method)": [[172, "networkx.algorithms.planarity.PlanarEmbedding.has_node"]], "has_predecessor() (planarembedding method)": [[173, "networkx.algorithms.planarity.PlanarEmbedding.has_predecessor"]], "has_successor() (planarembedding method)": [[174, "networkx.algorithms.planarity.PlanarEmbedding.has_successor"]], "in_degree (planarembedding property)": [[175, "networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[739, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[740, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[741, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[744, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[745, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.is_triad"]], "random_triad() (in module networkx.algorithms.triads)": [[747, "networkx.algorithms.triads.random_triad"]], "triad_type() (in module networkx.algorithms.triads)": [[748, "networkx.algorithms.triads.triad_type"]], "triadic_census() (in module networkx.algorithms.triads)": [[749, "networkx.algorithms.triads.triadic_census"]], "triads_by_type() (in module networkx.algorithms.triads)": [[750, "networkx.algorithms.triads.triads_by_type"]], "closeness_vitality() (in module networkx.algorithms.vitality)": [[751, "networkx.algorithms.vitality.closeness_vitality"]], "voronoi_cells() (in module networkx.algorithms.voronoi)": [[752, "networkx.algorithms.voronoi.voronoi_cells"]], "wiener_index() (in module networkx.algorithms.wiener)": [[753, "networkx.algorithms.wiener.wiener_index"]], "networkx.algorithms.graph_hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "networkx.algorithms.graphical": [[755, "module-networkx.algorithms.graphical"]], "networkx.algorithms.hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "networkx.algorithms.hybrid": [[757, "module-networkx.algorithms.hybrid"]], "networkx.algorithms.isolate": [[759, "module-networkx.algorithms.isolate"]], "networkx.algorithms.isomorphism": [[760, "module-networkx.algorithms.isomorphism"]], "networkx.algorithms.isomorphism.tree_isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "networkx.algorithms.isomorphism.vf2pp": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "networkx.algorithms.isomorphism.ismags": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "networkx.algorithms.isomorphism.isomorphvf2": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "networkx.algorithms.link_analysis.hits_alg": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "networkx.algorithms.link_analysis.pagerank_alg": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "networkx.algorithms.link_prediction": [[764, "module-networkx.algorithms.link_prediction"]], "networkx.algorithms.lowest_common_ancestors": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "networkx.algorithms.matching": [[766, "module-networkx.algorithms.matching"]], "networkx.algorithms.minors": [[767, "module-networkx.algorithms.minors"]], "networkx.algorithms.mis": [[768, "module-networkx.algorithms.mis"]], "networkx.algorithms.moral": [[769, "module-networkx.algorithms.moral"]], "networkx.algorithms.node_classification": [[770, "module-networkx.algorithms.node_classification"]], "networkx.algorithms.non_randomness": [[771, "module-networkx.algorithms.non_randomness"]], "networkx.algorithms.operators.all": [[772, "module-networkx.algorithms.operators.all"]], "networkx.algorithms.operators.binary": [[772, "module-networkx.algorithms.operators.binary"]], "networkx.algorithms.operators.product": [[772, "module-networkx.algorithms.operators.product"]], "networkx.algorithms.operators.unary": [[772, "module-networkx.algorithms.operators.unary"]], "networkx.algorithms.planar_drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "networkx.algorithms.planarity": [[774, "module-networkx.algorithms.planarity"]], "networkx.algorithms.polynomials": [[775, "module-networkx.algorithms.polynomials"]], "networkx.algorithms.reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "networkx.algorithms.regular": [[777, "module-networkx.algorithms.regular"]], "networkx.algorithms.richclub": [[778, "module-networkx.algorithms.richclub"]], "networkx.algorithms.shortest_paths.astar": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "networkx.algorithms.shortest_paths.dense": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "networkx.algorithms.shortest_paths.generic": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "networkx.algorithms.shortest_paths.unweighted": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], 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"networkx.Graph"]], "networkx.classes.backends": [[1038, "module-networkx.classes.backends"]], "networkx.classes.coreviews": [[1038, "module-networkx.classes.coreviews"]], "networkx.classes.filters": [[1038, "module-networkx.classes.filters"]], "networkx.classes.graphviews": [[1038, "module-networkx.classes.graphviews"]], "multidigraph (class in networkx)": [[1039, "networkx.MultiDiGraph"]], "multigraph (class in networkx)": [[1040, "networkx.MultiGraph"]], "networkx.convert": [[1041, "module-networkx.convert"]], "networkx.convert_matrix": [[1041, "module-networkx.convert_matrix"]], "networkx.drawing.layout": [[1042, "module-networkx.drawing.layout"]], "networkx.drawing.nx_agraph": [[1042, "module-networkx.drawing.nx_agraph"]], "networkx.drawing.nx_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.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)": 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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, "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, 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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, "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 52b8e5b9..4900adba 100644 --- a/tutorial-34.pdf +++ b/tutorial-34.pdf diff --git a/tutorial-35.pdf b/tutorial-35.pdf Binary files differindex 4e1b91c3..a880aba4 100644 --- a/tutorial-35.pdf +++ b/tutorial-35.pdf diff --git a/tutorial-36.pdf b/tutorial-36.pdf Binary files differindex 11019820..adb9a14c 100644 --- a/tutorial-36.pdf +++ b/tutorial-36.pdf diff --git a/tutorial.ipynb b/tutorial.ipynb index 93c2049a..18ed824b 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "556904a6", + "id": "f341cd7a", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,7 +17,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2f70944b", + "id": "915e25c5", "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "ce2e12a6", + "id": "3c66bbab", "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": "c9b79e88", + "id": "bf54140f", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "ca159ecf", + "id": "b944c843", "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": "fc8dd841", + "id": "abe72052", "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "markdown", - "id": "08a65628", + "id": "600188a8", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e942f2c4", + "id": "f505d7bc", "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "markdown", - "id": "e46c6c9e", + "id": "cdf97fea", "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": "4e05da68", + "id": "7bcc267d", "metadata": {}, "outputs": [], "source": [ @@ -125,7 +125,7 @@ }, { "cell_type": "markdown", - "id": "de5be6e6", + "id": "e5323595", "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": "acbd939c", + "id": "37c5e5e0", "metadata": {}, "outputs": [], "source": [ @@ -154,7 +154,7 @@ }, { "cell_type": "markdown", - "id": "fffcbb82", + "id": "00ca1374", "metadata": {}, "source": [ "by adding a list of edges," @@ -163,7 +163,7 @@ { "cell_type": "code", "execution_count": null, - "id": "01fd2e57", + "id": "c6dbe13d", "metadata": {}, "outputs": [], "source": [ @@ -172,7 +172,7 @@ }, { "cell_type": "markdown", - "id": "cf94d8f8", + "id": "9b8e3126", "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": "38de4c01", + "id": "35c254e3", "metadata": {}, "outputs": [], "source": [ @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "d4b29f95", + "id": "d8e3d02a", "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": "4642592f", + "id": "3b95cfba", "metadata": {}, "outputs": [], "source": [ @@ -213,7 +213,7 @@ }, { "cell_type": "markdown", - "id": "ab3bb044", + "id": "7a38efa7", "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": "6a3347ca", + "id": "de7c39bc", "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "992f34ff", + "id": "bba0fc4b", "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": "297c8f54", + "id": "dc32d21c", "metadata": {}, "outputs": [], "source": [ @@ -257,7 +257,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4ea2d38c", + "id": "57a7fc3a", "metadata": {}, "outputs": [], "source": [ @@ -272,7 +272,7 @@ }, { "cell_type": "markdown", - "id": "f996097b", + "id": "2ff133c5", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -292,7 +292,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b23270c7", + "id": "179dfe37", "metadata": {}, "outputs": [], "source": [ @@ -304,7 +304,7 @@ }, { "cell_type": "markdown", - "id": "5cc50bbb", + "id": "0ba17135", "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": "b1b77bcd", + "id": "600f437d", "metadata": {}, "outputs": [], "source": [ @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "e5b2404c", + "id": "2cbcffbf", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -343,7 +343,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a422dcc2", + "id": "267bffb7", "metadata": {}, "outputs": [], "source": [ @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "b9c7bdae", + "id": "d3b8d158", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -370,7 +370,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4bdf9108", + "id": "cfadd1c1", "metadata": {}, "outputs": [], "source": [ @@ -387,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "ed15df6b", + "id": "883c013f", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -416,7 +416,7 @@ { "cell_type": "code", "execution_count": null, - "id": "58ed22c2", + "id": "d4240f62", "metadata": {}, "outputs": [], "source": [ @@ -428,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "39c51bcd", + "id": "5a699950", "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": "6a6fc51c", + "id": "e7475845", "metadata": {}, "outputs": [], "source": [ @@ -450,7 +450,7 @@ }, { "cell_type": "markdown", - "id": "657dac76", + "id": "796777aa", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -461,7 +461,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ca2e8457", + "id": "ff8d3e4b", "metadata": {}, "outputs": [], "source": [ @@ -475,7 +475,7 @@ }, { "cell_type": "markdown", - "id": "282e9697", + "id": "c0d52456", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -484,7 +484,7 @@ { "cell_type": "code", "execution_count": null, - "id": "427c15e1", + "id": "7d957260", "metadata": {}, "outputs": [], "source": [ @@ -495,7 +495,7 @@ }, { "cell_type": "markdown", - "id": "d628c908", + "id": "68c56030", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -517,7 +517,7 @@ { "cell_type": "code", "execution_count": null, - "id": "036176aa", + "id": "3eb77ff4", "metadata": {}, "outputs": [], "source": [ @@ -527,7 +527,7 @@ }, { "cell_type": "markdown", - "id": "325d8a7f", + "id": "0639a17f", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -536,7 +536,7 @@ { "cell_type": "code", "execution_count": null, - "id": "afb95d5d", + "id": "34ad8489", "metadata": {}, "outputs": [], "source": [ @@ -546,7 +546,7 @@ }, { "cell_type": "markdown", - "id": "9acf916c", + "id": "b45c21ba", "metadata": {}, "source": [ "# Node attributes\n", @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a032aeba", + "id": "1b71fd2c", "metadata": {}, "outputs": [], "source": [ @@ -570,7 +570,7 @@ }, { "cell_type": "markdown", - "id": "7b00414e", + "id": "77bdd920", "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": "cdef5897", + "id": "3ed49146", "metadata": {}, "outputs": [], "source": [ @@ -598,7 +598,7 @@ }, { "cell_type": "markdown", - "id": "c9dab37d", + "id": "f6e3b9fd", "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": "16afcf25", + "id": "7ece9a9c", "metadata": {}, "outputs": [], "source": [ @@ -633,7 +633,7 @@ }, { "cell_type": "markdown", - "id": "7a167fd6", + "id": "64f109ba", "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": "c31c382b", + "id": "434f9992", "metadata": {}, "outputs": [], "source": [ @@ -655,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "26c42ce6", + "id": "f08e6c52", "metadata": {}, "source": [ "# Multigraphs\n", @@ -675,7 +675,7 @@ { "cell_type": "code", "execution_count": null, - "id": "bd6e9b0d", + "id": "41ade2d1", "metadata": {}, "outputs": [], "source": [ @@ -693,7 +693,7 @@ }, { "cell_type": "markdown", - "id": "d1d7ab2f", + "id": "ba8d4af7", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -713,7 +713,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8ee96e6f", + "id": "5fc6c20a", "metadata": {}, "outputs": [], "source": [ @@ -725,7 +725,7 @@ }, { "cell_type": "markdown", - "id": "74aa1028", + "id": "2522e834", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -736,7 +736,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c6199beb", + "id": "6b849c37", "metadata": {}, "outputs": [], "source": [ @@ -748,7 +748,7 @@ }, { "cell_type": "markdown", - "id": "13340f7e", + "id": "8c5b0ba4", "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": "a1389e73", + "id": "6fcf24d5", "metadata": {}, "outputs": [], "source": [ @@ -770,7 +770,7 @@ }, { "cell_type": "markdown", - "id": "7fde08c4", + "id": "ebd909bd", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -785,7 +785,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1cc203a1", + "id": "8d8a1355", "metadata": {}, "outputs": [], "source": [ @@ -799,7 +799,7 @@ }, { "cell_type": "markdown", - "id": "760be142", + "id": "a7db1188", "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": "0b019237", + "id": "3a8f10fa", "metadata": {}, "outputs": [], "source": [ @@ -819,7 +819,7 @@ }, { "cell_type": "markdown", - "id": "ff0a53a6", + "id": "1c8a9cef", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -838,7 +838,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5ae58915", + "id": "464ab17e", "metadata": {}, "outputs": [], "source": [ @@ -847,7 +847,7 @@ }, { "cell_type": "markdown", - "id": "c56c1964", + "id": "c58cc91e", "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": "059a8dec", + "id": "86ed6e67", "metadata": {}, "outputs": [], "source": [ @@ -870,7 +870,7 @@ }, { "cell_type": "markdown", - "id": "199339be", + "id": "d5151c7e", "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": "ee8c1c6b", + "id": "f88f1bce", "metadata": {}, "outputs": [], "source": [ @@ -889,7 +889,7 @@ }, { "cell_type": "markdown", - "id": "ef2fb3cc", + "id": "2e6bcc59", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -898,7 +898,7 @@ { "cell_type": "code", "execution_count": null, - "id": "21fe7c4f", + "id": "6e27cd84", "metadata": {}, "outputs": [], "source": [ @@ -919,7 +919,7 @@ }, { "cell_type": "markdown", - "id": "5f44eaab", + "id": "80dd6400", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -930,7 +930,7 @@ { "cell_type": "code", "execution_count": null, - "id": "21526fe5", + "id": "d399c4cb", "metadata": {}, "outputs": [], "source": [ @@ -941,7 +941,7 @@ }, { "cell_type": "markdown", - "id": "f99bb0b4", + "id": "ff07fe90", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -950,7 +950,7 @@ { "cell_type": "code", "execution_count": null, - "id": "09eea67d", + "id": "cbdb9a7b", "metadata": {}, "outputs": [], "source": [ @@ -960,7 +960,7 @@ }, { "cell_type": "markdown", - "id": "c0a0b338", + "id": "78300d30", "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": "2319e3f5", + "id": "7e0e5e01", "metadata": {}, "outputs": [], "source": [ @@ -985,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "a8415aea", + "id": "d999d94b", "metadata": {}, "source": [ "See Drawing for additional details." diff --git a/tutorial_full.ipynb b/tutorial_full.ipynb index 778e489e..67b5c61f 100644 --- a/tutorial_full.ipynb +++ b/tutorial_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "556904a6", + "id": "f341cd7a", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,13 +17,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "2f70944b", + "id": "915e25c5", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.016282Z", - "iopub.status.busy": "2023-01-04T14:37:25.016007Z", - "iopub.status.idle": "2023-01-04T14:37:25.093941Z", - "shell.execute_reply": "2023-01-04T14:37:25.093233Z" + "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" } }, "outputs": [], @@ -34,7 +34,7 @@ }, { "cell_type": "markdown", - "id": "ce2e12a6", + "id": "3c66bbab", "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": "c9b79e88", + "id": "bf54140f", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.097892Z", - "iopub.status.busy": "2023-01-04T14:37:25.097666Z", - "iopub.status.idle": "2023-01-04T14:37:25.100890Z", - "shell.execute_reply": "2023-01-04T14:37:25.100129Z" + "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" } }, "outputs": [], @@ -70,7 +70,7 @@ }, { "cell_type": "markdown", - "id": "ca159ecf", + "id": "b944c843", "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": "fc8dd841", + "id": "abe72052", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.104429Z", - "iopub.status.busy": "2023-01-04T14:37:25.104201Z", - "iopub.status.idle": "2023-01-04T14:37:25.107079Z", - "shell.execute_reply": "2023-01-04T14:37:25.106485Z" + "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" } }, "outputs": [], @@ -95,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "08a65628", + "id": "600188a8", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -117,13 +117,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "e942f2c4", + "id": "f505d7bc", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.109882Z", - "iopub.status.busy": "2023-01-04T14:37:25.109644Z", - "iopub.status.idle": "2023-01-04T14:37:25.113271Z", - "shell.execute_reply": "2023-01-04T14:37:25.112582Z" + "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" } }, "outputs": [], @@ -134,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "e46c6c9e", + "id": "cdf97fea", "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": "4e05da68", + "id": "7bcc267d", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.116349Z", - "iopub.status.busy": "2023-01-04T14:37:25.116126Z", - "iopub.status.idle": "2023-01-04T14:37:25.119072Z", - "shell.execute_reply": "2023-01-04T14:37:25.118448Z" + "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" } }, "outputs": [], @@ -160,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "de5be6e6", + "id": "e5323595", "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": "acbd939c", + "id": "37c5e5e0", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.122371Z", - "iopub.status.busy": "2023-01-04T14:37:25.122146Z", - "iopub.status.idle": "2023-01-04T14:37:25.125443Z", - "shell.execute_reply": "2023-01-04T14:37:25.124687Z" + "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" } }, "outputs": [], @@ -196,7 +196,7 @@ }, { "cell_type": "markdown", - "id": "fffcbb82", + "id": "00ca1374", "metadata": {}, "source": [ "by adding a list of edges," @@ -205,13 +205,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "01fd2e57", + "id": "c6dbe13d", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.128926Z", - "iopub.status.busy": "2023-01-04T14:37:25.128671Z", - "iopub.status.idle": "2023-01-04T14:37:25.131930Z", - "shell.execute_reply": "2023-01-04T14:37:25.131255Z" + "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" } }, "outputs": [], @@ -221,7 +221,7 @@ }, { "cell_type": "markdown", - "id": "cf94d8f8", + "id": "9b8e3126", "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": "38de4c01", + "id": "35c254e3", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.134962Z", - "iopub.status.busy": "2023-01-04T14:37:25.134765Z", - "iopub.status.idle": "2023-01-04T14:37:25.137669Z", - "shell.execute_reply": "2023-01-04T14:37:25.136978Z" + "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" } }, "outputs": [], @@ -250,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "d4b29f95", + "id": "d8e3d02a", "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": "4642592f", + "id": "3b95cfba", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.140949Z", - "iopub.status.busy": "2023-01-04T14:37:25.140711Z", - "iopub.status.idle": "2023-01-04T14:37:25.143671Z", - "shell.execute_reply": "2023-01-04T14:37:25.143027Z" + "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" } }, "outputs": [], @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "ab3bb044", + "id": "7a38efa7", "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": "6a3347ca", + "id": "de7c39bc", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.146775Z", - "iopub.status.busy": "2023-01-04T14:37:25.146554Z", - "iopub.status.idle": "2023-01-04T14:37:25.150610Z", - "shell.execute_reply": "2023-01-04T14:37:25.149912Z" + "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" } }, "outputs": [], @@ -307,7 +307,7 @@ }, { "cell_type": "markdown", - "id": "992f34ff", + "id": "bba0fc4b", "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": "297c8f54", + "id": "dc32d21c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.153956Z", - "iopub.status.busy": "2023-01-04T14:37:25.153734Z", - "iopub.status.idle": "2023-01-04T14:37:25.160443Z", - "shell.execute_reply": "2023-01-04T14:37:25.159727Z" + "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" } }, "outputs": [ @@ -345,13 +345,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "4ea2d38c", + "id": "57a7fc3a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.164807Z", - "iopub.status.busy": "2023-01-04T14:37:25.164566Z", - "iopub.status.idle": "2023-01-04T14:37:25.168807Z", - "shell.execute_reply": "2023-01-04T14:37:25.168205Z" + "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" } }, "outputs": [], @@ -367,7 +367,7 @@ }, { "cell_type": "markdown", - "id": "f996097b", + "id": "2ff133c5", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -387,13 +387,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "b23270c7", + "id": "179dfe37", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.171829Z", - "iopub.status.busy": "2023-01-04T14:37:25.171606Z", - "iopub.status.idle": "2023-01-04T14:37:25.176270Z", - "shell.execute_reply": "2023-01-04T14:37:25.175681Z" + "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" } }, "outputs": [ @@ -417,7 +417,7 @@ }, { "cell_type": "markdown", - "id": "5cc50bbb", + "id": "0ba17135", "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": "b1b77bcd", + "id": "600f437d", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.180292Z", - "iopub.status.busy": "2023-01-04T14:37:25.180053Z", - "iopub.status.idle": "2023-01-04T14:37:25.184636Z", - "shell.execute_reply": "2023-01-04T14:37:25.183923Z" + "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" } }, "outputs": [ @@ -457,7 +457,7 @@ }, { "cell_type": "markdown", - "id": "e5b2404c", + "id": "2cbcffbf", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -474,13 +474,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "a422dcc2", + "id": "267bffb7", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.188263Z", - "iopub.status.busy": "2023-01-04T14:37:25.188043Z", - "iopub.status.idle": "2023-01-04T14:37:25.191392Z", - "shell.execute_reply": "2023-01-04T14:37:25.190676Z" + "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" } }, "outputs": [], @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "b9c7bdae", + "id": "d3b8d158", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -508,13 +508,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "4bdf9108", + "id": "cfadd1c1", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.194428Z", - "iopub.status.busy": "2023-01-04T14:37:25.194189Z", - "iopub.status.idle": "2023-01-04T14:37:25.476331Z", - "shell.execute_reply": "2023-01-04T14:37:25.475414Z" + "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" } }, "outputs": [ @@ -543,7 +543,7 @@ }, { "cell_type": "markdown", - "id": "ed15df6b", + "id": "883c013f", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -572,13 +572,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "58ed22c2", + "id": "d4240f62", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.480229Z", - "iopub.status.busy": "2023-01-04T14:37:25.479853Z", - "iopub.status.idle": "2023-01-04T14:37:25.486702Z", - "shell.execute_reply": "2023-01-04T14:37:25.486029Z" + "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" } }, "outputs": [ @@ -602,7 +602,7 @@ }, { "cell_type": "markdown", - "id": "39c51bcd", + "id": "5a699950", "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": "6a6fc51c", + "id": "e7475845", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.489843Z", - "iopub.status.busy": "2023-01-04T14:37:25.489596Z", - "iopub.status.idle": "2023-01-04T14:37:25.494790Z", - "shell.execute_reply": "2023-01-04T14:37:25.494127Z" + "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" } }, "outputs": [ @@ -642,7 +642,7 @@ }, { "cell_type": "markdown", - "id": "657dac76", + "id": "796777aa", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -653,13 +653,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "ca2e8457", + "id": "ff8d3e4b", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.498727Z", - "iopub.status.busy": "2023-01-04T14:37:25.498499Z", - "iopub.status.idle": "2023-01-04T14:37:25.504018Z", - "shell.execute_reply": "2023-01-04T14:37:25.503112Z" + "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" } }, "outputs": [ @@ -685,7 +685,7 @@ }, { "cell_type": "markdown", - "id": "282e9697", + "id": "c0d52456", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -694,13 +694,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "427c15e1", + "id": "7d957260", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.508418Z", - "iopub.status.busy": "2023-01-04T14:37:25.508180Z", - "iopub.status.idle": "2023-01-04T14:37:25.512189Z", - "shell.execute_reply": "2023-01-04T14:37:25.511501Z" + "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" } }, "outputs": [ @@ -721,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "d628c908", + "id": "68c56030", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -743,13 +743,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "036176aa", + "id": "3eb77ff4", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.516276Z", - "iopub.status.busy": "2023-01-04T14:37:25.516034Z", - "iopub.status.idle": "2023-01-04T14:37:25.520433Z", - "shell.execute_reply": "2023-01-04T14:37:25.519790Z" + "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" } }, "outputs": [ @@ -771,7 +771,7 @@ }, { "cell_type": "markdown", - "id": "325d8a7f", + "id": "0639a17f", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -780,13 +780,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "afb95d5d", + "id": "34ad8489", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.524949Z", - "iopub.status.busy": "2023-01-04T14:37:25.524655Z", - "iopub.status.idle": "2023-01-04T14:37:25.529245Z", - "shell.execute_reply": "2023-01-04T14:37:25.528502Z" + "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" } }, "outputs": [ @@ -808,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "9acf916c", + "id": "b45c21ba", "metadata": {}, "source": [ "# Node attributes\n", @@ -819,13 +819,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "a032aeba", + "id": "1b71fd2c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.533639Z", - "iopub.status.busy": "2023-01-04T14:37:25.533413Z", - "iopub.status.idle": "2023-01-04T14:37:25.538582Z", - "shell.execute_reply": "2023-01-04T14:37:25.537855Z" + "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" } }, "outputs": [ @@ -850,7 +850,7 @@ }, { "cell_type": "markdown", - "id": "7b00414e", + "id": "77bdd920", "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": "cdef5897", + "id": "3ed49146", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.542567Z", - "iopub.status.busy": "2023-01-04T14:37:25.542342Z", - "iopub.status.idle": "2023-01-04T14:37:25.546841Z", - "shell.execute_reply": "2023-01-04T14:37:25.546160Z" + "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" } }, "outputs": [], @@ -885,7 +885,7 @@ }, { "cell_type": "markdown", - "id": "c9dab37d", + "id": "f6e3b9fd", "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": "16afcf25", + "id": "7ece9a9c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.550020Z", - "iopub.status.busy": "2023-01-04T14:37:25.549790Z", - "iopub.status.idle": "2023-01-04T14:37:25.555631Z", - "shell.execute_reply": "2023-01-04T14:37:25.554904Z" + "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" } }, "outputs": [ @@ -938,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "7a167fd6", + "id": "64f109ba", "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": "c31c382b", + "id": "434f9992", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.559653Z", - "iopub.status.busy": "2023-01-04T14:37:25.559409Z", - "iopub.status.idle": "2023-01-04T14:37:25.562660Z", - "shell.execute_reply": "2023-01-04T14:37:25.561971Z" + "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" } }, "outputs": [], @@ -967,7 +967,7 @@ }, { "cell_type": "markdown", - "id": "26c42ce6", + "id": "f08e6c52", "metadata": {}, "source": [ "# Multigraphs\n", @@ -987,13 +987,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "bd6e9b0d", + "id": "41ade2d1", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.565806Z", - "iopub.status.busy": "2023-01-04T14:37:25.565579Z", - "iopub.status.idle": "2023-01-04T14:37:25.572605Z", - "shell.execute_reply": "2023-01-04T14:37:25.571883Z" + "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" } }, "outputs": [ @@ -1023,7 +1023,7 @@ }, { "cell_type": "markdown", - "id": "d1d7ab2f", + "id": "ba8d4af7", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -1043,13 +1043,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "8ee96e6f", + "id": "5fc6c20a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.576622Z", - "iopub.status.busy": "2023-01-04T14:37:25.576395Z", - "iopub.status.idle": "2023-01-04T14:37:25.580954Z", - "shell.execute_reply": "2023-01-04T14:37:25.580225Z" + "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" } }, "outputs": [], @@ -1062,7 +1062,7 @@ }, { "cell_type": "markdown", - "id": "74aa1028", + "id": "2522e834", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -1073,13 +1073,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "c6199beb", + "id": "6b849c37", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.584244Z", - "iopub.status.busy": "2023-01-04T14:37:25.584002Z", - "iopub.status.idle": "2023-01-04T14:37:25.603489Z", - "shell.execute_reply": "2023-01-04T14:37:25.602629Z" + "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" } }, "outputs": [], @@ -1092,7 +1092,7 @@ }, { "cell_type": "markdown", - "id": "13340f7e", + "id": "8c5b0ba4", "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": "a1389e73", + "id": "6fcf24d5", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:25.607344Z", - "iopub.status.busy": "2023-01-04T14:37:25.607114Z", - "iopub.status.idle": "2023-01-04T14:37:26.161918Z", - "shell.execute_reply": "2023-01-04T14:37:26.160927Z" + "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" } }, "outputs": [], @@ -1121,7 +1121,7 @@ }, { "cell_type": "markdown", - "id": "7fde08c4", + "id": "ebd909bd", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -1136,13 +1136,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "1cc203a1", + "id": "8d8a1355", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:26.166889Z", - "iopub.status.busy": "2023-01-04T14:37:26.166625Z", - "iopub.status.idle": "2023-01-04T14:37:26.174584Z", - "shell.execute_reply": "2023-01-04T14:37:26.173964Z" + "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" } }, "outputs": [ @@ -1168,7 +1168,7 @@ }, { "cell_type": "markdown", - "id": "760be142", + "id": "a7db1188", "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": "0b019237", + "id": "3a8f10fa", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:26.177636Z", - "iopub.status.busy": "2023-01-04T14:37:26.177402Z", - "iopub.status.idle": "2023-01-04T14:37:26.182170Z", - "shell.execute_reply": "2023-01-04T14:37:26.181443Z" + "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" } }, "outputs": [ @@ -1206,7 +1206,7 @@ }, { "cell_type": "markdown", - "id": "ff0a53a6", + "id": "1c8a9cef", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -1225,13 +1225,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "5ae58915", + "id": "464ab17e", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:26.185974Z", - "iopub.status.busy": "2023-01-04T14:37:26.185741Z", - "iopub.status.idle": "2023-01-04T14:37:26.576044Z", - "shell.execute_reply": "2023-01-04T14:37:26.575110Z" + "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" } }, "outputs": [], @@ -1241,7 +1241,7 @@ }, { "cell_type": "markdown", - "id": "c56c1964", + "id": "c58cc91e", "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": "059a8dec", + "id": "86ed6e67", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:26.581391Z", - "iopub.status.busy": "2023-01-04T14:37:26.580458Z", - "iopub.status.idle": "2023-01-04T14:37:26.797664Z", - "shell.execute_reply": "2023-01-04T14:37:26.797077Z" + "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" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] @@ -1282,7 +1282,7 @@ }, { "cell_type": "markdown", - "id": "199339be", + "id": "d5151c7e", "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": "ee8c1c6b", + "id": "f88f1bce", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:26.803591Z", - "iopub.status.busy": "2023-01-04T14:37:26.803134Z", - "iopub.status.idle": "2023-01-04T14:37:26.806331Z", - "shell.execute_reply": "2023-01-04T14:37:26.805812Z" + "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" } }, "outputs": [], @@ -1308,7 +1308,7 @@ }, { "cell_type": "markdown", - "id": "ef2fb3cc", + "id": "2e6bcc59", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -1317,19 +1317,19 @@ { "cell_type": "code", "execution_count": 36, - "id": "21fe7c4f", + "id": "6e27cd84", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:26.809288Z", - "iopub.status.busy": "2023-01-04T14:37:26.808878Z", - "iopub.status.idle": "2023-01-04T14:37:27.138222Z", - "shell.execute_reply": "2023-01-04T14:37:27.137045Z" + "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" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 640x480 with 4 Axes>" ] @@ -1356,7 +1356,7 @@ }, { "cell_type": "markdown", - "id": "5f44eaab", + "id": "80dd6400", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -1367,13 +1367,13 @@ { "cell_type": "code", "execution_count": 37, - "id": "21526fe5", + "id": "d399c4cb", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:27.142651Z", - "iopub.status.busy": "2023-01-04T14:37:27.142151Z", - "iopub.status.idle": "2023-01-04T14:37:27.262438Z", - "shell.execute_reply": "2023-01-04T14:37:27.261708Z" + "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" } }, "outputs": [ @@ -1396,7 +1396,7 @@ }, { "cell_type": "markdown", - "id": "f99bb0b4", + "id": "ff07fe90", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -1405,19 +1405,19 @@ { "cell_type": "code", "execution_count": 38, - "id": "09eea67d", + "id": "cbdb9a7b", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:27.266526Z", - "iopub.status.busy": "2023-01-04T14:37:27.266275Z", - "iopub.status.idle": "2023-01-04T14:37:27.416551Z", - "shell.execute_reply": "2023-01-04T14:37:27.415934Z" + "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" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1433,7 +1433,7 @@ }, { "cell_type": "markdown", - "id": "c0a0b338", + "id": "78300d30", "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": "2319e3f5", + "id": "7e0e5e01", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T14:37:27.421594Z", - "iopub.status.busy": "2023-01-04T14:37:27.420095Z", - "iopub.status.idle": "2023-01-04T14:37:27.570210Z", - "shell.execute_reply": "2023-01-04T14:37:27.569579Z" + "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" } }, "outputs": [ @@ -1476,7 +1476,7 @@ }, { "cell_type": "markdown", - "id": "a8415aea", + "id": "d999d94b", "metadata": {}, "source": [ "See Drawing for additional details." |
