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| author | MridulS <mail@mriduls.com> | 2023-01-02 13:08:27 +0000 |
|---|---|---|
| committer | MridulS <mail@mriduls.com> | 2023-01-02 13:08:27 +0000 |
| commit | 0b9a02d6b3796e8ce4fed6cbce282fced15e486a (patch) | |
| tree | 8b48b18926291619367d4f9537892bd228bf7987 /_modules/networkx | |
| parent | 6ae99ab58d8b8ba50f66768c0f3aa4bb82b22196 (diff) | |
| download | networkx-0b9a02d6b3796e8ce4fed6cbce282fced15e486a.tar.gz | |
Deploying to gh-pages from @ networkx/networkx@71ad516a90c89c7294e32bf28e1f05c63c5f17e4 🚀
Diffstat (limited to '_modules/networkx')
240 files changed, 1734 insertions, 1736 deletions
diff --git a/_modules/networkx/algorithms/approximation/clique.html b/_modules/networkx/algorithms/approximation/clique.html index 3efa43f5..2ecec5e1 100644 --- a/_modules/networkx/algorithms/approximation/clique.html +++ b/_modules/networkx/algorithms/approximation/clique.html @@ -477,7 +477,7 @@ <div class="viewcode-block" id="maximum_independent_set"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.clique.maximum_independent_set.html#networkx.algorithms.approximation.clique.maximum_independent_set">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">maximum_independent_set</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns an approximate maximum independent set.</span> +<span class="w"> </span><span class="sd">"""Returns an approximate maximum independent set.</span> <span class="sd"> Independent set or stable set is a set of vertices in a graph, no two of</span> <span class="sd"> which are adjacent. That is, it is a set I of vertices such that for every</span> @@ -527,7 +527,7 @@ <div class="viewcode-block" id="max_clique"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.clique.max_clique.html#networkx.algorithms.approximation.clique.max_clique">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">max_clique</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Find the Maximum Clique</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Find the Maximum Clique</span> <span class="sd"> Finds the $O(|V|/(log|V|)^2)$ apx of maximum clique/independent set</span> <span class="sd"> in the worst case.</span> @@ -582,7 +582,7 @@ <div class="viewcode-block" id="clique_removal"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.clique.clique_removal.html#networkx.algorithms.approximation.clique.clique_removal">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">clique_removal</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Repeatedly remove cliques from the graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Repeatedly remove cliques from the graph.</span> <span class="sd"> Results in a $O(|V|/(\log |V|)^2)$ approximation of maximum clique</span> <span class="sd"> and independent set. Returns the largest independent set found, along</span> @@ -628,7 +628,7 @@ <div class="viewcode-block" id="large_clique_size"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.clique.large_clique_size.html#networkx.algorithms.approximation.clique.large_clique_size">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">large_clique_size</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Find the size of a large clique in a graph.</span> +<span class="w"> </span><span class="sd">"""Find the size of a large clique in a graph.</span> <span class="sd"> A *clique* is a subset of nodes in which each pair of nodes is</span> <span class="sd"> adjacent. This function is a heuristic for finding the size of a</span> @@ -745,7 +745,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/approximation/clustering_coefficient.html b/_modules/networkx/algorithms/approximation/clustering_coefficient.html index 4f5e25a3..c494aa3f 100644 --- a/_modules/networkx/algorithms/approximation/clustering_coefficient.html +++ b/_modules/networkx/algorithms/approximation/clustering_coefficient.html @@ -469,7 +469,7 @@ <div class="viewcode-block" id="average_clustering"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.clustering_coefficient.average_clustering.html#networkx.algorithms.approximation.clustering_coefficient.average_clustering">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">average_clustering</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">trials</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Estimates the average clustering coefficient of G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Estimates the average clustering coefficient of G.</span> <span class="sd"> The local clustering of each node in `G` is the fraction of triangles</span> <span class="sd"> that actually exist over all possible triangles in its neighborhood.</span> @@ -576,7 +576,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/approximation/connectivity.html b/_modules/networkx/algorithms/approximation/connectivity.html index d43dc9b3..218de1e1 100644 --- a/_modules/networkx/algorithms/approximation/connectivity.html +++ b/_modules/networkx/algorithms/approximation/connectivity.html @@ -476,7 +476,7 @@ <div class="viewcode-block" id="local_node_connectivity"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.connectivity.local_node_connectivity.html#networkx.algorithms.approximation.connectivity.local_node_connectivity">[docs]</a><span class="k">def</span> <span class="nf">local_node_connectivity</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute node connectivity between source and target.</span> +<span class="w"> </span><span class="sd">"""Compute node connectivity between source and target.</span> <span class="sd"> Pairwise or local node connectivity between two distinct and nonadjacent</span> <span class="sd"> nodes is the minimum number of nodes that must be removed (minimum</span> @@ -571,7 +571,7 @@ <div class="viewcode-block" id="node_connectivity"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.connectivity.node_connectivity.html#networkx.algorithms.approximation.connectivity.node_connectivity">[docs]</a><span class="k">def</span> <span class="nf">node_connectivity</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">t</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns an approximation for node connectivity for a graph or digraph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns an approximation for node connectivity for a graph or digraph G.</span> <span class="sd"> Node connectivity is equal to the minimum number of nodes that</span> <span class="sd"> must be removed to disconnect G or render it trivial. By Menger's theorem,</span> @@ -676,7 +676,7 @@ <div class="viewcode-block" id="all_pairs_node_connectivity"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity.html#networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity">[docs]</a><span class="k">def</span> <span class="nf">all_pairs_node_connectivity</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nbunch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute node connectivity between all pairs of nodes.</span> +<span class="w"> </span><span class="sd">"""Compute node connectivity between all pairs of nodes.</span> <span class="sd"> Pairwise or local node connectivity between two distinct and nonadjacent</span> <span class="sd"> nodes is the minimum number of nodes that must be removed (minimum</span> @@ -755,7 +755,7 @@ <span class="k">def</span> <span class="nf">_bidirectional_shortest_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">exclude</span><span class="p">):</span> - <span class="sd">"""Returns shortest path between source and target ignoring nodes in the</span> +<span class="w"> </span><span class="sd">"""Returns shortest path between source and target ignoring nodes in the</span> <span class="sd"> container 'exclude'.</span> <span class="sd"> Parameters</span> @@ -927,7 +927,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/approximation/distance_measures.html b/_modules/networkx/algorithms/approximation/distance_measures.html index 93f4e1b8..550b00be 100644 --- a/_modules/networkx/algorithms/approximation/distance_measures.html +++ b/_modules/networkx/algorithms/approximation/distance_measures.html @@ -471,7 +471,7 @@ <div class="viewcode-block" id="diameter"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.distance_measures.diameter.html#networkx.algorithms.approximation.distance_measures.diameter">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="k">def</span> <span class="nf">diameter</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a lower bound on the diameter of the graph G.</span> +<span class="w"> </span><span class="sd">"""Returns a lower bound on the diameter of the graph G.</span> <span class="sd"> The function computes a lower bound on the diameter (i.e., the maximum eccentricity)</span> <span class="sd"> of a directed or undirected graph G. The procedure used varies depending on the graph</span> @@ -538,7 +538,7 @@ <span class="k">def</span> <span class="nf">_two_sweep_undirected</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">seed</span><span class="p">):</span> - <span class="sd">"""Helper function for finding a lower bound on the diameter</span> +<span class="w"> </span><span class="sd">"""Helper function for finding a lower bound on the diameter</span> <span class="sd"> for undirected Graphs.</span> <span class="sd"> The idea is to pick the farthest node from a random node</span> @@ -564,7 +564,7 @@ <span class="k">def</span> <span class="nf">_two_sweep_directed</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">seed</span><span class="p">):</span> - <span class="sd">"""Helper function for finding a lower bound on the diameter</span> +<span class="w"> </span><span class="sd">"""Helper function for finding a lower bound on the diameter</span> <span class="sd"> for directed Graphs.</span> <span class="sd"> It implements 2-dSweep, the directed version of the 2-sweep algorithm.</span> @@ -652,7 +652,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/approximation/dominating_set.html b/_modules/networkx/algorithms/approximation/dominating_set.html index 6aeb086c..2a533abd 100644 --- a/_modules/networkx/algorithms/approximation/dominating_set.html +++ b/_modules/networkx/algorithms/approximation/dominating_set.html @@ -483,7 +483,7 @@ <span class="c1"># TODO Why doesn't this algorithm work for directed graphs?</span> <div class="viewcode-block" id="min_weighted_dominating_set"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set.html#networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">min_weighted_dominating_set</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a dominating set that approximates the minimum weight node</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a dominating set that approximates the minimum weight node</span> <span class="sd"> dominating set.</span> <span class="sd"> Parameters</span> @@ -530,7 +530,7 @@ <span class="n">dom_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span> <span class="k">def</span> <span class="nf">_cost</span><span class="p">(</span><span class="n">node_and_neighborhood</span><span class="p">):</span> - <span class="sd">"""Returns the cost-effectiveness of greedily choosing the given</span> +<span class="w"> </span><span class="sd">"""Returns the cost-effectiveness of greedily choosing the given</span> <span class="sd"> node.</span> <span class="sd"> `node_and_neighborhood` is a two-tuple comprising a node and its</span> @@ -563,7 +563,7 @@ <div class="viewcode-block" id="min_edge_dominating_set"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.dominating_set.min_edge_dominating_set.html#networkx.algorithms.approximation.dominating_set.min_edge_dominating_set">[docs]</a><span class="k">def</span> <span class="nf">min_edge_dominating_set</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns minimum cardinality edge dominating set.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns minimum cardinality edge dominating set.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -635,7 +635,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/approximation/kcomponents.html b/_modules/networkx/algorithms/approximation/kcomponents.html index e0accb66..045c2762 100644 --- a/_modules/networkx/algorithms/approximation/kcomponents.html +++ b/_modules/networkx/algorithms/approximation/kcomponents.html @@ -478,7 +478,7 @@ <div class="viewcode-block" id="k_components"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.kcomponents.k_components.html#networkx.algorithms.approximation.kcomponents.k_components">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">k_components</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">min_density</span><span class="o">=</span><span class="mf">0.95</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the approximate k-component structure of a graph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the approximate k-component structure of a graph G.</span> <span class="sd"> A `k`-component is a maximal subgraph of a graph G that has, at least,</span> <span class="sd"> node connectivity `k`: we need to remove at least `k` nodes to break it</span> @@ -657,7 +657,7 @@ <span class="k">class</span> <span class="nc">_AntiGraph</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Class for complement graphs.</span> <span class="sd"> The main goal is to be able to work with big and dense graphs with</span> @@ -678,7 +678,7 @@ <span class="n">edge_attr_dict_factory</span> <span class="o">=</span> <span class="n">single_edge_dict</span> <span class="c1"># type: ignore</span> <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span> - <span class="sd">"""Returns a dict of neighbors of node n in the dense graph.</span> +<span class="w"> </span><span class="sd">"""Returns a dict of neighbors of node n in the dense graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -697,7 +697,7 @@ <span class="p">}</span> <span class="k">def</span> <span class="nf">neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span> - <span class="sd">"""Returns an iterator over all neighbors of node n in the</span> +<span class="w"> </span><span class="sd">"""Returns an iterator over all neighbors of node n in the</span> <span class="sd"> dense graph.</span> <span class="sd"> """</span> <span class="k">try</span><span class="p">:</span> @@ -706,7 +706,7 @@ <span class="k">raise</span> <span class="n">NetworkXError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"The node </span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s2"> is not in the graph."</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">err</span> <span class="k">class</span> <span class="nc">AntiAtlasView</span><span class="p">(</span><span class="n">Mapping</span><span class="p">):</span> - <span class="sd">"""An adjacency inner dict for AntiGraph"""</span> +<span class="w"> </span><span class="sd">"""An adjacency inner dict for AntiGraph"""</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">node</span><span class="p">):</span> <span class="bp">self</span><span class="o">.</span><span class="n">_graph</span> <span class="o">=</span> <span class="n">graph</span> @@ -726,7 +726,7 @@ <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="n">nbr</span><span class="p">)</span> <span class="k">class</span> <span class="nc">AntiAdjacencyView</span><span class="p">(</span><span class="n">AntiAtlasView</span><span class="p">):</span> - <span class="sd">"""An adjacency outer dict for AntiGraph"""</span> +<span class="w"> </span><span class="sd">"""An adjacency outer dict for AntiGraph"""</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">):</span> <span class="bp">self</span><span class="o">.</span><span class="n">_graph</span> <span class="o">=</span> <span class="n">graph</span> @@ -748,7 +748,7 @@ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">AntiAdjacencyView</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="k">def</span> <span class="nf">subgraph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span> - <span class="sd">"""This subgraph method returns a full AntiGraph. Not a View"""</span> +<span class="w"> </span><span class="sd">"""This subgraph method returns a full AntiGraph. Not a View"""</span> <span class="n">nodes</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">nodes</span><span class="p">)</span> <span class="n">G</span> <span class="o">=</span> <span class="n">_AntiGraph</span><span class="p">()</span> <span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="n">nodes</span><span class="p">)</span> @@ -776,7 +776,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">degree</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns an iterator for (node, degree) and degree for single node.</span> +<span class="w"> </span><span class="sd">"""Returns an iterator for (node, degree) and degree for single node.</span> <span class="sd"> The node degree is the number of edges adjacent to the node.</span> @@ -814,7 +814,7 @@ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">AntiDegreeView</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="k">def</span> <span class="nf">adjacency</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns an iterator of (node, adjacency set) tuples for all nodes</span> +<span class="w"> </span><span class="sd">"""Returns an iterator of (node, adjacency set) tuples for all nodes</span> <span class="sd"> in the dense graph.</span> <span class="sd"> This is the fastest way to look at every edge.</span> @@ -880,7 +880,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/approximation/matching.html b/_modules/networkx/algorithms/approximation/matching.html index df5e1d03..48d5f4de 100644 --- a/_modules/networkx/algorithms/approximation/matching.html +++ b/_modules/networkx/algorithms/approximation/matching.html @@ -477,7 +477,7 @@ <div class="viewcode-block" id="min_maximal_matching"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.matching.min_maximal_matching.html#networkx.algorithms.approximation.matching.min_maximal_matching">[docs]</a><span class="k">def</span> <span class="nf">min_maximal_matching</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the minimum maximal matching of G. That is, out of all maximal</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the minimum maximal matching of G. That is, out of all maximal</span> <span class="sd"> matchings of the graph G, the smallest is returned.</span> <span class="sd"> Parameters</span> @@ -554,7 +554,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/approximation/maxcut.html b/_modules/networkx/algorithms/approximation/maxcut.html index bf2a5a18..e7d40048 100644 --- a/_modules/networkx/algorithms/approximation/maxcut.html +++ b/_modules/networkx/algorithms/approximation/maxcut.html @@ -470,7 +470,7 @@ <div class="viewcode-block" id="randomized_partitioning"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.maxcut.randomized_partitioning.html#networkx.algorithms.approximation.maxcut.randomized_partitioning">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">,</span> <span class="s2">"multigraph"</span><span class="p">)</span> <span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="k">def</span> <span class="nf">randomized_partitioning</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute a random partitioning of the graph nodes and its cut value.</span> +<span class="w"> </span><span class="sd">"""Compute a random partitioning of the graph nodes and its cut value.</span> <span class="sd"> A partitioning is calculated by observing each node</span> <span class="sd"> and deciding to add it to the partition with probability `p`,</span> @@ -514,7 +514,7 @@ <div class="viewcode-block" id="one_exchange"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.maxcut.one_exchange.html#networkx.algorithms.approximation.maxcut.one_exchange">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">,</span> <span class="s2">"multigraph"</span><span class="p">)</span> <span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">one_exchange</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">initial_cut</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute a partitioning of the graphs nodes and the corresponding cut value.</span> +<span class="w"> </span><span class="sd">"""Compute a partitioning of the graphs nodes and the corresponding cut value.</span> <span class="sd"> Use a greedy one exchange strategy to find a locally maximal cut</span> <span class="sd"> and its value, it works by finding the best node (one that gives</span> @@ -623,7 +623,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/approximation/ramsey.html b/_modules/networkx/algorithms/approximation/ramsey.html index 58560989..bd83ea28 100644 --- a/_modules/networkx/algorithms/approximation/ramsey.html +++ b/_modules/networkx/algorithms/approximation/ramsey.html @@ -475,7 +475,7 @@ <div class="viewcode-block" id="ramsey_R2"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.ramsey.ramsey_R2.html#networkx.algorithms.approximation.ramsey.ramsey_R2">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">ramsey_R2</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the largest clique and largest independent set in `G`.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the largest clique and largest independent set in `G`.</span> <span class="sd"> This can be used to estimate bounds for the 2-color</span> <span class="sd"> Ramsey number `R(2;s,t)` for `G`.</span> @@ -563,7 +563,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/approximation/steinertree.html b/_modules/networkx/algorithms/approximation/steinertree.html index 4c7e01a8..270ca630 100644 --- a/_modules/networkx/algorithms/approximation/steinertree.html +++ b/_modules/networkx/algorithms/approximation/steinertree.html @@ -471,7 +471,7 @@ <div class="viewcode-block" id="metric_closure"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.steinertree.metric_closure.html#networkx.algorithms.approximation.steinertree.metric_closure">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">metric_closure</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Return the metric closure of a graph.</span> +<span class="w"> </span><span class="sd">"""Return the metric closure of a graph.</span> <span class="sd"> The metric closure of a graph *G* is the complete graph in which each edge</span> <span class="sd"> is weighted by the shortest path distance between the nodes in *G* .</span> @@ -589,7 +589,7 @@ <div class="viewcode-block" id="steiner_tree"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.steinertree.steiner_tree.html#networkx.algorithms.approximation.steinertree.steiner_tree">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">steiner_tree</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">terminal_nodes</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Return an approximation to the minimum Steiner tree of a graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Return an approximation to the minimum Steiner tree of a graph.</span> <span class="sd"> The minimum Steiner tree of `G` w.r.t a set of `terminal_nodes` (also *S*)</span> <span class="sd"> is a tree within `G` that spans those nodes and has minimum size (sum of</span> @@ -729,7 +729,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/approximation/traveling_salesman.html b/_modules/networkx/algorithms/approximation/traveling_salesman.html index 85de471c..e3c284cf 100644 --- a/_modules/networkx/algorithms/approximation/traveling_salesman.html +++ b/_modules/networkx/algorithms/approximation/traveling_salesman.html @@ -513,7 +513,7 @@ <span class="k">def</span> <span class="nf">swap_two_nodes</span><span class="p">(</span><span class="n">soln</span><span class="p">,</span> <span class="n">seed</span><span class="p">):</span> - <span class="sd">"""Swap two nodes in `soln` to give a neighbor solution.</span> +<span class="w"> </span><span class="sd">"""Swap two nodes in `soln` to give a neighbor solution.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -548,7 +548,7 @@ <span class="k">def</span> <span class="nf">move_one_node</span><span class="p">(</span><span class="n">soln</span><span class="p">,</span> <span class="n">seed</span><span class="p">):</span> - <span class="sd">"""Move one node to another position to give a neighbor solution.</span> +<span class="w"> </span><span class="sd">"""Move one node to another position to give a neighbor solution.</span> <span class="sd"> The node to move and the position to move to are chosen randomly.</span> <span class="sd"> The first and last nodes are left untouched as soln must be a cycle</span> @@ -588,7 +588,7 @@ <div class="viewcode-block" id="christofides"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.traveling_salesman.christofides.html#networkx.algorithms.approximation.traveling_salesman.christofides">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">christofides</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">tree</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Approximate a solution of the traveling salesman problem</span> +<span class="w"> </span><span class="sd">"""Approximate a solution of the traveling salesman problem</span> <span class="sd"> Compute a 3/2-approximation of the traveling salesman problem</span> <span class="sd"> in a complete undirected graph using Christofides [1]_ algorithm.</span> @@ -647,7 +647,7 @@ <span class="k">def</span> <span class="nf">_shortcutting</span><span class="p">(</span><span class="n">circuit</span><span class="p">):</span> - <span class="sd">"""Remove duplicate nodes in the path"""</span> +<span class="w"> </span><span class="sd">"""Remove duplicate nodes in the path"""</span> <span class="n">nodes</span> <span class="o">=</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">circuit</span><span class="p">:</span> <span class="k">if</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">nodes</span><span class="p">:</span> @@ -660,7 +660,7 @@ <div class="viewcode-block" id="traveling_salesman_problem"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem.html#networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem">[docs]</a><span class="k">def</span> <span class="nf">traveling_salesman_problem</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cycle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Find the shortest path in `G` connecting specified nodes</span> +<span class="w"> </span><span class="sd">"""Find the shortest path in `G` connecting specified nodes</span> <span class="sd"> This function allows approximate solution to the traveling salesman</span> <span class="sd"> problem on networks that are not complete graphs and/or where the</span> @@ -799,7 +799,7 @@ <div class="viewcode-block" id="asadpour_atsp"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.traveling_salesman.asadpour_atsp.html#networkx.algorithms.approximation.traveling_salesman.asadpour_atsp">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">asadpour_atsp</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns an approximate solution to the traveling salesman problem.</span> <span class="sd"> This approximate solution is one of the best known approximations for the</span> @@ -948,7 +948,7 @@ <span class="k">def</span> <span class="nf">held_karp_ascent</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Minimizes the Held-Karp relaxation of the TSP for `G`</span> <span class="sd"> Solves the Held-Karp relaxation of the input complete digraph and scales</span> @@ -996,7 +996,7 @@ <span class="kn">import</span> <span class="nn">scipy.optimize</span> <span class="k">as</span> <span class="nn">optimize</span> <span class="k">def</span> <span class="nf">k_pi</span><span class="p">():</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Find the set of minimum 1-Arborescences for G at point pi.</span> <span class="sd"> Returns</span> @@ -1080,7 +1080,7 @@ <span class="k">return</span> <span class="n">minimum_1_arborescences</span> <span class="k">def</span> <span class="nf">direction_of_ascent</span><span class="p">():</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Find the direction of ascent at point pi.</span> <span class="sd"> See [1]_ for more information.</span> @@ -1146,7 +1146,7 @@ <span class="c1"># 5. GO TO 2</span> <span class="k">def</span> <span class="nf">find_epsilon</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Given the direction of ascent at pi, find the maximum distance we can go</span> <span class="sd"> in that direction.</span> @@ -1258,7 +1258,7 @@ <span class="k">def</span> <span class="nf">spanning_tree_distribution</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">z</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Find the asadpour exponential distribution of spanning trees.</span> <span class="sd"> Solves the Maximum Entropy Convex Program in the Asadpour algorithm [1]_</span> @@ -1289,7 +1289,7 @@ <span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">log</span> <span class="k">as</span> <span class="n">ln</span> <span class="k">def</span> <span class="nf">q</span><span class="p">(</span><span class="n">e</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> The value of q(e) is described in the Asadpour paper is "the</span> <span class="sd"> probability that edge e will be included in a spanning tree T that is</span> <span class="sd"> chosen with probability proportional to exp(gamma(T))" which</span> @@ -1368,7 +1368,7 @@ <div class="viewcode-block" id="greedy_tsp"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.traveling_salesman.greedy_tsp.html#networkx.algorithms.approximation.traveling_salesman.greedy_tsp">[docs]</a><span class="k">def</span> <span class="nf">greedy_tsp</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Return a low cost cycle starting at `source` and its cost.</span> +<span class="w"> </span><span class="sd">"""Return a low cost cycle starting at `source` and its cost.</span> <span class="sd"> This approximates a solution to the traveling salesman problem.</span> <span class="sd"> It finds a cycle of all the nodes that a salesman can visit in order</span> @@ -1471,7 +1471,7 @@ <span class="n">alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns an approximate solution to the traveling salesman problem.</span> +<span class="w"> </span><span class="sd">"""Returns an approximate solution to the traveling salesman problem.</span> <span class="sd"> This function uses simulated annealing to approximate the minimal cost</span> <span class="sd"> cycle through the nodes. Starting from a suboptimal solution, simulated</span> @@ -1689,7 +1689,7 @@ <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns an approximate solution to the traveling salesman problem.</span> +<span class="w"> </span><span class="sd">"""Returns an approximate solution to the traveling salesman problem.</span> <span class="sd"> This function uses threshold accepting methods to approximate the minimal cost</span> <span class="sd"> cycle through the nodes. Starting from a suboptimal solution, threshold</span> @@ -1946,7 +1946,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/approximation/treewidth.html b/_modules/networkx/algorithms/approximation/treewidth.html index 3d6a5a1a..025036ae 100644 --- a/_modules/networkx/algorithms/approximation/treewidth.html +++ b/_modules/networkx/algorithms/approximation/treewidth.html @@ -505,7 +505,7 @@ <div class="viewcode-block" id="treewidth_min_degree"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.treewidth.treewidth_min_degree.html#networkx.algorithms.approximation.treewidth.treewidth_min_degree">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">treewidth_min_degree</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns a treewidth decomposition using the Minimum Degree heuristic.</span> +<span class="w"> </span><span class="sd">"""Returns a treewidth decomposition using the Minimum Degree heuristic.</span> <span class="sd"> The heuristic chooses the nodes according to their degree, i.e., first</span> <span class="sd"> the node with the lowest degree is chosen, then the graph is updated</span> @@ -528,7 +528,7 @@ <div class="viewcode-block" id="treewidth_min_fill_in"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.treewidth.treewidth_min_fill_in.html#networkx.algorithms.approximation.treewidth.treewidth_min_fill_in">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">treewidth_min_fill_in</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns a treewidth decomposition using the Minimum Fill-in heuristic.</span> +<span class="w"> </span><span class="sd">"""Returns a treewidth decomposition using the Minimum Fill-in heuristic.</span> <span class="sd"> The heuristic chooses a node from the graph, where the number of edges</span> <span class="sd"> added turning the neighbourhood of the chosen node into clique is as</span> @@ -547,7 +547,7 @@ <span class="k">class</span> <span class="nc">MinDegreeHeuristic</span><span class="p">:</span> - <span class="sd">"""Implements the Minimum Degree heuristic.</span> +<span class="w"> </span><span class="sd">"""Implements the Minimum Degree heuristic.</span> <span class="sd"> The heuristic chooses the nodes according to their degree</span> <span class="sd"> (number of neighbours), i.e., first the node with the lowest degree is</span> @@ -594,7 +594,7 @@ <span class="k">def</span> <span class="nf">min_fill_in_heuristic</span><span class="p">(</span><span class="n">graph</span><span class="p">):</span> - <span class="sd">"""Implements the Minimum Degree heuristic.</span> +<span class="w"> </span><span class="sd">"""Implements the Minimum Degree heuristic.</span> <span class="sd"> Returns the node from the graph, where the number of edges added when</span> <span class="sd"> turning the neighbourhood of the chosen node into clique is as small as</span> @@ -639,7 +639,7 @@ <span class="k">def</span> <span class="nf">treewidth_decomp</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">heuristic</span><span class="o">=</span><span class="n">min_fill_in_heuristic</span><span class="p">):</span> - <span class="sd">"""Returns a treewidth decomposition using the passed heuristic.</span> +<span class="w"> </span><span class="sd">"""Returns a treewidth decomposition using the passed heuristic.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -761,7 +761,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/approximation/vertex_cover.html b/_modules/networkx/algorithms/approximation/vertex_cover.html index 6df1616f..fb5b7d8f 100644 --- a/_modules/networkx/algorithms/approximation/vertex_cover.html +++ b/_modules/networkx/algorithms/approximation/vertex_cover.html @@ -475,7 +475,7 @@ <div class="viewcode-block" id="min_weighted_vertex_cover"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover.html#networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover">[docs]</a><span class="k">def</span> <span class="nf">min_weighted_vertex_cover</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns an approximate minimum weighted vertex cover.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns an approximate minimum weighted vertex cover.</span> <span class="sd"> The set of nodes returned by this function is guaranteed to be a</span> <span class="sd"> vertex cover, and the total weight of the set is guaranteed to be at</span> @@ -592,7 +592,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/assortativity/connectivity.html b/_modules/networkx/algorithms/assortativity/connectivity.html index 01469c67..a9bab070 100644 --- a/_modules/networkx/algorithms/assortativity/connectivity.html +++ b/_modules/networkx/algorithms/assortativity/connectivity.html @@ -471,7 +471,7 @@ <div class="viewcode-block" id="average_degree_connectivity"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.assortativity.average_degree_connectivity.html#networkx.algorithms.assortativity.average_degree_connectivity">[docs]</a><span class="k">def</span> <span class="nf">average_degree_connectivity</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="s2">"in+out"</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="s2">"in+out"</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the average degree connectivity of graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the average degree connectivity of graph.</span> <span class="sd"> The average degree connectivity is the average nearest neighbor degree of</span> <span class="sd"> nodes with degree k. For weighted graphs, an analogous measure can</span> @@ -633,7 +633,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/assortativity/correlation.html b/_modules/networkx/algorithms/assortativity/correlation.html index 7082fdc9..b0b215ac 100644 --- a/_modules/networkx/algorithms/assortativity/correlation.html +++ b/_modules/networkx/algorithms/assortativity/correlation.html @@ -478,7 +478,7 @@ <div class="viewcode-block" id="degree_assortativity_coefficient"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.assortativity.degree_assortativity_coefficient.html#networkx.algorithms.assortativity.degree_assortativity_coefficient">[docs]</a><span class="k">def</span> <span class="nf">degree_assortativity_coefficient</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">"out"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"in"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute degree assortativity of graph.</span> +<span class="w"> </span><span class="sd">"""Compute degree assortativity of graph.</span> <span class="sd"> Assortativity measures the similarity of connections</span> <span class="sd"> in the graph with respect to the node degree.</span> @@ -562,7 +562,7 @@ <div class="viewcode-block" id="degree_pearson_correlation_coefficient"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.assortativity.degree_pearson_correlation_coefficient.html#networkx.algorithms.assortativity.degree_pearson_correlation_coefficient">[docs]</a><span class="k">def</span> <span class="nf">degree_pearson_correlation_coefficient</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">"out"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"in"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute degree assortativity of graph.</span> +<span class="w"> </span><span class="sd">"""Compute degree assortativity of graph.</span> <span class="sd"> Assortativity measures the similarity of connections</span> <span class="sd"> in the graph with respect to the node degree.</span> @@ -621,7 +621,7 @@ <div class="viewcode-block" id="attribute_assortativity_coefficient"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.assortativity.attribute_assortativity_coefficient.html#networkx.algorithms.assortativity.attribute_assortativity_coefficient">[docs]</a><span class="k">def</span> <span class="nf">attribute_assortativity_coefficient</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">attribute</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute assortativity for node attributes.</span> +<span class="w"> </span><span class="sd">"""Compute assortativity for node attributes.</span> <span class="sd"> Assortativity measures the similarity of connections</span> <span class="sd"> in the graph with respect to the given attribute.</span> @@ -667,7 +667,7 @@ <div class="viewcode-block" id="numeric_assortativity_coefficient"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.assortativity.numeric_assortativity_coefficient.html#networkx.algorithms.assortativity.numeric_assortativity_coefficient">[docs]</a><span class="k">def</span> <span class="nf">numeric_assortativity_coefficient</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">attribute</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute assortativity for numerical node attributes.</span> +<span class="w"> </span><span class="sd">"""Compute assortativity for numerical node attributes.</span> <span class="sd"> Assortativity measures the similarity of connections</span> <span class="sd"> in the graph with respect to the given numeric attribute.</span> @@ -716,7 +716,7 @@ <span class="k">def</span> <span class="nf">attribute_ac</span><span class="p">(</span><span class="n">M</span><span class="p">):</span> - <span class="sd">"""Compute assortativity for attribute matrix M.</span> +<span class="w"> </span><span class="sd">"""Compute assortativity for attribute matrix M.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -810,7 +810,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/assortativity/mixing.html b/_modules/networkx/algorithms/assortativity/mixing.html index 3a674dd1..181cf0f3 100644 --- a/_modules/networkx/algorithms/assortativity/mixing.html +++ b/_modules/networkx/algorithms/assortativity/mixing.html @@ -477,7 +477,7 @@ <div class="viewcode-block" id="attribute_mixing_dict"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.assortativity.attribute_mixing_dict.html#networkx.algorithms.assortativity.attribute_mixing_dict">[docs]</a><span class="k">def</span> <span class="nf">attribute_mixing_dict</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">attribute</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns dictionary representation of mixing matrix for attribute.</span> +<span class="w"> </span><span class="sd">"""Returns dictionary representation of mixing matrix for attribute.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -515,7 +515,7 @@ <div class="viewcode-block" id="attribute_mixing_matrix"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.assortativity.attribute_mixing_matrix.html#networkx.algorithms.assortativity.attribute_mixing_matrix">[docs]</a><span class="k">def</span> <span class="nf">attribute_mixing_matrix</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">attribute</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mapping</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns mixing matrix for attribute.</span> +<span class="w"> </span><span class="sd">"""Returns mixing matrix for attribute.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -574,7 +574,7 @@ <div class="viewcode-block" id="degree_mixing_dict"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.assortativity.degree_mixing_dict.html#networkx.algorithms.assortativity.degree_mixing_dict">[docs]</a><span class="k">def</span> <span class="nf">degree_mixing_dict</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">"out"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"in"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns dictionary representation of mixing matrix for degree.</span> +<span class="w"> </span><span class="sd">"""Returns dictionary representation of mixing matrix for degree.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -607,7 +607,7 @@ <div class="viewcode-block" id="degree_mixing_matrix"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.assortativity.degree_mixing_matrix.html#networkx.algorithms.assortativity.degree_mixing_matrix">[docs]</a><span class="k">def</span> <span class="nf">degree_mixing_matrix</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">"out"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"in"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">mapping</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Returns mixing matrix for attribute.</span> +<span class="w"> </span><span class="sd">"""Returns mixing matrix for attribute.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -672,7 +672,7 @@ <div class="viewcode-block" id="mixing_dict"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.assortativity.mixing_dict.html#networkx.algorithms.assortativity.mixing_dict">[docs]</a><span class="k">def</span> <span class="nf">mixing_dict</span><span class="p">(</span><span class="n">xy</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns a dictionary representation of mixing matrix.</span> +<span class="w"> </span><span class="sd">"""Returns a dictionary representation of mixing matrix.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -757,7 +757,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/assortativity/neighbor_degree.html b/_modules/networkx/algorithms/assortativity/neighbor_degree.html index b4bf73d1..08f5bafa 100644 --- a/_modules/networkx/algorithms/assortativity/neighbor_degree.html +++ b/_modules/networkx/algorithms/assortativity/neighbor_degree.html @@ -467,7 +467,7 @@ <div class="viewcode-block" id="average_neighbor_degree"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.assortativity.average_neighbor_degree.html#networkx.algorithms.assortativity.average_neighbor_degree">[docs]</a><span class="k">def</span> <span class="nf">average_neighbor_degree</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="s2">"out"</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="s2">"out"</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the average degree of the neighborhood of each node.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the average degree of the neighborhood of each node.</span> <span class="sd"> In an undirected graph, the neighborhood `N(i)` of node `i` contains the</span> <span class="sd"> nodes that are connected to `i` by an edge.</span> @@ -671,7 +671,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/assortativity/pairs.html b/_modules/networkx/algorithms/assortativity/pairs.html index 808d1ccd..7690d519 100644 --- a/_modules/networkx/algorithms/assortativity/pairs.html +++ b/_modules/networkx/algorithms/assortativity/pairs.html @@ -466,7 +466,7 @@ <div class="viewcode-block" id="node_attribute_xy"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.assortativity.node_attribute_xy.html#networkx.algorithms.assortativity.node_attribute_xy">[docs]</a><span class="k">def</span> <span class="nf">node_attribute_xy</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">attribute</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns iterator of node-attribute pairs for all edges in G.</span> +<span class="w"> </span><span class="sd">"""Returns iterator of node-attribute pairs for all edges in G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -520,7 +520,7 @@ <div class="viewcode-block" id="node_degree_xy"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.assortativity.node_degree_xy.html#networkx.algorithms.assortativity.node_degree_xy">[docs]</a><span class="k">def</span> <span class="nf">node_degree_xy</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">"out"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"in"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generate node degree-degree pairs for edges in G.</span> +<span class="w"> </span><span class="sd">"""Generate node degree-degree pairs for edges in G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -626,7 +626,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/asteroidal.html b/_modules/networkx/algorithms/asteroidal.html index ef63c572..4bbc0066 100644 --- a/_modules/networkx/algorithms/asteroidal.html +++ b/_modules/networkx/algorithms/asteroidal.html @@ -482,7 +482,7 @@ <div class="viewcode-block" id="find_asteroidal_triple"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.asteroidal.find_asteroidal_triple.html#networkx.algorithms.asteroidal.find_asteroidal_triple">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">find_asteroidal_triple</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Find an asteroidal triple in the given graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Find an asteroidal triple in the given graph.</span> <span class="sd"> An asteroidal triple is a triple of non-adjacent vertices such that</span> <span class="sd"> there exists a path between any two of them which avoids the closed</span> @@ -554,7 +554,7 @@ <div class="viewcode-block" id="is_at_free"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.asteroidal.is_at_free.html#networkx.algorithms.asteroidal.is_at_free">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">is_at_free</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Check if a graph is AT-free.</span> +<span class="w"> </span><span class="sd">"""Check if a graph is AT-free.</span> <span class="sd"> The method uses the `find_asteroidal_triple` method to recognize</span> <span class="sd"> an AT-free graph. If no asteroidal triple is found the graph is</span> @@ -587,7 +587,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">create_component_structure</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Create component structure for G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Create component structure for G.</span> <span class="sd"> A *component structure* is an `nxn` array, denoted `c`, where `n` is</span> <span class="sd"> the number of vertices, where each row and column corresponds to a vertex.</span> @@ -679,7 +679,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/bipartite/basic.html b/_modules/networkx/algorithms/bipartite/basic.html index 2ea45d83..fa01367b 100644 --- a/_modules/networkx/algorithms/bipartite/basic.html +++ b/_modules/networkx/algorithms/bipartite/basic.html @@ -481,7 +481,7 @@ <div class="viewcode-block" id="color"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.basic.color.html#networkx.algorithms.bipartite.basic.color">[docs]</a><span class="k">def</span> <span class="nf">color</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns a two-coloring of the graph.</span> +<span class="w"> </span><span class="sd">"""Returns a two-coloring of the graph.</span> <span class="sd"> Raises an exception if the graph is not bipartite.</span> @@ -547,7 +547,7 @@ <div class="viewcode-block" id="is_bipartite"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.basic.is_bipartite.html#networkx.algorithms.bipartite.basic.is_bipartite">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">is_bipartite</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns True if graph G is bipartite, False if not.</span> +<span class="w"> </span><span class="sd">"""Returns True if graph G is bipartite, False if not.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -572,7 +572,7 @@ <div class="viewcode-block" id="is_bipartite_node_set"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.basic.is_bipartite_node_set.html#networkx.algorithms.bipartite.basic.is_bipartite_node_set">[docs]</a><span class="k">def</span> <span class="nf">is_bipartite_node_set</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span> - <span class="sd">"""Returns True if nodes and G/nodes are a bipartition of G.</span> +<span class="w"> </span><span class="sd">"""Returns True if nodes and G/nodes are a bipartition of G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -616,7 +616,7 @@ <div class="viewcode-block" id="sets"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.basic.sets.html#networkx.algorithms.bipartite.basic.sets">[docs]</a><span class="k">def</span> <span class="nf">sets</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">top_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns bipartite node sets of graph G.</span> +<span class="w"> </span><span class="sd">"""Returns bipartite node sets of graph G.</span> <span class="sd"> Raises an exception if the graph is not bipartite or if the input</span> <span class="sd"> graph is disconnected and thus more than one valid solution exists.</span> @@ -682,7 +682,7 @@ <div class="viewcode-block" id="density"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.basic.density.html#networkx.algorithms.bipartite.basic.density">[docs]</a><span class="k">def</span> <span class="nf">density</span><span class="p">(</span><span class="n">B</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span> - <span class="sd">"""Returns density of bipartite graph B.</span> +<span class="w"> </span><span class="sd">"""Returns density of bipartite graph B.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -734,7 +734,7 @@ <div class="viewcode-block" id="degrees"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.basic.degrees.html#networkx.algorithms.bipartite.basic.degrees">[docs]</a><span class="k">def</span> <span class="nf">degrees</span><span class="p">(</span><span class="n">B</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the degrees of the two node sets in the bipartite graph B.</span> +<span class="w"> </span><span class="sd">"""Returns the degrees of the two node sets in the bipartite graph B.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -828,7 +828,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/bipartite/centrality.html b/_modules/networkx/algorithms/bipartite/centrality.html index 55aedd62..dc8ca164 100644 --- a/_modules/networkx/algorithms/bipartite/centrality.html +++ b/_modules/networkx/algorithms/bipartite/centrality.html @@ -467,7 +467,7 @@ <div class="viewcode-block" id="degree_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.centrality.degree_centrality.html#networkx.algorithms.bipartite.centrality.degree_centrality">[docs]</a><span class="k">def</span> <span class="nf">degree_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the degree centrality for nodes in a bipartite network.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the degree centrality for nodes in a bipartite network.</span> <span class="sd"> The degree centrality for a node `v` is the fraction of nodes</span> <span class="sd"> connected to it.</span> @@ -534,7 +534,7 @@ <div class="viewcode-block" id="betweenness_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.centrality.betweenness_centrality.html#networkx.algorithms.bipartite.centrality.betweenness_centrality">[docs]</a><span class="k">def</span> <span class="nf">betweenness_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute betweenness centrality for nodes in a bipartite network.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute betweenness centrality for nodes in a bipartite network.</span> <span class="sd"> Betweenness centrality of a node `v` is the sum of the</span> <span class="sd"> fraction of all-pairs shortest paths that pass through `v`.</span> @@ -630,7 +630,7 @@ <div class="viewcode-block" id="closeness_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.centrality.closeness_centrality.html#networkx.algorithms.bipartite.centrality.closeness_centrality">[docs]</a><span class="k">def</span> <span class="nf">closeness_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the closeness centrality for nodes in a bipartite network.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the closeness centrality for nodes in a bipartite network.</span> <span class="sd"> The closeness of a node is the distance to all other nodes in the</span> <span class="sd"> graph or in the case that the graph is not connected to all other nodes</span> @@ -778,7 +778,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/bipartite/cluster.html b/_modules/networkx/algorithms/bipartite/cluster.html index bb3a43b5..8c9687f5 100644 --- a/_modules/networkx/algorithms/bipartite/cluster.html +++ b/_modules/networkx/algorithms/bipartite/cluster.html @@ -493,7 +493,7 @@ <div class="viewcode-block" id="latapy_clustering"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.cluster.latapy_clustering.html#networkx.algorithms.bipartite.cluster.latapy_clustering">[docs]</a><span class="k">def</span> <span class="nf">latapy_clustering</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"dot"</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute a bipartite clustering coefficient for nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute a bipartite clustering coefficient for nodes.</span> <span class="sd"> The bipartie clustering coefficient is a measure of local density</span> <span class="sd"> of connections defined as [1]_:</span> @@ -597,7 +597,7 @@ <div class="viewcode-block" id="average_clustering"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.cluster.average_clustering.html#networkx.algorithms.bipartite.cluster.average_clustering">[docs]</a><span class="k">def</span> <span class="nf">average_clustering</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"dot"</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the average bipartite clustering coefficient.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the average bipartite clustering coefficient.</span> <span class="sd"> A clustering coefficient for the whole graph is the average,</span> @@ -673,7 +673,7 @@ <div class="viewcode-block" id="robins_alexander_clustering"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.cluster.robins_alexander_clustering.html#networkx.algorithms.bipartite.cluster.robins_alexander_clustering">[docs]</a><span class="k">def</span> <span class="nf">robins_alexander_clustering</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the bipartite clustering of G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the bipartite clustering of G.</span> <span class="sd"> Robins and Alexander [1]_ defined bipartite clustering coefficient as</span> <span class="sd"> four times the number of four cycles `C_4` divided by the number of</span> @@ -789,7 +789,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/bipartite/covering.html b/_modules/networkx/algorithms/bipartite/covering.html index ced1c691..5c11d584 100644 --- a/_modules/networkx/algorithms/bipartite/covering.html +++ b/_modules/networkx/algorithms/bipartite/covering.html @@ -473,7 +473,7 @@ <div class="viewcode-block" id="min_edge_cover"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.covering.min_edge_cover.html#networkx.algorithms.bipartite.covering.min_edge_cover">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">min_edge_cover</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">matching_algorithm</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a set of edges which constitutes</span> +<span class="w"> </span><span class="sd">"""Returns a set of edges which constitutes</span> <span class="sd"> the minimum edge cover of the graph.</span> <span class="sd"> The smallest edge cover can be found in polynomial time by finding</span> @@ -567,7 +567,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/bipartite/edgelist.html b/_modules/networkx/algorithms/bipartite/edgelist.html index bc5a4261..012ee0a2 100644 --- a/_modules/networkx/algorithms/bipartite/edgelist.html +++ b/_modules/networkx/algorithms/bipartite/edgelist.html @@ -493,7 +493,7 @@ <div class="viewcode-block" id="write_edgelist"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.edgelist.write_edgelist.html#networkx.algorithms.bipartite.edgelist.write_edgelist">[docs]</a><span class="nd">@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">"wb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">write_edgelist</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">comments</span><span class="o">=</span><span class="s2">"#"</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s2">" "</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">):</span> - <span class="sd">"""Write a bipartite graph as a list of edges.</span> +<span class="w"> </span><span class="sd">"""Write a bipartite graph as a list of edges.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -543,7 +543,7 @@ <div class="viewcode-block" id="generate_edgelist"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.edgelist.generate_edgelist.html#networkx.algorithms.bipartite.edgelist.generate_edgelist">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">generate_edgelist</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s2">" "</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Generate a single line of the bipartite graph G in edge list format.</span> +<span class="w"> </span><span class="sd">"""Generate a single line of the bipartite graph G in edge list format.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -612,7 +612,7 @@ <div class="viewcode-block" id="parse_edgelist"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.edgelist.parse_edgelist.html#networkx.algorithms.bipartite.edgelist.parse_edgelist">[docs]</a><span class="k">def</span> <span class="nf">parse_edgelist</span><span class="p">(</span> <span class="n">lines</span><span class="p">,</span> <span class="n">comments</span><span class="o">=</span><span class="s2">"#"</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nodetype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">True</span> <span class="p">):</span> - <span class="sd">"""Parse lines of an edge list representation of a bipartite graph.</span> +<span class="w"> </span><span class="sd">"""Parse lines of an edge list representation of a bipartite graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -740,7 +740,7 @@ <span class="n">edgetype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Read a bipartite graph from a list of edges.</span> +<span class="w"> </span><span class="sd">"""Read a bipartite graph from a list of edges.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -869,7 +869,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/bipartite/generators.html b/_modules/networkx/algorithms/bipartite/generators.html index 7451cb06..b3cdb41c 100644 --- a/_modules/networkx/algorithms/bipartite/generators.html +++ b/_modules/networkx/algorithms/bipartite/generators.html @@ -485,7 +485,7 @@ <div class="viewcode-block" id="complete_bipartite_graph"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.generators.complete_bipartite_graph.html#networkx.algorithms.bipartite.generators.complete_bipartite_graph">[docs]</a><span class="nd">@nodes_or_number</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="k">def</span> <span class="nf">complete_bipartite_graph</span><span class="p">(</span><span class="n">n1</span><span class="p">,</span> <span class="n">n2</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the complete bipartite graph `K_{n_1,n_2}`.</span> +<span class="w"> </span><span class="sd">"""Returns the complete bipartite graph `K_{n_1,n_2}`.</span> <span class="sd"> The graph is composed of two partitions with nodes 0 to (n1 - 1)</span> <span class="sd"> in the first and nodes n1 to (n1 + n2 - 1) in the second.</span> @@ -530,7 +530,7 @@ <div class="viewcode-block" id="configuration_model"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.generators.configuration_model.html#networkx.algorithms.bipartite.generators.configuration_model">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">configuration_model</span><span class="p">(</span><span class="n">aseq</span><span class="p">,</span> <span class="n">bseq</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random bipartite graph from two given degree sequences.</span> +<span class="w"> </span><span class="sd">"""Returns a random bipartite graph from two given degree sequences.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -600,7 +600,7 @@ <div class="viewcode-block" id="havel_hakimi_graph"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.generators.havel_hakimi_graph.html#networkx.algorithms.bipartite.generators.havel_hakimi_graph">[docs]</a><span class="k">def</span> <span class="nf">havel_hakimi_graph</span><span class="p">(</span><span class="n">aseq</span><span class="p">,</span> <span class="n">bseq</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a bipartite graph from two given degree sequences using a</span> +<span class="w"> </span><span class="sd">"""Returns a bipartite graph from two given degree sequences using a</span> <span class="sd"> Havel-Hakimi style construction.</span> <span class="sd"> The graph is composed of two partitions. Set A has nodes 0 to</span> @@ -674,7 +674,7 @@ <div class="viewcode-block" id="reverse_havel_hakimi_graph"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph.html#networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph">[docs]</a><span class="k">def</span> <span class="nf">reverse_havel_hakimi_graph</span><span class="p">(</span><span class="n">aseq</span><span class="p">,</span> <span class="n">bseq</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a bipartite graph from two given degree sequences using a</span> +<span class="w"> </span><span class="sd">"""Returns a bipartite graph from two given degree sequences using a</span> <span class="sd"> Havel-Hakimi style construction.</span> <span class="sd"> The graph is composed of two partitions. Set A has nodes 0 to</span> @@ -747,7 +747,7 @@ <div class="viewcode-block" id="alternating_havel_hakimi_graph"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph.html#networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph">[docs]</a><span class="k">def</span> <span class="nf">alternating_havel_hakimi_graph</span><span class="p">(</span><span class="n">aseq</span><span class="p">,</span> <span class="n">bseq</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a bipartite graph from two given degree sequences using</span> +<span class="w"> </span><span class="sd">"""Returns a bipartite graph from two given degree sequences using</span> <span class="sd"> an alternating Havel-Hakimi style construction.</span> <span class="sd"> The graph is composed of two partitions. Set A has nodes 0 to</span> @@ -825,7 +825,7 @@ <div class="viewcode-block" id="preferential_attachment_graph"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.generators.preferential_attachment_graph.html#networkx.algorithms.bipartite.generators.preferential_attachment_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">preferential_attachment_graph</span><span class="p">(</span><span class="n">aseq</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Create a bipartite graph with a preferential attachment model from</span> +<span class="w"> </span><span class="sd">"""Create a bipartite graph with a preferential attachment model from</span> <span class="sd"> a given single degree sequence.</span> <span class="sd"> The graph is composed of two partitions. Set A has nodes 0 to</span> @@ -896,7 +896,7 @@ <div class="viewcode-block" id="random_graph"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.generators.random_graph.html#networkx.algorithms.bipartite.generators.random_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns a bipartite random graph.</span> +<span class="w"> </span><span class="sd">"""Returns a bipartite random graph.</span> <span class="sd"> This is a bipartite version of the binomial (ErdÅ‘s-Rényi) graph.</span> <span class="sd"> The graph is composed of two partitions. Set A has nodes 0 to</span> @@ -982,7 +982,7 @@ <div class="viewcode-block" id="gnmk_random_graph"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.generators.gnmk_random_graph.html#networkx.algorithms.bipartite.generators.gnmk_random_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">gnmk_random_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns a random bipartite graph G_{n,m,k}.</span> +<span class="w"> </span><span class="sd">"""Returns a random bipartite graph G_{n,m,k}.</span> <span class="sd"> Produces a bipartite graph chosen randomly out of the set of all graphs</span> <span class="sd"> with n top nodes, m bottom nodes, and k edges.</span> @@ -1107,7 +1107,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/bipartite/matching.html b/_modules/networkx/algorithms/bipartite/matching.html index c389c45d..f3e29806 100644 --- a/_modules/networkx/algorithms/bipartite/matching.html +++ b/_modules/networkx/algorithms/bipartite/matching.html @@ -518,7 +518,7 @@ <div class="viewcode-block" id="hopcroft_karp_matching"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.matching.hopcroft_karp_matching.html#networkx.algorithms.bipartite.matching.hopcroft_karp_matching">[docs]</a><span class="k">def</span> <span class="nf">hopcroft_karp_matching</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">top_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the maximum cardinality matching of the bipartite graph `G`.</span> +<span class="w"> </span><span class="sd">"""Returns the maximum cardinality matching of the bipartite graph `G`.</span> <span class="sd"> A matching is a set of edges that do not share any nodes. A maximum</span> <span class="sd"> cardinality matching is a matching with the most edges possible. It</span> @@ -643,7 +643,7 @@ <div class="viewcode-block" id="eppstein_matching"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.matching.eppstein_matching.html#networkx.algorithms.bipartite.matching.eppstein_matching">[docs]</a><span class="k">def</span> <span class="nf">eppstein_matching</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">top_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the maximum cardinality matching of the bipartite graph `G`.</span> +<span class="w"> </span><span class="sd">"""Returns the maximum cardinality matching of the bipartite graph `G`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -776,7 +776,7 @@ <span class="k">def</span> <span class="nf">_is_connected_by_alternating_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">matched_edges</span><span class="p">,</span> <span class="n">unmatched_edges</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span> - <span class="sd">"""Returns True if and only if the vertex `v` is connected to one of</span> +<span class="w"> </span><span class="sd">"""Returns True if and only if the vertex `v` is connected to one of</span> <span class="sd"> the target vertices by an alternating path in `G`.</span> <span class="sd"> An *alternating path* is a path in which every other edge is in the</span> @@ -799,7 +799,7 @@ <span class="sd"> """</span> <span class="k">def</span> <span class="nf">_alternating_dfs</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">along_matched</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns True if and only if `u` is connected to one of the</span> +<span class="w"> </span><span class="sd">"""Returns True if and only if `u` is connected to one of the</span> <span class="sd"> targets by an alternating path.</span> <span class="sd"> `u` is a vertex in the graph `G`.</span> @@ -838,7 +838,7 @@ <span class="k">def</span> <span class="nf">_connected_by_alternating_paths</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">matching</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span> - <span class="sd">"""Returns the set of vertices that are connected to one of the target</span> +<span class="w"> </span><span class="sd">"""Returns the set of vertices that are connected to one of the target</span> <span class="sd"> vertices by an alternating path in `G` or are themselves a target.</span> <span class="sd"> An *alternating path* is a path in which every other edge is in the</span> @@ -876,7 +876,7 @@ <div class="viewcode-block" id="to_vertex_cover"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.matching.to_vertex_cover.html#networkx.algorithms.bipartite.matching.to_vertex_cover">[docs]</a><span class="k">def</span> <span class="nf">to_vertex_cover</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">matching</span><span class="p">,</span> <span class="n">top_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the minimum vertex cover corresponding to the given maximum</span> +<span class="w"> </span><span class="sd">"""Returns the minimum vertex cover corresponding to the given maximum</span> <span class="sd"> matching of the bipartite graph `G`.</span> <span class="sd"> Parameters</span> @@ -956,7 +956,7 @@ <div class="viewcode-block" id="minimum_weight_full_matching"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.matching.minimum_weight_full_matching.html#networkx.algorithms.bipartite.matching.minimum_weight_full_matching">[docs]</a><span class="k">def</span> <span class="nf">minimum_weight_full_matching</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">top_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a minimum weight full matching of the bipartite graph `G`.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a minimum weight full matching of the bipartite graph `G`.</span> <span class="sd"> Let :math:`G = ((U, V), E)` be a weighted bipartite graph with real weights</span> <span class="sd"> :math:`w : E \to \mathbb{R}`. This function then produces a matching</span> @@ -1091,7 +1091,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/bipartite/matrix.html b/_modules/networkx/algorithms/bipartite/matrix.html index 769bf14d..0c132da5 100644 --- a/_modules/networkx/algorithms/bipartite/matrix.html +++ b/_modules/networkx/algorithms/bipartite/matrix.html @@ -477,7 +477,7 @@ <div class="viewcode-block" id="biadjacency_matrix"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.matrix.biadjacency_matrix.html#networkx.algorithms.bipartite.matrix.biadjacency_matrix">[docs]</a><span class="k">def</span> <span class="nf">biadjacency_matrix</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">row_order</span><span class="p">,</span> <span class="n">column_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"csr"</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the biadjacency matrix of the bipartite graph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the biadjacency matrix of the bipartite graph G.</span> <span class="sd"> Let `G = (U, V, E)` be a bipartite graph with node sets</span> <span class="sd"> `U = u_{1},...,u_{r}` and `V = v_{1},...,v_{s}`. The biadjacency</span> @@ -574,7 +574,7 @@ <div class="viewcode-block" id="from_biadjacency_matrix"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.matrix.from_biadjacency_matrix.html#networkx.algorithms.bipartite.matrix.from_biadjacency_matrix">[docs]</a><span class="k">def</span> <span class="nf">from_biadjacency_matrix</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">edge_attribute</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Creates a new bipartite graph from a biadjacency matrix given as a</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Creates a new bipartite graph from a biadjacency matrix given as a</span> <span class="sd"> SciPy sparse array.</span> <span class="sd"> Parameters</span> @@ -678,7 +678,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/bipartite/projection.html b/_modules/networkx/algorithms/bipartite/projection.html index 0e7a1d1c..657548a1 100644 --- a/_modules/networkx/algorithms/bipartite/projection.html +++ b/_modules/networkx/algorithms/bipartite/projection.html @@ -476,7 +476,7 @@ <div class="viewcode-block" id="projected_graph"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.projection.projected_graph.html#networkx.algorithms.bipartite.projection.projected_graph">[docs]</a><span class="k">def</span> <span class="nf">projected_graph</span><span class="p">(</span><span class="n">B</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">multigraph</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the projection of B onto one of its node sets.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the projection of B onto one of its node sets.</span> <span class="sd"> Returns the graph G that is the projection of the bipartite graph B</span> <span class="sd"> onto the specified nodes. They retain their attributes and are connected</span> @@ -580,7 +580,7 @@ <div class="viewcode-block" id="weighted_projected_graph"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.projection.weighted_projected_graph.html#networkx.algorithms.bipartite.projection.weighted_projected_graph">[docs]</a><span class="nd">@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">weighted_projected_graph</span><span class="p">(</span><span class="n">B</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">ratio</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a weighted projection of B onto one of its node sets.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a weighted projection of B onto one of its node sets.</span> <span class="sd"> The weighted projected graph is the projection of the bipartite</span> <span class="sd"> network B onto the specified nodes with weights representing the</span> @@ -680,7 +680,7 @@ <div class="viewcode-block" id="collaboration_weighted_projected_graph"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph.html#networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph">[docs]</a><span class="nd">@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">collaboration_weighted_projected_graph</span><span class="p">(</span><span class="n">B</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Newman's weighted projection of B onto one of its node sets.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Newman's weighted projection of B onto one of its node sets.</span> <span class="sd"> The collaboration weighted projection is the projection of the</span> <span class="sd"> bipartite network B onto the specified nodes with weights assigned</span> @@ -774,7 +774,7 @@ <div class="viewcode-block" id="overlap_weighted_projected_graph"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph.html#networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph">[docs]</a><span class="nd">@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">overlap_weighted_projected_graph</span><span class="p">(</span><span class="n">B</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">jaccard</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Overlap weighted projection of B onto one of its node sets.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Overlap weighted projection of B onto one of its node sets.</span> <span class="sd"> The overlap weighted projection is the projection of the bipartite</span> <span class="sd"> network B onto the specified nodes with weights representing</span> @@ -873,7 +873,7 @@ <div class="viewcode-block" id="generic_weighted_projected_graph"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.projection.generic_weighted_projected_graph.html#networkx.algorithms.bipartite.projection.generic_weighted_projected_graph">[docs]</a><span class="nd">@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">generic_weighted_projected_graph</span><span class="p">(</span><span class="n">B</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">weight_function</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Weighted projection of B with a user-specified weight function.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Weighted projection of B with a user-specified weight function.</span> <span class="sd"> The bipartite network B is projected on to the specified nodes</span> <span class="sd"> with weights computed by a user-specified function. This function</span> @@ -1035,7 +1035,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/bipartite/redundancy.html b/_modules/networkx/algorithms/bipartite/redundancy.html index e2ac8668..325e602a 100644 --- a/_modules/networkx/algorithms/bipartite/redundancy.html +++ b/_modules/networkx/algorithms/bipartite/redundancy.html @@ -470,7 +470,7 @@ <div class="viewcode-block" id="node_redundancy"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.redundancy.node_redundancy.html#networkx.algorithms.bipartite.redundancy.node_redundancy">[docs]</a><span class="k">def</span> <span class="nf">node_redundancy</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Computes the node redundancy coefficients for the nodes in the bipartite</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Computes the node redundancy coefficients for the nodes in the bipartite</span> <span class="sd"> graph `G`.</span> <span class="sd"> The redundancy coefficient of a node `v` is the fraction of pairs of</span> @@ -555,7 +555,7 @@ <span class="k">def</span> <span class="nf">_node_redundancy</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Returns the redundancy of the node `v` in the bipartite graph `G`.</span> +<span class="w"> </span><span class="sd">"""Returns the redundancy of the node `v` in the bipartite graph `G`.</span> <span class="sd"> If `G` is a graph with `n` nodes, the redundancy of a node is the ratio</span> <span class="sd"> of the "overlap" of `v` to the maximum possible overlap of `v`</span> @@ -621,7 +621,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/bipartite/spectral.html b/_modules/networkx/algorithms/bipartite/spectral.html index 4a6bea15..d3693f38 100644 --- a/_modules/networkx/algorithms/bipartite/spectral.html +++ b/_modules/networkx/algorithms/bipartite/spectral.html @@ -470,7 +470,7 @@ <div class="viewcode-block" id="spectral_bipartivity"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.bipartite.spectral.spectral_bipartivity.html#networkx.algorithms.bipartite.spectral.spectral_bipartivity">[docs]</a><span class="k">def</span> <span class="nf">spectral_bipartivity</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns the spectral bipartivity.</span> +<span class="w"> </span><span class="sd">"""Returns the spectral bipartivity.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -580,7 +580,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/boundary.html b/_modules/networkx/algorithms/boundary.html index 73147d46..20e3d891 100644 --- a/_modules/networkx/algorithms/boundary.html +++ b/_modules/networkx/algorithms/boundary.html @@ -480,7 +480,7 @@ <div class="viewcode-block" id="edge_boundary"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.boundary.edge_boundary.html#networkx.algorithms.boundary.edge_boundary">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">edge_boundary</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nbunch1</span><span class="p">,</span> <span class="n">nbunch2</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the edge boundary of `nbunch1`.</span> +<span class="w"> </span><span class="sd">"""Returns the edge boundary of `nbunch1`.</span> <span class="sd"> The *edge boundary* of a set *S* with respect to a set *T* is the</span> <span class="sd"> set of edges (*u*, *v*) such that *u* is in *S* and *v* is in *T*.</span> @@ -557,7 +557,7 @@ <div class="viewcode-block" id="node_boundary"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.boundary.node_boundary.html#networkx.algorithms.boundary.node_boundary">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span><span class="p">()</span> <span class="k">def</span> <span class="nf">node_boundary</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nbunch1</span><span class="p">,</span> <span class="n">nbunch2</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the node boundary of `nbunch1`.</span> +<span class="w"> </span><span class="sd">"""Returns the node boundary of `nbunch1`.</span> <span class="sd"> The *node boundary* of a set *S* with respect to a set *T* is the</span> <span class="sd"> set of nodes *v* in *T* such that for some *u* in *S*, there is an</span> @@ -651,7 +651,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/bridges.html b/_modules/networkx/algorithms/bridges.html index 8eb952a4..9daee3f1 100644 --- a/_modules/networkx/algorithms/bridges.html +++ b/_modules/networkx/algorithms/bridges.html @@ -472,7 +472,7 @@ <div class="viewcode-block" id="bridges"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.bridges.bridges.html#networkx.algorithms.bridges.bridges">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">bridges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generate all bridges in a graph.</span> +<span class="w"> </span><span class="sd">"""Generate all bridges in a graph.</span> <span class="sd"> A *bridge* in a graph is an edge whose removal causes the number of</span> <span class="sd"> connected components of the graph to increase. Equivalently, a bridge is an</span> @@ -544,7 +544,7 @@ <div class="viewcode-block" id="has_bridges"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.bridges.has_bridges.html#networkx.algorithms.bridges.has_bridges">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">has_bridges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Decide whether a graph has any bridges.</span> +<span class="w"> </span><span class="sd">"""Decide whether a graph has any bridges.</span> <span class="sd"> A *bridge* in a graph is an edge whose removal causes the number of</span> <span class="sd"> connected components of the graph to increase.</span> @@ -604,7 +604,7 @@ <div class="viewcode-block" id="local_bridges"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.bridges.local_bridges.html#networkx.algorithms.bridges.local_bridges">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"multigraph"</span><span class="p">)</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">local_bridges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">with_span</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Iterate over local bridges of `G` optionally computing the span</span> +<span class="w"> </span><span class="sd">"""Iterate over local bridges of `G` optionally computing the span</span> <span class="sd"> A *local bridge* is an edge whose endpoints have no common neighbors.</span> <span class="sd"> That is, the edge is not part of a triangle in the graph.</span> @@ -714,7 +714,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/betweenness.html b/_modules/networkx/algorithms/centrality/betweenness.html index 03beade9..a9474c72 100644 --- a/_modules/networkx/algorithms/centrality/betweenness.html +++ b/_modules/networkx/algorithms/centrality/betweenness.html @@ -479,7 +479,7 @@ <span class="k">def</span> <span class="nf">betweenness_centrality</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">endpoints</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the shortest-path betweenness centrality for nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the shortest-path betweenness centrality for nodes.</span> <span class="sd"> Betweenness centrality of a node $v$ is the sum of the</span> <span class="sd"> fraction of all-pairs shortest paths that pass through $v$</span> @@ -614,7 +614,7 @@ <div class="viewcode-block" id="edge_betweenness_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.edge_betweenness_centrality.html#networkx.algorithms.centrality.edge_betweenness_centrality">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> <span class="k">def</span> <span class="nf">edge_betweenness_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute betweenness centrality for edges.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute betweenness centrality for edges.</span> <span class="sd"> Betweenness centrality of an edge $e$ is the sum of the</span> <span class="sd"> fraction of all-pairs shortest paths that pass through $e$</span> @@ -859,7 +859,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"graph"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">_add_edge_keys</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">betweenness</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Adds the corrected betweenness centrality (BC) values for multigraphs.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Adds the corrected betweenness centrality (BC) values for multigraphs.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -942,7 +942,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/betweenness_subset.html b/_modules/networkx/algorithms/centrality/betweenness_subset.html index faa70b64..60b79a6d 100644 --- a/_modules/networkx/algorithms/centrality/betweenness_subset.html +++ b/_modules/networkx/algorithms/centrality/betweenness_subset.html @@ -477,7 +477,7 @@ <div class="viewcode-block" id="betweenness_centrality_subset"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.betweenness_centrality_subset.html#networkx.algorithms.centrality.betweenness_centrality_subset">[docs]</a><span class="k">def</span> <span class="nf">betweenness_centrality_subset</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute betweenness centrality for a subset of nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute betweenness centrality for a subset of nodes.</span> <span class="sd"> .. math::</span> @@ -576,7 +576,7 @@ <div class="viewcode-block" id="edge_betweenness_centrality_subset"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.edge_betweenness_centrality_subset.html#networkx.algorithms.centrality.edge_betweenness_centrality_subset">[docs]</a><span class="k">def</span> <span class="nf">edge_betweenness_centrality_subset</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute betweenness centrality for edges for a subset of nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute betweenness centrality for edges for a subset of nodes.</span> <span class="sd"> .. math::</span> @@ -676,7 +676,7 @@ <span class="k">def</span> <span class="nf">_accumulate_edges_subset</span><span class="p">(</span><span class="n">betweenness</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">P</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span> - <span class="sd">"""edge_betweenness_centrality_subset helper."""</span> +<span class="w"> </span><span class="sd">"""edge_betweenness_centrality_subset helper."""</span> <span class="n">delta</span> <span class="o">=</span> <span class="nb">dict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">S</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="n">target_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span> <span class="k">while</span> <span class="n">S</span><span class="p">:</span> @@ -697,7 +697,7 @@ <span class="k">def</span> <span class="nf">_rescale</span><span class="p">(</span><span class="n">betweenness</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">normalized</span><span class="p">,</span> <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""betweenness_centrality_subset helper."""</span> +<span class="w"> </span><span class="sd">"""betweenness_centrality_subset helper."""</span> <span class="k">if</span> <span class="n">normalized</span><span class="p">:</span> <span class="k">if</span> <span class="n">n</span> <span class="o"><=</span> <span class="mi">2</span><span class="p">:</span> <span class="n">scale</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># no normalization b=0 for all nodes</span> @@ -715,7 +715,7 @@ <span class="k">def</span> <span class="nf">_rescale_e</span><span class="p">(</span><span class="n">betweenness</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">normalized</span><span class="p">,</span> <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""edge_betweenness_centrality_subset helper."""</span> +<span class="w"> </span><span class="sd">"""edge_betweenness_centrality_subset helper."""</span> <span class="k">if</span> <span class="n">normalized</span><span class="p">:</span> <span class="k">if</span> <span class="n">n</span> <span class="o"><=</span> <span class="mi">1</span><span class="p">:</span> <span class="n">scale</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># no normalization b=0 for all nodes</span> @@ -781,7 +781,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/closeness.html b/_modules/networkx/algorithms/centrality/closeness.html index 5d2b9511..6dec3eaa 100644 --- a/_modules/networkx/algorithms/centrality/closeness.html +++ b/_modules/networkx/algorithms/centrality/closeness.html @@ -474,7 +474,7 @@ <div class="viewcode-block" id="closeness_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.closeness_centrality.html#networkx.algorithms.centrality.closeness_centrality">[docs]</a><span class="k">def</span> <span class="nf">closeness_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">u</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">distance</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">wf_improved</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute closeness centrality for nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute closeness centrality for nodes.</span> <span class="sd"> Closeness centrality [1]_ of a node `u` is the reciprocal of the</span> <span class="sd"> average shortest path distance to `u` over all `n-1` reachable nodes.</span> @@ -602,7 +602,7 @@ <span class="k">def</span> <span class="nf">incremental_closeness_centrality</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">edge</span><span class="p">,</span> <span class="n">prev_cc</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">insertion</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">wf_improved</span><span class="o">=</span><span class="kc">True</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Incremental closeness centrality for nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Incremental closeness centrality for nodes.</span> <span class="sd"> Compute closeness centrality for nodes using level-based work filtering</span> <span class="sd"> as described in Incremental Algorithms for Closeness Centrality by Sariyuce et al.</span> @@ -791,7 +791,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/current_flow_betweenness.html b/_modules/networkx/algorithms/centrality/current_flow_betweenness.html index 9f829d5c..03b9bad5 100644 --- a/_modules/networkx/algorithms/centrality/current_flow_betweenness.html +++ b/_modules/networkx/algorithms/centrality/current_flow_betweenness.html @@ -494,7 +494,7 @@ <span class="n">kmax</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the approximate current-flow betweenness centrality for nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the approximate current-flow betweenness centrality for nodes.</span> <span class="sd"> Approximates the current-flow betweenness centrality within absolute</span> <span class="sd"> error of epsilon with high probability [1]_.</span> @@ -609,7 +609,7 @@ <span class="k">def</span> <span class="nf">current_flow_betweenness_centrality</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s2">"full"</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute current-flow betweenness centrality for nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute current-flow betweenness centrality for nodes.</span> <span class="sd"> Current-flow betweenness centrality uses an electrical current</span> <span class="sd"> model for information spreading in contrast to betweenness</span> @@ -705,7 +705,7 @@ <span class="k">def</span> <span class="nf">edge_current_flow_betweenness_centrality</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s2">"full"</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute current-flow betweenness centrality for edges.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute current-flow betweenness centrality for edges.</span> <span class="sd"> Current-flow betweenness centrality uses an electrical current</span> <span class="sd"> model for information spreading in contrast to betweenness</span> @@ -852,7 +852,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/current_flow_betweenness_subset.html b/_modules/networkx/algorithms/centrality/current_flow_betweenness_subset.html index 683fbf63..61eb3e7a 100644 --- a/_modules/networkx/algorithms/centrality/current_flow_betweenness_subset.html +++ b/_modules/networkx/algorithms/centrality/current_flow_betweenness_subset.html @@ -476,7 +476,7 @@ <span class="k">def</span> <span class="nf">current_flow_betweenness_centrality_subset</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s2">"lu"</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute current-flow betweenness centrality for subsets of nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute current-flow betweenness centrality for subsets of nodes.</span> <span class="sd"> Current-flow betweenness centrality uses an electrical current</span> <span class="sd"> model for information spreading in contrast to betweenness</span> @@ -585,7 +585,7 @@ <span class="k">def</span> <span class="nf">edge_current_flow_betweenness_centrality_subset</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s2">"lu"</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute current-flow betweenness centrality for edges using subsets</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute current-flow betweenness centrality for edges using subsets</span> <span class="sd"> of nodes.</span> <span class="sd"> Current-flow betweenness centrality uses an electrical current</span> @@ -736,7 +736,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/current_flow_closeness.html b/_modules/networkx/algorithms/centrality/current_flow_closeness.html index 73fd1859..abcee6a3 100644 --- a/_modules/networkx/algorithms/centrality/current_flow_closeness.html +++ b/_modules/networkx/algorithms/centrality/current_flow_closeness.html @@ -475,7 +475,7 @@ <div class="viewcode-block" id="current_flow_closeness_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.current_flow_closeness_centrality.html#networkx.algorithms.centrality.current_flow_closeness_centrality">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">current_flow_closeness_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s2">"lu"</span><span class="p">):</span> - <span class="sd">"""Compute current-flow closeness centrality for nodes.</span> +<span class="w"> </span><span class="sd">"""Compute current-flow closeness centrality for nodes.</span> <span class="sd"> Current-flow closeness centrality is variant of closeness</span> <span class="sd"> centrality based on effective resistance between nodes in</span> @@ -608,7 +608,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/degree_alg.html b/_modules/networkx/algorithms/centrality/degree_alg.html index d8de0bba..b6b2850a 100644 --- a/_modules/networkx/algorithms/centrality/degree_alg.html +++ b/_modules/networkx/algorithms/centrality/degree_alg.html @@ -470,7 +470,7 @@ <div class="viewcode-block" id="degree_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.degree_centrality.html#networkx.algorithms.centrality.degree_centrality">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">degree_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Compute the degree centrality for nodes.</span> +<span class="w"> </span><span class="sd">"""Compute the degree centrality for nodes.</span> <span class="sd"> The degree centrality for a node v is the fraction of nodes it</span> <span class="sd"> is connected to.</span> @@ -515,7 +515,7 @@ <div class="viewcode-block" id="in_degree_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.in_degree_centrality.html#networkx.algorithms.centrality.in_degree_centrality">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">in_degree_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Compute the in-degree centrality for nodes.</span> +<span class="w"> </span><span class="sd">"""Compute the in-degree centrality for nodes.</span> <span class="sd"> The in-degree centrality for a node v is the fraction of nodes its</span> <span class="sd"> incoming edges are connected to.</span> @@ -565,7 +565,7 @@ <div class="viewcode-block" id="out_degree_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.out_degree_centrality.html#networkx.algorithms.centrality.out_degree_centrality">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">out_degree_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Compute the out-degree centrality for nodes.</span> +<span class="w"> </span><span class="sd">"""Compute the out-degree centrality for nodes.</span> <span class="sd"> The out-degree centrality for a node v is the fraction of nodes its</span> <span class="sd"> outgoing edges are connected to.</span> @@ -661,7 +661,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/dispersion.html b/_modules/networkx/algorithms/centrality/dispersion.html index 86bd35b4..9149c300 100644 --- a/_modules/networkx/algorithms/centrality/dispersion.html +++ b/_modules/networkx/algorithms/centrality/dispersion.html @@ -467,7 +467,7 @@ <div class="viewcode-block" id="dispersion"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.dispersion.html#networkx.algorithms.centrality.dispersion">[docs]</a><span class="k">def</span> <span class="nf">dispersion</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">u</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">v</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="mf">0.0</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Calculate dispersion between `u` and `v` in `G`.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Calculate dispersion between `u` and `v` in `G`.</span> <span class="sd"> A link between two actors (`u` and `v`) has a high dispersion when their</span> <span class="sd"> mutual ties (`s` and `t`) are not well connected with each other.</span> @@ -515,7 +515,7 @@ <span class="sd"> """</span> <span class="k">def</span> <span class="nf">_dispersion</span><span class="p">(</span><span class="n">G_u</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""dispersion for all nodes 'v' in a ego network G_u of node 'u'"""</span> +<span class="w"> </span><span class="sd">"""dispersion for all nodes 'v' in a ego network G_u of node 'u'"""</span> <span class="n">u_nbrs</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">G_u</span><span class="p">[</span><span class="n">u</span><span class="p">])</span> <span class="n">ST</span> <span class="o">=</span> <span class="p">{</span><span class="n">n</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">G_u</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="k">if</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">u_nbrs</span><span class="p">}</span> <span class="n">set_uv</span> <span class="o">=</span> <span class="p">{</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">}</span> @@ -616,7 +616,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/eigenvector.html b/_modules/networkx/algorithms/centrality/eigenvector.html index fd04012e..2aebe6e1 100644 --- a/_modules/networkx/algorithms/centrality/eigenvector.html +++ b/_modules/networkx/algorithms/centrality/eigenvector.html @@ -473,7 +473,7 @@ <div class="viewcode-block" id="eigenvector_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.eigenvector_centrality.html#networkx.algorithms.centrality.eigenvector_centrality">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="nd">@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">eigenvector_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1.0e-6</span><span class="p">,</span> <span class="n">nstart</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the eigenvector centrality for the graph `G`.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the eigenvector centrality for the graph `G`.</span> <span class="sd"> Eigenvector centrality computes the centrality for a node based on the</span> <span class="sd"> centrality of its neighbors. The eigenvector centrality for node $i$ is</span> @@ -602,7 +602,7 @@ <div class="viewcode-block" id="eigenvector_centrality_numpy"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.eigenvector_centrality_numpy.html#networkx.algorithms.centrality.eigenvector_centrality_numpy">[docs]</a><span class="k">def</span> <span class="nf">eigenvector_centrality_numpy</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the eigenvector centrality for the graph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the eigenvector centrality for the graph G.</span> <span class="sd"> Eigenvector centrality computes the centrality for a node based on the</span> <span class="sd"> centrality of its neighbors. The eigenvector centrality for node $i$ is</span> @@ -742,7 +742,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/group.html b/_modules/networkx/algorithms/centrality/group.html index e3b08959..97e6518b 100644 --- a/_modules/networkx/algorithms/centrality/group.html +++ b/_modules/networkx/algorithms/centrality/group.html @@ -483,7 +483,7 @@ <div class="viewcode-block" id="group_betweenness_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.group_betweenness_centrality.html#networkx.algorithms.centrality.group_betweenness_centrality">[docs]</a><span class="k">def</span> <span class="nf">group_betweenness_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">endpoints</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the group betweenness centrality for a group of nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the group betweenness centrality for a group of nodes.</span> <span class="sd"> Group betweenness centrality of a group of nodes $C$ is the sum of the</span> <span class="sd"> fraction of all-pairs shortest paths that pass through any vertex in $C$</span> @@ -583,7 +583,7 @@ <span class="n">list_of_groups</span> <span class="o">=</span> <span class="kc">False</span> <span class="n">set_v</span> <span class="o">=</span> <span class="p">{</span><span class="n">node</span> <span class="k">for</span> <span class="n">group</span> <span class="ow">in</span> <span class="n">C</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">group</span><span class="p">}</span> <span class="k">if</span> <span class="n">set_v</span> <span class="o">-</span> <span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span> <span class="c1"># element(s) of C not in G</span> - <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NodeNotFound</span><span class="p">(</span><span class="sa">f</span><span class="s2">"The node(s) </span><span class="si">{</span><span class="n">set_v</span> <span class="o">-</span> <span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="si">}</span><span class="s2"> are in C but not in G."</span><span class="p">)</span> + <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NodeNotFound</span><span class="p">(</span><span class="sa">f</span><span class="s2">"The node(s) </span><span class="si">{</span><span class="n">set_v</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="si">}</span><span class="s2"> are in C but not in G."</span><span class="p">)</span> <span class="c1"># pre-processing</span> <span class="n">PB</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">D</span> <span class="o">=</span> <span class="n">_group_preprocessing</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">set_v</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span> @@ -701,7 +701,7 @@ <div class="viewcode-block" id="prominent_group"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.prominent_group.html#networkx.algorithms.centrality.prominent_group">[docs]</a><span class="k">def</span> <span class="nf">prominent_group</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">endpoints</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">greedy</span><span class="o">=</span><span class="kc">False</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Find the prominent group of size $k$ in graph $G$. The prominence of the</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Find the prominent group of size $k$ in graph $G$. The prominence of the</span> <span class="sd"> group is evaluated by the group betweenness centrality.</span> <span class="sd"> Group betweenness centrality of a group of nodes $C$ is the sum of the</span> @@ -806,7 +806,7 @@ <span class="k">if</span> <span class="n">C</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="n">C</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">C</span><span class="p">)</span> <span class="k">if</span> <span class="n">C</span> <span class="o">-</span> <span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span> <span class="c1"># element(s) of C not in G</span> - <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NodeNotFound</span><span class="p">(</span><span class="sa">f</span><span class="s2">"The node(s) </span><span class="si">{</span><span class="n">C</span> <span class="o">-</span> <span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="si">}</span><span class="s2"> are in C but not in G."</span><span class="p">)</span> + <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NodeNotFound</span><span class="p">(</span><span class="sa">f</span><span class="s2">"The node(s) </span><span class="si">{</span><span class="n">C</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="si">}</span><span class="s2"> are in C but not in G."</span><span class="p">)</span> <span class="n">nodes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span> <span class="o">-</span> <span class="n">C</span><span class="p">)</span> <span class="k">else</span><span class="p">:</span> <span class="n">nodes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">)</span> @@ -1005,7 +1005,7 @@ <div class="viewcode-block" id="group_closeness_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.group_closeness_centrality.html#networkx.algorithms.centrality.group_closeness_centrality">[docs]</a><span class="k">def</span> <span class="nf">group_closeness_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the group closeness centrality for a group of nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the group closeness centrality for a group of nodes.</span> <span class="sd"> Group closeness centrality of a group of nodes $S$ is a measure</span> <span class="sd"> of how close the group is to the other nodes in the graph.</span> @@ -1101,7 +1101,7 @@ <div class="viewcode-block" id="group_degree_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.group_degree_centrality.html#networkx.algorithms.centrality.group_degree_centrality">[docs]</a><span class="k">def</span> <span class="nf">group_degree_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">S</span><span class="p">):</span> - <span class="sd">"""Compute the group degree centrality for a group of nodes.</span> +<span class="w"> </span><span class="sd">"""Compute the group degree centrality for a group of nodes.</span> <span class="sd"> Group degree centrality of a group of nodes $S$ is the fraction</span> <span class="sd"> of non-group members connected to group members.</span> @@ -1152,7 +1152,7 @@ <div class="viewcode-block" id="group_in_degree_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.group_in_degree_centrality.html#networkx.algorithms.centrality.group_in_degree_centrality">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">group_in_degree_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">S</span><span class="p">):</span> - <span class="sd">"""Compute the group in-degree centrality for a group of nodes.</span> +<span class="w"> </span><span class="sd">"""Compute the group in-degree centrality for a group of nodes.</span> <span class="sd"> Group in-degree centrality of a group of nodes $S$ is the fraction</span> <span class="sd"> of non-group members connected to group members by incoming edges.</span> @@ -1198,7 +1198,7 @@ <div class="viewcode-block" id="group_out_degree_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.group_out_degree_centrality.html#networkx.algorithms.centrality.group_out_degree_centrality">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">group_out_degree_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">S</span><span class="p">):</span> - <span class="sd">"""Compute the group out-degree centrality for a group of nodes.</span> +<span class="w"> </span><span class="sd">"""Compute the group out-degree centrality for a group of nodes.</span> <span class="sd"> Group out-degree centrality of a group of nodes $S$ is the fraction</span> <span class="sd"> of non-group members connected to group members by outgoing edges.</span> @@ -1291,7 +1291,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/harmonic.html b/_modules/networkx/algorithms/centrality/harmonic.html index c4506f36..6abdf215 100644 --- a/_modules/networkx/algorithms/centrality/harmonic.html +++ b/_modules/networkx/algorithms/centrality/harmonic.html @@ -470,7 +470,7 @@ <div class="viewcode-block" id="harmonic_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.harmonic_centrality.html#networkx.algorithms.centrality.harmonic_centrality">[docs]</a><span class="k">def</span> <span class="nf">harmonic_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nbunch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">distance</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sources</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute harmonic centrality for nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute harmonic centrality for nodes.</span> <span class="sd"> Harmonic centrality [1]_ of a node `u` is the sum of the reciprocal</span> <span class="sd"> of the shortest path distances from all other nodes to `u`</span> @@ -591,7 +591,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/katz.html b/_modules/networkx/algorithms/centrality/katz.html index b0067c94..135b5be8 100644 --- a/_modules/networkx/algorithms/centrality/katz.html +++ b/_modules/networkx/algorithms/centrality/katz.html @@ -482,7 +482,7 @@ <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the Katz centrality for the nodes of the graph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the Katz centrality for the nodes of the graph G.</span> <span class="sd"> Katz centrality computes the centrality for a node based on the centrality</span> <span class="sd"> of its neighbors. It is a generalization of the eigenvector centrality. The</span> @@ -659,7 +659,7 @@ <div class="viewcode-block" id="katz_centrality_numpy"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.katz_centrality_numpy.html#networkx.algorithms.centrality.katz_centrality_numpy">[docs]</a><span class="nd">@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">katz_centrality_numpy</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the Katz centrality for the graph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the Katz centrality for the graph G.</span> <span class="sd"> Katz centrality computes the centrality for a node based on the centrality</span> <span class="sd"> of its neighbors. It is a generalization of the eigenvector centrality. The</span> @@ -846,7 +846,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/load.html b/_modules/networkx/algorithms/centrality/load.html index 7505e60b..50ba5326 100644 --- a/_modules/networkx/algorithms/centrality/load.html +++ b/_modules/networkx/algorithms/centrality/load.html @@ -470,7 +470,7 @@ <span class="k">def</span> <span class="nf">newman_betweenness_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">v</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute load centrality for nodes.</span> +<span class="w"> </span><span class="sd">"""Compute load centrality for nodes.</span> <span class="sd"> The load centrality of a node is the fraction of all shortest</span> <span class="sd"> paths that pass through that node.</span> @@ -545,7 +545,7 @@ <span class="k">def</span> <span class="nf">_node_betweenness</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Node betweenness_centrality helper:</span> +<span class="w"> </span><span class="sd">"""Node betweenness_centrality helper:</span> <span class="sd"> See betweenness_centrality for what you probably want.</span> <span class="sd"> This actually computes "load" and not betweenness.</span> @@ -599,7 +599,7 @@ <div class="viewcode-block" id="edge_load_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.edge_load_centrality.html#networkx.algorithms.centrality.edge_load_centrality">[docs]</a><span class="k">def</span> <span class="nf">edge_load_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Compute edge load.</span> +<span class="w"> </span><span class="sd">"""Compute edge load.</span> <span class="sd"> WARNING: This concept of edge load has not been analysed</span> <span class="sd"> or discussed outside of NetworkX that we know of.</span> @@ -634,7 +634,7 @@ <span class="k">def</span> <span class="nf">_edge_betweenness</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Edge betweenness helper."""</span> +<span class="w"> </span><span class="sd">"""Edge betweenness helper."""</span> <span class="c1"># get the predecessor data</span> <span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">length</span><span class="p">)</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">predecessor</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="n">cutoff</span><span class="p">,</span> <span class="n">return_seen</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># order the nodes by path length</span> @@ -709,7 +709,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/percolation.html b/_modules/networkx/algorithms/centrality/percolation.html index 992c84c5..6f084a9b 100644 --- a/_modules/networkx/algorithms/centrality/percolation.html +++ b/_modules/networkx/algorithms/centrality/percolation.html @@ -475,7 +475,7 @@ <div class="viewcode-block" id="percolation_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.percolation_centrality.html#networkx.algorithms.centrality.percolation_centrality">[docs]</a><span class="k">def</span> <span class="nf">percolation_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">attribute</span><span class="o">=</span><span class="s2">"percolation"</span><span class="p">,</span> <span class="n">states</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the percolation centrality for nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the percolation centrality for nodes.</span> <span class="sd"> Percolation centrality of a node $v$, at a given time, is defined</span> <span class="sd"> as the proportion of ‘percolated paths’ that go through that node.</span> @@ -636,7 +636,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/reaching.html b/_modules/networkx/algorithms/centrality/reaching.html index 63685af8..382c159a 100644 --- a/_modules/networkx/algorithms/centrality/reaching.html +++ b/_modules/networkx/algorithms/centrality/reaching.html @@ -470,7 +470,7 @@ <span class="k">def</span> <span class="nf">_average_weight</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the average weight of an edge in a weighted path.</span> +<span class="w"> </span><span class="sd">"""Returns the average weight of an edge in a weighted path.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -495,7 +495,7 @@ <div class="viewcode-block" id="global_reaching_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.global_reaching_centrality.html#networkx.algorithms.centrality.global_reaching_centrality">[docs]</a><span class="k">def</span> <span class="nf">global_reaching_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns the global reaching centrality of a directed graph.</span> +<span class="w"> </span><span class="sd">"""Returns the global reaching centrality of a directed graph.</span> <span class="sd"> The *global reaching centrality* of a weighted directed graph is the</span> <span class="sd"> average over all nodes of the difference between the local reaching</span> @@ -582,7 +582,7 @@ <div class="viewcode-block" id="local_reaching_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.local_reaching_centrality.html#networkx.algorithms.centrality.local_reaching_centrality">[docs]</a><span class="k">def</span> <span class="nf">local_reaching_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">paths</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns the local reaching centrality of a node in a directed</span> +<span class="w"> </span><span class="sd">"""Returns the local reaching centrality of a node in a directed</span> <span class="sd"> graph.</span> <span class="sd"> The *local reaching centrality* of a node in a directed graph is the</span> @@ -716,7 +716,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/second_order.html b/_modules/networkx/algorithms/centrality/second_order.html index 51f21551..175c0eed 100644 --- a/_modules/networkx/algorithms/centrality/second_order.html +++ b/_modules/networkx/algorithms/centrality/second_order.html @@ -503,7 +503,7 @@ <div class="viewcode-block" id="second_order_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.second_order_centrality.html#networkx.algorithms.centrality.second_order_centrality">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">second_order_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Compute the second order centrality for nodes of G.</span> +<span class="w"> </span><span class="sd">"""Compute the second order centrality for nodes of G.</span> <span class="sd"> The second order centrality of a given node is the standard deviation of</span> <span class="sd"> the return times to that node of a perpetual random walk on G:</span> @@ -645,7 +645,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/subgraph_alg.html b/_modules/networkx/algorithms/centrality/subgraph_alg.html index f19298fe..dbf1aed1 100644 --- a/_modules/networkx/algorithms/centrality/subgraph_alg.html +++ b/_modules/networkx/algorithms/centrality/subgraph_alg.html @@ -478,7 +478,7 @@ <div class="viewcode-block" id="subgraph_centrality_exp"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.subgraph_centrality_exp.html#networkx.algorithms.centrality.subgraph_centrality_exp">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">subgraph_centrality_exp</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the subgraph centrality for each node of G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the subgraph centrality for each node of G.</span> <span class="sd"> Subgraph centrality of a node `n` is the sum of weighted closed</span> <span class="sd"> walks of all lengths starting and ending at node `n`. The weights</span> @@ -562,7 +562,7 @@ <div class="viewcode-block" id="subgraph_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.subgraph_centrality.html#networkx.algorithms.centrality.subgraph_centrality">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">subgraph_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns subgraph centrality for each node in G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns subgraph centrality for each node in G.</span> <span class="sd"> Subgraph centrality of a node `n` is the sum of weighted closed</span> <span class="sd"> walks of all lengths starting and ending at node `n`. The weights</span> @@ -652,7 +652,7 @@ <div class="viewcode-block" id="communicability_betweenness_centrality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.communicability_betweenness_centrality.html#networkx.algorithms.centrality.communicability_betweenness_centrality">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">communicability_betweenness_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns subgraph communicability for all pairs of nodes in G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns subgraph communicability for all pairs of nodes in G.</span> <span class="sd"> Communicability betweenness measure makes use of the number of walks</span> <span class="sd"> connecting every pair of nodes as the basis of a betweenness centrality</span> @@ -754,7 +754,7 @@ <div class="viewcode-block" id="estrada_index"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.estrada_index.html#networkx.algorithms.centrality.estrada_index">[docs]</a><span class="k">def</span> <span class="nf">estrada_index</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the Estrada index of a the graph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the Estrada index of a the graph G.</span> <span class="sd"> The Estrada Index is a topological index of folding or 3D "compactness" ([1]_).</span> @@ -850,7 +850,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/trophic.html b/_modules/networkx/algorithms/centrality/trophic.html index f28c54e0..257a3141 100644 --- a/_modules/networkx/algorithms/centrality/trophic.html +++ b/_modules/networkx/algorithms/centrality/trophic.html @@ -470,7 +470,7 @@ <div class="viewcode-block" id="trophic_levels"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.trophic_levels.html#networkx.algorithms.centrality.trophic_levels">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">trophic_levels</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the trophic levels of nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the trophic levels of nodes.</span> <span class="sd"> The trophic level of a node $i$ is</span> @@ -545,7 +545,7 @@ <div class="viewcode-block" id="trophic_differences"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.trophic_differences.html#networkx.algorithms.centrality.trophic_differences">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">trophic_differences</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the trophic differences of the edges of a directed graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the trophic differences of the edges of a directed graph.</span> <span class="sd"> The trophic difference $x_ij$ for each edge is defined in Johnson et al.</span> <span class="sd"> [1]_ as:</span> @@ -579,7 +579,7 @@ <div class="viewcode-block" id="trophic_incoherence_parameter"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.trophic_incoherence_parameter.html#networkx.algorithms.centrality.trophic_incoherence_parameter">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">trophic_incoherence_parameter</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">cannibalism</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the trophic incoherence parameter of a graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the trophic incoherence parameter of a graph.</span> <span class="sd"> Trophic coherence is defined as the homogeneity of the distribution of</span> <span class="sd"> trophic distances: the more similar, the more coherent. This is measured by</span> @@ -671,7 +671,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/centrality/voterank_alg.html b/_modules/networkx/algorithms/centrality/voterank_alg.html index 0539dd35..dd8f0c2f 100644 --- a/_modules/networkx/algorithms/centrality/voterank_alg.html +++ b/_modules/networkx/algorithms/centrality/voterank_alg.html @@ -467,7 +467,7 @@ <div class="viewcode-block" id="voterank"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.centrality.voterank.html#networkx.algorithms.centrality.voterank">[docs]</a><span class="k">def</span> <span class="nf">voterank</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">number_of_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Select a list of influential nodes in a graph using VoteRank algorithm</span> +<span class="w"> </span><span class="sd">"""Select a list of influential nodes in a graph using VoteRank algorithm</span> <span class="sd"> VoteRank [1]_ computes a ranking of the nodes in a graph G based on a</span> <span class="sd"> voting scheme. With VoteRank, all nodes vote for each of its in-neighbours</span> @@ -604,7 +604,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/chains.html b/_modules/networkx/algorithms/chains.html index 0cc9c910..29396974 100644 --- a/_modules/networkx/algorithms/chains.html +++ b/_modules/networkx/algorithms/chains.html @@ -472,7 +472,7 @@ <div class="viewcode-block" id="chain_decomposition"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.chains.chain_decomposition.html#networkx.algorithms.chains.chain_decomposition">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">chain_decomposition</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the chain decomposition of a graph.</span> +<span class="w"> </span><span class="sd">"""Returns the chain decomposition of a graph.</span> <span class="sd"> The *chain decomposition* of a graph with respect a depth-first</span> <span class="sd"> search tree is a set of cycles or paths derived from the set of</span> @@ -527,7 +527,7 @@ <span class="sd"> """</span> <span class="k">def</span> <span class="nf">_dfs_cycle_forest</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Builds a directed graph composed of cycles from the given graph.</span> +<span class="w"> </span><span class="sd">"""Builds a directed graph composed of cycles from the given graph.</span> <span class="sd"> `G` is an undirected simple graph. `root` is a node in the graph</span> <span class="sd"> from which the depth-first search is started.</span> @@ -583,7 +583,7 @@ <span class="k">return</span> <span class="n">H</span><span class="p">,</span> <span class="n">nodes</span> <span class="k">def</span> <span class="nf">_build_chain</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">visited</span><span class="p">):</span> - <span class="sd">"""Generate the chain starting from the given nontree edge.</span> +<span class="w"> </span><span class="sd">"""Generate the chain starting from the given nontree edge.</span> <span class="sd"> `G` is a DFS cycle graph as constructed by</span> <span class="sd"> :func:`_dfs_cycle_graph`. The edge (`u`, `v`) is a nontree edge</span> @@ -683,7 +683,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/chordal.html b/_modules/networkx/algorithms/chordal.html index 25da1ab0..f8f39247 100644 --- a/_modules/networkx/algorithms/chordal.html +++ b/_modules/networkx/algorithms/chordal.html @@ -485,14 +485,14 @@ <span class="k">class</span> <span class="nc">NetworkXTreewidthBoundExceeded</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">NetworkXException</span><span class="p">):</span> - <span class="sd">"""Exception raised when a treewidth bound has been provided and it has</span> +<span class="w"> </span><span class="sd">"""Exception raised when a treewidth bound has been provided and it has</span> <span class="sd"> been exceeded"""</span> <div class="viewcode-block" id="is_chordal"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.chordal.is_chordal.html#networkx.algorithms.chordal.is_chordal">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">is_chordal</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Checks whether G is a chordal graph.</span> +<span class="w"> </span><span class="sd">"""Checks whether G is a chordal graph.</span> <span class="sd"> A graph is chordal if every cycle of length at least 4 has a chord</span> <span class="sd"> (an edge joining two nodes not adjacent in the cycle).</span> @@ -547,7 +547,7 @@ <div class="viewcode-block" id="find_induced_nodes"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.chordal.find_induced_nodes.html#networkx.algorithms.chordal.find_induced_nodes">[docs]</a><span class="k">def</span> <span class="nf">find_induced_nodes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">treewidth_bound</span><span class="o">=</span><span class="n">sys</span><span class="o">.</span><span class="n">maxsize</span><span class="p">):</span> - <span class="sd">"""Returns the set of induced nodes in the path from s to t.</span> +<span class="w"> </span><span class="sd">"""Returns the set of induced nodes in the path from s to t.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -624,7 +624,7 @@ <div class="viewcode-block" id="chordal_graph_cliques"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.chordal.chordal_graph_cliques.html#networkx.algorithms.chordal.chordal_graph_cliques">[docs]</a><span class="k">def</span> <span class="nf">chordal_graph_cliques</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns all maximal cliques of a chordal graph.</span> +<span class="w"> </span><span class="sd">"""Returns all maximal cliques of a chordal graph.</span> <span class="sd"> The algorithm breaks the graph in connected components and performs a</span> <span class="sd"> maximum cardinality search in each component to get the cliques.</span> @@ -696,7 +696,7 @@ <div class="viewcode-block" id="chordal_graph_treewidth"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.chordal.chordal_graph_treewidth.html#networkx.algorithms.chordal.chordal_graph_treewidth">[docs]</a><span class="k">def</span> <span class="nf">chordal_graph_treewidth</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the treewidth of the chordal graph G.</span> +<span class="w"> </span><span class="sd">"""Returns the treewidth of the chordal graph G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -749,7 +749,7 @@ <span class="k">def</span> <span class="nf">_is_complete_graph</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns True if G is a complete graph."""</span> +<span class="w"> </span><span class="sd">"""Returns True if G is a complete graph."""</span> <span class="k">if</span> <span class="n">nx</span><span class="o">.</span><span class="n">number_of_selfloops</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</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">"Self loop found in _is_complete_graph()"</span><span class="p">)</span> <span class="n">n</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">()</span> @@ -761,7 +761,7 @@ <span class="k">def</span> <span class="nf">_find_missing_edge</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Given a non-complete graph G, returns a missing edge."""</span> +<span class="w"> </span><span class="sd">"""Given a non-complete graph G, returns a missing edge."""</span> <span class="n">nodes</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">G</span><span class="p">:</span> <span class="n">missing</span> <span class="o">=</span> <span class="n">nodes</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">G</span><span class="p">[</span><span class="n">u</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">+</span> <span class="p">[</span><span class="n">u</span><span class="p">])</span> @@ -770,7 +770,7 @@ <span class="k">def</span> <span class="nf">_max_cardinality_node</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">choices</span><span class="p">,</span> <span class="n">wanna_connect</span><span class="p">):</span> - <span class="sd">"""Returns a the node in choices that has more connections in G</span> +<span class="w"> </span><span class="sd">"""Returns a the node in choices that has more connections in G</span> <span class="sd"> to nodes in wanna_connect.</span> <span class="sd"> """</span> <span class="n">max_number</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> @@ -783,7 +783,7 @@ <span class="k">def</span> <span class="nf">_find_chordality_breaker</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">treewidth_bound</span><span class="o">=</span><span class="n">sys</span><span class="o">.</span><span class="n">maxsize</span><span class="p">):</span> - <span class="sd">"""Given a graph G, starts a max cardinality search</span> +<span class="w"> </span><span class="sd">"""Given a graph G, starts a max cardinality search</span> <span class="sd"> (starting from s if s is given and from an arbitrary node otherwise)</span> <span class="sd"> trying to find a non-chordal cycle.</span> @@ -821,7 +821,7 @@ <div class="viewcode-block" id="complete_to_chordal_graph"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.chordal.complete_to_chordal_graph.html#networkx.algorithms.chordal.complete_to_chordal_graph">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">complete_to_chordal_graph</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Return a copy of G completed to a chordal graph</span> +<span class="w"> </span><span class="sd">"""Return a copy of G completed to a chordal graph</span> <span class="sd"> Adds edges to a copy of G to create a chordal graph. A graph G=(V,E) is</span> <span class="sd"> called chordal if for each cycle with length bigger than 3, there exist</span> @@ -941,7 +941,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/clique.html b/_modules/networkx/algorithms/clique.html index aea7ae67..5e97ead2 100644 --- a/_modules/networkx/algorithms/clique.html +++ b/_modules/networkx/algorithms/clique.html @@ -493,7 +493,7 @@ <div class="viewcode-block" id="enumerate_all_cliques"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.clique.enumerate_all_cliques.html#networkx.algorithms.clique.enumerate_all_cliques">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">enumerate_all_cliques</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns all cliques in an undirected graph.</span> +<span class="w"> </span><span class="sd">"""Returns all cliques in an undirected graph.</span> <span class="sd"> This function returns an iterator over cliques, each of which is a</span> <span class="sd"> list of nodes. The iteration is ordered by cardinality of the</span> @@ -564,7 +564,7 @@ <div class="viewcode-block" id="find_cliques"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.clique.find_cliques.html#networkx.algorithms.clique.find_cliques">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">find_cliques</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns all maximal cliques in an undirected graph.</span> +<span class="w"> </span><span class="sd">"""Returns all maximal cliques in an undirected graph.</span> <span class="sd"> For each node *n*, a *maximal clique for n* is a largest complete</span> <span class="sd"> subgraph containing *n*. The largest maximal clique is sometimes</span> @@ -698,7 +698,7 @@ <span class="c1"># TODO Should this also be not implemented for directed graphs?</span> <div class="viewcode-block" id="find_cliques_recursive"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.clique.find_cliques_recursive.html#networkx.algorithms.clique.find_cliques_recursive">[docs]</a><span class="k">def</span> <span class="nf">find_cliques_recursive</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns all maximal cliques in a graph.</span> +<span class="w"> </span><span class="sd">"""Returns all maximal cliques in a graph.</span> <span class="sd"> For each node *v*, a *maximal clique for v* is a largest complete</span> <span class="sd"> subgraph containing *v*. The largest maximal clique is sometimes</span> @@ -815,7 +815,7 @@ <div class="viewcode-block" id="make_max_clique_graph"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.clique.make_max_clique_graph.html#networkx.algorithms.clique.make_max_clique_graph">[docs]</a><span class="k">def</span> <span class="nf">make_max_clique_graph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the maximal clique graph of the given graph.</span> +<span class="w"> </span><span class="sd">"""Returns the maximal clique graph of the given graph.</span> <span class="sd"> The nodes of the maximal clique graph of `G` are the cliques of</span> <span class="sd"> `G` and an edge joins two cliques if the cliques are not disjoint.</span> @@ -861,7 +861,7 @@ <div class="viewcode-block" id="make_clique_bipartite"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.clique.make_clique_bipartite.html#networkx.algorithms.clique.make_clique_bipartite">[docs]</a><span class="k">def</span> <span class="nf">make_clique_bipartite</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">fpos</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the bipartite clique graph corresponding to `G`.</span> +<span class="w"> </span><span class="sd">"""Returns the bipartite clique graph corresponding to `G`.</span> <span class="sd"> In the returned bipartite graph, the "bottom" nodes are the nodes of</span> <span class="sd"> `G` and the "top" nodes represent the maximal cliques of `G`.</span> @@ -909,7 +909,7 @@ <div class="viewcode-block" id="graph_clique_number"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.clique.graph_clique_number.html#networkx.algorithms.clique.graph_clique_number">[docs]</a><span class="k">def</span> <span class="nf">graph_clique_number</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">cliques</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the clique number of the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the clique number of the graph.</span> <span class="sd"> The *clique number* of a graph is the size of the largest clique in</span> <span class="sd"> the graph.</span> @@ -944,7 +944,7 @@ <div class="viewcode-block" id="graph_number_of_cliques"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.clique.graph_number_of_cliques.html#networkx.algorithms.clique.graph_number_of_cliques">[docs]</a><span class="k">def</span> <span class="nf">graph_number_of_cliques</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">cliques</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the number of maximal cliques in the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the number of maximal cliques in the graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -974,7 +974,7 @@ <div class="viewcode-block" id="node_clique_number"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.clique.node_clique_number.html#networkx.algorithms.clique.node_clique_number">[docs]</a><span class="k">def</span> <span class="nf">node_clique_number</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cliques</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">separate_nodes</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns the size of the largest maximal clique containing each given node.</span> +<span class="w"> </span><span class="sd">"""Returns the size of the largest maximal clique containing each given node.</span> <span class="sd"> Returns a single or list depending on input nodes.</span> <span class="sd"> An optional list of cliques can be input if already computed.</span> @@ -1037,7 +1037,7 @@ <div class="viewcode-block" id="number_of_cliques"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.clique.number_of_cliques.html#networkx.algorithms.clique.number_of_cliques">[docs]</a><span class="k">def</span> <span class="nf">number_of_cliques</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cliques</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the number of maximal cliques for each node.</span> +<span class="w"> </span><span class="sd">"""Returns the number of maximal cliques for each node.</span> <span class="sd"> Returns a single or list depending on input nodes.</span> <span class="sd"> Optional list of cliques can be input if already computed.</span> @@ -1060,7 +1060,7 @@ <div class="viewcode-block" id="cliques_containing_node"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.clique.cliques_containing_node.html#networkx.algorithms.clique.cliques_containing_node">[docs]</a><span class="k">def</span> <span class="nf">cliques_containing_node</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cliques</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a list of cliques containing the given node.</span> +<span class="w"> </span><span class="sd">"""Returns a list of cliques containing the given node.</span> <span class="sd"> Returns a single list or list of lists depending on input nodes.</span> <span class="sd"> Optional list of cliques can be input if already computed.</span> @@ -1083,7 +1083,7 @@ <span class="k">class</span> <span class="nc">MaxWeightClique</span><span class="p">:</span> - <span class="sd">"""A class for the maximum weight clique algorithm.</span> +<span class="w"> </span><span class="sd">"""A class for the maximum weight clique algorithm.</span> <span class="sd"> This class is a helper for the `max_weight_clique` function. The class</span> <span class="sd"> should not normally be used directly.</span> @@ -1126,7 +1126,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">node_weights</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="n">weight</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">()}</span> <span class="k">def</span> <span class="nf">update_incumbent_if_improved</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">C_weight</span><span class="p">):</span> - <span class="sd">"""Update the incumbent if the node set C has greater weight.</span> +<span class="w"> </span><span class="sd">"""Update the incumbent if the node set C has greater weight.</span> <span class="sd"> C is assumed to be a clique.</span> <span class="sd"> """</span> @@ -1135,7 +1135,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">incumbent_weight</span> <span class="o">=</span> <span class="n">C_weight</span> <span class="k">def</span> <span class="nf">greedily_find_independent_set</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">P</span><span class="p">):</span> - <span class="sd">"""Greedily find an independent set of nodes from a set of</span> +<span class="w"> </span><span class="sd">"""Greedily find an independent set of nodes from a set of</span> <span class="sd"> nodes P."""</span> <span class="n">independent_set</span> <span class="o">=</span> <span class="p">[]</span> <span class="n">P</span> <span class="o">=</span> <span class="n">P</span><span class="p">[:]</span> @@ -1146,7 +1146,7 @@ <span class="k">return</span> <span class="n">independent_set</span> <span class="k">def</span> <span class="nf">find_branching_nodes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">P</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span> - <span class="sd">"""Find a set of nodes to branch on."""</span> +<span class="w"> </span><span class="sd">"""Find a set of nodes to branch on."""</span> <span class="n">residual_wt</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">node_weights</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">P</span><span class="p">}</span> <span class="n">total_wt</span> <span class="o">=</span> <span class="mi">0</span> <span class="n">P</span> <span class="o">=</span> <span class="n">P</span><span class="p">[:]</span> @@ -1162,7 +1162,7 @@ <span class="k">return</span> <span class="n">P</span> <span class="k">def</span> <span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">C_weight</span><span class="p">,</span> <span class="n">P</span><span class="p">):</span> - <span class="sd">"""Look for the best clique that contains all the nodes in C and zero or</span> +<span class="w"> </span><span class="sd">"""Look for the best clique that contains all the nodes in C and zero or</span> <span class="sd"> more of the nodes in P, backtracking if it can be shown that no such</span> <span class="sd"> clique has greater weight than the incumbent.</span> <span class="sd"> """</span> @@ -1177,7 +1177,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">new_C</span><span class="p">,</span> <span class="n">new_C_weight</span><span class="p">,</span> <span class="n">new_P</span><span class="p">)</span> <span class="k">def</span> <span class="nf">find_max_weight_clique</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Find a maximum weight clique."""</span> +<span class="w"> </span><span class="sd">"""Find a maximum weight clique."""</span> <span class="c1"># Sort nodes in reverse order of degree for speed</span> <span class="n">nodes</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="o">.</span><span class="n">degree</span><span class="p">(</span><span class="n">v</span><span class="p">),</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="n">nodes</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">nodes</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">node_weights</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="o">></span> <span class="mi">0</span><span class="p">]</span> @@ -1186,7 +1186,7 @@ <div class="viewcode-block" id="max_weight_clique"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.clique.max_weight_clique.html#networkx.algorithms.clique.max_weight_clique">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">max_weight_clique</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Find a maximum weight clique in G.</span> +<span class="w"> </span><span class="sd">"""Find a maximum weight clique in G.</span> <span class="sd"> A *clique* in a graph is a set of nodes such that every two distinct nodes</span> <span class="sd"> are adjacent. The *weight* of a clique is the sum of the weights of its</span> @@ -1288,7 +1288,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/cluster.html b/_modules/networkx/algorithms/cluster.html index dc215547..cf2fed2b 100644 --- a/_modules/networkx/algorithms/cluster.html +++ b/_modules/networkx/algorithms/cluster.html @@ -482,7 +482,7 @@ <div class="viewcode-block" id="triangles"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cluster.triangles.html#networkx.algorithms.cluster.triangles">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span><span class="p">(</span><span class="s2">"triangles"</span><span class="p">)</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">triangles</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute the number of triangles.</span> +<span class="w"> </span><span class="sd">"""Compute the number of triangles.</span> <span class="sd"> Finds the number of triangles that include a node as one vertex.</span> @@ -525,7 +525,7 @@ <span class="nd">@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">_triangles_and_degree_iter</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Return an iterator of (node, degree, triangles, generalized degree).</span> +<span class="w"> </span><span class="sd">"""Return an iterator of (node, degree, triangles, generalized degree).</span> <span class="sd"> This double counts triangles so you may want to divide by 2.</span> <span class="sd"> See degree(), triangles() and generalized_degree() for definitions</span> @@ -546,7 +546,7 @@ <span class="nd">@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">_weighted_triangles_and_degree_iter</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Return an iterator of (node, degree, weighted_triangles).</span> +<span class="w"> </span><span class="sd">"""Return an iterator of (node, degree, weighted_triangles).</span> <span class="sd"> Used for weighted clustering.</span> <span class="sd"> Note: this returns the geometric average weight of edges in the triangle.</span> @@ -587,7 +587,7 @@ <span class="nd">@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">_directed_triangles_and_degree_iter</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Return an iterator of</span> +<span class="w"> </span><span class="sd">"""Return an iterator of</span> <span class="sd"> (node, total_degree, reciprocal_degree, directed_triangles).</span> <span class="sd"> Used for directed clustering.</span> @@ -621,7 +621,7 @@ <span class="nd">@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">_directed_weighted_triangles_and_degree_iter</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Return an iterator of</span> +<span class="w"> </span><span class="sd">"""Return an iterator of</span> <span class="sd"> (node, total_degree, reciprocal_degree, directed_weighted_triangles).</span> <span class="sd"> Used for directed weighted clustering.</span> @@ -685,7 +685,7 @@ <div class="viewcode-block" id="average_clustering"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cluster.average_clustering.html#networkx.algorithms.cluster.average_clustering">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">"average_clustering"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">average_clustering</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">count_zeros</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the average clustering coefficient for the graph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the average clustering coefficient for the graph G.</span> <span class="sd"> The clustering coefficient for the graph is the average,</span> @@ -745,7 +745,7 @@ <div class="viewcode-block" id="clustering"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cluster.clustering.html#networkx.algorithms.cluster.clustering">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">"clustering"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">clustering</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the clustering coefficient for nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the clustering coefficient for nodes.</span> <span class="sd"> For unweighted graphs, the clustering of a node :math:`u`</span> <span class="sd"> is the fraction of possible triangles through that node that exist,</span> @@ -859,7 +859,7 @@ <div class="viewcode-block" id="transitivity"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cluster.transitivity.html#networkx.algorithms.cluster.transitivity">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span><span class="p">(</span><span class="s2">"transitivity"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">transitivity</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute graph transitivity, the fraction of all possible triangles</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute graph transitivity, the fraction of all possible triangles</span> <span class="sd"> present in G.</span> <span class="sd"> Possible triangles are identified by the number of "triads"</span> @@ -898,7 +898,7 @@ <div class="viewcode-block" id="square_clustering"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cluster.square_clustering.html#networkx.algorithms.cluster.square_clustering">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">"square_clustering"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">square_clustering</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the squares clustering coefficient for nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the squares clustering coefficient for nodes.</span> <span class="sd"> For each node return the fraction of possible squares that exist at</span> <span class="sd"> the node [1]_</span> @@ -977,7 +977,7 @@ <div class="viewcode-block" id="generalized_degree"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cluster.generalized_degree.html#networkx.algorithms.cluster.generalized_degree">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span><span class="p">(</span><span class="s2">"generalized_degree"</span><span class="p">)</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">generalized_degree</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the generalized degree for nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the generalized degree for nodes.</span> <span class="sd"> For each node, the generalized degree shows how many edges of given</span> <span class="sd"> triangle multiplicity the node is connected to. The triangle multiplicity</span> @@ -1087,7 +1087,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/coloring/equitable_coloring.html b/_modules/networkx/algorithms/coloring/equitable_coloring.html index 921c1e82..a5c4df25 100644 --- a/_modules/networkx/algorithms/coloring/equitable_coloring.html +++ b/_modules/networkx/algorithms/coloring/equitable_coloring.html @@ -473,7 +473,7 @@ <span class="k">def</span> <span class="nf">is_coloring</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">coloring</span><span class="p">):</span> - <span class="sd">"""Determine if the coloring is a valid coloring for the graph G."""</span> +<span class="w"> </span><span class="sd">"""Determine if the coloring is a valid coloring for the graph G."""</span> <span class="c1"># Verify that the coloring is valid.</span> <span class="k">for</span> <span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">d</span><span class="p">)</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="k">if</span> <span class="n">coloring</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">==</span> <span class="n">coloring</span><span class="p">[</span><span class="n">d</span><span class="p">]:</span> @@ -482,7 +482,7 @@ <span class="k">def</span> <span class="nf">is_equitable</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">coloring</span><span class="p">,</span> <span class="n">num_colors</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Determines if the coloring is valid and equitable for the graph G."""</span> +<span class="w"> </span><span class="sd">"""Determines if the coloring is valid and equitable for the graph G."""</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">is_coloring</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">coloring</span><span class="p">):</span> <span class="k">return</span> <span class="kc">False</span> @@ -538,7 +538,7 @@ <span class="k">def</span> <span class="nf">change_color</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">L</span><span class="p">):</span> - <span class="sd">"""Change the color of 'u' from X to Y and update N, H, F, C."""</span> +<span class="w"> </span><span class="sd">"""Change the color of 'u' from X to Y and update N, H, F, C."""</span> <span class="k">assert</span> <span class="n">F</span><span class="p">[</span><span class="n">u</span><span class="p">]</span> <span class="o">==</span> <span class="n">X</span> <span class="ow">and</span> <span class="n">X</span> <span class="o">!=</span> <span class="n">Y</span> <span class="c1"># Change the class of 'u' from X to Y</span> @@ -568,7 +568,7 @@ <span class="k">def</span> <span class="nf">move_witnesses</span><span class="p">(</span><span class="n">src_color</span><span class="p">,</span> <span class="n">dst_color</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">T_cal</span><span class="p">,</span> <span class="n">L</span><span class="p">):</span> - <span class="sd">"""Move witness along a path from src_color to dst_color."""</span> +<span class="w"> </span><span class="sd">"""Move witness along a path from src_color to dst_color."""</span> <span class="n">X</span> <span class="o">=</span> <span class="n">src_color</span> <span class="k">while</span> <span class="n">X</span> <span class="o">!=</span> <span class="n">dst_color</span><span class="p">:</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">T_cal</span><span class="p">[</span><span class="n">X</span><span class="p">]</span> @@ -579,7 +579,7 @@ <span class="k">def</span> <span class="nf">pad_graph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">num_colors</span><span class="p">):</span> - <span class="sd">"""Add a disconnected complete clique K_p such that the number of nodes in</span> +<span class="w"> </span><span class="sd">"""Add a disconnected complete clique K_p such that the number of nodes in</span> <span class="sd"> the graph becomes a multiple of `num_colors`.</span> <span class="sd"> Assumes that the graph's nodes are labelled using integers.</span> @@ -604,7 +604,7 @@ <span class="k">def</span> <span class="nf">procedure_P</span><span class="p">(</span><span class="n">V_minus</span><span class="p">,</span> <span class="n">V_plus</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">L</span><span class="p">,</span> <span class="n">excluded_colors</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Procedure P as described in the paper."""</span> +<span class="w"> </span><span class="sd">"""Procedure P as described in the paper."""</span> <span class="k">if</span> <span class="n">excluded_colors</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="n">excluded_colors</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span> @@ -859,7 +859,7 @@ <div class="viewcode-block" id="equitable_color"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.coloring.equitable_color.html#networkx.algorithms.coloring.equitable_color">[docs]</a><span class="k">def</span> <span class="nf">equitable_color</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">num_colors</span><span class="p">):</span> - <span class="sd">"""Provides equitable (r + 1)-coloring for nodes of G in O(r * n^2) time</span> +<span class="w"> </span><span class="sd">"""Provides equitable (r + 1)-coloring for nodes of G in O(r * n^2) time</span> <span class="sd"> if deg(G) <= r. The algorithm is described in [1]_.</span> <span class="sd"> Attempts to color a graph using r colors, where no neighbors of a node</span> @@ -919,7 +919,7 @@ <span class="k">if</span> <span class="n">r_</span> <span class="o">>=</span> <span class="n">num_colors</span><span class="p">:</span> <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXAlgorithmError</span><span class="p">(</span> <span class="sa">f</span><span class="s2">"Graph has maximum degree </span><span class="si">{</span><span class="n">r_</span><span class="si">}</span><span class="s2">, needs "</span> - <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">r_</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2"> (> </span><span class="si">{</span><span class="n">num_colors</span><span class="si">}</span><span class="s2">) colors for guaranteed coloring."</span> + <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">r_</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2"> (> </span><span class="si">{</span><span class="n">num_colors</span><span class="si">}</span><span class="s2">) colors for guaranteed coloring."</span> <span class="p">)</span> <span class="c1"># Ensure that the number of nodes in G is a multiple of (r + 1)</span> @@ -1027,7 +1027,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/coloring/greedy_coloring.html b/_modules/networkx/algorithms/coloring/greedy_coloring.html index 56243af4..56f93c01 100644 --- a/_modules/networkx/algorithms/coloring/greedy_coloring.html +++ b/_modules/networkx/algorithms/coloring/greedy_coloring.html @@ -484,7 +484,7 @@ <div class="viewcode-block" id="strategy_largest_first"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.coloring.strategy_largest_first.html#networkx.algorithms.coloring.strategy_largest_first">[docs]</a><span class="k">def</span> <span class="nf">strategy_largest_first</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">colors</span><span class="p">):</span> - <span class="sd">"""Returns a list of the nodes of ``G`` in decreasing order by</span> +<span class="w"> </span><span class="sd">"""Returns a list of the nodes of ``G`` in decreasing order by</span> <span class="sd"> degree.</span> <span class="sd"> ``G`` is a NetworkX graph. ``colors`` is ignored.</span> @@ -495,7 +495,7 @@ <div class="viewcode-block" id="strategy_random_sequential"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.coloring.strategy_random_sequential.html#networkx.algorithms.coloring.strategy_random_sequential">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">strategy_random_sequential</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">colors</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random permutation of the nodes of ``G`` as a list.</span> +<span class="w"> </span><span class="sd">"""Returns a random permutation of the nodes of ``G`` as a list.</span> <span class="sd"> ``G`` is a NetworkX graph. ``colors`` is ignored.</span> @@ -509,7 +509,7 @@ <div class="viewcode-block" id="strategy_smallest_last"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.coloring.strategy_smallest_last.html#networkx.algorithms.coloring.strategy_smallest_last">[docs]</a><span class="k">def</span> <span class="nf">strategy_smallest_last</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">colors</span><span class="p">):</span> - <span class="sd">"""Returns a deque of the nodes of ``G``, "smallest" last.</span> +<span class="w"> </span><span class="sd">"""Returns a deque of the nodes of ``G``, "smallest" last.</span> <span class="sd"> Specifically, the degrees of each node are tracked in a bucket queue.</span> <span class="sd"> From this, the node of minimum degree is repeatedly popped from the</span> @@ -566,7 +566,7 @@ <span class="k">def</span> <span class="nf">_maximal_independent_set</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns a maximal independent set of nodes in ``G`` by repeatedly</span> +<span class="w"> </span><span class="sd">"""Returns a maximal independent set of nodes in ``G`` by repeatedly</span> <span class="sd"> choosing an independent node of minimum degree (with respect to the</span> <span class="sd"> subgraph of unchosen nodes).</span> @@ -582,7 +582,7 @@ <div class="viewcode-block" id="strategy_independent_set"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.coloring.strategy_independent_set.html#networkx.algorithms.coloring.strategy_independent_set">[docs]</a><span class="k">def</span> <span class="nf">strategy_independent_set</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">colors</span><span class="p">):</span> - <span class="sd">"""Uses a greedy independent set removal strategy to determine the</span> +<span class="w"> </span><span class="sd">"""Uses a greedy independent set removal strategy to determine the</span> <span class="sd"> colors.</span> <span class="sd"> This function updates ``colors`` **in-place** and return ``None``,</span> @@ -606,7 +606,7 @@ <div class="viewcode-block" id="strategy_connected_sequential_bfs"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.coloring.strategy_connected_sequential_bfs.html#networkx.algorithms.coloring.strategy_connected_sequential_bfs">[docs]</a><span class="k">def</span> <span class="nf">strategy_connected_sequential_bfs</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">colors</span><span class="p">):</span> - <span class="sd">"""Returns an iterable over nodes in ``G`` in the order given by a</span> +<span class="w"> </span><span class="sd">"""Returns an iterable over nodes in ``G`` in the order given by a</span> <span class="sd"> breadth-first traversal.</span> <span class="sd"> The generated sequence has the property that for each node except</span> @@ -619,7 +619,7 @@ <div class="viewcode-block" id="strategy_connected_sequential_dfs"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.coloring.strategy_connected_sequential_dfs.html#networkx.algorithms.coloring.strategy_connected_sequential_dfs">[docs]</a><span class="k">def</span> <span class="nf">strategy_connected_sequential_dfs</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">colors</span><span class="p">):</span> - <span class="sd">"""Returns an iterable over nodes in ``G`` in the order given by a</span> +<span class="w"> </span><span class="sd">"""Returns an iterable over nodes in ``G`` in the order given by a</span> <span class="sd"> depth-first traversal.</span> <span class="sd"> The generated sequence has the property that for each node except</span> @@ -632,7 +632,7 @@ <div class="viewcode-block" id="strategy_connected_sequential"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.coloring.strategy_connected_sequential.html#networkx.algorithms.coloring.strategy_connected_sequential">[docs]</a><span class="k">def</span> <span class="nf">strategy_connected_sequential</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">colors</span><span class="p">,</span> <span class="n">traversal</span><span class="o">=</span><span class="s2">"bfs"</span><span class="p">):</span> - <span class="sd">"""Returns an iterable over nodes in ``G`` in the order given by a</span> +<span class="w"> </span><span class="sd">"""Returns an iterable over nodes in ``G`` in the order given by a</span> <span class="sd"> breadth-first or depth-first traversal.</span> <span class="sd"> ``traversal`` must be one of the strings ``'dfs'`` or ``'bfs'``,</span> @@ -664,7 +664,7 @@ <div class="viewcode-block" id="strategy_saturation_largest_first"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.coloring.strategy_saturation_largest_first.html#networkx.algorithms.coloring.strategy_saturation_largest_first">[docs]</a><span class="k">def</span> <span class="nf">strategy_saturation_largest_first</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">colors</span><span class="p">):</span> - <span class="sd">"""Iterates over all the nodes of ``G`` in "saturation order" (also</span> +<span class="w"> </span><span class="sd">"""Iterates over all the nodes of ``G`` in "saturation order" (also</span> <span class="sd"> known as "DSATUR").</span> <span class="sd"> ``G`` is a NetworkX graph. ``colors`` is a dictionary mapping nodes of</span> @@ -725,7 +725,7 @@ <div class="viewcode-block" id="greedy_color"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.coloring.greedy_color.html#networkx.algorithms.coloring.greedy_color">[docs]</a><span class="k">def</span> <span class="nf">greedy_color</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">strategy</span><span class="o">=</span><span class="s2">"largest_first"</span><span class="p">,</span> <span class="n">interchange</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Color a graph using various strategies of greedy graph coloring.</span> +<span class="w"> </span><span class="sd">"""Color a graph using various strategies of greedy graph coloring.</span> <span class="sd"> Attempts to color a graph using as few colors as possible, where no</span> <span class="sd"> neighbours of a node can have same color as the node itself. The</span> @@ -901,7 +901,7 @@ <span class="k">def</span> <span class="nf">_greedy_coloring_with_interchange</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span> - <span class="sd">"""Return a coloring for `orginal_graph` using interchange approach</span> +<span class="w"> </span><span class="sd">"""Return a coloring for `orginal_graph` using interchange approach</span> <span class="sd"> This procedure is an adaption of the algorithm described by [1]_,</span> <span class="sd"> and is an implementation of coloring with interchange. Please be</span> @@ -1075,7 +1075,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/communicability_alg.html b/_modules/networkx/algorithms/communicability_alg.html index 55c28089..1dfc1f31 100644 --- a/_modules/networkx/algorithms/communicability_alg.html +++ b/_modules/networkx/algorithms/communicability_alg.html @@ -473,7 +473,7 @@ <div class="viewcode-block" id="communicability"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.communicability_alg.communicability.html#networkx.algorithms.communicability_alg.communicability">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">communicability</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns communicability between all pairs of nodes in G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns communicability between all pairs of nodes in G.</span> <span class="sd"> The communicability between pairs of nodes in G is the sum of</span> <span class="sd"> walks of different lengths starting at node u and ending at node v.</span> @@ -554,7 +554,7 @@ <div class="viewcode-block" id="communicability_exp"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.communicability_alg.communicability_exp.html#networkx.algorithms.communicability_alg.communicability_exp">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">communicability_exp</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns communicability between all pairs of nodes in G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns communicability between all pairs of nodes in G.</span> <span class="sd"> Communicability between pair of node (u,v) of node in G is the sum of</span> <span class="sd"> walks of different lengths starting at node u and ending at node v.</span> @@ -673,7 +673,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/community/asyn_fluid.html b/_modules/networkx/algorithms/community/asyn_fluid.html index b5211f16..20e96907 100644 --- a/_modules/networkx/algorithms/community/asyn_fluid.html +++ b/_modules/networkx/algorithms/community/asyn_fluid.html @@ -475,7 +475,7 @@ <div class="viewcode-block" id="asyn_fluidc"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.community.asyn_fluid.asyn_fluidc.html#networkx.algorithms.community.asyn_fluid.asyn_fluidc">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">,</span> <span class="s2">"multigraph"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">asyn_fluidc</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns communities in `G` as detected by Fluid Communities algorithm.</span> +<span class="w"> </span><span class="sd">"""Returns communities in `G` as detected by Fluid Communities algorithm.</span> <span class="sd"> The asynchronous fluid communities algorithm is described in</span> <span class="sd"> [1]_. The algorithm is based on the simple idea of fluids interacting</span> @@ -659,7 +659,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/community/centrality.html b/_modules/networkx/algorithms/community/centrality.html index 45de9736..9b254f8f 100644 --- a/_modules/networkx/algorithms/community/centrality.html +++ b/_modules/networkx/algorithms/community/centrality.html @@ -469,7 +469,7 @@ <div class="viewcode-block" id="girvan_newman"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.community.centrality.girvan_newman.html#networkx.algorithms.community.centrality.girvan_newman">[docs]</a><span class="k">def</span> <span class="nf">girvan_newman</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">most_valuable_edge</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Finds communities in a graph using the Girvan–Newman method.</span> +<span class="w"> </span><span class="sd">"""Finds communities in a graph using the Girvan–Newman method.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -592,7 +592,7 @@ <span class="k">if</span> <span class="n">most_valuable_edge</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="k">def</span> <span class="nf">most_valuable_edge</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the edge with the highest betweenness centrality</span> +<span class="w"> </span><span class="sd">"""Returns the edge with the highest betweenness centrality</span> <span class="sd"> in the graph `G`.</span> <span class="sd"> """</span> @@ -611,7 +611,7 @@ <span class="k">def</span> <span class="nf">_without_most_central_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">most_valuable_edge</span><span class="p">):</span> - <span class="sd">"""Returns the connected components of the graph that results from</span> +<span class="w"> </span><span class="sd">"""Returns the connected components of the graph that results from</span> <span class="sd"> repeatedly removing the most "valuable" edge in the graph.</span> <span class="sd"> `G` must be a non-empty graph. This function modifies the graph `G`</span> @@ -682,7 +682,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/community/community_utils.html b/_modules/networkx/algorithms/community/community_utils.html index 45ed92e2..6fd1f51c 100644 --- a/_modules/networkx/algorithms/community/community_utils.html +++ b/_modules/networkx/algorithms/community/community_utils.html @@ -467,7 +467,7 @@ <div class="viewcode-block" id="is_partition"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.community.community_utils.is_partition.html#networkx.algorithms.community.community_utils.is_partition">[docs]</a><span class="k">def</span> <span class="nf">is_partition</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">communities</span><span class="p">):</span> - <span class="sd">"""Returns *True* if `communities` is a partition of the nodes of `G`.</span> +<span class="w"> </span><span class="sd">"""Returns *True* if `communities` is a partition of the nodes of `G`.</span> <span class="sd"> A partition of a universe set is a family of pairwise disjoint sets</span> <span class="sd"> whose union is the entire universe set.</span> @@ -539,7 +539,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/community/kclique.html b/_modules/networkx/algorithms/community/kclique.html index d7fec04a..22546db6 100644 --- a/_modules/networkx/algorithms/community/kclique.html +++ b/_modules/networkx/algorithms/community/kclique.html @@ -469,7 +469,7 @@ <div class="viewcode-block" id="k_clique_communities"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.community.kclique.k_clique_communities.html#networkx.algorithms.community.kclique.k_clique_communities">[docs]</a><span class="k">def</span> <span class="nf">k_clique_communities</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">cliques</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Find k-clique communities in graph using the percolation method.</span> +<span class="w"> </span><span class="sd">"""Find k-clique communities in graph using the percolation method.</span> <span class="sd"> A k-clique community is the union of all cliques of size k that</span> <span class="sd"> can be reached through adjacent (sharing k-1 nodes) k-cliques.</span> @@ -591,7 +591,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/community/kernighan_lin.html b/_modules/networkx/algorithms/community/kernighan_lin.html index f7bc53cc..19f3c947 100644 --- a/_modules/networkx/algorithms/community/kernighan_lin.html +++ b/_modules/networkx/algorithms/community/kernighan_lin.html @@ -473,7 +473,7 @@ <span class="k">def</span> <span class="nf">_kernighan_lin_sweep</span><span class="p">(</span><span class="n">edges</span><span class="p">,</span> <span class="n">side</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> This is a modified form of Kernighan-Lin, which moves single nodes at a</span> <span class="sd"> time, alternating between sides to keep the bisection balanced. We keep</span> <span class="sd"> two min-heaps of swap costs to make optimal-next-move selection fast.</span> @@ -506,7 +506,7 @@ <div class="viewcode-block" id="kernighan_lin_bisection"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection.html#networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">kernighan_lin_bisection</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">partition</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Partition a graph into two blocks using the Kernighan–Lin</span> +<span class="w"> </span><span class="sd">"""Partition a graph into two blocks using the Kernighan–Lin</span> <span class="sd"> algorithm.</span> <span class="sd"> This algorithm partitions a network into two sets by iteratively</span> @@ -649,7 +649,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/community/label_propagation.html b/_modules/networkx/algorithms/community/label_propagation.html index eabd86e8..31376342 100644 --- a/_modules/networkx/algorithms/community/label_propagation.html +++ b/_modules/networkx/algorithms/community/label_propagation.html @@ -474,7 +474,7 @@ <div class="viewcode-block" id="asyn_lpa_communities"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.community.label_propagation.asyn_lpa_communities.html#networkx.algorithms.community.label_propagation.asyn_lpa_communities">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">asyn_lpa_communities</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns communities in `G` as detected by asynchronous label</span> +<span class="w"> </span><span class="sd">"""Returns communities in `G` as detected by asynchronous label</span> <span class="sd"> propagation.</span> <span class="sd"> The asynchronous label propagation algorithm is described in</span> @@ -570,7 +570,7 @@ <div class="viewcode-block" id="label_propagation_communities"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.community.label_propagation.label_propagation_communities.html#networkx.algorithms.community.label_propagation.label_propagation_communities">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">label_propagation_communities</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Generates community sets determined by label propagation</span> +<span class="w"> </span><span class="sd">"""Generates community sets determined by label propagation</span> <span class="sd"> Finds communities in `G` using a semi-synchronous label propagation</span> <span class="sd"> method [1]_. This method combines the advantages of both the synchronous</span> @@ -614,7 +614,7 @@ <span class="k">def</span> <span class="nf">_color_network</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Colors the network so that neighboring nodes all have distinct colors.</span> +<span class="w"> </span><span class="sd">"""Colors the network so that neighboring nodes all have distinct colors.</span> <span class="sd"> Returns a dict keyed by color to a set of nodes with that color.</span> <span class="sd"> """</span> @@ -629,7 +629,7 @@ <span class="k">def</span> <span class="nf">_labeling_complete</span><span class="p">(</span><span class="n">labeling</span><span class="p">,</span> <span class="n">G</span><span class="p">):</span> - <span class="sd">"""Determines whether or not LPA is done.</span> +<span class="w"> </span><span class="sd">"""Determines whether or not LPA is done.</span> <span class="sd"> Label propagation is complete when all nodes have a label that is</span> <span class="sd"> in the set of highest frequency labels amongst its neighbors.</span> @@ -642,7 +642,7 @@ <span class="k">def</span> <span class="nf">_most_frequent_labels</span><span class="p">(</span><span class="n">node</span><span class="p">,</span> <span class="n">labeling</span><span class="p">,</span> <span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns a set of all labels with maximum frequency in `labeling`.</span> +<span class="w"> </span><span class="sd">"""Returns a set of all labels with maximum frequency in `labeling`.</span> <span class="sd"> Input `labeling` should be a dict keyed by node to labels.</span> <span class="sd"> """</span> @@ -658,7 +658,7 @@ <span class="k">def</span> <span class="nf">_update_label</span><span class="p">(</span><span class="n">node</span><span class="p">,</span> <span class="n">labeling</span><span class="p">,</span> <span class="n">G</span><span class="p">):</span> - <span class="sd">"""Updates the label of a node using the Prec-Max tie breaking algorithm</span> +<span class="w"> </span><span class="sd">"""Updates the label of a node using the Prec-Max tie breaking algorithm</span> <span class="sd"> The algorithm is explained in: 'Community Detection via Semi-Synchronous</span> <span class="sd"> Label Propagation Algorithms' Cordasco and Gargano, 2011</span> @@ -721,7 +721,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/community/louvain.html b/_modules/networkx/algorithms/community/louvain.html index f8bd0603..89674161 100644 --- a/_modules/networkx/algorithms/community/louvain.html +++ b/_modules/networkx/algorithms/community/louvain.html @@ -477,7 +477,7 @@ <span class="k">def</span> <span class="nf">louvain_communities</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">resolution</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">0.0000001</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Find the best partition of a graph using the Louvain Community Detection</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Find the best partition of a graph using the Louvain Community Detection</span> <span class="sd"> Algorithm.</span> <span class="sd"> Louvain Community Detection Algorithm is a simple method to extract the community</span> @@ -579,7 +579,7 @@ <span class="k">def</span> <span class="nf">louvain_partitions</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">resolution</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">0.0000001</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Yields partitions for each level of the Louvain Community Detection Algorithm</span> +<span class="w"> </span><span class="sd">"""Yields partitions for each level of the Louvain Community Detection Algorithm</span> <span class="sd"> Louvain Community Detection Algorithm is a simple method to extract the community</span> <span class="sd"> structure of a network. This is a heuristic method based on modularity optimization. [1]_</span> @@ -657,7 +657,7 @@ <span class="k">def</span> <span class="nf">_one_level</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">partition</span><span class="p">,</span> <span class="n">resolution</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">is_directed</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Calculate one level of the Louvain partitions tree</span> +<span class="w"> </span><span class="sd">"""Calculate one level of the Louvain partitions tree</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -763,7 +763,7 @@ <span class="k">def</span> <span class="nf">_neighbor_weights</span><span class="p">(</span><span class="n">nbrs</span><span class="p">,</span> <span class="n">node2com</span><span class="p">):</span> - <span class="sd">"""Calculate weights between node and its neighbor communities.</span> +<span class="w"> </span><span class="sd">"""Calculate weights between node and its neighbor communities.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -780,7 +780,7 @@ <span class="k">def</span> <span class="nf">_gen_graph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">partition</span><span class="p">):</span> - <span class="sd">"""Generate a new graph based on the partitions of a given graph"""</span> +<span class="w"> </span><span class="sd">"""Generate a new graph based on the partitions of a given graph"""</span> <span class="n">H</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="vm">__class__</span><span class="p">()</span> <span class="n">node2com</span> <span class="o">=</span> <span class="p">{}</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">part</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">partition</span><span class="p">):</span> @@ -800,7 +800,7 @@ <span class="k">def</span> <span class="nf">_convert_multigraph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">is_directed</span><span class="p">):</span> - <span class="sd">"""Convert a Multigraph to normal Graph"""</span> +<span class="w"> </span><span class="sd">"""Convert a Multigraph to normal Graph"""</span> <span class="k">if</span> <span class="n">is_directed</span><span class="p">:</span> <span class="n">H</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="k">else</span><span class="p">:</span> @@ -863,7 +863,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/community/lukes.html b/_modules/networkx/algorithms/community/lukes.html index ed442b61..a5912a24 100644 --- a/_modules/networkx/algorithms/community/lukes.html +++ b/_modules/networkx/algorithms/community/lukes.html @@ -490,7 +490,7 @@ <div class="viewcode-block" id="lukes_partitioning"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.community.lukes.lukes_partitioning.html#networkx.algorithms.community.lukes.lukes_partitioning">[docs]</a><span class="k">def</span> <span class="nf">lukes_partitioning</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">max_size</span><span class="p">,</span> <span class="n">node_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">edge_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Optimal partitioning of a weighted tree using the Lukes algorithm.</span> +<span class="w"> </span><span class="sd">"""Optimal partitioning of a weighted tree using the Lukes algorithm.</span> <span class="sd"> This algorithm partitions a connected, acyclic graph featuring integer</span> <span class="sd"> node weights and float edge weights. The resulting clusters are such</span> @@ -739,7 +739,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/community/modularity_max.html b/_modules/networkx/algorithms/community/modularity_max.html index 686585de..5d49fa9a 100644 --- a/_modules/networkx/algorithms/community/modularity_max.html +++ b/_modules/networkx/algorithms/community/modularity_max.html @@ -477,7 +477,7 @@ <span class="k">def</span> <span class="nf">_greedy_modularity_communities_generator</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">resolution</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Yield community partitions of G and the modularity change at each step.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Yield community partitions of G and the modularity change at each step.</span> <span class="sd"> This function performs Clauset-Newman-Moore greedy modularity maximization [2]_</span> <span class="sd"> At each step of the process it yields the change in modularity that will occur in</span> @@ -693,7 +693,7 @@ <span class="n">cutoff</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">best_n</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Find communities in G using greedy modularity maximization.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Find communities in G using greedy modularity maximization.</span> <span class="sd"> This function uses Clauset-Newman-Moore greedy modularity maximization [2]_</span> <span class="sd"> to find the community partition with the largest modularity.</span> @@ -817,7 +817,7 @@ <div class="viewcode-block" id="naive_greedy_modularity_communities"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities.html#networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">naive_greedy_modularity_communities</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">resolution</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Find communities in G using greedy modularity maximization.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Find communities in G using greedy modularity maximization.</span> <span class="sd"> This implementation is O(n^4), much slower than alternatives, but it is</span> <span class="sd"> provided as an easy-to-understand reference implementation.</span> @@ -960,7 +960,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/community/quality.html b/_modules/networkx/algorithms/community/quality.html index 9f1862e1..42b8008b 100644 --- a/_modules/networkx/algorithms/community/quality.html +++ b/_modules/networkx/algorithms/community/quality.html @@ -478,7 +478,7 @@ <span class="k">class</span> <span class="nc">NotAPartition</span><span class="p">(</span><span class="n">NetworkXError</span><span class="p">):</span> - <span class="sd">"""Raised if a given collection is not a partition."""</span> +<span class="w"> </span><span class="sd">"""Raised if a given collection is not a partition."""</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G</span><span class="p">,</span> <span class="n">collection</span><span class="p">):</span> <span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">collection</span><span class="si">}</span><span class="s2"> is not a valid partition of the graph </span><span class="si">{</span><span class="n">G</span><span class="si">}</span><span class="s2">"</span> @@ -486,7 +486,7 @@ <span class="k">def</span> <span class="nf">_require_partition</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">partition</span><span class="p">):</span> - <span class="sd">"""Decorator to check that a valid partition is input to a function</span> +<span class="w"> </span><span class="sd">"""Decorator to check that a valid partition is input to a function</span> <span class="sd"> Raises :exc:`networkx.NetworkXError` if the partition is not valid.</span> @@ -524,7 +524,7 @@ <span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">intra_community_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">partition</span><span class="p">):</span> - <span class="sd">"""Returns the number of intra-community edges for a partition of `G`.</span> +<span class="w"> </span><span class="sd">"""Returns the number of intra-community edges for a partition of `G`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -542,7 +542,7 @@ <span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">inter_community_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">partition</span><span class="p">):</span> - <span class="sd">"""Returns the number of inter-community edges for a partition of `G`.</span> +<span class="w"> </span><span class="sd">"""Returns the number of inter-community edges for a partition of `G`.</span> <span class="sd"> according to the given</span> <span class="sd"> partition of the nodes of `G`.</span> @@ -573,7 +573,7 @@ <span class="k">def</span> <span class="nf">inter_community_non_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">partition</span><span class="p">):</span> - <span class="sd">"""Returns the number of inter-community non-edges according to the</span> +<span class="w"> </span><span class="sd">"""Returns the number of inter-community non-edges according to the</span> <span class="sd"> given partition of the nodes of `G`.</span> <span class="sd"> Parameters</span> @@ -605,7 +605,7 @@ <div class="viewcode-block" id="modularity"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.community.quality.modularity.html#networkx.algorithms.community.quality.modularity">[docs]</a><span class="k">def</span> <span class="nf">modularity</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">communities</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">resolution</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the modularity of the given partition of the graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the modularity of the given partition of the graph.</span> <span class="sd"> Modularity is defined in [1]_ as</span> @@ -717,7 +717,7 @@ <div class="viewcode-block" id="partition_quality"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.community.quality.partition_quality.html#networkx.algorithms.community.quality.partition_quality">[docs]</a><span class="nd">@require_partition</span> <span class="k">def</span> <span class="nf">partition_quality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">partition</span><span class="p">):</span> - <span class="sd">"""Returns the coverage and performance of a partition of G.</span> +<span class="w"> </span><span class="sd">"""Returns the coverage and performance of a partition of G.</span> <span class="sd"> The *coverage* of a partition is the ratio of the number of</span> <span class="sd"> intra-community edges to the total number of edges in the graph.</span> @@ -854,7 +854,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/components/attracting.html b/_modules/networkx/algorithms/components/attracting.html index db3593fe..d994b60b 100644 --- a/_modules/networkx/algorithms/components/attracting.html +++ b/_modules/networkx/algorithms/components/attracting.html @@ -474,7 +474,7 @@ <div class="viewcode-block" id="attracting_components"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.attracting_components.html#networkx.algorithms.components.attracting_components">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">attracting_components</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Generates the attracting components in `G`.</span> +<span class="w"> </span><span class="sd">"""Generates the attracting components in `G`.</span> <span class="sd"> An attracting component in a directed graph `G` is a strongly connected</span> <span class="sd"> component with the property that a random walker on the graph will never</span> @@ -517,7 +517,7 @@ <div class="viewcode-block" id="number_attracting_components"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.number_attracting_components.html#networkx.algorithms.components.number_attracting_components">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">number_attracting_components</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the number of attracting components in `G`.</span> +<span class="w"> </span><span class="sd">"""Returns the number of attracting components in `G`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -545,7 +545,7 @@ <div class="viewcode-block" id="is_attracting_component"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.is_attracting_component.html#networkx.algorithms.components.is_attracting_component">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">is_attracting_component</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns True if `G` consists of a single attracting component.</span> +<span class="w"> </span><span class="sd">"""Returns True if `G` consists of a single attracting component.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -623,7 +623,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/components/biconnected.html b/_modules/networkx/algorithms/components/biconnected.html index 73cb04ef..61be38ad 100644 --- a/_modules/networkx/algorithms/components/biconnected.html +++ b/_modules/networkx/algorithms/components/biconnected.html @@ -476,7 +476,7 @@ <div class="viewcode-block" id="is_biconnected"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.is_biconnected.html#networkx.algorithms.components.is_biconnected">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">is_biconnected</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns True if the graph is biconnected, False otherwise.</span> +<span class="w"> </span><span class="sd">"""Returns True if the graph is biconnected, False otherwise.</span> <span class="sd"> A graph is biconnected if, and only if, it cannot be disconnected by</span> <span class="sd"> removing only one node (and all edges incident on that node). If</span> @@ -556,7 +556,7 @@ <div class="viewcode-block" id="biconnected_component_edges"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.biconnected_component_edges.html#networkx.algorithms.components.biconnected_component_edges">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">biconnected_component_edges</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns a generator of lists of edges, one list for each biconnected</span> +<span class="w"> </span><span class="sd">"""Returns a generator of lists of edges, one list for each biconnected</span> <span class="sd"> component of the input graph.</span> <span class="sd"> Biconnected components are maximal subgraphs such that the removal of a</span> @@ -628,7 +628,7 @@ <div class="viewcode-block" id="biconnected_components"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.biconnected_components.html#networkx.algorithms.components.biconnected_components">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">biconnected_components</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns a generator of sets of nodes, one set for each biconnected</span> +<span class="w"> </span><span class="sd">"""Returns a generator of sets of nodes, one set for each biconnected</span> <span class="sd"> component of the graph</span> <span class="sd"> Biconnected components are maximal subgraphs such that the removal of a</span> @@ -720,7 +720,7 @@ <div class="viewcode-block" id="articulation_points"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.articulation_points.html#networkx.algorithms.components.articulation_points">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">articulation_points</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Yield the articulation points, or cut vertices, of a graph.</span> +<span class="w"> </span><span class="sd">"""Yield the articulation points, or cut vertices, of a graph.</span> <span class="sd"> An articulation point or cut vertex is any node whose removal (along with</span> <span class="sd"> all its incident edges) increases the number of connected components of</span> @@ -900,7 +900,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/components/connected.html b/_modules/networkx/algorithms/components/connected.html index e875fdbd..59ae2a4d 100644 --- a/_modules/networkx/algorithms/components/connected.html +++ b/_modules/networkx/algorithms/components/connected.html @@ -478,7 +478,7 @@ <div class="viewcode-block" id="connected_components"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.connected_components.html#networkx.algorithms.components.connected_components">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">connected_components</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Generate connected components.</span> +<span class="w"> </span><span class="sd">"""Generate connected components.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -532,7 +532,7 @@ <div class="viewcode-block" id="number_connected_components"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.number_connected_components.html#networkx.algorithms.components.number_connected_components">[docs]</a><span class="k">def</span> <span class="nf">number_connected_components</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the number of connected components.</span> +<span class="w"> </span><span class="sd">"""Returns the number of connected components.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -566,7 +566,7 @@ <div class="viewcode-block" id="is_connected"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.is_connected.html#networkx.algorithms.components.is_connected">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">is_connected</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns True if the graph is connected, False otherwise.</span> +<span class="w"> </span><span class="sd">"""Returns True if the graph is connected, False otherwise.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -611,7 +611,7 @@ <div class="viewcode-block" id="node_connected_component"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.node_connected_component.html#networkx.algorithms.components.node_connected_component">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">node_connected_component</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span> - <span class="sd">"""Returns the set of nodes in the component of graph containing node n.</span> +<span class="w"> </span><span class="sd">"""Returns the set of nodes in the component of graph containing node n.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -650,7 +650,7 @@ <span class="k">def</span> <span class="nf">_plain_bfs</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">):</span> - <span class="sd">"""A fast BFS node generator"""</span> +<span class="w"> </span><span class="sd">"""A fast BFS node generator"""</span> <span class="n">G_adj</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">adj</span> <span class="n">seen</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span> <span class="n">nextlevel</span> <span class="o">=</span> <span class="p">{</span><span class="n">source</span><span class="p">}</span> @@ -713,7 +713,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/components/semiconnected.html b/_modules/networkx/algorithms/components/semiconnected.html index 72b0e3bb..8ceab779 100644 --- a/_modules/networkx/algorithms/components/semiconnected.html +++ b/_modules/networkx/algorithms/components/semiconnected.html @@ -470,7 +470,7 @@ <div class="viewcode-block" id="is_semiconnected"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.is_semiconnected.html#networkx.algorithms.components.is_semiconnected">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">is_semiconnected</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">topo_order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns True if the graph is semiconnected, False otherwise.</span> +<span class="w"> </span><span class="sd">"""Returns True if the graph is semiconnected, False otherwise.</span> <span class="sd"> A graph is semiconnected if, and only if, for any pair of nodes, either one</span> <span class="sd"> is reachable from the other, or they are mutually reachable.</span> @@ -576,7 +576,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/components/strongly_connected.html b/_modules/networkx/algorithms/components/strongly_connected.html index cc351e59..1232f536 100644 --- a/_modules/networkx/algorithms/components/strongly_connected.html +++ b/_modules/networkx/algorithms/components/strongly_connected.html @@ -478,7 +478,7 @@ <div class="viewcode-block" id="strongly_connected_components"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.strongly_connected_components.html#networkx.algorithms.components.strongly_connected_components">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">strongly_connected_components</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Generate nodes in strongly connected components of graph.</span> +<span class="w"> </span><span class="sd">"""Generate nodes in strongly connected components of graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -576,7 +576,7 @@ <div class="viewcode-block" id="kosaraju_strongly_connected_components"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.kosaraju_strongly_connected_components.html#networkx.algorithms.components.kosaraju_strongly_connected_components">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">kosaraju_strongly_connected_components</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generate nodes in strongly connected components of graph.</span> +<span class="w"> </span><span class="sd">"""Generate nodes in strongly connected components of graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -637,7 +637,7 @@ <div class="viewcode-block" id="strongly_connected_components_recursive"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.strongly_connected_components_recursive.html#networkx.algorithms.components.strongly_connected_components_recursive">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">strongly_connected_components_recursive</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Generate nodes in strongly connected components of graph.</span> +<span class="w"> </span><span class="sd">"""Generate nodes in strongly connected components of graph.</span> <span class="sd"> Recursive version of algorithm.</span> @@ -730,7 +730,7 @@ <div class="viewcode-block" id="number_strongly_connected_components"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.number_strongly_connected_components.html#networkx.algorithms.components.number_strongly_connected_components">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">number_strongly_connected_components</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns number of strongly connected components in graph.</span> +<span class="w"> </span><span class="sd">"""Returns number of strongly connected components in graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -768,7 +768,7 @@ <div class="viewcode-block" id="is_strongly_connected"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.is_strongly_connected.html#networkx.algorithms.components.is_strongly_connected">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">is_strongly_connected</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Test directed graph for strong connectivity.</span> +<span class="w"> </span><span class="sd">"""Test directed graph for strong connectivity.</span> <span class="sd"> A directed graph is strongly connected if and only if every vertex in</span> <span class="sd"> the graph is reachable from every other vertex.</span> @@ -811,7 +811,7 @@ <span class="sd"> """</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXPointlessConcept</span><span class="p">(</span> - <span class="sd">"""Connectivity is undefined for the null graph."""</span> +<span class="w"> </span><span class="sd">"""Connectivity is undefined for the null graph."""</span> <span class="p">)</span> <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">strongly_connected_components</span><span class="p">(</span><span class="n">G</span><span class="p">)))</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">G</span><span class="p">)</span></div> @@ -819,7 +819,7 @@ <div class="viewcode-block" id="condensation"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.condensation.html#networkx.algorithms.components.condensation">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">condensation</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">scc</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the condensation of G.</span> +<span class="w"> </span><span class="sd">"""Returns the condensation of G.</span> <span class="sd"> The condensation of G is the graph with each of the strongly connected</span> <span class="sd"> components contracted into a single node.</span> @@ -950,7 +950,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/components/weakly_connected.html b/_modules/networkx/algorithms/components/weakly_connected.html index d00efaf7..3e1b4d94 100644 --- a/_modules/networkx/algorithms/components/weakly_connected.html +++ b/_modules/networkx/algorithms/components/weakly_connected.html @@ -475,7 +475,7 @@ <div class="viewcode-block" id="weakly_connected_components"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.weakly_connected_components.html#networkx.algorithms.components.weakly_connected_components">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">weakly_connected_components</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Generate weakly connected components of G.</span> +<span class="w"> </span><span class="sd">"""Generate weakly connected components of G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -530,7 +530,7 @@ <div class="viewcode-block" id="number_weakly_connected_components"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.number_weakly_connected_components.html#networkx.algorithms.components.number_weakly_connected_components">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">number_weakly_connected_components</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the number of weakly connected components in G.</span> +<span class="w"> </span><span class="sd">"""Returns the number of weakly connected components in G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -569,7 +569,7 @@ <div class="viewcode-block" id="is_weakly_connected"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.components.is_weakly_connected.html#networkx.algorithms.components.is_weakly_connected">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">is_weakly_connected</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Test directed graph for weak connectivity.</span> +<span class="w"> </span><span class="sd">"""Test directed graph for weak connectivity.</span> <span class="sd"> A directed graph is weakly connected if and only if the graph</span> <span class="sd"> is connected when the direction of the edge between nodes is ignored.</span> @@ -618,14 +618,14 @@ <span class="sd"> """</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXPointlessConcept</span><span class="p">(</span> - <span class="sd">"""Connectivity is undefined for the null graph."""</span> +<span class="w"> </span><span class="sd">"""Connectivity is undefined for the null graph."""</span> <span class="p">)</span> <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">weakly_connected_components</span><span class="p">(</span><span class="n">G</span><span class="p">)))</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">G</span><span class="p">)</span></div> <span class="k">def</span> <span class="nf">_plain_bfs</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">):</span> - <span class="sd">"""A fast BFS node generator</span> +<span class="w"> </span><span class="sd">"""A fast BFS node generator</span> <span class="sd"> The direction of the edge between nodes is ignored.</span> @@ -697,7 +697,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/connectivity/connectivity.html b/_modules/networkx/algorithms/connectivity/connectivity.html index 23d5b4f7..0d39d855 100644 --- a/_modules/networkx/algorithms/connectivity/connectivity.html +++ b/_modules/networkx/algorithms/connectivity/connectivity.html @@ -497,7 +497,7 @@ <div class="viewcode-block" id="local_node_connectivity"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.connectivity.local_node_connectivity.html#networkx.algorithms.connectivity.connectivity.local_node_connectivity">[docs]</a><span class="k">def</span> <span class="nf">local_node_connectivity</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">auxiliary</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">residual</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Computes local node connectivity for nodes s and t.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Computes local node connectivity for nodes s and t.</span> <span class="sd"> Local node connectivity for two non adjacent nodes s and t is the</span> <span class="sd"> minimum number of nodes that must be removed (along with their incident</span> @@ -673,7 +673,7 @@ <div class="viewcode-block" id="node_connectivity"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.connectivity.node_connectivity.html#networkx.algorithms.connectivity.connectivity.node_connectivity">[docs]</a><span class="k">def</span> <span class="nf">node_connectivity</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">t</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns node connectivity for a graph or digraph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns node connectivity for a graph or digraph G.</span> <span class="sd"> Node connectivity is equal to the minimum number of nodes that</span> <span class="sd"> must be removed to disconnect G or render it trivial. If source</span> @@ -813,7 +813,7 @@ <div class="viewcode-block" id="average_node_connectivity"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.connectivity.average_node_connectivity.html#networkx.algorithms.connectivity.connectivity.average_node_connectivity">[docs]</a><span class="k">def</span> <span class="nf">average_node_connectivity</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the average connectivity of a graph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the average connectivity of a graph G.</span> <span class="sd"> The average connectivity `\bar{\kappa}` of a graph G is the average</span> <span class="sd"> of local node connectivity over all pairs of nodes of G [1]_ .</span> @@ -881,7 +881,7 @@ <div class="viewcode-block" id="all_pairs_node_connectivity"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity.html#networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity">[docs]</a><span class="k">def</span> <span class="nf">all_pairs_node_connectivity</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nbunch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute node connectivity between all pairs of nodes of G.</span> +<span class="w"> </span><span class="sd">"""Compute node connectivity between all pairs of nodes of G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -950,7 +950,7 @@ <div class="viewcode-block" id="local_edge_connectivity"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.connectivity.local_edge_connectivity.html#networkx.algorithms.connectivity.connectivity.local_edge_connectivity">[docs]</a><span class="k">def</span> <span class="nf">local_edge_connectivity</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">auxiliary</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">residual</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns local edge connectivity for nodes s and t in G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns local edge connectivity for nodes s and t in G.</span> <span class="sd"> Local edge connectivity for two nodes s and t is the minimum number</span> <span class="sd"> of edges that must be removed to disconnect them.</span> @@ -1109,7 +1109,7 @@ <div class="viewcode-block" id="edge_connectivity"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.connectivity.edge_connectivity.html#networkx.algorithms.connectivity.connectivity.edge_connectivity">[docs]</a><span class="k">def</span> <span class="nf">edge_connectivity</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">t</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the edge connectivity of the graph or digraph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the edge connectivity of the graph or digraph G.</span> <span class="sd"> The edge connectivity is equal to the minimum number of edges that</span> <span class="sd"> must be removed to disconnect G or render it trivial. If source</span> @@ -1324,7 +1324,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/connectivity/cuts.html b/_modules/networkx/algorithms/connectivity/cuts.html index 543c02eb..56616899 100644 --- a/_modules/networkx/algorithms/connectivity/cuts.html +++ b/_modules/networkx/algorithms/connectivity/cuts.html @@ -485,7 +485,7 @@ <div class="viewcode-block" id="minimum_st_edge_cut"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.cuts.minimum_st_edge_cut.html#networkx.algorithms.connectivity.cuts.minimum_st_edge_cut">[docs]</a><span class="k">def</span> <span class="nf">minimum_st_edge_cut</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">auxiliary</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">residual</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the edges of the cut-set of a minimum (s, t)-cut.</span> +<span class="w"> </span><span class="sd">"""Returns the edges of the cut-set of a minimum (s, t)-cut.</span> <span class="sd"> This function returns the set of edges of minimum cardinality that,</span> <span class="sd"> if removed, would destroy all paths among source and target in G.</span> @@ -617,7 +617,7 @@ <div class="viewcode-block" id="minimum_st_node_cut"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.cuts.minimum_st_node_cut.html#networkx.algorithms.connectivity.cuts.minimum_st_node_cut">[docs]</a><span class="k">def</span> <span class="nf">minimum_st_node_cut</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">auxiliary</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">residual</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a set of nodes of minimum cardinality that disconnect source</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a set of nodes of minimum cardinality that disconnect source</span> <span class="sd"> from target in G.</span> <span class="sd"> This function returns the set of nodes of minimum cardinality that,</span> @@ -755,7 +755,7 @@ <div class="viewcode-block" id="minimum_node_cut"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.cuts.minimum_node_cut.html#networkx.algorithms.connectivity.cuts.minimum_node_cut">[docs]</a><span class="k">def</span> <span class="nf">minimum_node_cut</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">t</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a set of nodes of minimum cardinality that disconnects G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a set of nodes of minimum cardinality that disconnects G.</span> <span class="sd"> If source and target nodes are provided, this function returns the</span> <span class="sd"> set of nodes of minimum cardinality that, if removed, would destroy</span> @@ -900,7 +900,7 @@ <div class="viewcode-block" id="minimum_edge_cut"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.cuts.minimum_edge_cut.html#networkx.algorithms.connectivity.cuts.minimum_edge_cut">[docs]</a><span class="k">def</span> <span class="nf">minimum_edge_cut</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">t</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a set of edges of minimum cardinality that disconnects G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a set of edges of minimum cardinality that disconnects G.</span> <span class="sd"> If source and target nodes are provided, this function returns the</span> <span class="sd"> set of edges of minimum cardinality that, if removed, would break</span> @@ -1111,7 +1111,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/connectivity/disjoint_paths.html b/_modules/networkx/algorithms/connectivity/disjoint_paths.html index 2aa39364..9c0471c4 100644 --- a/_modules/networkx/algorithms/connectivity/disjoint_paths.html +++ b/_modules/networkx/algorithms/connectivity/disjoint_paths.html @@ -485,7 +485,7 @@ <div class="viewcode-block" id="edge_disjoint_paths"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths.html#networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths">[docs]</a><span class="k">def</span> <span class="nf">edge_disjoint_paths</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">auxiliary</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">residual</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Returns the edges disjoint paths between source and target.</span> +<span class="w"> </span><span class="sd">"""Returns the edges disjoint paths between source and target.</span> <span class="sd"> Edge disjoint paths are paths that do not share any edge. The</span> <span class="sd"> number of edge disjoint paths between source and target is equal</span> @@ -689,7 +689,7 @@ <div class="viewcode-block" id="node_disjoint_paths"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths.html#networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths">[docs]</a><span class="k">def</span> <span class="nf">node_disjoint_paths</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">auxiliary</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">residual</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Computes node disjoint paths between source and target.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Computes node disjoint paths between source and target.</span> <span class="sd"> Node disjoint paths are paths that only share their first and last</span> <span class="sd"> nodes. The number of node independent paths between two nodes is</span> @@ -902,7 +902,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/connectivity/edge_augmentation.html b/_modules/networkx/algorithms/connectivity/edge_augmentation.html index 61f238bf..84d8553c 100644 --- a/_modules/networkx/algorithms/connectivity/edge_augmentation.html +++ b/_modules/networkx/algorithms/connectivity/edge_augmentation.html @@ -488,7 +488,7 @@ <div class="viewcode-block" id="is_k_edge_connected"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected.html#networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">is_k_edge_connected</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""Tests to see if a graph is k-edge-connected.</span> +<span class="w"> </span><span class="sd">"""Tests to see if a graph is k-edge-connected.</span> <span class="sd"> Is it impossible to disconnect the graph by removing fewer than k edges?</span> <span class="sd"> If so, then G is k-edge-connected.</span> @@ -538,7 +538,7 @@ <div class="viewcode-block" id="is_locally_k_edge_connected"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected.html#networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">is_locally_k_edge_connected</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""Tests to see if an edge in a graph is locally k-edge-connected.</span> +<span class="w"> </span><span class="sd">"""Tests to see if an edge in a graph is locally k-edge-connected.</span> <span class="sd"> Is it impossible to disconnect s and t by removing fewer than k edges?</span> <span class="sd"> If so, then s and t are locally k-edge-connected in G.</span> @@ -595,7 +595,7 @@ <div class="viewcode-block" id="k_edge_augmentation"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">k_edge_augmentation</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">avail</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">partial</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Finds set of edges to k-edge-connect G.</span> +<span class="w"> </span><span class="sd">"""Finds set of edges to k-edge-connect G.</span> <span class="sd"> Adding edges from the augmentation to G make it impossible to disconnect G</span> <span class="sd"> unless k or more edges are removed. This function uses the most efficient</span> @@ -708,7 +708,7 @@ <span class="k">if</span> <span class="n">k</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"k must be a positive integer, not </span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> <span class="k">elif</span> <span class="n">G</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">()</span> <span class="o"><</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:</span> - <span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"impossible to </span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2"> connect in graph with less than </span><span class="si">{</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2"> nodes"</span> + <span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"impossible to </span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2"> connect in graph with less than </span><span class="si">{</span><span class="n">k</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2"> nodes"</span> <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXUnfeasible</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span> <span class="k">elif</span> <span class="n">avail</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">avail</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">nx</span><span class="o">.</span><span class="n">is_k_edge_connected</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> @@ -745,7 +745,7 @@ <span class="k">def</span> <span class="nf">partial_k_edge_augmentation</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">avail</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Finds augmentation that k-edge-connects as much of the graph as possible.</span> +<span class="w"> </span><span class="sd">"""Finds augmentation that k-edge-connects as much of the graph as possible.</span> <span class="sd"> When a k-edge-augmentation is not possible, we can still try to find a</span> <span class="sd"> small set of edges that partially k-edge-connects as much of the graph as</span> @@ -798,7 +798,7 @@ <span class="sd"> """</span> <span class="k">def</span> <span class="nf">_edges_between_disjoint</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">only1</span><span class="p">,</span> <span class="n">only2</span><span class="p">):</span> - <span class="sd">"""finds edges between disjoint nodes"""</span> +<span class="w"> </span><span class="sd">"""finds edges between disjoint nodes"""</span> <span class="n">only1_adj</span> <span class="o">=</span> <span class="p">{</span><span class="n">u</span><span class="p">:</span> <span class="nb">set</span><span class="p">(</span><span class="n">H</span><span class="o">.</span><span class="n">adj</span><span class="p">[</span><span class="n">u</span><span class="p">])</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">only1</span><span class="p">}</span> <span class="k">for</span> <span class="n">u</span><span class="p">,</span> <span class="n">neighbs</span> <span class="ow">in</span> <span class="n">only1_adj</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> <span class="c1"># Find the neighbors of u in only1 that are also in only2</span> @@ -847,7 +847,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"multigraph"</span><span class="p">)</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">one_edge_augmentation</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">avail</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">partial</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Finds minimum weight set of edges to connect G.</span> +<span class="w"> </span><span class="sd">"""Finds minimum weight set of edges to connect G.</span> <span class="sd"> Equivalent to :func:`k_edge_augmentation` when k=1. Adding the resulting</span> <span class="sd"> edges to G will make it 1-edge-connected. The solution is optimal for both</span> @@ -901,7 +901,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"multigraph"</span><span class="p">)</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">bridge_augmentation</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">avail</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Finds the a set of edges that bridge connects G.</span> +<span class="w"> </span><span class="sd">"""Finds the a set of edges that bridge connects G.</span> <span class="sd"> Equivalent to :func:`k_edge_augmentation` when k=2, and partial=False.</span> <span class="sd"> Adding the resulting edges to G will make it 2-edge-connected. If no</span> @@ -953,12 +953,12 @@ <span class="k">def</span> <span class="nf">_ordered</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Returns the nodes in an undirected edge in lower-triangular order"""</span> +<span class="w"> </span><span class="sd">"""Returns the nodes in an undirected edge in lower-triangular order"""</span> <span class="k">return</span> <span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="k">if</span> <span class="n">u</span> <span class="o"><</span> <span class="n">v</span> <span class="k">else</span> <span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">u</span><span class="p">)</span> <span class="k">def</span> <span class="nf">_unpack_available_edges</span><span class="p">(</span><span class="n">avail</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">G</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Helper to separate avail into edges and corresponding weights"""</span> +<span class="w"> </span><span class="sd">"""Helper to separate avail into edges and corresponding weights"""</span> <span class="k">if</span> <span class="n">weight</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="n">weight</span> <span class="o">=</span> <span class="s2">"weight"</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">avail</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> @@ -987,7 +987,7 @@ <span class="k">def</span> <span class="nf">_lightest_meta_edges</span><span class="p">(</span><span class="n">mapping</span><span class="p">,</span> <span class="n">avail_uv</span><span class="p">,</span> <span class="n">avail_w</span><span class="p">):</span> - <span class="sd">"""Maps available edges in the original graph to edges in the metagraph.</span> +<span class="w"> </span><span class="sd">"""Maps available edges in the original graph to edges in the metagraph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1036,7 +1036,7 @@ <span class="k">def</span> <span class="nf">unconstrained_one_edge_augmentation</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Finds the smallest set of edges to connect G.</span> +<span class="w"> </span><span class="sd">"""Finds the smallest set of edges to connect G.</span> <span class="sd"> This is a variant of the unweighted MST problem.</span> <span class="sd"> If G is not empty, a feasible solution always exists.</span> @@ -1078,7 +1078,7 @@ <span class="k">def</span> <span class="nf">weighted_one_edge_augmentation</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">avail</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">partial</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Finds the minimum weight set of edges to connect G if one exists.</span> +<span class="w"> </span><span class="sd">"""Finds the minimum weight set of edges to connect G if one exists.</span> <span class="sd"> This is a variant of the weighted MST problem.</span> @@ -1146,7 +1146,7 @@ <span class="k">def</span> <span class="nf">unconstrained_bridge_augmentation</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Finds an optimal 2-edge-augmentation of G using the fewest edges.</span> +<span class="w"> </span><span class="sd">"""Finds an optimal 2-edge-augmentation of G using the fewest edges.</span> <span class="sd"> This is an implementation of the algorithm detailed in [1]_.</span> <span class="sd"> The basic idea is to construct a meta-graph of bridge-ccs, connect leaf</span> @@ -1300,7 +1300,7 @@ <span class="k">def</span> <span class="nf">weighted_bridge_augmentation</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">avail</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Finds an approximate min-weight 2-edge-augmentation of G.</span> +<span class="w"> </span><span class="sd">"""Finds an approximate min-weight 2-edge-augmentation of G.</span> <span class="sd"> This is an implementation of the approximation algorithm detailed in [1]_.</span> <span class="sd"> It chooses a set of edges from avail to add to G that renders it</span> @@ -1470,7 +1470,7 @@ <span class="k">def</span> <span class="nf">_minimum_rooted_branching</span><span class="p">(</span><span class="n">D</span><span class="p">,</span> <span class="n">root</span><span class="p">):</span> - <span class="sd">"""Helper function to compute a minimum rooted branching (aka rooted</span> +<span class="w"> </span><span class="sd">"""Helper function to compute a minimum rooted branching (aka rooted</span> <span class="sd"> arborescence)</span> <span class="sd"> Before the branching can be computed, the directed graph must be rooted by</span> @@ -1494,7 +1494,7 @@ <span class="k">def</span> <span class="nf">collapse</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">grouped_nodes</span><span class="p">):</span> - <span class="sd">"""Collapses each group of nodes into a single node.</span> +<span class="w"> </span><span class="sd">"""Collapses each group of nodes into a single node.</span> <span class="sd"> This is similar to condensation, but works on undirected graphs.</span> @@ -1565,7 +1565,7 @@ <span class="k">def</span> <span class="nf">complement_edges</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns only the edges in the complement of G</span> +<span class="w"> </span><span class="sd">"""Returns only the edges in the complement of G</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1602,7 +1602,7 @@ <span class="k">def</span> <span class="nf">_compat_shuffle</span><span class="p">(</span><span class="n">rng</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span> - <span class="sd">"""wrapper around rng.shuffle for python 2 compatibility reasons"""</span> +<span class="w"> </span><span class="sd">"""wrapper around rng.shuffle for python 2 compatibility reasons"""</span> <span class="n">rng</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> @@ -1610,7 +1610,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"multigraph"</span><span class="p">)</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">greedy_k_edge_augmentation</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">avail</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Greedy algorithm for finding a k-edge-augmentation</span> +<span class="w"> </span><span class="sd">"""Greedy algorithm for finding a k-edge-augmentation</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1768,7 +1768,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/connectivity/edge_kcomponents.html b/_modules/networkx/algorithms/connectivity/edge_kcomponents.html index 8623023f..e8794442 100644 --- a/_modules/networkx/algorithms/connectivity/edge_kcomponents.html +++ b/_modules/networkx/algorithms/connectivity/edge_kcomponents.html @@ -488,7 +488,7 @@ <div class="viewcode-block" id="k_edge_components"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.edge_kcomponents.k_edge_components.html#networkx.algorithms.connectivity.edge_kcomponents.k_edge_components">[docs]</a><span class="nd">@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">k_edge_components</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""Generates nodes in each maximal k-edge-connected component in G.</span> +<span class="w"> </span><span class="sd">"""Generates nodes in each maximal k-edge-connected component in G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -571,7 +571,7 @@ <div class="viewcode-block" id="k_edge_subgraphs"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs.html#networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs">[docs]</a><span class="nd">@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">k_edge_subgraphs</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""Generates nodes in each maximal k-edge-connected subgraph in G.</span> +<span class="w"> </span><span class="sd">"""Generates nodes in each maximal k-edge-connected subgraph in G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -648,7 +648,7 @@ <span class="k">def</span> <span class="nf">_k_edge_subgraphs_nodes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""Helper to get the nodes from the subgraphs.</span> +<span class="w"> </span><span class="sd">"""Helper to get the nodes from the subgraphs.</span> <span class="sd"> This allows k_edge_subgraphs to return a generator.</span> <span class="sd"> """</span> @@ -659,7 +659,7 @@ <div class="viewcode-block" id="bridge_components"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.edge_kcomponents.bridge_components.html#networkx.algorithms.connectivity.edge_kcomponents.bridge_components">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">bridge_components</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Finds all bridge-connected components G.</span> +<span class="w"> </span><span class="sd">"""Finds all bridge-connected components G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -700,7 +700,7 @@ <div class="viewcode-block" id="EdgeComponentAuxGraph"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.html#networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph">[docs]</a><span class="k">class</span> <span class="nc">EdgeComponentAuxGraph</span><span class="p">:</span> - <span class="sa">r</span><span class="sd">"""A simple algorithm to find all k-edge-connected components in a graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""A simple algorithm to find all k-edge-connected components in a graph.</span> <span class="sd"> Constructing the AuxillaryGraph (which may take some time) allows for the</span> <span class="sd"> k-edge-ccs to be found in linear time for arbitrary k.</span> @@ -774,7 +774,7 @@ <span class="c1"># @not_implemented_for('multigraph') # TODO: fix decor for classmethods</span> <div class="viewcode-block" id="EdgeComponentAuxGraph.construct"><a class="viewcode-back" href="../../../../reference/algorithms/generated/generated/networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct.html#networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct">[docs]</a> <span class="nd">@classmethod</span> <span class="k">def</span> <span class="nf">construct</span><span class="p">(</span><span class="n">EdgeComponentAuxGraph</span><span class="p">,</span> <span class="n">G</span><span class="p">):</span> - <span class="sd">"""Builds an auxiliary graph encoding edge-connectivity between nodes.</span> +<span class="w"> </span><span class="sd">"""Builds an auxiliary graph encoding edge-connectivity between nodes.</span> <span class="sd"> Notes</span> <span class="sd"> -----</span> @@ -841,7 +841,7 @@ <span class="k">return</span> <span class="bp">self</span></div> <div class="viewcode-block" id="EdgeComponentAuxGraph.k_edge_components"><a class="viewcode-back" href="../../../../reference/algorithms/generated/generated/networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components.html#networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components">[docs]</a> <span class="k">def</span> <span class="nf">k_edge_components</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""Queries the auxiliary graph for k-edge-connected components.</span> +<span class="w"> </span><span class="sd">"""Queries the auxiliary graph for k-edge-connected components.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -874,7 +874,7 @@ <span class="k">yield from</span> <span class="n">nx</span><span class="o">.</span><span class="n">connected_components</span><span class="p">(</span><span class="n">R</span><span class="p">)</span></div> <div class="viewcode-block" id="EdgeComponentAuxGraph.k_edge_subgraphs"><a class="viewcode-back" href="../../../../reference/algorithms/generated/generated/networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs.html#networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs">[docs]</a> <span class="k">def</span> <span class="nf">k_edge_subgraphs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""Queries the auxiliary graph for k-edge-connected subgraphs.</span> +<span class="w"> </span><span class="sd">"""Queries the auxiliary graph for k-edge-connected subgraphs.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -919,7 +919,7 @@ <span class="k">def</span> <span class="nf">_low_degree_nodes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">nbunch</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Helper for finding nodes with degree less than k."""</span> +<span class="w"> </span><span class="sd">"""Helper for finding nodes with degree less than k."""</span> <span class="c1"># Nodes with degree less than k cannot be k-edge-connected.</span> <span class="k">if</span> <span class="n">G</span><span class="o">.</span><span class="n">is_directed</span><span class="p">():</span> <span class="c1"># Consider both in and out degree in the directed case</span> @@ -940,7 +940,7 @@ <span class="k">def</span> <span class="nf">_high_degree_components</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""Helper for filtering components that can't be k-edge-connected.</span> +<span class="w"> </span><span class="sd">"""Helper for filtering components that can't be k-edge-connected.</span> <span class="sd"> Removes and generates each node with degree less than k. Then generates</span> <span class="sd"> remaining components where all nodes have degree at least k.</span> @@ -965,7 +965,7 @@ <span class="k">def</span> <span class="nf">general_k_edge_subgraphs</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""General algorithm to find all maximal k-edge-connected subgraphs in G.</span> +<span class="w"> </span><span class="sd">"""General algorithm to find all maximal k-edge-connected subgraphs in G.</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> @@ -1093,7 +1093,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/connectivity/kcomponents.html b/_modules/networkx/algorithms/connectivity/kcomponents.html index 693dcc41..193495fe 100644 --- a/_modules/networkx/algorithms/connectivity/kcomponents.html +++ b/_modules/networkx/algorithms/connectivity/kcomponents.html @@ -481,7 +481,7 @@ <div class="viewcode-block" id="k_components"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.kcomponents.k_components.html#networkx.algorithms.connectivity.kcomponents.k_components">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">k_components</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the k-component structure of a graph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the k-component structure of a graph G.</span> <span class="sd"> A `k`-component is a maximal subgraph of a graph G that has, at least,</span> <span class="sd"> node connectivity `k`: we need to remove at least `k` nodes to break it</span> @@ -618,7 +618,7 @@ <span class="k">def</span> <span class="nf">_consolidate</span><span class="p">(</span><span class="n">sets</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""Merge sets that share k or more elements.</span> +<span class="w"> </span><span class="sd">"""Merge sets that share k or more elements.</span> <span class="sd"> See: http://rosettacode.org/wiki/Set_consolidation</span> @@ -736,7 +736,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/connectivity/kcutsets.html b/_modules/networkx/algorithms/connectivity/kcutsets.html index 986e1abe..c9190cb6 100644 --- a/_modules/networkx/algorithms/connectivity/kcutsets.html +++ b/_modules/networkx/algorithms/connectivity/kcutsets.html @@ -485,7 +485,7 @@ <div class="viewcode-block" id="all_node_cuts"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.kcutsets.all_node_cuts.html#networkx.algorithms.connectivity.kcutsets.all_node_cuts">[docs]</a><span class="k">def</span> <span class="nf">all_node_cuts</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns all minimum k cutsets of an undirected graph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns all minimum k cutsets of an undirected graph G.</span> <span class="sd"> This implementation is based on Kanevsky's algorithm [1]_ for finding all</span> <span class="sd"> minimum-size node cut-sets of an undirected graph G; ie the set (or sets)</span> @@ -685,7 +685,7 @@ <span class="k">def</span> <span class="nf">_is_separating_set</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">cut</span><span class="p">):</span> - <span class="sd">"""Assumes that the input graph is connected"""</span> +<span class="w"> </span><span class="sd">"""Assumes that the input graph is connected"""</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cut</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span> <span class="k">return</span> <span class="kc">True</span> @@ -744,7 +744,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/connectivity/stoerwagner.html b/_modules/networkx/algorithms/connectivity/stoerwagner.html index d13be75d..f46c1ec0 100644 --- a/_modules/networkx/algorithms/connectivity/stoerwagner.html +++ b/_modules/networkx/algorithms/connectivity/stoerwagner.html @@ -476,7 +476,7 @@ <div class="viewcode-block" id="stoer_wagner"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.stoerwagner.stoer_wagner.html#networkx.algorithms.connectivity.stoerwagner.stoer_wagner">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">stoer_wagner</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">heap</span><span class="o">=</span><span class="n">BinaryHeap</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the weighted minimum edge cut using the Stoer-Wagner algorithm.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the weighted minimum edge cut using the Stoer-Wagner algorithm.</span> <span class="sd"> Determine the minimum edge cut of a connected graph using the</span> <span class="sd"> Stoer-Wagner algorithm. In weighted cases, all weights must be</span> @@ -661,7 +661,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/connectivity/utils.html b/_modules/networkx/algorithms/connectivity/utils.html index 975084fc..a1a59b07 100644 --- a/_modules/networkx/algorithms/connectivity/utils.html +++ b/_modules/networkx/algorithms/connectivity/utils.html @@ -470,7 +470,7 @@ <div class="viewcode-block" id="build_auxiliary_node_connectivity"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity.html#networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity">[docs]</a><span class="k">def</span> <span class="nf">build_auxiliary_node_connectivity</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Creates a directed graph D from an undirected graph G to compute flow</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Creates a directed graph D from an undirected graph G to compute flow</span> <span class="sd"> based node connectivity.</span> <span class="sd"> For an undirected graph G having `n` nodes and `m` edges we derive a</span> @@ -522,7 +522,7 @@ <div class="viewcode-block" id="build_auxiliary_edge_connectivity"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity.html#networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity">[docs]</a><span class="k">def</span> <span class="nf">build_auxiliary_edge_connectivity</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Auxiliary digraph for computing flow based edge connectivity</span> +<span class="w"> </span><span class="sd">"""Auxiliary digraph for computing flow based edge connectivity</span> <span class="sd"> If the input graph is undirected, we replace each edge (`u`,`v`) with</span> <span class="sd"> two reciprocal arcs (`u`, `v`) and (`v`, `u`) and then we set the attribute</span> @@ -597,7 +597,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/core.html b/_modules/networkx/algorithms/core.html index 8a1c3119..68607fce 100644 --- a/_modules/networkx/algorithms/core.html +++ b/_modules/networkx/algorithms/core.html @@ -509,7 +509,7 @@ <div class="viewcode-block" id="core_number"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.core.core_number.html#networkx.algorithms.core.core_number">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="nd">@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">core_number</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the core number for each vertex.</span> +<span class="w"> </span><span class="sd">"""Returns the core number for each vertex.</span> <span class="sd"> A k-core is a maximal subgraph that contains nodes of degree k or more.</span> @@ -579,7 +579,7 @@ <span class="k">def</span> <span class="nf">_core_subgraph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k_filter</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">core</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the subgraph induced by nodes passing filter `k_filter`.</span> +<span class="w"> </span><span class="sd">"""Returns the subgraph induced by nodes passing filter `k_filter`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -607,7 +607,7 @@ <div class="viewcode-block" id="k_core"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.core.k_core.html#networkx.algorithms.core.k_core">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">k_core</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">core_number</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the k-core of G.</span> +<span class="w"> </span><span class="sd">"""Returns the k-core of G.</span> <span class="sd"> A k-core is a maximal subgraph that contains nodes of degree k or more.</span> @@ -659,7 +659,7 @@ <div class="viewcode-block" id="k_shell"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.core.k_shell.html#networkx.algorithms.core.k_shell">[docs]</a><span class="k">def</span> <span class="nf">k_shell</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">core_number</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the k-shell of G.</span> +<span class="w"> </span><span class="sd">"""Returns the k-shell of G.</span> <span class="sd"> The k-shell is the subgraph induced by nodes with core number k.</span> <span class="sd"> That is, nodes in the k-core that are not in the (k+1)-core.</span> @@ -718,7 +718,7 @@ <div class="viewcode-block" id="k_crust"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.core.k_crust.html#networkx.algorithms.core.k_crust">[docs]</a><span class="k">def</span> <span class="nf">k_crust</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">core_number</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the k-crust of G.</span> +<span class="w"> </span><span class="sd">"""Returns the k-crust of G.</span> <span class="sd"> The k-crust is the graph G with the edges of the k-core removed</span> <span class="sd"> and isolated nodes found after the removal of edges are also removed.</span> @@ -777,7 +777,7 @@ <div class="viewcode-block" id="k_corona"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.core.k_corona.html#networkx.algorithms.core.k_corona">[docs]</a><span class="k">def</span> <span class="nf">k_corona</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">core_number</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the k-corona of G.</span> +<span class="w"> </span><span class="sd">"""Returns the k-corona of G.</span> <span class="sd"> The k-corona is the subgraph of nodes in the k-core which have</span> <span class="sd"> exactly k neighbours in the k-core.</span> @@ -834,7 +834,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">k_truss</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""Returns the k-truss of `G`.</span> +<span class="w"> </span><span class="sd">"""Returns the k-truss of `G`.</span> <span class="sd"> The k-truss is the maximal induced subgraph of `G` which contains at least</span> <span class="sd"> three vertices where every edge is incident to at least `k-2` triangles.</span> @@ -903,7 +903,7 @@ <div class="viewcode-block" id="onion_layers"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.core.onion_layers.html#networkx.algorithms.core.onion_layers">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"multigraph"</span><span class="p">)</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">onion_layers</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the layer of each vertex in an onion decomposition of the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the layer of each vertex in an onion decomposition of the graph.</span> <span class="sd"> The onion decomposition refines the k-core decomposition by providing</span> <span class="sd"> information on the internal organization of each k-shell. It is usually</span> @@ -1046,7 +1046,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/covering.html b/_modules/networkx/algorithms/covering.html index b3927816..e8efd5fe 100644 --- a/_modules/networkx/algorithms/covering.html +++ b/_modules/networkx/algorithms/covering.html @@ -475,7 +475,7 @@ <div class="viewcode-block" id="min_edge_cover"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.covering.min_edge_cover.html#networkx.algorithms.covering.min_edge_cover">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">min_edge_cover</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">matching_algorithm</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the min cardinality edge cover of the graph as a set of edges.</span> +<span class="w"> </span><span class="sd">"""Returns the min cardinality edge cover of the graph as a set of edges.</span> <span class="sd"> A smallest edge cover can be found in polynomial time by finding</span> <span class="sd"> a maximum matching and extending it greedily so that all nodes</span> @@ -569,7 +569,7 @@ <div class="viewcode-block" id="is_edge_cover"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.covering.is_edge_cover.html#networkx.algorithms.covering.is_edge_cover">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">is_edge_cover</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">cover</span><span class="p">):</span> - <span class="sd">"""Decides whether a set of edges is a valid edge cover of the graph.</span> +<span class="w"> </span><span class="sd">"""Decides whether a set of edges is a valid edge cover of the graph.</span> <span class="sd"> Given a set of edges, whether it is an edge covering can</span> <span class="sd"> be decided if we just check whether all nodes of the graph</span> @@ -652,7 +652,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/cuts.html b/_modules/networkx/algorithms/cuts.html index 14508e1c..1dfbeef8 100644 --- a/_modules/networkx/algorithms/cuts.html +++ b/_modules/networkx/algorithms/cuts.html @@ -486,7 +486,7 @@ <div class="viewcode-block" id="cut_size"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cuts.cut_size.html#networkx.algorithms.cuts.cut_size">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">cut_size</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the size of the cut between two sets of nodes.</span> +<span class="w"> </span><span class="sd">"""Returns the size of the cut between two sets of nodes.</span> <span class="sd"> A *cut* is a partition of the nodes of a graph into two sets. The</span> <span class="sd"> *cut size* is the sum of the weights of the edges "between" the two</span> @@ -549,7 +549,7 @@ <div class="viewcode-block" id="volume"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cuts.volume.html#networkx.algorithms.cuts.volume">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">volume</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the volume of a set of nodes.</span> +<span class="w"> </span><span class="sd">"""Returns the volume of a set of nodes.</span> <span class="sd"> The *volume* of a set *S* is the sum of the (out-)degrees of nodes</span> <span class="sd"> in *S* (taking into account parallel edges in multigraphs). [1]</span> @@ -592,7 +592,7 @@ <div class="viewcode-block" id="normalized_cut_size"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cuts.normalized_cut_size.html#networkx.algorithms.cuts.normalized_cut_size">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">normalized_cut_size</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the normalized size of the cut between two sets of nodes.</span> +<span class="w"> </span><span class="sd">"""Returns the normalized size of the cut between two sets of nodes.</span> <span class="sd"> The *normalized cut size* is the cut size times the sum of the</span> <span class="sd"> reciprocal sizes of the volumes of the two sets. [1]</span> @@ -645,7 +645,7 @@ <div class="viewcode-block" id="conductance"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cuts.conductance.html#networkx.algorithms.cuts.conductance">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">conductance</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the conductance of two sets of nodes.</span> +<span class="w"> </span><span class="sd">"""Returns the conductance of two sets of nodes.</span> <span class="sd"> The *conductance* is the quotient of the cut size and the smaller of</span> <span class="sd"> the volumes of the two sets. [1]</span> @@ -693,7 +693,7 @@ <div class="viewcode-block" id="edge_expansion"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cuts.edge_expansion.html#networkx.algorithms.cuts.edge_expansion">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">edge_expansion</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the edge expansion between two node sets.</span> +<span class="w"> </span><span class="sd">"""Returns the edge expansion between two node sets.</span> <span class="sd"> The *edge expansion* is the quotient of the cut size and the smaller</span> <span class="sd"> of the cardinalities of the two sets. [1]</span> @@ -740,7 +740,7 @@ <div class="viewcode-block" id="mixing_expansion"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cuts.mixing_expansion.html#networkx.algorithms.cuts.mixing_expansion">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">mixing_expansion</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the mixing expansion between two node sets.</span> +<span class="w"> </span><span class="sd">"""Returns the mixing expansion between two node sets.</span> <span class="sd"> The *mixing expansion* is the quotient of the cut size and twice the</span> <span class="sd"> number of edges in the graph. [1]</span> @@ -788,7 +788,7 @@ <span class="c1"># denominator become `min(len(S), len(T))`?</span> <div class="viewcode-block" id="node_expansion"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cuts.node_expansion.html#networkx.algorithms.cuts.node_expansion">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">node_expansion</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">S</span><span class="p">):</span> - <span class="sd">"""Returns the node expansion of the set `S`.</span> +<span class="w"> </span><span class="sd">"""Returns the node expansion of the set `S`.</span> <span class="sd"> The *node expansion* is the quotient of the size of the node</span> <span class="sd"> boundary of *S* and the cardinality of *S*. [1]</span> @@ -828,7 +828,7 @@ <span class="c1"># denominator become `min(len(S), len(T))`?</span> <div class="viewcode-block" id="boundary_expansion"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cuts.boundary_expansion.html#networkx.algorithms.cuts.boundary_expansion">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">boundary_expansion</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">S</span><span class="p">):</span> - <span class="sd">"""Returns the boundary expansion of the set `S`.</span> +<span class="w"> </span><span class="sd">"""Returns the boundary expansion of the set `S`.</span> <span class="sd"> The *boundary expansion* is the quotient of the size</span> <span class="sd"> of the node boundary and the cardinality of *S*. [1]</span> @@ -912,7 +912,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/cycles.html b/_modules/networkx/algorithms/cycles.html index 6ee41fd0..591aa56e 100644 --- a/_modules/networkx/algorithms/cycles.html +++ b/_modules/networkx/algorithms/cycles.html @@ -484,7 +484,7 @@ <div class="viewcode-block" id="cycle_basis"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cycles.cycle_basis.html#networkx.algorithms.cycles.cycle_basis">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">cycle_basis</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a list of cycles which form a basis for cycles of G.</span> +<span class="w"> </span><span class="sd">"""Returns a list of cycles which form a basis for cycles of G.</span> <span class="sd"> A basis for cycles of a network is a minimal collection of</span> <span class="sd"> cycles such that any cycle in the network can be written</span> @@ -560,7 +560,7 @@ <div class="viewcode-block" id="simple_cycles"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cycles.simple_cycles.html#networkx.algorithms.cycles.simple_cycles">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">simple_cycles</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Find simple cycles (elementary circuits) of a directed graph.</span> +<span class="w"> </span><span class="sd">"""Find simple cycles (elementary circuits) of a directed graph.</span> <span class="sd"> A `simple cycle`, or `elementary circuit`, is a closed path where</span> <span class="sd"> no node appears twice. Two elementary circuits are distinct if they</span> @@ -687,7 +687,7 @@ <div class="viewcode-block" id="recursive_simple_cycles"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cycles.recursive_simple_cycles.html#networkx.algorithms.cycles.recursive_simple_cycles">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">recursive_simple_cycles</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Find simple cycles (elementary circuits) of a directed graph.</span> +<span class="w"> </span><span class="sd">"""Find simple cycles (elementary circuits) of a directed graph.</span> <span class="sd"> A `simple cycle`, or `elementary circuit`, is a closed path where</span> <span class="sd"> no node appears twice. Two elementary circuits are distinct if they</span> @@ -734,7 +734,7 @@ <span class="sd"> """</span> <span class="c1"># Jon Olav Vik, 2010-08-09</span> <span class="k">def</span> <span class="nf">_unblock</span><span class="p">(</span><span class="n">thisnode</span><span class="p">):</span> - <span class="sd">"""Recursively unblock and remove nodes from B[thisnode]."""</span> +<span class="w"> </span><span class="sd">"""Recursively unblock and remove nodes from B[thisnode]."""</span> <span class="k">if</span> <span class="n">blocked</span><span class="p">[</span><span class="n">thisnode</span><span class="p">]:</span> <span class="n">blocked</span><span class="p">[</span><span class="n">thisnode</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span> <span class="k">while</span> <span class="n">B</span><span class="p">[</span><span class="n">thisnode</span><span class="p">]:</span> @@ -795,7 +795,7 @@ <div class="viewcode-block" id="find_cycle"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cycles.find_cycle.html#networkx.algorithms.cycles.find_cycle">[docs]</a><span class="k">def</span> <span class="nf">find_cycle</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a cycle found via depth-first traversal.</span> +<span class="w"> </span><span class="sd">"""Returns a cycle found via depth-first traversal.</span> <span class="sd"> The cycle is a list of edges indicating the cyclic path.</span> <span class="sd"> Orientation of directed edges is controlled by `orientation`.</span> @@ -956,7 +956,7 @@ <div class="viewcode-block" id="minimum_cycle_basis"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.cycles.minimum_cycle_basis.html#networkx.algorithms.cycles.minimum_cycle_basis">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">minimum_cycle_basis</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a minimum weight cycle basis for G</span> +<span class="w"> </span><span class="sd">"""Returns a minimum weight cycle basis for G</span> <span class="sd"> Minimum weight means a cycle basis for which the total weight</span> <span class="sd"> (length for unweighted graphs) of all the cycles is minimum.</span> @@ -1025,7 +1025,7 @@ <span class="k">def</span> <span class="nf">_min_cycle</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">orth</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Computes the minimum weight cycle in G,</span> <span class="sd"> orthogonal to the vector orth as per [p. 338, 1]</span> <span class="sd"> """</span> @@ -1069,7 +1069,7 @@ <span class="k">def</span> <span class="nf">_path_to_cycle</span><span class="p">(</span><span class="n">path</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Removes the edges from path that occur even number of times.</span> <span class="sd"> Returns a set of edges</span> <span class="sd"> """</span> @@ -1129,7 +1129,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/d_separation.html b/_modules/networkx/algorithms/d_separation.html index c437ec20..f95c23cb 100644 --- a/_modules/networkx/algorithms/d_separation.html +++ b/_modules/networkx/algorithms/d_separation.html @@ -591,7 +591,7 @@ <div class="viewcode-block" id="d_separated"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.d_separation.d_separated.html#networkx.algorithms.d_separation.d_separated">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">d_separated</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return whether node sets ``x`` and ``y`` are d-separated by ``z``.</span> <span class="sd"> Parameters</span> @@ -677,7 +677,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">minimal_d_separator</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Compute a minimal d-separating set between 'u' and 'v'.</span> +<span class="w"> </span><span class="sd">"""Compute a minimal d-separating set between 'u' and 'v'.</span> <span class="sd"> A d-separating set in a DAG is a set of nodes that blocks all paths</span> <span class="sd"> between the two nodes, 'u' and 'v'. This function</span> @@ -759,7 +759,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">is_minimal_d_separator</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">z</span><span class="p">):</span> - <span class="sd">"""Determine if a d-separating set is minimal.</span> +<span class="w"> </span><span class="sd">"""Determine if a d-separating set is minimal.</span> <span class="sd"> A d-separating set, `z`, in a DAG is a set of nodes that blocks</span> <span class="sd"> all paths between the two nodes, `u` and `v`. This function</span> @@ -861,7 +861,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">_bfs_with_marks</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">start_node</span><span class="p">,</span> <span class="n">check_set</span><span class="p">):</span> - <span class="sd">"""Breadth-first-search with markings.</span> +<span class="w"> </span><span class="sd">"""Breadth-first-search with markings.</span> <span class="sd"> Performs BFS starting from ``start_node`` and whenever a node</span> <span class="sd"> inside ``check_set`` is met, it is "marked". Once a node is marked,</span> @@ -953,7 +953,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/dag.html b/_modules/networkx/algorithms/dag.html index 6ba9c1b3..6cf6ed34 100644 --- a/_modules/networkx/algorithms/dag.html +++ b/_modules/networkx/algorithms/dag.html @@ -501,7 +501,7 @@ <div class="viewcode-block" id="descendants"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dag.descendants.html#networkx.algorithms.dag.descendants">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">descendants</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">):</span> - <span class="sd">"""Returns all nodes reachable from `source` in `G`.</span> +<span class="w"> </span><span class="sd">"""Returns all nodes reachable from `source` in `G`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -538,7 +538,7 @@ <div class="viewcode-block" id="ancestors"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dag.ancestors.html#networkx.algorithms.dag.ancestors">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">ancestors</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">):</span> - <span class="sd">"""Returns all nodes having a path to `source` in `G`.</span> +<span class="w"> </span><span class="sd">"""Returns all nodes having a path to `source` in `G`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -574,7 +574,7 @@ <span class="k">def</span> <span class="nf">has_cycle</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Decides whether the directed graph has a cycle."""</span> +<span class="w"> </span><span class="sd">"""Decides whether the directed graph has a cycle."""</span> <span class="k">try</span><span class="p">:</span> <span class="c1"># Feed the entire iterator into a zero-length deque.</span> <span class="n">deque</span><span class="p">(</span><span class="n">topological_sort</span><span class="p">(</span><span class="n">G</span><span class="p">),</span> <span class="n">maxlen</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> @@ -585,7 +585,7 @@ <div class="viewcode-block" id="is_directed_acyclic_graph"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dag.is_directed_acyclic_graph.html#networkx.algorithms.dag.is_directed_acyclic_graph">[docs]</a><span class="k">def</span> <span class="nf">is_directed_acyclic_graph</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns True if the graph `G` is a directed acyclic graph (DAG) or</span> +<span class="w"> </span><span class="sd">"""Returns True if the graph `G` is a directed acyclic graph (DAG) or</span> <span class="sd"> False if not.</span> <span class="sd"> Parameters</span> @@ -625,7 +625,7 @@ <div class="viewcode-block" id="topological_generations"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dag.topological_generations.html#networkx.algorithms.dag.topological_generations">[docs]</a><span class="k">def</span> <span class="nf">topological_generations</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Stratifies a DAG into generations.</span> +<span class="w"> </span><span class="sd">"""Stratifies a DAG into generations.</span> <span class="sd"> A topological generation is node collection in which ancestors of a node in each</span> <span class="sd"> generation are guaranteed to be in a previous generation, and any descendants of</span> @@ -702,7 +702,7 @@ <div class="viewcode-block" id="topological_sort"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dag.topological_sort.html#networkx.algorithms.dag.topological_sort">[docs]</a><span class="k">def</span> <span class="nf">topological_sort</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns a generator of nodes in topologically sorted order.</span> +<span class="w"> </span><span class="sd">"""Returns a generator of nodes in topologically sorted order.</span> <span class="sd"> A topological sort is a nonunique permutation of the nodes of a</span> <span class="sd"> directed graph such that an edge from u to v implies that u</span> @@ -770,7 +770,7 @@ <div class="viewcode-block" id="lexicographical_topological_sort"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dag.lexicographical_topological_sort.html#networkx.algorithms.dag.lexicographical_topological_sort">[docs]</a><span class="k">def</span> <span class="nf">lexicographical_topological_sort</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generate the nodes in the unique lexicographical topological sort order.</span> +<span class="w"> </span><span class="sd">"""Generate the nodes in the unique lexicographical topological sort order.</span> <span class="sd"> Generates a unique ordering of nodes by first sorting topologically (for which there are often</span> <span class="sd"> multiple valid orderings) and then additionally by sorting lexicographically.</span> @@ -912,7 +912,7 @@ <div class="viewcode-block" id="all_topological_sorts"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dag.all_topological_sorts.html#networkx.algorithms.dag.all_topological_sorts">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">all_topological_sorts</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns a generator of _all_ topological sorts of the directed graph G.</span> +<span class="w"> </span><span class="sd">"""Returns a generator of _all_ topological sorts of the directed graph G.</span> <span class="sd"> A topological sort is a nonunique permutation of the nodes such that an</span> <span class="sd"> edge from u to v implies that u appears before v in the topological sort</span> @@ -1030,7 +1030,7 @@ <div class="viewcode-block" id="is_aperiodic"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dag.is_aperiodic.html#networkx.algorithms.dag.is_aperiodic">[docs]</a><span class="k">def</span> <span class="nf">is_aperiodic</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns True if `G` is aperiodic.</span> +<span class="w"> </span><span class="sd">"""Returns True if `G` is aperiodic.</span> <span class="sd"> A directed graph is aperiodic if there is no integer k > 1 that</span> <span class="sd"> divides the length of every cycle in the graph.</span> @@ -1121,7 +1121,7 @@ <div class="viewcode-block" id="transitive_closure"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dag.transitive_closure.html#networkx.algorithms.dag.transitive_closure">[docs]</a><span class="k">def</span> <span class="nf">transitive_closure</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">reflexive</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns transitive closure of a graph</span> +<span class="w"> </span><span class="sd">"""Returns transitive closure of a graph</span> <span class="sd"> The transitive closure of G = (V,E) is a graph G+ = (V,E+) such that</span> <span class="sd"> for all v, w in V there is an edge (v, w) in E+ if and only if there</span> @@ -1213,7 +1213,7 @@ <div class="viewcode-block" id="transitive_closure_dag"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dag.transitive_closure_dag.html#networkx.algorithms.dag.transitive_closure_dag">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">transitive_closure_dag</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">topo_order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the transitive closure of a directed acyclic graph.</span> +<span class="w"> </span><span class="sd">"""Returns the transitive closure of a directed acyclic graph.</span> <span class="sd"> This function is faster than the function `transitive_closure`, but fails</span> <span class="sd"> if the graph has a cycle.</span> @@ -1269,7 +1269,7 @@ <div class="viewcode-block" id="transitive_reduction"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dag.transitive_reduction.html#networkx.algorithms.dag.transitive_reduction">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">transitive_reduction</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns transitive reduction of a directed graph</span> +<span class="w"> </span><span class="sd">"""Returns transitive reduction of a directed graph</span> <span class="sd"> The transitive reduction of G = (V,E) is a graph G- = (V,E-) such that</span> <span class="sd"> for all v,w in V there is an edge (v,w) in E- if and only if (v,w) is</span> @@ -1341,7 +1341,7 @@ <div class="viewcode-block" id="antichains"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dag.antichains.html#networkx.algorithms.dag.antichains">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">antichains</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">topo_order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generates antichains from a directed acyclic graph (DAG).</span> +<span class="w"> </span><span class="sd">"""Generates antichains from a directed acyclic graph (DAG).</span> <span class="sd"> An antichain is a subset of a partially ordered set such that any</span> <span class="sd"> two elements in the subset are incomparable.</span> @@ -1407,7 +1407,7 @@ <div class="viewcode-block" id="dag_longest_path"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dag.dag_longest_path.html#networkx.algorithms.dag.dag_longest_path">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">dag_longest_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">default_weight</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">topo_order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the longest path in a directed acyclic graph (DAG).</span> +<span class="w"> </span><span class="sd">"""Returns the longest path in a directed acyclic graph (DAG).</span> <span class="sd"> If `G` has edges with `weight` attribute the edge data are used as</span> <span class="sd"> weight values.</span> @@ -1502,7 +1502,7 @@ <div class="viewcode-block" id="dag_longest_path_length"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dag.dag_longest_path_length.html#networkx.algorithms.dag.dag_longest_path_length">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">dag_longest_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">default_weight</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span> - <span class="sd">"""Returns the longest path length in a DAG</span> +<span class="w"> </span><span class="sd">"""Returns the longest path length in a DAG</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1553,7 +1553,7 @@ <span class="k">def</span> <span class="nf">root_to_leaf_paths</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Yields root-to-leaf paths in a directed acyclic graph.</span> +<span class="w"> </span><span class="sd">"""Yields root-to-leaf paths in a directed acyclic graph.</span> <span class="sd"> `G` must be a directed acyclic graph. If not, the behavior of this</span> <span class="sd"> function is undefined. A "root" in this graph is a node of in-degree</span> @@ -1573,7 +1573,7 @@ <div class="viewcode-block" id="dag_to_branching"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dag.dag_to_branching.html#networkx.algorithms.dag.dag_to_branching">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"multigraph"</span><span class="p">)</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">dag_to_branching</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns a branching representing all (overlapping) paths from</span> +<span class="w"> </span><span class="sd">"""Returns a branching representing all (overlapping) paths from</span> <span class="sd"> root nodes to leaf nodes in the given directed acyclic graph.</span> <span class="sd"> As described in :mod:`networkx.algorithms.tree.recognition`, a</span> @@ -1670,7 +1670,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">compute_v_structures</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Iterate through the graph to compute all v-structures.</span> +<span class="w"> </span><span class="sd">"""Iterate through the graph to compute all v-structures.</span> <span class="sd"> V-structures are triples in the directed graph where</span> <span class="sd"> two parent nodes point to the same child and the two parent nodes</span> @@ -1747,7 +1747,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/distance_measures.html b/_modules/networkx/algorithms/distance_measures.html index b6582a26..b67c0356 100644 --- a/_modules/networkx/algorithms/distance_measures.html +++ b/_modules/networkx/algorithms/distance_measures.html @@ -478,7 +478,7 @@ <span class="k">def</span> <span class="nf">_extrema_bounding</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">compute</span><span class="o">=</span><span class="s2">"diameter"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute requested extreme distance metric of undirected graph G</span> +<span class="w"> </span><span class="sd">"""Compute requested extreme distance metric of undirected graph G</span> <span class="sd"> Computation is based on smart lower and upper bounds, and in practice</span> <span class="sd"> linear in the number of nodes, rather than quadratic (except for some</span> @@ -699,7 +699,7 @@ <div class="viewcode-block" id="eccentricity"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.distance_measures.eccentricity.html#networkx.algorithms.distance_measures.eccentricity">[docs]</a><span class="k">def</span> <span class="nf">eccentricity</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">v</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sp</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the eccentricity of nodes in G.</span> +<span class="w"> </span><span class="sd">"""Returns the eccentricity of nodes in G.</span> <span class="sd"> The eccentricity of a node v is the maximum distance from v to</span> <span class="sd"> all other nodes in G.</span> @@ -787,7 +787,7 @@ <div class="viewcode-block" id="diameter"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.distance_measures.diameter.html#networkx.algorithms.distance_measures.diameter">[docs]</a><span class="k">def</span> <span class="nf">diameter</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">e</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">usebounds</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the diameter of the graph G.</span> +<span class="w"> </span><span class="sd">"""Returns the diameter of the graph G.</span> <span class="sd"> The diameter is the maximum eccentricity.</span> @@ -842,7 +842,7 @@ <div class="viewcode-block" id="periphery"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.distance_measures.periphery.html#networkx.algorithms.distance_measures.periphery">[docs]</a><span class="k">def</span> <span class="nf">periphery</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">e</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">usebounds</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the periphery of the graph G.</span> +<span class="w"> </span><span class="sd">"""Returns the periphery of the graph G.</span> <span class="sd"> The periphery is the set of nodes with eccentricity equal to the diameter.</span> @@ -900,7 +900,7 @@ <div class="viewcode-block" id="radius"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.distance_measures.radius.html#networkx.algorithms.distance_measures.radius">[docs]</a><span class="k">def</span> <span class="nf">radius</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">e</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">usebounds</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the radius of the graph G.</span> +<span class="w"> </span><span class="sd">"""Returns the radius of the graph G.</span> <span class="sd"> The radius is the minimum eccentricity.</span> @@ -952,7 +952,7 @@ <div class="viewcode-block" id="center"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.distance_measures.center.html#networkx.algorithms.distance_measures.center">[docs]</a><span class="k">def</span> <span class="nf">center</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">e</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">usebounds</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the center of the graph G.</span> +<span class="w"> </span><span class="sd">"""Returns the center of the graph G.</span> <span class="sd"> The center is the set of nodes with eccentricity equal to radius.</span> @@ -1010,7 +1010,7 @@ <div class="viewcode-block" id="barycenter"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.distance_measures.barycenter.html#networkx.algorithms.distance_measures.barycenter">[docs]</a><span class="k">def</span> <span class="nf">barycenter</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">attr</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sp</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Calculate barycenter of a connected graph, optionally with edge weights.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Calculate barycenter of a connected graph, optionally with edge weights.</span> <span class="sd"> The :dfn:`barycenter` a</span> <span class="sd"> :func:`connected <networkx.algorithms.components.is_connected>` graph</span> @@ -1088,7 +1088,7 @@ <span class="k">def</span> <span class="nf">_count_lu_permutations</span><span class="p">(</span><span class="n">perm_array</span><span class="p">):</span> - <span class="sd">"""Counts the number of permutations in SuperLU perm_c or perm_r"""</span> +<span class="w"> </span><span class="sd">"""Counts the number of permutations in SuperLU perm_c or perm_r"""</span> <span class="n">perm_cnt</span> <span class="o">=</span> <span class="mi">0</span> <span class="n">arr</span> <span class="o">=</span> <span class="n">perm_array</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">arr</span><span class="p">)):</span> @@ -1103,7 +1103,7 @@ <div class="viewcode-block" id="resistance_distance"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.distance_measures.resistance_distance.html#networkx.algorithms.distance_measures.resistance_distance">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">resistance_distance</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodeA</span><span class="p">,</span> <span class="n">nodeB</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">invert_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns the resistance distance between node A and node B on graph G.</span> +<span class="w"> </span><span class="sd">"""Returns the resistance distance between node A and node B on graph G.</span> <span class="sd"> The resistance distance between two nodes of a graph is akin to treating</span> <span class="sd"> the graph as a grid of resistorses with a resistance equal to the provided</span> @@ -1277,7 +1277,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/distance_regular.html b/_modules/networkx/algorithms/distance_regular.html index b326bfe8..d4909128 100644 --- a/_modules/networkx/algorithms/distance_regular.html +++ b/_modules/networkx/algorithms/distance_regular.html @@ -481,7 +481,7 @@ <div class="viewcode-block" id="is_distance_regular"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.distance_regular.is_distance_regular.html#networkx.algorithms.distance_regular.is_distance_regular">[docs]</a><span class="k">def</span> <span class="nf">is_distance_regular</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns True if the graph is distance regular, False otherwise.</span> +<span class="w"> </span><span class="sd">"""Returns True if the graph is distance regular, False otherwise.</span> <span class="sd"> A connected graph G is distance-regular if for any nodes x,y</span> <span class="sd"> and any integers i,j=0,1,...,d (where d is the graph</span> @@ -528,7 +528,7 @@ <div class="viewcode-block" id="global_parameters"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.distance_regular.global_parameters.html#networkx.algorithms.distance_regular.global_parameters">[docs]</a><span class="k">def</span> <span class="nf">global_parameters</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span> - <span class="sd">"""Returns global parameters for a given intersection array.</span> +<span class="w"> </span><span class="sd">"""Returns global parameters for a given intersection array.</span> <span class="sd"> Given a distance-regular graph G with integers b_i, c_i,i = 0,....,d</span> <span class="sd"> such that for any 2 vertices x,y in G at a distance i=d(x,y), there</span> @@ -573,7 +573,7 @@ <div class="viewcode-block" id="intersection_array"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.distance_regular.intersection_array.html#networkx.algorithms.distance_regular.intersection_array">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">,</span> <span class="s2">"multigraph"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">intersection_array</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the intersection array of a distance-regular graph.</span> +<span class="w"> </span><span class="sd">"""Returns the intersection array of a distance-regular graph.</span> <span class="sd"> Given a distance-regular graph G with integers b_i, c_i,i = 0,....,d</span> <span class="sd"> such that for any 2 vertices x,y in G at a distance i=d(x,y), there</span> @@ -642,7 +642,7 @@ <span class="c1"># TODO There is a definition for directed strongly regular graphs.</span> <div class="viewcode-block" id="is_strongly_regular"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.distance_regular.is_strongly_regular.html#networkx.algorithms.distance_regular.is_strongly_regular">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">,</span> <span class="s2">"multigraph"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">is_strongly_regular</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns True if and only if the given graph is strongly</span> +<span class="w"> </span><span class="sd">"""Returns True if and only if the given graph is strongly</span> <span class="sd"> regular.</span> <span class="sd"> An undirected graph is *strongly regular* if</span> @@ -743,7 +743,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/dominance.html b/_modules/networkx/algorithms/dominance.html index 5c95b752..9085730b 100644 --- a/_modules/networkx/algorithms/dominance.html +++ b/_modules/networkx/algorithms/dominance.html @@ -475,7 +475,7 @@ <div class="viewcode-block" id="immediate_dominators"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dominance.immediate_dominators.html#networkx.algorithms.dominance.immediate_dominators">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">immediate_dominators</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">start</span><span class="p">):</span> - <span class="sd">"""Returns the immediate dominators of all nodes of a directed graph.</span> +<span class="w"> </span><span class="sd">"""Returns the immediate dominators of all nodes of a directed graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -547,7 +547,7 @@ <div class="viewcode-block" id="dominance_frontiers"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dominance.dominance_frontiers.html#networkx.algorithms.dominance.dominance_frontiers">[docs]</a><span class="k">def</span> <span class="nf">dominance_frontiers</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">start</span><span class="p">):</span> - <span class="sd">"""Returns the dominance frontiers of all nodes of a directed graph.</span> +<span class="w"> </span><span class="sd">"""Returns the dominance frontiers of all nodes of a directed graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -645,7 +645,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/dominating.html b/_modules/networkx/algorithms/dominating.html index 205087b2..123655a1 100644 --- a/_modules/networkx/algorithms/dominating.html +++ b/_modules/networkx/algorithms/dominating.html @@ -471,7 +471,7 @@ <div class="viewcode-block" id="dominating_set"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dominating.dominating_set.html#networkx.algorithms.dominating.dominating_set">[docs]</a><span class="k">def</span> <span class="nf">dominating_set</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">start_with</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Finds a dominating set for the graph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Finds a dominating set for the graph G.</span> <span class="sd"> A *dominating set* for a graph with node set *V* is a subset *D* of</span> <span class="sd"> *V* such that every node not in *D* is adjacent to at least one</span> @@ -529,7 +529,7 @@ <div class="viewcode-block" id="is_dominating_set"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.dominating.is_dominating_set.html#networkx.algorithms.dominating.is_dominating_set">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">is_dominating_set</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nbunch</span><span class="p">):</span> - <span class="sd">"""Checks if `nbunch` is a dominating set for `G`.</span> +<span class="w"> </span><span class="sd">"""Checks if `nbunch` is a dominating set for `G`.</span> <span class="sd"> A *dominating set* for a graph with node set *V* is a subset *D* of</span> <span class="sd"> *V* such that every node not in *D* is adjacent to at least one</span> @@ -605,7 +605,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/efficiency_measures.html b/_modules/networkx/algorithms/efficiency_measures.html index 88b4f8ae..652ce0b2 100644 --- a/_modules/networkx/algorithms/efficiency_measures.html +++ b/_modules/networkx/algorithms/efficiency_measures.html @@ -473,7 +473,7 @@ <div class="viewcode-block" id="efficiency"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.efficiency_measures.efficiency.html#networkx.algorithms.efficiency_measures.efficiency">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">efficiency</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Returns the efficiency of a pair of nodes in a graph.</span> +<span class="w"> </span><span class="sd">"""Returns the efficiency of a pair of nodes in a graph.</span> <span class="sd"> The *efficiency* of a pair of nodes is the multiplicative inverse of the</span> <span class="sd"> shortest path distance between the nodes [1]_. Returns 0 if no path</span> @@ -523,7 +523,7 @@ <div class="viewcode-block" id="global_efficiency"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.efficiency_measures.global_efficiency.html#networkx.algorithms.efficiency_measures.global_efficiency">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">global_efficiency</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the average global efficiency of the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the average global efficiency of the graph.</span> <span class="sd"> The *efficiency* of a pair of nodes in a graph is the multiplicative</span> <span class="sd"> inverse of the shortest path distance between the nodes. The *average</span> @@ -583,7 +583,7 @@ <div class="viewcode-block" id="local_efficiency"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.efficiency_measures.local_efficiency.html#networkx.algorithms.efficiency_measures.local_efficiency">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">local_efficiency</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the average local efficiency of the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the average local efficiency of the graph.</span> <span class="sd"> The *efficiency* of a pair of nodes in a graph is the multiplicative</span> <span class="sd"> inverse of the shortest path distance between the nodes. The *local</span> @@ -677,7 +677,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/euler.html b/_modules/networkx/algorithms/euler.html index 7f0a4cbb..0afa0b74 100644 --- a/_modules/networkx/algorithms/euler.html +++ b/_modules/networkx/algorithms/euler.html @@ -481,7 +481,7 @@ <div class="viewcode-block" id="is_eulerian"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.euler.is_eulerian.html#networkx.algorithms.euler.is_eulerian">[docs]</a><span class="k">def</span> <span class="nf">is_eulerian</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns True if and only if `G` is Eulerian.</span> +<span class="w"> </span><span class="sd">"""Returns True if and only if `G` is Eulerian.</span> <span class="sd"> A graph is *Eulerian* if it has an Eulerian circuit. An *Eulerian</span> <span class="sd"> circuit* is a closed walk that includes each edge of a graph exactly</span> @@ -532,7 +532,7 @@ <div class="viewcode-block" id="is_semieulerian"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.euler.is_semieulerian.html#networkx.algorithms.euler.is_semieulerian">[docs]</a><span class="k">def</span> <span class="nf">is_semieulerian</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Return True iff `G` is semi-Eulerian.</span> +<span class="w"> </span><span class="sd">"""Return True iff `G` is semi-Eulerian.</span> <span class="sd"> G is semi-Eulerian if it has an Eulerian path but no Eulerian circuit.</span> @@ -545,7 +545,7 @@ <span class="k">def</span> <span class="nf">_find_path_start</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Return a suitable starting vertex for an Eulerian path.</span> +<span class="w"> </span><span class="sd">"""Return a suitable starting vertex for an Eulerian path.</span> <span class="sd"> If no path exists, return None.</span> <span class="sd"> """</span> @@ -616,7 +616,7 @@ <div class="viewcode-block" id="eulerian_circuit"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.euler.eulerian_circuit.html#networkx.algorithms.euler.eulerian_circuit">[docs]</a><span class="k">def</span> <span class="nf">eulerian_circuit</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns an iterator over the edges of an Eulerian circuit in `G`.</span> +<span class="w"> </span><span class="sd">"""Returns an iterator over the edges of an Eulerian circuit in `G`.</span> <span class="sd"> An *Eulerian circuit* is a closed walk that includes each edge of a</span> <span class="sd"> graph exactly once.</span> @@ -696,7 +696,7 @@ <div class="viewcode-block" id="has_eulerian_path"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.euler.has_eulerian_path.html#networkx.algorithms.euler.has_eulerian_path">[docs]</a><span class="k">def</span> <span class="nf">has_eulerian_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Return True iff `G` has an Eulerian path.</span> +<span class="w"> </span><span class="sd">"""Return True iff `G` has an Eulerian path.</span> <span class="sd"> An Eulerian path is a path in a graph which uses each edge of a graph</span> <span class="sd"> exactly once. If `source` is specified, then this function checks</span> @@ -790,7 +790,7 @@ <div class="viewcode-block" id="eulerian_path"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.euler.eulerian_path.html#networkx.algorithms.euler.eulerian_path">[docs]</a><span class="k">def</span> <span class="nf">eulerian_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Return an iterator over the edges of an Eulerian path in `G`.</span> +<span class="w"> </span><span class="sd">"""Return an iterator over the edges of an Eulerian path in `G`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -845,7 +845,7 @@ <div class="viewcode-block" id="eulerize"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.euler.eulerize.html#networkx.algorithms.euler.eulerize">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">eulerize</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Transforms a graph into an Eulerian graph.</span> +<span class="w"> </span><span class="sd">"""Transforms a graph into an Eulerian graph.</span> <span class="sd"> If `G` is Eulerian the result is `G` as a MultiGraph, otherwise the result is a smallest</span> <span class="sd"> (in terms of the number of edges) multigraph whose underlying simple graph is `G`.</span> @@ -975,7 +975,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/flow/boykovkolmogorov.html b/_modules/networkx/algorithms/flow/boykovkolmogorov.html index eea1cf92..f456aad0 100644 --- a/_modules/networkx/algorithms/flow/boykovkolmogorov.html +++ b/_modules/networkx/algorithms/flow/boykovkolmogorov.html @@ -476,7 +476,7 @@ <div class="viewcode-block" id="boykov_kolmogorov"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.boykov_kolmogorov.html#networkx.algorithms.flow.boykov_kolmogorov">[docs]</a><span class="k">def</span> <span class="nf">boykov_kolmogorov</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="s2">"capacity"</span><span class="p">,</span> <span class="n">residual</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">value_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Find a maximum single-commodity flow using Boykov-Kolmogorov algorithm.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Find a maximum single-commodity flow using Boykov-Kolmogorov algorithm.</span> <span class="sd"> This function returns the residual network resulting after computing</span> <span class="sd"> the maximum flow. See below for details about the conventions</span> @@ -652,7 +652,7 @@ <span class="n">R_pred</span> <span class="o">=</span> <span class="n">R</span><span class="o">.</span><span class="n">pred</span> <span class="k">def</span> <span class="nf">grow</span><span class="p">():</span> - <span class="sd">"""Bidirectional breadth-first search for the growth stage.</span> +<span class="w"> </span><span class="sd">"""Bidirectional breadth-first search for the growth stage.</span> <span class="sd"> Returns a connecting edge, that is and edge that connects</span> <span class="sd"> a node from the source search tree with a node from the</span> @@ -687,7 +687,7 @@ <span class="k">return</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span> <span class="k">def</span> <span class="nf">augment</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Augmentation stage.</span> +<span class="w"> </span><span class="sd">"""Augmentation stage.</span> <span class="sd"> Reconstruct path and determine its residual capacity.</span> <span class="sd"> We start from a connecting edge, which links a node</span> @@ -735,7 +735,7 @@ <span class="k">return</span> <span class="n">flow</span> <span class="k">def</span> <span class="nf">adopt</span><span class="p">():</span> - <span class="sd">"""Adoption stage.</span> +<span class="w"> </span><span class="sd">"""Adoption stage.</span> <span class="sd"> Reconstruct search trees by adopting or discarding orphans.</span> <span class="sd"> During augmentation stage some edges got saturated and thus</span> @@ -879,7 +879,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/flow/capacityscaling.html b/_modules/networkx/algorithms/flow/capacityscaling.html index edaf0b98..dbf60083 100644 --- a/_modules/networkx/algorithms/flow/capacityscaling.html +++ b/_modules/networkx/algorithms/flow/capacityscaling.html @@ -476,7 +476,7 @@ <span class="k">def</span> <span class="nf">_detect_unboundedness</span><span class="p">(</span><span class="n">R</span><span class="p">):</span> - <span class="sd">"""Detect infinite-capacity negative cycles."""</span> +<span class="w"> </span><span class="sd">"""Detect infinite-capacity negative cycles."""</span> <span class="n">G</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="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="n">R</span><span class="p">)</span> @@ -503,7 +503,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">_build_residual_network</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">demand</span><span class="p">,</span> <span class="n">capacity</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> - <span class="sd">"""Build a residual network and initialize a zero flow."""</span> +<span class="w"> </span><span class="sd">"""Build a residual network and initialize a zero flow."""</span> <span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="n">u</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">demand</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">G</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXUnfeasible</span><span class="p">(</span><span class="s2">"Sum of the demands should be 0."</span><span class="p">)</span> @@ -569,7 +569,7 @@ <span class="k">def</span> <span class="nf">_build_flow_dict</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">R</span><span class="p">,</span> <span class="n">capacity</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> - <span class="sd">"""Build a flow dictionary from a residual network."""</span> +<span class="w"> </span><span class="sd">"""Build a flow dictionary from a residual network."""</span> <span class="n">inf</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s2">"inf"</span><span class="p">)</span> <span class="n">flow_dict</span> <span class="o">=</span> <span class="p">{}</span> <span class="k">if</span> <span class="n">G</span><span class="o">.</span><span class="n">is_multigraph</span><span class="p">():</span> @@ -615,7 +615,7 @@ <div class="viewcode-block" id="capacity_scaling"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.capacity_scaling.html#networkx.algorithms.flow.capacity_scaling">[docs]</a><span class="k">def</span> <span class="nf">capacity_scaling</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">demand</span><span class="o">=</span><span class="s2">"demand"</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="s2">"capacity"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">heap</span><span class="o">=</span><span class="n">BinaryHeap</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Find a minimum cost flow satisfying all demands in digraph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Find a minimum cost flow satisfying all demands in digraph G.</span> <span class="sd"> This is a capacity scaling successive shortest augmenting path algorithm.</span> @@ -916,7 +916,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/flow/dinitz_alg.html b/_modules/networkx/algorithms/flow/dinitz_alg.html index f26fc3c1..28f39557 100644 --- a/_modules/networkx/algorithms/flow/dinitz_alg.html +++ b/_modules/networkx/algorithms/flow/dinitz_alg.html @@ -474,7 +474,7 @@ <div class="viewcode-block" id="dinitz"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.dinitz.html#networkx.algorithms.flow.dinitz">[docs]</a><span class="k">def</span> <span class="nf">dinitz</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="s2">"capacity"</span><span class="p">,</span> <span class="n">residual</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">value_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Find a maximum single-commodity flow using Dinitz' algorithm.</span> +<span class="w"> </span><span class="sd">"""Find a maximum single-commodity flow using Dinitz' algorithm.</span> <span class="sd"> This function returns the residual network resulting after computing</span> <span class="sd"> the maximum flow. See below for details about the conventions</span> @@ -643,7 +643,7 @@ <span class="k">return</span> <span class="n">parents</span> <span class="k">def</span> <span class="nf">depth_first_search</span><span class="p">(</span><span class="n">parents</span><span class="p">):</span> - <span class="sd">"""Build a path using DFS starting from the sink"""</span> +<span class="w"> </span><span class="sd">"""Build a path using DFS starting from the sink"""</span> <span class="n">path</span> <span class="o">=</span> <span class="p">[]</span> <span class="n">u</span> <span class="o">=</span> <span class="n">t</span> <span class="n">flow</span> <span class="o">=</span> <span class="n">INF</span> @@ -723,7 +723,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/flow/edmondskarp.html b/_modules/networkx/algorithms/flow/edmondskarp.html index abef7969..8c2ee927 100644 --- a/_modules/networkx/algorithms/flow/edmondskarp.html +++ b/_modules/networkx/algorithms/flow/edmondskarp.html @@ -472,7 +472,7 @@ <span class="k">def</span> <span class="nf">edmonds_karp_core</span><span class="p">(</span><span class="n">R</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">cutoff</span><span class="p">):</span> - <span class="sd">"""Implementation of the Edmonds-Karp algorithm."""</span> +<span class="w"> </span><span class="sd">"""Implementation of the Edmonds-Karp algorithm."""</span> <span class="n">R_nodes</span> <span class="o">=</span> <span class="n">R</span><span class="o">.</span><span class="n">nodes</span> <span class="n">R_pred</span> <span class="o">=</span> <span class="n">R</span><span class="o">.</span><span class="n">pred</span> <span class="n">R_succ</span> <span class="o">=</span> <span class="n">R</span><span class="o">.</span><span class="n">succ</span> @@ -480,7 +480,7 @@ <span class="n">inf</span> <span class="o">=</span> <span class="n">R</span><span class="o">.</span><span class="n">graph</span><span class="p">[</span><span class="s2">"inf"</span><span class="p">]</span> <span class="k">def</span> <span class="nf">augment</span><span class="p">(</span><span class="n">path</span><span class="p">):</span> - <span class="sd">"""Augment flow along a path from s to t."""</span> +<span class="w"> </span><span class="sd">"""Augment flow along a path from s to t."""</span> <span class="c1"># Determine the path residual capacity.</span> <span class="n">flow</span> <span class="o">=</span> <span class="n">inf</span> <span class="n">it</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> @@ -501,7 +501,7 @@ <span class="k">return</span> <span class="n">flow</span> <span class="k">def</span> <span class="nf">bidirectional_bfs</span><span class="p">():</span> - <span class="sd">"""Bidirectional breadth-first search for an augmenting path."""</span> +<span class="w"> </span><span class="sd">"""Bidirectional breadth-first search for an augmenting path."""</span> <span class="n">pred</span> <span class="o">=</span> <span class="p">{</span><span class="n">s</span><span class="p">:</span> <span class="kc">None</span><span class="p">}</span> <span class="n">q_s</span> <span class="o">=</span> <span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="n">succ</span> <span class="o">=</span> <span class="p">{</span><span class="n">t</span><span class="p">:</span> <span class="kc">None</span><span class="p">}</span> @@ -555,7 +555,7 @@ <span class="k">def</span> <span class="nf">edmonds_karp_impl</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">capacity</span><span class="p">,</span> <span class="n">residual</span><span class="p">,</span> <span class="n">cutoff</span><span class="p">):</span> - <span class="sd">"""Implementation of the Edmonds-Karp algorithm."""</span> +<span class="w"> </span><span class="sd">"""Implementation of the Edmonds-Karp algorithm."""</span> <span class="k">if</span> <span class="n">s</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">G</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="nb">str</span><span class="p">(</span><span class="n">s</span><span class="p">)</span><span class="si">}</span><span class="s2"> not in graph"</span><span class="p">)</span> <span class="k">if</span> <span class="n">t</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">G</span><span class="p">:</span> @@ -583,7 +583,7 @@ <div class="viewcode-block" id="edmonds_karp"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.edmonds_karp.html#networkx.algorithms.flow.edmonds_karp">[docs]</a><span class="k">def</span> <span class="nf">edmonds_karp</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="s2">"capacity"</span><span class="p">,</span> <span class="n">residual</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">value_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Find a maximum single-commodity flow using the Edmonds-Karp algorithm.</span> +<span class="w"> </span><span class="sd">"""Find a maximum single-commodity flow using the Edmonds-Karp algorithm.</span> <span class="sd"> This function returns the residual network resulting after computing</span> <span class="sd"> the maximum flow. See below for details about the conventions</span> @@ -751,7 +751,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/flow/gomory_hu.html b/_modules/networkx/algorithms/flow/gomory_hu.html index 24afe5b4..03d2775a 100644 --- a/_modules/networkx/algorithms/flow/gomory_hu.html +++ b/_modules/networkx/algorithms/flow/gomory_hu.html @@ -477,7 +477,7 @@ <div class="viewcode-block" id="gomory_hu_tree"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.gomory_hu_tree.html#networkx.algorithms.flow.gomory_hu_tree">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">gomory_hu_tree</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="s2">"capacity"</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the Gomory-Hu tree of an undirected graph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the Gomory-Hu tree of an undirected graph G.</span> <span class="sd"> A Gomory-Hu tree of an undirected graph with capacities is a</span> <span class="sd"> weighted tree that represents the minimum s-t cuts for all s-t</span> @@ -688,7 +688,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/flow/maxflow.html b/_modules/networkx/algorithms/flow/maxflow.html index 8553181d..7c1a0fdc 100644 --- a/_modules/networkx/algorithms/flow/maxflow.html +++ b/_modules/networkx/algorithms/flow/maxflow.html @@ -480,7 +480,7 @@ <div class="viewcode-block" id="maximum_flow"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.maximum_flow.html#networkx.algorithms.flow.maximum_flow">[docs]</a><span class="k">def</span> <span class="nf">maximum_flow</span><span class="p">(</span><span class="n">flowG</span><span class="p">,</span> <span class="n">_s</span><span class="p">,</span> <span class="n">_t</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="s2">"capacity"</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> - <span class="sd">"""Find a maximum single-commodity flow.</span> +<span class="w"> </span><span class="sd">"""Find a maximum single-commodity flow.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -626,7 +626,7 @@ <div class="viewcode-block" id="maximum_flow_value"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.maximum_flow_value.html#networkx.algorithms.flow.maximum_flow_value">[docs]</a><span class="k">def</span> <span class="nf">maximum_flow_value</span><span class="p">(</span><span class="n">flowG</span><span class="p">,</span> <span class="n">_s</span><span class="p">,</span> <span class="n">_t</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="s2">"capacity"</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> - <span class="sd">"""Find the value of maximum single-commodity flow.</span> +<span class="w"> </span><span class="sd">"""Find the value of maximum single-commodity flow.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -765,7 +765,7 @@ <div class="viewcode-block" id="minimum_cut"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.minimum_cut.html#networkx.algorithms.flow.minimum_cut">[docs]</a><span class="k">def</span> <span class="nf">minimum_cut</span><span class="p">(</span><span class="n">flowG</span><span class="p">,</span> <span class="n">_s</span><span class="p">,</span> <span class="n">_t</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="s2">"capacity"</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> - <span class="sd">"""Compute the value and the node partition of a minimum (s, t)-cut.</span> +<span class="w"> </span><span class="sd">"""Compute the value and the node partition of a minimum (s, t)-cut.</span> <span class="sd"> Use the max-flow min-cut theorem, i.e., the capacity of a minimum</span> <span class="sd"> capacity cut is equal to the flow value of a maximum flow.</span> @@ -928,7 +928,7 @@ <div class="viewcode-block" id="minimum_cut_value"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.minimum_cut_value.html#networkx.algorithms.flow.minimum_cut_value">[docs]</a><span class="k">def</span> <span class="nf">minimum_cut_value</span><span class="p">(</span><span class="n">flowG</span><span class="p">,</span> <span class="n">_s</span><span class="p">,</span> <span class="n">_t</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="s2">"capacity"</span><span class="p">,</span> <span class="n">flow_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> - <span class="sd">"""Compute the value of a minimum (s, t)-cut.</span> +<span class="w"> </span><span class="sd">"""Compute the value of a minimum (s, t)-cut.</span> <span class="sd"> Use the max-flow min-cut theorem, i.e., the capacity of a minimum</span> <span class="sd"> capacity cut is equal to the flow value of a maximum flow.</span> @@ -1115,7 +1115,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/flow/mincost.html b/_modules/networkx/algorithms/flow/mincost.html index ab2bfb03..8bfc9daf 100644 --- a/_modules/networkx/algorithms/flow/mincost.html +++ b/_modules/networkx/algorithms/flow/mincost.html @@ -471,7 +471,7 @@ <div class="viewcode-block" id="min_cost_flow_cost"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.min_cost_flow_cost.html#networkx.algorithms.flow.min_cost_flow_cost">[docs]</a><span class="k">def</span> <span class="nf">min_cost_flow_cost</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">demand</span><span class="o">=</span><span class="s2">"demand"</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="s2">"capacity"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Find the cost of a minimum cost flow satisfying all demands in digraph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Find the cost of a minimum cost flow satisfying all demands in digraph G.</span> <span class="sd"> G is a digraph with edge costs and capacities and in which nodes</span> <span class="sd"> have demand, i.e., they want to send or receive some amount of</span> @@ -560,7 +560,7 @@ <div class="viewcode-block" id="min_cost_flow"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.min_cost_flow.html#networkx.algorithms.flow.min_cost_flow">[docs]</a><span class="k">def</span> <span class="nf">min_cost_flow</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">demand</span><span class="o">=</span><span class="s2">"demand"</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="s2">"capacity"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a minimum cost flow satisfying all demands in digraph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a minimum cost flow satisfying all demands in digraph G.</span> <span class="sd"> G is a digraph with edge costs and capacities and in which nodes</span> <span class="sd"> have demand, i.e., they want to send or receive some amount of</span> @@ -648,7 +648,7 @@ <div class="viewcode-block" id="cost_of_flow"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.cost_of_flow.html#networkx.algorithms.flow.cost_of_flow">[docs]</a><span class="k">def</span> <span class="nf">cost_of_flow</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">flowDict</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Compute the cost of the flow given by flowDict on graph G.</span> +<span class="w"> </span><span class="sd">"""Compute the cost of the flow given by flowDict on graph G.</span> <span class="sd"> Note that this function does not check for the validity of the</span> <span class="sd"> flow flowDict. This function will fail if the graph G and the</span> @@ -692,7 +692,7 @@ <div class="viewcode-block" id="max_flow_min_cost"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.max_flow_min_cost.html#networkx.algorithms.flow.max_flow_min_cost">[docs]</a><span class="k">def</span> <span class="nf">max_flow_min_cost</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="s2">"capacity"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns a maximum (s, t)-flow of minimum cost.</span> +<span class="w"> </span><span class="sd">"""Returns a maximum (s, t)-flow of minimum cost.</span> <span class="sd"> G is a digraph with edge costs and capacities. There is a source</span> <span class="sd"> node s and a sink node t. This function finds a maximum flow from</span> @@ -843,7 +843,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/flow/networksimplex.html b/_modules/networkx/algorithms/flow/networksimplex.html index 50c056f4..e125a490 100644 --- a/_modules/networkx/algorithms/flow/networksimplex.html +++ b/_modules/networkx/algorithms/flow/networksimplex.html @@ -548,7 +548,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">_spanning_tree_initialized</span> <span class="o">=</span> <span class="kc">True</span> <span class="c1"># True only if all the assignments pass</span> <span class="k">def</span> <span class="nf">find_apex</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">q</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Find the lowest common ancestor of nodes p and q in the spanning tree.</span> <span class="sd"> """</span> <span class="n">size_p</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">subtree_size</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> @@ -570,7 +570,7 @@ <span class="k">return</span> <span class="n">p</span> <span class="k">def</span> <span class="nf">trace_path</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">w</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the nodes and edges on the path from node p to its ancestor w.</span> <span class="sd"> """</span> <span class="n">Wn</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span><span class="p">]</span> @@ -582,7 +582,7 @@ <span class="k">return</span> <span class="n">Wn</span><span class="p">,</span> <span class="n">We</span> <span class="k">def</span> <span class="nf">find_cycle</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">q</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the nodes and edges on the cycle containing edge i == (p, q)</span> <span class="sd"> when the latter is added to the spanning tree.</span> @@ -601,7 +601,7 @@ <span class="k">return</span> <span class="n">Wn</span><span class="p">,</span> <span class="n">We</span> <span class="k">def</span> <span class="nf">augment_flow</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Wn</span><span class="p">,</span> <span class="n">We</span><span class="p">,</span> <span class="n">f</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Augment f units of flow along a cycle represented by Wn and We.</span> <span class="sd"> """</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">We</span><span class="p">,</span> <span class="n">Wn</span><span class="p">):</span> @@ -611,7 +611,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">edge_flow</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-=</span> <span class="n">f</span> <span class="k">def</span> <span class="nf">trace_subtree</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Yield the nodes in the subtree rooted at a node p.</span> <span class="sd"> """</span> <span class="k">yield</span> <span class="n">p</span> @@ -621,7 +621,7 @@ <span class="k">yield</span> <span class="n">p</span> <span class="k">def</span> <span class="nf">remove_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Remove an edge (s, t) where parent[t] == s from the spanning tree.</span> <span class="sd"> """</span> <span class="n">size_t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">subtree_size</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> @@ -645,7 +645,7 @@ <span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="k">def</span> <span class="nf">make_root</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">q</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Make a node q the root of its containing subtree.</span> <span class="sd"> """</span> <span class="n">ancestors</span> <span class="o">=</span> <span class="p">[]</span> @@ -683,7 +683,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">last_descendent_dft</span><span class="p">[</span><span class="n">q</span><span class="p">]</span> <span class="o">=</span> <span class="n">last_p</span> <span class="k">def</span> <span class="nf">add_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">q</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Add an edge (p, q) to the spanning tree where q is the root of a subtree.</span> <span class="sd"> """</span> <span class="n">last_p</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_descendent_dft</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> @@ -707,7 +707,7 @@ <span class="n">p</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="k">def</span> <span class="nf">update_potentials</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">q</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Update the potentials of the nodes in the subtree rooted at a node</span> <span class="sd"> q connected to its parent p by an edge i.</span> <span class="sd"> """</span> @@ -719,7 +719,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">node_potentials</span><span class="p">[</span><span class="n">q</span><span class="p">]</span> <span class="o">+=</span> <span class="n">d</span> <span class="k">def</span> <span class="nf">reduced_cost</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span> - <span class="sd">"""Returns the reduced cost of an edge i."""</span> +<span class="w"> </span><span class="sd">"""Returns the reduced cost of an edge i."""</span> <span class="n">c</span> <span class="o">=</span> <span class="p">(</span> <span class="bp">self</span><span class="o">.</span><span class="n">edge_weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">node_potentials</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">edge_sources</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> @@ -728,7 +728,7 @@ <span class="k">return</span> <span class="n">c</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">edge_flow</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="o">-</span><span class="n">c</span> <span class="k">def</span> <span class="nf">find_entering_edges</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Yield entering edges until none can be found."""</span> +<span class="w"> </span><span class="sd">"""Yield entering edges until none can be found."""</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">edge_count</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="k">return</span> @@ -770,7 +770,7 @@ <span class="c1"># optimal.</span> <span class="k">def</span> <span class="nf">residual_capacity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span> - <span class="sd">"""Returns the residual capacity of an edge i in the direction away</span> +<span class="w"> </span><span class="sd">"""Returns the residual capacity of an edge i in the direction away</span> <span class="sd"> from its endpoint p.</span> <span class="sd"> """</span> <span class="k">return</span> <span class="p">(</span> @@ -780,7 +780,7 @@ <span class="p">)</span> <span class="k">def</span> <span class="nf">find_leaving_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Wn</span><span class="p">,</span> <span class="n">We</span><span class="p">):</span> - <span class="sd">"""Returns the leaving edge in a cycle represented by Wn and We."""</span> +<span class="w"> </span><span class="sd">"""Returns the leaving edge in a cycle represented by Wn and We."""</span> <span class="n">j</span><span class="p">,</span> <span class="n">s</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">reversed</span><span class="p">(</span><span class="n">We</span><span class="p">),</span> <span class="nb">reversed</span><span class="p">(</span><span class="n">Wn</span><span class="p">)),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">i_p</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">residual_capacity</span><span class="p">(</span><span class="o">*</span><span class="n">i_p</span><span class="p">),</span> @@ -791,7 +791,7 @@ <div class="viewcode-block" id="network_simplex"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.network_simplex.html#networkx.algorithms.flow.network_simplex">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">network_simplex</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">demand</span><span class="o">=</span><span class="s2">"demand"</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="s2">"capacity"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Find a minimum cost flow satisfying all demands in digraph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Find a minimum cost flow satisfying all demands in digraph G.</span> <span class="sd"> This is a primal network simplex algorithm that uses the leaving</span> <span class="sd"> arc rule to prevent cycling.</span> @@ -1084,7 +1084,7 @@ <span class="n">flow_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">n</span><span class="p">:</span> <span class="p">{}</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">DEAF</span><span class="o">.</span><span class="n">node_list</span><span class="p">}</span> <span class="k">def</span> <span class="nf">add_entry</span><span class="p">(</span><span class="n">e</span><span class="p">):</span> - <span class="sd">"""Add a flow dict entry."""</span> +<span class="w"> </span><span class="sd">"""Add a flow dict entry."""</span> <span class="n">d</span> <span class="o">=</span> <span class="n">flow_dict</span><span class="p">[</span><span class="n">e</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">e</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">2</span><span class="p">]:</span> <span class="k">try</span><span class="p">:</span> @@ -1176,7 +1176,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/flow/preflowpush.html b/_modules/networkx/algorithms/flow/preflowpush.html index 04e5cd8f..eac762cc 100644 --- a/_modules/networkx/algorithms/flow/preflowpush.html +++ b/_modules/networkx/algorithms/flow/preflowpush.html @@ -483,7 +483,7 @@ <span class="k">def</span> <span class="nf">preflow_push_impl</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">capacity</span><span class="p">,</span> <span class="n">residual</span><span class="p">,</span> <span class="n">global_relabel_freq</span><span class="p">,</span> <span class="n">value_only</span><span class="p">):</span> - <span class="sd">"""Implementation of the highest-label preflow-push algorithm."""</span> +<span class="w"> </span><span class="sd">"""Implementation of the highest-label preflow-push algorithm."""</span> <span class="k">if</span> <span class="n">s</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">G</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="nb">str</span><span class="p">(</span><span class="n">s</span><span class="p">)</span><span class="si">}</span><span class="s2"> not in graph"</span><span class="p">)</span> <span class="k">if</span> <span class="n">t</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">G</span><span class="p">:</span> @@ -514,7 +514,7 @@ <span class="n">e</span><span class="p">[</span><span class="s2">"flow"</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> <span class="k">def</span> <span class="nf">reverse_bfs</span><span class="p">(</span><span class="n">src</span><span class="p">):</span> - <span class="sd">"""Perform a reverse breadth-first search from src in the residual</span> +<span class="w"> </span><span class="sd">"""Perform a reverse breadth-first search from src in the residual</span> <span class="sd"> network.</span> <span class="sd"> """</span> <span class="n">heights</span> <span class="o">=</span> <span class="p">{</span><span class="n">src</span><span class="p">:</span> <span class="mi">0</span><span class="p">}</span> @@ -551,7 +551,7 @@ <span class="n">R_nodes</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="s2">"curr_edge"</span><span class="p">]</span> <span class="o">=</span> <span class="n">CurrentEdge</span><span class="p">(</span><span class="n">R_succ</span><span class="p">[</span><span class="n">u</span><span class="p">])</span> <span class="k">def</span> <span class="nf">push</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">flow</span><span class="p">):</span> - <span class="sd">"""Push flow units of flow from u to v."""</span> +<span class="w"> </span><span class="sd">"""Push flow units of flow from u to v."""</span> <span class="n">R_succ</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="n">v</span><span class="p">][</span><span class="s2">"flow"</span><span class="p">]</span> <span class="o">+=</span> <span class="n">flow</span> <span class="n">R_succ</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="n">u</span><span class="p">][</span><span class="s2">"flow"</span><span class="p">]</span> <span class="o">-=</span> <span class="n">flow</span> <span class="n">R_nodes</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="s2">"excess"</span><span class="p">]</span> <span class="o">-=</span> <span class="n">flow</span> @@ -575,7 +575,7 @@ <span class="n">level</span><span class="o">.</span><span class="n">inactive</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">u</span><span class="p">)</span> <span class="k">def</span> <span class="nf">activate</span><span class="p">(</span><span class="n">v</span><span class="p">):</span> - <span class="sd">"""Move a node from the inactive set to the active set of its level."""</span> +<span class="w"> </span><span class="sd">"""Move a node from the inactive set to the active set of its level."""</span> <span class="k">if</span> <span class="n">v</span> <span class="o">!=</span> <span class="n">s</span> <span class="ow">and</span> <span class="n">v</span> <span class="o">!=</span> <span class="n">t</span><span class="p">:</span> <span class="n">level</span> <span class="o">=</span> <span class="n">levels</span><span class="p">[</span><span class="n">R_nodes</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="s2">"height"</span><span class="p">]]</span> <span class="k">if</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">level</span><span class="o">.</span><span class="n">inactive</span><span class="p">:</span> @@ -583,7 +583,7 @@ <span class="n">level</span><span class="o">.</span><span class="n">active</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">def</span> <span class="nf">relabel</span><span class="p">(</span><span class="n">u</span><span class="p">):</span> - <span class="sd">"""Relabel a node to create an admissible edge."""</span> +<span class="w"> </span><span class="sd">"""Relabel a node to create an admissible edge."""</span> <span class="n">grt</span><span class="o">.</span><span class="n">add_work</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">R_succ</span><span class="p">[</span><span class="n">u</span><span class="p">]))</span> <span class="k">return</span> <span class="p">(</span> <span class="nb">min</span><span class="p">(</span> @@ -595,7 +595,7 @@ <span class="p">)</span> <span class="k">def</span> <span class="nf">discharge</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">is_phase1</span><span class="p">):</span> - <span class="sd">"""Discharge a node until it becomes inactive or, during phase 1 (see</span> +<span class="w"> </span><span class="sd">"""Discharge a node until it becomes inactive or, during phase 1 (see</span> <span class="sd"> below), its height reaches at least n. The node is known to have the</span> <span class="sd"> largest height among active nodes.</span> <span class="sd"> """</span> @@ -636,7 +636,7 @@ <span class="k">return</span> <span class="n">next_height</span> <span class="k">def</span> <span class="nf">gap_heuristic</span><span class="p">(</span><span class="n">height</span><span class="p">):</span> - <span class="sd">"""Apply the gap heuristic."""</span> +<span class="w"> </span><span class="sd">"""Apply the gap heuristic."""</span> <span class="c1"># Move all nodes at levels (height + 1) to max_height to level n + 1.</span> <span class="k">for</span> <span class="n">level</span> <span class="ow">in</span> <span class="n">islice</span><span class="p">(</span><span class="n">levels</span><span class="p">,</span> <span class="n">height</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">max_height</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">level</span><span class="o">.</span><span class="n">active</span><span class="p">:</span> @@ -649,7 +649,7 @@ <span class="n">level</span><span class="o">.</span><span class="n">inactive</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span> <span class="k">def</span> <span class="nf">global_relabel</span><span class="p">(</span><span class="n">from_sink</span><span class="p">):</span> - <span class="sd">"""Apply the global relabeling heuristic."""</span> +<span class="w"> </span><span class="sd">"""Apply the global relabeling heuristic."""</span> <span class="n">src</span> <span class="o">=</span> <span class="n">t</span> <span class="k">if</span> <span class="n">from_sink</span> <span class="k">else</span> <span class="n">s</span> <span class="n">heights</span> <span class="o">=</span> <span class="n">reverse_bfs</span><span class="p">(</span><span class="n">src</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">from_sink</span><span class="p">:</span> @@ -754,7 +754,7 @@ <div class="viewcode-block" id="preflow_push"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.preflow_push.html#networkx.algorithms.flow.preflow_push">[docs]</a><span class="k">def</span> <span class="nf">preflow_push</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="s2">"capacity"</span><span class="p">,</span> <span class="n">residual</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">global_relabel_freq</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">value_only</span><span class="o">=</span><span class="kc">False</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Find a maximum single-commodity flow using the highest-label</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Find a maximum single-commodity flow using the highest-label</span> <span class="sd"> preflow-push algorithm.</span> <span class="sd"> This function returns the residual network resulting after computing</span> @@ -935,7 +935,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/flow/shortestaugmentingpath.html b/_modules/networkx/algorithms/flow/shortestaugmentingpath.html index 27ebd430..d66305e1 100644 --- a/_modules/networkx/algorithms/flow/shortestaugmentingpath.html +++ b/_modules/networkx/algorithms/flow/shortestaugmentingpath.html @@ -476,7 +476,7 @@ <span class="k">def</span> <span class="nf">shortest_augmenting_path_impl</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">capacity</span><span class="p">,</span> <span class="n">residual</span><span class="p">,</span> <span class="n">two_phase</span><span class="p">,</span> <span class="n">cutoff</span><span class="p">):</span> - <span class="sd">"""Implementation of the shortest augmenting path algorithm."""</span> +<span class="w"> </span><span class="sd">"""Implementation of the shortest augmenting path algorithm."""</span> <span class="k">if</span> <span class="n">s</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">G</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="nb">str</span><span class="p">(</span><span class="n">s</span><span class="p">)</span><span class="si">}</span><span class="s2"> not in graph"</span><span class="p">)</span> <span class="k">if</span> <span class="n">t</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">G</span><span class="p">:</span> @@ -531,7 +531,7 @@ <span class="n">inf</span> <span class="o">=</span> <span class="n">R</span><span class="o">.</span><span class="n">graph</span><span class="p">[</span><span class="s2">"inf"</span><span class="p">]</span> <span class="k">def</span> <span class="nf">augment</span><span class="p">(</span><span class="n">path</span><span class="p">):</span> - <span class="sd">"""Augment flow along a path from s to t."""</span> +<span class="w"> </span><span class="sd">"""Augment flow along a path from s to t."""</span> <span class="c1"># Determine the path residual capacity.</span> <span class="n">flow</span> <span class="o">=</span> <span class="n">inf</span> <span class="n">it</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> @@ -552,7 +552,7 @@ <span class="k">return</span> <span class="n">flow</span> <span class="k">def</span> <span class="nf">relabel</span><span class="p">(</span><span class="n">u</span><span class="p">):</span> - <span class="sd">"""Relabel a node to create an admissible edge."""</span> +<span class="w"> </span><span class="sd">"""Relabel a node to create an admissible edge."""</span> <span class="n">height</span> <span class="o">=</span> <span class="n">n</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="n">attr</span> <span class="ow">in</span> <span class="n">R_succ</span><span class="p">[</span><span class="n">u</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> <span class="k">if</span> <span class="n">attr</span><span class="p">[</span><span class="s2">"flow"</span><span class="p">]</span> <span class="o"><</span> <span class="n">attr</span><span class="p">[</span><span class="s2">"capacity"</span><span class="p">]:</span> @@ -636,7 +636,7 @@ <span class="n">two_phase</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Find a maximum single-commodity flow using the shortest augmenting path</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Find a maximum single-commodity flow using the shortest augmenting path</span> <span class="sd"> algorithm.</span> <span class="sd"> This function returns the residual network resulting after computing</span> @@ -810,7 +810,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/flow/utils.html b/_modules/networkx/algorithms/flow/utils.html index 3a345604..5d37c97e 100644 --- a/_modules/networkx/algorithms/flow/utils.html +++ b/_modules/networkx/algorithms/flow/utils.html @@ -480,7 +480,7 @@ <span class="k">class</span> <span class="nc">CurrentEdge</span><span class="p">:</span> - <span class="sd">"""Mechanism for iterating over out-edges incident to a node in a circular</span> +<span class="w"> </span><span class="sd">"""Mechanism for iterating over out-edges incident to a node in a circular</span> <span class="sd"> manner. StopIteration exception is raised when wraparound occurs.</span> <span class="sd"> """</span> @@ -507,7 +507,7 @@ <span class="k">class</span> <span class="nc">Level</span><span class="p">:</span> - <span class="sd">"""Active and inactive nodes in a level."""</span> +<span class="w"> </span><span class="sd">"""Active and inactive nodes in a level."""</span> <span class="vm">__slots__</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"active"</span><span class="p">,</span> <span class="s2">"inactive"</span><span class="p">)</span> @@ -517,7 +517,7 @@ <span class="k">class</span> <span class="nc">GlobalRelabelThreshold</span><span class="p">:</span> - <span class="sd">"""Measurement of work before the global relabeling heuristic should be</span> +<span class="w"> </span><span class="sd">"""Measurement of work before the global relabeling heuristic should be</span> <span class="sd"> applied.</span> <span class="sd"> """</span> @@ -536,7 +536,7 @@ <div class="viewcode-block" id="build_residual_network"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.flow.build_residual_network.html#networkx.algorithms.flow.build_residual_network">[docs]</a><span class="k">def</span> <span class="nf">build_residual_network</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">capacity</span><span class="p">):</span> - <span class="sd">"""Build a residual network and initialize a zero flow.</span> +<span class="w"> </span><span class="sd">"""Build a residual network and initialize a zero flow.</span> <span class="sd"> The residual network :samp:`R` from an input graph :samp:`G` has the</span> <span class="sd"> same nodes as :samp:`G`. :samp:`R` is a DiGraph that contains a pair</span> @@ -617,7 +617,7 @@ <span class="k">def</span> <span class="nf">detect_unboundedness</span><span class="p">(</span><span class="n">R</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span> - <span class="sd">"""Detect an infinite-capacity s-t path in R."""</span> +<span class="w"> </span><span class="sd">"""Detect an infinite-capacity s-t path in R."""</span> <span class="n">q</span> <span class="o">=</span> <span class="n">deque</span><span class="p">([</span><span class="n">s</span><span class="p">])</span> <span class="n">seen</span> <span class="o">=</span> <span class="p">{</span><span class="n">s</span><span class="p">}</span> <span class="n">inf</span> <span class="o">=</span> <span class="n">R</span><span class="o">.</span><span class="n">graph</span><span class="p">[</span><span class="s2">"inf"</span><span class="p">]</span> @@ -634,7 +634,7 @@ <span class="k">def</span> <span class="nf">build_flow_dict</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">R</span><span class="p">):</span> - <span class="sd">"""Build a flow dictionary from a residual network."""</span> +<span class="w"> </span><span class="sd">"""Build a flow dictionary from a residual network."""</span> <span class="n">flow_dict</span> <span class="o">=</span> <span class="p">{}</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">G</span><span class="p">:</span> <span class="n">flow_dict</span><span class="p">[</span><span class="n">u</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">G</span><span class="p">[</span><span class="n">u</span><span class="p">]}</span> @@ -693,7 +693,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/graph_hashing.html b/_modules/networkx/algorithms/graph_hashing.html index 21e26642..b94e024d 100644 --- a/_modules/networkx/algorithms/graph_hashing.html +++ b/_modules/networkx/algorithms/graph_hashing.html @@ -487,7 +487,7 @@ <span class="k">def</span> <span class="nf">_neighborhood_aggregate</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">node</span><span class="p">,</span> <span class="n">node_labels</span><span class="p">,</span> <span class="n">edge_attr</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Compute new labels for given node by aggregating</span> <span class="sd"> the labels of each node's neighbors.</span> <span class="sd"> """</span> @@ -501,7 +501,7 @@ <div class="viewcode-block" id="weisfeiler_lehman_graph_hash"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash.html#networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash">[docs]</a><span class="k">def</span> <span class="nf">weisfeiler_lehman_graph_hash</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">edge_attr</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">node_attr</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">digest_size</span><span class="o">=</span><span class="mi">16</span> <span class="p">):</span> - <span class="sd">"""Return Weisfeiler Lehman (WL) graph hash.</span> +<span class="w"> </span><span class="sd">"""Return Weisfeiler Lehman (WL) graph hash.</span> <span class="sd"> The function iteratively aggregates and hashes neighbourhoods of each node.</span> <span class="sd"> After each node's neighbors are hashed to obtain updated node labels,</span> @@ -595,7 +595,7 @@ <span class="sd"> """</span> <span class="k">def</span> <span class="nf">weisfeiler_lehman_step</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">edge_attr</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Apply neighborhood aggregation to each node</span> <span class="sd"> in the graph.</span> <span class="sd"> Computes a dictionary with labels for each node.</span> @@ -623,7 +623,7 @@ <div class="viewcode-block" id="weisfeiler_lehman_subgraph_hashes"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes.html#networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes">[docs]</a><span class="k">def</span> <span class="nf">weisfeiler_lehman_subgraph_hashes</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">edge_attr</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">node_attr</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">digest_size</span><span class="o">=</span><span class="mi">16</span> <span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return a dictionary of subgraph hashes by node.</span> <span class="sd"> The dictionary is keyed by node to a list of hashes in increasingly</span> @@ -741,7 +741,7 @@ <span class="sd"> """</span> <span class="k">def</span> <span class="nf">weisfeiler_lehman_step</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">node_subgraph_hashes</span><span class="p">,</span> <span class="n">edge_attr</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Apply neighborhood aggregation to each node</span> <span class="sd"> in the graph.</span> <span class="sd"> Computes a dictionary with labels for each node.</span> @@ -816,7 +816,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/graphical.html b/_modules/networkx/algorithms/graphical.html index 0553a3fb..b7fce3d7 100644 --- a/_modules/networkx/algorithms/graphical.html +++ b/_modules/networkx/algorithms/graphical.html @@ -478,7 +478,7 @@ <div class="viewcode-block" id="is_graphical"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.graphical.is_graphical.html#networkx.algorithms.graphical.is_graphical">[docs]</a><span class="k">def</span> <span class="nf">is_graphical</span><span class="p">(</span><span class="n">sequence</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"eg"</span><span class="p">):</span> - <span class="sd">"""Returns True if sequence is a valid degree sequence.</span> +<span class="w"> </span><span class="sd">"""Returns True if sequence is a valid degree sequence.</span> <span class="sd"> A degree sequence is valid if some graph can realize it.</span> @@ -550,7 +550,7 @@ <div class="viewcode-block" id="is_valid_degree_sequence_havel_hakimi"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi.html#networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi">[docs]</a><span class="k">def</span> <span class="nf">is_valid_degree_sequence_havel_hakimi</span><span class="p">(</span><span class="n">deg_sequence</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns True if deg_sequence can be realized by a simple graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns True if deg_sequence can be realized by a simple graph.</span> <span class="sd"> The validation proceeds using the Havel-Hakimi theorem</span> <span class="sd"> [havel1955]_, [hakimi1962]_, [CL1996]_.</span> @@ -626,7 +626,7 @@ <div class="viewcode-block" id="is_valid_degree_sequence_erdos_gallai"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai.html#networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai">[docs]</a><span class="k">def</span> <span class="nf">is_valid_degree_sequence_erdos_gallai</span><span class="p">(</span><span class="n">deg_sequence</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns True if deg_sequence can be realized by a simple graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns True if deg_sequence can be realized by a simple graph.</span> <span class="sd"> The validation is done using the ErdÅ‘s-Gallai theorem [EG1960]_.</span> @@ -703,7 +703,7 @@ <div class="viewcode-block" id="is_multigraphical"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.graphical.is_multigraphical.html#networkx.algorithms.graphical.is_multigraphical">[docs]</a><span class="k">def</span> <span class="nf">is_multigraphical</span><span class="p">(</span><span class="n">sequence</span><span class="p">):</span> - <span class="sd">"""Returns True if some multigraph can realize the sequence.</span> +<span class="w"> </span><span class="sd">"""Returns True if some multigraph can realize the sequence.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -740,7 +740,7 @@ <div class="viewcode-block" id="is_pseudographical"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.graphical.is_pseudographical.html#networkx.algorithms.graphical.is_pseudographical">[docs]</a><span class="k">def</span> <span class="nf">is_pseudographical</span><span class="p">(</span><span class="n">sequence</span><span class="p">):</span> - <span class="sd">"""Returns True if some pseudograph can realize the sequence.</span> +<span class="w"> </span><span class="sd">"""Returns True if some pseudograph can realize the sequence.</span> <span class="sd"> Every nonnegative integer sequence with an even sum is pseudographical</span> <span class="sd"> (see [1]_).</span> @@ -773,7 +773,7 @@ <div class="viewcode-block" id="is_digraphical"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.graphical.is_digraphical.html#networkx.algorithms.graphical.is_digraphical">[docs]</a><span class="k">def</span> <span class="nf">is_digraphical</span><span class="p">(</span><span class="n">in_sequence</span><span class="p">,</span> <span class="n">out_sequence</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns True if some directed graph can realize the in- and out-degree</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns True if some directed graph can realize the in- and out-degree</span> <span class="sd"> sequences.</span> <span class="sd"> Parameters</span> @@ -917,7 +917,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/hierarchy.html b/_modules/networkx/algorithms/hierarchy.html index ff7a61f8..48a0787f 100644 --- a/_modules/networkx/algorithms/hierarchy.html +++ b/_modules/networkx/algorithms/hierarchy.html @@ -470,7 +470,7 @@ <div class="viewcode-block" id="flow_hierarchy"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.hierarchy.flow_hierarchy.html#networkx.algorithms.hierarchy.flow_hierarchy">[docs]</a><span class="k">def</span> <span class="nf">flow_hierarchy</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the flow hierarchy of a directed network.</span> +<span class="w"> </span><span class="sd">"""Returns the flow hierarchy of a directed network.</span> <span class="sd"> Flow hierarchy is defined as the fraction of edges not participating</span> <span class="sd"> in cycles in a directed graph [1]_.</span> @@ -559,7 +559,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/hybrid.html b/_modules/networkx/algorithms/hybrid.html index 2da1c5cd..71d62f62 100644 --- a/_modules/networkx/algorithms/hybrid.html +++ b/_modules/networkx/algorithms/hybrid.html @@ -474,7 +474,7 @@ <div class="viewcode-block" id="kl_connected_subgraph"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.hybrid.kl_connected_subgraph.html#networkx.algorithms.hybrid.kl_connected_subgraph">[docs]</a><span class="k">def</span> <span class="nf">kl_connected_subgraph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">low_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">same_as_graph</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns the maximum locally `(k, l)`-connected subgraph of `G`.</span> +<span class="w"> </span><span class="sd">"""Returns the maximum locally `(k, l)`-connected subgraph of `G`.</span> <span class="sd"> A graph is locally `(k, l)`-connected if for each edge `(u, v)` in the</span> <span class="sd"> graph there are at least `l` edge-disjoint paths of length at most `k`</span> @@ -578,7 +578,7 @@ <div class="viewcode-block" id="is_kl_connected"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.hybrid.is_kl_connected.html#networkx.algorithms.hybrid.is_kl_connected">[docs]</a><span class="k">def</span> <span class="nf">is_kl_connected</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">low_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns True if and only if `G` is locally `(k, l)`-connected.</span> +<span class="w"> </span><span class="sd">"""Returns True if and only if `G` is locally `(k, l)`-connected.</span> <span class="sd"> A graph is locally `(k, l)`-connected if for each edge `(u, v)` in the</span> <span class="sd"> graph there are at least `l` edge-disjoint paths of length at most `k`</span> @@ -705,7 +705,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/isolate.html b/_modules/networkx/algorithms/isolate.html index 7a1d5c6d..6df3744c 100644 --- a/_modules/networkx/algorithms/isolate.html +++ b/_modules/networkx/algorithms/isolate.html @@ -471,7 +471,7 @@ <div class="viewcode-block" id="is_isolate"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.isolate.is_isolate.html#networkx.algorithms.isolate.is_isolate">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">is_isolate</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span> - <span class="sd">"""Determines whether a node is an isolate.</span> +<span class="w"> </span><span class="sd">"""Determines whether a node is an isolate.</span> <span class="sd"> An *isolate* is a node with no neighbors (that is, with degree</span> <span class="sd"> zero). For directed graphs, this means no in-neighbors and no</span> @@ -504,7 +504,7 @@ <div class="viewcode-block" id="isolates"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.isolate.isolates.html#networkx.algorithms.isolate.isolates">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">isolates</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Iterator over isolates in the graph.</span> +<span class="w"> </span><span class="sd">"""Iterator over isolates in the graph.</span> <span class="sd"> An *isolate* is a node with no neighbors (that is, with degree</span> <span class="sd"> zero). For directed graphs, this means no in-neighbors and no</span> @@ -550,7 +550,7 @@ <div class="viewcode-block" id="number_of_isolates"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.isolate.number_of_isolates.html#networkx.algorithms.isolate.number_of_isolates">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">number_of_isolates</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the number of isolates in the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the number of isolates in the graph.</span> <span class="sd"> An *isolate* is a node with no neighbors (that is, with degree</span> <span class="sd"> zero). For directed graphs, this means no in-neighbors and no</span> @@ -619,7 +619,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/isomorphism/ismags.html b/_modules/networkx/algorithms/isomorphism/ismags.html index d10ca9d7..475e53d7 100644 --- a/_modules/networkx/algorithms/isomorphism/ismags.html +++ b/_modules/networkx/algorithms/isomorphism/ismags.html @@ -578,7 +578,7 @@ <span class="k">def</span> <span class="nf">are_all_equal</span><span class="p">(</span><span class="n">iterable</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns ``True`` if and only if all elements in `iterable` are equal; and</span> <span class="sd"> ``False`` otherwise.</span> @@ -608,7 +608,7 @@ <span class="k">def</span> <span class="nf">make_partitions</span><span class="p">(</span><span class="n">items</span><span class="p">,</span> <span class="n">test</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Partitions items into sets based on the outcome of ``test(item1, item2)``.</span> <span class="sd"> Pairs of items for which `test` returns `True` end up in the same set.</span> @@ -646,7 +646,7 @@ <span class="k">def</span> <span class="nf">partition_to_color</span><span class="p">(</span><span class="n">partitions</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Creates a dictionary with for every item in partition for every partition</span> <span class="sd"> in partitions the index of partition in partitions.</span> @@ -667,7 +667,7 @@ <span class="k">def</span> <span class="nf">intersect</span><span class="p">(</span><span class="n">collection_of_sets</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Given an collection of sets, returns the intersection of those sets.</span> <span class="sd"> Parameters</span> @@ -688,7 +688,7 @@ <div class="viewcode-block" id="ISMAGS"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.ISMAGS.html#networkx.algorithms.isomorphism.ISMAGS">[docs]</a><span class="k">class</span> <span class="nc">ISMAGS</span><span class="p">:</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Implements the ISMAGS subgraph matching algorith. [1]_ ISMAGS stands for</span> <span class="sd"> "Index-based Subgraph Matching Algorithm with General Symmetries". As the</span> <span class="sd"> name implies, it is symmetry aware and will only generate non-symmetric</span> @@ -731,7 +731,7 @@ <span class="sd"> """</span> <div class="viewcode-block" id="ISMAGS.__init__"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.ISMAGS.html#networkx.algorithms.isomorphism.ISMAGS.__init__">[docs]</a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">subgraph</span><span class="p">,</span> <span class="n">node_match</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">edge_match</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cache</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> <span class="sd"> graph: networkx.Graph</span> @@ -904,7 +904,7 @@ <span class="k">return</span> <span class="n">comparer</span> <div class="viewcode-block" id="ISMAGS.find_isomorphisms"><a class="viewcode-back" href="../../../../reference/algorithms/generated/generated/networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms.html#networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms">[docs]</a> <span class="k">def</span> <span class="nf">find_isomorphisms</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">symmetry</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Find all subgraph isomorphisms between subgraph and graph</span> +<span class="w"> </span><span class="sd">"""Find all subgraph isomorphisms between subgraph and graph</span> <span class="sd"> Finds isomorphisms where :attr:`subgraph` <= :attr:`graph`.</span> @@ -953,7 +953,7 @@ <span class="nd">@staticmethod</span> <span class="k">def</span> <span class="nf">_find_neighbor_color_count</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">node</span><span class="p">,</span> <span class="n">node_color</span><span class="p">,</span> <span class="n">edge_color</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> For `node` in `graph`, count the number of edges of a specific color</span> <span class="sd"> it has to nodes of a specific color.</span> <span class="sd"> """</span> @@ -969,7 +969,7 @@ <span class="k">return</span> <span class="n">counts</span> <span class="k">def</span> <span class="nf">_get_lookahead_candidates</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns a mapping of {subgraph node: collection of graph nodes} for</span> <span class="sd"> which the graph nodes are feasible candidates for the subgraph node, as</span> <span class="sd"> determined by looking ahead one edge.</span> @@ -1001,7 +1001,7 @@ <span class="k">return</span> <span class="n">candidates</span> <div class="viewcode-block" id="ISMAGS.largest_common_subgraph"><a class="viewcode-back" href="../../../../reference/algorithms/generated/generated/networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph.html#networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph">[docs]</a> <span class="k">def</span> <span class="nf">largest_common_subgraph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">symmetry</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Find the largest common induced subgraphs between :attr:`subgraph` and</span> <span class="sd"> :attr:`graph`.</span> @@ -1040,7 +1040,7 @@ <span class="k">return</span></div> <div class="viewcode-block" id="ISMAGS.analyze_symmetry"><a class="viewcode-back" href="../../../../reference/algorithms/generated/generated/networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry.html#networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry">[docs]</a> <span class="k">def</span> <span class="nf">analyze_symmetry</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">node_partitions</span><span class="p">,</span> <span class="n">edge_colors</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Find a minimal set of permutations and corresponding co-sets that</span> <span class="sd"> describe the symmetry of `graph`, given the node and edge equalities</span> <span class="sd"> given by `node_partitions` and `edge_colors`, respectively.</span> @@ -1095,7 +1095,7 @@ <span class="k">return</span> <span class="n">permutations</span><span class="p">,</span> <span class="n">cosets</span></div> <div class="viewcode-block" id="ISMAGS.is_isomorphic"><a class="viewcode-back" href="../../../../reference/algorithms/generated/generated/networkx.algorithms.isomorphism.ISMAGS.is_isomorphic.html#networkx.algorithms.isomorphism.ISMAGS.is_isomorphic">[docs]</a> <span class="k">def</span> <span class="nf">is_isomorphic</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">symmetry</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns True if :attr:`graph` is isomorphic to :attr:`subgraph` and</span> <span class="sd"> False otherwise.</span> @@ -1108,7 +1108,7 @@ <span class="p">)</span></div> <div class="viewcode-block" id="ISMAGS.subgraph_is_isomorphic"><a class="viewcode-back" href="../../../../reference/algorithms/generated/generated/networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic.html#networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic">[docs]</a> <span class="k">def</span> <span class="nf">subgraph_is_isomorphic</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">symmetry</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns True if a subgraph of :attr:`graph` is isomorphic to</span> <span class="sd"> :attr:`subgraph` and False otherwise.</span> @@ -1123,7 +1123,7 @@ <span class="k">return</span> <span class="n">isom</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span></div> <div class="viewcode-block" id="ISMAGS.isomorphisms_iter"><a class="viewcode-back" href="../../../../reference/algorithms/generated/generated/networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter.html#networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter">[docs]</a> <span class="k">def</span> <span class="nf">isomorphisms_iter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">symmetry</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Does the same as :meth:`find_isomorphisms` if :attr:`graph` and</span> <span class="sd"> :attr:`subgraph` have the same number of nodes.</span> <span class="sd"> """</span> @@ -1131,11 +1131,11 @@ <span class="k">yield from</span> <span class="bp">self</span><span class="o">.</span><span class="n">subgraph_isomorphisms_iter</span><span class="p">(</span><span class="n">symmetry</span><span class="o">=</span><span class="n">symmetry</span><span class="p">)</span></div> <div class="viewcode-block" id="ISMAGS.subgraph_isomorphisms_iter"><a class="viewcode-back" href="../../../../reference/algorithms/generated/generated/networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter.html#networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter">[docs]</a> <span class="k">def</span> <span class="nf">subgraph_isomorphisms_iter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">symmetry</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Alternative name for :meth:`find_isomorphisms`."""</span> +<span class="w"> </span><span class="sd">"""Alternative name for :meth:`find_isomorphisms`."""</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">find_isomorphisms</span><span class="p">(</span><span class="n">symmetry</span><span class="p">)</span></div> <span class="k">def</span> <span class="nf">_find_nodecolor_candidates</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Per node in subgraph find all nodes in graph that have the same color.</span> <span class="sd"> """</span> <span class="n">candidates</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">set</span><span class="p">)</span> @@ -1153,7 +1153,7 @@ <span class="nd">@staticmethod</span> <span class="k">def</span> <span class="nf">_make_constraints</span><span class="p">(</span><span class="n">cosets</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Turn cosets into constraints.</span> <span class="sd"> """</span> <span class="n">constraints</span> <span class="o">=</span> <span class="p">[]</span> @@ -1166,7 +1166,7 @@ <span class="nd">@staticmethod</span> <span class="k">def</span> <span class="nf">_find_node_edge_color</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">node_colors</span><span class="p">,</span> <span class="n">edge_colors</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> For every node in graph, come up with a color that combines 1) the</span> <span class="sd"> color of the node, and 2) the number of edges of a color to each type</span> <span class="sd"> of node.</span> @@ -1191,7 +1191,7 @@ <span class="nd">@staticmethod</span> <span class="k">def</span> <span class="nf">_get_permutations_by_length</span><span class="p">(</span><span class="n">items</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Get all permutations of items, but only permute items with the same</span> <span class="sd"> length.</span> @@ -1215,7 +1215,7 @@ <span class="nd">@classmethod</span> <span class="k">def</span> <span class="nf">_refine_node_partitions</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">node_partitions</span><span class="p">,</span> <span class="n">edge_colors</span><span class="p">,</span> <span class="n">branch</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Given a partition of nodes in graph, make the partitions smaller such</span> <span class="sd"> that all nodes in a partition have 1) the same color, and 2) the same</span> <span class="sd"> number of edges to specific other partitions.</span> @@ -1265,7 +1265,7 @@ <span class="k">yield from</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_refine_node_partitions</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">n_p</span><span class="p">,</span> <span class="n">edge_colors</span><span class="p">,</span> <span class="n">branch</span><span class="p">)</span> <span class="k">def</span> <span class="nf">_edges_of_same_color</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sgn1</span><span class="p">,</span> <span class="n">sgn2</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns all edges in :attr:`graph` that have the same colour as the</span> <span class="sd"> edge between sgn1 and sgn2 in :attr:`subgraph`.</span> <span class="sd"> """</span> @@ -1282,7 +1282,7 @@ <span class="k">return</span> <span class="n">g_edges</span> <span class="k">def</span> <span class="nf">_map_nodes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sgn</span><span class="p">,</span> <span class="n">candidates</span><span class="p">,</span> <span class="n">constraints</span><span class="p">,</span> <span class="n">mapping</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">to_be_mapped</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Find all subgraph isomorphisms honoring constraints.</span> <span class="sd"> """</span> <span class="k">if</span> <span class="n">mapping</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> @@ -1362,7 +1362,7 @@ <span class="c1"># del mapping[sgn]</span> <span class="k">def</span> <span class="nf">_largest_common_subgraph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">candidates</span><span class="p">,</span> <span class="n">constraints</span><span class="p">,</span> <span class="n">to_be_mapped</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Find all largest common subgraphs honoring constraints.</span> <span class="sd"> """</span> <span class="k">if</span> <span class="n">to_be_mapped</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> @@ -1433,7 +1433,7 @@ <span class="nd">@staticmethod</span> <span class="k">def</span> <span class="nf">_remove_node</span><span class="p">(</span><span class="n">node</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">constraints</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns a new set where node has been removed from nodes, subject to</span> <span class="sd"> symmetry constraints. We know, that for every constraint we have</span> <span class="sd"> those subgraph nodes are equal. So whenever we would remove the</span> @@ -1450,7 +1450,7 @@ <span class="nd">@staticmethod</span> <span class="k">def</span> <span class="nf">_find_permutations</span><span class="p">(</span><span class="n">top_partitions</span><span class="p">,</span> <span class="n">bottom_partitions</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return the pairs of top/bottom partitions where the partitions are</span> <span class="sd"> different. Ensures that all partitions in both top and bottom</span> <span class="sd"> partitions have size 1.</span> @@ -1470,7 +1470,7 @@ <span class="nd">@staticmethod</span> <span class="k">def</span> <span class="nf">_update_orbits</span><span class="p">(</span><span class="n">orbits</span><span class="p">,</span> <span class="n">permutations</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Update orbits based on permutations. Orbits is modified in place.</span> <span class="sd"> For every pair of items in permutations their respective orbits are</span> <span class="sd"> merged.</span> @@ -1501,7 +1501,7 @@ <span class="n">graph</span><span class="p">,</span> <span class="n">edge_colors</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Generate new partitions from top and bottom_partitions where t_node is</span> <span class="sd"> coupled to b_node. pair_idx is the index of the partitions where t_ and</span> <span class="sd"> b_node can be found.</span> @@ -1541,7 +1541,7 @@ <span class="n">orbits</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cosets</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Processes ordered pair partitions as per the reference paper. Finds and</span> <span class="sd"> returns all permutations and cosets that leave the graph unchanged.</span> <span class="sd"> """</span> @@ -1682,7 +1682,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/isomorphism/isomorph.html b/_modules/networkx/algorithms/isomorphism/isomorph.html index d663ba40..640975ae 100644 --- a/_modules/networkx/algorithms/isomorphism/isomorph.html +++ b/_modules/networkx/algorithms/isomorphism/isomorph.html @@ -476,7 +476,7 @@ <div class="viewcode-block" id="could_be_isomorphic"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.could_be_isomorphic.html#networkx.algorithms.isomorphism.could_be_isomorphic">[docs]</a><span class="k">def</span> <span class="nf">could_be_isomorphic</span><span class="p">(</span><span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">):</span> - <span class="sd">"""Returns False if graphs are definitely not isomorphic.</span> +<span class="w"> </span><span class="sd">"""Returns False if graphs are definitely not isomorphic.</span> <span class="sd"> True does NOT guarantee isomorphism.</span> <span class="sd"> Parameters</span> @@ -521,7 +521,7 @@ <div class="viewcode-block" id="fast_could_be_isomorphic"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.fast_could_be_isomorphic.html#networkx.algorithms.isomorphism.fast_could_be_isomorphic">[docs]</a><span class="k">def</span> <span class="nf">fast_could_be_isomorphic</span><span class="p">(</span><span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">):</span> - <span class="sd">"""Returns False if graphs are definitely not isomorphic.</span> +<span class="w"> </span><span class="sd">"""Returns False if graphs are definitely not isomorphic.</span> <span class="sd"> True does NOT guarantee isomorphism.</span> @@ -561,7 +561,7 @@ <div class="viewcode-block" id="faster_could_be_isomorphic"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.faster_could_be_isomorphic.html#networkx.algorithms.isomorphism.faster_could_be_isomorphic">[docs]</a><span class="k">def</span> <span class="nf">faster_could_be_isomorphic</span><span class="p">(</span><span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">):</span> - <span class="sd">"""Returns False if graphs are definitely not isomorphic.</span> +<span class="w"> </span><span class="sd">"""Returns False if graphs are definitely not isomorphic.</span> <span class="sd"> True does NOT guarantee isomorphism.</span> @@ -593,7 +593,7 @@ <div class="viewcode-block" id="is_isomorphic"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.is_isomorphic.html#networkx.algorithms.isomorphism.is_isomorphic">[docs]</a><span class="k">def</span> <span class="nf">is_isomorphic</span><span class="p">(</span><span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">,</span> <span class="n">node_match</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">edge_match</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns True if the graphs G1 and G2 are isomorphic and False otherwise.</span> +<span class="w"> </span><span class="sd">"""Returns True if the graphs G1 and G2 are isomorphic and False otherwise.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -750,7 +750,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/isomorphism/isomorphvf2.html b/_modules/networkx/algorithms/isomorphism/isomorphvf2.html index a537cca9..0166cdd5 100644 --- a/_modules/networkx/algorithms/isomorphism/isomorphvf2.html +++ b/_modules/networkx/algorithms/isomorphism/isomorphvf2.html @@ -608,13 +608,13 @@ <span class="k">class</span> <span class="nc">GraphMatcher</span><span class="p">:</span> - <span class="sd">"""Implementation of VF2 algorithm for matching undirected graphs.</span> +<span class="w"> </span><span class="sd">"""Implementation of VF2 algorithm for matching undirected graphs.</span> <span class="sd"> Suitable for Graph and MultiGraph instances.</span> <span class="sd"> """</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">):</span> - <span class="sd">"""Initialize GraphMatcher.</span> +<span class="w"> </span><span class="sd">"""Initialize GraphMatcher.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -650,7 +650,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> <span class="k">def</span> <span class="nf">reset_recursion_limit</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Restores the recursion limit."""</span> +<span class="w"> </span><span class="sd">"""Restores the recursion limit."""</span> <span class="c1"># TODO:</span> <span class="c1"># Currently, we use recursion and set the recursion level higher.</span> <span class="c1"># It would be nice to restore the level, but because the</span> @@ -663,7 +663,7 @@ <span class="n">sys</span><span class="o">.</span><span class="n">setrecursionlimit</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">old_recursion_limit</span><span class="p">)</span> <div class="viewcode-block" id="GraphMatcher.candidate_pairs_iter"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter.html#networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter">[docs]</a> <span class="k">def</span> <span class="nf">candidate_pairs_iter</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Iterator over candidate pairs of nodes in G1 and G2."""</span> +<span class="w"> </span><span class="sd">"""Iterator over candidate pairs of nodes in G1 and G2."""</span> <span class="c1"># All computations are done using the current state!</span> @@ -696,7 +696,7 @@ <span class="c1"># For all other cases, we don't have any candidate pairs.</span> <div class="viewcode-block" id="GraphMatcher.initialize"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.GraphMatcher.initialize.html#networkx.algorithms.isomorphism.GraphMatcher.initialize">[docs]</a> <span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Reinitializes the state of the algorithm.</span> +<span class="w"> </span><span class="sd">"""Reinitializes the state of the algorithm.</span> <span class="sd"> This method should be redefined if using something other than GMState.</span> <span class="sd"> If only subclassing GraphMatcher, a redefinition is not necessary.</span> @@ -727,7 +727,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">mapping</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">core_1</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span></div> <div class="viewcode-block" id="GraphMatcher.is_isomorphic"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic.html#networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic">[docs]</a> <span class="k">def</span> <span class="nf">is_isomorphic</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns True if G1 and G2 are isomorphic graphs."""</span> +<span class="w"> </span><span class="sd">"""Returns True if G1 and G2 are isomorphic graphs."""</span> <span class="c1"># Let's do two very quick checks!</span> <span class="c1"># QUESTION: Should we call faster_graph_could_be_isomorphic(G1,G2)?</span> @@ -750,14 +750,14 @@ <span class="k">return</span> <span class="kc">False</span></div> <div class="viewcode-block" id="GraphMatcher.isomorphisms_iter"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter.html#networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter">[docs]</a> <span class="k">def</span> <span class="nf">isomorphisms_iter</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Generator over isomorphisms between G1 and G2."""</span> +<span class="w"> </span><span class="sd">"""Generator over isomorphisms between G1 and G2."""</span> <span class="c1"># Declare that we are looking for a graph-graph isomorphism.</span> <span class="bp">self</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="s2">"graph"</span> <span class="bp">self</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> <span class="k">yield from</span> <span class="bp">self</span><span class="o">.</span><span class="n">match</span><span class="p">()</span></div> <div class="viewcode-block" id="GraphMatcher.match"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.GraphMatcher.match.html#networkx.algorithms.isomorphism.GraphMatcher.match">[docs]</a> <span class="k">def</span> <span class="nf">match</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Extends the isomorphism mapping.</span> +<span class="w"> </span><span class="sd">"""Extends the isomorphism mapping.</span> <span class="sd"> This function is called recursively to determine if a complete</span> <span class="sd"> isomorphism can be found between G1 and G2. It cleans up the class</span> @@ -782,7 +782,7 @@ <span class="n">newstate</span><span class="o">.</span><span class="n">restore</span><span class="p">()</span></div> <span class="k">def</span> <span class="nf">semantic_feasibility</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G1_node</span><span class="p">,</span> <span class="n">G2_node</span><span class="p">):</span> - <span class="sd">"""Returns True if adding (G1_node, G2_node) is symantically feasible.</span> +<span class="w"> </span><span class="sd">"""Returns True if adding (G1_node, G2_node) is symantically feasible.</span> <span class="sd"> The semantic feasibility function should return True if it is</span> <span class="sd"> acceptable to add the candidate pair (G1_node, G2_node) to the current</span> @@ -822,7 +822,7 @@ <span class="k">return</span> <span class="kc">True</span> <div class="viewcode-block" id="GraphMatcher.subgraph_is_isomorphic"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic.html#networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic">[docs]</a> <span class="k">def</span> <span class="nf">subgraph_is_isomorphic</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns True if a subgraph of G1 is isomorphic to G2."""</span> +<span class="w"> </span><span class="sd">"""Returns True if a subgraph of G1 is isomorphic to G2."""</span> <span class="k">try</span><span class="p">:</span> <span class="n">x</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">subgraph_isomorphisms_iter</span><span class="p">())</span> <span class="k">return</span> <span class="kc">True</span> @@ -830,7 +830,7 @@ <span class="k">return</span> <span class="kc">False</span></div> <span class="k">def</span> <span class="nf">subgraph_is_monomorphic</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns True if a subgraph of G1 is monomorphic to G2."""</span> +<span class="w"> </span><span class="sd">"""Returns True if a subgraph of G1 is monomorphic to G2."""</span> <span class="k">try</span><span class="p">:</span> <span class="n">x</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">subgraph_monomorphisms_iter</span><span class="p">())</span> <span class="k">return</span> <span class="kc">True</span> @@ -840,14 +840,14 @@ <span class="c1"># subgraph_is_isomorphic.__doc__ += "\n" + subgraph.replace('\n','\n'+indent)</span> <div class="viewcode-block" id="GraphMatcher.subgraph_isomorphisms_iter"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter.html#networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter">[docs]</a> <span class="k">def</span> <span class="nf">subgraph_isomorphisms_iter</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Generator over isomorphisms between a subgraph of G1 and G2."""</span> +<span class="w"> </span><span class="sd">"""Generator over isomorphisms between a subgraph of G1 and G2."""</span> <span class="c1"># Declare that we are looking for graph-subgraph isomorphism.</span> <span class="bp">self</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="s2">"subgraph"</span> <span class="bp">self</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> <span class="k">yield from</span> <span class="bp">self</span><span class="o">.</span><span class="n">match</span><span class="p">()</span></div> <span class="k">def</span> <span class="nf">subgraph_monomorphisms_iter</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Generator over monomorphisms between a subgraph of G1 and G2."""</span> +<span class="w"> </span><span class="sd">"""Generator over monomorphisms between a subgraph of G1 and G2."""</span> <span class="c1"># Declare that we are looking for graph-subgraph monomorphism.</span> <span class="bp">self</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="s2">"mono"</span> <span class="bp">self</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> @@ -856,7 +856,7 @@ <span class="c1"># subgraph_isomorphisms_iter.__doc__ += "\n" + subgraph.replace('\n','\n'+indent)</span> <div class="viewcode-block" id="GraphMatcher.syntactic_feasibility"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility.html#networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility">[docs]</a> <span class="k">def</span> <span class="nf">syntactic_feasibility</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G1_node</span><span class="p">,</span> <span class="n">G2_node</span><span class="p">):</span> - <span class="sd">"""Returns True if adding (G1_node, G2_node) is syntactically feasible.</span> +<span class="w"> </span><span class="sd">"""Returns True if adding (G1_node, G2_node) is syntactically feasible.</span> <span class="sd"> This function returns True if it is adding the candidate pair</span> <span class="sd"> to the current partial isomorphism/monomorphism mapping is allowable.</span> @@ -980,13 +980,13 @@ <span class="k">class</span> <span class="nc">DiGraphMatcher</span><span class="p">(</span><span class="n">GraphMatcher</span><span class="p">):</span> - <span class="sd">"""Implementation of VF2 algorithm for matching directed graphs.</span> +<span class="w"> </span><span class="sd">"""Implementation of VF2 algorithm for matching directed graphs.</span> <span class="sd"> Suitable for DiGraph and MultiDiGraph instances.</span> <span class="sd"> """</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">):</span> - <span class="sd">"""Initialize DiGraphMatcher.</span> +<span class="w"> </span><span class="sd">"""Initialize DiGraphMatcher.</span> <span class="sd"> G1 and G2 should be nx.Graph or nx.MultiGraph instances.</span> @@ -1002,7 +1002,7 @@ <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">)</span> <div class="viewcode-block" id="DiGraphMatcher.candidate_pairs_iter"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter.html#networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter">[docs]</a> <span class="k">def</span> <span class="nf">candidate_pairs_iter</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Iterator over candidate pairs of nodes in G1 and G2."""</span> +<span class="w"> </span><span class="sd">"""Iterator over candidate pairs of nodes in G1 and G2."""</span> <span class="c1"># All computations are done using the current state!</span> @@ -1049,7 +1049,7 @@ <span class="c1"># For all other cases, we don't have any candidate pairs.</span> <div class="viewcode-block" id="DiGraphMatcher.initialize"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.DiGraphMatcher.initialize.html#networkx.algorithms.isomorphism.DiGraphMatcher.initialize">[docs]</a> <span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Reinitializes the state of the algorithm.</span> +<span class="w"> </span><span class="sd">"""Reinitializes the state of the algorithm.</span> <span class="sd"> This method should be redefined if using something other than DiGMState.</span> <span class="sd"> If only subclassing GraphMatcher, a redefinition is not necessary.</span> @@ -1083,7 +1083,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">mapping</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">core_1</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span></div> <div class="viewcode-block" id="DiGraphMatcher.syntactic_feasibility"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility.html#networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility">[docs]</a> <span class="k">def</span> <span class="nf">syntactic_feasibility</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G1_node</span><span class="p">,</span> <span class="n">G2_node</span><span class="p">):</span> - <span class="sd">"""Returns True if adding (G1_node, G2_node) is syntactically feasible.</span> +<span class="w"> </span><span class="sd">"""Returns True if adding (G1_node, G2_node) is syntactically feasible.</span> <span class="sd"> This function returns True if it is adding the candidate pair</span> <span class="sd"> to the current partial isomorphism/monomorphism mapping is allowable.</span> @@ -1307,7 +1307,7 @@ <span class="k">class</span> <span class="nc">GMState</span><span class="p">:</span> - <span class="sd">"""Internal representation of state for the GraphMatcher class.</span> +<span class="w"> </span><span class="sd">"""Internal representation of state for the GraphMatcher class.</span> <span class="sd"> This class is used internally by the GraphMatcher class. It is used</span> <span class="sd"> only to store state specific data. There will be at most G2.order() of</span> @@ -1316,7 +1316,7 @@ <span class="sd"> """</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">GM</span><span class="p">,</span> <span class="n">G1_node</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">G2_node</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Initializes GMState object.</span> +<span class="w"> </span><span class="sd">"""Initializes GMState object.</span> <span class="sd"> Pass in the GraphMatcher to which this GMState belongs and the</span> <span class="sd"> new node pair that will be added to the GraphMatcher's current</span> @@ -1379,7 +1379,7 @@ <span class="n">GM</span><span class="o">.</span><span class="n">inout_2</span><span class="p">[</span><span class="n">node</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">depth</span> <span class="k">def</span> <span class="nf">restore</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Deletes the GMState object and restores the class variables."""</span> +<span class="w"> </span><span class="sd">"""Deletes the GMState object and restores the class variables."""</span> <span class="c1"># First we remove the node that was added from the core vectors.</span> <span class="c1"># Watch out! G1_node == 0 should evaluate to True.</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">G1_node</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">G2_node</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> @@ -1395,7 +1395,7 @@ <span class="k">class</span> <span class="nc">DiGMState</span><span class="p">:</span> - <span class="sd">"""Internal representation of state for the DiGraphMatcher class.</span> +<span class="w"> </span><span class="sd">"""Internal representation of state for the DiGraphMatcher class.</span> <span class="sd"> This class is used internally by the DiGraphMatcher class. It is used</span> <span class="sd"> only to store state specific data. There will be at most G2.order() of</span> @@ -1405,7 +1405,7 @@ <span class="sd"> """</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">GM</span><span class="p">,</span> <span class="n">G1_node</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">G2_node</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Initializes DiGMState object.</span> +<span class="w"> </span><span class="sd">"""Initializes DiGMState object.</span> <span class="sd"> Pass in the DiGraphMatcher to which this DiGMState belongs and the</span> <span class="sd"> new node pair that will be added to the GraphMatcher's current</span> @@ -1508,7 +1508,7 @@ <span class="n">GM</span><span class="o">.</span><span class="n">out_2</span><span class="p">[</span><span class="n">node</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">depth</span> <span class="k">def</span> <span class="nf">restore</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Deletes the DiGMState object and restores the class variables."""</span> +<span class="w"> </span><span class="sd">"""Deletes the DiGMState object and restores the class variables."""</span> <span class="c1"># First we remove the node that was added from the core vectors.</span> <span class="c1"># Watch out! G1_node == 0 should evaluate to True.</span> @@ -1573,7 +1573,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/isomorphism/matchhelpers.html b/_modules/networkx/algorithms/isomorphism/matchhelpers.html index 8818825c..dca838b0 100644 --- a/_modules/networkx/algorithms/isomorphism/matchhelpers.html +++ b/_modules/networkx/algorithms/isomorphism/matchhelpers.html @@ -482,14 +482,14 @@ <span class="k">def</span> <span class="nf">copyfunc</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a deepcopy of a function."""</span> +<span class="w"> </span><span class="sd">"""Returns a deepcopy of a function."""</span> <span class="k">return</span> <span class="n">types</span><span class="o">.</span><span class="n">FunctionType</span><span class="p">(</span> <span class="n">f</span><span class="o">.</span><span class="vm">__code__</span><span class="p">,</span> <span class="n">f</span><span class="o">.</span><span class="vm">__globals__</span><span class="p">,</span> <span class="n">name</span> <span class="ow">or</span> <span class="n">f</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">f</span><span class="o">.</span><span class="vm">__defaults__</span><span class="p">,</span> <span class="n">f</span><span class="o">.</span><span class="vm">__closure__</span> <span class="p">)</span> <span class="k">def</span> <span class="nf">allclose</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="mf">1.0000000000000001e-05</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-08</span><span class="p">):</span> - <span class="sd">"""Returns True if x and y are sufficiently close, elementwise.</span> +<span class="w"> </span><span class="sd">"""Returns True if x and y are sufficiently close, elementwise.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -744,7 +744,7 @@ <div class="viewcode-block" id="generic_multiedge_match"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.generic_multiedge_match.html#networkx.algorithms.isomorphism.generic_multiedge_match">[docs]</a><span class="k">def</span> <span class="nf">generic_multiedge_match</span><span class="p">(</span><span class="n">attr</span><span class="p">,</span> <span class="n">default</span><span class="p">,</span> <span class="n">op</span><span class="p">):</span> - <span class="sd">"""Returns a comparison function for a generic attribute.</span> +<span class="w"> </span><span class="sd">"""Returns a comparison function for a generic attribute.</span> <span class="sd"> The value(s) of the attr(s) are compared using the specified</span> <span class="sd"> operators. If all the attributes are equal, then the constructed</span> @@ -867,7 +867,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/isomorphism/tree_isomorphism.html b/_modules/networkx/algorithms/isomorphism/tree_isomorphism.html index fb9d05ea..bddf86ea 100644 --- a/_modules/networkx/algorithms/isomorphism/tree_isomorphism.html +++ b/_modules/networkx/algorithms/isomorphism/tree_isomorphism.html @@ -488,7 +488,7 @@ <span class="k">def</span> <span class="nf">root_trees</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">root1</span><span class="p">,</span> <span class="n">t2</span><span class="p">,</span> <span class="n">root2</span><span class="p">):</span> - <span class="sd">"""Create a single digraph dT of free trees t1 and t2</span> +<span class="w"> </span><span class="sd">"""Create a single digraph dT of free trees t1 and t2</span> <span class="sd"> # with roots root1 and root2 respectively</span> <span class="sd"> # rename the nodes with consecutive integers</span> <span class="sd"> # so that all nodes get a unique name between both trees</span> @@ -564,7 +564,7 @@ <div class="viewcode-block" id="rooted_tree_isomorphism"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism.html#networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism">[docs]</a><span class="k">def</span> <span class="nf">rooted_tree_isomorphism</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">root1</span><span class="p">,</span> <span class="n">t2</span><span class="p">,</span> <span class="n">root2</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Given two rooted trees `t1` and `t2`,</span> <span class="sd"> with roots `root1` and `root2` respectivly</span> <span class="sd"> this routine will determine if they are isomorphic.</span> @@ -671,7 +671,7 @@ <div class="viewcode-block" id="tree_isomorphism"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism.html#networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">,</span> <span class="s2">"multigraph"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">tree_isomorphism</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Given two undirected (or free) trees `t1` and `t2`,</span> <span class="sd"> this routine will determine if they are isomorphic.</span> <span class="sd"> It returns the isomorphism, a mapping of the nodes of `t1` onto the nodes</span> @@ -791,7 +791,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/isomorphism/vf2pp.html b/_modules/networkx/algorithms/isomorphism/vf2pp.html index 47fca9ea..8379e377 100644 --- a/_modules/networkx/algorithms/isomorphism/vf2pp.html +++ b/_modules/networkx/algorithms/isomorphism/vf2pp.html @@ -560,7 +560,7 @@ <div class="viewcode-block" id="vf2pp_isomorphism"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism.html#networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism">[docs]</a><span class="k">def</span> <span class="nf">vf2pp_isomorphism</span><span class="p">(</span><span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">,</span> <span class="n">node_label</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">default_label</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Return an isomorphic mapping between `G1` and `G2` if it exists.</span> +<span class="w"> </span><span class="sd">"""Return an isomorphic mapping between `G1` and `G2` if it exists.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -590,7 +590,7 @@ <div class="viewcode-block" id="vf2pp_is_isomorphic"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic.html#networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic">[docs]</a><span class="k">def</span> <span class="nf">vf2pp_is_isomorphic</span><span class="p">(</span><span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">,</span> <span class="n">node_label</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">default_label</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Examines whether G1 and G2 are isomorphic.</span> +<span class="w"> </span><span class="sd">"""Examines whether G1 and G2 are isomorphic.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -618,7 +618,7 @@ <div class="viewcode-block" id="vf2pp_all_isomorphisms"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms.html#networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms">[docs]</a><span class="k">def</span> <span class="nf">vf2pp_all_isomorphisms</span><span class="p">(</span><span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">,</span> <span class="n">node_label</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">default_label</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Yields all the possible mappings between G1 and G2.</span> +<span class="w"> </span><span class="sd">"""Yields all the possible mappings between G1 and G2.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -746,7 +746,7 @@ <span class="k">def</span> <span class="nf">_initialize_parameters</span><span class="p">(</span><span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">,</span> <span class="n">G2_degree</span><span class="p">,</span> <span class="n">node_label</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">default_label</span><span class="o">=-</span><span class="mi">1</span><span class="p">):</span> - <span class="sd">"""Initializes all the necessary parameters for VF2++</span> +<span class="w"> </span><span class="sd">"""Initializes all the necessary parameters for VF2++</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -870,7 +870,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/isomorphism/vf2userfunc.html b/_modules/networkx/algorithms/isomorphism/vf2userfunc.html index 800ca5f4..310610e2 100644 --- a/_modules/networkx/algorithms/isomorphism/vf2userfunc.html +++ b/_modules/networkx/algorithms/isomorphism/vf2userfunc.html @@ -500,7 +500,7 @@ <span class="k">def</span> <span class="nf">_semantic_feasibility</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G1_node</span><span class="p">,</span> <span class="n">G2_node</span><span class="p">):</span> - <span class="sd">"""Returns True if mapping G1_node to G2_node is semantically feasible."""</span> +<span class="w"> </span><span class="sd">"""Returns True if mapping G1_node to G2_node is semantically feasible."""</span> <span class="c1"># Make sure the nodes match</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">node_match</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="n">nm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">node_match</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G1</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="n">G1_node</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">G2</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="n">G2_node</span><span class="p">])</span> @@ -535,10 +535,10 @@ <span class="k">class</span> <span class="nc">GraphMatcher</span><span class="p">(</span><span class="n">vf2</span><span class="o">.</span><span class="n">GraphMatcher</span><span class="p">):</span> - <span class="sd">"""VF2 isomorphism checker for undirected graphs."""</span> +<span class="w"> </span><span class="sd">"""VF2 isomorphism checker for undirected graphs."""</span> <div class="viewcode-block" id="GraphMatcher.__init__"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.GraphMatcher.__init__.html#networkx.algorithms.isomorphism.GraphMatcher.__init__">[docs]</a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">,</span> <span class="n">node_match</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">edge_match</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Initialize graph matcher.</span> +<span class="w"> </span><span class="sd">"""Initialize graph matcher.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -582,10 +582,10 @@ <span class="k">class</span> <span class="nc">DiGraphMatcher</span><span class="p">(</span><span class="n">vf2</span><span class="o">.</span><span class="n">DiGraphMatcher</span><span class="p">):</span> - <span class="sd">"""VF2 isomorphism checker for directed graphs."""</span> +<span class="w"> </span><span class="sd">"""VF2 isomorphism checker for directed graphs."""</span> <div class="viewcode-block" id="DiGraphMatcher.__init__"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.DiGraphMatcher.__init__.html#networkx.algorithms.isomorphism.DiGraphMatcher.__init__">[docs]</a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">,</span> <span class="n">node_match</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">edge_match</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Initialize graph matcher.</span> +<span class="w"> </span><span class="sd">"""Initialize graph matcher.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -626,7 +626,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">G2_adj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G2</span><span class="o">.</span><span class="n">adj</span></div> <div class="viewcode-block" id="DiGraphMatcher.semantic_feasibility"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility.html#networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility">[docs]</a> <span class="k">def</span> <span class="nf">semantic_feasibility</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G1_node</span><span class="p">,</span> <span class="n">G2_node</span><span class="p">):</span> - <span class="sd">"""Returns True if mapping G1_node to G2_node is semantically feasible."""</span> +<span class="w"> </span><span class="sd">"""Returns True if mapping G1_node to G2_node is semantically feasible."""</span> <span class="c1"># Test node_match and also test edge_match on successors</span> <span class="n">feasible</span> <span class="o">=</span> <span class="n">_semantic_feasibility</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G1_node</span><span class="p">,</span> <span class="n">G2_node</span><span class="p">)</span> @@ -649,13 +649,13 @@ <span class="k">class</span> <span class="nc">MultiGraphMatcher</span><span class="p">(</span><span class="n">GraphMatcher</span><span class="p">):</span> - <span class="sd">"""VF2 isomorphism checker for undirected multigraphs."""</span> +<span class="w"> </span><span class="sd">"""VF2 isomorphism checker for undirected multigraphs."""</span> <span class="k">pass</span> <span class="k">class</span> <span class="nc">MultiDiGraphMatcher</span><span class="p">(</span><span class="n">DiGraphMatcher</span><span class="p">):</span> - <span class="sd">"""VF2 isomorphism checker for directed multigraphs."""</span> +<span class="w"> </span><span class="sd">"""VF2 isomorphism checker for directed multigraphs."""</span> <span class="k">pass</span> </pre></div> @@ -709,7 +709,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/link_analysis/hits_alg.html b/_modules/networkx/algorithms/link_analysis/hits_alg.html index ee6c543a..ddb890c9 100644 --- a/_modules/networkx/algorithms/link_analysis/hits_alg.html +++ b/_modules/networkx/algorithms/link_analysis/hits_alg.html @@ -470,7 +470,7 @@ <div class="viewcode-block" id="hits"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.link_analysis.hits_alg.hits.html#networkx.algorithms.link_analysis.hits_alg.hits">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">hits</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1.0e-8</span><span class="p">,</span> <span class="n">nstart</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns HITS hubs and authorities values for nodes.</span> +<span class="w"> </span><span class="sd">"""Returns HITS hubs and authorities values for nodes.</span> <span class="sd"> The HITS algorithm computes two numbers for a node.</span> <span class="sd"> Authorities estimates the node value based on the incoming links.</span> @@ -609,7 +609,7 @@ <span class="k">def</span> <span class="nf">_hits_numpy</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns HITS hubs and authorities values for nodes.</span> +<span class="w"> </span><span class="sd">"""Returns HITS hubs and authorities values for nodes.</span> <span class="sd"> The HITS algorithm computes two numbers for a node.</span> <span class="sd"> Authorities estimates the node value based on the incoming links.</span> @@ -693,7 +693,7 @@ <span class="k">def</span> <span class="nf">_hits_scipy</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1.0e-6</span><span class="p">,</span> <span class="n">nstart</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns HITS hubs and authorities values for nodes.</span> +<span class="w"> </span><span class="sd">"""Returns HITS hubs and authorities values for nodes.</span> <span class="sd"> The HITS algorithm computes two numbers for a node.</span> @@ -847,7 +847,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/link_analysis/pagerank_alg.html b/_modules/networkx/algorithms/link_analysis/pagerank_alg.html index 179c6930..cba43835 100644 --- a/_modules/networkx/algorithms/link_analysis/pagerank_alg.html +++ b/_modules/networkx/algorithms/link_analysis/pagerank_alg.html @@ -480,7 +480,7 @@ <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">dangling</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns the PageRank of the nodes in the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the PageRank of the nodes in the graph.</span> <span class="sd"> PageRank computes a ranking of the nodes in the graph G based on</span> <span class="sd"> the structure of the incoming links. It was originally designed as</span> @@ -638,7 +638,7 @@ <span class="k">def</span> <span class="nf">google_matrix</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.85</span><span class="p">,</span> <span class="n">personalization</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">dangling</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Returns the Google matrix of the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the Google matrix of the graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -733,7 +733,7 @@ <span class="k">def</span> <span class="nf">_pagerank_numpy</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.85</span><span class="p">,</span> <span class="n">personalization</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">dangling</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Returns the PageRank of the nodes in the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the PageRank of the nodes in the graph.</span> <span class="sd"> PageRank computes a ranking of the nodes in the graph G based on</span> <span class="sd"> the structure of the incoming links. It was originally designed as</span> @@ -827,7 +827,7 @@ <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">dangling</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns the PageRank of the nodes in the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the PageRank of the nodes in the graph.</span> <span class="sd"> PageRank computes a ranking of the nodes in the graph G based on</span> <span class="sd"> the structure of the incoming links. It was originally designed as</span> @@ -1010,7 +1010,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/link_prediction.html b/_modules/networkx/algorithms/link_prediction.html index d1c1c472..e7df6b9b 100644 --- a/_modules/networkx/algorithms/link_prediction.html +++ b/_modules/networkx/algorithms/link_prediction.html @@ -484,7 +484,7 @@ <span class="k">def</span> <span class="nf">_apply_prediction</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">ebunch</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Applies the given function to each edge in the specified iterable</span> +<span class="w"> </span><span class="sd">"""Applies the given function to each edge in the specified iterable</span> <span class="sd"> of edges.</span> <span class="sd"> `G` is an instance of :class:`networkx.Graph`.</span> @@ -506,7 +506,7 @@ <div class="viewcode-block" id="resource_allocation_index"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.link_prediction.resource_allocation_index.html#networkx.algorithms.link_prediction.resource_allocation_index">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">resource_allocation_index</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">ebunch</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the resource allocation index of all node pairs in ebunch.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the resource allocation index of all node pairs in ebunch.</span> <span class="sd"> Resource allocation index of `u` and `v` is defined as</span> @@ -561,7 +561,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">jaccard_coefficient</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">ebunch</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the Jaccard coefficient of all node pairs in ebunch.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the Jaccard coefficient of all node pairs in ebunch.</span> <span class="sd"> Jaccard coefficient of nodes `u` and `v` is defined as</span> @@ -617,7 +617,7 @@ <div class="viewcode-block" id="adamic_adar_index"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.link_prediction.adamic_adar_index.html#networkx.algorithms.link_prediction.adamic_adar_index">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">adamic_adar_index</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">ebunch</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the Adamic-Adar index of all node pairs in ebunch.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the Adamic-Adar index of all node pairs in ebunch.</span> <span class="sd"> Adamic-Adar index of `u` and `v` is defined as</span> @@ -672,7 +672,7 @@ <div class="viewcode-block" id="common_neighbor_centrality"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.link_prediction.common_neighbor_centrality.html#networkx.algorithms.link_prediction.common_neighbor_centrality">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">common_neighbor_centrality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">ebunch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Return the CCPA score for each pair of nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Return the CCPA score for each pair of nodes.</span> <span class="sd"> Compute the Common Neighbor and Centrality based Parameterized Algorithm(CCPA)</span> <span class="sd"> score of all node pairs in ebunch.</span> @@ -769,7 +769,7 @@ <div class="viewcode-block" id="preferential_attachment"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.link_prediction.preferential_attachment.html#networkx.algorithms.link_prediction.preferential_attachment">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">preferential_attachment</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">ebunch</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the preferential attachment score of all node pairs in ebunch.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the preferential attachment score of all node pairs in ebunch.</span> <span class="sd"> Preferential attachment score of `u` and `v` is defined as</span> @@ -822,7 +822,7 @@ <div class="viewcode-block" id="cn_soundarajan_hopcroft"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.link_prediction.cn_soundarajan_hopcroft.html#networkx.algorithms.link_prediction.cn_soundarajan_hopcroft">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">cn_soundarajan_hopcroft</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">ebunch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">community</span><span class="o">=</span><span class="s2">"community"</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Count the number of common neighbors of all node pairs in ebunch</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Count the number of common neighbors of all node pairs in ebunch</span> <span class="sd"> using community information.</span> <span class="sd"> For two nodes $u$ and $v$, this function computes the number of</span> @@ -896,7 +896,7 @@ <div class="viewcode-block" id="ra_index_soundarajan_hopcroft"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft.html#networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">ra_index_soundarajan_hopcroft</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">ebunch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">community</span><span class="o">=</span><span class="s2">"community"</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the resource allocation index of all node pairs in</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the resource allocation index of all node pairs in</span> <span class="sd"> ebunch using community information.</span> <span class="sd"> For two nodes $u$ and $v$, this function computes the resource</span> @@ -971,7 +971,7 @@ <div class="viewcode-block" id="within_inter_cluster"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.link_prediction.within_inter_cluster.html#networkx.algorithms.link_prediction.within_inter_cluster">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">within_inter_cluster</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">ebunch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">delta</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">community</span><span class="o">=</span><span class="s2">"community"</span><span class="p">):</span> - <span class="sd">"""Compute the ratio of within- and inter-cluster common neighbors</span> +<span class="w"> </span><span class="sd">"""Compute the ratio of within- and inter-cluster common neighbors</span> <span class="sd"> of all node pairs in ebunch.</span> <span class="sd"> For two nodes `u` and `v`, if a common neighbor `w` belongs to the</span> @@ -1052,7 +1052,7 @@ <span class="k">def</span> <span class="nf">_community</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">community</span><span class="p">):</span> - <span class="sd">"""Get the community of the given node."""</span> +<span class="w"> </span><span class="sd">"""Get the community of the given node."""</span> <span class="n">node_u</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="n">u</span><span class="p">]</span> <span class="k">try</span><span class="p">:</span> <span class="k">return</span> <span class="n">node_u</span><span class="p">[</span><span class="n">community</span><span class="p">]</span> @@ -1109,7 +1109,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/lowest_common_ancestors.html b/_modules/networkx/algorithms/lowest_common_ancestors.html index 401c7bb8..a6f48279 100644 --- a/_modules/networkx/algorithms/lowest_common_ancestors.html +++ b/_modules/networkx/algorithms/lowest_common_ancestors.html @@ -478,7 +478,7 @@ <div class="viewcode-block" id="all_pairs_lowest_common_ancestor"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor.html#networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">all_pairs_lowest_common_ancestor</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">pairs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Return the lowest common ancestor of all pairs or the provided pairs</span> +<span class="w"> </span><span class="sd">"""Return the lowest common ancestor of all pairs or the provided pairs</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -540,7 +540,7 @@ <span class="k">for</span> <span class="n">pair</span> <span class="ow">in</span> <span class="n">pairs</span><span class="p">:</span> <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">pair</span><span class="p">)</span> <span class="o">-</span> <span class="n">nodeset</span><span class="p">:</span> <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NodeNotFound</span><span class="p">(</span> - <span class="sa">f</span><span class="s2">"Node(s) </span><span class="si">{</span><span class="nb">set</span><span class="p">(</span><span class="n">pair</span><span class="p">)</span> <span class="o">-</span> <span class="n">nodeset</span><span class="si">}</span><span class="s2"> from pair </span><span class="si">{</span><span class="n">pair</span><span class="si">}</span><span class="s2"> not in G."</span> + <span class="sa">f</span><span class="s2">"Node(s) </span><span class="si">{</span><span class="nb">set</span><span class="p">(</span><span class="n">pair</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">nodeset</span><span class="si">}</span><span class="s2"> from pair </span><span class="si">{</span><span class="n">pair</span><span class="si">}</span><span class="s2"> not in G."</span> <span class="p">)</span> <span class="c1"># Once input validation is done, construct the generator</span> @@ -575,7 +575,7 @@ <div class="viewcode-block" id="lowest_common_ancestor"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor.html#networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">lowest_common_ancestor</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">node1</span><span class="p">,</span> <span class="n">node2</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute the lowest common ancestor of the given pair of nodes.</span> +<span class="w"> </span><span class="sd">"""Compute the lowest common ancestor of the given pair of nodes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -612,7 +612,7 @@ <div class="viewcode-block" id="tree_all_pairs_lowest_common_ancestor"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor.html#networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">tree_all_pairs_lowest_common_ancestor</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pairs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Yield the lowest common ancestor for sets of pairs in a tree.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Yield the lowest common ancestor for sets of pairs in a tree.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -777,7 +777,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/matching.html b/_modules/networkx/algorithms/matching.html index 0acd3ebc..c413ddd7 100644 --- a/_modules/networkx/algorithms/matching.html +++ b/_modules/networkx/algorithms/matching.html @@ -481,7 +481,7 @@ <div class="viewcode-block" id="maximal_matching"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.matching.maximal_matching.html#networkx.algorithms.matching.maximal_matching">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"multigraph"</span><span class="p">)</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">maximal_matching</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Find a maximal matching in the graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Find a maximal matching in the graph.</span> <span class="sd"> A matching is a subset of edges in which no node occurs more than once.</span> <span class="sd"> A maximal matching cannot add more edges and still be a matching.</span> @@ -520,7 +520,7 @@ <span class="k">def</span> <span class="nf">matching_dict_to_set</span><span class="p">(</span><span class="n">matching</span><span class="p">):</span> - <span class="sd">"""Converts matching dict format to matching set format</span> +<span class="w"> </span><span class="sd">"""Converts matching dict format to matching set format</span> <span class="sd"> Converts a dictionary representing a matching (as returned by</span> <span class="sd"> :func:`max_weight_matching`) to a set representing a matching (as</span> @@ -545,7 +545,7 @@ <div class="viewcode-block" id="is_matching"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.matching.is_matching.html#networkx.algorithms.matching.is_matching">[docs]</a><span class="k">def</span> <span class="nf">is_matching</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">matching</span><span class="p">):</span> - <span class="sd">"""Return True if ``matching`` is a valid matching of ``G``</span> +<span class="w"> </span><span class="sd">"""Return True if ``matching`` is a valid matching of ``G``</span> <span class="sd"> A *matching* in a graph is a set of edges in which no two distinct</span> <span class="sd"> edges share a common endpoint. Each node is incident to at most one</span> @@ -605,7 +605,7 @@ <div class="viewcode-block" id="is_maximal_matching"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.matching.is_maximal_matching.html#networkx.algorithms.matching.is_maximal_matching">[docs]</a><span class="k">def</span> <span class="nf">is_maximal_matching</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">matching</span><span class="p">):</span> - <span class="sd">"""Return True if ``matching`` is a maximal matching of ``G``</span> +<span class="w"> </span><span class="sd">"""Return True if ``matching`` is a maximal matching of ``G``</span> <span class="sd"> A *maximal matching* in a graph is a matching in which adding any</span> <span class="sd"> edge would cause the set to no longer be a valid matching.</span> @@ -666,7 +666,7 @@ <div class="viewcode-block" id="is_perfect_matching"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.matching.is_perfect_matching.html#networkx.algorithms.matching.is_perfect_matching">[docs]</a><span class="k">def</span> <span class="nf">is_perfect_matching</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">matching</span><span class="p">):</span> - <span class="sd">"""Return True if ``matching`` is a perfect matching for ``G``</span> +<span class="w"> </span><span class="sd">"""Return True if ``matching`` is a perfect matching for ``G``</span> <span class="sd"> A *perfect matching* in a graph is a matching in which exactly one edge</span> <span class="sd"> is incident upon each vertex.</span> @@ -719,7 +719,7 @@ <div class="viewcode-block" id="min_weight_matching"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.matching.min_weight_matching.html#networkx.algorithms.matching.min_weight_matching">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"multigraph"</span><span class="p">)</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">min_weight_matching</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Computing a minimum-weight maximal matching of G.</span> +<span class="w"> </span><span class="sd">"""Computing a minimum-weight maximal matching of G.</span> <span class="sd"> Use the maximum-weight algorithm with edge weights subtracted</span> <span class="sd"> from the maximum weight of all edges.</span> @@ -779,7 +779,7 @@ <div class="viewcode-block" id="max_weight_matching"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.matching.max_weight_matching.html#networkx.algorithms.matching.max_weight_matching">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"multigraph"</span><span class="p">)</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">max_weight_matching</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">maxcardinality</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Compute a maximum-weighted matching of G.</span> +<span class="w"> </span><span class="sd">"""Compute a maximum-weighted matching of G.</span> <span class="sd"> A matching is a subset of edges in which no node occurs more than once.</span> <span class="sd"> The weight of a matching is the sum of the weights of its edges.</span> @@ -852,12 +852,12 @@ <span class="c1">#</span> <span class="k">class</span> <span class="nc">NoNode</span><span class="p">:</span> - <span class="sd">"""Dummy value which is different from any node."""</span> +<span class="w"> </span><span class="sd">"""Dummy value which is different from any node."""</span> <span class="k">pass</span> <span class="k">class</span> <span class="nc">Blossom</span><span class="p">:</span> - <span class="sd">"""Representation of a non-trivial blossom or sub-blossom."""</span> +<span class="w"> </span><span class="sd">"""Representation of a non-trivial blossom or sub-blossom."""</span> <span class="vm">__slots__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"childs"</span><span class="p">,</span> <span class="s2">"edges"</span><span class="p">,</span> <span class="s2">"mybestedges"</span><span class="p">]</span> @@ -1620,7 +1620,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/minors/contraction.html b/_modules/networkx/algorithms/minors/contraction.html index 8f6025f2..90cd96a0 100644 --- a/_modules/networkx/algorithms/minors/contraction.html +++ b/_modules/networkx/algorithms/minors/contraction.html @@ -481,7 +481,7 @@ <div class="viewcode-block" id="equivalence_classes"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.minors.equivalence_classes.html#networkx.algorithms.minors.equivalence_classes">[docs]</a><span class="k">def</span> <span class="nf">equivalence_classes</span><span class="p">(</span><span class="n">iterable</span><span class="p">,</span> <span class="n">relation</span><span class="p">):</span> - <span class="sd">"""Returns equivalence classes of `relation` when applied to `iterable`.</span> +<span class="w"> </span><span class="sd">"""Returns equivalence classes of `relation` when applied to `iterable`.</span> <span class="sd"> The equivalence classes, or blocks, consist of objects from `iterable`</span> <span class="sd"> which are all equivalent. They are defined to be equivalent if the</span> @@ -566,7 +566,7 @@ <span class="n">relabel</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns the quotient graph of `G` under the specified equivalence</span> +<span class="w"> </span><span class="sd">"""Returns the quotient graph of `G` under the specified equivalence</span> <span class="sd"> relation on nodes.</span> <span class="sd"> Parameters</span> @@ -796,7 +796,7 @@ <span class="n">relabel</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Construct the quotient graph assuming input has been checked"""</span> +<span class="w"> </span><span class="sd">"""Construct the quotient graph assuming input has been checked"""</span> <span class="k">if</span> <span class="n">create_using</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="n">H</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="vm">__class__</span><span class="p">()</span> <span class="k">else</span><span class="p">:</span> @@ -872,7 +872,7 @@ <div class="viewcode-block" id="contracted_nodes"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.minors.contracted_nodes.html#networkx.algorithms.minors.contracted_nodes">[docs]</a><span class="k">def</span> <span class="nf">contracted_nodes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">self_loops</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns the graph that results from contracting `u` and `v`.</span> +<span class="w"> </span><span class="sd">"""Returns the graph that results from contracting `u` and `v`.</span> <span class="sd"> Node contraction identifies the two nodes as a single node incident to any</span> <span class="sd"> edge that was incident to the original two nodes.</span> @@ -994,7 +994,7 @@ <div class="viewcode-block" id="contracted_edge"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.minors.contracted_edge.html#networkx.algorithms.minors.contracted_edge">[docs]</a><span class="k">def</span> <span class="nf">contracted_edge</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">edge</span><span class="p">,</span> <span class="n">self_loops</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns the graph that results from contracting the specified edge.</span> +<span class="w"> </span><span class="sd">"""Returns the graph that results from contracting the specified edge.</span> <span class="sd"> Edge contraction identifies the two endpoints of the edge as a single node</span> <span class="sd"> incident to any edge that was incident to the original two nodes. A graph</span> @@ -1111,7 +1111,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/mis.html b/_modules/networkx/algorithms/mis.html index dfca32a0..d9143cb7 100644 --- a/_modules/networkx/algorithms/mis.html +++ b/_modules/networkx/algorithms/mis.html @@ -474,7 +474,7 @@ <div class="viewcode-block" id="maximal_independent_set"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.mis.maximal_independent_set.html#networkx.algorithms.mis.maximal_independent_set">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">maximal_independent_set</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random maximal independent set guaranteed to contain</span> +<span class="w"> </span><span class="sd">"""Returns a random maximal independent set guaranteed to contain</span> <span class="sd"> a given set of nodes.</span> <span class="sd"> An independent set is a set of nodes such that the subgraph</span> @@ -588,7 +588,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/moral.html b/_modules/networkx/algorithms/moral.html index ca6a6ceb..ff45b514 100644 --- a/_modules/networkx/algorithms/moral.html +++ b/_modules/networkx/algorithms/moral.html @@ -472,7 +472,7 @@ <div class="viewcode-block" id="moral_graph"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.moral.moral_graph.html#networkx.algorithms.moral.moral_graph">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">moral_graph</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Return the Moral Graph</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Return the Moral Graph</span> <span class="sd"> Returns the moralized graph of a given directed graph.</span> @@ -569,7 +569,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/node_classification.html b/_modules/networkx/algorithms/node_classification.html index 5c270d5a..43d7b891 100644 --- a/_modules/networkx/algorithms/node_classification.html +++ b/_modules/networkx/algorithms/node_classification.html @@ -492,7 +492,7 @@ <div class="viewcode-block" id="harmonic_function"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.node_classification.harmonic_function.html#networkx.algorithms.node_classification.harmonic_function">[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">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">harmonic_function</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">label_name</span><span class="o">=</span><span class="s2">"label"</span><span class="p">):</span> - <span class="sd">"""Node classification by Harmonic function</span> +<span class="w"> </span><span class="sd">"""Node classification by Harmonic function</span> <span class="sd"> Function for computing Harmonic function algorithm by Zhu et al.</span> @@ -569,7 +569,7 @@ <div class="viewcode-block" id="local_and_global_consistency"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.node_classification.local_and_global_consistency.html#networkx.algorithms.node_classification.local_and_global_consistency">[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">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">local_and_global_consistency</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.99</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">label_name</span><span class="o">=</span><span class="s2">"label"</span><span class="p">):</span> - <span class="sd">"""Node classification by Local and Global Consistency</span> +<span class="w"> </span><span class="sd">"""Node classification by Local and Global Consistency</span> <span class="sd"> Function for computing Local and global consistency algorithm by Zhou et al.</span> @@ -646,7 +646,7 @@ <span class="k">def</span> <span class="nf">_get_label_info</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">label_name</span><span class="p">):</span> - <span class="sd">"""Get and return information of labels from the input graph</span> +<span class="w"> </span><span class="sd">"""Get and return information of labels from the input graph</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -730,7 +730,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/non_randomness.html b/_modules/networkx/algorithms/non_randomness.html index 45abfb74..4e97da33 100644 --- a/_modules/networkx/algorithms/non_randomness.html +++ b/_modules/networkx/algorithms/non_randomness.html @@ -475,7 +475,7 @@ <div class="viewcode-block" id="non_randomness"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.non_randomness.non_randomness.html#networkx.algorithms.non_randomness.non_randomness">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">non_randomness</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Compute the non-randomness of graph G.</span> +<span class="w"> </span><span class="sd">"""Compute the non-randomness of graph G.</span> <span class="sd"> The first returned value nr is the sum of non-randomness values of all</span> <span class="sd"> edges within the graph (where the non-randomness of an edge tends to be</span> @@ -607,7 +607,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/operators/all.html b/_modules/networkx/algorithms/operators/all.html index 2f311782..f3a6855a 100644 --- a/_modules/networkx/algorithms/operators/all.html +++ b/_modules/networkx/algorithms/operators/all.html @@ -471,7 +471,7 @@ <div class="viewcode-block" id="union_all"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.all.union_all.html#networkx.algorithms.operators.all.union_all">[docs]</a><span class="k">def</span> <span class="nf">union_all</span><span class="p">(</span><span class="n">graphs</span><span class="p">,</span> <span class="n">rename</span><span class="o">=</span><span class="p">()):</span> - <span class="sd">"""Returns the union of all graphs.</span> +<span class="w"> </span><span class="sd">"""Returns the union of all graphs.</span> <span class="sd"> The graphs must be disjoint, otherwise an exception is raised.</span> @@ -554,7 +554,7 @@ <div class="viewcode-block" id="disjoint_union_all"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.all.disjoint_union_all.html#networkx.algorithms.operators.all.disjoint_union_all">[docs]</a><span class="k">def</span> <span class="nf">disjoint_union_all</span><span class="p">(</span><span class="n">graphs</span><span class="p">):</span> - <span class="sd">"""Returns the disjoint union of all graphs.</span> +<span class="w"> </span><span class="sd">"""Returns the disjoint union of all graphs.</span> <span class="sd"> This operation forces distinct integer node labels starting with 0</span> <span class="sd"> for the first graph in the list and numbering consecutively.</span> @@ -594,7 +594,7 @@ <div class="viewcode-block" id="compose_all"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.all.compose_all.html#networkx.algorithms.operators.all.compose_all">[docs]</a><span class="k">def</span> <span class="nf">compose_all</span><span class="p">(</span><span class="n">graphs</span><span class="p">):</span> - <span class="sd">"""Returns the composition of all graphs.</span> +<span class="w"> </span><span class="sd">"""Returns the composition of all graphs.</span> <span class="sd"> Composition is the simple union of the node sets and edge sets.</span> <span class="sd"> The node sets of the supplied graphs need not be disjoint.</span> @@ -645,7 +645,7 @@ <div class="viewcode-block" id="intersection_all"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.all.intersection_all.html#networkx.algorithms.operators.all.intersection_all">[docs]</a><span class="k">def</span> <span class="nf">intersection_all</span><span class="p">(</span><span class="n">graphs</span><span class="p">):</span> - <span class="sd">"""Returns a new graph that contains only the nodes and the edges that exist in</span> +<span class="w"> </span><span class="sd">"""Returns a new graph that contains only the nodes and the edges that exist in</span> <span class="sd"> all graphs.</span> <span class="sd"> Parameters</span> @@ -743,7 +743,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/operators/binary.html b/_modules/networkx/algorithms/operators/binary.html index 1bb3cd6e..d766ce23 100644 --- a/_modules/networkx/algorithms/operators/binary.html +++ b/_modules/networkx/algorithms/operators/binary.html @@ -478,7 +478,7 @@ <div class="viewcode-block" id="union"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.binary.union.html#networkx.algorithms.operators.binary.union">[docs]</a><span class="k">def</span> <span class="nf">union</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">rename</span><span class="o">=</span><span class="p">()):</span> - <span class="sd">"""Combine graphs G and H. The names of nodes must be unique.</span> +<span class="w"> </span><span class="sd">"""Combine graphs G and H. The names of nodes must be unique.</span> <span class="sd"> A name collision between the graphs will raise an exception.</span> @@ -533,7 +533,7 @@ <div class="viewcode-block" id="disjoint_union"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.binary.disjoint_union.html#networkx.algorithms.operators.binary.disjoint_union">[docs]</a><span class="k">def</span> <span class="nf">disjoint_union</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">H</span><span class="p">):</span> - <span class="sd">"""Combine graphs G and H. The nodes are assumed to be unique (disjoint).</span> +<span class="w"> </span><span class="sd">"""Combine graphs G and H. The nodes are assumed to be unique (disjoint).</span> <span class="sd"> This algorithm automatically relabels nodes to avoid name collisions.</span> @@ -586,7 +586,7 @@ <div class="viewcode-block" id="intersection"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.binary.intersection.html#networkx.algorithms.operators.binary.intersection">[docs]</a><span class="k">def</span> <span class="nf">intersection</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">H</span><span class="p">):</span> - <span class="sd">"""Returns a new graph that contains only the nodes and the edges that exist in</span> +<span class="w"> </span><span class="sd">"""Returns a new graph that contains only the nodes and the edges that exist in</span> <span class="sd"> both G and H.</span> <span class="sd"> Parameters</span> @@ -630,7 +630,7 @@ <div class="viewcode-block" id="difference"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.binary.difference.html#networkx.algorithms.operators.binary.difference">[docs]</a><span class="k">def</span> <span class="nf">difference</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">H</span><span class="p">):</span> - <span class="sd">"""Returns a new graph that contains the edges that exist in G but not in H.</span> +<span class="w"> </span><span class="sd">"""Returns a new graph that contains the edges that exist in G but not in H.</span> <span class="sd"> The node sets of H and G must be the same.</span> @@ -684,7 +684,7 @@ <div class="viewcode-block" id="symmetric_difference"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.binary.symmetric_difference.html#networkx.algorithms.operators.binary.symmetric_difference">[docs]</a><span class="k">def</span> <span class="nf">symmetric_difference</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">H</span><span class="p">):</span> - <span class="sd">"""Returns new graph with edges that exist in either G or H but not both.</span> +<span class="w"> </span><span class="sd">"""Returns new graph with edges that exist in either G or H but not both.</span> <span class="sd"> The node sets of H and G must be the same.</span> @@ -746,7 +746,7 @@ <div class="viewcode-block" id="compose"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.binary.compose.html#networkx.algorithms.operators.binary.compose">[docs]</a><span class="k">def</span> <span class="nf">compose</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">H</span><span class="p">):</span> - <span class="sd">"""Compose graph G with H by combining nodes and edges into a single graph.</span> +<span class="w"> </span><span class="sd">"""Compose graph G with H by combining nodes and edges into a single graph.</span> <span class="sd"> The node sets and edges sets do not need to be disjoint.</span> @@ -822,7 +822,7 @@ <div class="viewcode-block" id="full_join"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.binary.full_join.html#networkx.algorithms.operators.binary.full_join">[docs]</a><span class="k">def</span> <span class="nf">full_join</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">rename</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">)):</span> - <span class="sd">"""Returns the full join of graphs G and H.</span> +<span class="w"> </span><span class="sd">"""Returns the full join of graphs G and H.</span> <span class="sd"> Full join is the union of G and H in which all edges between</span> <span class="sd"> G and H are added.</span> @@ -948,7 +948,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/operators/product.html b/_modules/networkx/algorithms/operators/product.html index c97eb533..310beb8e 100644 --- a/_modules/networkx/algorithms/operators/product.html +++ b/_modules/networkx/algorithms/operators/product.html @@ -586,7 +586,7 @@ <div class="viewcode-block" id="tensor_product"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.product.tensor_product.html#networkx.algorithms.operators.product.tensor_product">[docs]</a><span class="k">def</span> <span class="nf">tensor_product</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">H</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the tensor product of G and H.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the tensor product of G and H.</span> <span class="sd"> The tensor product $P$ of the graphs $G$ and $H$ has a node set that</span> <span class="sd"> is the tensor product of the node sets, $V(P)=V(G) \times V(H)$.</span> @@ -641,7 +641,7 @@ <div class="viewcode-block" id="cartesian_product"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.product.cartesian_product.html#networkx.algorithms.operators.product.cartesian_product">[docs]</a><span class="k">def</span> <span class="nf">cartesian_product</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">H</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the Cartesian product of G and H.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the Cartesian product of G and H.</span> <span class="sd"> The Cartesian product $P$ of the graphs $G$ and $H$ has a node set that</span> <span class="sd"> is the Cartesian product of the node sets, $V(P)=V(G) \times V(H)$.</span> @@ -692,7 +692,7 @@ <div class="viewcode-block" id="lexicographic_product"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.product.lexicographic_product.html#networkx.algorithms.operators.product.lexicographic_product">[docs]</a><span class="k">def</span> <span class="nf">lexicographic_product</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">H</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the lexicographic product of G and H.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the lexicographic product of G and H.</span> <span class="sd"> The lexicographical product $P$ of the graphs $G$ and $H$ has a node set</span> <span class="sd"> that is the Cartesian product of the node sets, $V(P)=V(G) \times V(H)$.</span> @@ -744,7 +744,7 @@ <div class="viewcode-block" id="strong_product"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.product.strong_product.html#networkx.algorithms.operators.product.strong_product">[docs]</a><span class="k">def</span> <span class="nf">strong_product</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">H</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the strong product of G and H.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the strong product of G and H.</span> <span class="sd"> The strong product $P$ of the graphs $G$ and $H$ has a node set that</span> <span class="sd"> is the Cartesian product of the node sets, $V(P)=V(G) \times V(H)$.</span> @@ -801,7 +801,7 @@ <div class="viewcode-block" id="power"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.product.power.html#networkx.algorithms.operators.product.power">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">power</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""Returns the specified power of a graph.</span> +<span class="w"> </span><span class="sd">"""Returns the specified power of a graph.</span> <span class="sd"> The $k$th power of a simple graph $G$, denoted $G^k$, is a</span> <span class="sd"> graph on the same set of nodes in which two distinct nodes $u$ and</span> @@ -889,7 +889,7 @@ <div class="viewcode-block" id="rooted_product"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.product.rooted_product.html#networkx.algorithms.operators.product.rooted_product">[docs]</a><span class="nd">@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">rooted_product</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">root</span><span class="p">):</span> - <span class="sd">"""Return the rooted product of graphs G and H rooted at root in H.</span> +<span class="w"> </span><span class="sd">"""Return the rooted product of graphs G and H rooted at root in H.</span> <span class="sd"> A new graph is constructed representing the rooted product of</span> <span class="sd"> the inputted graphs, G and H, with a root in H.</span> @@ -928,7 +928,7 @@ <div class="viewcode-block" id="corona_product"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.product.corona_product.html#networkx.algorithms.operators.product.corona_product">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">corona_product</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">H</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the Corona product of G and H.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the Corona product of G and H.</span> <span class="sd"> The corona product of $G$ and $H$ is the graph $C = G \circ H$ obtained by</span> <span class="sd"> taking one copy of $G$, called the center graph, $|V(G)|$ copies of $H$,</span> @@ -1039,7 +1039,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/operators/unary.html b/_modules/networkx/algorithms/operators/unary.html index 59814f96..15408756 100644 --- a/_modules/networkx/algorithms/operators/unary.html +++ b/_modules/networkx/algorithms/operators/unary.html @@ -468,7 +468,7 @@ <div class="viewcode-block" id="complement"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.unary.complement.html#networkx.algorithms.operators.unary.complement">[docs]</a><span class="k">def</span> <span class="nf">complement</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the graph complement of G.</span> +<span class="w"> </span><span class="sd">"""Returns the graph complement of G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -503,7 +503,7 @@ <div class="viewcode-block" id="reverse"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.operators.unary.reverse.html#networkx.algorithms.operators.unary.reverse">[docs]</a><span class="k">def</span> <span class="nf">reverse</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns the reverse directed graph of G.</span> +<span class="w"> </span><span class="sd">"""Returns the reverse directed graph of G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -586,7 +586,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/planar_drawing.html b/_modules/networkx/algorithms/planar_drawing.html index 09cdd9a4..fb6c5154 100644 --- a/_modules/networkx/algorithms/planar_drawing.html +++ b/_modules/networkx/algorithms/planar_drawing.html @@ -469,7 +469,7 @@ <div class="viewcode-block" id="combinatorial_embedding_to_pos"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos.html#networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos">[docs]</a><span class="k">def</span> <span class="nf">combinatorial_embedding_to_pos</span><span class="p">(</span><span class="n">embedding</span><span class="p">,</span> <span class="n">fully_triangulate</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Assigns every node a (x, y) position based on the given embedding</span> +<span class="w"> </span><span class="sd">"""Assigns every node a (x, y) position based on the given embedding</span> <span class="sd"> The algorithm iteratively inserts nodes of the input graph in a certain</span> <span class="sd"> order and rearranges previously inserted nodes so that the planar drawing</span> @@ -589,7 +589,7 @@ <span class="k">def</span> <span class="nf">set_position</span><span class="p">(</span><span class="n">parent</span><span class="p">,</span> <span class="n">tree</span><span class="p">,</span> <span class="n">remaining_nodes</span><span class="p">,</span> <span class="n">delta_x</span><span class="p">,</span> <span class="n">y_coordinate</span><span class="p">,</span> <span class="n">pos</span><span class="p">):</span> - <span class="sd">"""Helper method to calculate the absolute position of nodes."""</span> +<span class="w"> </span><span class="sd">"""Helper method to calculate the absolute position of nodes."""</span> <span class="n">child</span> <span class="o">=</span> <span class="n">tree</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span> <span class="n">parent_node_x</span> <span class="o">=</span> <span class="n">pos</span><span class="p">[</span><span class="n">parent</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">child</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> @@ -601,7 +601,7 @@ <span class="k">def</span> <span class="nf">get_canonical_ordering</span><span class="p">(</span><span class="n">embedding</span><span class="p">,</span> <span class="n">outer_face</span><span class="p">):</span> - <span class="sd">"""Returns a canonical ordering of the nodes</span> +<span class="w"> </span><span class="sd">"""Returns a canonical ordering of the nodes</span> <span class="sd"> The canonical ordering of nodes (v1, ..., vn) must fulfill the following</span> <span class="sd"> conditions:</span> @@ -768,7 +768,7 @@ <span class="k">def</span> <span class="nf">triangulate_face</span><span class="p">(</span><span class="n">embedding</span><span class="p">,</span> <span class="n">v1</span><span class="p">,</span> <span class="n">v2</span><span class="p">):</span> - <span class="sd">"""Triangulates the face given by half edge (v, w)</span> +<span class="w"> </span><span class="sd">"""Triangulates the face given by half edge (v, w)</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -797,7 +797,7 @@ <span class="k">def</span> <span class="nf">triangulate_embedding</span><span class="p">(</span><span class="n">embedding</span><span class="p">,</span> <span class="n">fully_triangulate</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Triangulates the embedding.</span> +<span class="w"> </span><span class="sd">"""Triangulates the embedding.</span> <span class="sd"> Traverses faces of the embedding and adds edges to a copy of the</span> <span class="sd"> embedding to triangulate it.</span> @@ -866,7 +866,7 @@ <span class="k">def</span> <span class="nf">make_bi_connected</span><span class="p">(</span><span class="n">embedding</span><span class="p">,</span> <span class="n">starting_node</span><span class="p">,</span> <span class="n">outgoing_node</span><span class="p">,</span> <span class="n">edges_counted</span><span class="p">):</span> - <span class="sd">"""Triangulate a face and make it 2-connected</span> +<span class="w"> </span><span class="sd">"""Triangulate a face and make it 2-connected</span> <span class="sd"> This method also adds all edges on the face to `edges_counted`.</span> @@ -976,7 +976,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/planarity.html b/_modules/networkx/algorithms/planarity.html index 13145296..b8853a46 100644 --- a/_modules/networkx/algorithms/planarity.html +++ b/_modules/networkx/algorithms/planarity.html @@ -469,7 +469,7 @@ <div class="viewcode-block" id="is_planar"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.planarity.is_planar.html#networkx.algorithms.planarity.is_planar">[docs]</a><span class="k">def</span> <span class="nf">is_planar</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns True if and only if `G` is planar.</span> +<span class="w"> </span><span class="sd">"""Returns True if and only if `G` is planar.</span> <span class="sd"> A graph is *planar* iff it can be drawn in a plane without</span> <span class="sd"> any edge intersections.</span> @@ -501,7 +501,7 @@ <div class="viewcode-block" id="check_planarity"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.planarity.check_planarity.html#networkx.algorithms.planarity.check_planarity">[docs]</a><span class="k">def</span> <span class="nf">check_planarity</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">counterexample</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Check if a graph is planar and return a counterexample or an embedding.</span> +<span class="w"> </span><span class="sd">"""Check if a graph is planar and return a counterexample or an embedding.</span> <span class="sd"> A graph is planar iff it can be drawn in a plane without</span> <span class="sd"> any edge intersections.</span> @@ -576,7 +576,7 @@ <span class="k">def</span> <span class="nf">check_planarity_recursive</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">counterexample</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Recursive version of :meth:`check_planarity`."""</span> +<span class="w"> </span><span class="sd">"""Recursive version of :meth:`check_planarity`."""</span> <span class="n">planarity_state</span> <span class="o">=</span> <span class="n">LRPlanarity</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="n">embedding</span> <span class="o">=</span> <span class="n">planarity_state</span><span class="o">.</span><span class="n">lr_planarity_recursive</span><span class="p">()</span> <span class="k">if</span> <span class="n">embedding</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> @@ -591,7 +591,7 @@ <span class="k">def</span> <span class="nf">get_counterexample</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Obtains a Kuratowski subgraph.</span> +<span class="w"> </span><span class="sd">"""Obtains a Kuratowski subgraph.</span> <span class="sd"> Raises nx.NetworkXException if G is planar.</span> @@ -629,7 +629,7 @@ <span class="k">def</span> <span class="nf">get_counterexample_recursive</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Recursive version of :meth:`get_counterexample`."""</span> +<span class="w"> </span><span class="sd">"""Recursive version of :meth:`get_counterexample`."""</span> <span class="c1"># copy graph</span> <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> @@ -651,7 +651,7 @@ <span class="k">class</span> <span class="nc">Interval</span><span class="p">:</span> - <span class="sd">"""Represents a set of return edges.</span> +<span class="w"> </span><span class="sd">"""Represents a set of return edges.</span> <span class="sd"> All return edges in an interval induce a same constraint on the contained</span> <span class="sd"> edges, which means that all edges must either have a left orientation or</span> @@ -663,15 +663,15 @@ <span class="bp">self</span><span class="o">.</span><span class="n">high</span> <span class="o">=</span> <span class="n">high</span> <span class="k">def</span> <span class="nf">empty</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Check if the interval is empty"""</span> +<span class="w"> </span><span class="sd">"""Check if the interval is empty"""</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">low</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">high</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns a copy of this interval"""</span> +<span class="w"> </span><span class="sd">"""Returns a copy of this interval"""</span> <span class="k">return</span> <span class="n">Interval</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">low</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">high</span><span class="p">)</span> <span class="k">def</span> <span class="nf">conflicting</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">planarity_state</span><span class="p">):</span> - <span class="sd">"""Returns True if interval I conflicts with edge b"""</span> +<span class="w"> </span><span class="sd">"""Returns True if interval I conflicts with edge b"""</span> <span class="k">return</span> <span class="p">(</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">empty</span><span class="p">()</span> <span class="ow">and</span> <span class="n">planarity_state</span><span class="o">.</span><span class="n">lowpt</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">high</span><span class="p">]</span> <span class="o">></span> <span class="n">planarity_state</span><span class="o">.</span><span class="n">lowpt</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> @@ -679,7 +679,7 @@ <span class="k">class</span> <span class="nc">ConflictPair</span><span class="p">:</span> - <span class="sd">"""Represents a different constraint between two intervals.</span> +<span class="w"> </span><span class="sd">"""Represents a different constraint between two intervals.</span> <span class="sd"> The edges in the left interval must have a different orientation than</span> <span class="sd"> the one in the right interval.</span> @@ -690,13 +690,13 @@ <span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">=</span> <span class="n">right</span> <span class="k">def</span> <span class="nf">swap</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Swap left and right intervals"""</span> +<span class="w"> </span><span class="sd">"""Swap left and right intervals"""</span> <span class="n">temp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">=</span> <span class="n">temp</span> <span class="k">def</span> <span class="nf">lowest</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">planarity_state</span><span class="p">):</span> - <span class="sd">"""Returns the lowest lowpoint of a conflict pair"""</span> +<span class="w"> </span><span class="sd">"""Returns the lowest lowpoint of a conflict pair"""</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">left</span><span class="o">.</span><span class="n">empty</span><span class="p">():</span> <span class="k">return</span> <span class="n">planarity_state</span><span class="o">.</span><span class="n">lowpt</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">right</span><span class="o">.</span><span class="n">low</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span><span class="o">.</span><span class="n">empty</span><span class="p">():</span> @@ -707,14 +707,14 @@ <span class="k">def</span> <span class="nf">top_of_stack</span><span class="p">(</span><span class="n">l</span><span class="p">):</span> - <span class="sd">"""Returns the element on top of the stack."""</span> +<span class="w"> </span><span class="sd">"""Returns the element on top of the stack."""</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">l</span><span class="p">:</span> <span class="k">return</span> <span class="kc">None</span> <span class="k">return</span> <span class="n">l</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">class</span> <span class="nc">LRPlanarity</span><span class="p">:</span> - <span class="sd">"""A class to maintain the state during planarity check."""</span> +<span class="w"> </span><span class="sd">"""A class to maintain the state during planarity check."""</span> <span class="vm">__slots__</span> <span class="o">=</span> <span class="p">[</span> <span class="s2">"G"</span><span class="p">,</span> @@ -778,7 +778,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">embedding</span> <span class="o">=</span> <span class="n">PlanarEmbedding</span><span class="p">()</span> <span class="k">def</span> <span class="nf">lr_planarity</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Execute the LR planarity test.</span> +<span class="w"> </span><span class="sd">"""Execute the LR planarity test.</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> @@ -857,7 +857,7 @@ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">embedding</span> <span class="k">def</span> <span class="nf">lr_planarity_recursive</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Recursive version of :meth:`lr_planarity`."""</span> +<span class="w"> </span><span class="sd">"""Recursive version of :meth:`lr_planarity`."""</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="o">.</span><span class="n">order</span><span class="p">()</span> <span class="o">></span> <span class="mi">2</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="o">.</span><span class="n">size</span><span class="p">()</span> <span class="o">></span> <span class="mi">3</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="o">.</span><span class="n">order</span><span class="p">()</span> <span class="o">-</span> <span class="mi">6</span><span class="p">:</span> <span class="c1"># graph is not planar</span> <span class="k">return</span> <span class="kc">None</span> @@ -904,7 +904,7 @@ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">embedding</span> <span class="k">def</span> <span class="nf">dfs_orientation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Orient the graph by DFS, compute lowpoints and nesting order."""</span> +<span class="w"> </span><span class="sd">"""Orient the graph by DFS, compute lowpoints and nesting order."""</span> <span class="c1"># the recursion stack</span> <span class="n">dfs_stack</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="c1"># index of next edge to handle in adjacency list of each node</span> @@ -957,7 +957,7 @@ <span class="n">ind</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span> <span class="k">def</span> <span class="nf">dfs_orientation_recursive</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Recursive version of :meth:`dfs_orientation`."""</span> +<span class="w"> </span><span class="sd">"""Recursive version of :meth:`dfs_orientation`."""</span> <span class="n">e</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent_edge</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="p">[</span><span class="n">v</span><span class="p">]:</span> <span class="k">if</span> <span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">DG</span><span class="o">.</span><span class="n">edges</span> <span class="ow">or</span> <span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">DG</span><span class="o">.</span><span class="n">edges</span><span class="p">:</span> @@ -990,7 +990,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">lowpt2</span><span class="p">[</span><span class="n">e</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lowpt2</span><span class="p">[</span><span class="n">e</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">lowpt2</span><span class="p">[</span><span class="n">vw</span><span class="p">])</span> <span class="k">def</span> <span class="nf">dfs_testing</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Test for LR partition."""</span> +<span class="w"> </span><span class="sd">"""Test for LR partition."""</span> <span class="c1"># the recursion stack</span> <span class="n">dfs_stack</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="c1"># index of next edge to handle in adjacency list of each node</span> @@ -1039,7 +1039,7 @@ <span class="k">return</span> <span class="kc">True</span> <span class="k">def</span> <span class="nf">dfs_testing_recursive</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Recursive version of :meth:`dfs_testing`."""</span> +<span class="w"> </span><span class="sd">"""Recursive version of :meth:`dfs_testing`."""</span> <span class="n">e</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent_edge</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ordered_adjs</span><span class="p">[</span><span class="n">v</span><span class="p">]:</span> <span class="n">ei</span> <span class="o">=</span> <span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span> @@ -1149,7 +1149,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">ref</span><span class="p">[</span><span class="n">e</span><span class="p">]</span> <span class="o">=</span> <span class="n">hr</span> <span class="k">def</span> <span class="nf">dfs_embedding</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Completes the embedding."""</span> +<span class="w"> </span><span class="sd">"""Completes the embedding."""</span> <span class="c1"># the recursion stack</span> <span class="n">dfs_stack</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="c1"># index of next edge to handle in adjacency list of each node</span> @@ -1178,7 +1178,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">left_ref</span><span class="p">[</span><span class="n">w</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span> <span class="k">def</span> <span class="nf">dfs_embedding_recursive</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Recursive version of :meth:`dfs_embedding`."""</span> +<span class="w"> </span><span class="sd">"""Recursive version of :meth:`dfs_embedding`."""</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ordered_adjs</span><span class="p">[</span><span class="n">v</span><span class="p">]:</span> <span class="n">ei</span> <span class="o">=</span> <span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span> <span class="k">if</span> <span class="n">ei</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent_edge</span><span class="p">[</span><span class="n">w</span><span class="p">]:</span> <span class="c1"># tree edge</span> @@ -1196,7 +1196,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">left_ref</span><span class="p">[</span><span class="n">w</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span> <span class="k">def</span> <span class="nf">sign</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">e</span><span class="p">):</span> - <span class="sd">"""Resolve the relative side of an edge to the absolute side."""</span> +<span class="w"> </span><span class="sd">"""Resolve the relative side of an edge to the absolute side."""</span> <span class="c1"># the recursion stack</span> <span class="n">dfs_stack</span> <span class="o">=</span> <span class="p">[</span><span class="n">e</span><span class="p">]</span> <span class="c1"># dict to remember reference edges</span> @@ -1216,7 +1216,7 @@ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">side</span><span class="p">[</span><span class="n">e</span><span class="p">]</span> <span class="k">def</span> <span class="nf">sign_recursive</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">e</span><span class="p">):</span> - <span class="sd">"""Recursive version of :meth:`sign`."""</span> +<span class="w"> </span><span class="sd">"""Recursive version of :meth:`sign`."""</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ref</span><span class="p">[</span><span class="n">e</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">side</span><span class="p">[</span><span class="n">e</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">side</span><span class="p">[</span><span class="n">e</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">sign_recursive</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ref</span><span class="p">[</span><span class="n">e</span><span class="p">])</span> <span class="bp">self</span><span class="o">.</span><span class="n">ref</span><span class="p">[</span><span class="n">e</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span> @@ -1224,7 +1224,7 @@ <div class="viewcode-block" id="PlanarEmbedding"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.planarity.PlanarEmbedding.html#networkx.algorithms.planarity.PlanarEmbedding">[docs]</a><span class="k">class</span> <span class="nc">PlanarEmbedding</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">):</span> - <span class="sd">"""Represents a planar graph with its planar embedding.</span> +<span class="w"> </span><span class="sd">"""Represents a planar graph with its planar embedding.</span> <span class="sd"> The planar embedding is given by a `combinatorial embedding</span> <span class="sd"> <https://en.wikipedia.org/wiki/Graph_embedding#Combinatorial_embedding>`_.</span> @@ -1317,7 +1317,7 @@ <span class="sd"> """</span> <div class="viewcode-block" id="PlanarEmbedding.get_data"><a class="viewcode-back" href="../../../reference/algorithms/generated/generated/networkx.algorithms.planarity.PlanarEmbedding.get_data.html#networkx.algorithms.planarity.PlanarEmbedding.get_data">[docs]</a> <span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Converts the adjacency structure into a better readable structure.</span> +<span class="w"> </span><span class="sd">"""Converts the adjacency structure into a better readable structure.</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> @@ -1336,7 +1336,7 @@ <span class="k">return</span> <span class="n">embedding</span></div> <div class="viewcode-block" id="PlanarEmbedding.set_data"><a class="viewcode-back" href="../../../reference/algorithms/generated/generated/networkx.algorithms.planarity.PlanarEmbedding.set_data.html#networkx.algorithms.planarity.PlanarEmbedding.set_data">[docs]</a> <span class="k">def</span> <span class="nf">set_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span> - <span class="sd">"""Inserts edges according to given sorted neighbor list.</span> +<span class="w"> </span><span class="sd">"""Inserts edges according to given sorted neighbor list.</span> <span class="sd"> The input format is the same as the output format of get_data().</span> @@ -1356,7 +1356,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">add_half_edge_first</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span></div> <div class="viewcode-block" id="PlanarEmbedding.neighbors_cw_order"><a class="viewcode-back" href="../../../reference/algorithms/generated/generated/networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order.html#networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order">[docs]</a> <span class="k">def</span> <span class="nf">neighbors_cw_order</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Generator for the neighbors of v in clockwise order.</span> +<span class="w"> </span><span class="sd">"""Generator for the neighbors of v in clockwise order.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1378,7 +1378,7 @@ <span class="n">current_node</span> <span class="o">=</span> <span class="bp">self</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="n">current_node</span><span class="p">][</span><span class="s2">"cw"</span><span class="p">]</span></div> <div class="viewcode-block" id="PlanarEmbedding.check_structure"><a class="viewcode-back" href="../../../reference/algorithms/generated/generated/networkx.algorithms.planarity.PlanarEmbedding.check_structure.html#networkx.algorithms.planarity.PlanarEmbedding.check_structure">[docs]</a> <span class="k">def</span> <span class="nf">check_structure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Runs without exceptions if this object is valid.</span> +<span class="w"> </span><span class="sd">"""Runs without exceptions if this object is valid.</span> <span class="sd"> Checks that the following properties are fulfilled:</span> @@ -1437,7 +1437,7 @@ <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXException</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span></div> <div class="viewcode-block" id="PlanarEmbedding.add_half_edge_ccw"><a class="viewcode-back" href="../../../reference/algorithms/generated/generated/networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw.html#networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw">[docs]</a> <span class="k">def</span> <span class="nf">add_half_edge_ccw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start_node</span><span class="p">,</span> <span class="n">end_node</span><span class="p">,</span> <span class="n">reference_neighbor</span><span class="p">):</span> - <span class="sd">"""Adds a half-edge from start_node to end_node.</span> +<span class="w"> </span><span class="sd">"""Adds a half-edge from start_node to end_node.</span> <span class="sd"> The half-edge is added counter clockwise next to the existing half-edge</span> <span class="sd"> (start_node, reference_neighbor).</span> @@ -1478,7 +1478,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="n">start_node</span><span class="p">][</span><span class="s2">"first_nbr"</span><span class="p">]</span> <span class="o">=</span> <span class="n">end_node</span></div> <div class="viewcode-block" id="PlanarEmbedding.add_half_edge_cw"><a class="viewcode-back" href="../../../reference/algorithms/generated/generated/networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw.html#networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw">[docs]</a> <span class="k">def</span> <span class="nf">add_half_edge_cw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start_node</span><span class="p">,</span> <span class="n">end_node</span><span class="p">,</span> <span class="n">reference_neighbor</span><span class="p">):</span> - <span class="sd">"""Adds a half-edge from start_node to end_node.</span> +<span class="w"> </span><span class="sd">"""Adds a half-edge from start_node to end_node.</span> <span class="sd"> The half-edge is added clockwise next to the existing half-edge</span> <span class="sd"> (start_node, reference_neighbor).</span> @@ -1526,7 +1526,7 @@ <span class="bp">self</span><span class="p">[</span><span class="n">start_node</span><span class="p">][</span><span class="n">end_node</span><span class="p">][</span><span class="s2">"ccw"</span><span class="p">]</span> <span class="o">=</span> <span class="n">reference_neighbor</span></div> <div class="viewcode-block" id="PlanarEmbedding.connect_components"><a class="viewcode-back" href="../../../reference/algorithms/generated/generated/networkx.algorithms.planarity.PlanarEmbedding.connect_components.html#networkx.algorithms.planarity.PlanarEmbedding.connect_components">[docs]</a> <span class="k">def</span> <span class="nf">connect_components</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">):</span> - <span class="sd">"""Adds half-edges for (v, w) and (w, v) at some position.</span> +<span class="w"> </span><span class="sd">"""Adds half-edges for (v, w) and (w, v) at some position.</span> <span class="sd"> This method should only be called if v and w are in different</span> <span class="sd"> components, or it might break the embedding.</span> @@ -1550,7 +1550,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">add_half_edge_first</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span></div> <div class="viewcode-block" id="PlanarEmbedding.add_half_edge_first"><a class="viewcode-back" href="../../../reference/algorithms/generated/generated/networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first.html#networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first">[docs]</a> <span class="k">def</span> <span class="nf">add_half_edge_first</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start_node</span><span class="p">,</span> <span class="n">end_node</span><span class="p">):</span> - <span class="sd">"""The added half-edge is inserted at the first position in the order.</span> +<span class="w"> </span><span class="sd">"""The added half-edge is inserted at the first position in the order.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1570,7 +1570,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">add_half_edge_ccw</span><span class="p">(</span><span class="n">start_node</span><span class="p">,</span> <span class="n">end_node</span><span class="p">,</span> <span class="n">reference</span><span class="p">)</span></div> <div class="viewcode-block" id="PlanarEmbedding.next_face_half_edge"><a class="viewcode-back" href="../../../reference/algorithms/generated/generated/networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge.html#networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge">[docs]</a> <span class="k">def</span> <span class="nf">next_face_half_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">):</span> - <span class="sd">"""Returns the following half-edge left of a face.</span> +<span class="w"> </span><span class="sd">"""Returns the following half-edge left of a face.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1585,7 +1585,7 @@ <span class="k">return</span> <span class="n">w</span><span class="p">,</span> <span class="n">new_node</span></div> <div class="viewcode-block" id="PlanarEmbedding.traverse_face"><a class="viewcode-back" href="../../../reference/algorithms/generated/generated/networkx.algorithms.planarity.PlanarEmbedding.traverse_face.html#networkx.algorithms.planarity.PlanarEmbedding.traverse_face">[docs]</a> <span class="k">def</span> <span class="nf">traverse_face</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">mark_half_edges</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns nodes on the face that belong to the half-edge (v, w).</span> +<span class="w"> </span><span class="sd">"""Returns nodes on the face that belong to the half-edge (v, w).</span> <span class="sd"> The face that is traversed lies to the right of the half-edge (in an</span> <span class="sd"> orientation where v is below w).</span> @@ -1628,7 +1628,7 @@ <span class="k">return</span> <span class="n">face_nodes</span></div> <div class="viewcode-block" id="PlanarEmbedding.is_directed"><a class="viewcode-back" href="../../../reference/algorithms/generated/generated/networkx.algorithms.planarity.PlanarEmbedding.is_directed.html#networkx.algorithms.planarity.PlanarEmbedding.is_directed">[docs]</a> <span class="k">def</span> <span class="nf">is_directed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""A valid PlanarEmbedding is undirected.</span> +<span class="w"> </span><span class="sd">"""A valid PlanarEmbedding is undirected.</span> <span class="sd"> All reverse edges are contained, i.e. for every existing</span> <span class="sd"> half-edge (v, w) the half-edge in the opposite direction (w, v) is also</span> @@ -1686,7 +1686,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/polynomials.html b/_modules/networkx/algorithms/polynomials.html index f42dd64c..09211792 100644 --- a/_modules/networkx/algorithms/polynomials.html +++ b/_modules/networkx/algorithms/polynomials.html @@ -494,7 +494,7 @@ <div class="viewcode-block" id="tutte_polynomial"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.polynomials.tutte_polynomial.html#networkx.algorithms.polynomials.tutte_polynomial">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">tutte_polynomial</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the Tutte polynomial of `G`</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the Tutte polynomial of `G`</span> <span class="sd"> This function computes the Tutte polynomial via an iterative version of</span> <span class="sd"> the deletion-contraction algorithm.</span> @@ -643,7 +643,7 @@ <div class="viewcode-block" id="chromatic_polynomial"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.polynomials.chromatic_polynomial.html#networkx.algorithms.polynomials.chromatic_polynomial">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">chromatic_polynomial</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the chromatic polynomial of `G`</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the chromatic polynomial of `G`</span> <span class="sd"> This function computes the chromatic polynomial via an iterative version of</span> <span class="sd"> the deletion-contraction algorithm.</span> @@ -815,7 +815,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/reciprocity.html b/_modules/networkx/algorithms/reciprocity.html index caa04463..12e28869 100644 --- a/_modules/networkx/algorithms/reciprocity.html +++ b/_modules/networkx/algorithms/reciprocity.html @@ -473,7 +473,7 @@ <div class="viewcode-block" id="reciprocity"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.reciprocity.reciprocity.html#networkx.algorithms.reciprocity.reciprocity">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">,</span> <span class="s2">"multigraph"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">reciprocity</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Compute the reciprocity in a directed graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compute the reciprocity in a directed graph.</span> <span class="sd"> The reciprocity of a directed graph is defined as the ratio</span> <span class="sd"> of the number of edges pointing in both directions to the total</span> @@ -521,7 +521,7 @@ <span class="k">def</span> <span class="nf">_reciprocity_iter</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span> - <span class="sd">"""Return an iterator of (node, reciprocity)."""</span> +<span class="w"> </span><span class="sd">"""Return an iterator of (node, reciprocity)."""</span> <span class="n">n</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">nbunch_iter</span><span class="p">(</span><span class="n">nodes</span><span class="p">)</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">n</span><span class="p">:</span> <span class="n">pred</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">predecessors</span><span class="p">(</span><span class="n">node</span><span class="p">))</span> @@ -541,7 +541,7 @@ <div class="viewcode-block" id="overall_reciprocity"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.reciprocity.overall_reciprocity.html#networkx.algorithms.reciprocity.overall_reciprocity">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">,</span> <span class="s2">"multigraph"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">overall_reciprocity</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Compute the reciprocity for the whole graph.</span> +<span class="w"> </span><span class="sd">"""Compute the reciprocity for the whole graph.</span> <span class="sd"> See the doc of reciprocity for the definition.</span> @@ -609,7 +609,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/regular.html b/_modules/networkx/algorithms/regular.html index 63e54ebb..20f36e84 100644 --- a/_modules/networkx/algorithms/regular.html +++ b/_modules/networkx/algorithms/regular.html @@ -470,7 +470,7 @@ <div class="viewcode-block" id="is_regular"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.regular.is_regular.html#networkx.algorithms.regular.is_regular">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">is_regular</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Determines whether the graph ``G`` is a regular graph.</span> +<span class="w"> </span><span class="sd">"""Determines whether the graph ``G`` is a regular graph.</span> <span class="sd"> A regular graph is a graph where each vertex has the same degree. A</span> <span class="sd"> regular digraph is a graph where the indegree and outdegree of each</span> @@ -507,7 +507,7 @@ <div class="viewcode-block" id="is_k_regular"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.regular.is_k_regular.html#networkx.algorithms.regular.is_k_regular">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">is_k_regular</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""Determines whether the graph ``G`` is a k-regular graph.</span> +<span class="w"> </span><span class="sd">"""Determines whether the graph ``G`` is a k-regular graph.</span> <span class="sd"> A k-regular graph is a graph where each vertex has degree k.</span> @@ -533,7 +533,7 @@ <div class="viewcode-block" id="k_factor"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.regular.k_factor.html#networkx.algorithms.regular.k_factor">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">k_factor</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">matching_weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Compute a k-factor of G</span> +<span class="w"> </span><span class="sd">"""Compute a k-factor of G</span> <span class="sd"> A k-factor of a graph is a spanning k-regular subgraph.</span> <span class="sd"> A spanning k-regular subgraph of G is a subgraph that contains</span> @@ -723,7 +723,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/richclub.html b/_modules/networkx/algorithms/richclub.html index 0c024ba7..80626a6f 100644 --- a/_modules/networkx/algorithms/richclub.html +++ b/_modules/networkx/algorithms/richclub.html @@ -474,7 +474,7 @@ <div class="viewcode-block" id="rich_club_coefficient"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.richclub.rich_club_coefficient.html#networkx.algorithms.richclub.rich_club_coefficient">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">rich_club_coefficient</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">Q</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the rich-club coefficient of the graph `G`.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the rich-club coefficient of the graph `G`.</span> <span class="sd"> For each degree *k*, the *rich-club coefficient* is the ratio of the</span> <span class="sd"> number of actual to the number of potential edges for nodes with</span> @@ -549,7 +549,7 @@ <span class="k">def</span> <span class="nf">_compute_rc</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the rich-club coefficient for each degree in the graph</span> +<span class="w"> </span><span class="sd">"""Returns the rich-club coefficient for each degree in the graph</span> <span class="sd"> `G`.</span> <span class="sd"> `G` is an undirected graph without multiedges.</span> @@ -632,7 +632,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/shortest_paths/astar.html b/_modules/networkx/algorithms/shortest_paths/astar.html index 7179d006..3bf7bd4d 100644 --- a/_modules/networkx/algorithms/shortest_paths/astar.html +++ b/_modules/networkx/algorithms/shortest_paths/astar.html @@ -473,7 +473,7 @@ <div class="viewcode-block" id="astar_path"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.astar.astar_path.html#networkx.algorithms.shortest_paths.astar.astar_path">[docs]</a><span class="k">def</span> <span class="nf">astar_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">heuristic</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns a list of nodes in a shortest path between source and target</span> +<span class="w"> </span><span class="sd">"""Returns a list of nodes in a shortest path between source and target</span> <span class="sd"> using the A* ("A-star") algorithm.</span> <span class="sd"> There may be more than one shortest path. This returns only one.</span> @@ -619,7 +619,7 @@ <div class="viewcode-block" id="astar_path_length"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.astar.astar_path_length.html#networkx.algorithms.shortest_paths.astar.astar_path_length">[docs]</a><span class="k">def</span> <span class="nf">astar_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">heuristic</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns the length of the shortest path between source and target using</span> +<span class="w"> </span><span class="sd">"""Returns the length of the shortest path between source and target using</span> <span class="sd"> the A* ("A-star") algorithm.</span> <span class="sd"> Parameters</span> @@ -722,7 +722,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/shortest_paths/dense.html b/_modules/networkx/algorithms/shortest_paths/dense.html index 4b084769..b7b67c15 100644 --- a/_modules/networkx/algorithms/shortest_paths/dense.html +++ b/_modules/networkx/algorithms/shortest_paths/dense.html @@ -474,7 +474,7 @@ <div class="viewcode-block" id="floyd_warshall_numpy"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy.html#networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy">[docs]</a><span class="k">def</span> <span class="nf">floyd_warshall_numpy</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Find all-pairs shortest path lengths using Floyd's algorithm.</span> +<span class="w"> </span><span class="sd">"""Find all-pairs shortest path lengths using Floyd's algorithm.</span> <span class="sd"> This algorithm for finding shortest paths takes advantage of</span> <span class="sd"> matrix representations of a graph and works well for dense</span> @@ -537,7 +537,7 @@ <div class="viewcode-block" id="floyd_warshall_predecessor_and_distance"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance.html#networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance">[docs]</a><span class="k">def</span> <span class="nf">floyd_warshall_predecessor_and_distance</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Find all-pairs shortest path lengths using Floyd's algorithm.</span> +<span class="w"> </span><span class="sd">"""Find all-pairs shortest path lengths using Floyd's algorithm.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -619,7 +619,7 @@ <div class="viewcode-block" id="reconstruct_path"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.dense.reconstruct_path.html#networkx.algorithms.shortest_paths.dense.reconstruct_path">[docs]</a><span class="k">def</span> <span class="nf">reconstruct_path</span><span class="p">(</span><span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">predecessors</span><span class="p">):</span> - <span class="sd">"""Reconstruct a path from source to target using the predecessors</span> +<span class="w"> </span><span class="sd">"""Reconstruct a path from source to target using the predecessors</span> <span class="sd"> dict as returned by floyd_warshall_predecessor_and_distance</span> <span class="sd"> Parameters</span> @@ -662,7 +662,7 @@ <div class="viewcode-block" id="floyd_warshall"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.dense.floyd_warshall.html#networkx.algorithms.shortest_paths.dense.floyd_warshall">[docs]</a><span class="k">def</span> <span class="nf">floyd_warshall</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Find all-pairs shortest path lengths using Floyd's algorithm.</span> +<span class="w"> </span><span class="sd">"""Find all-pairs shortest path lengths using Floyd's algorithm.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -745,7 +745,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/shortest_paths/generic.html b/_modules/networkx/algorithms/shortest_paths/generic.html index 0f058311..5fdf61f3 100644 --- a/_modules/networkx/algorithms/shortest_paths/generic.html +++ b/_modules/networkx/algorithms/shortest_paths/generic.html @@ -481,7 +481,7 @@ <div class="viewcode-block" id="has_path"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.generic.has_path.html#networkx.algorithms.shortest_paths.generic.has_path">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">has_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span> - <span class="sd">"""Returns *True* if *G* has a path from *source* to *target*.</span> +<span class="w"> </span><span class="sd">"""Returns *True* if *G* has a path from *source* to *target*.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -502,7 +502,7 @@ <div class="viewcode-block" id="shortest_path"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.generic.shortest_path.html#networkx.algorithms.shortest_paths.generic.shortest_path">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">shortest_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"dijkstra"</span><span class="p">):</span> - <span class="sd">"""Compute shortest paths in the graph.</span> +<span class="w"> </span><span class="sd">"""Compute shortest paths in the graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -637,7 +637,7 @@ <div class="viewcode-block" id="shortest_path_length"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.generic.shortest_path_length.html#networkx.algorithms.shortest_paths.generic.shortest_path_length">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">shortest_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"dijkstra"</span><span class="p">):</span> - <span class="sd">"""Compute shortest path lengths in the graph.</span> +<span class="w"> </span><span class="sd">"""Compute shortest path lengths in the graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -780,7 +780,7 @@ <div class="viewcode-block" id="average_shortest_path_length"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.generic.average_shortest_path_length.html#networkx.algorithms.shortest_paths.generic.average_shortest_path_length">[docs]</a><span class="k">def</span> <span class="nf">average_shortest_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the average shortest path length.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the average shortest path length.</span> <span class="sd"> The average shortest path length is</span> @@ -896,7 +896,7 @@ <div class="viewcode-block" id="all_shortest_paths"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.generic.all_shortest_paths.html#networkx.algorithms.shortest_paths.generic.all_shortest_paths">[docs]</a><span class="k">def</span> <span class="nf">all_shortest_paths</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"dijkstra"</span><span class="p">):</span> - <span class="sd">"""Compute all shortest simple paths in the graph.</span> +<span class="w"> </span><span class="sd">"""Compute all shortest simple paths in the graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -973,7 +973,7 @@ <span class="k">def</span> <span class="nf">_build_paths_from_predecessors</span><span class="p">(</span><span class="n">sources</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span> - <span class="sd">"""Compute all simple paths to target, given the predecessors found in</span> +<span class="w"> </span><span class="sd">"""Compute all simple paths to target, given the predecessors found in</span> <span class="sd"> pred, terminating when any source in sources is found.</span> <span class="sd"> Parameters</span> @@ -1088,7 +1088,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/shortest_paths/unweighted.html b/_modules/networkx/algorithms/shortest_paths/unweighted.html index 3cbeecde..f8565ac0 100644 --- a/_modules/networkx/algorithms/shortest_paths/unweighted.html +++ b/_modules/networkx/algorithms/shortest_paths/unweighted.html @@ -479,7 +479,7 @@ <div class="viewcode-block" id="single_source_shortest_path_length"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length.html#networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length">[docs]</a><span class="k">def</span> <span class="nf">single_source_shortest_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute the shortest path lengths from source to all reachable nodes.</span> +<span class="w"> </span><span class="sd">"""Compute the shortest path lengths from source to all reachable nodes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -523,7 +523,7 @@ <span class="k">def</span> <span class="nf">_single_shortest_path_length</span><span class="p">(</span><span class="n">adj</span><span class="p">,</span> <span class="n">firstlevel</span><span class="p">,</span> <span class="n">cutoff</span><span class="p">):</span> - <span class="sd">"""Yields (node, level) in a breadth first search</span> +<span class="w"> </span><span class="sd">"""Yields (node, level) in a breadth first search</span> <span class="sd"> Shortest Path Length helper function</span> <span class="sd"> Parameters</span> @@ -557,7 +557,7 @@ <div class="viewcode-block" id="single_target_shortest_path_length"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length.html#networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length">[docs]</a><span class="k">def</span> <span class="nf">single_target_shortest_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute the shortest path lengths to target from all reachable nodes.</span> +<span class="w"> </span><span class="sd">"""Compute the shortest path lengths to target from all reachable nodes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -604,7 +604,7 @@ <div class="viewcode-block" id="all_pairs_shortest_path_length"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length.html#networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length">[docs]</a><span class="k">def</span> <span class="nf">all_pairs_shortest_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Computes the shortest path lengths between all nodes in `G`.</span> +<span class="w"> </span><span class="sd">"""Computes the shortest path lengths between all nodes in `G`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -648,7 +648,7 @@ <div class="viewcode-block" id="bidirectional_shortest_path"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path.html#networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path">[docs]</a><span class="k">def</span> <span class="nf">bidirectional_shortest_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span> - <span class="sd">"""Returns a list of nodes in a shortest path between source and target.</span> +<span class="w"> </span><span class="sd">"""Returns a list of nodes in a shortest path between source and target.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -704,7 +704,7 @@ <span class="k">def</span> <span class="nf">_bidirectional_pred_succ</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span> - <span class="sd">"""Bidirectional shortest path helper.</span> +<span class="w"> </span><span class="sd">"""Bidirectional shortest path helper.</span> <span class="sd"> Returns (pred, succ, w) where</span> <span class="sd"> pred is a dictionary of predecessors from w to the source, and</span> @@ -756,7 +756,7 @@ <div class="viewcode-block" id="single_source_shortest_path"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path.html#networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path">[docs]</a><span class="k">def</span> <span class="nf">single_source_shortest_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute shortest path between source</span> +<span class="w"> </span><span class="sd">"""Compute shortest path between source</span> <span class="sd"> and all other nodes reachable from source.</span> <span class="sd"> Parameters</span> @@ -806,7 +806,7 @@ <span class="k">def</span> <span class="nf">_single_shortest_path</span><span class="p">(</span><span class="n">adj</span><span class="p">,</span> <span class="n">firstlevel</span><span class="p">,</span> <span class="n">paths</span><span class="p">,</span> <span class="n">cutoff</span><span class="p">,</span> <span class="n">join</span><span class="p">):</span> - <span class="sd">"""Returns shortest paths</span> +<span class="w"> </span><span class="sd">"""Returns shortest paths</span> <span class="sd"> Shortest Path helper function</span> <span class="sd"> Parameters</span> @@ -839,7 +839,7 @@ <div class="viewcode-block" id="single_target_shortest_path"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path.html#networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path">[docs]</a><span class="k">def</span> <span class="nf">single_target_shortest_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute shortest path to target from all nodes that reach target.</span> +<span class="w"> </span><span class="sd">"""Compute shortest path to target from all nodes that reach target.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -890,7 +890,7 @@ <div class="viewcode-block" id="all_pairs_shortest_path"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path.html#networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path">[docs]</a><span class="k">def</span> <span class="nf">all_pairs_shortest_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute shortest paths between all nodes.</span> +<span class="w"> </span><span class="sd">"""Compute shortest paths between all nodes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -923,7 +923,7 @@ <div class="viewcode-block" id="predecessor"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.predecessor.html#networkx.algorithms.shortest_paths.unweighted.predecessor">[docs]</a><span class="k">def</span> <span class="nf">predecessor</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">return_seen</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns dict of predecessors for the path from source to all nodes in G.</span> +<span class="w"> </span><span class="sd">"""Returns dict of predecessors for the path from source to all nodes in G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1055,7 +1055,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/shortest_paths/weighted.html b/_modules/networkx/algorithms/shortest_paths/weighted.html index a2e23f33..61c9e9e5 100644 --- a/_modules/networkx/algorithms/shortest_paths/weighted.html +++ b/_modules/networkx/algorithms/shortest_paths/weighted.html @@ -502,7 +502,7 @@ <span class="k">def</span> <span class="nf">_weight_function</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> - <span class="sd">"""Returns a function that returns the weight of an edge.</span> +<span class="w"> </span><span class="sd">"""Returns a function that returns the weight of an edge.</span> <span class="sd"> The returned function is specifically suitable for input to</span> <span class="sd"> functions :func:`_dijkstra` and :func:`_bellman_ford_relaxation`.</span> @@ -542,7 +542,7 @@ <div class="viewcode-block" id="dijkstra_path"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.dijkstra_path.html#networkx.algorithms.shortest_paths.weighted.dijkstra_path">[docs]</a><span class="k">def</span> <span class="nf">dijkstra_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns the shortest weighted path from source to target in G.</span> +<span class="w"> </span><span class="sd">"""Returns the shortest weighted path from source to target in G.</span> <span class="sd"> Uses Dijkstra's Method to compute the shortest weighted path</span> <span class="sd"> between two nodes in a graph.</span> @@ -623,7 +623,7 @@ <div class="viewcode-block" id="dijkstra_path_length"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.dijkstra_path_length.html#networkx.algorithms.shortest_paths.weighted.dijkstra_path_length">[docs]</a><span class="k">def</span> <span class="nf">dijkstra_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns the shortest weighted path length in G from source to target.</span> +<span class="w"> </span><span class="sd">"""Returns the shortest weighted path length in G from source to target.</span> <span class="sd"> Uses Dijkstra's Method to compute the shortest weighted path length</span> <span class="sd"> between two nodes in a graph.</span> @@ -702,7 +702,7 @@ <div class="viewcode-block" id="single_source_dijkstra_path"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path.html#networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path">[docs]</a><span class="k">def</span> <span class="nf">single_source_dijkstra_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Find shortest weighted paths in G from a source node.</span> +<span class="w"> </span><span class="sd">"""Find shortest weighted paths in G from a source node.</span> <span class="sd"> Compute shortest path between source and all other reachable</span> <span class="sd"> nodes for a weighted graph.</span> @@ -766,7 +766,7 @@ <div class="viewcode-block" id="single_source_dijkstra_path_length"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length.html#networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length">[docs]</a><span class="k">def</span> <span class="nf">single_source_dijkstra_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Find shortest weighted path lengths in G from a source node.</span> +<span class="w"> </span><span class="sd">"""Find shortest weighted path lengths in G from a source node.</span> <span class="sd"> Compute the shortest path length between source and all other</span> <span class="sd"> reachable nodes for a weighted graph.</span> @@ -837,7 +837,7 @@ <div class="viewcode-block" id="single_source_dijkstra"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.single_source_dijkstra.html#networkx.algorithms.shortest_paths.weighted.single_source_dijkstra">[docs]</a><span class="k">def</span> <span class="nf">single_source_dijkstra</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Find shortest weighted paths and lengths from a source node.</span> +<span class="w"> </span><span class="sd">"""Find shortest weighted paths and lengths from a source node.</span> <span class="sd"> Compute the shortest path length between source and all other</span> <span class="sd"> reachable nodes for a weighted graph.</span> @@ -938,7 +938,7 @@ <div class="viewcode-block" id="multi_source_dijkstra_path"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path.html#networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path">[docs]</a><span class="k">def</span> <span class="nf">multi_source_dijkstra_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Find shortest weighted paths in G from a given set of source</span> +<span class="w"> </span><span class="sd">"""Find shortest weighted paths in G from a given set of source</span> <span class="sd"> nodes.</span> <span class="sd"> Compute shortest path between any of the source nodes and all other</span> @@ -1011,7 +1011,7 @@ <div class="viewcode-block" id="multi_source_dijkstra_path_length"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length.html#networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length">[docs]</a><span class="k">def</span> <span class="nf">multi_source_dijkstra_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Find shortest weighted path lengths in G from a given set of</span> +<span class="w"> </span><span class="sd">"""Find shortest weighted path lengths in G from a given set of</span> <span class="sd"> source nodes.</span> <span class="sd"> Compute the shortest path length between any of the source nodes and</span> @@ -1092,7 +1092,7 @@ <div class="viewcode-block" id="multi_source_dijkstra"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra.html#networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra">[docs]</a><span class="k">def</span> <span class="nf">multi_source_dijkstra</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Find shortest weighted paths and lengths from a given set of</span> +<span class="w"> </span><span class="sd">"""Find shortest weighted paths and lengths from a given set of</span> <span class="sd"> source nodes.</span> <span class="sd"> Uses Dijkstra's algorithm to compute the shortest paths and lengths</span> @@ -1211,7 +1211,7 @@ <span class="k">def</span> <span class="nf">_dijkstra</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">pred</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">paths</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Uses Dijkstra's algorithm to find shortest weighted paths from a</span> +<span class="w"> </span><span class="sd">"""Uses Dijkstra's algorithm to find shortest weighted paths from a</span> <span class="sd"> single source.</span> <span class="sd"> This is a convenience function for :func:`_dijkstra_multisource`</span> @@ -1227,7 +1227,7 @@ <span class="k">def</span> <span class="nf">_dijkstra_multisource</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">pred</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">paths</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Uses Dijkstra's algorithm to find shortest weighted paths</span> +<span class="w"> </span><span class="sd">"""Uses Dijkstra's algorithm to find shortest weighted paths</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1328,7 +1328,7 @@ <div class="viewcode-block" id="dijkstra_predecessor_and_distance"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance.html#networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance">[docs]</a><span class="k">def</span> <span class="nf">dijkstra_predecessor_and_distance</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Compute weighted shortest path length and predecessors.</span> +<span class="w"> </span><span class="sd">"""Compute weighted shortest path length and predecessors.</span> <span class="sd"> Uses Dijkstra's Method to obtain the shortest weighted paths</span> <span class="sd"> and return dictionaries of predecessors for each node and</span> @@ -1400,7 +1400,7 @@ <div class="viewcode-block" id="all_pairs_dijkstra"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra.html#networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra">[docs]</a><span class="k">def</span> <span class="nf">all_pairs_dijkstra</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Find shortest weighted paths and lengths between all nodes.</span> +<span class="w"> </span><span class="sd">"""Find shortest weighted paths and lengths between all nodes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1468,7 +1468,7 @@ <div class="viewcode-block" id="all_pairs_dijkstra_path_length"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length.html#networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length">[docs]</a><span class="k">def</span> <span class="nf">all_pairs_dijkstra_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Compute shortest path lengths between all nodes in a weighted graph.</span> +<span class="w"> </span><span class="sd">"""Compute shortest path lengths between all nodes in a weighted graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1526,7 +1526,7 @@ <div class="viewcode-block" id="all_pairs_dijkstra_path"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path.html#networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path">[docs]</a><span class="k">def</span> <span class="nf">all_pairs_dijkstra_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Compute shortest paths between all nodes in a weighted graph.</span> +<span class="w"> </span><span class="sd">"""Compute shortest paths between all nodes in a weighted graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1580,7 +1580,7 @@ <div class="viewcode-block" id="bellman_ford_predecessor_and_distance"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance.html#networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance">[docs]</a><span class="k">def</span> <span class="nf">bellman_ford_predecessor_and_distance</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">heuristic</span><span class="o">=</span><span class="kc">False</span> <span class="p">):</span> - <span class="sd">"""Compute shortest path lengths and predecessors on shortest paths</span> +<span class="w"> </span><span class="sd">"""Compute shortest path lengths and predecessors on shortest paths</span> <span class="sd"> in weighted graphs.</span> <span class="sd"> The algorithm has a running time of $O(mn)$ where $n$ is the number of</span> @@ -1709,7 +1709,7 @@ <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">heuristic</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Calls relaxation loop for Bellman–Ford algorithm and builds paths</span> +<span class="w"> </span><span class="sd">"""Calls relaxation loop for Bellman–Ford algorithm and builds paths</span> <span class="sd"> This is an implementation of the SPFA variant.</span> <span class="sd"> See https://en.wikipedia.org/wiki/Shortest_Path_Faster_Algorithm</span> @@ -1801,7 +1801,7 @@ <span class="n">dist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">heuristic</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Inner Relaxation loop for Bellman–Ford algorithm.</span> +<span class="w"> </span><span class="sd">"""Inner Relaxation loop for Bellman–Ford algorithm.</span> <span class="sd"> This is an implementation of the SPFA variant.</span> <span class="sd"> See https://en.wikipedia.org/wiki/Shortest_Path_Faster_Algorithm</span> @@ -1918,7 +1918,7 @@ <div class="viewcode-block" id="bellman_ford_path"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.bellman_ford_path.html#networkx.algorithms.shortest_paths.weighted.bellman_ford_path">[docs]</a><span class="k">def</span> <span class="nf">bellman_ford_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns the shortest path from source to target in a weighted graph G.</span> +<span class="w"> </span><span class="sd">"""Returns the shortest path from source to target in a weighted graph G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1976,7 +1976,7 @@ <div class="viewcode-block" id="bellman_ford_path_length"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length.html#networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length">[docs]</a><span class="k">def</span> <span class="nf">bellman_ford_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns the shortest path length from source to target</span> +<span class="w"> </span><span class="sd">"""Returns the shortest path length from source to target</span> <span class="sd"> in a weighted graph.</span> <span class="sd"> Parameters</span> @@ -2046,7 +2046,7 @@ <div class="viewcode-block" id="single_source_bellman_ford_path"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path.html#networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path">[docs]</a><span class="k">def</span> <span class="nf">single_source_bellman_ford_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Compute shortest path between source and all other reachable</span> +<span class="w"> </span><span class="sd">"""Compute shortest path between source and all other reachable</span> <span class="sd"> nodes for a weighted graph.</span> <span class="sd"> Parameters</span> @@ -2101,7 +2101,7 @@ <div class="viewcode-block" id="single_source_bellman_ford_path_length"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length.html#networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length">[docs]</a><span class="k">def</span> <span class="nf">single_source_bellman_ford_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Compute the shortest path length between source and all other</span> +<span class="w"> </span><span class="sd">"""Compute the shortest path length between source and all other</span> <span class="sd"> reachable nodes for a weighted graph.</span> <span class="sd"> Parameters</span> @@ -2163,7 +2163,7 @@ <div class="viewcode-block" id="single_source_bellman_ford"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford.html#networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford">[docs]</a><span class="k">def</span> <span class="nf">single_source_bellman_ford</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Compute shortest paths and lengths in a weighted graph G.</span> +<span class="w"> </span><span class="sd">"""Compute shortest paths and lengths in a weighted graph G.</span> <span class="sd"> Uses Bellman-Ford algorithm for shortest paths.</span> @@ -2256,7 +2256,7 @@ <div class="viewcode-block" id="all_pairs_bellman_ford_path_length"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length.html#networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length">[docs]</a><span class="k">def</span> <span class="nf">all_pairs_bellman_ford_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Compute shortest path lengths between all nodes in a weighted graph.</span> +<span class="w"> </span><span class="sd">"""Compute shortest path lengths between all nodes in a weighted graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -2310,7 +2310,7 @@ <div class="viewcode-block" id="all_pairs_bellman_ford_path"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path.html#networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path">[docs]</a><span class="k">def</span> <span class="nf">all_pairs_bellman_ford_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Compute shortest paths between all nodes in a weighted graph.</span> +<span class="w"> </span><span class="sd">"""Compute shortest paths between all nodes in a weighted graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -2358,7 +2358,7 @@ <div class="viewcode-block" id="goldberg_radzik"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.goldberg_radzik.html#networkx.algorithms.shortest_paths.weighted.goldberg_radzik">[docs]</a><span class="k">def</span> <span class="nf">goldberg_radzik</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Compute shortest path lengths and predecessors on shortest paths</span> +<span class="w"> </span><span class="sd">"""Compute shortest path lengths and predecessors on shortest paths</span> <span class="sd"> in weighted graphs.</span> <span class="sd"> The algorithm has a running time of $O(mn)$ where $n$ is the number of</span> @@ -2450,7 +2450,7 @@ <span class="n">pred</span> <span class="o">=</span> <span class="p">{</span><span class="n">source</span><span class="p">:</span> <span class="kc">None</span><span class="p">}</span> <span class="k">def</span> <span class="nf">topo_sort</span><span class="p">(</span><span class="n">relabeled</span><span class="p">):</span> - <span class="sd">"""Topologically sort nodes relabeled in the previous round and detect</span> +<span class="w"> </span><span class="sd">"""Topologically sort nodes relabeled in the previous round and detect</span> <span class="sd"> negative cycles.</span> <span class="sd"> """</span> <span class="c1"># List of nodes to scan in this round. Denoted by A in Goldberg and</span> @@ -2506,7 +2506,7 @@ <span class="k">return</span> <span class="n">to_scan</span> <span class="k">def</span> <span class="nf">relax</span><span class="p">(</span><span class="n">to_scan</span><span class="p">):</span> - <span class="sd">"""Relax out-edges of relabeled nodes."""</span> +<span class="w"> </span><span class="sd">"""Relax out-edges of relabeled nodes."""</span> <span class="n">relabeled</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span> <span class="c1"># Scan nodes in to_scan in topological order and relax incident</span> <span class="c1"># out-edges. Add the relabled nodes to labeled.</span> @@ -2533,7 +2533,7 @@ <div class="viewcode-block" id="negative_edge_cycle"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.negative_edge_cycle.html#networkx.algorithms.shortest_paths.weighted.negative_edge_cycle">[docs]</a><span class="k">def</span> <span class="nf">negative_edge_cycle</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">heuristic</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns True if there exists a negative edge cycle anywhere in G.</span> +<span class="w"> </span><span class="sd">"""Returns True if there exists a negative edge cycle anywhere in G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -2600,7 +2600,7 @@ <div class="viewcode-block" id="find_negative_cycle"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.find_negative_cycle.html#networkx.algorithms.shortest_paths.weighted.find_negative_cycle">[docs]</a><span class="k">def</span> <span class="nf">find_negative_cycle</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns a cycle with negative total weight if it exists.</span> +<span class="w"> </span><span class="sd">"""Returns a cycle with negative total weight if it exists.</span> <span class="sd"> Bellman-Ford is used to find shortest_paths. That algorithm</span> <span class="sd"> stops if there exists a negative cycle. This algorithm</span> @@ -2692,7 +2692,7 @@ <div class="viewcode-block" id="bidirectional_dijkstra"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra.html#networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra">[docs]</a><span class="k">def</span> <span class="nf">bidirectional_dijkstra</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Dijkstra's algorithm for shortest paths using bidirectional search.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Dijkstra's algorithm for shortest paths using bidirectional search.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -2839,7 +2839,7 @@ <div class="viewcode-block" id="johnson"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.johnson.html#networkx.algorithms.shortest_paths.weighted.johnson">[docs]</a><span class="k">def</span> <span class="nf">johnson</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Uses Johnson's Algorithm to compute shortest paths.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Uses Johnson's Algorithm to compute shortest paths.</span> <span class="sd"> Johnson's Algorithm finds a shortest path between each pair of</span> <span class="sd"> nodes in a weighted graph even if negative weights are present.</span> @@ -2977,7 +2977,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/similarity.html b/_modules/networkx/algorithms/similarity.html index 12ea90f1..61cc66f9 100644 --- a/_modules/networkx/algorithms/similarity.html +++ b/_modules/networkx/algorithms/similarity.html @@ -515,7 +515,7 @@ <span class="n">upper_bound</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns GED (graph edit distance) between graphs G1 and G2.</span> +<span class="w"> </span><span class="sd">"""Returns GED (graph edit distance) between graphs G1 and G2.</span> <span class="sd"> Graph edit distance is a graph similarity measure analogous to</span> <span class="sd"> Levenshtein distance for strings. It is defined as minimum cost</span> @@ -684,7 +684,7 @@ <span class="n">edge_ins_cost</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">upper_bound</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns all minimum-cost edit paths transforming G1 to G2.</span> +<span class="w"> </span><span class="sd">"""Returns all minimum-cost edit paths transforming G1 to G2.</span> <span class="sd"> Graph edit path is a sequence of node and edge edit operations</span> <span class="sd"> transforming graph G1 to graph isomorphic to G2. Edit operations</span> @@ -846,7 +846,7 @@ <span class="n">edge_ins_cost</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">upper_bound</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns consecutive approximations of GED (graph edit distance)</span> +<span class="w"> </span><span class="sd">"""Returns consecutive approximations of GED (graph edit distance)</span> <span class="sd"> between graphs G1 and G2.</span> <span class="sd"> Graph edit distance is a graph similarity measure analogous to</span> @@ -999,7 +999,7 @@ <span class="n">roots</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""GED (graph edit distance) calculation: advanced interface.</span> +<span class="w"> </span><span class="sd">"""GED (graph edit distance) calculation: advanced interface.</span> <span class="sd"> Graph edit path is a sequence of node and edge edit operations</span> <span class="sd"> transforming graph G1 to graph isomorphic to G2. Edit operations</span> @@ -1188,7 +1188,7 @@ <span class="k">return</span> <span class="n">rind</span> <span class="k">def</span> <span class="nf">match_edges</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">pending_g</span><span class="p">,</span> <span class="n">pending_h</span><span class="p">,</span> <span class="n">Ce</span><span class="p">,</span> <span class="n">matched_uv</span><span class="o">=</span><span class="p">[]):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Parameters:</span> <span class="sd"> u, v: matched vertices, u=None or v=None for</span> <span class="sd"> deletion/insertion</span> @@ -1291,7 +1291,7 @@ <span class="k">def</span> <span class="nf">get_edit_ops</span><span class="p">(</span> <span class="n">matched_uv</span><span class="p">,</span> <span class="n">pending_u</span><span class="p">,</span> <span class="n">pending_v</span><span class="p">,</span> <span class="n">Cv</span><span class="p">,</span> <span class="n">pending_g</span><span class="p">,</span> <span class="n">pending_h</span><span class="p">,</span> <span class="n">Ce</span><span class="p">,</span> <span class="n">matched_cost</span> <span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Parameters:</span> <span class="sd"> matched_uv: partial vertex edit path</span> <span class="sd"> list of tuples (u, v) of vertex mappings u<->v,</span> @@ -1398,7 +1398,7 @@ <span class="n">Ce</span><span class="p">,</span> <span class="n">matched_cost</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Parameters:</span> <span class="sd"> matched_uv: partial vertex edit path</span> <span class="sd"> list of tuples (u, v) of vertex mappings u<->v,</span> @@ -1679,7 +1679,7 @@ <span class="n">max_iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns the SimRank similarity of nodes in the graph ``G``.</span> +<span class="w"> </span><span class="sd">"""Returns the SimRank similarity of nodes in the graph ``G``.</span> <span class="sd"> SimRank is a similarity metric that says "two objects are considered</span> <span class="sd"> to be similar if they are referenced by similar objects." [1]_.</span> @@ -1801,7 +1801,7 @@ <span class="n">max_iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns the SimRank similarity of nodes in the graph ``G``.</span> +<span class="w"> </span><span class="sd">"""Returns the SimRank similarity of nodes in the graph ``G``.</span> <span class="sd"> This pure Python version is provided for pedagogical purposes.</span> @@ -1861,7 +1861,7 @@ <span class="n">max_iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Calculate SimRank of nodes in ``G`` using matrices with ``numpy``.</span> +<span class="w"> </span><span class="sd">"""Calculate SimRank of nodes in ``G`` using matrices with ``numpy``.</span> <span class="sd"> The SimRank algorithm for determining node similarity is defined in</span> <span class="sd"> [1]_.</span> @@ -1963,7 +1963,7 @@ <div class="viewcode-block" id="panther_similarity"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.similarity.panther_similarity.html#networkx.algorithms.similarity.panther_similarity">[docs]</a><span class="k">def</span> <span class="nf">panther_similarity</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">path_length</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">delta</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the Panther similarity of nodes in the graph `G` to node ``v``.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the Panther similarity of nodes in the graph `G` to node ``v``.</span> <span class="sd"> Panther is a similarity metric that says "two objects are considered</span> <span class="sd"> to be similar if they frequently appear on the same paths." [1]_.</span> @@ -2068,7 +2068,7 @@ <div class="viewcode-block" id="generate_random_paths"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.similarity.generate_random_paths.html#networkx.algorithms.similarity.generate_random_paths">[docs]</a><span class="k">def</span> <span class="nf">generate_random_paths</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">,</span> <span class="n">path_length</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">index_map</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Randomly generate `sample_size` paths of length `path_length`.</span> +<span class="w"> </span><span class="sd">"""Randomly generate `sample_size` paths of length `path_length`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -2214,7 +2214,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/simple_paths.html b/_modules/networkx/algorithms/simple_paths.html index 5645d675..77138848 100644 --- a/_modules/networkx/algorithms/simple_paths.html +++ b/_modules/networkx/algorithms/simple_paths.html @@ -478,7 +478,7 @@ <div class="viewcode-block" id="is_simple_path"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.simple_paths.is_simple_path.html#networkx.algorithms.simple_paths.is_simple_path">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">is_simple_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span> - <span class="sd">"""Returns True if and only if `nodes` form a simple path in `G`.</span> +<span class="w"> </span><span class="sd">"""Returns True if and only if `nodes` form a simple path in `G`.</span> <span class="sd"> A *simple path* in a graph is a nonempty sequence of nodes in which</span> <span class="sd"> no node appears more than once in the sequence, and each adjacent</span> @@ -555,7 +555,7 @@ <div class="viewcode-block" id="all_simple_paths"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.simple_paths.all_simple_paths.html#networkx.algorithms.simple_paths.all_simple_paths">[docs]</a><span class="k">def</span> <span class="nf">all_simple_paths</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generate all simple paths in the graph G from source to target.</span> +<span class="w"> </span><span class="sd">"""Generate all simple paths in the graph G from source to target.</span> <span class="sd"> A simple path is a path with no repeated nodes.</span> @@ -784,7 +784,7 @@ <div class="viewcode-block" id="all_simple_edge_paths"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.simple_paths.all_simple_edge_paths.html#networkx.algorithms.simple_paths.all_simple_edge_paths">[docs]</a><span class="k">def</span> <span class="nf">all_simple_edge_paths</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generate lists of edges for all simple paths in G from source to target.</span> +<span class="w"> </span><span class="sd">"""Generate lists of edges for all simple paths in G from source to target.</span> <span class="sd"> A simple path is a path with no repeated nodes.</span> @@ -905,7 +905,7 @@ <div class="viewcode-block" id="shortest_simple_paths"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.simple_paths.shortest_simple_paths.html#networkx.algorithms.simple_paths.shortest_simple_paths">[docs]</a><span class="nd">@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">shortest_simple_paths</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generate all simple paths in the graph G from source to target,</span> +<span class="w"> </span><span class="sd">"""Generate all simple paths in the graph G from source to target,</span> <span class="sd"> starting from shortest ones.</span> <span class="sd"> A simple path is a path with no repeated nodes.</span> @@ -1075,7 +1075,7 @@ <span class="k">def</span> <span class="nf">_bidirectional_shortest_path</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">ignore_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ignore_edges</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Returns the shortest path between source and target ignoring</span> +<span class="w"> </span><span class="sd">"""Returns the shortest path between source and target ignoring</span> <span class="sd"> nodes and edges in the containers ignore_nodes and ignore_edges.</span> <span class="sd"> This is a custom modification of the standard bidirectional shortest</span> @@ -1136,7 +1136,7 @@ <span class="k">def</span> <span class="nf">_bidirectional_pred_succ</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">ignore_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ignore_edges</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Bidirectional shortest path helper.</span> +<span class="w"> </span><span class="sd">"""Bidirectional shortest path helper.</span> <span class="sd"> Returns (pred,succ,w) where</span> <span class="sd"> pred is a dictionary of predecessors from w to the source, and</span> <span class="sd"> succ is a dictionary of successors from w to the target.</span> @@ -1243,7 +1243,7 @@ <span class="k">def</span> <span class="nf">_bidirectional_dijkstra</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">ignore_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ignore_edges</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Dijkstra's algorithm for shortest paths using bidirectional search.</span> +<span class="w"> </span><span class="sd">"""Dijkstra's algorithm for shortest paths using bidirectional search.</span> <span class="sd"> This function returns the shortest path between source and target</span> <span class="sd"> ignoring nodes and edges in the containers ignore_nodes and</span> @@ -1487,7 +1487,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/smallworld.html b/_modules/networkx/algorithms/smallworld.html index a7215f9d..4800d028 100644 --- a/_modules/networkx/algorithms/smallworld.html +++ b/_modules/networkx/algorithms/smallworld.html @@ -487,7 +487,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">random_reference</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">niter</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">connectivity</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Compute a random graph by swapping edges of a given graph.</span> +<span class="w"> </span><span class="sd">"""Compute a random graph by swapping edges of a given graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -584,7 +584,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">lattice_reference</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">niter</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">D</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">connectivity</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Latticize the given graph by swapping edges.</span> +<span class="w"> </span><span class="sd">"""Latticize the given graph by swapping edges.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -707,7 +707,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">sigma</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">niter</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">nrand</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the small-world coefficient (sigma) of the given graph.</span> +<span class="w"> </span><span class="sd">"""Returns the small-world coefficient (sigma) of the given graph.</span> <span class="sd"> The small-world coefficient is defined as:</span> <span class="sd"> sigma = C/Cr / L/Lr</span> @@ -775,7 +775,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">omega</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">niter</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrand</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the small-world coefficient (omega) of a graph</span> +<span class="w"> </span><span class="sd">"""Returns the small-world coefficient (omega) of a graph</span> <span class="sd"> The small-world coefficient of a graph G is:</span> @@ -911,7 +911,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/smetric.html b/_modules/networkx/algorithms/smetric.html index ad527382..00cd4176 100644 --- a/_modules/networkx/algorithms/smetric.html +++ b/_modules/networkx/algorithms/smetric.html @@ -468,7 +468,7 @@ <div class="viewcode-block" id="s_metric"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.smetric.s_metric.html#networkx.algorithms.smetric.s_metric">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">s_metric</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns the s-metric of graph.</span> +<span class="w"> </span><span class="sd">"""Returns the s-metric of graph.</span> <span class="sd"> The s-metric is defined as the sum of the products deg(u)*deg(v)</span> <span class="sd"> for every edge (u,v) in G. If norm is provided construct the</span> @@ -551,7 +551,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/sparsifiers.html b/_modules/networkx/algorithms/sparsifiers.html index 4b0da1a7..e889a918 100644 --- a/_modules/networkx/algorithms/sparsifiers.html +++ b/_modules/networkx/algorithms/sparsifiers.html @@ -474,7 +474,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">spanner</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">stretch</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a spanner of the given graph with the given stretch.</span> +<span class="w"> </span><span class="sd">"""Returns a spanner of the given graph with the given stretch.</span> <span class="sd"> A spanner of a graph G = (V, E) with stretch t is a subgraph</span> <span class="sd"> H = (V, E_S) such that E_S is a subset of E and the distance between</span> @@ -649,7 +649,7 @@ <span class="k">def</span> <span class="nf">_setup_residual_graph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> - <span class="sd">"""Setup residual graph as a copy of G with unique edges weights.</span> +<span class="w"> </span><span class="sd">"""Setup residual graph as a copy of G with unique edges weights.</span> <span class="sd"> The node set of the residual graph corresponds to the set V' from</span> <span class="sd"> the Baswana-Sen paper and the edge set corresponds to the set E'</span> @@ -685,7 +685,7 @@ <span class="k">def</span> <span class="nf">_lightest_edge_dicts</span><span class="p">(</span><span class="n">residual_graph</span><span class="p">,</span> <span class="n">clustering</span><span class="p">,</span> <span class="n">node</span><span class="p">):</span> - <span class="sd">"""Find the lightest edge to each cluster.</span> +<span class="w"> </span><span class="sd">"""Find the lightest edge to each cluster.</span> <span class="sd"> Searches for the minimum-weight edge to each cluster adjacent to</span> <span class="sd"> the given node.</span> @@ -731,7 +731,7 @@ <span class="k">def</span> <span class="nf">_add_edge_to_spanner</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">residual_graph</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> - <span class="sd">"""Add the edge {u, v} to the spanner H and take weight from</span> +<span class="w"> </span><span class="sd">"""Add the edge {u, v} to the spanner H and take weight from</span> <span class="sd"> the residual graph.</span> <span class="sd"> Parameters</span> @@ -806,7 +806,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/structuralholes.html b/_modules/networkx/algorithms/structuralholes.html index 8e70f7df..a1f3c86c 100644 --- a/_modules/networkx/algorithms/structuralholes.html +++ b/_modules/networkx/algorithms/structuralholes.html @@ -470,7 +470,7 @@ <span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">mutual_weight</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the sum of the weights of the edge from `u` to `v` and</span> +<span class="w"> </span><span class="sd">"""Returns the sum of the weights of the edge from `u` to `v` and</span> <span class="sd"> the edge from `v` to `u` in `G`.</span> <span class="sd"> `weight` is the edge data key that represents the edge weight. If</span> @@ -492,7 +492,7 @@ <span class="k">def</span> <span class="nf">normalized_mutual_weight</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="nb">sum</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns normalized mutual weight of the edges from `u` to `v`</span> +<span class="w"> </span><span class="sd">"""Returns normalized mutual weight of the edges from `u` to `v`</span> <span class="sd"> with respect to the mutual weights of the neighbors of `u` in `G`.</span> <span class="sd"> `norm` specifies how the normalization factor is computed. It must</span> @@ -512,7 +512,7 @@ <div class="viewcode-block" id="effective_size"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.structuralholes.effective_size.html#networkx.algorithms.structuralholes.effective_size">[docs]</a><span class="k">def</span> <span class="nf">effective_size</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the effective size of all nodes in the graph ``G``.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the effective size of all nodes in the graph ``G``.</span> <span class="sd"> The *effective size* of a node's ego network is based on the concept</span> <span class="sd"> of redundancy. A person's ego network has redundancy to the extent</span> @@ -624,7 +624,7 @@ <div class="viewcode-block" id="constraint"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.structuralholes.constraint.html#networkx.algorithms.structuralholes.constraint">[docs]</a><span class="k">def</span> <span class="nf">constraint</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the constraint on all nodes in the graph ``G``.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the constraint on all nodes in the graph ``G``.</span> <span class="sd"> The *constraint* is a measure of the extent to which a node *v* is</span> <span class="sd"> invested in those nodes that are themselves invested in the</span> @@ -684,7 +684,7 @@ <div class="viewcode-block" id="local_constraint"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.structuralholes.local_constraint.html#networkx.algorithms.structuralholes.local_constraint">[docs]</a><span class="k">def</span> <span class="nf">local_constraint</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the local constraint on the node ``u`` with respect to</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the local constraint on the node ``u`` with respect to</span> <span class="sd"> the node ``v`` in the graph ``G``.</span> <span class="sd"> Formally, the *local constraint on u with respect to v*, denoted</span> @@ -791,7 +791,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/summarization.html b/_modules/networkx/algorithms/summarization.html index b733d299..7bd44172 100644 --- a/_modules/networkx/algorithms/summarization.html +++ b/_modules/networkx/algorithms/summarization.html @@ -529,7 +529,7 @@ <div class="viewcode-block" id="dedensify"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.summarization.dedensify.html#networkx.algorithms.summarization.dedensify">[docs]</a><span class="k">def</span> <span class="nf">dedensify</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Compresses neighborhoods around high-degree nodes</span> +<span class="w"> </span><span class="sd">"""Compresses neighborhoods around high-degree nodes</span> <span class="sd"> Reduces the number of edges to high-degree nodes by adding compressor nodes</span> <span class="sd"> that summarize multiple edges of the same type to high-degree nodes (nodes</span> @@ -686,7 +686,7 @@ <span class="n">supernode_attribute</span><span class="p">,</span> <span class="n">superedge_attribute</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Build the summary graph from the data structures produced in the SNAP aggregation algorithm</span> <span class="sd"> Used in the SNAP aggregation algorithm to build the output summary graph and supernode</span> @@ -766,7 +766,7 @@ <span class="k">def</span> <span class="nf">_snap_eligible_group</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">group_lookup</span><span class="p">,</span> <span class="n">edge_types</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Determines if a group is eligible to be split.</span> <span class="sd"> A group is eligible to be split if all nodes in the group have edges of the same type(s)</span> @@ -818,7 +818,7 @@ <span class="k">def</span> <span class="nf">_snap_split</span><span class="p">(</span><span class="n">groups</span><span class="p">,</span> <span class="n">neighbor_info</span><span class="p">,</span> <span class="n">group_lookup</span><span class="p">,</span> <span class="n">group_id</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Splits a group based on edge types and updates the groups accordingly</span> <span class="sd"> Splits the group with the given group_id based on the edge types</span> @@ -874,7 +874,7 @@ <span class="n">supernode_attribute</span><span class="o">=</span><span class="s2">"group"</span><span class="p">,</span> <span class="n">superedge_attribute</span><span class="o">=</span><span class="s2">"types"</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Creates a summary graph based on attributes and connectivity.</span> +<span class="w"> </span><span class="sd">"""Creates a summary graph based on attributes and connectivity.</span> <span class="sd"> This function uses the Summarization by Grouping Nodes on Attributes</span> <span class="sd"> and Pairwise edges (SNAP) algorithm for summarizing a given</span> @@ -1068,7 +1068,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/swap.html b/_modules/networkx/algorithms/swap.html index 25308ce5..27e7bbe0 100644 --- a/_modules/networkx/algorithms/swap.html +++ b/_modules/networkx/algorithms/swap.html @@ -475,7 +475,7 @@ <div class="viewcode-block" id="directed_edge_swap"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.swap.directed_edge_swap.html#networkx.algorithms.swap.directed_edge_swap">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <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">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">directed_edge_swap</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">nswap</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_tries</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Swap three edges in a directed graph while keeping the node degrees fixed.</span> +<span class="w"> </span><span class="sd">"""Swap three edges in a directed graph while keeping the node degrees fixed.</span> <span class="sd"> A directed edge swap swaps three edges such that a -> b -> c -> d becomes</span> <span class="sd"> a -> c -> b -> d. This pattern of swapping allows all possible states with the</span> @@ -594,7 +594,7 @@ <div class="viewcode-block" id="double_edge_swap"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.swap.double_edge_swap.html#networkx.algorithms.swap.double_edge_swap">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">double_edge_swap</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nswap</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_tries</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Swap two edges in the graph while keeping the node degrees fixed.</span> +<span class="w"> </span><span class="sd">"""Swap two edges in the graph while keeping the node degrees fixed.</span> <span class="sd"> A double-edge swap removes two randomly chosen edges u-v and x-y</span> <span class="sd"> and creates the new edges u-x and v-y::</span> @@ -691,7 +691,7 @@ <div class="viewcode-block" id="connected_double_edge_swap"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.swap.connected_double_edge_swap.html#networkx.algorithms.swap.connected_double_edge_swap">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">connected_double_edge_swap</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nswap</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">_window_threshold</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Attempts the specified number of double-edge swaps in the graph `G`.</span> +<span class="w"> </span><span class="sd">"""Attempts the specified number of double-edge swaps in the graph `G`.</span> <span class="sd"> A double-edge swap removes two randomly chosen edges `(u, v)` and `(x,</span> <span class="sd"> y)` and creates the new edges `(u, x)` and `(v, y)`::</span> @@ -914,7 +914,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/threshold.html b/_modules/networkx/algorithms/threshold.html index afb3444d..45c6128d 100644 --- a/_modules/networkx/algorithms/threshold.html +++ b/_modules/networkx/algorithms/threshold.html @@ -473,7 +473,7 @@ <div class="viewcode-block" id="is_threshold_graph"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.threshold.is_threshold_graph.html#networkx.algorithms.threshold.is_threshold_graph">[docs]</a><span class="k">def</span> <span class="nf">is_threshold_graph</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns `True` if `G` is a threshold graph.</span> <span class="sd"> Parameters</span> @@ -504,7 +504,7 @@ <span class="k">def</span> <span class="nf">is_threshold_sequence</span><span class="p">(</span><span class="n">degree_sequence</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns True if the sequence is a threshold degree seqeunce.</span> <span class="sd"> Uses the property that a threshold graph must be constructed by</span> @@ -527,7 +527,7 @@ <span class="k">def</span> <span class="nf">creation_sequence</span><span class="p">(</span><span class="n">degree_sequence</span><span class="p">,</span> <span class="n">with_labels</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">compact</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Determines the creation sequence for the given threshold degree sequence.</span> <span class="sd"> The creation sequence is a list of single characters 'd'</span> @@ -586,7 +586,7 @@ <span class="k">def</span> <span class="nf">make_compact</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the creation sequence in a compact form</span> <span class="sd"> that is the number of 'i's and 'd's alternating.</span> @@ -630,7 +630,7 @@ <span class="k">def</span> <span class="nf">uncompact</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Converts a compact creation sequence for a threshold</span> <span class="sd"> graph to a standard creation sequence (unlabeled).</span> <span class="sd"> If the creation_sequence is already standard, return it.</span> @@ -654,7 +654,7 @@ <span class="k">def</span> <span class="nf">creation_sequence_to_weights</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns a list of node weights which create the threshold</span> <span class="sd"> graph designated by the creation sequence. The weights</span> <span class="sd"> are scaled so that the threshold is 1.0. The order of the</span> @@ -703,7 +703,7 @@ <span class="k">def</span> <span class="nf">weights_to_creation_sequence</span><span class="p">(</span> <span class="n">weights</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">with_labels</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">compact</span><span class="o">=</span><span class="kc">False</span> <span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns a creation sequence for a threshold graph</span> <span class="sd"> determined by the weights and threshold given as input.</span> <span class="sd"> If the sum of two node weights is greater than the</span> @@ -764,7 +764,7 @@ <span class="c1"># Manipulating NetworkX.Graphs in context of threshold graphs</span> <span class="k">def</span> <span class="nf">threshold_graph</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Create a threshold graph from the creation sequence or compact</span> <span class="sd"> creation_sequence.</span> @@ -815,7 +815,7 @@ <span class="k">def</span> <span class="nf">find_alternating_4_cycle</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns False if there aren't any alternating 4 cycles.</span> <span class="sd"> Otherwise returns the cycle as [a,b,c,d] where (a,b)</span> <span class="sd"> and (c,d) are edges and (a,c) and (b,d) are not.</span> @@ -830,7 +830,7 @@ <div class="viewcode-block" id="find_threshold_graph"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.threshold.find_threshold_graph.html#networkx.algorithms.threshold.find_threshold_graph">[docs]</a><span class="k">def</span> <span class="nf">find_threshold_graph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns a threshold subgraph that is close to largest in `G`.</span> <span class="sd"> The threshold graph will contain the largest degree node in G.</span> @@ -864,7 +864,7 @@ <span class="k">def</span> <span class="nf">find_creation_sequence</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Find a threshold subgraph that is close to largest in G.</span> <span class="sd"> Returns the labeled creation sequence of that threshold graph.</span> <span class="sd"> """</span> @@ -896,7 +896,7 @@ <span class="c1"># Properties of Threshold Graphs</span> <span class="k">def</span> <span class="nf">triangles</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Compute number of triangles in the threshold graph with the</span> <span class="sd"> given creation sequence.</span> <span class="sd"> """</span> @@ -916,7 +916,7 @@ <span class="k">def</span> <span class="nf">triangle_sequence</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return triangle sequence for the given threshold graph creation sequence.</span> <span class="sd"> """</span> @@ -944,7 +944,7 @@ <span class="k">def</span> <span class="nf">cluster_sequence</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return cluster sequence for the given threshold graph creation sequence.</span> <span class="sd"> """</span> <span class="n">triseq</span> <span class="o">=</span> <span class="n">triangle_sequence</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">)</span> @@ -961,7 +961,7 @@ <span class="k">def</span> <span class="nf">degree_sequence</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return degree sequence for the threshold graph with the given</span> <span class="sd"> creation sequence</span> <span class="sd"> """</span> @@ -978,7 +978,7 @@ <span class="k">def</span> <span class="nf">density</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return the density of the graph with this creation_sequence.</span> <span class="sd"> The density is the fraction of possible edges present.</span> <span class="sd"> """</span> @@ -990,7 +990,7 @@ <span class="k">def</span> <span class="nf">degree_correlation</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return the degree-degree correlation over all edges.</span> <span class="sd"> """</span> <span class="n">cs</span> <span class="o">=</span> <span class="n">creation_sequence</span> @@ -1024,7 +1024,7 @@ <span class="k">def</span> <span class="nf">shortest_path</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Find the shortest path between u and v in a</span> <span class="sd"> threshold graph G with the given creation_sequence.</span> @@ -1077,7 +1077,7 @@ <span class="k">def</span> <span class="nf">shortest_path_length</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return the shortest path length from indicated node to</span> <span class="sd"> every other node for the threshold graph with the given</span> <span class="sd"> creation sequence.</span> @@ -1123,7 +1123,7 @@ <span class="k">def</span> <span class="nf">betweenness_sequence</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return betweenness for the threshold graph with the given creation</span> <span class="sd"> sequence. The result is unscaled. To scale the values</span> <span class="sd"> to the iterval [0,1] divide by (n-1)*(n-2).</span> @@ -1163,7 +1163,7 @@ <span class="k">def</span> <span class="nf">eigenvectors</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return a 2-tuple of Laplacian eigenvalues and eigenvectors</span> <span class="sd"> for the threshold network with creation_sequence.</span> <span class="sd"> The first value is a list of eigenvalues.</span> @@ -1221,7 +1221,7 @@ <span class="k">def</span> <span class="nf">spectral_projection</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">eigenpairs</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the coefficients of each eigenvector</span> <span class="sd"> in a projection of the vector u onto the normalized</span> <span class="sd"> eigenvectors which are contained in eigenpairs.</span> @@ -1242,7 +1242,7 @@ <span class="k">def</span> <span class="nf">eigenvalues</span><span class="p">(</span><span class="n">creation_sequence</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return sequence of eigenvalues of the Laplacian of the threshold</span> <span class="sd"> graph for the given creation_sequence.</span> @@ -1285,7 +1285,7 @@ <span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_threshold_sequence</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Create a random threshold sequence of size n.</span> <span class="sd"> A creation sequence is built by randomly choosing d's with</span> <span class="sd"> probabiliy p and i's with probability 1-p.</span> @@ -1319,7 +1319,7 @@ <span class="c1"># be (or be called from) a single routine with a more descriptive name</span> <span class="c1"># and a keyword parameter?</span> <span class="k">def</span> <span class="nf">right_d_threshold_sequence</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Create a skewed threshold graph with a given number</span> <span class="sd"> of vertices (n) and a given number of edges (m).</span> @@ -1353,7 +1353,7 @@ <span class="k">def</span> <span class="nf">left_d_threshold_sequence</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Create a skewed threshold graph with a given number</span> <span class="sd"> of vertices (n) and a given number of edges (m).</span> @@ -1389,7 +1389,7 @@ <span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">swap_d</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">p_split</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">p_combine</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Perform a "swap" operation on a threshold sequence.</span> <span class="sd"> The swap preserves the number of nodes and edges</span> @@ -1486,7 +1486,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/tournament.html b/_modules/networkx/algorithms/tournament.html index 8515dc62..101e895c 100644 --- a/_modules/networkx/algorithms/tournament.html +++ b/_modules/networkx/algorithms/tournament.html @@ -498,7 +498,7 @@ <span class="k">def</span> <span class="nf">index_satisfying</span><span class="p">(</span><span class="n">iterable</span><span class="p">,</span> <span class="n">condition</span><span class="p">):</span> - <span class="sd">"""Returns the index of the first element in `iterable` that</span> +<span class="w"> </span><span class="sd">"""Returns the index of the first element in `iterable` that</span> <span class="sd"> satisfies the given condition.</span> <span class="sd"> If no such element is found (that is, when the iterable is</span> @@ -528,7 +528,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="nd">@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">is_tournament</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns True if and only if `G` is a tournament.</span> +<span class="w"> </span><span class="sd">"""Returns True if and only if `G` is a tournament.</span> <span class="sd"> A tournament is a directed graph, with neither self-loops nor</span> <span class="sd"> multi-edges, in which there is exactly one directed edge joining</span> @@ -567,7 +567,7 @@ <div class="viewcode-block" id="hamiltonian_path"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.tournament.hamiltonian_path.html#networkx.algorithms.tournament.hamiltonian_path">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="nd">@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">hamiltonian_path</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns a Hamiltonian path in the given tournament graph.</span> +<span class="w"> </span><span class="sd">"""Returns a Hamiltonian path in the given tournament graph.</span> <span class="sd"> Each tournament has a Hamiltonian path. If furthermore, the</span> <span class="sd"> tournament is strongly connected, then the returned Hamiltonian path</span> @@ -612,7 +612,7 @@ <div class="viewcode-block" id="random_tournament"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.tournament.random_tournament.html#networkx.algorithms.tournament.random_tournament">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_tournament</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a random tournament graph on `n` nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a random tournament graph on `n` nodes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -647,7 +647,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="nd">@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">score_sequence</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the score sequence for the given tournament graph.</span> +<span class="w"> </span><span class="sd">"""Returns the score sequence for the given tournament graph.</span> <span class="sd"> The score sequence is the sorted list of the out-degrees of the</span> <span class="sd"> nodes of the graph.</span> @@ -677,7 +677,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="nd">@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">tournament_matrix</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the tournament matrix for the given tournament graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the tournament matrix for the given tournament graph.</span> <span class="sd"> This function requires SciPy.</span> @@ -719,7 +719,7 @@ <div class="viewcode-block" id="is_reachable"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.tournament.is_reachable.html#networkx.algorithms.tournament.is_reachable">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="nd">@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">is_reachable</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span> - <span class="sd">"""Decides whether there is a path from `s` to `t` in the</span> +<span class="w"> </span><span class="sd">"""Decides whether there is a path from `s` to `t` in the</span> <span class="sd"> tournament.</span> <span class="sd"> This function is more theoretically efficient than the reachability</span> @@ -773,7 +773,7 @@ <span class="sd"> """</span> <span class="k">def</span> <span class="nf">two_neighborhood</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Returns the set of nodes at distance at most two from `v`.</span> +<span class="w"> </span><span class="sd">"""Returns the set of nodes at distance at most two from `v`.</span> <span class="sd"> `G` must be a graph and `v` a node in that graph.</span> @@ -788,7 +788,7 @@ <span class="p">}</span> <span class="k">def</span> <span class="nf">is_closed</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span> - <span class="sd">"""Decides whether the given set of nodes is closed.</span> +<span class="w"> </span><span class="sd">"""Decides whether the given set of nodes is closed.</span> <span class="sd"> A set *S* of nodes is *closed* if for each node *u* in the graph</span> <span class="sd"> not in *S* and for each node *v* in *S*, there is an edge from</span> @@ -806,7 +806,7 @@ <div class="viewcode-block" id="is_strongly_connected"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.tournament.is_strongly_connected.html#networkx.algorithms.tournament.is_strongly_connected">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="nd">@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">is_strongly_connected</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Decides whether the given tournament is strongly connected.</span> +<span class="w"> </span><span class="sd">"""Decides whether the given tournament is strongly connected.</span> <span class="sd"> This function is more theoretically efficient than the</span> <span class="sd"> :func:`~networkx.algorithms.components.is_strongly_connected`</span> @@ -906,7 +906,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/traversal/beamsearch.html b/_modules/networkx/algorithms/traversal/beamsearch.html index 63c1ef6f..e8c9dd3d 100644 --- a/_modules/networkx/algorithms/traversal/beamsearch.html +++ b/_modules/networkx/algorithms/traversal/beamsearch.html @@ -469,7 +469,7 @@ <div class="viewcode-block" id="bfs_beam_edges"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.beamsearch.bfs_beam_edges.html#networkx.algorithms.traversal.beamsearch.bfs_beam_edges">[docs]</a><span class="k">def</span> <span class="nf">bfs_beam_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Iterates over edges in a beam search.</span> +<span class="w"> </span><span class="sd">"""Iterates over edges in a beam search.</span> <span class="sd"> The beam search is a generalized breadth-first search in which only</span> <span class="sd"> the "best" *w* neighbors of the current node are enqueued, where *w*</span> @@ -539,7 +539,7 @@ <span class="n">width</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="k">def</span> <span class="nf">successors</span><span class="p">(</span><span class="n">v</span><span class="p">):</span> - <span class="sd">"""Returns a list of the best neighbors of a node.</span> +<span class="w"> </span><span class="sd">"""Returns a list of the best neighbors of a node.</span> <span class="sd"> `v` is a node in the graph `G`.</span> @@ -616,7 +616,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/traversal/breadth_first_search.html b/_modules/networkx/algorithms/traversal/breadth_first_search.html index 1d2283da..a89dc704 100644 --- a/_modules/networkx/algorithms/traversal/breadth_first_search.html +++ b/_modules/networkx/algorithms/traversal/breadth_first_search.html @@ -477,7 +477,7 @@ <span class="k">def</span> <span class="nf">generic_bfs_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">neighbors</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">depth_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sort_neighbors</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Iterate over edges in a breadth-first search.</span> +<span class="w"> </span><span class="sd">"""Iterate over edges in a breadth-first search.</span> <span class="sd"> The breadth-first search begins at `source` and enqueues the</span> <span class="sd"> neighbors of newly visited nodes specified by the `neighbors`</span> @@ -553,7 +553,7 @@ <div class="viewcode-block" id="bfs_edges"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.breadth_first_search.bfs_edges.html#networkx.algorithms.traversal.breadth_first_search.bfs_edges">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">bfs_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">depth_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sort_neighbors</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Iterate over edges in a breadth-first-search starting at source.</span> +<span class="w"> </span><span class="sd">"""Iterate over edges in a breadth-first-search starting at source.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -634,7 +634,7 @@ <div class="viewcode-block" id="bfs_tree"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.breadth_first_search.bfs_tree.html#networkx.algorithms.traversal.breadth_first_search.bfs_tree">[docs]</a><span class="k">def</span> <span class="nf">bfs_tree</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">depth_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sort_neighbors</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns an oriented tree constructed from of a breadth-first-search</span> +<span class="w"> </span><span class="sd">"""Returns an oriented tree constructed from of a breadth-first-search</span> <span class="sd"> starting at source.</span> <span class="sd"> Parameters</span> @@ -700,7 +700,7 @@ <div class="viewcode-block" id="bfs_predecessors"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.breadth_first_search.bfs_predecessors.html#networkx.algorithms.traversal.breadth_first_search.bfs_predecessors">[docs]</a><span class="k">def</span> <span class="nf">bfs_predecessors</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">depth_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sort_neighbors</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns an iterator of predecessors in breadth-first-search from source.</span> +<span class="w"> </span><span class="sd">"""Returns an iterator of predecessors in breadth-first-search from source.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -764,7 +764,7 @@ <div class="viewcode-block" id="bfs_successors"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.breadth_first_search.bfs_successors.html#networkx.algorithms.traversal.breadth_first_search.bfs_successors">[docs]</a><span class="k">def</span> <span class="nf">bfs_successors</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">depth_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sort_neighbors</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns an iterator of successors in breadth-first-search from source.</span> +<span class="w"> </span><span class="sd">"""Returns an iterator of successors in breadth-first-search from source.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -836,7 +836,7 @@ <div class="viewcode-block" id="bfs_layers"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.breadth_first_search.bfs_layers.html#networkx.algorithms.traversal.breadth_first_search.bfs_layers">[docs]</a><span class="k">def</span> <span class="nf">bfs_layers</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">sources</span><span class="p">):</span> - <span class="sd">"""Returns an iterator of all the layers in breadth-first search traversal.</span> +<span class="w"> </span><span class="sd">"""Returns an iterator of all the layers in breadth-first search traversal.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -887,7 +887,7 @@ <div class="viewcode-block" id="descendants_at_distance"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.breadth_first_search.descendants_at_distance.html#networkx.algorithms.traversal.breadth_first_search.descendants_at_distance">[docs]</a><span class="k">def</span> <span class="nf">descendants_at_distance</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">distance</span><span class="p">):</span> - <span class="sd">"""Returns all nodes at a fixed `distance` from `source` in `G`.</span> +<span class="w"> </span><span class="sd">"""Returns all nodes at a fixed `distance` from `source` in `G`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -974,7 +974,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/traversal/depth_first_search.html b/_modules/networkx/algorithms/traversal/depth_first_search.html index ba8878df..ef9b18e7 100644 --- a/_modules/networkx/algorithms/traversal/depth_first_search.html +++ b/_modules/networkx/algorithms/traversal/depth_first_search.html @@ -478,7 +478,7 @@ <div class="viewcode-block" id="dfs_edges"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.depth_first_search.dfs_edges.html#networkx.algorithms.traversal.depth_first_search.dfs_edges">[docs]</a><span class="k">def</span> <span class="nf">dfs_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">depth_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Iterate over edges in a depth-first-search (DFS).</span> +<span class="w"> </span><span class="sd">"""Iterate over edges in a depth-first-search (DFS).</span> <span class="sd"> Perform a depth-first-search over the nodes of `G` and yield</span> <span class="sd"> the edges in order. This may not generate all edges in `G`</span> @@ -559,7 +559,7 @@ <div class="viewcode-block" id="dfs_tree"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.depth_first_search.dfs_tree.html#networkx.algorithms.traversal.depth_first_search.dfs_tree">[docs]</a><span class="k">def</span> <span class="nf">dfs_tree</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">depth_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns oriented tree constructed from a depth-first-search from source.</span> +<span class="w"> </span><span class="sd">"""Returns oriented tree constructed from a depth-first-search from source.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -604,7 +604,7 @@ <div class="viewcode-block" id="dfs_predecessors"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.depth_first_search.dfs_predecessors.html#networkx.algorithms.traversal.depth_first_search.dfs_predecessors">[docs]</a><span class="k">def</span> <span class="nf">dfs_predecessors</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">depth_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns dictionary of predecessors in depth-first-search from source.</span> +<span class="w"> </span><span class="sd">"""Returns dictionary of predecessors in depth-first-search from source.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -654,7 +654,7 @@ <div class="viewcode-block" id="dfs_successors"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.depth_first_search.dfs_successors.html#networkx.algorithms.traversal.depth_first_search.dfs_successors">[docs]</a><span class="k">def</span> <span class="nf">dfs_successors</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">depth_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns dictionary of successors in depth-first-search from source.</span> +<span class="w"> </span><span class="sd">"""Returns dictionary of successors in depth-first-search from source.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -707,7 +707,7 @@ <div class="viewcode-block" id="dfs_postorder_nodes"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes.html#networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes">[docs]</a><span class="k">def</span> <span class="nf">dfs_postorder_nodes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">depth_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generate nodes in a depth-first-search post-ordering starting at source.</span> +<span class="w"> </span><span class="sd">"""Generate nodes in a depth-first-search post-ordering starting at source.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -758,7 +758,7 @@ <div class="viewcode-block" id="dfs_preorder_nodes"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes.html#networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes">[docs]</a><span class="k">def</span> <span class="nf">dfs_preorder_nodes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">depth_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generate nodes in a depth-first-search pre-ordering starting at source.</span> +<span class="w"> </span><span class="sd">"""Generate nodes in a depth-first-search pre-ordering starting at source.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -809,7 +809,7 @@ <div class="viewcode-block" id="dfs_labeled_edges"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges.html#networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges">[docs]</a><span class="k">def</span> <span class="nf">dfs_labeled_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">depth_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Iterate over edges in a depth-first-search (DFS) labeled by type.</span> +<span class="w"> </span><span class="sd">"""Iterate over edges in a depth-first-search (DFS) labeled by type.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -960,7 +960,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/traversal/edgebfs.html b/_modules/networkx/algorithms/traversal/edgebfs.html index 3d50a559..5fdf851f 100644 --- a/_modules/networkx/algorithms/traversal/edgebfs.html +++ b/_modules/networkx/algorithms/traversal/edgebfs.html @@ -480,7 +480,7 @@ <div class="viewcode-block" id="edge_bfs"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.edgebfs.edge_bfs.html#networkx.algorithms.traversal.edgebfs.edge_bfs">[docs]</a><span class="k">def</span> <span class="nf">edge_bfs</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""A directed, breadth-first-search of edges in `G`, beginning at `source`.</span> +<span class="w"> </span><span class="sd">"""A directed, breadth-first-search of edges in `G`, beginning at `source`.</span> <span class="sd"> Yield the edges of G in a breadth-first-search order continuing until</span> <span class="sd"> all edges are generated.</span> @@ -688,7 +688,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/traversal/edgedfs.html b/_modules/networkx/algorithms/traversal/edgedfs.html index ccb9b575..a23c1ce6 100644 --- a/_modules/networkx/algorithms/traversal/edgedfs.html +++ b/_modules/networkx/algorithms/traversal/edgedfs.html @@ -478,7 +478,7 @@ <div class="viewcode-block" id="edge_dfs"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.traversal.edgedfs.edge_dfs.html#networkx.algorithms.traversal.edgedfs.edge_dfs">[docs]</a><span class="k">def</span> <span class="nf">edge_dfs</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""A directed, depth-first-search of edges in `G`, beginning at `source`.</span> +<span class="w"> </span><span class="sd">"""A directed, depth-first-search of edges in `G`, beginning at `source`.</span> <span class="sd"> Yield the edges of G in a depth-first-search order continuing until</span> <span class="sd"> all edges are generated.</span> @@ -686,7 +686,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/tree/branchings.html b/_modules/networkx/algorithms/tree/branchings.html index f7eb644d..4f521d08 100644 --- a/_modules/networkx/algorithms/tree/branchings.html +++ b/_modules/networkx/algorithms/tree/branchings.html @@ -536,7 +536,7 @@ <div class="viewcode-block" id="branching_weight"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.branchings.branching_weight.html#networkx.algorithms.tree.branchings.branching_weight">[docs]</a><span class="k">def</span> <span class="nf">branching_weight</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">attr</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the total weight of a branching.</span> <span class="sd"> You must access this function through the networkx.algorithms.tree module.</span> @@ -570,7 +570,7 @@ <div class="viewcode-block" id="greedy_branching"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.branchings.greedy_branching.html#networkx.algorithms.tree.branchings.greedy_branching">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> <span class="k">def</span> <span class="nf">greedy_branching</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">attr</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s2">"max"</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns a branching obtained through a greedy algorithm.</span> <span class="sd"> This algorithm is wrong, and cannot give a proper optimal branching.</span> @@ -649,7 +649,7 @@ <span class="k">class</span> <span class="nc">MultiDiGraph_EdgeKey</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">MultiDiGraph</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> MultiDiGraph which assigns unique keys to every edge.</span> <span class="sd"> Adds a dictionary edge_index which maps edge keys to (u, v, data) tuples.</span> @@ -689,7 +689,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">remove_node</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="k">def</span> <span class="nf">add_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u_for_edge</span><span class="p">,</span> <span class="n">v_for_edge</span><span class="p">,</span> <span class="n">key_for_edge</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Key is now required.</span> <span class="sd"> """</span> @@ -720,7 +720,7 @@ <span class="k">def</span> <span class="nf">get_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the edge keys of the unique path between u and v.</span> <span class="sd"> This is not a generic function. G must be a branching and an instance of</span> @@ -744,7 +744,7 @@ <div class="viewcode-block" id="Edmonds"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.branchings.Edmonds.html#networkx.algorithms.tree.branchings.Edmonds">[docs]</a><span class="k">class</span> <span class="nc">Edmonds</span><span class="p">:</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Edmonds algorithm [1]_ for finding optimal branchings and spanning</span> <span class="sd"> arborescences.</span> @@ -857,7 +857,7 @@ <span class="n">partition</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns a branching from G.</span> <span class="sd"> Parameters</span> @@ -903,7 +903,7 @@ <span class="n">G_pred</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">pred</span> <span class="k">def</span> <span class="nf">desired_edge</span><span class="p">(</span><span class="n">v</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Find the edge directed toward v with maximal weight.</span> <span class="sd"> If an edge partition exists in this graph, return the included edge</span> @@ -1081,7 +1081,7 @@ <span class="n">H</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_original</span><span class="o">.</span><span class="vm">__class__</span><span class="p">()</span> <span class="k">def</span> <span class="nf">is_root</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">edgekeys</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns True if `u` is a root node in G.</span> <span class="sd"> Node `u` will be a root node if its in-degree, restricted to the</span> @@ -1292,7 +1292,7 @@ <div class="viewcode-block" id="ArborescenceIterator"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.branchings.ArborescenceIterator.html#networkx.algorithms.tree.branchings.ArborescenceIterator">[docs]</a><span class="k">class</span> <span class="nc">ArborescenceIterator</span><span class="p">:</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Iterate over all spanning arborescences of a graph in either increasing or</span> <span class="sd"> decreasing cost.</span> @@ -1314,7 +1314,7 @@ <span class="nd">@dataclass</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">class</span> <span class="nc">Partition</span><span class="p">:</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> This dataclass represents a partition and stores a dict with the edge</span> <span class="sd"> data and the weight of the minimum spanning arborescence of the</span> <span class="sd"> partition dict.</span> @@ -1329,7 +1329,7 @@ <span class="p">)</span> <div class="viewcode-block" id="ArborescenceIterator.__init__"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.branchings.ArborescenceIterator.html#networkx.algorithms.tree.branchings.ArborescenceIterator.__init__">[docs]</a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">minimum</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">init_partition</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Initialize the iterator</span> <span class="sd"> Parameters</span> @@ -1372,7 +1372,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">init_partition</span> <span class="o">=</span> <span class="kc">None</span></div> <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> <span class="sd"> ArborescenceIterator</span> @@ -1404,7 +1404,7 @@ <span class="k">return</span> <span class="bp">self</span> <span class="k">def</span> <span class="fm">__next__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> <span class="sd"> (multi)Graph</span> @@ -1429,7 +1429,7 @@ <span class="k">return</span> <span class="n">next_arborescence</span> <span class="k">def</span> <span class="nf">_partition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">partition</span><span class="p">,</span> <span class="n">partition_arborescence</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Create new partitions based of the minimum spanning tree of the</span> <span class="sd"> current minimum partition.</span> @@ -1469,7 +1469,7 @@ <span class="n">p1</span><span class="o">.</span><span class="n">partition_dict</span> <span class="o">=</span> <span class="n">p2</span><span class="o">.</span><span class="n">partition_dict</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="k">def</span> <span class="nf">_write_partition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">partition</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Writes the desired partition into the graph to calculate the minimum</span> <span class="sd"> spanning tree. Also, if one incoming edge is included, mark all others</span> <span class="sd"> as excluded so that if that vertex is merged during Edmonds' algorithm</span> @@ -1503,7 +1503,7 @@ <span class="n">d</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">partition_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">EdgePartition</span><span class="o">.</span><span class="n">EXCLUDED</span> <span class="k">def</span> <span class="nf">_clear_partition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Removes partition data from the graph</span> <span class="sd"> """</span> <span class="k">for</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">d</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">data</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> @@ -1560,7 +1560,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/tree/coding.html b/_modules/networkx/algorithms/tree/coding.html index 2592c833..7201bd2b 100644 --- a/_modules/networkx/algorithms/tree/coding.html +++ b/_modules/networkx/algorithms/tree/coding.html @@ -487,7 +487,7 @@ <div class="viewcode-block" id="NotATree"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.coding.NotATree.html#networkx.algorithms.tree.coding.NotATree">[docs]</a><span class="k">class</span> <span class="nc">NotATree</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">NetworkXException</span><span class="p">):</span> - <span class="sd">"""Raised when a function expects a tree (that is, a connected</span> +<span class="w"> </span><span class="sd">"""Raised when a function expects a tree (that is, a connected</span> <span class="sd"> undirected graph with no cycles) but gets a non-tree graph as input</span> <span class="sd"> instead.</span> @@ -496,7 +496,7 @@ <div class="viewcode-block" id="to_nested_tuple"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.coding.to_nested_tuple.html#networkx.algorithms.tree.coding.to_nested_tuple">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">to_nested_tuple</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">root</span><span class="p">,</span> <span class="n">canonical_form</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns a nested tuple representation of the given tree.</span> +<span class="w"> </span><span class="sd">"""Returns a nested tuple representation of the given tree.</span> <span class="sd"> The nested tuple representation of a tree is defined</span> <span class="sd"> recursively. The tree with one node and no edges is represented by</span> @@ -562,7 +562,7 @@ <span class="sd"> """</span> <span class="k">def</span> <span class="nf">_make_tuple</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">root</span><span class="p">,</span> <span class="n">_parent</span><span class="p">):</span> - <span class="sd">"""Recursively compute the nested tuple representation of the</span> +<span class="w"> </span><span class="sd">"""Recursively compute the nested tuple representation of the</span> <span class="sd"> given rooted tree.</span> <span class="sd"> ``_parent`` is the parent node of ``root`` in the supertree in</span> @@ -591,7 +591,7 @@ <div class="viewcode-block" id="from_nested_tuple"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.coding.from_nested_tuple.html#networkx.algorithms.tree.coding.from_nested_tuple">[docs]</a><span class="k">def</span> <span class="nf">from_nested_tuple</span><span class="p">(</span><span class="n">sequence</span><span class="p">,</span> <span class="n">sensible_relabeling</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns the rooted tree corresponding to the given nested tuple.</span> +<span class="w"> </span><span class="sd">"""Returns the rooted tree corresponding to the given nested tuple.</span> <span class="sd"> The nested tuple representation of a tree is defined</span> <span class="sd"> recursively. The tree with one node and no edges is represented by</span> @@ -641,7 +641,7 @@ <span class="sd"> """</span> <span class="k">def</span> <span class="nf">_make_tree</span><span class="p">(</span><span class="n">sequence</span><span class="p">):</span> - <span class="sd">"""Recursively creates a tree from the given sequence of nested</span> +<span class="w"> </span><span class="sd">"""Recursively creates a tree from the given sequence of nested</span> <span class="sd"> tuples.</span> <span class="sd"> This function employs the :func:`~networkx.tree.join` function</span> @@ -675,7 +675,7 @@ <div class="viewcode-block" id="to_prufer_sequence"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.coding.to_prufer_sequence.html#networkx.algorithms.tree.coding.to_prufer_sequence">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">to_prufer_sequence</span><span class="p">(</span><span class="n">T</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the Prüfer sequence of the given tree.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the Prüfer sequence of the given tree.</span> <span class="sd"> A *Prüfer sequence* is a list of *n* - 2 numbers between 0 and</span> <span class="sd"> *n* - 1, inclusive. The tree corresponding to a given Prüfer</span> @@ -775,7 +775,7 @@ <div class="viewcode-block" id="from_prufer_sequence"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.coding.from_prufer_sequence.html#networkx.algorithms.tree.coding.from_prufer_sequence">[docs]</a><span class="k">def</span> <span class="nf">from_prufer_sequence</span><span class="p">(</span><span class="n">sequence</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the tree corresponding to the given Prüfer sequence.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the tree corresponding to the given Prüfer sequence.</span> <span class="sd"> A *Prüfer sequence* is a list of *n* - 2 numbers between 0 and</span> <span class="sd"> *n* - 1, inclusive. The tree corresponding to a given Prüfer</span> @@ -910,7 +910,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/tree/decomposition.html b/_modules/networkx/algorithms/tree/decomposition.html index 9b894257..3015dd5e 100644 --- a/_modules/networkx/algorithms/tree/decomposition.html +++ b/_modules/networkx/algorithms/tree/decomposition.html @@ -474,7 +474,7 @@ <div class="viewcode-block" id="junction_tree"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.decomposition.junction_tree.html#networkx.algorithms.tree.decomposition.junction_tree">[docs]</a><span class="nd">@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">junction_tree</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a junction tree of a given graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a junction tree of a given graph.</span> <span class="sd"> A junction tree (or clique tree) is constructed from a (un)directed graph G.</span> <span class="sd"> The tree is constructed based on a moralized and triangulated version of G.</span> @@ -599,7 +599,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/tree/mst.html b/_modules/networkx/algorithms/tree/mst.html index 12764cf6..2ac894cf 100644 --- a/_modules/networkx/algorithms/tree/mst.html +++ b/_modules/networkx/algorithms/tree/mst.html @@ -489,7 +489,7 @@ <span class="k">class</span> <span class="nc">EdgePartition</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> An enum to store the state of an edge partition. The enum is written to the</span> <span class="sd"> edges of a graph before being pasted to `kruskal_mst_edges`. Options are:</span> @@ -507,7 +507,7 @@ <span class="k">def</span> <span class="nf">boruvka_mst_edges</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">minimum</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_nan</span><span class="o">=</span><span class="kc">False</span> <span class="p">):</span> - <span class="sd">"""Iterate over edges of a Borůvka's algorithm min/max spanning tree.</span> +<span class="w"> </span><span class="sd">"""Iterate over edges of a Borůvka's algorithm min/max spanning tree.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -541,7 +541,7 @@ <span class="n">forest</span> <span class="o">=</span> <span class="n">UnionFind</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="k">def</span> <span class="nf">best_edge</span><span class="p">(</span><span class="n">component</span><span class="p">):</span> - <span class="sd">"""Returns the optimum (minimum or maximum) edge on the edge</span> +<span class="w"> </span><span class="sd">"""Returns the optimum (minimum or maximum) edge on the edge</span> <span class="sd"> boundary of the given set of nodes.</span> <span class="sd"> A return value of ``None`` indicates an empty boundary.</span> @@ -603,7 +603,7 @@ <span class="k">def</span> <span class="nf">kruskal_mst_edges</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">minimum</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_nan</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">partition</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Iterate over edge of a Kruskal's algorithm min/max spanning tree.</span> <span class="sd"> Parameters</span> @@ -649,7 +649,7 @@ <span class="k">else</span><span class="p">:</span> <span class="n">edges</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Sort the edges of the graph with respect to the partition data. </span> <span class="sd"> Edges are returned in the following order:</span> @@ -711,7 +711,7 @@ <span class="k">def</span> <span class="nf">prim_mst_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">minimum</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_nan</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Iterate over edges of Prim's algorithm min/max spanning tree.</span> +<span class="w"> </span><span class="sd">"""Iterate over edges of Prim's algorithm min/max spanning tree.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -758,7 +758,7 @@ <span class="k">if</span> <span class="n">isnan</span><span class="p">(</span><span class="n">wt</span><span class="p">):</span> <span class="k">if</span> <span class="n">ignore_nan</span><span class="p">:</span> <span class="k">continue</span> - <span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"NaN found as an edge weight. Edge </span><span class="si">{</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span> + <span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"NaN found as an edge weight. Edge </span><span class="si">{</span><span class="p">(</span><span class="n">u</span><span class="p">,</span><span class="w"> </span><span class="n">v</span><span class="p">,</span><span class="w"> </span><span class="n">k</span><span class="p">,</span><span class="w"> </span><span class="n">d</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span> <span class="n">push</span><span class="p">(</span><span class="n">frontier</span><span class="p">,</span> <span class="p">(</span><span class="n">wt</span><span class="p">,</span> <span class="nb">next</span><span class="p">(</span><span class="n">c</span><span class="p">),</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">d</span><span class="p">))</span> <span class="k">else</span><span class="p">:</span> @@ -767,7 +767,7 @@ <span class="k">if</span> <span class="n">isnan</span><span class="p">(</span><span class="n">wt</span><span class="p">):</span> <span class="k">if</span> <span class="n">ignore_nan</span><span class="p">:</span> <span class="k">continue</span> - <span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"NaN found as an edge weight. Edge </span><span class="si">{</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span> + <span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"NaN found as an edge weight. Edge </span><span class="si">{</span><span class="p">(</span><span class="n">u</span><span class="p">,</span><span class="w"> </span><span class="n">v</span><span class="p">,</span><span class="w"> </span><span class="n">d</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span> <span class="n">push</span><span class="p">(</span><span class="n">frontier</span><span class="p">,</span> <span class="p">(</span><span class="n">wt</span><span class="p">,</span> <span class="nb">next</span><span class="p">(</span><span class="n">c</span><span class="p">),</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">d</span><span class="p">))</span> <span class="k">while</span> <span class="n">nodes</span> <span class="ow">and</span> <span class="n">frontier</span><span class="p">:</span> @@ -818,7 +818,7 @@ <span class="k">def</span> <span class="nf">minimum_spanning_edges</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">algorithm</span><span class="o">=</span><span class="s2">"kruskal"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_nan</span><span class="o">=</span><span class="kc">False</span> <span class="p">):</span> - <span class="sd">"""Generate edges in a minimum spanning forest of an undirected</span> +<span class="w"> </span><span class="sd">"""Generate edges in a minimum spanning forest of an undirected</span> <span class="sd"> weighted graph.</span> <span class="sd"> A minimum spanning tree is a subgraph of the graph (a tree)</span> @@ -912,7 +912,7 @@ <span class="k">def</span> <span class="nf">maximum_spanning_edges</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">algorithm</span><span class="o">=</span><span class="s2">"kruskal"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_nan</span><span class="o">=</span><span class="kc">False</span> <span class="p">):</span> - <span class="sd">"""Generate edges in a maximum spanning forest of an undirected</span> +<span class="w"> </span><span class="sd">"""Generate edges in a maximum spanning forest of an undirected</span> <span class="sd"> weighted graph.</span> <span class="sd"> A maximum spanning tree is a subgraph of the graph (a tree)</span> @@ -1002,7 +1002,7 @@ <div class="viewcode-block" id="minimum_spanning_tree"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.mst.minimum_spanning_tree.html#networkx.algorithms.tree.mst.minimum_spanning_tree">[docs]</a><span class="k">def</span> <span class="nf">minimum_spanning_tree</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">algorithm</span><span class="o">=</span><span class="s2">"kruskal"</span><span class="p">,</span> <span class="n">ignore_nan</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns a minimum spanning tree or forest on an undirected graph `G`.</span> +<span class="w"> </span><span class="sd">"""Returns a minimum spanning tree or forest on an undirected graph `G`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1063,7 +1063,7 @@ <span class="k">def</span> <span class="nf">partition_spanning_tree</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">minimum</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">partition</span><span class="o">=</span><span class="s2">"partition"</span><span class="p">,</span> <span class="n">ignore_nan</span><span class="o">=</span><span class="kc">False</span> <span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Find a spanning tree while respecting a partition of edges.</span> <span class="sd"> Edges can be flagged as either `INLCUDED` which are required to be in the</span> @@ -1124,7 +1124,7 @@ <div class="viewcode-block" id="maximum_spanning_tree"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.mst.maximum_spanning_tree.html#networkx.algorithms.tree.mst.maximum_spanning_tree">[docs]</a><span class="k">def</span> <span class="nf">maximum_spanning_tree</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">algorithm</span><span class="o">=</span><span class="s2">"kruskal"</span><span class="p">,</span> <span class="n">ignore_nan</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns a maximum spanning tree or forest on an undirected graph `G`.</span> +<span class="w"> </span><span class="sd">"""Returns a maximum spanning tree or forest on an undirected graph `G`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1187,7 +1187,7 @@ <div class="viewcode-block" id="random_spanning_tree"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.mst.random_spanning_tree.html#networkx.algorithms.tree.mst.random_spanning_tree">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_spanning_tree</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">multiplicative</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Sample a random spanning tree using the edges weights of `G`.</span> <span class="sd"> This function supports two different methods for determining the</span> @@ -1231,7 +1231,7 @@ <span class="sd"> """</span> <span class="k">def</span> <span class="nf">find_node</span><span class="p">(</span><span class="n">merged_nodes</span><span class="p">,</span> <span class="n">node</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> We can think of clusters of contracted nodes as having one</span> <span class="sd"> representative in the graph. Each node which is not in merged_nodes</span> <span class="sd"> is still its own representative. Since a representative can be later</span> @@ -1263,7 +1263,7 @@ <span class="k">return</span> <span class="n">rep</span> <span class="k">def</span> <span class="nf">prepare_graph</span><span class="p">():</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> For the graph `G`, remove all edges not in the set `V` and then</span> <span class="sd"> contract all edges in the set `U`.</span> @@ -1306,7 +1306,7 @@ <span class="k">return</span> <span class="n">merged_nodes</span><span class="p">,</span> <span class="n">result</span> <span class="k">def</span> <span class="nf">spanning_tree_total_weight</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Find the sum of weights of the spanning trees of `G` using the</span> <span class="sd"> approioate `method`.</span> @@ -1408,7 +1408,7 @@ <div class="viewcode-block" id="SpanningTreeIterator"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.mst.SpanningTreeIterator.html#networkx.algorithms.tree.mst.SpanningTreeIterator">[docs]</a><span class="k">class</span> <span class="nc">SpanningTreeIterator</span><span class="p">:</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Iterate over all spanning trees of a graph in either increasing or</span> <span class="sd"> decreasing cost.</span> @@ -1429,7 +1429,7 @@ <span class="nd">@dataclass</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">class</span> <span class="nc">Partition</span><span class="p">:</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> This dataclass represents a partition and stores a dict with the edge</span> <span class="sd"> data and the weight of the minimum spanning tree of the partition dict.</span> <span class="sd"> """</span> @@ -1443,7 +1443,7 @@ <span class="p">)</span> <div class="viewcode-block" id="SpanningTreeIterator.__init__"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.mst.SpanningTreeIterator.html#networkx.algorithms.tree.mst.SpanningTreeIterator.__init__">[docs]</a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">minimum</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_nan</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Initialize the iterator</span> <span class="sd"> Parameters</span> @@ -1472,7 +1472,7 @@ <span class="p">)</span></div> <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> <span class="sd"> SpanningTreeIterator</span> @@ -1491,7 +1491,7 @@ <span class="k">return</span> <span class="bp">self</span> <span class="k">def</span> <span class="fm">__next__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> <span class="sd"> (multi)Graph</span> @@ -1513,7 +1513,7 @@ <span class="k">return</span> <span class="n">next_tree</span> <span class="k">def</span> <span class="nf">_partition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">partition</span><span class="p">,</span> <span class="n">partition_tree</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Create new partitions based of the minimum spanning tree of the</span> <span class="sd"> current minimum partition.</span> @@ -1550,7 +1550,7 @@ <span class="n">p1</span><span class="o">.</span><span class="n">partition_dict</span> <span class="o">=</span> <span class="n">p2</span><span class="o">.</span><span class="n">partition_dict</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="k">def</span> <span class="nf">_write_partition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">partition</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Writes the desired partition into the graph to calculate the minimum</span> <span class="sd"> spanning tree.</span> @@ -1567,7 +1567,7 @@ <span class="n">d</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">partition_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">EdgePartition</span><span class="o">.</span><span class="n">OPEN</span> <span class="k">def</span> <span class="nf">_clear_partition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Removes partition data from the graph</span> <span class="sd"> """</span> <span class="k">for</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">d</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">data</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> @@ -1624,7 +1624,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/tree/operations.html b/_modules/networkx/algorithms/tree/operations.html index 7caf82cc..90520311 100644 --- a/_modules/networkx/algorithms/tree/operations.html +++ b/_modules/networkx/algorithms/tree/operations.html @@ -471,7 +471,7 @@ <div class="viewcode-block" id="join"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.operations.join.html#networkx.algorithms.tree.operations.join">[docs]</a><span class="k">def</span> <span class="nf">join</span><span class="p">(</span><span class="n">rooted_trees</span><span class="p">,</span> <span class="n">label_attribute</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a new rooted tree with a root node joined with the roots</span> +<span class="w"> </span><span class="sd">"""Returns a new rooted tree with a root node joined with the roots</span> <span class="sd"> of each of the given rooted trees.</span> <span class="sd"> Parameters</span> @@ -618,7 +618,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/tree/recognition.html b/_modules/networkx/algorithms/tree/recognition.html index 5a6885bc..9c10710b 100644 --- a/_modules/networkx/algorithms/tree/recognition.html +++ b/_modules/networkx/algorithms/tree/recognition.html @@ -543,7 +543,7 @@ <div class="viewcode-block" id="is_arborescence"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.recognition.is_arborescence.html#networkx.algorithms.tree.recognition.is_arborescence">[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">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">is_arborescence</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns True if `G` is an arborescence.</span> <span class="sd"> An arborescence is a directed tree with maximum in-degree equal to 1.</span> @@ -582,7 +582,7 @@ <div class="viewcode-block" id="is_branching"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.recognition.is_branching.html#networkx.algorithms.tree.recognition.is_branching">[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">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">is_branching</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns True if `G` is a branching.</span> <span class="sd"> A branching is a directed forest with maximum in-degree equal to 1.</span> @@ -620,7 +620,7 @@ <div class="viewcode-block" id="is_forest"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.recognition.is_forest.html#networkx.algorithms.tree.recognition.is_forest">[docs]</a><span class="k">def</span> <span class="nf">is_forest</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns True if `G` is a forest.</span> <span class="sd"> A forest is a graph with no undirected cycles.</span> @@ -676,7 +676,7 @@ <div class="viewcode-block" id="is_tree"><a class="viewcode-back" href="../../../../reference/algorithms/generated/networkx.algorithms.tree.recognition.is_tree.html#networkx.algorithms.tree.recognition.is_tree">[docs]</a><span class="k">def</span> <span class="nf">is_tree</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns True if `G` is a tree.</span> <span class="sd"> A tree is a connected graph with no undirected cycles.</span> @@ -781,7 +781,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/triads.html b/_modules/networkx/algorithms/triads.html index e9c9cef3..394588ed 100644 --- a/_modules/networkx/algorithms/triads.html +++ b/_modules/networkx/algorithms/triads.html @@ -580,7 +580,7 @@ <span class="k">def</span> <span class="nf">_tricode</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">w</span><span class="p">):</span> - <span class="sd">"""Returns the integer code of the given triad.</span> +<span class="w"> </span><span class="sd">"""Returns the integer code of the given triad.</span> <span class="sd"> This is some fancy magic that comes from Batagelj and Mrvar's paper. It</span> <span class="sd"> treats each edge joining a pair of `v`, `u`, and `w` as a bit in</span> @@ -593,7 +593,7 @@ <div class="viewcode-block" id="triadic_census"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.triads.triadic_census.html#networkx.algorithms.triads.triadic_census">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">triadic_census</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Determines the triadic census of a directed graph.</span> +<span class="w"> </span><span class="sd">"""Determines the triadic census of a directed graph.</span> <span class="sd"> The triadic census is a count of how many of the 16 possible types of</span> <span class="sd"> triads are present in a directed graph. If a list of nodes is passed, then</span> @@ -740,7 +740,7 @@ <div class="viewcode-block" id="is_triad"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.triads.is_triad.html#networkx.algorithms.triads.is_triad">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span><span class="p">()</span> <span class="k">def</span> <span class="nf">is_triad</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns True if the graph G is a triad, else False.</span> +<span class="w"> </span><span class="sd">"""Returns True if the graph G is a triad, else False.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -770,7 +770,7 @@ <div class="viewcode-block" id="all_triplets"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.triads.all_triplets.html#networkx.algorithms.triads.all_triplets">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">all_triplets</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns a generator of all possible sets of 3 nodes in a DiGraph.</span> +<span class="w"> </span><span class="sd">"""Returns a generator of all possible sets of 3 nodes in a DiGraph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -795,7 +795,7 @@ <div class="viewcode-block" id="all_triads"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.triads.all_triads.html#networkx.algorithms.triads.all_triads">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">all_triads</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""A generator of all possible triads in G.</span> +<span class="w"> </span><span class="sd">"""A generator of all possible triads in G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -825,7 +825,7 @@ <div class="viewcode-block" id="triads_by_type"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.triads.triads_by_type.html#networkx.algorithms.triads.triads_by_type">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">triads_by_type</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns a list of all triads for each triad type in a directed graph.</span> +<span class="w"> </span><span class="sd">"""Returns a list of all triads for each triad type in a directed graph.</span> <span class="sd"> There are exactly 16 different types of triads possible. Suppose 1, 2, 3 are three</span> <span class="sd"> nodes, they will be classified as a particular triad type if their connections</span> <span class="sd"> are as follows:</span> @@ -887,7 +887,7 @@ <div class="viewcode-block" id="triad_type"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.triads.triad_type.html#networkx.algorithms.triads.triad_type">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">triad_type</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the sociological triad type for a triad.</span> +<span class="w"> </span><span class="sd">"""Returns the sociological triad type for a triad.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -985,7 +985,7 @@ <div class="viewcode-block" id="random_triad"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.triads.random_triad.html#networkx.algorithms.triads.random_triad">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_triad</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random triad from a directed graph.</span> +<span class="w"> </span><span class="sd">"""Returns a random triad from a directed graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1120,7 +1120,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/vitality.html b/_modules/networkx/algorithms/vitality.html index c0a1036e..789c9102 100644 --- a/_modules/networkx/algorithms/vitality.html +++ b/_modules/networkx/algorithms/vitality.html @@ -472,7 +472,7 @@ <div class="viewcode-block" id="closeness_vitality"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.vitality.closeness_vitality.html#networkx.algorithms.vitality.closeness_vitality">[docs]</a><span class="k">def</span> <span class="nf">closeness_vitality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">node</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">wiener_index</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the closeness vitality for nodes in the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the closeness vitality for nodes in the graph.</span> <span class="sd"> The *closeness vitality* of a node, defined in Section 3.6.2 of [1],</span> <span class="sd"> is the change in the sum of distances between all node pairs when</span> @@ -587,7 +587,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/voronoi.html b/_modules/networkx/algorithms/voronoi.html index 1fcad711..d8cc0aa0 100644 --- a/_modules/networkx/algorithms/voronoi.html +++ b/_modules/networkx/algorithms/voronoi.html @@ -469,7 +469,7 @@ <div class="viewcode-block" id="voronoi_cells"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.voronoi.voronoi_cells.html#networkx.algorithms.voronoi.voronoi_cells">[docs]</a><span class="k">def</span> <span class="nf">voronoi_cells</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">center_nodes</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns the Voronoi cells centered at `center_nodes` with respect</span> +<span class="w"> </span><span class="sd">"""Returns the Voronoi cells centered at `center_nodes` with respect</span> <span class="sd"> to the shortest-path distance metric.</span> <span class="sd"> If *C* is a set of nodes in the graph and *c* is an element of *C*,</span> @@ -597,7 +597,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/algorithms/wiener.html b/_modules/networkx/algorithms/wiener.html index adc0a32f..ba5e24d3 100644 --- a/_modules/networkx/algorithms/wiener.html +++ b/_modules/networkx/algorithms/wiener.html @@ -476,7 +476,7 @@ <div class="viewcode-block" id="wiener_index"><a class="viewcode-back" href="../../../reference/algorithms/generated/networkx.algorithms.wiener.wiener_index.html#networkx.algorithms.wiener.wiener_index">[docs]</a><span class="k">def</span> <span class="nf">wiener_index</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the Wiener index of the given graph.</span> +<span class="w"> </span><span class="sd">"""Returns the Wiener index of the given graph.</span> <span class="sd"> The *Wiener index* of a graph is the sum of the shortest-path</span> <span class="sd"> distances between each pair of reachable nodes. For pairs of nodes</span> @@ -588,7 +588,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/classes/backends.html b/_modules/networkx/classes/backends.html index 49c8337b..1a00bf6f 100644 --- a/_modules/networkx/classes/backends.html +++ b/_modules/networkx/classes/backends.html @@ -532,7 +532,7 @@ <span class="k">class</span> <span class="nc">PluginInfo</span><span class="p">:</span> - <span class="sd">"""Lazily loaded entry_points plugin information"""</span> +<span class="w"> </span><span class="sd">"""Lazily loaded entry_points plugin information"""</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="bp">self</span><span class="o">.</span><span class="n">_items</span> <span class="o">=</span> <span class="kc">None</span> @@ -572,7 +572,7 @@ <div class="viewcode-block" id="_dispatch"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.classes.backends._dispatch.html#networkx.classes.backends._dispatch">[docs]</a><span class="k">def</span> <span class="nf">_dispatch</span><span class="p">(</span><span class="n">func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Dispatches to a backend algorithm</span> +<span class="w"> </span><span class="sd">"""Dispatches to a backend algorithm</span> <span class="sd"> when the first argument is a backend graph-like object.</span> <span class="sd"> """</span> <span class="c1"># Allow any of the following decorator forms:</span> @@ -610,7 +610,7 @@ <span class="k">def</span> <span class="nf">test_override_dispatch</span><span class="p">(</span><span class="n">func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Auto-converts the first argument into the backend equivalent,</span> +<span class="w"> </span><span class="sd">"""Auto-converts the first argument into the backend equivalent,</span> <span class="sd"> causing the dispatching mechanism to trigger for every</span> <span class="sd"> decorated algorithm."""</span> <span class="k">if</span> <span class="n">func</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> @@ -678,7 +678,7 @@ <span class="k">def</span> <span class="nf">_mark_tests</span><span class="p">(</span><span class="n">items</span><span class="p">):</span> - <span class="sd">"""Allow backend to mark tests (skip or xfail) if they aren't able to correctly handle them"""</span> +<span class="w"> </span><span class="sd">"""Allow backend to mark tests (skip or xfail) if they aren't able to correctly handle them"""</span> <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"NETWORKX_GRAPH_CONVERT"</span><span class="p">):</span> <span class="n">plugin_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"NETWORKX_GRAPH_CONVERT"</span><span class="p">]</span> <span class="n">backend</span> <span class="o">=</span> <span class="n">plugins</span><span class="p">[</span><span class="n">plugin_name</span><span class="p">]</span><span class="o">.</span><span class="n">load</span><span class="p">()</span> @@ -735,7 +735,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/classes/coreviews.html b/_modules/networkx/classes/coreviews.html index 99cf611f..35dd22d2 100644 --- a/_modules/networkx/classes/coreviews.html +++ b/_modules/networkx/classes/coreviews.html @@ -483,7 +483,7 @@ <div class="viewcode-block" id="AtlasView"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.classes.coreviews.AtlasView.html#networkx.classes.coreviews.AtlasView">[docs]</a><span class="k">class</span> <span class="nc">AtlasView</span><span class="p">(</span><span class="n">Mapping</span><span class="p">):</span> - <span class="sd">"""An AtlasView is a Read-only Mapping of Mappings.</span> +<span class="w"> </span><span class="sd">"""An AtlasView is a Read-only Mapping of Mappings.</span> <span class="sd"> It is a View into a dict-of-dict data structure.</span> <span class="sd"> The inner level of dict is read-write. But the</span> @@ -526,7 +526,7 @@ <div class="viewcode-block" id="AdjacencyView"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.classes.coreviews.AdjacencyView.html#networkx.classes.coreviews.AdjacencyView">[docs]</a><span class="k">class</span> <span class="nc">AdjacencyView</span><span class="p">(</span><span class="n">AtlasView</span><span class="p">):</span> - <span class="sd">"""An AdjacencyView is a Read-only Map of Maps of Maps.</span> +<span class="w"> </span><span class="sd">"""An AdjacencyView is a Read-only Map of Maps of Maps.</span> <span class="sd"> It is a View into a dict-of-dict-of-dict data structure.</span> <span class="sd"> The inner level of dict is read-write. But the</span> @@ -548,7 +548,7 @@ <div class="viewcode-block" id="MultiAdjacencyView"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.classes.coreviews.MultiAdjacencyView.html#networkx.classes.coreviews.MultiAdjacencyView">[docs]</a><span class="k">class</span> <span class="nc">MultiAdjacencyView</span><span class="p">(</span><span class="n">AdjacencyView</span><span class="p">):</span> - <span class="sd">"""An MultiAdjacencyView is a Read-only Map of Maps of Maps of Maps.</span> +<span class="w"> </span><span class="sd">"""An MultiAdjacencyView is a Read-only Map of Maps of Maps of Maps.</span> <span class="sd"> It is a View into a dict-of-dict-of-dict-of-dict data structure.</span> <span class="sd"> The inner level of dict is read-write. But the</span> @@ -570,7 +570,7 @@ <div class="viewcode-block" id="UnionAtlas"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.classes.coreviews.UnionAtlas.html#networkx.classes.coreviews.UnionAtlas">[docs]</a><span class="k">class</span> <span class="nc">UnionAtlas</span><span class="p">(</span><span class="n">Mapping</span><span class="p">):</span> - <span class="sd">"""A read-only union of two atlases (dict-of-dict).</span> +<span class="w"> </span><span class="sd">"""A read-only union of two atlases (dict-of-dict).</span> <span class="sd"> The two dict-of-dicts represent the inner dict of</span> <span class="sd"> an Adjacency: `G.succ[node]` and `G.pred[node]`.</span> @@ -625,7 +625,7 @@ <div class="viewcode-block" id="UnionAdjacency"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.classes.coreviews.UnionAdjacency.html#networkx.classes.coreviews.UnionAdjacency">[docs]</a><span class="k">class</span> <span class="nc">UnionAdjacency</span><span class="p">(</span><span class="n">Mapping</span><span class="p">):</span> - <span class="sd">"""A read-only union of dict Adjacencies as a Map of Maps of Maps.</span> +<span class="w"> </span><span class="sd">"""A read-only union of dict Adjacencies as a Map of Maps of Maps.</span> <span class="sd"> The two input dict-of-dict-of-dicts represent the union of</span> <span class="sd"> `G.succ` and `G.pred`. Return values are UnionAtlas</span> @@ -677,7 +677,7 @@ <div class="viewcode-block" id="UnionMultiInner"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.classes.coreviews.UnionMultiInner.html#networkx.classes.coreviews.UnionMultiInner">[docs]</a><span class="k">class</span> <span class="nc">UnionMultiInner</span><span class="p">(</span><span class="n">UnionAtlas</span><span class="p">):</span> - <span class="sd">"""A read-only union of two inner dicts of MultiAdjacencies.</span> +<span class="w"> </span><span class="sd">"""A read-only union of two inner dicts of MultiAdjacencies.</span> <span class="sd"> The two input dict-of-dict-of-dicts represent the union of</span> <span class="sd"> `G.succ[node]` and `G.pred[node]` for MultiDiGraphs.</span> @@ -708,7 +708,7 @@ <div class="viewcode-block" id="UnionMultiAdjacency"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.classes.coreviews.UnionMultiAdjacency.html#networkx.classes.coreviews.UnionMultiAdjacency">[docs]</a><span class="k">class</span> <span class="nc">UnionMultiAdjacency</span><span class="p">(</span><span class="n">UnionAdjacency</span><span class="p">):</span> - <span class="sd">"""A read-only union of two dict MultiAdjacencies.</span> +<span class="w"> </span><span class="sd">"""A read-only union of two dict MultiAdjacencies.</span> <span class="sd"> The two input dict-of-dict-of-dict-of-dicts represent the union of</span> <span class="sd"> `G.succ` and `G.pred` for MultiDiGraphs. Return values are UnionAdjacency.</span> @@ -879,7 +879,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/classes/digraph.html b/_modules/networkx/classes/digraph.html index 932102bc..58b34f4e 100644 --- a/_modules/networkx/classes/digraph.html +++ b/_modules/networkx/classes/digraph.html @@ -482,7 +482,7 @@ <span class="k">class</span> <span class="nc">_CachedPropertyResetterAdjAndSucc</span><span class="p">:</span> - <span class="sd">"""Data Descriptor class that syncs and resets cached properties adj and succ</span> +<span class="w"> </span><span class="sd">"""Data Descriptor class that syncs and resets cached properties adj and succ</span> <span class="sd"> The cached properties `adj` and `succ` are reset whenever `_adj` or `_succ`</span> <span class="sd"> are set to new objects. In addition, the attributes `_succ` and `_adj`</span> @@ -509,7 +509,7 @@ <span class="k">class</span> <span class="nc">_CachedPropertyResetterPred</span><span class="p">:</span> - <span class="sd">"""Data Descriptor class for _pred that resets ``pred`` cached_property when needed</span> +<span class="w"> </span><span class="sd">"""Data Descriptor class for _pred that resets ``pred`` cached_property when needed</span> <span class="sd"> This assumes that the ``cached_property`` ``G.pred`` should be reset whenever</span> <span class="sd"> ``G._pred`` is set to a new value.</span> @@ -531,7 +531,7 @@ <div class="viewcode-block" id="DiGraph"><a class="viewcode-back" href="../../../reference/classes/digraph.html#networkx.DiGraph">[docs]</a><span class="k">class</span> <span class="nc">DiGraph</span><span class="p">(</span><span class="n">Graph</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Base class for directed graphs.</span> <span class="sd"> A DiGraph stores nodes and edges with optional data, or attributes.</span> @@ -777,7 +777,7 @@ <span class="n">_pred</span> <span class="o">=</span> <span class="n">_CachedPropertyResetterPred</span><span class="p">()</span> <div class="viewcode-block" id="DiGraph.__init__"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.DiGraph.__init__.html#networkx.DiGraph.__init__">[docs]</a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">incoming_graph_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Initialize a graph with edges, name, or graph attributes.</span> +<span class="w"> </span><span class="sd">"""Initialize a graph with edges, name, or graph attributes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -826,7 +826,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">adj</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Graph adjacency object holding the neighbors of each node.</span> +<span class="w"> </span><span class="sd">"""Graph adjacency object holding the neighbors of each node.</span> <span class="sd"> This object is a read-only dict-like structure with node keys</span> <span class="sd"> and neighbor-dict values. The neighbor-dict is keyed by neighbor</span> @@ -845,7 +845,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">succ</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Graph adjacency object holding the successors of each node.</span> +<span class="w"> </span><span class="sd">"""Graph adjacency object holding the successors of each node.</span> <span class="sd"> This object is a read-only dict-like structure with node keys</span> <span class="sd"> and neighbor-dict values. The neighbor-dict is keyed by neighbor</span> @@ -866,7 +866,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">pred</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Graph adjacency object holding the predecessors of each node.</span> +<span class="w"> </span><span class="sd">"""Graph adjacency object holding the predecessors of each node.</span> <span class="sd"> This object is a read-only dict-like structure with node keys</span> <span class="sd"> and neighbor-dict values. The neighbor-dict is keyed by neighbor</span> @@ -881,7 +881,7 @@ <span class="k">return</span> <span class="n">AdjacencyView</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pred</span><span class="p">)</span> <div class="viewcode-block" id="DiGraph.add_node"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.DiGraph.add_node.html#networkx.DiGraph.add_node">[docs]</a> <span class="k">def</span> <span class="nf">add_node</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node_for_adding</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Add a single node `node_for_adding` and update node attributes.</span> +<span class="w"> </span><span class="sd">"""Add a single node `node_for_adding` and update node attributes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -930,7 +930,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">_node</span><span class="p">[</span><span class="n">node_for_adding</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">attr</span><span class="p">)</span></div> <div class="viewcode-block" id="DiGraph.add_nodes_from"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.DiGraph.add_nodes_from.html#networkx.DiGraph.add_nodes_from">[docs]</a> <span class="k">def</span> <span class="nf">add_nodes_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nodes_for_adding</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Add multiple nodes.</span> +<span class="w"> </span><span class="sd">"""Add multiple nodes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1008,7 +1008,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">_node</span><span class="p">[</span><span class="n">n</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">newdict</span><span class="p">)</span></div> <div class="viewcode-block" id="DiGraph.remove_node"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.DiGraph.remove_node.html#networkx.DiGraph.remove_node">[docs]</a> <span class="k">def</span> <span class="nf">remove_node</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span> - <span class="sd">"""Remove node n.</span> +<span class="w"> </span><span class="sd">"""Remove node n.</span> <span class="sd"> Removes the node n and all adjacent edges.</span> <span class="sd"> Attempting to remove a non-existent node will raise an exception.</span> @@ -1050,7 +1050,7 @@ <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pred</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="c1"># remove node from pred</span></div> <div class="viewcode-block" id="DiGraph.remove_nodes_from"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.DiGraph.remove_nodes_from.html#networkx.DiGraph.remove_nodes_from">[docs]</a> <span class="k">def</span> <span class="nf">remove_nodes_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span> - <span class="sd">"""Remove multiple nodes.</span> +<span class="w"> </span><span class="sd">"""Remove multiple nodes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1104,7 +1104,7 @@ <span class="k">pass</span> <span class="c1"># silent failure on remove</span></div> <div class="viewcode-block" id="DiGraph.add_edge"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.DiGraph.add_edge.html#networkx.DiGraph.add_edge">[docs]</a> <span class="k">def</span> <span class="nf">add_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u_of_edge</span><span class="p">,</span> <span class="n">v_of_edge</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Add an edge between u and v.</span> +<span class="w"> </span><span class="sd">"""Add an edge between u and v.</span> <span class="sd"> The nodes u and v will be automatically added if they are</span> <span class="sd"> not already in the graph.</span> @@ -1174,7 +1174,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">_pred</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="n">u</span><span class="p">]</span> <span class="o">=</span> <span class="n">datadict</span></div> <div class="viewcode-block" id="DiGraph.add_edges_from"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.DiGraph.add_edges_from.html#networkx.DiGraph.add_edges_from">[docs]</a> <span class="k">def</span> <span class="nf">add_edges_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ebunch_to_add</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Add all the edges in ebunch_to_add.</span> +<span class="w"> </span><span class="sd">"""Add all the edges in ebunch_to_add.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1256,7 +1256,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">_pred</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="n">u</span><span class="p">]</span> <span class="o">=</span> <span class="n">datadict</span></div> <div class="viewcode-block" id="DiGraph.remove_edge"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.DiGraph.remove_edge.html#networkx.DiGraph.remove_edge">[docs]</a> <span class="k">def</span> <span class="nf">remove_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Remove the edge between u and v.</span> +<span class="w"> </span><span class="sd">"""Remove the edge between u and v.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1289,7 +1289,7 @@ <span class="k">raise</span> <span class="n">NetworkXError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"The edge </span><span class="si">{</span><span class="n">u</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"> not in graph."</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">err</span></div> <div class="viewcode-block" id="DiGraph.remove_edges_from"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.DiGraph.remove_edges_from.html#networkx.DiGraph.remove_edges_from">[docs]</a> <span class="k">def</span> <span class="nf">remove_edges_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ebunch</span><span class="p">):</span> - <span class="sd">"""Remove all edges specified in ebunch.</span> +<span class="w"> </span><span class="sd">"""Remove all edges specified in ebunch.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1321,21 +1321,21 @@ <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pred</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="n">u</span><span class="p">]</span></div> <span class="k">def</span> <span class="nf">has_successor</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Returns True if node u has successor v.</span> +<span class="w"> </span><span class="sd">"""Returns True if node u has successor v.</span> <span class="sd"> This is true if graph has the edge u->v.</span> <span class="sd"> """</span> <span class="k">return</span> <span class="n">u</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_succ</span> <span class="ow">and</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_succ</span><span class="p">[</span><span class="n">u</span><span class="p">]</span> <span class="k">def</span> <span class="nf">has_predecessor</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Returns True if node u has predecessor v.</span> +<span class="w"> </span><span class="sd">"""Returns True if node u has predecessor v.</span> <span class="sd"> This is true if graph has the edge u<-v.</span> <span class="sd"> """</span> <span class="k">return</span> <span class="n">u</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pred</span> <span class="ow">and</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pred</span><span class="p">[</span><span class="n">u</span><span class="p">]</span> <div class="viewcode-block" id="DiGraph.successors"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.DiGraph.successors.html#networkx.DiGraph.successors">[docs]</a> <span class="k">def</span> <span class="nf">successors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span> - <span class="sd">"""Returns an iterator over successor nodes of n.</span> +<span class="w"> </span><span class="sd">"""Returns an iterator over successor nodes of n.</span> <span class="sd"> A successor of n is a node m such that there exists a directed</span> <span class="sd"> edge from n to m.</span> @@ -1367,7 +1367,7 @@ <span class="n">neighbors</span> <span class="o">=</span> <span class="n">successors</span> <div class="viewcode-block" id="DiGraph.predecessors"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.DiGraph.predecessors.html#networkx.DiGraph.predecessors">[docs]</a> <span class="k">def</span> <span class="nf">predecessors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span> - <span class="sd">"""Returns an iterator over predecessor nodes of n.</span> +<span class="w"> </span><span class="sd">"""Returns an iterator over predecessor nodes of n.</span> <span class="sd"> A predecessor of n is a node m such that there exists a directed</span> <span class="sd"> edge from m to n.</span> @@ -1393,7 +1393,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">edges</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""An OutEdgeView of the DiGraph as G.edges or G.edges().</span> +<span class="w"> </span><span class="sd">"""An OutEdgeView of the DiGraph as G.edges or G.edges().</span> <span class="sd"> edges(self, nbunch=None, data=False, default=None)</span> @@ -1463,7 +1463,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">in_edges</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""A view of the in edges of the graph as G.in_edges or G.in_edges().</span> +<span class="w"> </span><span class="sd">"""A view of the in edges of the graph as G.in_edges or G.in_edges().</span> <span class="sd"> in_edges(self, nbunch=None, data=False, default=None):</span> @@ -1503,7 +1503,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">degree</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""A DegreeView for the Graph as G.degree or G.degree().</span> +<span class="w"> </span><span class="sd">"""A DegreeView for the Graph as G.degree or G.degree().</span> <span class="sd"> The node degree is the number of edges adjacent to the node.</span> <span class="sd"> The weighted node degree is the sum of the edge weights for</span> @@ -1547,7 +1547,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">in_degree</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""An InDegreeView for (node, in_degree) or in_degree for single node.</span> +<span class="w"> </span><span class="sd">"""An InDegreeView for (node, in_degree) or in_degree for single node.</span> <span class="sd"> The node in_degree is the number of edges pointing to the node.</span> <span class="sd"> The weighted node degree is the sum of the edge weights for</span> @@ -1594,7 +1594,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">out_degree</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""An OutDegreeView for (node, out_degree)</span> +<span class="w"> </span><span class="sd">"""An OutDegreeView for (node, out_degree)</span> <span class="sd"> The node out_degree is the number of edges pointing out of the node.</span> <span class="sd"> The weighted node degree is the sum of the edge weights for</span> @@ -1640,7 +1640,7 @@ <span class="k">return</span> <span class="n">OutDegreeView</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <div class="viewcode-block" id="DiGraph.clear"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.DiGraph.clear.html#networkx.DiGraph.clear">[docs]</a> <span class="k">def</span> <span class="nf">clear</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Remove all nodes and edges from the graph.</span> +<span class="w"> </span><span class="sd">"""Remove all nodes and edges from the graph.</span> <span class="sd"> This also removes the name, and all graph, node, and edge attributes.</span> @@ -1660,7 +1660,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span></div> <div class="viewcode-block" id="DiGraph.clear_edges"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.DiGraph.clear_edges.html#networkx.DiGraph.clear_edges">[docs]</a> <span class="k">def</span> <span class="nf">clear_edges</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Remove all edges from the graph without altering nodes.</span> +<span class="w"> </span><span class="sd">"""Remove all edges from the graph without altering nodes.</span> <span class="sd"> Examples</span> <span class="sd"> --------</span> @@ -1678,15 +1678,15 @@ <span class="n">successor_dict</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span></div> <span class="k">def</span> <span class="nf">is_multigraph</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns True if graph is a multigraph, False otherwise."""</span> +<span class="w"> </span><span class="sd">"""Returns True if graph is a multigraph, False otherwise."""</span> <span class="k">return</span> <span class="kc">False</span> <span class="k">def</span> <span class="nf">is_directed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns True if graph is directed, False otherwise."""</span> +<span class="w"> </span><span class="sd">"""Returns True if graph is directed, False otherwise."""</span> <span class="k">return</span> <span class="kc">True</span> <div class="viewcode-block" id="DiGraph.to_undirected"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.DiGraph.to_undirected.html#networkx.DiGraph.to_undirected">[docs]</a> <span class="k">def</span> <span class="nf">to_undirected</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">reciprocal</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">as_view</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns an undirected representation of the digraph.</span> +<span class="w"> </span><span class="sd">"""Returns an undirected representation of the digraph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1765,7 +1765,7 @@ <span class="k">return</span> <span class="n">G</span></div> <div class="viewcode-block" id="DiGraph.reverse"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.DiGraph.reverse.html#networkx.DiGraph.reverse">[docs]</a> <span class="k">def</span> <span class="nf">reverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns the reverse of the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the reverse of the graph.</span> <span class="sd"> The reverse is a graph with the same nodes and edges</span> <span class="sd"> but with the directions of the edges reversed.</span> @@ -1835,7 +1835,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/classes/filters.html b/_modules/networkx/classes/filters.html index 49431334..8c4bfc87 100644 --- a/_modules/networkx/classes/filters.html +++ b/_modules/networkx/classes/filters.html @@ -587,7 +587,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/classes/function.html b/_modules/networkx/classes/function.html index a61c946a..bec045a0 100644 --- a/_modules/networkx/classes/function.html +++ b/_modules/networkx/classes/function.html @@ -515,12 +515,12 @@ <div class="viewcode-block" id="nodes"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.nodes.html#networkx.classes.function.nodes">[docs]</a><span class="k">def</span> <span class="nf">nodes</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns an iterator over the graph nodes."""</span> +<span class="w"> </span><span class="sd">"""Returns an iterator over the graph nodes."""</span> <span class="k">return</span> <span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">()</span></div> <div class="viewcode-block" id="edges"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.edges.html#networkx.classes.function.edges">[docs]</a><span class="k">def</span> <span class="nf">edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nbunch</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns an edge view of edges incident to nodes in nbunch.</span> +<span class="w"> </span><span class="sd">"""Returns an edge view of edges incident to nodes in nbunch.</span> <span class="sd"> Return all edges if nbunch is unspecified or nbunch=None.</span> @@ -530,29 +530,29 @@ <div class="viewcode-block" id="degree"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.degree.html#networkx.classes.function.degree">[docs]</a><span class="k">def</span> <span class="nf">degree</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nbunch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a degree view of single node or of nbunch of nodes.</span> +<span class="w"> </span><span class="sd">"""Returns a degree view of single node or of nbunch of nodes.</span> <span class="sd"> If nbunch is omitted, then return degrees of *all* nodes.</span> <span class="sd"> """</span> <span class="k">return</span> <span class="n">G</span><span class="o">.</span><span class="n">degree</span><span class="p">(</span><span class="n">nbunch</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span></div> <div class="viewcode-block" id="neighbors"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.neighbors.html#networkx.classes.function.neighbors">[docs]</a><span class="k">def</span> <span class="nf">neighbors</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span> - <span class="sd">"""Returns a list of nodes connected to node n."""</span> +<span class="w"> </span><span class="sd">"""Returns a list of nodes connected to node n."""</span> <span class="k">return</span> <span class="n">G</span><span class="o">.</span><span class="n">neighbors</span><span class="p">(</span><span class="n">n</span><span class="p">)</span></div> <div class="viewcode-block" id="number_of_nodes"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.number_of_nodes.html#networkx.classes.function.number_of_nodes">[docs]</a><span class="k">def</span> <span class="nf">number_of_nodes</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the number of nodes in the graph."""</span> +<span class="w"> </span><span class="sd">"""Returns the number of nodes in the graph."""</span> <span class="k">return</span> <span class="n">G</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">()</span></div> <div class="viewcode-block" id="number_of_edges"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.number_of_edges.html#networkx.classes.function.number_of_edges">[docs]</a><span class="k">def</span> <span class="nf">number_of_edges</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the number of edges in the graph."""</span> +<span class="w"> </span><span class="sd">"""Returns the number of edges in the graph."""</span> <span class="k">return</span> <span class="n">G</span><span class="o">.</span><span class="n">number_of_edges</span><span class="p">()</span></div> <div class="viewcode-block" id="density"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.density.html#networkx.classes.function.density">[docs]</a><span class="k">def</span> <span class="nf">density</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the density of a graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the density of a graph.</span> <span class="sd"> The density for undirected graphs is</span> @@ -587,7 +587,7 @@ <div class="viewcode-block" id="degree_histogram"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.degree_histogram.html#networkx.classes.function.degree_histogram">[docs]</a><span class="k">def</span> <span class="nf">degree_histogram</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns a list of the frequency of each degree value.</span> +<span class="w"> </span><span class="sd">"""Returns a list of the frequency of each degree value.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -610,17 +610,17 @@ <div class="viewcode-block" id="is_directed"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.is_directed.html#networkx.classes.function.is_directed">[docs]</a><span class="k">def</span> <span class="nf">is_directed</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Return True if graph is directed."""</span> +<span class="w"> </span><span class="sd">"""Return True if graph is directed."""</span> <span class="k">return</span> <span class="n">G</span><span class="o">.</span><span class="n">is_directed</span><span class="p">()</span></div> <span class="k">def</span> <span class="nf">frozen</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> - <span class="sd">"""Dummy method for raising errors when trying to modify frozen graphs"""</span> +<span class="w"> </span><span class="sd">"""Dummy method for raising errors when trying to modify frozen graphs"""</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">"Frozen graph can't be modified"</span><span class="p">)</span> <div class="viewcode-block" id="freeze"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.freeze.html#networkx.classes.function.freeze">[docs]</a><span class="k">def</span> <span class="nf">freeze</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Modify graph to prevent further change by adding or removing</span> +<span class="w"> </span><span class="sd">"""Modify graph to prevent further change by adding or removing</span> <span class="sd"> nodes or edges.</span> <span class="sd"> Node and edge data can still be modified.</span> @@ -670,7 +670,7 @@ <div class="viewcode-block" id="is_frozen"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.is_frozen.html#networkx.classes.function.is_frozen">[docs]</a><span class="k">def</span> <span class="nf">is_frozen</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns True if graph is frozen.</span> +<span class="w"> </span><span class="sd">"""Returns True if graph is frozen.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -688,7 +688,7 @@ <div class="viewcode-block" id="add_star"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.add_star.html#networkx.classes.function.add_star">[docs]</a><span class="k">def</span> <span class="nf">add_star</span><span class="p">(</span><span class="n">G_to_add_to</span><span class="p">,</span> <span class="n">nodes_for_star</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Add a star to Graph G_to_add_to.</span> +<span class="w"> </span><span class="sd">"""Add a star to Graph G_to_add_to.</span> <span class="sd"> The first node in `nodes_for_star` is the middle of the star.</span> <span class="sd"> It is connected to all other nodes.</span> @@ -723,7 +723,7 @@ <div class="viewcode-block" id="add_path"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.add_path.html#networkx.classes.function.add_path">[docs]</a><span class="k">def</span> <span class="nf">add_path</span><span class="p">(</span><span class="n">G_to_add_to</span><span class="p">,</span> <span class="n">nodes_for_path</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Add a path to the Graph G_to_add_to.</span> +<span class="w"> </span><span class="sd">"""Add a path to the Graph G_to_add_to.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -755,7 +755,7 @@ <div class="viewcode-block" id="add_cycle"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.add_cycle.html#networkx.classes.function.add_cycle">[docs]</a><span class="k">def</span> <span class="nf">add_cycle</span><span class="p">(</span><span class="n">G_to_add_to</span><span class="p">,</span> <span class="n">nodes_for_cycle</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Add a cycle to the Graph G_to_add_to.</span> +<span class="w"> </span><span class="sd">"""Add a cycle to the Graph G_to_add_to.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -789,7 +789,7 @@ <div class="viewcode-block" id="subgraph"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.subgraph.html#networkx.classes.function.subgraph">[docs]</a><span class="k">def</span> <span class="nf">subgraph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nbunch</span><span class="p">):</span> - <span class="sd">"""Returns the subgraph induced on nodes in nbunch.</span> +<span class="w"> </span><span class="sd">"""Returns the subgraph induced on nodes in nbunch.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -812,7 +812,7 @@ <div class="viewcode-block" id="induced_subgraph"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.induced_subgraph.html#networkx.classes.function.induced_subgraph">[docs]</a><span class="k">def</span> <span class="nf">induced_subgraph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nbunch</span><span class="p">):</span> - <span class="sd">"""Returns a SubGraph view of `G` showing only nodes in nbunch.</span> +<span class="w"> </span><span class="sd">"""Returns a SubGraph view of `G` showing only nodes in nbunch.</span> <span class="sd"> The induced subgraph of a graph on a set of nodes N is the</span> <span class="sd"> graph with nodes N and edges from G which have both ends in N.</span> @@ -858,7 +858,7 @@ <div class="viewcode-block" id="edge_subgraph"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.edge_subgraph.html#networkx.classes.function.edge_subgraph">[docs]</a><span class="k">def</span> <span class="nf">edge_subgraph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">edges</span><span class="p">):</span> - <span class="sd">"""Returns a view of the subgraph induced by the specified edges.</span> +<span class="w"> </span><span class="sd">"""Returns a view of the subgraph induced by the specified edges.</span> <span class="sd"> The induced subgraph contains each edge in `edges` and each</span> <span class="sd"> node incident to any of those edges.</span> @@ -917,7 +917,7 @@ <div class="viewcode-block" id="restricted_view"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.restricted_view.html#networkx.classes.function.restricted_view">[docs]</a><span class="k">def</span> <span class="nf">restricted_view</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">edges</span><span class="p">):</span> - <span class="sd">"""Returns a view of `G` with hidden nodes and edges.</span> +<span class="w"> </span><span class="sd">"""Returns a view of `G` with hidden nodes and edges.</span> <span class="sd"> The resulting subgraph filters out node `nodes` and edges `edges`.</span> <span class="sd"> Filtered out nodes also filter out any of their edges.</span> @@ -973,7 +973,7 @@ <div class="viewcode-block" id="to_directed"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.to_directed.html#networkx.classes.function.to_directed">[docs]</a><span class="k">def</span> <span class="nf">to_directed</span><span class="p">(</span><span class="n">graph</span><span class="p">):</span> - <span class="sd">"""Returns a directed view of the graph `graph`.</span> +<span class="w"> </span><span class="sd">"""Returns a directed view of the graph `graph`.</span> <span class="sd"> Identical to graph.to_directed(as_view=True)</span> <span class="sd"> Note that graph.to_directed defaults to `as_view=False`</span> @@ -983,7 +983,7 @@ <div class="viewcode-block" id="to_undirected"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.to_undirected.html#networkx.classes.function.to_undirected">[docs]</a><span class="k">def</span> <span class="nf">to_undirected</span><span class="p">(</span><span class="n">graph</span><span class="p">):</span> - <span class="sd">"""Returns an undirected view of the graph `graph`.</span> +<span class="w"> </span><span class="sd">"""Returns an undirected view of the graph `graph`.</span> <span class="sd"> Identical to graph.to_undirected(as_view=True)</span> <span class="sd"> Note that graph.to_undirected defaults to `as_view=False`</span> @@ -993,7 +993,7 @@ <div class="viewcode-block" id="create_empty_copy"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.create_empty_copy.html#networkx.classes.function.create_empty_copy">[docs]</a><span class="k">def</span> <span class="nf">create_empty_copy</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">with_data</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns a copy of the graph G with all of the edges removed.</span> +<span class="w"> </span><span class="sd">"""Returns a copy of the graph G with all of the edges removed.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1016,7 +1016,7 @@ <div class="viewcode-block" id="set_node_attributes"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.set_node_attributes.html#networkx.classes.function.set_node_attributes">[docs]</a><span class="k">def</span> <span class="nf">set_node_attributes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Sets node attributes from a given value or dictionary of values.</span> +<span class="w"> </span><span class="sd">"""Sets node attributes from a given value or dictionary of values.</span> <span class="sd"> .. Warning:: The call order of arguments `values` and `name`</span> <span class="sd"> switched between v1.x & v2.x.</span> @@ -1116,7 +1116,7 @@ <div class="viewcode-block" id="get_node_attributes"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.get_node_attributes.html#networkx.classes.function.get_node_attributes">[docs]</a><span class="k">def</span> <span class="nf">get_node_attributes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span> - <span class="sd">"""Get node attributes from graph</span> +<span class="w"> </span><span class="sd">"""Get node attributes from graph</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1141,7 +1141,7 @@ <div class="viewcode-block" id="set_edge_attributes"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.set_edge_attributes.html#networkx.classes.function.set_edge_attributes">[docs]</a><span class="k">def</span> <span class="nf">set_edge_attributes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Sets edge attributes from a given value or dictionary of values.</span> +<span class="w"> </span><span class="sd">"""Sets edge attributes from a given value or dictionary of values.</span> <span class="sd"> .. Warning:: The call order of arguments `values` and `name`</span> <span class="sd"> switched between v1.x & v2.x.</span> @@ -1278,7 +1278,7 @@ <div class="viewcode-block" id="get_edge_attributes"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.get_edge_attributes.html#networkx.classes.function.get_edge_attributes">[docs]</a><span class="k">def</span> <span class="nf">get_edge_attributes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span> - <span class="sd">"""Get edge attributes from graph</span> +<span class="w"> </span><span class="sd">"""Get edge attributes from graph</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1309,7 +1309,7 @@ <div class="viewcode-block" id="all_neighbors"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.all_neighbors.html#networkx.classes.function.all_neighbors">[docs]</a><span class="k">def</span> <span class="nf">all_neighbors</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">node</span><span class="p">):</span> - <span class="sd">"""Returns all of the neighbors of a node in the graph.</span> +<span class="w"> </span><span class="sd">"""Returns all of the neighbors of a node in the graph.</span> <span class="sd"> If the graph is directed returns predecessors as well as successors.</span> @@ -1334,7 +1334,7 @@ <div class="viewcode-block" id="non_neighbors"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.non_neighbors.html#networkx.classes.function.non_neighbors">[docs]</a><span class="k">def</span> <span class="nf">non_neighbors</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">node</span><span class="p">):</span> - <span class="sd">"""Returns the non-neighbors of the node in the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the non-neighbors of the node in the graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1354,7 +1354,7 @@ <div class="viewcode-block" id="non_edges"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.non_edges.html#networkx.classes.function.non_edges">[docs]</a><span class="k">def</span> <span class="nf">non_edges</span><span class="p">(</span><span class="n">graph</span><span class="p">):</span> - <span class="sd">"""Returns the non-existent edges in the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the non-existent edges in the graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1380,7 +1380,7 @@ <div class="viewcode-block" id="common_neighbors"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.common_neighbors.html#networkx.classes.function.common_neighbors">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">common_neighbors</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Returns the common neighbors of two nodes in a graph.</span> +<span class="w"> </span><span class="sd">"""Returns the common neighbors of two nodes in a graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1417,7 +1417,7 @@ <div class="viewcode-block" id="is_weighted"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.is_weighted.html#networkx.classes.function.is_weighted">[docs]</a><span class="k">def</span> <span class="nf">is_weighted</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">edge</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns True if `G` has weighted edges.</span> +<span class="w"> </span><span class="sd">"""Returns True if `G` has weighted edges.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1470,7 +1470,7 @@ <div class="viewcode-block" id="is_negatively_weighted"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.is_negatively_weighted.html#networkx.classes.function.is_negatively_weighted">[docs]</a><span class="k">def</span> <span class="nf">is_negatively_weighted</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">edge</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns True if `G` has negatively weighted edges.</span> +<span class="w"> </span><span class="sd">"""Returns True if `G` has negatively weighted edges.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1523,7 +1523,7 @@ <div class="viewcode-block" id="is_empty"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.is_empty.html#networkx.classes.function.is_empty">[docs]</a><span class="k">def</span> <span class="nf">is_empty</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns True if `G` has no edges.</span> +<span class="w"> </span><span class="sd">"""Returns True if `G` has no edges.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1546,7 +1546,7 @@ <div class="viewcode-block" id="nodes_with_selfloops"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.nodes_with_selfloops.html#networkx.classes.function.nodes_with_selfloops">[docs]</a><span class="k">def</span> <span class="nf">nodes_with_selfloops</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns an iterator over nodes with self loops.</span> +<span class="w"> </span><span class="sd">"""Returns an iterator over nodes with self loops.</span> <span class="sd"> A node with a self loop has an edge with both ends adjacent</span> <span class="sd"> to that node.</span> @@ -1573,7 +1573,7 @@ <div class="viewcode-block" id="selfloop_edges"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.selfloop_edges.html#networkx.classes.function.selfloop_edges">[docs]</a><span class="k">def</span> <span class="nf">selfloop_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns an iterator over selfloop edges.</span> +<span class="w"> </span><span class="sd">"""Returns an iterator over selfloop edges.</span> <span class="sd"> A selfloop edge has the same node at both ends.</span> @@ -1672,7 +1672,7 @@ <div class="viewcode-block" id="number_of_selfloops"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.number_of_selfloops.html#networkx.classes.function.number_of_selfloops">[docs]</a><span class="k">def</span> <span class="nf">number_of_selfloops</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the number of selfloop edges.</span> +<span class="w"> </span><span class="sd">"""Returns the number of selfloop edges.</span> <span class="sd"> A selfloop edge has the same node at both ends.</span> @@ -1697,7 +1697,7 @@ <div class="viewcode-block" id="is_path"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.is_path.html#networkx.classes.function.is_path">[docs]</a><span class="k">def</span> <span class="nf">is_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span> - <span class="sd">"""Returns whether or not the specified path exists.</span> +<span class="w"> </span><span class="sd">"""Returns whether or not the specified path exists.</span> <span class="sd"> For it to return True, every node on the path must exist and</span> <span class="sd"> each consecutive pair must be connected via one or more edges.</span> @@ -1723,7 +1723,7 @@ <div class="viewcode-block" id="path_weight"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.path_weight.html#networkx.classes.function.path_weight">[docs]</a><span class="k">def</span> <span class="nf">path_weight</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> - <span class="sd">"""Returns total cost associated with specified path and weight</span> +<span class="w"> </span><span class="sd">"""Returns total cost associated with specified path and weight</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1809,7 +1809,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/classes/graph.html b/_modules/networkx/classes/graph.html index e58ec4ea..84a65957 100644 --- a/_modules/networkx/classes/graph.html +++ b/_modules/networkx/classes/graph.html @@ -483,7 +483,7 @@ <span class="k">class</span> <span class="nc">_CachedPropertyResetterAdj</span><span class="p">:</span> - <span class="sd">"""Data Descriptor class for _adj that resets ``adj`` cached_property when needed</span> +<span class="w"> </span><span class="sd">"""Data Descriptor class for _adj that resets ``adj`` cached_property when needed</span> <span class="sd"> This assumes that the ``cached_property`` ``G.adj`` should be reset whenever</span> <span class="sd"> ``G._adj`` is set to a new value.</span> @@ -505,7 +505,7 @@ <span class="k">class</span> <span class="nc">_CachedPropertyResetterNode</span><span class="p">:</span> - <span class="sd">"""Data Descriptor class for _node that resets ``nodes`` cached_property when needed</span> +<span class="w"> </span><span class="sd">"""Data Descriptor class for _node that resets ``nodes`` cached_property when needed</span> <span class="sd"> This assumes that the ``cached_property`` ``G.node`` should be reset whenever</span> <span class="sd"> ``G._node`` is set to a new value.</span> @@ -527,7 +527,7 @@ <div class="viewcode-block" id="Graph"><a class="viewcode-back" href="../../../reference/classes/graph.html#networkx.Graph">[docs]</a><span class="k">class</span> <span class="nc">Graph</span><span class="p">:</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Base class for undirected graphs.</span> <span class="sd"> A Graph stores nodes and edges with optional data, or attributes.</span> @@ -777,7 +777,7 @@ <span class="n">graph_attr_dict_factory</span> <span class="o">=</span> <span class="nb">dict</span> <span class="k">def</span> <span class="nf">to_directed_class</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns the class to use for empty directed copies.</span> +<span class="w"> </span><span class="sd">"""Returns the class to use for empty directed copies.</span> <span class="sd"> If you subclass the base classes, use this to designate</span> <span class="sd"> what directed class to use for `to_directed()` copies.</span> @@ -785,7 +785,7 @@ <span class="k">return</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span> <span class="k">def</span> <span class="nf">to_undirected_class</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns the class to use for empty undirected copies.</span> +<span class="w"> </span><span class="sd">"""Returns the class to use for empty undirected copies.</span> <span class="sd"> If you subclass the base classes, use this to designate</span> <span class="sd"> what directed class to use for `to_directed()` copies.</span> @@ -793,7 +793,7 @@ <span class="k">return</span> <span class="n">Graph</span> <div class="viewcode-block" id="Graph.__init__"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.__init__.html#networkx.Graph.__init__">[docs]</a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">incoming_graph_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Initialize a graph with edges, name, or graph attributes.</span> +<span class="w"> </span><span class="sd">"""Initialize a graph with edges, name, or graph attributes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -836,7 +836,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">adj</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Graph adjacency object holding the neighbors of each node.</span> +<span class="w"> </span><span class="sd">"""Graph adjacency object holding the neighbors of each node.</span> <span class="sd"> This object is a read-only dict-like structure with node keys</span> <span class="sd"> and neighbor-dict values. The neighbor-dict is keyed by neighbor</span> @@ -855,7 +855,7 @@ <span class="nd">@property</span> <span class="k">def</span> <span class="nf">name</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""String identifier of the graph.</span> +<span class="w"> </span><span class="sd">"""String identifier of the graph.</span> <span class="sd"> This graph attribute appears in the attribute dict G.graph</span> <span class="sd"> keyed by the string `"name"`. as well as an attribute (technically</span> @@ -868,7 +868,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">[</span><span class="s2">"name"</span><span class="p">]</span> <span class="o">=</span> <span class="n">s</span> <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns a short summary of the graph.</span> +<span class="w"> </span><span class="sd">"""Returns a short summary of the graph.</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> @@ -896,7 +896,7 @@ <span class="p">)</span> <div class="viewcode-block" id="Graph.__iter__"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.__iter__.html#networkx.Graph.__iter__">[docs]</a> <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Iterate over the nodes. Use: 'for n in G'.</span> +<span class="w"> </span><span class="sd">"""Iterate over the nodes. Use: 'for n in G'.</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> @@ -914,7 +914,7 @@ <span class="k">return</span> <span class="nb">iter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_node</span><span class="p">)</span></div> <div class="viewcode-block" id="Graph.__contains__"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.__contains__.html#networkx.Graph.__contains__">[docs]</a> <span class="k">def</span> <span class="fm">__contains__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span> - <span class="sd">"""Returns True if n is a node, False otherwise. Use: 'n in G'.</span> +<span class="w"> </span><span class="sd">"""Returns True if n is a node, False otherwise. Use: 'n in G'.</span> <span class="sd"> Examples</span> <span class="sd"> --------</span> @@ -928,7 +928,7 @@ <span class="k">return</span> <span class="kc">False</span></div> <div class="viewcode-block" id="Graph.__len__"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.__len__.html#networkx.Graph.__len__">[docs]</a> <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns the number of nodes in the graph. Use: 'len(G)'.</span> +<span class="w"> </span><span class="sd">"""Returns the number of nodes in the graph. Use: 'len(G)'.</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> @@ -950,7 +950,7 @@ <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_node</span><span class="p">)</span></div> <div class="viewcode-block" id="Graph.__getitem__"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.__getitem__.html#networkx.Graph.__getitem__">[docs]</a> <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span> - <span class="sd">"""Returns a dict of neighbors of node n. Use: 'G[n]'.</span> +<span class="w"> </span><span class="sd">"""Returns a dict of neighbors of node n. Use: 'G[n]'.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -976,7 +976,7 @@ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">adj</span><span class="p">[</span><span class="n">n</span><span class="p">]</span></div> <div class="viewcode-block" id="Graph.add_node"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.add_node.html#networkx.Graph.add_node">[docs]</a> <span class="k">def</span> <span class="nf">add_node</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node_for_adding</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Add a single node `node_for_adding` and update node attributes.</span> +<span class="w"> </span><span class="sd">"""Add a single node `node_for_adding` and update node attributes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1024,7 +1024,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">_node</span><span class="p">[</span><span class="n">node_for_adding</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">attr</span><span class="p">)</span></div> <div class="viewcode-block" id="Graph.add_nodes_from"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.add_nodes_from.html#networkx.Graph.add_nodes_from">[docs]</a> <span class="k">def</span> <span class="nf">add_nodes_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nodes_for_adding</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Add multiple nodes.</span> +<span class="w"> </span><span class="sd">"""Add multiple nodes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1101,7 +1101,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">_node</span><span class="p">[</span><span class="n">n</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">newdict</span><span class="p">)</span></div> <div class="viewcode-block" id="Graph.remove_node"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.remove_node.html#networkx.Graph.remove_node">[docs]</a> <span class="k">def</span> <span class="nf">remove_node</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span> - <span class="sd">"""Remove node n.</span> +<span class="w"> </span><span class="sd">"""Remove node n.</span> <span class="sd"> Removes the node n and all adjacent edges.</span> <span class="sd"> Attempting to remove a non-existent node will raise an exception.</span> @@ -1141,7 +1141,7 @@ <span class="k">del</span> <span class="n">adj</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="c1"># now remove node</span></div> <div class="viewcode-block" id="Graph.remove_nodes_from"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.remove_nodes_from.html#networkx.Graph.remove_nodes_from">[docs]</a> <span class="k">def</span> <span class="nf">remove_nodes_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span> - <span class="sd">"""Remove multiple nodes.</span> +<span class="w"> </span><span class="sd">"""Remove multiple nodes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1194,7 +1194,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">nodes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""A NodeView of the Graph as G.nodes or G.nodes().</span> +<span class="w"> </span><span class="sd">"""A NodeView of the Graph as G.nodes or G.nodes().</span> <span class="sd"> Can be used as `G.nodes` for data lookup and for set-like operations.</span> <span class="sd"> Can also be used as `G.nodes(data='color', default=None)` to return a</span> @@ -1286,7 +1286,7 @@ <span class="k">return</span> <span class="n">NodeView</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <div class="viewcode-block" id="Graph.number_of_nodes"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.number_of_nodes.html#networkx.Graph.number_of_nodes">[docs]</a> <span class="k">def</span> <span class="nf">number_of_nodes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns the number of nodes in the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the number of nodes in the graph.</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> @@ -1307,7 +1307,7 @@ <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_node</span><span class="p">)</span></div> <div class="viewcode-block" id="Graph.order"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.order.html#networkx.Graph.order">[docs]</a> <span class="k">def</span> <span class="nf">order</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns the number of nodes in the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the number of nodes in the graph.</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> @@ -1328,7 +1328,7 @@ <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_node</span><span class="p">)</span></div> <div class="viewcode-block" id="Graph.has_node"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.has_node.html#networkx.Graph.has_node">[docs]</a> <span class="k">def</span> <span class="nf">has_node</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span> - <span class="sd">"""Returns True if the graph contains the node n.</span> +<span class="w"> </span><span class="sd">"""Returns True if the graph contains the node n.</span> <span class="sd"> Identical to `n in G`</span> @@ -1354,7 +1354,7 @@ <span class="k">return</span> <span class="kc">False</span></div> <div class="viewcode-block" id="Graph.add_edge"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.add_edge.html#networkx.Graph.add_edge">[docs]</a> <span class="k">def</span> <span class="nf">add_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u_of_edge</span><span class="p">,</span> <span class="n">v_of_edge</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Add an edge between u and v.</span> +<span class="w"> </span><span class="sd">"""Add an edge between u and v.</span> <span class="sd"> The nodes u and v will be automatically added if they are</span> <span class="sd"> not already in the graph.</span> @@ -1422,7 +1422,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="n">u</span><span class="p">]</span> <span class="o">=</span> <span class="n">datadict</span></div> <div class="viewcode-block" id="Graph.add_edges_from"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.add_edges_from.html#networkx.Graph.add_edges_from">[docs]</a> <span class="k">def</span> <span class="nf">add_edges_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ebunch_to_add</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Add all the edges in ebunch_to_add.</span> +<span class="w"> </span><span class="sd">"""Add all the edges in ebunch_to_add.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1502,7 +1502,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="n">u</span><span class="p">]</span> <span class="o">=</span> <span class="n">datadict</span></div> <div class="viewcode-block" id="Graph.add_weighted_edges_from"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.add_weighted_edges_from.html#networkx.Graph.add_weighted_edges_from">[docs]</a> <span class="k">def</span> <span class="nf">add_weighted_edges_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ebunch_to_add</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Add weighted edges in `ebunch_to_add` with specified weight attr</span> +<span class="w"> </span><span class="sd">"""Add weighted edges in `ebunch_to_add` with specified weight attr</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1552,7 +1552,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">(((</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="p">{</span><span class="n">weight</span><span class="p">:</span> <span class="n">d</span><span class="p">})</span> <span class="k">for</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">ebunch_to_add</span><span class="p">),</span> <span class="o">**</span><span class="n">attr</span><span class="p">)</span></div> <div class="viewcode-block" id="Graph.remove_edge"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.remove_edge.html#networkx.Graph.remove_edge">[docs]</a> <span class="k">def</span> <span class="nf">remove_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Remove the edge between u and v.</span> +<span class="w"> </span><span class="sd">"""Remove the edge between u and v.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1585,7 +1585,7 @@ <span class="k">raise</span> <span class="n">NetworkXError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"The edge </span><span class="si">{</span><span class="n">u</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"> is not in the graph"</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">err</span></div> <div class="viewcode-block" id="Graph.remove_edges_from"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.remove_edges_from.html#networkx.Graph.remove_edges_from">[docs]</a> <span class="k">def</span> <span class="nf">remove_edges_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ebunch</span><span class="p">):</span> - <span class="sd">"""Remove all edges specified in ebunch.</span> +<span class="w"> </span><span class="sd">"""Remove all edges specified in ebunch.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1619,7 +1619,7 @@ <span class="k">del</span> <span class="n">adj</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="n">u</span><span class="p">]</span></div> <div class="viewcode-block" id="Graph.update"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.update.html#networkx.Graph.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">edges</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Update the graph using nodes/edges/graphs as input.</span> +<span class="w"> </span><span class="sd">"""Update the graph using nodes/edges/graphs as input.</span> <span class="sd"> Like dict.update, this method takes a graph as input, adding the</span> <span class="sd"> graph's nodes and edges to this graph. It can also take two inputs:</span> @@ -1735,7 +1735,7 @@ <span class="k">raise</span> <span class="n">NetworkXError</span><span class="p">(</span><span class="s2">"update needs nodes or edges input"</span><span class="p">)</span></div> <div class="viewcode-block" id="Graph.has_edge"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.has_edge.html#networkx.Graph.has_edge">[docs]</a> <span class="k">def</span> <span class="nf">has_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Returns True if the edge (u, v) is in the graph.</span> +<span class="w"> </span><span class="sd">"""Returns True if the edge (u, v) is in the graph.</span> <span class="sd"> This is the same as `v in G[u]` without KeyError exceptions.</span> @@ -1776,7 +1776,7 @@ <span class="k">return</span> <span class="kc">False</span></div> <div class="viewcode-block" id="Graph.neighbors"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.neighbors.html#networkx.Graph.neighbors">[docs]</a> <span class="k">def</span> <span class="nf">neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span> - <span class="sd">"""Returns an iterator over all neighbors of node n.</span> +<span class="w"> </span><span class="sd">"""Returns an iterator over all neighbors of node n.</span> <span class="sd"> This is identical to `iter(G[n])`</span> @@ -1820,7 +1820,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">edges</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""An EdgeView of the Graph as G.edges or G.edges().</span> +<span class="w"> </span><span class="sd">"""An EdgeView of the Graph as G.edges or G.edges().</span> <span class="sd"> edges(self, nbunch=None, data=False, default=None)</span> @@ -1876,7 +1876,7 @@ <span class="k">return</span> <span class="n">EdgeView</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <div class="viewcode-block" id="Graph.get_edge_data"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.get_edge_data.html#networkx.Graph.get_edge_data">[docs]</a> <span class="k">def</span> <span class="nf">get_edge_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the attribute dictionary associated with edge (u, v).</span> +<span class="w"> </span><span class="sd">"""Returns the attribute dictionary associated with edge (u, v).</span> <span class="sd"> This is identical to `G[u][v]` except the default is returned</span> <span class="sd"> instead of an exception if the edge doesn't exist.</span> @@ -1922,7 +1922,7 @@ <span class="k">return</span> <span class="n">default</span></div> <div class="viewcode-block" id="Graph.adjacency"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.adjacency.html#networkx.Graph.adjacency">[docs]</a> <span class="k">def</span> <span class="nf">adjacency</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns an iterator over (node, adjacency dict) tuples for all nodes.</span> +<span class="w"> </span><span class="sd">"""Returns an iterator over (node, adjacency dict) tuples for all nodes.</span> <span class="sd"> For directed graphs, only outgoing neighbors/adjacencies are included.</span> @@ -1943,7 +1943,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">degree</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""A DegreeView for the Graph as G.degree or G.degree().</span> +<span class="w"> </span><span class="sd">"""A DegreeView for the Graph as G.degree or G.degree().</span> <span class="sd"> The node degree is the number of edges adjacent to the node.</span> <span class="sd"> The weighted node degree is the sum of the edge weights for</span> @@ -1980,7 +1980,7 @@ <span class="k">return</span> <span class="n">DegreeView</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <div class="viewcode-block" id="Graph.clear"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.clear.html#networkx.Graph.clear">[docs]</a> <span class="k">def</span> <span class="nf">clear</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Remove all nodes and edges from the graph.</span> +<span class="w"> </span><span class="sd">"""Remove all nodes and edges from the graph.</span> <span class="sd"> This also removes the name, and all graph, node, and edge attributes.</span> @@ -1999,7 +1999,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span></div> <div class="viewcode-block" id="Graph.clear_edges"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.clear_edges.html#networkx.Graph.clear_edges">[docs]</a> <span class="k">def</span> <span class="nf">clear_edges</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Remove all edges from the graph without altering nodes.</span> +<span class="w"> </span><span class="sd">"""Remove all edges from the graph without altering nodes.</span> <span class="sd"> Examples</span> <span class="sd"> --------</span> @@ -2014,15 +2014,15 @@ <span class="n">neighbours_dict</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span></div> <span class="k">def</span> <span class="nf">is_multigraph</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns True if graph is a multigraph, False otherwise."""</span> +<span class="w"> </span><span class="sd">"""Returns True if graph is a multigraph, False otherwise."""</span> <span class="k">return</span> <span class="kc">False</span> <span class="k">def</span> <span class="nf">is_directed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns True if graph is directed, False otherwise."""</span> +<span class="w"> </span><span class="sd">"""Returns True if graph is directed, False otherwise."""</span> <span class="k">return</span> <span class="kc">False</span> <div class="viewcode-block" id="Graph.copy"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.copy.html#networkx.Graph.copy">[docs]</a> <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">as_view</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns a copy of the graph.</span> +<span class="w"> </span><span class="sd">"""Returns a copy of the graph.</span> <span class="sd"> The copy method by default returns an independent shallow copy</span> <span class="sd"> of the graph and attributes. That is, if an attribute is a</span> @@ -2111,7 +2111,7 @@ <span class="k">return</span> <span class="n">G</span></div> <div class="viewcode-block" id="Graph.to_directed"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.to_directed.html#networkx.Graph.to_directed">[docs]</a> <span class="k">def</span> <span class="nf">to_directed</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">as_view</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns a directed representation of the graph.</span> +<span class="w"> </span><span class="sd">"""Returns a directed representation of the graph.</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> @@ -2167,7 +2167,7 @@ <span class="k">return</span> <span class="n">G</span></div> <div class="viewcode-block" id="Graph.to_undirected"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.to_undirected.html#networkx.Graph.to_undirected">[docs]</a> <span class="k">def</span> <span class="nf">to_undirected</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">as_view</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns an undirected copy of the graph.</span> +<span class="w"> </span><span class="sd">"""Returns an undirected copy of the graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -2224,7 +2224,7 @@ <span class="k">return</span> <span class="n">G</span></div> <div class="viewcode-block" id="Graph.subgraph"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.subgraph.html#networkx.Graph.subgraph">[docs]</a> <span class="k">def</span> <span class="nf">subgraph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span> - <span class="sd">"""Returns a SubGraph view of the subgraph induced on `nodes`.</span> +<span class="w"> </span><span class="sd">"""Returns a SubGraph view of the subgraph induced on `nodes`.</span> <span class="sd"> The induced subgraph of the graph contains the nodes in `nodes`</span> <span class="sd"> and the edges between those nodes.</span> @@ -2288,7 +2288,7 @@ <span class="k">return</span> <span class="n">subgraph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">induced_nodes</span><span class="p">)</span></div> <div class="viewcode-block" id="Graph.edge_subgraph"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.edge_subgraph.html#networkx.Graph.edge_subgraph">[docs]</a> <span class="k">def</span> <span class="nf">edge_subgraph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">edges</span><span class="p">):</span> - <span class="sd">"""Returns the subgraph induced by the specified edges.</span> +<span class="w"> </span><span class="sd">"""Returns the subgraph induced by the specified edges.</span> <span class="sd"> The induced subgraph contains each edge in `edges` and each</span> <span class="sd"> node incident to any one of those edges.</span> @@ -2328,7 +2328,7 @@ <span class="k">return</span> <span class="n">nx</span><span class="o">.</span><span class="n">edge_subgraph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">edges</span><span class="p">)</span></div> <div class="viewcode-block" id="Graph.size"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.size.html#networkx.Graph.size">[docs]</a> <span class="k">def</span> <span class="nf">size</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the number of edges or total of all edge weights.</span> +<span class="w"> </span><span class="sd">"""Returns the number of edges or total of all edge weights.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -2371,7 +2371,7 @@ <span class="k">return</span> <span class="n">s</span> <span class="o">//</span> <span class="mi">2</span> <span class="k">if</span> <span class="n">weight</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">s</span> <span class="o">/</span> <span class="mi">2</span></div> <div class="viewcode-block" id="Graph.number_of_edges"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.number_of_edges.html#networkx.Graph.number_of_edges">[docs]</a> <span class="k">def</span> <span class="nf">number_of_edges</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">v</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the number of edges between two nodes.</span> +<span class="w"> </span><span class="sd">"""Returns the number of edges between two nodes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -2423,7 +2423,7 @@ <span class="k">return</span> <span class="mi">0</span></div> <div class="viewcode-block" id="Graph.nbunch_iter"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.Graph.nbunch_iter.html#networkx.Graph.nbunch_iter">[docs]</a> <span class="k">def</span> <span class="nf">nbunch_iter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nbunch</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns an iterator over nodes contained in nbunch that are</span> +<span class="w"> </span><span class="sd">"""Returns an iterator over nodes contained in nbunch that are</span> <span class="sd"> also in the graph.</span> <span class="sd"> The nodes in nbunch are checked for membership in the graph</span> @@ -2540,7 +2540,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/classes/graphviews.html b/_modules/networkx/classes/graphviews.html index f572123e..1ad32f53 100644 --- a/_modules/networkx/classes/graphviews.html +++ b/_modules/networkx/classes/graphviews.html @@ -535,7 +535,7 @@ <div class="viewcode-block" id="subgraph_view"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.subgraph_view.html#networkx.classes.graphviews.subgraph_view">[docs]</a><span class="k">def</span> <span class="nf">subgraph_view</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">filter_node</span><span class="o">=</span><span class="n">no_filter</span><span class="p">,</span> <span class="n">filter_edge</span><span class="o">=</span><span class="n">no_filter</span><span class="p">):</span> - <span class="sd">"""View of `G` applying a filter on nodes and edges.</span> +<span class="w"> </span><span class="sd">"""View of `G` applying a filter on nodes and edges.</span> <span class="sd"> `subgraph_view` provides a read-only view of the input graph that excludes</span> <span class="sd"> nodes and edges based on the outcome of two filter functions `filter_node`</span> @@ -635,7 +635,7 @@ <div class="viewcode-block" id="reverse_view"><a class="viewcode-back" href="../../../reference/generated/networkx.classes.function.reverse_view.html#networkx.classes.graphviews.reverse_view">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">reverse_view</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""View of `G` with edge directions reversed</span> +<span class="w"> </span><span class="sd">"""View of `G` with edge directions reversed</span> <span class="sd"> `reverse_view` returns a read-only view of the input graph where</span> <span class="sd"> edge directions are reversed.</span> @@ -717,7 +717,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/classes/multidigraph.html b/_modules/networkx/classes/multidigraph.html index 4ec51584..0c34a199 100644 --- a/_modules/networkx/classes/multidigraph.html +++ b/_modules/networkx/classes/multidigraph.html @@ -483,7 +483,7 @@ <div class="viewcode-block" id="MultiDiGraph"><a class="viewcode-back" href="../../../reference/classes/multidigraph.html#networkx.MultiDiGraph">[docs]</a><span class="k">class</span> <span class="nc">MultiDiGraph</span><span class="p">(</span><span class="n">MultiGraph</span><span class="p">,</span> <span class="n">DiGraph</span><span class="p">):</span> - <span class="sd">"""A directed graph class that can store multiedges.</span> +<span class="w"> </span><span class="sd">"""A directed graph class that can store multiedges.</span> <span class="sd"> Multiedges are multiple edges between two nodes. Each edge</span> <span class="sd"> can hold optional data or attributes.</span> @@ -762,7 +762,7 @@ <span class="c1"># edge_attr_dict_factory = dict</span> <div class="viewcode-block" id="MultiDiGraph.__init__"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiDiGraph.__init__.html#networkx.MultiDiGraph.__init__">[docs]</a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">incoming_graph_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">multigraph_input</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Initialize a graph with edges, name, or graph attributes.</span> +<span class="w"> </span><span class="sd">"""Initialize a graph with edges, name, or graph attributes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -826,7 +826,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">adj</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Graph adjacency object holding the neighbors of each node.</span> +<span class="w"> </span><span class="sd">"""Graph adjacency object holding the neighbors of each node.</span> <span class="sd"> This object is a read-only dict-like structure with node keys</span> <span class="sd"> and neighbor-dict values. The neighbor-dict is keyed by neighbor</span> @@ -845,7 +845,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">succ</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Graph adjacency object holding the successors of each node.</span> +<span class="w"> </span><span class="sd">"""Graph adjacency object holding the successors of each node.</span> <span class="sd"> This object is a read-only dict-like structure with node keys</span> <span class="sd"> and neighbor-dict values. The neighbor-dict is keyed by neighbor</span> @@ -864,7 +864,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">pred</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Graph adjacency object holding the predecessors of each node.</span> +<span class="w"> </span><span class="sd">"""Graph adjacency object holding the predecessors of each node.</span> <span class="sd"> This object is a read-only dict-like structure with node keys</span> <span class="sd"> and neighbor-dict values. The neighbor-dict is keyed by neighbor</span> @@ -877,7 +877,7 @@ <span class="k">return</span> <span class="n">MultiAdjacencyView</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pred</span><span class="p">)</span> <div class="viewcode-block" id="MultiDiGraph.add_edge"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiDiGraph.add_edge.html#networkx.MultiDiGraph.add_edge">[docs]</a> <span class="k">def</span> <span class="nf">add_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u_for_edge</span><span class="p">,</span> <span class="n">v_for_edge</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Add an edge between u and v.</span> +<span class="w"> </span><span class="sd">"""Add an edge between u and v.</span> <span class="sd"> The nodes u and v will be automatically added if they are</span> <span class="sd"> not already in the graph.</span> @@ -974,7 +974,7 @@ <span class="k">return</span> <span class="n">key</span></div> <div class="viewcode-block" id="MultiDiGraph.remove_edge"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiDiGraph.remove_edge.html#networkx.MultiDiGraph.remove_edge">[docs]</a> <span class="k">def</span> <span class="nf">remove_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Remove an edge between u and v.</span> +<span class="w"> </span><span class="sd">"""Remove an edge between u and v.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1049,7 +1049,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">edges</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""An OutMultiEdgeView of the Graph as G.edges or G.edges().</span> +<span class="w"> </span><span class="sd">"""An OutMultiEdgeView of the Graph as G.edges or G.edges().</span> <span class="sd"> edges(self, nbunch=None, data=False, keys=False, default=None)</span> @@ -1137,7 +1137,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">in_edges</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""A view of the in edges of the graph as G.in_edges or G.in_edges().</span> +<span class="w"> </span><span class="sd">"""A view of the in edges of the graph as G.in_edges or G.in_edges().</span> <span class="sd"> in_edges(self, nbunch=None, data=False, keys=False, default=None)</span> @@ -1171,7 +1171,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">degree</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""A DegreeView for the Graph as G.degree or G.degree().</span> +<span class="w"> </span><span class="sd">"""A DegreeView for the Graph as G.degree or G.degree().</span> <span class="sd"> The node degree is the number of edges adjacent to the node.</span> <span class="sd"> The weighted node degree is the sum of the edge weights for</span> @@ -1219,7 +1219,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">in_degree</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""A DegreeView for (node, in_degree) or in_degree for single node.</span> +<span class="w"> </span><span class="sd">"""A DegreeView for (node, in_degree) or in_degree for single node.</span> <span class="sd"> The node in-degree is the number of edges pointing in to the node.</span> <span class="sd"> The weighted node degree is the sum of the edge weights for</span> @@ -1270,7 +1270,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">out_degree</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns an iterator for (node, out-degree) or out-degree for single node.</span> +<span class="w"> </span><span class="sd">"""Returns an iterator for (node, out-degree) or out-degree for single node.</span> <span class="sd"> out_degree(self, nbunch=None, weight=None)</span> @@ -1319,15 +1319,15 @@ <span class="k">return</span> <span class="n">OutMultiDegreeView</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="k">def</span> <span class="nf">is_multigraph</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns True if graph is a multigraph, False otherwise."""</span> +<span class="w"> </span><span class="sd">"""Returns True if graph is a multigraph, False otherwise."""</span> <span class="k">return</span> <span class="kc">True</span> <span class="k">def</span> <span class="nf">is_directed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns True if graph is directed, False otherwise."""</span> +<span class="w"> </span><span class="sd">"""Returns True if graph is directed, False otherwise."""</span> <span class="k">return</span> <span class="kc">True</span> <div class="viewcode-block" id="MultiDiGraph.to_undirected"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiDiGraph.to_undirected.html#networkx.MultiDiGraph.to_undirected">[docs]</a> <span class="k">def</span> <span class="nf">to_undirected</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">reciprocal</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">as_view</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns an undirected representation of the digraph.</span> +<span class="w"> </span><span class="sd">"""Returns an undirected representation of the digraph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1402,7 +1402,7 @@ <span class="k">return</span> <span class="n">G</span></div> <div class="viewcode-block" id="MultiDiGraph.reverse"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiDiGraph.reverse.html#networkx.MultiDiGraph.reverse">[docs]</a> <span class="k">def</span> <span class="nf">reverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Returns the reverse of the graph.</span> +<span class="w"> </span><span class="sd">"""Returns the reverse of the graph.</span> <span class="sd"> The reverse is a graph with the same nodes and edges</span> <span class="sd"> but with the directions of the edges reversed.</span> @@ -1475,7 +1475,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/classes/multigraph.html b/_modules/networkx/classes/multigraph.html index a8db1308..12e61e50 100644 --- a/_modules/networkx/classes/multigraph.html +++ b/_modules/networkx/classes/multigraph.html @@ -476,7 +476,7 @@ <div class="viewcode-block" id="MultiGraph"><a class="viewcode-back" href="../../../reference/classes/multigraph.html#networkx.MultiGraph">[docs]</a><span class="k">class</span> <span class="nc">MultiGraph</span><span class="p">(</span><span class="n">Graph</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> An undirected graph class that can store multiedges.</span> <span class="sd"> Multiedges are multiple edges between two nodes. Each edge</span> @@ -755,7 +755,7 @@ <span class="c1"># edge_attr_dict_factory = dict</span> <span class="k">def</span> <span class="nf">to_directed_class</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns the class to use for empty directed copies.</span> +<span class="w"> </span><span class="sd">"""Returns the class to use for empty directed copies.</span> <span class="sd"> If you subclass the base classes, use this to designate</span> <span class="sd"> what directed class to use for `to_directed()` copies.</span> @@ -763,7 +763,7 @@ <span class="k">return</span> <span class="n">nx</span><span class="o">.</span><span class="n">MultiDiGraph</span> <span class="k">def</span> <span class="nf">to_undirected_class</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns the class to use for empty undirected copies.</span> +<span class="w"> </span><span class="sd">"""Returns the class to use for empty undirected copies.</span> <span class="sd"> If you subclass the base classes, use this to designate</span> <span class="sd"> what directed class to use for `to_directed()` copies.</span> @@ -771,7 +771,7 @@ <span class="k">return</span> <span class="n">MultiGraph</span> <div class="viewcode-block" id="MultiGraph.__init__"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.__init__.html#networkx.MultiGraph.__init__">[docs]</a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">incoming_graph_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">multigraph_input</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Initialize a graph with edges, name, or graph attributes.</span> +<span class="w"> </span><span class="sd">"""Initialize a graph with edges, name, or graph attributes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -835,7 +835,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">adj</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Graph adjacency object holding the neighbors of each node.</span> +<span class="w"> </span><span class="sd">"""Graph adjacency object holding the neighbors of each node.</span> <span class="sd"> This object is a read-only dict-like structure with node keys</span> <span class="sd"> and neighbor-dict values. The neighbor-dict is keyed by neighbor</span> @@ -863,7 +863,7 @@ <span class="k">return</span> <span class="n">MultiAdjacencyView</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">)</span> <div class="viewcode-block" id="MultiGraph.new_edge_key"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.new_edge_key.html#networkx.MultiGraph.new_edge_key">[docs]</a> <span class="k">def</span> <span class="nf">new_edge_key</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> - <span class="sd">"""Returns an unused key for edges between nodes `u` and `v`.</span> +<span class="w"> </span><span class="sd">"""Returns an unused key for edges between nodes `u` and `v`.</span> <span class="sd"> The nodes `u` and `v` do not need to be already in the graph.</span> @@ -892,7 +892,7 @@ <span class="k">return</span> <span class="n">key</span></div> <div class="viewcode-block" id="MultiGraph.add_edge"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.add_edge.html#networkx.MultiGraph.add_edge">[docs]</a> <span class="k">def</span> <span class="nf">add_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u_for_edge</span><span class="p">,</span> <span class="n">v_for_edge</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Add an edge between u and v.</span> +<span class="w"> </span><span class="sd">"""Add an edge between u and v.</span> <span class="sd"> The nodes u and v will be automatically added if they are</span> <span class="sd"> not already in the graph.</span> @@ -987,7 +987,7 @@ <span class="k">return</span> <span class="n">key</span></div> <div class="viewcode-block" id="MultiGraph.add_edges_from"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.add_edges_from.html#networkx.MultiGraph.add_edges_from">[docs]</a> <span class="k">def</span> <span class="nf">add_edges_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ebunch_to_add</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span> - <span class="sd">"""Add all the edges in ebunch_to_add.</span> +<span class="w"> </span><span class="sd">"""Add all the edges in ebunch_to_add.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1083,7 +1083,7 @@ <span class="k">return</span> <span class="n">keylist</span></div> <div class="viewcode-block" id="MultiGraph.remove_edge"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.remove_edge.html#networkx.MultiGraph.remove_edge">[docs]</a> <span class="k">def</span> <span class="nf">remove_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Remove an edge between u and v.</span> +<span class="w"> </span><span class="sd">"""Remove an edge between u and v.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1161,7 +1161,7 @@ <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="n">u</span><span class="p">]</span></div> <div class="viewcode-block" id="MultiGraph.remove_edges_from"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.remove_edges_from.html#networkx.MultiGraph.remove_edges_from">[docs]</a> <span class="k">def</span> <span class="nf">remove_edges_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ebunch</span><span class="p">):</span> - <span class="sd">"""Remove all edges specified in ebunch.</span> +<span class="w"> </span><span class="sd">"""Remove all edges specified in ebunch.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1219,7 +1219,7 @@ <span class="k">pass</span></div> <div class="viewcode-block" id="MultiGraph.has_edge"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.has_edge.html#networkx.MultiGraph.has_edge">[docs]</a> <span class="k">def</span> <span class="nf">has_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns True if the graph has an edge between nodes u and v.</span> +<span class="w"> </span><span class="sd">"""Returns True if the graph has an edge between nodes u and v.</span> <span class="sd"> This is the same as `v in G[u] or key in G[u][v]`</span> <span class="sd"> without KeyError exceptions.</span> @@ -1280,7 +1280,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">edges</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns an iterator over the edges.</span> +<span class="w"> </span><span class="sd">"""Returns an iterator over the edges.</span> <span class="sd"> edges(self, nbunch=None, data=False, keys=False, default=None)</span> @@ -1355,7 +1355,7 @@ <span class="k">return</span> <span class="n">MultiEdgeView</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <div class="viewcode-block" id="MultiGraph.get_edge_data"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.get_edge_data.html#networkx.MultiGraph.get_edge_data">[docs]</a> <span class="k">def</span> <span class="nf">get_edge_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the attribute dictionary associated with edge (u, v,</span> +<span class="w"> </span><span class="sd">"""Returns the attribute dictionary associated with edge (u, v,</span> <span class="sd"> key).</span> <span class="sd"> If a key is not provided, returns a dictionary mapping edge keys</span> @@ -1433,7 +1433,7 @@ <span class="nd">@cached_property</span> <span class="k">def</span> <span class="nf">degree</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""A DegreeView for the Graph as G.degree or G.degree().</span> +<span class="w"> </span><span class="sd">"""A DegreeView for the Graph as G.degree or G.degree().</span> <span class="sd"> The node degree is the number of edges adjacent to the node.</span> <span class="sd"> The weighted node degree is the sum of the edge weights for</span> @@ -1472,15 +1472,15 @@ <span class="k">return</span> <span class="n">MultiDegreeView</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="k">def</span> <span class="nf">is_multigraph</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns True if graph is a multigraph, False otherwise."""</span> +<span class="w"> </span><span class="sd">"""Returns True if graph is a multigraph, False otherwise."""</span> <span class="k">return</span> <span class="kc">True</span> <span class="k">def</span> <span class="nf">is_directed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns True if graph is directed, False otherwise."""</span> +<span class="w"> </span><span class="sd">"""Returns True if graph is directed, False otherwise."""</span> <span class="k">return</span> <span class="kc">False</span> <div class="viewcode-block" id="MultiGraph.copy"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.copy.html#networkx.MultiGraph.copy">[docs]</a> <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">as_view</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns a copy of the graph.</span> +<span class="w"> </span><span class="sd">"""Returns a copy of the graph.</span> <span class="sd"> The copy method by default returns an independent shallow copy</span> <span class="sd"> of the graph and attributes. That is, if an attribute is a</span> @@ -1570,7 +1570,7 @@ <span class="k">return</span> <span class="n">G</span></div> <div class="viewcode-block" id="MultiGraph.to_directed"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.to_directed.html#networkx.MultiGraph.to_directed">[docs]</a> <span class="k">def</span> <span class="nf">to_directed</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">as_view</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns a directed representation of the graph.</span> +<span class="w"> </span><span class="sd">"""Returns a directed representation of the graph.</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> @@ -1631,7 +1631,7 @@ <span class="k">return</span> <span class="n">G</span></div> <div class="viewcode-block" id="MultiGraph.to_undirected"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.to_undirected.html#networkx.MultiGraph.to_undirected">[docs]</a> <span class="k">def</span> <span class="nf">to_undirected</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">as_view</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns an undirected copy of the graph.</span> +<span class="w"> </span><span class="sd">"""Returns an undirected copy of the graph.</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> @@ -1684,7 +1684,7 @@ <span class="k">return</span> <span class="n">G</span></div> <div class="viewcode-block" id="MultiGraph.number_of_edges"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.number_of_edges.html#networkx.MultiGraph.number_of_edges">[docs]</a> <span class="k">def</span> <span class="nf">number_of_edges</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">v</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the number of edges between two nodes.</span> +<span class="w"> </span><span class="sd">"""Returns the number of edges between two nodes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1791,7 +1791,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/convert.html b/_modules/networkx/convert.html index 1197f25f..a78ff4ae 100644 --- a/_modules/networkx/convert.html +++ b/_modules/networkx/convert.html @@ -495,7 +495,7 @@ <div class="viewcode-block" id="to_networkx_graph"><a class="viewcode-back" href="../../reference/generated/networkx.convert.to_networkx_graph.html#networkx.convert.to_networkx_graph">[docs]</a><span class="k">def</span> <span class="nf">to_networkx_graph</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">multigraph_input</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Make a NetworkX graph from a known data structure.</span> +<span class="w"> </span><span class="sd">"""Make a NetworkX graph from a known data structure.</span> <span class="sd"> The preferred way to call this is automatically</span> <span class="sd"> from the class constructor</span> @@ -640,7 +640,7 @@ <div class="viewcode-block" id="to_dict_of_lists"><a class="viewcode-back" href="../../reference/generated/networkx.convert.to_dict_of_lists.html#networkx.convert.to_dict_of_lists">[docs]</a><span class="k">def</span> <span class="nf">to_dict_of_lists</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns adjacency representation of graph as a dictionary of lists.</span> +<span class="w"> </span><span class="sd">"""Returns adjacency representation of graph as a dictionary of lists.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -665,7 +665,7 @@ <div class="viewcode-block" id="from_dict_of_lists"><a class="viewcode-back" href="../../reference/generated/networkx.convert.from_dict_of_lists.html#networkx.convert.from_dict_of_lists">[docs]</a><span class="k">def</span> <span class="nf">from_dict_of_lists</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a graph from a dictionary of lists.</span> +<span class="w"> </span><span class="sd">"""Returns a graph from a dictionary of lists.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -705,7 +705,7 @@ <div class="viewcode-block" id="to_dict_of_dicts"><a class="viewcode-back" href="../../reference/generated/networkx.convert.to_dict_of_dicts.html#networkx.convert.to_dict_of_dicts">[docs]</a><span class="k">def</span> <span class="nf">to_dict_of_dicts</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">edge_data</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns adjacency representation of graph as a dictionary of dictionaries.</span> +<span class="w"> </span><span class="sd">"""Returns adjacency representation of graph as a dictionary of dictionaries.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -826,7 +826,7 @@ <div class="viewcode-block" id="from_dict_of_dicts"><a class="viewcode-back" href="../../reference/generated/networkx.convert.from_dict_of_dicts.html#networkx.convert.from_dict_of_dicts">[docs]</a><span class="k">def</span> <span class="nf">from_dict_of_dicts</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">multigraph_input</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns a graph from a dictionary of dictionaries.</span> +<span class="w"> </span><span class="sd">"""Returns a graph from a dictionary of dictionaries.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -912,7 +912,7 @@ <div class="viewcode-block" id="to_edgelist"><a class="viewcode-back" href="../../reference/generated/networkx.convert.to_edgelist.html#networkx.convert.to_edgelist">[docs]</a><span class="k">def</span> <span class="nf">to_edgelist</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a list of edges in the graph.</span> +<span class="w"> </span><span class="sd">"""Returns a list of edges in the graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -929,7 +929,7 @@ <div class="viewcode-block" id="from_edgelist"><a class="viewcode-back" href="../../reference/generated/networkx.convert.from_edgelist.html#networkx.convert.from_edgelist">[docs]</a><span class="k">def</span> <span class="nf">from_edgelist</span><span class="p">(</span><span class="n">edgelist</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a graph from a list of edges.</span> +<span class="w"> </span><span class="sd">"""Returns a graph from a list of edges.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1003,7 +1003,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/convert_matrix.html b/_modules/networkx/convert_matrix.html index ea3650f5..7bf92f2e 100644 --- a/_modules/networkx/convert_matrix.html +++ b/_modules/networkx/convert_matrix.html @@ -515,7 +515,7 @@ <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">nonedge</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns the graph adjacency matrix as a Pandas DataFrame.</span> +<span class="w"> </span><span class="sd">"""Returns the graph adjacency matrix as a Pandas DataFrame.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -613,7 +613,7 @@ <div class="viewcode-block" id="from_pandas_adjacency"><a class="viewcode-back" href="../../reference/generated/networkx.convert_matrix.from_pandas_adjacency.html#networkx.convert_matrix.from_pandas_adjacency">[docs]</a><span class="k">def</span> <span class="nf">from_pandas_adjacency</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a graph from Pandas DataFrame.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a graph from Pandas DataFrame.</span> <span class="sd"> The Pandas DataFrame is interpreted as an adjacency matrix for the graph.</span> @@ -680,7 +680,7 @@ <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">edge_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns the graph edge list as a Pandas DataFrame.</span> +<span class="w"> </span><span class="sd">"""Returns the graph edge list as a Pandas DataFrame.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -771,7 +771,7 @@ <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">edge_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns a graph from Pandas DataFrame containing an edge list.</span> +<span class="w"> </span><span class="sd">"""Returns a graph from Pandas DataFrame containing an edge list.</span> <span class="sd"> The Pandas DataFrame should contain at least two columns of node names and</span> <span class="sd"> zero or more columns of edge attributes. Each row will be processed as one</span> @@ -927,7 +927,7 @@ <div class="viewcode-block" id="to_scipy_sparse_array"><a class="viewcode-back" href="../../reference/generated/networkx.convert_matrix.to_scipy_sparse_array.html#networkx.convert_matrix.to_scipy_sparse_array">[docs]</a><span class="k">def</span> <span class="nf">to_scipy_sparse_array</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"csr"</span><span class="p">):</span> - <span class="sd">"""Returns the graph adjacency matrix as a SciPy sparse array.</span> +<span class="w"> </span><span class="sd">"""Returns the graph adjacency matrix as a SciPy sparse array.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1061,7 +1061,7 @@ <span class="k">def</span> <span class="nf">_csr_gen_triples</span><span class="p">(</span><span class="n">A</span><span class="p">):</span> - <span class="sd">"""Converts a SciPy sparse array in **Compressed Sparse Row** format to</span> +<span class="w"> </span><span class="sd">"""Converts a SciPy sparse array in **Compressed Sparse Row** format to</span> <span class="sd"> an iterable of weighted edge triples.</span> <span class="sd"> """</span> @@ -1073,7 +1073,7 @@ <span class="k">def</span> <span class="nf">_csc_gen_triples</span><span class="p">(</span><span class="n">A</span><span class="p">):</span> - <span class="sd">"""Converts a SciPy sparse array in **Compressed Sparse Column** format to</span> +<span class="w"> </span><span class="sd">"""Converts a SciPy sparse array in **Compressed Sparse Column** format to</span> <span class="sd"> an iterable of weighted edge triples.</span> <span class="sd"> """</span> @@ -1085,7 +1085,7 @@ <span class="k">def</span> <span class="nf">_coo_gen_triples</span><span class="p">(</span><span class="n">A</span><span class="p">):</span> - <span class="sd">"""Converts a SciPy sparse array in **Coordinate** format to an iterable</span> +<span class="w"> </span><span class="sd">"""Converts a SciPy sparse array in **Coordinate** format to an iterable</span> <span class="sd"> of weighted edge triples.</span> <span class="sd"> """</span> @@ -1094,7 +1094,7 @@ <span class="k">def</span> <span class="nf">_dok_gen_triples</span><span class="p">(</span><span class="n">A</span><span class="p">):</span> - <span class="sd">"""Converts a SciPy sparse array in **Dictionary of Keys** format to an</span> +<span class="w"> </span><span class="sd">"""Converts a SciPy sparse array in **Dictionary of Keys** format to an</span> <span class="sd"> iterable of weighted edge triples.</span> <span class="sd"> """</span> @@ -1103,7 +1103,7 @@ <span class="k">def</span> <span class="nf">_generate_weighted_edges</span><span class="p">(</span><span class="n">A</span><span class="p">):</span> - <span class="sd">"""Returns an iterable over (u, v, w) triples, where u and v are adjacent</span> +<span class="w"> </span><span class="sd">"""Returns an iterable over (u, v, w) triples, where u and v are adjacent</span> <span class="sd"> vertices and w is the weight of the edge joining u and v.</span> <span class="sd"> `A` is a SciPy sparse array (in any format).</span> @@ -1122,7 +1122,7 @@ <div class="viewcode-block" id="from_scipy_sparse_array"><a class="viewcode-back" href="../../reference/generated/networkx.convert_matrix.from_scipy_sparse_array.html#networkx.convert_matrix.from_scipy_sparse_array">[docs]</a><span class="k">def</span> <span class="nf">from_scipy_sparse_array</span><span class="p">(</span> <span class="n">A</span><span class="p">,</span> <span class="n">parallel_edges</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">edge_attribute</span><span class="o">=</span><span class="s2">"weight"</span> <span class="p">):</span> - <span class="sd">"""Creates a new graph from an adjacency matrix given as a SciPy sparse</span> +<span class="w"> </span><span class="sd">"""Creates a new graph from an adjacency matrix given as a SciPy sparse</span> <span class="sd"> array.</span> <span class="sd"> Parameters</span> @@ -1233,7 +1233,7 @@ <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">nonedge</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns the graph adjacency matrix as a NumPy array.</span> +<span class="w"> </span><span class="sd">"""Returns the graph adjacency matrix as a NumPy array.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1392,7 +1392,7 @@ <span class="c1"># Input validation</span> <span class="n">nodeset</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">nodelist</span><span class="p">)</span> <span class="k">if</span> <span class="n">nodeset</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">G</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">"Nodes </span><span class="si">{</span><span class="n">nodeset</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">G</span><span class="p">)</span><span class="si">}</span><span class="s2"> in nodelist is not in G"</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">"Nodes </span><span class="si">{</span><span class="n">nodeset</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="nb">set</span><span class="p">(</span><span class="n">G</span><span class="p">)</span><span class="si">}</span><span class="s2"> in nodelist is not in G"</span><span class="p">)</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">nodeset</span><span class="p">)</span> <span class="o"><</span> <span class="n">nlen</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">"nodelist contains duplicates."</span><span class="p">)</span> @@ -1463,7 +1463,7 @@ <div class="viewcode-block" id="from_numpy_array"><a class="viewcode-back" href="../../reference/generated/networkx.convert_matrix.from_numpy_array.html#networkx.convert_matrix.from_numpy_array">[docs]</a><span class="k">def</span> <span class="nf">from_numpy_array</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">parallel_edges</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a graph from a 2D NumPy array.</span> +<span class="w"> </span><span class="sd">"""Returns a graph from a 2D NumPy array.</span> <span class="sd"> The 2D NumPy array is interpreted as an adjacency matrix for the graph.</span> @@ -1674,7 +1674,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/drawing/layout.html b/_modules/networkx/drawing/layout.html index 5e2a18b8..0afdd047 100644 --- a/_modules/networkx/drawing/layout.html +++ b/_modules/networkx/drawing/layout.html @@ -522,7 +522,7 @@ <div class="viewcode-block" id="random_layout"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.layout.random_layout.html#networkx.drawing.layout.random_layout">[docs]</a><span class="nd">@np_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_layout</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Position nodes uniformly at random in the unit square.</span> +<span class="w"> </span><span class="sd">"""Position nodes uniformly at random in the unit square.</span> <span class="sd"> For every node, a position is generated by choosing each of dim</span> <span class="sd"> coordinates uniformly at random on the interval [0.0, 1.0).</span> @@ -571,7 +571,7 @@ <div class="viewcode-block" id="circular_layout"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.layout.circular_layout.html#networkx.drawing.layout.circular_layout">[docs]</a><span class="k">def</span> <span class="nf">circular_layout</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> <span class="c1"># dim=2 only</span> - <span class="sd">"""Position nodes on a circle.</span> +<span class="w"> </span><span class="sd">"""Position nodes on a circle.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -638,7 +638,7 @@ <div class="viewcode-block" id="shell_layout"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.layout.shell_layout.html#networkx.drawing.layout.shell_layout">[docs]</a><span class="k">def</span> <span class="nf">shell_layout</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nlist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">rotate</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> - <span class="sd">"""Position nodes in concentric circles.</span> +<span class="w"> </span><span class="sd">"""Position nodes in concentric circles.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -731,7 +731,7 @@ <div class="viewcode-block" id="bipartite_layout"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.layout.bipartite_layout.html#networkx.drawing.layout.bipartite_layout">[docs]</a><span class="k">def</span> <span class="nf">bipartite_layout</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">align</span><span class="o">=</span><span class="s2">"vertical"</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">aspect_ratio</span><span class="o">=</span><span class="mi">4</span> <span class="o">/</span> <span class="mi">3</span> <span class="p">):</span> - <span class="sd">"""Position nodes in two straight lines.</span> +<span class="w"> </span><span class="sd">"""Position nodes in two straight lines.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -820,7 +820,7 @@ <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Position nodes using Fruchterman-Reingold force-directed algorithm.</span> +<span class="w"> </span><span class="sd">"""Position nodes using Fruchterman-Reingold force-directed algorithm.</span> <span class="sd"> The algorithm simulates a force-directed representation of the network</span> <span class="sd"> treating edges as springs holding nodes close, while treating nodes</span> @@ -1102,7 +1102,7 @@ <div class="viewcode-block" id="kamada_kawai_layout"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.layout.kamada_kawai_layout.html#networkx.drawing.layout.kamada_kawai_layout">[docs]</a><span class="k">def</span> <span class="nf">kamada_kawai_layout</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">dist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span> <span class="p">):</span> - <span class="sd">"""Position nodes using Kamada-Kawai path-length cost-function.</span> +<span class="w"> </span><span class="sd">"""Position nodes using Kamada-Kawai path-length cost-function.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1225,7 +1225,7 @@ <div class="viewcode-block" id="spectral_layout"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.layout.spectral_layout.html#networkx.drawing.layout.spectral_layout">[docs]</a><span class="k">def</span> <span class="nf">spectral_layout</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> - <span class="sd">"""Position nodes using the eigenvectors of the graph Laplacian.</span> +<span class="w"> </span><span class="sd">"""Position nodes using the eigenvectors of the graph Laplacian.</span> <span class="sd"> Using the unnormalized Laplacian, the layout shows possible clusters of</span> <span class="sd"> nodes which are an approximation of the ratio cut. If dim is the number of</span> @@ -1354,7 +1354,7 @@ <div class="viewcode-block" id="planar_layout"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.layout.planar_layout.html#networkx.drawing.layout.planar_layout">[docs]</a><span class="k">def</span> <span class="nf">planar_layout</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> - <span class="sd">"""Position nodes without edge intersections.</span> +<span class="w"> </span><span class="sd">"""Position nodes without edge intersections.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1411,7 +1411,7 @@ <div class="viewcode-block" id="spiral_layout"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.layout.spiral_layout.html#networkx.drawing.layout.spiral_layout">[docs]</a><span class="k">def</span> <span class="nf">spiral_layout</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">resolution</span><span class="o">=</span><span class="mf">0.35</span><span class="p">,</span> <span class="n">equidistant</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Position nodes in a spiral layout.</span> +<span class="w"> </span><span class="sd">"""Position nodes in a spiral layout.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1490,7 +1490,7 @@ <div class="viewcode-block" id="multipartite_layout"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.layout.multipartite_layout.html#networkx.drawing.layout.multipartite_layout">[docs]</a><span class="k">def</span> <span class="nf">multipartite_layout</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">subset_key</span><span class="o">=</span><span class="s2">"subset"</span><span class="p">,</span> <span class="n">align</span><span class="o">=</span><span class="s2">"vertical"</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Position nodes in layers of straight lines.</span> +<span class="w"> </span><span class="sd">"""Position nodes in layers of straight lines.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1583,7 +1583,7 @@ <span class="n">dt</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Arf layout for networkx</span> +<span class="w"> </span><span class="sd">"""Arf layout for networkx</span> <span class="sd"> The attractive and repulsive forces (arf) layout [1]</span> <span class="sd"> improves the spring layout in three ways. First, it</span> @@ -1687,7 +1687,7 @@ <div class="viewcode-block" id="rescale_layout"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.layout.rescale_layout.html#networkx.drawing.layout.rescale_layout">[docs]</a><span class="k">def</span> <span class="nf">rescale_layout</span><span class="p">(</span><span class="n">pos</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span> - <span class="sd">"""Returns scaled position array to (-scale, scale) in all axes.</span> +<span class="w"> </span><span class="sd">"""Returns scaled position array to (-scale, scale) in all axes.</span> <span class="sd"> The function acts on NumPy arrays which hold position information.</span> <span class="sd"> Each position is one row of the array. The dimension of the space</span> @@ -1728,7 +1728,7 @@ <div class="viewcode-block" id="rescale_layout_dict"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.layout.rescale_layout_dict.html#networkx.drawing.layout.rescale_layout_dict">[docs]</a><span class="k">def</span> <span class="nf">rescale_layout_dict</span><span class="p">(</span><span class="n">pos</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span> - <span class="sd">"""Return a dictionary of scaled positions keyed by node</span> +<span class="w"> </span><span class="sd">"""Return a dictionary of scaled positions keyed by node</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1814,7 +1814,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/drawing/nx_agraph.html b/_modules/networkx/drawing/nx_agraph.html index 7f187a6d..a18c0e02 100644 --- a/_modules/networkx/drawing/nx_agraph.html +++ b/_modules/networkx/drawing/nx_agraph.html @@ -497,7 +497,7 @@ <div class="viewcode-block" id="from_agraph"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_agraph.from_agraph.html#networkx.drawing.nx_agraph.from_agraph">[docs]</a><span class="k">def</span> <span class="nf">from_agraph</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a NetworkX Graph or DiGraph from a PyGraphviz graph.</span> +<span class="w"> </span><span class="sd">"""Returns a NetworkX Graph or DiGraph from a PyGraphviz graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -573,7 +573,7 @@ <div class="viewcode-block" id="to_agraph"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_agraph.to_agraph.html#networkx.drawing.nx_agraph.to_agraph">[docs]</a><span class="k">def</span> <span class="nf">to_agraph</span><span class="p">(</span><span class="n">N</span><span class="p">):</span> - <span class="sd">"""Returns a pygraphviz graph from a NetworkX graph N.</span> +<span class="w"> </span><span class="sd">"""Returns a pygraphviz graph from a NetworkX graph N.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -639,7 +639,7 @@ <div class="viewcode-block" id="write_dot"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_agraph.write_dot.html#networkx.drawing.nx_agraph.write_dot">[docs]</a><span class="k">def</span> <span class="nf">write_dot</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span> - <span class="sd">"""Write NetworkX graph G to Graphviz dot format on path.</span> +<span class="w"> </span><span class="sd">"""Write NetworkX graph G to Graphviz dot format on path.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -661,7 +661,7 @@ <div class="viewcode-block" id="read_dot"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_agraph.read_dot.html#networkx.drawing.nx_agraph.read_dot">[docs]</a><span class="k">def</span> <span class="nf">read_dot</span><span class="p">(</span><span class="n">path</span><span class="p">):</span> - <span class="sd">"""Returns a NetworkX graph from a dot file on path.</span> +<span class="w"> </span><span class="sd">"""Returns a NetworkX graph from a dot file on path.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -681,7 +681,7 @@ <div class="viewcode-block" id="graphviz_layout"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_agraph.graphviz_layout.html#networkx.drawing.nx_agraph.graphviz_layout">[docs]</a><span class="k">def</span> <span class="nf">graphviz_layout</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"neato"</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="s2">""</span><span class="p">):</span> - <span class="sd">"""Create node positions for G using Graphviz.</span> +<span class="w"> </span><span class="sd">"""Create node positions for G using Graphviz.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -715,7 +715,7 @@ <div class="viewcode-block" id="pygraphviz_layout"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_agraph.pygraphviz_layout.html#networkx.drawing.nx_agraph.pygraphviz_layout">[docs]</a><span class="k">def</span> <span class="nf">pygraphviz_layout</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"neato"</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="s2">""</span><span class="p">):</span> - <span class="sd">"""Create node positions for G using Graphviz.</span> +<span class="w"> </span><span class="sd">"""Create node positions for G using Graphviz.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -780,7 +780,7 @@ <span class="k">def</span> <span class="nf">view_pygraphviz</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">edgelabel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"dot"</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="s2">""</span><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s2">""</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span> <span class="p">):</span> - <span class="sd">"""Views the graph G using the specified layout algorithm.</span> +<span class="w"> </span><span class="sd">"""Views the graph G using the specified layout algorithm.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -972,7 +972,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/drawing/nx_pydot.html b/_modules/networkx/drawing/nx_pydot.html index 9ea4f296..750562a6 100644 --- a/_modules/networkx/drawing/nx_pydot.html +++ b/_modules/networkx/drawing/nx_pydot.html @@ -500,7 +500,7 @@ <div class="viewcode-block" id="write_dot"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_pydot.write_dot.html#networkx.drawing.nx_pydot.write_dot">[docs]</a><span class="nd">@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_dot</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span> - <span class="sd">"""Write NetworkX graph G to Graphviz dot format on path.</span> +<span class="w"> </span><span class="sd">"""Write NetworkX graph G to Graphviz dot format on path.</span> <span class="sd"> Path can be a string or a file handle.</span> <span class="sd"> """</span> @@ -518,7 +518,7 @@ <div class="viewcode-block" id="read_dot"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_pydot.read_dot.html#networkx.drawing.nx_pydot.read_dot">[docs]</a><span class="nd">@open_file</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"r"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">read_dot</span><span class="p">(</span><span class="n">path</span><span class="p">):</span> - <span class="sd">"""Returns a NetworkX :class:`MultiGraph` or :class:`MultiDiGraph` from the</span> +<span class="w"> </span><span class="sd">"""Returns a NetworkX :class:`MultiGraph` or :class:`MultiDiGraph` from the</span> <span class="sd"> dot file with the passed path.</span> <span class="sd"> If this file contains multiple graphs, only the first such graph is</span> @@ -559,7 +559,7 @@ <div class="viewcode-block" id="from_pydot"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_pydot.from_pydot.html#networkx.drawing.nx_pydot.from_pydot">[docs]</a><span class="k">def</span> <span class="nf">from_pydot</span><span class="p">(</span><span class="n">P</span><span class="p">):</span> - <span class="sd">"""Returns a NetworkX graph from a Pydot graph.</span> +<span class="w"> </span><span class="sd">"""Returns a NetworkX graph from a Pydot graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -663,7 +663,7 @@ <div class="viewcode-block" id="to_pydot"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_pydot.to_pydot.html#networkx.drawing.nx_pydot.to_pydot">[docs]</a><span class="k">def</span> <span class="nf">to_pydot</span><span class="p">(</span><span class="n">N</span><span class="p">):</span> - <span class="sd">"""Returns a pydot graph from a NetworkX graph N.</span> +<span class="w"> </span><span class="sd">"""Returns a pydot graph from a NetworkX graph N.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -780,7 +780,7 @@ <div class="viewcode-block" id="graphviz_layout"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_pydot.graphviz_layout.html#networkx.drawing.nx_pydot.graphviz_layout">[docs]</a><span class="k">def</span> <span class="nf">graphviz_layout</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"neato"</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Create node positions using Pydot and Graphviz.</span> +<span class="w"> </span><span class="sd">"""Create node positions using Pydot and Graphviz.</span> <span class="sd"> Returns a dictionary of positions keyed by node.</span> @@ -821,7 +821,7 @@ <div class="viewcode-block" id="pydot_layout"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_pydot.pydot_layout.html#networkx.drawing.nx_pydot.pydot_layout">[docs]</a><span class="k">def</span> <span class="nf">pydot_layout</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"neato"</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Create node positions using :mod:`pydot` and Graphviz.</span> +<span class="w"> </span><span class="sd">"""Create node positions using :mod:`pydot` and Graphviz.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -964,7 +964,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/drawing/nx_pylab.html b/_modules/networkx/drawing/nx_pylab.html index 5f119c1b..c7ced280 100644 --- a/_modules/networkx/drawing/nx_pylab.html +++ b/_modules/networkx/drawing/nx_pylab.html @@ -510,7 +510,7 @@ <div class="viewcode-block" id="draw"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_pylab.draw.html#networkx.drawing.nx_pylab.draw">[docs]</a><span class="k">def</span> <span class="nf">draw</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="kc">None</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwds</span><span class="p">):</span> - <span class="sd">"""Draw the graph G with Matplotlib.</span> +<span class="w"> </span><span class="sd">"""Draw the graph G with Matplotlib.</span> <span class="sd"> Draw the graph as a simple representation with no node</span> <span class="sd"> labels or edge labels and using the full Matplotlib figure area</span> @@ -588,7 +588,7 @@ <div class="viewcode-block" id="draw_networkx"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#networkx.drawing.nx_pylab.draw_networkx">[docs]</a><span class="k">def</span> <span class="nf">draw_networkx</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="kc">None</span><span class="p">,</span> <span class="n">arrows</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">with_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwds</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Draw the graph G using Matplotlib.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Draw the graph G using Matplotlib.</span> <span class="sd"> Draw the graph with Matplotlib with options for node positions,</span> <span class="sd"> labeling, titles, and many other drawing features.</span> @@ -787,7 +787,7 @@ <span class="n">label</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">margins</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Draw the nodes of the graph G.</span> +<span class="w"> </span><span class="sd">"""Draw the nodes of the graph G.</span> <span class="sd"> This draws only the nodes of the graph G.</span> @@ -949,7 +949,7 @@ <span class="n">min_source_margin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">min_target_margin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Draw the edges of the graph G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Draw the edges of the graph G.</span> <span class="sd"> This draws only the edges of the graph G.</span> @@ -1403,7 +1403,7 @@ <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">clip_on</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Draw node labels on the graph G.</span> +<span class="w"> </span><span class="sd">"""Draw node labels on the graph G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1529,7 +1529,7 @@ <span class="n">rotate</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">clip_on</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Draw edge labels.</span> +<span class="w"> </span><span class="sd">"""Draw edge labels.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1683,7 +1683,7 @@ <div class="viewcode-block" id="draw_circular"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_pylab.draw_circular.html#networkx.drawing.nx_pylab.draw_circular">[docs]</a><span class="k">def</span> <span class="nf">draw_circular</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> - <span class="sd">"""Draw the graph `G` with a circular layout.</span> +<span class="w"> </span><span class="sd">"""Draw the graph `G` with a circular layout.</span> <span class="sd"> This is a convenience function equivalent to::</span> @@ -1717,7 +1717,7 @@ <div class="viewcode-block" id="draw_kamada_kawai"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_pylab.draw_kamada_kawai.html#networkx.drawing.nx_pylab.draw_kamada_kawai">[docs]</a><span class="k">def</span> <span class="nf">draw_kamada_kawai</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> - <span class="sd">"""Draw the graph `G` with a Kamada-Kawai force-directed layout.</span> +<span class="w"> </span><span class="sd">"""Draw the graph `G` with a Kamada-Kawai force-directed layout.</span> <span class="sd"> This is a convenience function equivalent to::</span> @@ -1752,7 +1752,7 @@ <div class="viewcode-block" id="draw_random"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_pylab.draw_random.html#networkx.drawing.nx_pylab.draw_random">[docs]</a><span class="k">def</span> <span class="nf">draw_random</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> - <span class="sd">"""Draw the graph `G` with a random layout.</span> +<span class="w"> </span><span class="sd">"""Draw the graph `G` with a random layout.</span> <span class="sd"> This is a convenience function equivalent to::</span> @@ -1786,7 +1786,7 @@ <div class="viewcode-block" id="draw_spectral"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_pylab.draw_spectral.html#networkx.drawing.nx_pylab.draw_spectral">[docs]</a><span class="k">def</span> <span class="nf">draw_spectral</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> - <span class="sd">"""Draw the graph `G` with a spectral 2D layout.</span> +<span class="w"> </span><span class="sd">"""Draw the graph `G` with a spectral 2D layout.</span> <span class="sd"> This is a convenience function equivalent to::</span> @@ -1823,7 +1823,7 @@ <div class="viewcode-block" id="draw_spring"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_pylab.draw_spring.html#networkx.drawing.nx_pylab.draw_spring">[docs]</a><span class="k">def</span> <span class="nf">draw_spring</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> - <span class="sd">"""Draw the graph `G` with a spring layout.</span> +<span class="w"> </span><span class="sd">"""Draw the graph `G` with a spring layout.</span> <span class="sd"> This is a convenience function equivalent to::</span> @@ -1861,7 +1861,7 @@ <div class="viewcode-block" id="draw_shell"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_pylab.draw_shell.html#networkx.drawing.nx_pylab.draw_shell">[docs]</a><span class="k">def</span> <span class="nf">draw_shell</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nlist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> - <span class="sd">"""Draw networkx graph `G` with shell layout.</span> +<span class="w"> </span><span class="sd">"""Draw networkx graph `G` with shell layout.</span> <span class="sd"> This is a convenience function equivalent to::</span> @@ -1900,7 +1900,7 @@ <div class="viewcode-block" id="draw_planar"><a class="viewcode-back" href="../../../reference/generated/networkx.drawing.nx_pylab.draw_planar.html#networkx.drawing.nx_pylab.draw_planar">[docs]</a><span class="k">def</span> <span class="nf">draw_planar</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> - <span class="sd">"""Draw a planar networkx graph `G` with planar layout.</span> +<span class="w"> </span><span class="sd">"""Draw a planar networkx graph `G` with planar layout.</span> <span class="sd"> This is a convenience function equivalent to::</span> @@ -1939,7 +1939,7 @@ <span class="k">def</span> <span class="nf">apply_alpha</span><span class="p">(</span><span class="n">colors</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">elem_list</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Apply an alpha (or list of alphas) to the colors provided.</span> +<span class="w"> </span><span class="sd">"""Apply an alpha (or list of alphas) to the colors provided.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -2064,7 +2064,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/exception.html b/_modules/networkx/exception.html index 9e1855a4..c665dcb7 100644 --- a/_modules/networkx/exception.html +++ b/_modules/networkx/exception.html @@ -488,15 +488,15 @@ <div class="viewcode-block" id="NetworkXException"><a class="viewcode-back" href="../../reference/exceptions.html#networkx.NetworkXException">[docs]</a><span class="k">class</span> <span class="nc">NetworkXException</span><span class="p">(</span><span class="ne">Exception</span><span class="p">):</span> - <span class="sd">"""Base class for exceptions in NetworkX."""</span></div> +<span class="w"> </span><span class="sd">"""Base class for exceptions in NetworkX."""</span></div> <div class="viewcode-block" id="NetworkXError"><a class="viewcode-back" href="../../reference/exceptions.html#networkx.NetworkXError">[docs]</a><span class="k">class</span> <span class="nc">NetworkXError</span><span class="p">(</span><span class="n">NetworkXException</span><span class="p">):</span> - <span class="sd">"""Exception for a serious error in NetworkX"""</span></div> +<span class="w"> </span><span class="sd">"""Exception for a serious error in NetworkX"""</span></div> <div class="viewcode-block" id="NetworkXPointlessConcept"><a class="viewcode-back" href="../../reference/exceptions.html#networkx.NetworkXPointlessConcept">[docs]</a><span class="k">class</span> <span class="nc">NetworkXPointlessConcept</span><span class="p">(</span><span class="n">NetworkXException</span><span class="p">):</span> - <span class="sd">"""Raised when a null graph is provided as input to an algorithm</span> +<span class="w"> </span><span class="sd">"""Raised when a null graph is provided as input to an algorithm</span> <span class="sd"> that cannot use it.</span> <span class="sd"> The null graph is sometimes considered a pointless concept [1]_,</span> @@ -512,46 +512,46 @@ <div class="viewcode-block" id="NetworkXAlgorithmError"><a class="viewcode-back" href="../../reference/exceptions.html#networkx.NetworkXAlgorithmError">[docs]</a><span class="k">class</span> <span class="nc">NetworkXAlgorithmError</span><span class="p">(</span><span class="n">NetworkXException</span><span class="p">):</span> - <span class="sd">"""Exception for unexpected termination of algorithms."""</span></div> +<span class="w"> </span><span class="sd">"""Exception for unexpected termination of algorithms."""</span></div> <div class="viewcode-block" id="NetworkXUnfeasible"><a class="viewcode-back" href="../../reference/exceptions.html#networkx.NetworkXUnfeasible">[docs]</a><span class="k">class</span> <span class="nc">NetworkXUnfeasible</span><span class="p">(</span><span class="n">NetworkXAlgorithmError</span><span class="p">):</span> - <span class="sd">"""Exception raised by algorithms trying to solve a problem</span> +<span class="w"> </span><span class="sd">"""Exception raised by algorithms trying to solve a problem</span> <span class="sd"> instance that has no feasible solution."""</span></div> <div class="viewcode-block" id="NetworkXNoPath"><a class="viewcode-back" href="../../reference/exceptions.html#networkx.NetworkXNoPath">[docs]</a><span class="k">class</span> <span class="nc">NetworkXNoPath</span><span class="p">(</span><span class="n">NetworkXUnfeasible</span><span class="p">):</span> - <span class="sd">"""Exception for algorithms that should return a path when running</span> +<span class="w"> </span><span class="sd">"""Exception for algorithms that should return a path when running</span> <span class="sd"> on graphs where such a path does not exist."""</span></div> <div class="viewcode-block" id="NetworkXNoCycle"><a class="viewcode-back" href="../../reference/exceptions.html#networkx.NetworkXNoCycle">[docs]</a><span class="k">class</span> <span class="nc">NetworkXNoCycle</span><span class="p">(</span><span class="n">NetworkXUnfeasible</span><span class="p">):</span> - <span class="sd">"""Exception for algorithms that should return a cycle when running</span> +<span class="w"> </span><span class="sd">"""Exception for algorithms that should return a cycle when running</span> <span class="sd"> on graphs where such a cycle does not exist."""</span></div> <div class="viewcode-block" id="HasACycle"><a class="viewcode-back" href="../../reference/exceptions.html#networkx.HasACycle">[docs]</a><span class="k">class</span> <span class="nc">HasACycle</span><span class="p">(</span><span class="n">NetworkXException</span><span class="p">):</span> - <span class="sd">"""Raised if a graph has a cycle when an algorithm expects that it</span> +<span class="w"> </span><span class="sd">"""Raised if a graph has a cycle when an algorithm expects that it</span> <span class="sd"> will have no cycles.</span> <span class="sd"> """</span></div> <div class="viewcode-block" id="NetworkXUnbounded"><a class="viewcode-back" href="../../reference/exceptions.html#networkx.NetworkXUnbounded">[docs]</a><span class="k">class</span> <span class="nc">NetworkXUnbounded</span><span class="p">(</span><span class="n">NetworkXAlgorithmError</span><span class="p">):</span> - <span class="sd">"""Exception raised by algorithms trying to solve a maximization</span> +<span class="w"> </span><span class="sd">"""Exception raised by algorithms trying to solve a maximization</span> <span class="sd"> or a minimization problem instance that is unbounded."""</span></div> <div class="viewcode-block" id="NetworkXNotImplemented"><a class="viewcode-back" href="../../reference/exceptions.html#networkx.NetworkXNotImplemented">[docs]</a><span class="k">class</span> <span class="nc">NetworkXNotImplemented</span><span class="p">(</span><span class="n">NetworkXException</span><span class="p">):</span> - <span class="sd">"""Exception raised by algorithms not implemented for a type of graph."""</span></div> +<span class="w"> </span><span class="sd">"""Exception raised by algorithms not implemented for a type of graph."""</span></div> <div class="viewcode-block" id="NodeNotFound"><a class="viewcode-back" href="../../reference/exceptions.html#networkx.NodeNotFound">[docs]</a><span class="k">class</span> <span class="nc">NodeNotFound</span><span class="p">(</span><span class="n">NetworkXException</span><span class="p">):</span> - <span class="sd">"""Exception raised if requested node is not present in the graph"""</span></div> +<span class="w"> </span><span class="sd">"""Exception raised if requested node is not present in the graph"""</span></div> <div class="viewcode-block" id="AmbiguousSolution"><a class="viewcode-back" href="../../reference/exceptions.html#networkx.AmbiguousSolution">[docs]</a><span class="k">class</span> <span class="nc">AmbiguousSolution</span><span class="p">(</span><span class="n">NetworkXException</span><span class="p">):</span> - <span class="sd">"""Raised if more than one valid solution exists for an intermediary step</span> +<span class="w"> </span><span class="sd">"""Raised if more than one valid solution exists for an intermediary step</span> <span class="sd"> of an algorithm.</span> <span class="sd"> In the face of ambiguity, refuse the temptation to guess.</span> @@ -563,7 +563,7 @@ <div class="viewcode-block" id="ExceededMaxIterations"><a class="viewcode-back" href="../../reference/exceptions.html#networkx.ExceededMaxIterations">[docs]</a><span class="k">class</span> <span class="nc">ExceededMaxIterations</span><span class="p">(</span><span class="n">NetworkXException</span><span class="p">):</span> - <span class="sd">"""Raised if a loop iterates too many times without breaking.</span> +<span class="w"> </span><span class="sd">"""Raised if a loop iterates too many times without breaking.</span> <span class="sd"> This may occur, for example, in an algorithm that computes</span> <span class="sd"> progressively better approximations to a value but exceeds an</span> @@ -573,7 +573,7 @@ <div class="viewcode-block" id="PowerIterationFailedConvergence"><a class="viewcode-back" href="../../reference/exceptions.html#networkx.PowerIterationFailedConvergence">[docs]</a><span class="k">class</span> <span class="nc">PowerIterationFailedConvergence</span><span class="p">(</span><span class="n">ExceededMaxIterations</span><span class="p">):</span> - <span class="sd">"""Raised when the power iteration method fails to converge within a</span> +<span class="w"> </span><span class="sd">"""Raised when the power iteration method fails to converge within a</span> <span class="sd"> specified iteration limit.</span> <span class="sd"> `num_iterations` is the number of iterations that have been</span> @@ -637,7 +637,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/atlas.html b/_modules/networkx/generators/atlas.html index 3edd4853..400416be 100644 --- a/_modules/networkx/generators/atlas.html +++ b/_modules/networkx/generators/atlas.html @@ -518,7 +518,7 @@ <span class="k">def</span> <span class="nf">_generate_graphs</span><span class="p">():</span> - <span class="sd">"""Sequentially read the file containing the edge list data for the</span> +<span class="w"> </span><span class="sd">"""Sequentially read the file containing the edge list data for the</span> <span class="sd"> graphs in the atlas and generate the graphs one at a time.</span> <span class="sd"> This function reads the file given in :data:`.ATLAS_FILE`.</span> @@ -552,7 +552,7 @@ <div class="viewcode-block" id="graph_atlas"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.atlas.graph_atlas.html#networkx.generators.atlas.graph_atlas">[docs]</a><span class="k">def</span> <span class="nf">graph_atlas</span><span class="p">(</span><span class="n">i</span><span class="p">):</span> - <span class="sd">"""Returns graph number `i` from the Graph Atlas.</span> +<span class="w"> </span><span class="sd">"""Returns graph number `i` from the Graph Atlas.</span> <span class="sd"> For more information, see :func:`.graph_atlas_g`.</span> @@ -590,7 +590,7 @@ <div class="viewcode-block" id="graph_atlas_g"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.atlas.graph_atlas_g.html#networkx.generators.atlas.graph_atlas_g">[docs]</a><span class="k">def</span> <span class="nf">graph_atlas_g</span><span class="p">():</span> - <span class="sd">"""Returns the list of all graphs with up to seven nodes named in the</span> +<span class="w"> </span><span class="sd">"""Returns the list of all graphs with up to seven nodes named in the</span> <span class="sd"> Graph Atlas.</span> <span class="sd"> The graphs are listed in increasing order by</span> @@ -689,7 +689,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/classic.html b/_modules/networkx/generators/classic.html index bd5d97aa..f95736b0 100644 --- a/_modules/networkx/generators/classic.html +++ b/_modules/networkx/generators/classic.html @@ -528,7 +528,7 @@ <div class="viewcode-block" id="full_rary_tree"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.full_rary_tree.html#networkx.generators.classic.full_rary_tree">[docs]</a><span class="k">def</span> <span class="nf">full_rary_tree</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Creates a full r-ary tree of `n` nodes.</span> +<span class="w"> </span><span class="sd">"""Creates a full r-ary tree of `n` nodes.</span> <span class="sd"> Sometimes called a k-ary, n-ary, or m-ary tree.</span> <span class="sd"> "... all non-leaf nodes have exactly r children and all levels</span> @@ -561,7 +561,7 @@ <div class="viewcode-block" id="balanced_tree"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.balanced_tree.html#networkx.generators.classic.balanced_tree">[docs]</a><span class="k">def</span> <span class="nf">balanced_tree</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the perfectly balanced `r`-ary tree of height `h`.</span> +<span class="w"> </span><span class="sd">"""Returns the perfectly balanced `r`-ary tree of height `h`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -606,7 +606,7 @@ <div class="viewcode-block" id="barbell_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.barbell_graph.html#networkx.generators.classic.barbell_graph">[docs]</a><span class="k">def</span> <span class="nf">barbell_graph</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">m2</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the Barbell Graph: two complete graphs connected by a path.</span> +<span class="w"> </span><span class="sd">"""Returns the Barbell Graph: two complete graphs connected by a path.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -674,7 +674,7 @@ <div class="viewcode-block" id="binomial_tree"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.binomial_tree.html#networkx.generators.classic.binomial_tree">[docs]</a><span class="k">def</span> <span class="nf">binomial_tree</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the Binomial Tree of order n.</span> +<span class="w"> </span><span class="sd">"""Returns the Binomial Tree of order n.</span> <span class="sd"> The binomial tree of order 0 consists of a single node. A binomial tree of order k</span> <span class="sd"> is defined recursively by linking two binomial trees of order k-1: the root of one is</span> @@ -708,7 +708,7 @@ <div class="viewcode-block" id="complete_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.complete_graph.html#networkx.generators.classic.complete_graph">[docs]</a><span class="nd">@nodes_or_number</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">def</span> <span class="nf">complete_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Return the complete graph `K_n` with n nodes.</span> +<span class="w"> </span><span class="sd">"""Return the complete graph `K_n` with n nodes.</span> <span class="sd"> A complete graph on `n` nodes means that all pairs</span> <span class="sd"> of distinct nodes have an edge connecting them.</span> @@ -750,7 +750,7 @@ <div class="viewcode-block" id="circular_ladder_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.circular_ladder_graph.html#networkx.generators.classic.circular_ladder_graph">[docs]</a><span class="k">def</span> <span class="nf">circular_ladder_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the circular ladder graph $CL_n$ of length n.</span> +<span class="w"> </span><span class="sd">"""Returns the circular ladder graph $CL_n$ of length n.</span> <span class="sd"> $CL_n$ consists of two concentric n-cycles in which</span> <span class="sd"> each of the n pairs of concentric nodes are joined by an edge.</span> @@ -765,7 +765,7 @@ <div class="viewcode-block" id="circulant_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.circulant_graph.html#networkx.generators.classic.circulant_graph">[docs]</a><span class="k">def</span> <span class="nf">circulant_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the circulant graph $Ci_n(x_1, x_2, ..., x_m)$ with $n$ nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the circulant graph $Ci_n(x_1, x_2, ..., x_m)$ with $n$ nodes.</span> <span class="sd"> The circulant graph $Ci_n(x_1, ..., x_m)$ consists of $n$ nodes $0, ..., n-1$</span> <span class="sd"> such that node $i$ is connected to nodes $(i + x) \mod n$ and $(i - x) \mod n$</span> @@ -838,7 +838,7 @@ <div class="viewcode-block" id="cycle_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.cycle_graph.html#networkx.generators.classic.cycle_graph">[docs]</a><span class="nd">@nodes_or_number</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">def</span> <span class="nf">cycle_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the cycle graph $C_n$ of cyclically connected nodes.</span> +<span class="w"> </span><span class="sd">"""Returns the cycle graph $C_n$ of cyclically connected nodes.</span> <span class="sd"> $C_n$ is a path with its two end-nodes connected.</span> @@ -864,7 +864,7 @@ <div class="viewcode-block" id="dorogovtsev_goltsev_mendes_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.dorogovtsev_goltsev_mendes_graph.html#networkx.generators.classic.dorogovtsev_goltsev_mendes_graph">[docs]</a><span class="k">def</span> <span class="nf">dorogovtsev_goltsev_mendes_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the hierarchically constructed Dorogovtsev-Goltsev-Mendes graph.</span> +<span class="w"> </span><span class="sd">"""Returns the hierarchically constructed Dorogovtsev-Goltsev-Mendes graph.</span> <span class="sd"> n is the generation.</span> <span class="sd"> See: arXiv:/cond-mat/0112143 by Dorogovtsev, Goltsev and Mendes.</span> @@ -892,7 +892,7 @@ <div class="viewcode-block" id="empty_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.empty_graph.html#networkx.generators.classic.empty_graph">[docs]</a><span class="nd">@nodes_or_number</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">def</span> <span class="nf">empty_graph</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="n">Graph</span><span class="p">):</span> - <span class="sd">"""Returns the empty graph with n nodes and zero edges.</span> +<span class="w"> </span><span class="sd">"""Returns the empty graph with n nodes and zero edges.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -985,7 +985,7 @@ <div class="viewcode-block" id="ladder_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.ladder_graph.html#networkx.generators.classic.ladder_graph">[docs]</a><span class="k">def</span> <span class="nf">ladder_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the Ladder graph of length n.</span> +<span class="w"> </span><span class="sd">"""Returns the Ladder graph of length n.</span> <span class="sd"> This is two paths of n nodes, with</span> <span class="sd"> each pair connected by a single edge.</span> @@ -1004,7 +1004,7 @@ <div class="viewcode-block" id="lollipop_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.lollipop_graph.html#networkx.generators.classic.lollipop_graph">[docs]</a><span class="nd">@nodes_or_number</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="k">def</span> <span class="nf">lollipop_graph</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the Lollipop Graph; `K_m` connected to `P_n`.</span> +<span class="w"> </span><span class="sd">"""Returns the Lollipop Graph; `K_m` connected to `P_n`.</span> <span class="sd"> This is the Barbell Graph without the right barbell.</span> @@ -1060,7 +1060,7 @@ <div class="viewcode-block" id="null_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.null_graph.html#networkx.generators.classic.null_graph">[docs]</a><span class="k">def</span> <span class="nf">null_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the Null graph with no nodes or edges.</span> +<span class="w"> </span><span class="sd">"""Returns the Null graph with no nodes or edges.</span> <span class="sd"> See empty_graph for the use of create_using.</span> @@ -1071,7 +1071,7 @@ <div class="viewcode-block" id="path_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.path_graph.html#networkx.generators.classic.path_graph">[docs]</a><span class="nd">@nodes_or_number</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">def</span> <span class="nf">path_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the Path graph `P_n` of linearly connected nodes.</span> +<span class="w"> </span><span class="sd">"""Returns the Path graph `P_n` of linearly connected nodes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1092,7 +1092,7 @@ <div class="viewcode-block" id="star_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.star_graph.html#networkx.generators.classic.star_graph">[docs]</a><span class="nd">@nodes_or_number</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">def</span> <span class="nf">star_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Return the star graph</span> +<span class="w"> </span><span class="sd">"""Return the star graph</span> <span class="sd"> The star graph consists of one center node connected to n outer nodes.</span> @@ -1125,13 +1125,13 @@ <div class="viewcode-block" id="trivial_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.trivial_graph.html#networkx.generators.classic.trivial_graph">[docs]</a><span class="k">def</span> <span class="nf">trivial_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Return the Trivial graph with one node (with label 0) and no edges."""</span> +<span class="w"> </span><span class="sd">"""Return the Trivial graph with one node (with label 0) and no edges."""</span> <span class="n">G</span> <span class="o">=</span> <span class="n">empty_graph</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">create_using</span><span class="p">)</span> <span class="k">return</span> <span class="n">G</span></div> <div class="viewcode-block" id="turan_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.turan_graph.html#networkx.generators.classic.turan_graph">[docs]</a><span class="k">def</span> <span class="nf">turan_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">r</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Return the Turan Graph</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Return the Turan Graph</span> <span class="sd"> The Turan Graph is a complete multipartite graph on $n$ nodes</span> <span class="sd"> with $r$ disjoint subsets. That is, edges connect each node to</span> @@ -1165,7 +1165,7 @@ <div class="viewcode-block" id="wheel_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.wheel_graph.html#networkx.generators.classic.wheel_graph">[docs]</a><span class="nd">@nodes_or_number</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">def</span> <span class="nf">wheel_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Return the wheel graph</span> +<span class="w"> </span><span class="sd">"""Return the wheel graph</span> <span class="sd"> The wheel graph consists of a hub node connected to a cycle of (n-1) nodes.</span> @@ -1195,7 +1195,7 @@ <div class="viewcode-block" id="complete_multipartite_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.complete_multipartite_graph.html#networkx.generators.classic.complete_multipartite_graph">[docs]</a><span class="k">def</span> <span class="nf">complete_multipartite_graph</span><span class="p">(</span><span class="o">*</span><span class="n">subset_sizes</span><span class="p">):</span> - <span class="sd">"""Returns the complete multipartite graph with the specified subset sizes.</span> +<span class="w"> </span><span class="sd">"""Returns the complete multipartite graph with the specified subset sizes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1326,7 +1326,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/cographs.html b/_modules/networkx/generators/cographs.html index a3586e90..0eec65f1 100644 --- a/_modules/networkx/generators/cographs.html +++ b/_modules/networkx/generators/cographs.html @@ -482,7 +482,7 @@ <div class="viewcode-block" id="random_cograph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.cographs.random_cograph.html#networkx.generators.cographs.random_cograph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_cograph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a random cograph with $2 ^ n$ nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a random cograph with $2 ^ n$ nodes.</span> <span class="sd"> A cograph is a graph containing no path on four vertices.</span> <span class="sd"> Cographs or $P_4$-free graphs can be obtained from a single vertex</span> @@ -578,7 +578,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/community.html b/_modules/networkx/generators/community.html index 275fe412..f85e1e9c 100644 --- a/_modules/networkx/generators/community.html +++ b/_modules/networkx/generators/community.html @@ -483,7 +483,7 @@ <div class="viewcode-block" id="caveman_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.community.caveman_graph.html#networkx.generators.community.caveman_graph">[docs]</a><span class="k">def</span> <span class="nf">caveman_graph</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""Returns a caveman graph of `l` cliques of size `k`.</span> +<span class="w"> </span><span class="sd">"""Returns a caveman graph of `l` cliques of size `k`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -529,7 +529,7 @@ <div class="viewcode-block" id="connected_caveman_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.community.connected_caveman_graph.html#networkx.generators.community.connected_caveman_graph">[docs]</a><span class="k">def</span> <span class="nf">connected_caveman_graph</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""Returns a connected caveman graph of `l` cliques of size `k`.</span> +<span class="w"> </span><span class="sd">"""Returns a connected caveman graph of `l` cliques of size `k`.</span> <span class="sd"> The connected caveman graph is formed by creating `n` cliques of size</span> <span class="sd"> `k`, then a single edge in each clique is rewired to a node in an</span> @@ -583,7 +583,7 @@ <div class="viewcode-block" id="relaxed_caveman_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.community.relaxed_caveman_graph.html#networkx.generators.community.relaxed_caveman_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">relaxed_caveman_graph</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a relaxed caveman graph.</span> +<span class="w"> </span><span class="sd">"""Returns a relaxed caveman graph.</span> <span class="sd"> A relaxed caveman graph starts with `l` cliques of size `k`. Edges are</span> <span class="sd"> then randomly rewired with probability `p` to link different cliques.</span> @@ -634,7 +634,7 @@ <div class="viewcode-block" id="random_partition_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.community.random_partition_graph.html#networkx.generators.community.random_partition_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_partition_graph</span><span class="p">(</span><span class="n">sizes</span><span class="p">,</span> <span class="n">p_in</span><span class="p">,</span> <span class="n">p_out</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns the random partition graph with a partition of sizes.</span> +<span class="w"> </span><span class="sd">"""Returns the random partition graph with a partition of sizes.</span> <span class="sd"> A partition graph is a graph of communities with sizes defined by</span> <span class="sd"> s in sizes. Nodes in the same group are connected with probability</span> @@ -712,7 +712,7 @@ <div class="viewcode-block" id="planted_partition_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.community.planted_partition_graph.html#networkx.generators.community.planted_partition_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> <span class="k">def</span> <span class="nf">planted_partition_graph</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p_in</span><span class="p">,</span> <span class="n">p_out</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns the planted l-partition graph.</span> +<span class="w"> </span><span class="sd">"""Returns the planted l-partition graph.</span> <span class="sd"> This model partitions a graph with n=l*k vertices in</span> <span class="sd"> l groups with k vertices each. Vertices of the same</span> @@ -767,7 +767,7 @@ <div class="viewcode-block" id="gaussian_random_partition_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.community.gaussian_random_partition_graph.html#networkx.generators.community.gaussian_random_partition_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span> <span class="k">def</span> <span class="nf">gaussian_random_partition_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">p_in</span><span class="p">,</span> <span class="n">p_out</span><span class="p">,</span> <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generate a Gaussian random partition graph.</span> +<span class="w"> </span><span class="sd">"""Generate a Gaussian random partition graph.</span> <span class="sd"> A Gaussian random partition graph is created by creating k partitions</span> <span class="sd"> each with a size drawn from a normal distribution with mean s and variance</span> @@ -842,7 +842,7 @@ <div class="viewcode-block" id="ring_of_cliques"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.community.ring_of_cliques.html#networkx.generators.community.ring_of_cliques">[docs]</a><span class="k">def</span> <span class="nf">ring_of_cliques</span><span class="p">(</span><span class="n">num_cliques</span><span class="p">,</span> <span class="n">clique_size</span><span class="p">):</span> - <span class="sd">"""Defines a "ring of cliques" graph.</span> +<span class="w"> </span><span class="sd">"""Defines a "ring of cliques" graph.</span> <span class="sd"> A ring of cliques graph is consisting of cliques, connected through single</span> <span class="sd"> links. Each clique is a complete graph.</span> @@ -897,7 +897,7 @@ <div class="viewcode-block" id="windmill_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.community.windmill_graph.html#networkx.generators.community.windmill_graph">[docs]</a><span class="k">def</span> <span class="nf">windmill_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> - <span class="sd">"""Generate a windmill graph.</span> +<span class="w"> </span><span class="sd">"""Generate a windmill graph.</span> <span class="sd"> A windmill graph is a graph of `n` cliques each of size `k` that are all</span> <span class="sd"> joined at one node.</span> <span class="sd"> It can be thought of as taking a disjoint union of `n` cliques of size `k`,</span> @@ -952,7 +952,7 @@ <span class="k">def</span> <span class="nf">stochastic_block_model</span><span class="p">(</span> <span class="n">sizes</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">selfloops</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">True</span> <span class="p">):</span> - <span class="sd">"""Returns a stochastic block model graph.</span> +<span class="w"> </span><span class="sd">"""Returns a stochastic block model graph.</span> <span class="sd"> This model partitions the nodes in blocks of arbitrary sizes, and places</span> <span class="sd"> edges between pairs of nodes independently, with a probability that depends</span> @@ -1118,7 +1118,7 @@ <span class="k">def</span> <span class="nf">_zipf_rv_below</span><span class="p">(</span><span class="n">gamma</span><span class="p">,</span> <span class="n">xmin</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="n">seed</span><span class="p">):</span> - <span class="sd">"""Returns a random value chosen from the bounded Zipf distribution.</span> +<span class="w"> </span><span class="sd">"""Returns a random value chosen from the bounded Zipf distribution.</span> <span class="sd"> Repeatedly draws values from the Zipf distribution until the</span> <span class="sd"> threshold is met, then returns that value.</span> @@ -1130,7 +1130,7 @@ <span class="k">def</span> <span class="nf">_powerlaw_sequence</span><span class="p">(</span><span class="n">gamma</span><span class="p">,</span> <span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">,</span> <span class="n">condition</span><span class="p">,</span> <span class="n">length</span><span class="p">,</span> <span class="n">max_iters</span><span class="p">,</span> <span class="n">seed</span><span class="p">):</span> - <span class="sd">"""Returns a list of numbers obeying a constrained power law distribution.</span> +<span class="w"> </span><span class="sd">"""Returns a list of numbers obeying a constrained power law distribution.</span> <span class="sd"> ``gamma`` and ``low`` are the parameters for the Zipf distribution.</span> @@ -1161,7 +1161,7 @@ <span class="k">def</span> <span class="nf">_hurwitz_zeta</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">tolerance</span><span class="p">):</span> - <span class="sd">"""The Hurwitz zeta function, or the Riemann zeta function of two arguments.</span> +<span class="w"> </span><span class="sd">"""The Hurwitz zeta function, or the Riemann zeta function of two arguments.</span> <span class="sd"> ``x`` must be greater than one and ``q`` must be positive.</span> @@ -1179,7 +1179,7 @@ <span class="k">def</span> <span class="nf">_generate_min_degree</span><span class="p">(</span><span class="n">gamma</span><span class="p">,</span> <span class="n">average_degree</span><span class="p">,</span> <span class="n">max_degree</span><span class="p">,</span> <span class="n">tolerance</span><span class="p">,</span> <span class="n">max_iters</span><span class="p">):</span> - <span class="sd">"""Returns a minimum degree from the given average degree."""</span> +<span class="w"> </span><span class="sd">"""Returns a minimum degree from the given average degree."""</span> <span class="c1"># Defines zeta function whether or not Scipy is available</span> <span class="k">try</span><span class="p">:</span> <span class="kn">from</span> <span class="nn">scipy.special</span> <span class="kn">import</span> <span class="n">zeta</span> @@ -1211,7 +1211,7 @@ <span class="k">def</span> <span class="nf">_generate_communities</span><span class="p">(</span><span class="n">degree_seq</span><span class="p">,</span> <span class="n">community_sizes</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">max_iters</span><span class="p">,</span> <span class="n">seed</span><span class="p">):</span> - <span class="sd">"""Returns a list of sets, each of which represents a community.</span> +<span class="w"> </span><span class="sd">"""Returns a list of sets, each of which represents a community.</span> <span class="sd"> ``degree_seq`` is the degree sequence that must be met by the</span> <span class="sd"> graph.</span> @@ -1276,7 +1276,7 @@ <span class="n">max_iters</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the LFR benchmark graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the LFR benchmark graph.</span> <span class="sd"> This algorithm proceeds as follows:</span> @@ -1573,7 +1573,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/degree_seq.html b/_modules/networkx/generators/degree_seq.html index 71c51514..7ab8faa5 100644 --- a/_modules/networkx/generators/degree_seq.html +++ b/_modules/networkx/generators/degree_seq.html @@ -486,7 +486,7 @@ <span class="k">def</span> <span class="nf">_to_stublist</span><span class="p">(</span><span class="n">degree_sequence</span><span class="p">):</span> - <span class="sd">"""Returns a list of degree-repeated node numbers.</span> +<span class="w"> </span><span class="sd">"""Returns a list of degree-repeated node numbers.</span> <span class="sd"> ``degree_sequence`` is a list of nonnegative integers representing</span> <span class="sd"> the degrees of nodes in a graph.</span> @@ -520,7 +520,7 @@ <span class="k">def</span> <span class="nf">_configuration_model</span><span class="p">(</span> <span class="n">deg_sequence</span><span class="p">,</span> <span class="n">create_using</span><span class="p">,</span> <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">in_deg_sequence</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Helper function for generating either undirected or directed</span> +<span class="w"> </span><span class="sd">"""Helper function for generating either undirected or directed</span> <span class="sd"> configuration model graphs.</span> <span class="sd"> ``deg_sequence`` is a list of nonnegative integers representing the</span> @@ -588,7 +588,7 @@ <div class="viewcode-block" id="configuration_model"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.degree_seq.configuration_model.html#networkx.generators.degree_seq.configuration_model">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">configuration_model</span><span class="p">(</span><span class="n">deg_sequence</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random graph with the given degree sequence.</span> +<span class="w"> </span><span class="sd">"""Returns a random graph with the given degree sequence.</span> <span class="sd"> The configuration model generates a random pseudograph (graph with</span> <span class="sd"> parallel edges and self loops) by randomly assigning edges to</span> @@ -692,7 +692,7 @@ <span class="k">def</span> <span class="nf">directed_configuration_model</span><span class="p">(</span> <span class="n">in_degree_sequence</span><span class="p">,</span> <span class="n">out_degree_sequence</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Returns a directed_random graph with the given degree sequences.</span> +<span class="w"> </span><span class="sd">"""Returns a directed_random graph with the given degree sequences.</span> <span class="sd"> The configuration model generates a random directed pseudograph</span> <span class="sd"> (graph with parallel edges and self loops) by randomly assigning</span> @@ -792,7 +792,7 @@ <div class="viewcode-block" id="expected_degree_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.degree_seq.expected_degree_graph.html#networkx.generators.degree_seq.expected_degree_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="k">def</span> <span class="nf">expected_degree_graph</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">selfloops</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a random graph with given expected degrees.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a random graph with given expected degrees.</span> <span class="sd"> Given a sequence of expected degrees $W=(w_0,w_1,\ldots,w_{n-1})$</span> <span class="sd"> of length $n$ this algorithm assigns an edge between node $u$ and</span> @@ -900,7 +900,7 @@ <div class="viewcode-block" id="havel_hakimi_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.degree_seq.havel_hakimi_graph.html#networkx.generators.degree_seq.havel_hakimi_graph">[docs]</a><span class="k">def</span> <span class="nf">havel_hakimi_graph</span><span class="p">(</span><span class="n">deg_sequence</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a simple graph with given degree sequence constructed</span> +<span class="w"> </span><span class="sd">"""Returns a simple graph with given degree sequence constructed</span> <span class="sd"> using the Havel-Hakimi algorithm.</span> <span class="sd"> Parameters</span> @@ -992,7 +992,7 @@ <div class="viewcode-block" id="directed_havel_hakimi_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.degree_seq.directed_havel_hakimi_graph.html#networkx.generators.degree_seq.directed_havel_hakimi_graph">[docs]</a><span class="k">def</span> <span class="nf">directed_havel_hakimi_graph</span><span class="p">(</span><span class="n">in_deg_sequence</span><span class="p">,</span> <span class="n">out_deg_sequence</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a directed graph with the given degree sequences.</span> +<span class="w"> </span><span class="sd">"""Returns a directed graph with the given degree sequences.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1103,7 +1103,7 @@ <div class="viewcode-block" id="degree_sequence_tree"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.degree_seq.degree_sequence_tree.html#networkx.generators.degree_seq.degree_sequence_tree">[docs]</a><span class="k">def</span> <span class="nf">degree_sequence_tree</span><span class="p">(</span><span class="n">deg_sequence</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Make a tree for the given degree sequence.</span> +<span class="w"> </span><span class="sd">"""Make a tree for the given degree sequence.</span> <span class="sd"> A tree has #nodes-#edges=1 so</span> <span class="sd"> the degree sequence must have</span> @@ -1149,7 +1149,7 @@ <div class="viewcode-block" id="random_degree_sequence_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.degree_seq.random_degree_sequence_graph.html#networkx.generators.degree_seq.random_degree_sequence_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_degree_sequence_graph</span><span class="p">(</span><span class="n">sequence</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tries</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a simple random graph with the given degree sequence.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a simple random graph with the given degree sequence.</span> <span class="sd"> If the maximum degree $d_m$ in the sequence is $O(m^{1/4})$ then the</span> <span class="sd"> algorithm produces almost uniform random graphs in $O(m d_m)$ time</span> @@ -1270,7 +1270,7 @@ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">remaining_degree</span><span class="p">[</span><span class="n">u</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">remaining_degree</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="o">/</span> <span class="n">norm</span> <span class="k">def</span> <span class="nf">suitable_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Returns True if and only if an arbitrary remaining node can</span> +<span class="w"> </span><span class="sd">"""Returns True if and only if an arbitrary remaining node can</span> <span class="sd"> potentially be joined with some other remaining node.</span> <span class="sd"> """</span> @@ -1373,7 +1373,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/directed.html b/_modules/networkx/generators/directed.html index ee55d446..eeb82d95 100644 --- a/_modules/networkx/generators/directed.html +++ b/_modules/networkx/generators/directed.html @@ -485,7 +485,7 @@ <div class="viewcode-block" id="gn_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.directed.gn_graph.html#networkx.generators.directed.gn_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">gn_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the growing network (GN) digraph with `n` nodes.</span> +<span class="w"> </span><span class="sd">"""Returns the growing network (GN) digraph with `n` nodes.</span> <span class="sd"> The GN graph is built by adding nodes one at a time with a link to one</span> <span class="sd"> previously added node. The target node for the link is chosen with</span> @@ -552,7 +552,7 @@ <div class="viewcode-block" id="gnr_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.directed.gnr_graph.html#networkx.generators.directed.gnr_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">gnr_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the growing network with redirection (GNR) digraph with `n`</span> +<span class="w"> </span><span class="sd">"""Returns the growing network with redirection (GNR) digraph with `n`</span> <span class="sd"> nodes and redirection probability `p`.</span> <span class="sd"> The GNR graph is built by adding nodes one at a time with a link to one</span> @@ -605,7 +605,7 @@ <div class="viewcode-block" id="gnc_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.directed.gnc_graph.html#networkx.generators.directed.gnc_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">gnc_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the growing network with copying (GNC) digraph with `n` nodes.</span> +<span class="w"> </span><span class="sd">"""Returns the growing network with copying (GNC) digraph with `n` nodes.</span> <span class="sd"> The GNC graph is built by adding nodes one at a time with a link to one</span> <span class="sd"> previously added node (chosen uniformly at random) and to all of that</span> @@ -654,7 +654,7 @@ <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">initial_graph</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns a scale-free directed graph.</span> +<span class="w"> </span><span class="sd">"""Returns a scale-free directed graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -829,7 +829,7 @@ <span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_uniform_k_out_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">self_loops</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">with_replacement</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random `k`-out graph with uniform attachment.</span> +<span class="w"> </span><span class="sd">"""Returns a random `k`-out graph with uniform attachment.</span> <span class="sd"> A random `k`-out graph with uniform attachment is a multidigraph</span> <span class="sd"> generated by the following algorithm. For each node *u*, choose</span> @@ -909,7 +909,7 @@ <div class="viewcode-block" id="random_k_out_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.directed.random_k_out_graph.html#networkx.generators.directed.random_k_out_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_k_out_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">self_loops</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random `k`-out graph with preferential attachment.</span> +<span class="w"> </span><span class="sd">"""Returns a random `k`-out graph with preferential attachment.</span> <span class="sd"> A random `k`-out graph with preferential attachment is a</span> <span class="sd"> multidigraph generated by the following algorithm.</span> @@ -1042,7 +1042,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/duplication.html b/_modules/networkx/generators/duplication.html index 8bdbe166..870a9578 100644 --- a/_modules/networkx/generators/duplication.html +++ b/_modules/networkx/generators/duplication.html @@ -477,7 +477,7 @@ <div class="viewcode-block" id="partial_duplication_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.duplication.partial_duplication_graph.html#networkx.generators.duplication.partial_duplication_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> <span class="k">def</span> <span class="nf">partial_duplication_graph</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="n">p</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random graph using the partial duplication model.</span> +<span class="w"> </span><span class="sd">"""Returns a random graph using the partial duplication model.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -551,7 +551,7 @@ <div class="viewcode-block" id="duplication_divergence_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.duplication.duplication_divergence_graph.html#networkx.generators.duplication.duplication_divergence_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">duplication_divergence_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns an undirected graph using the duplication-divergence model.</span> +<span class="w"> </span><span class="sd">"""Returns an undirected graph using the duplication-divergence model.</span> <span class="sd"> A graph of `n` nodes is created by duplicating the initial nodes</span> <span class="sd"> and retaining edges incident to the original nodes with a retention</span> @@ -673,7 +673,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/ego.html b/_modules/networkx/generators/ego.html index 3f67ebae..df2b7df6 100644 --- a/_modules/networkx/generators/ego.html +++ b/_modules/networkx/generators/ego.html @@ -471,7 +471,7 @@ <div class="viewcode-block" id="ego_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.ego.ego_graph.html#networkx.generators.ego.ego_graph">[docs]</a><span class="nd">@nx</span><span class="o">.</span><span class="n">_dispatch</span> <span class="k">def</span> <span class="nf">ego_graph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">undirected</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">distance</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns induced subgraph of neighbors centered at node n within</span> +<span class="w"> </span><span class="sd">"""Returns induced subgraph of neighbors centered at node n within</span> <span class="sd"> a given radius.</span> <span class="sd"> Parameters</span> @@ -577,7 +577,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/expanders.html b/_modules/networkx/generators/expanders.html index caf923b2..8b49e567 100644 --- a/_modules/networkx/generators/expanders.html +++ b/_modules/networkx/generators/expanders.html @@ -504,7 +504,7 @@ <span class="c1"># (x, (y + (2*x + 2)) % n),</span> <span class="c1">#</span> <div class="viewcode-block" id="margulis_gabber_galil_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.expanders.margulis_gabber_galil_graph.html#networkx.generators.expanders.margulis_gabber_galil_graph">[docs]</a><span class="k">def</span> <span class="nf">margulis_gabber_galil_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the Margulis-Gabber-Galil undirected MultiGraph on `n^2` nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the Margulis-Gabber-Galil undirected MultiGraph on `n^2` nodes.</span> <span class="sd"> The undirected MultiGraph is regular with degree `8`. Nodes are integer</span> <span class="sd"> pairs. The second-largest eigenvalue of the adjacency matrix of the graph</span> @@ -546,7 +546,7 @@ <div class="viewcode-block" id="chordal_cycle_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.expanders.chordal_cycle_graph.html#networkx.generators.expanders.chordal_cycle_graph">[docs]</a><span class="k">def</span> <span class="nf">chordal_cycle_graph</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the chordal cycle graph on `p` nodes.</span> +<span class="w"> </span><span class="sd">"""Returns the chordal cycle graph on `p` nodes.</span> <span class="sd"> The returned graph is a cycle graph on `p` nodes with chords joining each</span> <span class="sd"> vertex `x` to its inverse modulo `p`. This graph is a (mildly explicit)</span> @@ -609,7 +609,7 @@ <div class="viewcode-block" id="paley_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.expanders.paley_graph.html#networkx.generators.expanders.paley_graph">[docs]</a><span class="k">def</span> <span class="nf">paley_graph</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the Paley (p-1)/2-regular graph on p nodes.</span> +<span class="w"> </span><span class="sd">"""Returns the Paley (p-1)/2-regular graph on p nodes.</span> <span class="sd"> The returned graph is a graph on Z/pZ with edges between x and y</span> <span class="sd"> if and only if x-y is a nonzero square in Z/pZ.</span> @@ -715,7 +715,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/geometric.html b/_modules/networkx/generators/geometric.html index 77cfb9e2..69c901d6 100644 --- a/_modules/networkx/generators/geometric.html +++ b/_modules/networkx/generators/geometric.html @@ -483,7 +483,7 @@ <div class="viewcode-block" id="geometric_edges"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.geometric.geometric_edges.html#networkx.generators.geometric.geometric_edges">[docs]</a><span class="k">def</span> <span class="nf">geometric_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">radius</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> - <span class="sd">"""Returns edge list of node pairs within `radius` of each other.</span> +<span class="w"> </span><span class="sd">"""Returns edge list of node pairs within `radius` of each other.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -544,7 +544,7 @@ <span class="k">def</span> <span class="nf">_geometric_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">radius</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Implements `geometric_edges` without input validation. See `geometric_edges`</span> <span class="sd"> for complete docstring.</span> <span class="sd"> """</span> @@ -571,7 +571,7 @@ <div class="viewcode-block" id="random_geometric_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.geometric.random_geometric_graph.html#networkx.generators.geometric.random_geometric_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_geometric_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">radius</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random geometric graph in the unit cube of dimensions `dim`.</span> +<span class="w"> </span><span class="sd">"""Returns a random geometric graph in the unit cube of dimensions `dim`.</span> <span class="sd"> The random geometric graph model places `n` nodes uniformly at</span> <span class="sd"> random in the unit cube. Two nodes are joined by an edge if the</span> @@ -661,7 +661,7 @@ <span class="k">def</span> <span class="nf">soft_random_geometric_graph</span><span class="p">(</span> <span class="n">n</span><span class="p">,</span> <span class="n">radius</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">p_dist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a soft random geometric graph in the unit cube.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a soft random geometric graph in the unit cube.</span> <span class="sd"> The soft random geometric graph [1] model places `n` nodes uniformly at</span> <span class="sd"> random in the unit cube in dimension `dim`. Two nodes of distance, `dist`,</span> @@ -787,7 +787,7 @@ <span class="k">def</span> <span class="nf">geographical_threshold_graph</span><span class="p">(</span> <span class="n">n</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">p_dist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a geographical threshold graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a geographical threshold graph.</span> <span class="sd"> The geographical threshold graph model places $n$ nodes uniformly at</span> <span class="sd"> random in a rectangular domain. Each node $u$ is assigned a weight</span> @@ -937,7 +937,7 @@ <span class="k">def</span> <span class="nf">waxman_graph</span><span class="p">(</span> <span class="n">n</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">L</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="p">(</span><span class="mi">0</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="mi">1</span><span class="p">),</span> <span class="n">metric</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a Waxman random graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a Waxman random graph.</span> <span class="sd"> The Waxman random graph model places `n` nodes uniformly at random</span> <span class="sd"> in a rectangular domain. Each pair of nodes at distance `d` is</span> @@ -1056,7 +1056,7 @@ <div class="viewcode-block" id="navigable_small_world_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.geometric.navigable_small_world_graph.html#networkx.generators.geometric.navigable_small_world_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> <span class="k">def</span> <span class="nf">navigable_small_world_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a navigable small-world graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a navigable small-world graph.</span> <span class="sd"> A navigable small-world graph is a directed grid with additional long-range</span> <span class="sd"> connections that are chosen randomly.</span> @@ -1130,7 +1130,7 @@ <span class="k">def</span> <span class="nf">thresholded_random_geometric_graph</span><span class="p">(</span> <span class="n">n</span><span class="p">,</span> <span class="n">radius</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a thresholded random geometric graph in the unit cube.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a thresholded random geometric graph in the unit cube.</span> <span class="sd"> The thresholded random geometric graph [1] model places `n` nodes</span> <span class="sd"> uniformly at random in the unit cube of dimensions `dim`. Each node</span> @@ -1298,7 +1298,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/harary_graph.html b/_modules/networkx/generators/harary_graph.html index 33e71290..d91f2b54 100644 --- a/_modules/networkx/generators/harary_graph.html +++ b/_modules/networkx/generators/harary_graph.html @@ -485,7 +485,7 @@ <div class="viewcode-block" id="hnm_harary_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.harary_graph.hnm_harary_graph.html#networkx.generators.harary_graph.hnm_harary_graph">[docs]</a><span class="k">def</span> <span class="nf">hnm_harary_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the Harary graph with given numbers of nodes and edges.</span> +<span class="w"> </span><span class="sd">"""Returns the Harary graph with given numbers of nodes and edges.</span> <span class="sd"> The Harary graph $H_{n,m}$ is the graph that maximizes node connectivity</span> <span class="sd"> with $n$ nodes and $m$ edges.</span> @@ -576,7 +576,7 @@ <div class="viewcode-block" id="hkn_harary_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.harary_graph.hkn_harary_graph.html#networkx.generators.harary_graph.hkn_harary_graph">[docs]</a><span class="k">def</span> <span class="nf">hkn_harary_graph</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the Harary graph with given node connectivity and node number.</span> +<span class="w"> </span><span class="sd">"""Returns the Harary graph with given node connectivity and node number.</span> <span class="sd"> The Harary graph $H_{k,n}$ is the graph that minimizes the number of</span> <span class="sd"> edges needed with given node connectivity $k$ and node number $n$.</span> @@ -709,7 +709,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/internet_as_graphs.html b/_modules/networkx/generators/internet_as_graphs.html index dc135f7b..2e48a8e1 100644 --- a/_modules/networkx/generators/internet_as_graphs.html +++ b/_modules/networkx/generators/internet_as_graphs.html @@ -470,7 +470,7 @@ <span class="k">def</span> <span class="nf">uniform_int_from_avg</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">seed</span><span class="p">):</span> - <span class="sd">"""Pick a random integer with uniform probability.</span> +<span class="w"> </span><span class="sd">"""Pick a random integer with uniform probability.</span> <span class="sd"> Returns a random integer uniformly taken from a distribution with</span> <span class="sd"> minimum value 'a' and average value 'm', X~U(a,b), E[X]=m, X in N where</span> @@ -497,7 +497,7 @@ <span class="k">def</span> <span class="nf">choose_pref_attach</span><span class="p">(</span><span class="n">degs</span><span class="p">,</span> <span class="n">seed</span><span class="p">):</span> - <span class="sd">"""Pick a random value, with a probability given by its weight.</span> +<span class="w"> </span><span class="sd">"""Pick a random value, with a probability given by its weight.</span> <span class="sd"> Returns a random choice among degs keys, each of which has a</span> <span class="sd"> probability proportional to the corresponding dictionary value.</span> @@ -532,10 +532,10 @@ <span class="k">class</span> <span class="nc">AS_graph_generator</span><span class="p">:</span> - <span class="sd">"""Generates random internet AS graphs."""</span> +<span class="w"> </span><span class="sd">"""Generates random internet AS graphs."""</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">seed</span><span class="p">):</span> - <span class="sd">"""Initializes variables. Immediate numbers are taken from [1].</span> +<span class="w"> </span><span class="sd">"""Initializes variables. Immediate numbers are taken from [1].</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -575,7 +575,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">t_c</span> <span class="o">=</span> <span class="mf">0.125</span> <span class="c1"># probability C's provider is T</span> <span class="k">def</span> <span class="nf">t_graph</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Generates the core mesh network of tier one nodes of a AS graph.</span> +<span class="w"> </span><span class="sd">"""Generates the core mesh network of tier one nodes of a AS graph.</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> @@ -603,7 +603,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="n">kind</span><span class="p">,</span> <span class="n">customer</span><span class="o">=</span><span class="n">customer</span><span class="p">)</span> <span class="k">def</span> <span class="nf">choose_peer_pref_attach</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node_list</span><span class="p">):</span> - <span class="sd">"""Pick a node with a probability weighted by its peer degree.</span> +<span class="w"> </span><span class="sd">"""Pick a node with a probability weighted by its peer degree.</span> <span class="sd"> Pick a node from node_list with preferential attachment</span> <span class="sd"> computed only on their peer degree</span> @@ -615,7 +615,7 @@ <span class="k">return</span> <span class="n">choose_pref_attach</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span> <span class="k">def</span> <span class="nf">choose_node_pref_attach</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node_list</span><span class="p">):</span> - <span class="sd">"""Pick a node with a probability weighted by its degree.</span> +<span class="w"> </span><span class="sd">"""Pick a node with a probability weighted by its degree.</span> <span class="sd"> Pick a node from node_list with preferential attachment</span> <span class="sd"> computed on their degree</span> @@ -625,7 +625,7 @@ <span class="k">return</span> <span class="n">choose_pref_attach</span><span class="p">(</span><span class="n">degs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span> <span class="k">def</span> <span class="nf">add_customer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">):</span> - <span class="sd">"""Keep the dictionaries 'customers' and 'providers' consistent."""</span> +<span class="w"> </span><span class="sd">"""Keep the dictionaries 'customers' and 'providers' consistent."""</span> <span class="bp">self</span><span class="o">.</span><span class="n">customers</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">providers</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">j</span><span class="p">)</span> @@ -634,7 +634,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">providers</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="k">def</span> <span class="nf">add_node</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">kind</span><span class="p">,</span> <span class="n">reg2prob</span><span class="p">,</span> <span class="n">avg_deg</span><span class="p">,</span> <span class="n">t_edge_prob</span><span class="p">):</span> - <span class="sd">"""Add a node and its customer transit edges to the graph.</span> +<span class="w"> </span><span class="sd">"""Add a node and its customer transit edges to the graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -693,7 +693,7 @@ <span class="k">return</span> <span class="n">i</span> <span class="k">def</span> <span class="nf">add_m_peering_link</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">to_kind</span><span class="p">):</span> - <span class="sd">"""Add a peering link between two middle tier (M) nodes.</span> +<span class="w"> </span><span class="sd">"""Add a peering link between two middle tier (M) nodes.</span> <span class="sd"> Target node j is drawn considering a preferential attachment based on</span> <span class="sd"> other M node peering degree.</span> @@ -733,7 +733,7 @@ <span class="k">return</span> <span class="kc">False</span> <span class="k">def</span> <span class="nf">add_cp_peering_link</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cp</span><span class="p">,</span> <span class="n">to_kind</span><span class="p">):</span> - <span class="sd">"""Add a peering link to a content provider (CP) node.</span> +<span class="w"> </span><span class="sd">"""Add a peering link to a content provider (CP) node.</span> <span class="sd"> Target node j can be CP or M and it is drawn uniformely among the nodes</span> <span class="sd"> belonging to the same region as cp.</span> @@ -780,7 +780,7 @@ <span class="k">return</span> <span class="kc">False</span> <span class="k">def</span> <span class="nf">graph_regions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rn</span><span class="p">):</span> - <span class="sd">"""Initializes AS network regions.</span> +<span class="w"> </span><span class="sd">"""Initializes AS network regions.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -793,7 +793,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">regions</span><span class="p">[</span><span class="s2">"REG"</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)]</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span> <span class="k">def</span> <span class="nf">add_peering_links</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">from_kind</span><span class="p">,</span> <span class="n">to_kind</span><span class="p">):</span> - <span class="sd">"""Utility function to add peering links among node groups."""</span> +<span class="w"> </span><span class="sd">"""Utility function to add peering links among node groups."""</span> <span class="n">peer_link_method</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">from_kind</span> <span class="o">==</span> <span class="s2">"M"</span><span class="p">:</span> <span class="n">peer_link_method</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">add_m_peering_link</span> @@ -811,7 +811,7 @@ <span class="n">peer_link_method</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">to_kind</span><span class="p">)</span> <span class="k">def</span> <span class="nf">generate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Generates a random AS network graph as described in [1].</span> +<span class="w"> </span><span class="sd">"""Generates a random AS network graph as described in [1].</span> <span class="sd"> Returns</span> <span class="sd"> -------</span> @@ -861,7 +861,7 @@ <div class="viewcode-block" id="random_internet_as_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.internet_as_graphs.random_internet_as_graph.html#networkx.generators.internet_as_graphs.random_internet_as_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_internet_as_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generates a random undirected graph resembling the Internet AS network</span> +<span class="w"> </span><span class="sd">"""Generates a random undirected graph resembling the Internet AS network</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -952,7 +952,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/intersection.html b/_modules/networkx/generators/intersection.html index 36f200ef..620aaa6f 100644 --- a/_modules/networkx/generators/intersection.html +++ b/_modules/networkx/generators/intersection.html @@ -476,7 +476,7 @@ <div class="viewcode-block" id="uniform_random_intersection_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.intersection.uniform_random_intersection_graph.html#networkx.generators.intersection.uniform_random_intersection_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">uniform_random_intersection_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a uniform random intersection graph.</span> +<span class="w"> </span><span class="sd">"""Returns a uniform random intersection graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -511,7 +511,7 @@ <div class="viewcode-block" id="k_random_intersection_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.intersection.k_random_intersection_graph.html#networkx.generators.intersection.k_random_intersection_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">k_random_intersection_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a intersection graph with randomly chosen attribute sets for</span> +<span class="w"> </span><span class="sd">"""Returns a intersection graph with randomly chosen attribute sets for</span> <span class="sd"> each node that are of equal size (k).</span> <span class="sd"> Parameters</span> @@ -546,7 +546,7 @@ <div class="viewcode-block" id="general_random_intersection_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.intersection.general_random_intersection_graph.html#networkx.generators.intersection.general_random_intersection_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">general_random_intersection_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random intersection graph with independent probabilities</span> +<span class="w"> </span><span class="sd">"""Returns a random intersection graph with independent probabilities</span> <span class="sd"> for connections between node and attribute sets.</span> <span class="sd"> Parameters</span> @@ -633,7 +633,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/interval_graph.html b/_modules/networkx/generators/interval_graph.html index 97f6eb6a..62b7c24f 100644 --- a/_modules/networkx/generators/interval_graph.html +++ b/_modules/networkx/generators/interval_graph.html @@ -472,7 +472,7 @@ <div class="viewcode-block" id="interval_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.interval_graph.interval_graph.html#networkx.generators.interval_graph.interval_graph">[docs]</a><span class="k">def</span> <span class="nf">interval_graph</span><span class="p">(</span><span class="n">intervals</span><span class="p">):</span> - <span class="sd">"""Generates an interval graph for a list of intervals given.</span> +<span class="w"> </span><span class="sd">"""Generates an interval graph for a list of intervals given.</span> <span class="sd"> In graph theory, an interval graph is an undirected graph formed from a set</span> <span class="sd"> of closed intervals on the real line, with a vertex for each interval</span> @@ -582,7 +582,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/joint_degree_seq.html b/_modules/networkx/generators/joint_degree_seq.html index 635746e9..d86e6d4a 100644 --- a/_modules/networkx/generators/joint_degree_seq.html +++ b/_modules/networkx/generators/joint_degree_seq.html @@ -475,7 +475,7 @@ <div class="viewcode-block" id="is_valid_joint_degree"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.joint_degree_seq.is_valid_joint_degree.html#networkx.generators.joint_degree_seq.is_valid_joint_degree">[docs]</a><span class="k">def</span> <span class="nf">is_valid_joint_degree</span><span class="p">(</span><span class="n">joint_degrees</span><span class="p">):</span> - <span class="sd">"""Checks whether the given joint degree dictionary is realizable.</span> +<span class="w"> </span><span class="sd">"""Checks whether the given joint degree dictionary is realizable.</span> <span class="sd"> A *joint degree dictionary* is a dictionary of dictionaries, in</span> <span class="sd"> which entry ``joint_degrees[k][l]`` is an integer representing the</span> @@ -541,7 +541,7 @@ <span class="k">def</span> <span class="nf">_neighbor_switch</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">unsat</span><span class="p">,</span> <span class="n">h_node_residual</span><span class="p">,</span> <span class="n">avoid_node_id</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Releases one free stub for ``w``, while preserving joint degree in G.</span> +<span class="w"> </span><span class="sd">"""Releases one free stub for ``w``, while preserving joint degree in G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -605,7 +605,7 @@ <div class="viewcode-block" id="joint_degree_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.joint_degree_seq.joint_degree_graph.html#networkx.generators.joint_degree_seq.joint_degree_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="k">def</span> <span class="nf">joint_degree_graph</span><span class="p">(</span><span class="n">joint_degrees</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generates a random simple graph with the given joint degree dictionary.</span> +<span class="w"> </span><span class="sd">"""Generates a random simple graph with the given joint degree dictionary.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -751,7 +751,7 @@ <div class="viewcode-block" id="is_valid_directed_joint_degree"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.joint_degree_seq.is_valid_directed_joint_degree.html#networkx.generators.joint_degree_seq.is_valid_directed_joint_degree">[docs]</a><span class="k">def</span> <span class="nf">is_valid_directed_joint_degree</span><span class="p">(</span><span class="n">in_degrees</span><span class="p">,</span> <span class="n">out_degrees</span><span class="p">,</span> <span class="n">nkk</span><span class="p">):</span> - <span class="sd">"""Checks whether the given directed joint degree input is realizable</span> +<span class="w"> </span><span class="sd">"""Checks whether the given directed joint degree input is realizable</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -826,7 +826,7 @@ <span class="k">def</span> <span class="nf">_directed_neighbor_switch</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">unsat</span><span class="p">,</span> <span class="n">h_node_residual_out</span><span class="p">,</span> <span class="n">chords</span><span class="p">,</span> <span class="n">h_partition_in</span><span class="p">,</span> <span class="n">partition</span> <span class="p">):</span> - <span class="sd">"""Releases one free stub for node w, while preserving joint degree in G.</span> +<span class="w"> </span><span class="sd">"""Releases one free stub for node w, while preserving joint degree in G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -889,7 +889,7 @@ <span class="k">def</span> <span class="nf">_directed_neighbor_switch_rev</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">unsat</span><span class="p">,</span> <span class="n">h_node_residual_in</span><span class="p">,</span> <span class="n">chords</span><span class="p">,</span> <span class="n">h_partition_out</span><span class="p">,</span> <span class="n">partition</span> <span class="p">):</span> - <span class="sd">"""The reverse of directed_neighbor_switch.</span> +<span class="w"> </span><span class="sd">"""The reverse of directed_neighbor_switch.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -940,7 +940,7 @@ <div class="viewcode-block" id="directed_joint_degree_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.joint_degree_seq.directed_joint_degree_graph.html#networkx.generators.joint_degree_seq.directed_joint_degree_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">directed_joint_degree_graph</span><span class="p">(</span><span class="n">in_degrees</span><span class="p">,</span> <span class="n">out_degrees</span><span class="p">,</span> <span class="n">nkk</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generates a random simple directed graph with the joint degree.</span> +<span class="w"> </span><span class="sd">"""Generates a random simple directed graph with the joint degree.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1182,7 +1182,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/lattice.html b/_modules/networkx/generators/lattice.html index 014f63b7..574b0f9d 100644 --- a/_modules/networkx/generators/lattice.html +++ b/_modules/networkx/generators/lattice.html @@ -496,7 +496,7 @@ <div class="viewcode-block" id="grid_2d_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.lattice.grid_2d_graph.html#networkx.generators.lattice.grid_2d_graph">[docs]</a><span class="nd">@nodes_or_number</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="k">def</span> <span class="nf">grid_2d_graph</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">periodic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the two-dimensional grid graph.</span> +<span class="w"> </span><span class="sd">"""Returns the two-dimensional grid graph.</span> <span class="sd"> The grid graph has each node connected to its four nearest neighbors.</span> @@ -548,7 +548,7 @@ <div class="viewcode-block" id="grid_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.lattice.grid_graph.html#networkx.generators.lattice.grid_graph">[docs]</a><span class="k">def</span> <span class="nf">grid_graph</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">periodic</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns the *n*-dimensional grid graph.</span> +<span class="w"> </span><span class="sd">"""Returns the *n*-dimensional grid graph.</span> <span class="sd"> The dimension *n* is the length of the list `dim` and the size in</span> <span class="sd"> each dimension is the value of the corresponding list element.</span> @@ -604,7 +604,7 @@ <div class="viewcode-block" id="hypercube_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.lattice.hypercube_graph.html#networkx.generators.lattice.hypercube_graph">[docs]</a><span class="k">def</span> <span class="nf">hypercube_graph</span><span class="p">(</span><span class="n">n</span><span class="p">):</span> - <span class="sd">"""Returns the *n*-dimensional hypercube graph.</span> +<span class="w"> </span><span class="sd">"""Returns the *n*-dimensional hypercube graph.</span> <span class="sd"> The nodes are the integers between 0 and ``2 ** n - 1``, inclusive.</span> @@ -632,7 +632,7 @@ <div class="viewcode-block" id="triangular_lattice_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.lattice.triangular_lattice_graph.html#networkx.generators.lattice.triangular_lattice_graph">[docs]</a><span class="k">def</span> <span class="nf">triangular_lattice_graph</span><span class="p">(</span> <span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">periodic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">with_positions</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the $m$ by $n$ triangular lattice graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the $m$ by $n$ triangular lattice graph.</span> <span class="sd"> The `triangular lattice graph`_ is a two-dimensional `grid graph`_ in</span> <span class="sd"> which each square unit has a diagonal edge (each grid unit has a chord).</span> @@ -732,7 +732,7 @@ <div class="viewcode-block" id="hexagonal_lattice_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.lattice.hexagonal_lattice_graph.html#networkx.generators.lattice.hexagonal_lattice_graph">[docs]</a><span class="k">def</span> <span class="nf">hexagonal_lattice_graph</span><span class="p">(</span> <span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">periodic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">with_positions</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Returns an `m` by `n` hexagonal lattice graph.</span> +<span class="w"> </span><span class="sd">"""Returns an `m` by `n` hexagonal lattice graph.</span> <span class="sd"> The *hexagonal lattice graph* is a graph whose nodes and edges are</span> <span class="sd"> the `hexagonal tiling`_ of the plane.</span> @@ -873,7 +873,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/line.html b/_modules/networkx/generators/line.html index 66215f95..7d5f4fee 100644 --- a/_modules/networkx/generators/line.html +++ b/_modules/networkx/generators/line.html @@ -474,7 +474,7 @@ <div class="viewcode-block" id="line_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.line.line_graph.html#networkx.generators.line.line_graph">[docs]</a><span class="k">def</span> <span class="nf">line_graph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the line graph of the graph or digraph `G`.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the line graph of the graph or digraph `G`.</span> <span class="sd"> The line graph of a graph `G` has a node for each edge in `G` and an</span> <span class="sd"> edge joining those nodes if the two edges in `G` share a common node. For</span> @@ -582,7 +582,7 @@ <span class="k">def</span> <span class="nf">_lg_directed</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the line graph L of the (multi)digraph G.</span> +<span class="w"> </span><span class="sd">"""Returns the line graph L of the (multi)digraph G.</span> <span class="sd"> Edges in G appear as nodes in L, represented as tuples of the form (u,v)</span> <span class="sd"> or (u,v,key) if G is a multidigraph. A node in L corresponding to the edge</span> @@ -612,7 +612,7 @@ <span class="k">def</span> <span class="nf">_lg_undirected</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">selfloops</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the line graph L of the (multi)graph G.</span> +<span class="w"> </span><span class="sd">"""Returns the line graph L of the (multi)graph G.</span> <span class="sd"> Edges in G appear as nodes in L, represented as sorted tuples of the form</span> <span class="sd"> (u,v), or (u,v,key) if G is a multigraph. A node in L corresponding to</span> @@ -678,7 +678,7 @@ <div class="viewcode-block" id="inverse_line_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.line.inverse_line_graph.html#networkx.generators.line.inverse_line_graph">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">inverse_line_graph</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns the inverse line graph of graph G.</span> +<span class="w"> </span><span class="sd">"""Returns the inverse line graph of graph G.</span> <span class="sd"> If H is a graph, and G is the line graph of H, such that G = L(H).</span> <span class="sd"> Then H is the inverse line graph of G.</span> @@ -741,6 +741,13 @@ <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="n">msg</span><span class="p">)</span> + <span class="k">if</span> <span class="n">nx</span><span class="o">.</span><span class="n">number_of_selfloops</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span> + <span class="s2">"A line graph as generated by NetworkX has no selfloops, so G has no "</span> + <span class="s2">"inverse line graph. Please remove the selfloops from G and try again."</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="n">msg</span><span class="p">)</span> + <span class="n">starting_cell</span> <span class="o">=</span> <span class="n">_select_starting_cell</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="n">P</span> <span class="o">=</span> <span class="n">_find_partition</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">starting_cell</span><span class="p">)</span> <span class="c1"># count how many times each vertex appears in the partition set</span> @@ -763,7 +770,7 @@ <span class="k">def</span> <span class="nf">_triangles</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">e</span><span class="p">):</span> - <span class="sd">"""Return list of all triangles containing edge e"""</span> +<span class="w"> </span><span class="sd">"""Return list of all triangles containing edge e"""</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">e</span> <span class="k">if</span> <span class="n">u</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">G</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">"Vertex </span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2"> not in graph"</span><span class="p">)</span> @@ -777,7 +784,7 @@ <span class="k">def</span> <span class="nf">_odd_triangle</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">T</span><span class="p">):</span> - <span class="sd">"""Test whether T is an odd triangle in G</span> +<span class="w"> </span><span class="sd">"""Test whether T is an odd triangle in G</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -820,7 +827,7 @@ <span class="k">def</span> <span class="nf">_find_partition</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">starting_cell</span><span class="p">):</span> - <span class="sd">"""Find a partition of the vertices of G into cells of complete graphs</span> +<span class="w"> </span><span class="sd">"""Find a partition of the vertices of G into cells of complete graphs</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -843,13 +850,9 @@ <span class="n">partitioned_vertices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">starting_cell</span><span class="p">)</span> <span class="k">while</span> <span class="n">G_partition</span><span class="o">.</span><span class="n">number_of_edges</span><span class="p">()</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># there are still edges left and so more cells to be made</span> - <span class="n">u</span> <span class="o">=</span> <span class="n">partitioned_vertices</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> + <span class="n">u</span> <span class="o">=</span> <span class="n">partitioned_vertices</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span> <span class="n">deg_u</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">G_partition</span><span class="p">[</span><span class="n">u</span><span class="p">])</span> - <span class="k">if</span> <span class="n">deg_u</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> - <span class="c1"># if u has no edges left in G_partition then we have found</span> - <span class="c1"># all of its cells so we do not need to keep looking</span> - <span class="n">partitioned_vertices</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span> - <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">deg_u</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># if u still has edges then we need to find its other cell</span> <span class="c1"># this other cell must be a complete subgraph or else G is</span> <span class="c1"># not a line graph</span> @@ -869,7 +872,7 @@ <span class="k">def</span> <span class="nf">_select_starting_cell</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">starting_edge</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Select a cell to initiate _find_partition</span> +<span class="w"> </span><span class="sd">"""Select a cell to initiate _find_partition</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -896,6 +899,8 @@ <span class="n">e</span> <span class="o">=</span> <span class="n">arbitrary_element</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">())</span> <span class="k">else</span><span class="p">:</span> <span class="n">e</span> <span class="o">=</span> <span class="n">starting_edge</span> + <span class="k">if</span> <span class="n">e</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">():</span> + <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Vertex </span><span class="si">{</span><span class="n">e</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2"> not in graph"</span><span class="p">)</span> <span class="k">if</span> <span class="n">e</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">G</span><span class="p">[</span><span class="n">e</span><span class="p">[</span><span class="mi">0</span><span class="p">]]:</span> <span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"starting_edge (</span><span class="si">{</span><span class="n">e</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">e</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">) is not in the Graph"</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> @@ -910,10 +915,10 @@ <span class="n">T</span> <span class="o">=</span> <span class="n">e_triangles</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="n">T</span> <span class="c1"># ab was original edge so check the other 2 edges</span> - <span class="n">ac_edges</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">_triangles</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">c</span><span class="p">))]</span> - <span class="n">bc_edges</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">_triangles</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">))]</span> - <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ac_edges</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> - <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">bc_edges</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">ac_edges</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">_triangles</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">c</span><span class="p">)))</span> + <span class="n">bc_edges</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">_triangles</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)))</span> + <span class="k">if</span> <span class="n">ac_edges</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> + <span class="k">if</span> <span class="n">bc_edges</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> <span class="n">starting_cell</span> <span class="o">=</span> <span class="n">T</span> <span class="k">else</span><span class="p">:</span> <span class="k">return</span> <span class="n">_select_starting_cell</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">starting_edge</span><span class="o">=</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">))</span> @@ -932,29 +937,22 @@ <span class="n">starting_cell</span> <span class="o">=</span> <span class="n">T</span> <span class="k">elif</span> <span class="n">r</span> <span class="o">-</span> <span class="mi">1</span> <span class="o"><=</span> <span class="n">s</span> <span class="o"><=</span> <span class="n">r</span><span class="p">:</span> <span class="c1"># check if odd triangles containing e form complete subgraph</span> - <span class="c1"># there must be exactly s+2 of them</span> - <span class="c1"># and they must all be connected</span> <span class="n">triangle_nodes</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">T</span> <span class="ow">in</span> <span class="n">odd_triangles</span><span class="p">:</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">T</span><span class="p">:</span> <span class="n">triangle_nodes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> - <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">triangle_nodes</span><span class="p">)</span> <span class="o">==</span> <span class="n">s</span> <span class="o">+</span> <span class="mi">2</span><span class="p">:</span> - <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">triangle_nodes</span><span class="p">:</span> - <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">triangle_nodes</span><span class="p">:</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="p">(</span><span class="n">v</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">G</span><span class="p">[</span><span class="n">u</span><span class="p">]):</span> - <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span> - <span class="s2">"G is not a line graph (odd triangles "</span> - <span class="s2">"do not form complete subgraph)"</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="n">msg</span><span class="p">)</span> - <span class="c1"># otherwise then we can use this as the starting cell</span> - <span class="n">starting_cell</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">triangle_nodes</span><span class="p">)</span> - <span class="k">else</span><span class="p">:</span> - <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span> - <span class="s2">"G is not a line graph (odd triangles "</span> - <span class="s2">"do not form complete subgraph)"</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="n">msg</span><span class="p">)</span> + + <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">triangle_nodes</span><span class="p">:</span> + <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">triangle_nodes</span><span class="p">:</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="p">(</span><span class="n">v</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">G</span><span class="p">[</span><span class="n">u</span><span class="p">]):</span> + <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span> + <span class="s2">"G is not a line graph (odd triangles "</span> + <span class="s2">"do not form complete subgraph)"</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="n">msg</span><span class="p">)</span> + <span class="c1"># otherwise then we can use this as the starting cell</span> + <span class="n">starting_cell</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">triangle_nodes</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span> <span class="s2">"G is not a line graph (incorrect number of "</span> @@ -1013,7 +1011,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/mycielski.html b/_modules/networkx/generators/mycielski.html index 3965a0cb..2adba60d 100644 --- a/_modules/networkx/generators/mycielski.html +++ b/_modules/networkx/generators/mycielski.html @@ -475,7 +475,7 @@ <div class="viewcode-block" id="mycielskian"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.mycielski.mycielskian.html#networkx.generators.mycielski.mycielskian">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">mycielskian</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the Mycielskian of a simple, undirected graph G</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the Mycielskian of a simple, undirected graph G</span> <span class="sd"> The Mycielskian of graph preserves a graph's triangle free</span> <span class="sd"> property while increasing the chromatic number by 1.</span> @@ -531,7 +531,7 @@ <div class="viewcode-block" id="mycielski_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.mycielski.mycielski_graph.html#networkx.generators.mycielski.mycielski_graph">[docs]</a><span class="k">def</span> <span class="nf">mycielski_graph</span><span class="p">(</span><span class="n">n</span><span class="p">):</span> - <span class="sd">"""Generator for the n_th Mycielski Graph.</span> +<span class="w"> </span><span class="sd">"""Generator for the n_th Mycielski Graph.</span> <span class="sd"> The Mycielski family of graphs is an infinite set of graphs.</span> <span class="sd"> :math:`M_1` is the singleton graph, :math:`M_2` is two vertices with an</span> @@ -620,7 +620,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/nonisomorphic_trees.html b/_modules/networkx/generators/nonisomorphic_trees.html index 9a7ec02a..ad9c6df0 100644 --- a/_modules/networkx/generators/nonisomorphic_trees.html +++ b/_modules/networkx/generators/nonisomorphic_trees.html @@ -476,7 +476,7 @@ <div class="viewcode-block" id="nonisomorphic_trees"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.nonisomorphic_trees.nonisomorphic_trees.html#networkx.generators.nonisomorphic_trees.nonisomorphic_trees">[docs]</a><span class="k">def</span> <span class="nf">nonisomorphic_trees</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="n">create</span><span class="o">=</span><span class="s2">"graph"</span><span class="p">):</span> - <span class="sd">"""Returns a list of nonisomporphic trees</span> +<span class="w"> </span><span class="sd">"""Returns a list of nonisomporphic trees</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -515,7 +515,7 @@ <div class="viewcode-block" id="number_of_nonisomorphic_trees"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees.html#networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees">[docs]</a><span class="k">def</span> <span class="nf">number_of_nonisomorphic_trees</span><span class="p">(</span><span class="n">order</span><span class="p">):</span> - <span class="sd">"""Returns the number of nonisomorphic trees</span> +<span class="w"> </span><span class="sd">"""Returns the number of nonisomorphic trees</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -534,7 +534,7 @@ <span class="k">def</span> <span class="nf">_next_rooted_tree</span><span class="p">(</span><span class="n">predecessor</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""One iteration of the Beyer-Hedetniemi algorithm."""</span> +<span class="w"> </span><span class="sd">"""One iteration of the Beyer-Hedetniemi algorithm."""</span> <span class="k">if</span> <span class="n">p</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="n">p</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">predecessor</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> @@ -553,7 +553,7 @@ <span class="k">def</span> <span class="nf">_next_tree</span><span class="p">(</span><span class="n">candidate</span><span class="p">):</span> - <span class="sd">"""One iteration of the Wright, Richmond, Odlyzko and McKay</span> +<span class="w"> </span><span class="sd">"""One iteration of the Wright, Richmond, Odlyzko and McKay</span> <span class="sd"> algorithm."""</span> <span class="c1"># valid representation of a free tree if:</span> @@ -592,7 +592,7 @@ <span class="k">def</span> <span class="nf">_split_tree</span><span class="p">(</span><span class="n">layout</span><span class="p">):</span> - <span class="sd">"""Returns a tuple of two layouts, one containing the left</span> +<span class="w"> </span><span class="sd">"""Returns a tuple of two layouts, one containing the left</span> <span class="sd"> subtree of the root vertex, and one containing the original tree</span> <span class="sd"> with the left subtree removed."""</span> @@ -615,7 +615,7 @@ <span class="k">def</span> <span class="nf">_layout_to_matrix</span><span class="p">(</span><span class="n">layout</span><span class="p">):</span> - <span class="sd">"""Create the adjacency matrix for the tree specified by the</span> +<span class="w"> </span><span class="sd">"""Create the adjacency matrix for the tree specified by the</span> <span class="sd"> given layout (level sequence)."""</span> <span class="n">result</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">layout</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">layout</span><span class="p">))]</span> @@ -635,7 +635,7 @@ <span class="k">def</span> <span class="nf">_layout_to_graph</span><span class="p">(</span><span class="n">layout</span><span class="p">):</span> - <span class="sd">"""Create a NetworkX Graph for the tree specified by the</span> +<span class="w"> </span><span class="sd">"""Create a NetworkX Graph for the tree specified by the</span> <span class="sd"> given layout(level sequence)"""</span> <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span> <span class="n">stack</span> <span class="o">=</span> <span class="p">[]</span> @@ -702,7 +702,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/random_clustered.html b/_modules/networkx/generators/random_clustered.html index d9e2bdf8..c711813d 100644 --- a/_modules/networkx/generators/random_clustered.html +++ b/_modules/networkx/generators/random_clustered.html @@ -471,7 +471,7 @@ <div class="viewcode-block" id="random_clustered_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_clustered.random_clustered_graph.html#networkx.generators.random_clustered.random_clustered_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_clustered_graph</span><span class="p">(</span><span class="n">joint_degree_sequence</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Generate a random graph with the given joint independent edge degree and</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Generate a random graph with the given joint independent edge degree and</span> <span class="sd"> triangle degree sequence.</span> <span class="sd"> This uses a configuration model-like approach to generate a random graph</span> @@ -628,7 +628,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/random_graphs.html b/_modules/networkx/generators/random_graphs.html index c1fda8c6..ca1ddb16 100644 --- a/_modules/networkx/generators/random_graphs.html +++ b/_modules/networkx/generators/random_graphs.html @@ -501,7 +501,7 @@ <div class="viewcode-block" id="fast_gnp_random_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.fast_gnp_random_graph.html#networkx.generators.random_graphs.fast_gnp_random_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">fast_gnp_random_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns a $G_{n,p}$ random graph, also known as an ErdÅ‘s-Rényi graph or</span> +<span class="w"> </span><span class="sd">"""Returns a $G_{n,p}$ random graph, also known as an ErdÅ‘s-Rényi graph or</span> <span class="sd"> a binomial graph.</span> <span class="sd"> Parameters</span> @@ -572,7 +572,7 @@ <div class="viewcode-block" id="gnp_random_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.gnp_random_graph.html#networkx.generators.random_graphs.gnp_random_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">gnp_random_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns a $G_{n,p}$ random graph, also known as an ErdÅ‘s-Rényi graph</span> +<span class="w"> </span><span class="sd">"""Returns a $G_{n,p}$ random graph, also known as an ErdÅ‘s-Rényi graph</span> <span class="sd"> or a binomial graph.</span> <span class="sd"> The $G_{n,p}$ model chooses each of the possible edges with probability $p$.</span> @@ -636,7 +636,7 @@ <div class="viewcode-block" id="dense_gnm_random_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.dense_gnm_random_graph.html#networkx.generators.random_graphs.dense_gnm_random_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">dense_gnm_random_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a $G_{n,m}$ random graph.</span> +<span class="w"> </span><span class="sd">"""Returns a $G_{n,m}$ random graph.</span> <span class="sd"> In the $G_{n,m}$ model, a graph is chosen uniformly at random from the set</span> <span class="sd"> of all graphs with $n$ nodes and $m$ edges.</span> @@ -697,7 +697,7 @@ <div class="viewcode-block" id="gnm_random_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.gnm_random_graph.html#networkx.generators.random_graphs.gnm_random_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">gnm_random_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Returns a $G_{n,m}$ random graph.</span> +<span class="w"> </span><span class="sd">"""Returns a $G_{n,m}$ random graph.</span> <span class="sd"> In the $G_{n,m}$ model, a graph is chosen uniformly at random from the set</span> <span class="sd"> of all graphs with $n$ nodes and $m$ edges.</span> @@ -752,7 +752,7 @@ <div class="viewcode-block" id="newman_watts_strogatz_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.newman_watts_strogatz_graph.html#networkx.generators.random_graphs.newman_watts_strogatz_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">newman_watts_strogatz_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a Newman–Watts–Strogatz small-world graph.</span> +<span class="w"> </span><span class="sd">"""Returns a Newman–Watts–Strogatz small-world graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -822,7 +822,7 @@ <div class="viewcode-block" id="watts_strogatz_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.watts_strogatz_graph.html#networkx.generators.random_graphs.watts_strogatz_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">watts_strogatz_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a Watts–Strogatz small-world graph.</span> +<span class="w"> </span><span class="sd">"""Returns a Watts–Strogatz small-world graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -896,7 +896,7 @@ <div class="viewcode-block" id="connected_watts_strogatz_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.connected_watts_strogatz_graph.html#networkx.generators.random_graphs.connected_watts_strogatz_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> <span class="k">def</span> <span class="nf">connected_watts_strogatz_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">tries</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a connected Watts–Strogatz small-world graph.</span> +<span class="w"> </span><span class="sd">"""Returns a connected Watts–Strogatz small-world graph.</span> <span class="sd"> Attempts to generate a connected graph by repeated generation of</span> <span class="sd"> Watts–Strogatz small-world graphs. An exception is raised if the maximum</span> @@ -948,7 +948,7 @@ <div class="viewcode-block" id="random_regular_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.random_regular_graph.html#networkx.generators.random_graphs.random_regular_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_regular_graph</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a random $d$-regular graph on $n$ nodes.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a random $d$-regular graph on $n$ nodes.</span> <span class="sd"> The resulting graph has no self-loops or parallel edges.</span> @@ -1060,7 +1060,7 @@ <span class="k">def</span> <span class="nf">_random_subset</span><span class="p">(</span><span class="n">seq</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">rng</span><span class="p">):</span> - <span class="sd">"""Return m unique elements from seq.</span> +<span class="w"> </span><span class="sd">"""Return m unique elements from seq.</span> <span class="sd"> This differs from random.sample which can return repeated</span> <span class="sd"> elements if seq holds repeated elements.</span> @@ -1076,7 +1076,7 @@ <div class="viewcode-block" id="barabasi_albert_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.barabasi_albert_graph.html#networkx.generators.random_graphs.barabasi_albert_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">barabasi_albert_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">initial_graph</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random graph using Barabási–Albert preferential attachment</span> +<span class="w"> </span><span class="sd">"""Returns a random graph using Barabási–Albert preferential attachment</span> <span class="sd"> A graph of $n$ nodes is grown by attaching new nodes each with $m$</span> <span class="sd"> edges that are preferentially attached to existing nodes with high degree.</span> @@ -1148,7 +1148,7 @@ <div class="viewcode-block" id="dual_barabasi_albert_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.dual_barabasi_albert_graph.html#networkx.generators.random_graphs.dual_barabasi_albert_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> <span class="k">def</span> <span class="nf">dual_barabasi_albert_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m1</span><span class="p">,</span> <span class="n">m2</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">initial_graph</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random graph using dual Barabási–Albert preferential attachment</span> +<span class="w"> </span><span class="sd">"""Returns a random graph using dual Barabási–Albert preferential attachment</span> <span class="sd"> A graph of $n$ nodes is grown by attaching new nodes each with either $m_1$</span> <span class="sd"> edges (with probability $p$) or $m_2$ edges (with probability $1-p$) that</span> @@ -1215,7 +1215,7 @@ <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">initial_graph</span><span class="p">)</span> <span class="o"><</span> <span class="nb">max</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">m2</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">initial_graph</span><span class="p">)</span> <span class="o">></span> <span class="n">n</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">"Barabási–Albert initial graph must have between "</span> - <span class="sa">f</span><span class="s2">"max(m1, m2) = </span><span class="si">{</span><span class="nb">max</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">m2</span><span class="p">)</span><span class="si">}</span><span class="s2"> and n = </span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s2"> nodes"</span> + <span class="sa">f</span><span class="s2">"max(m1, m2) = </span><span class="si">{</span><span class="nb">max</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span><span class="w"> </span><span class="n">m2</span><span class="p">)</span><span class="si">}</span><span class="s2"> and n = </span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s2"> nodes"</span> <span class="p">)</span> <span class="n">G</span> <span class="o">=</span> <span class="n">initial_graph</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> @@ -1247,7 +1247,7 @@ <div class="viewcode-block" id="extended_barabasi_albert_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.extended_barabasi_albert_graph.html#networkx.generators.random_graphs.extended_barabasi_albert_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> <span class="k">def</span> <span class="nf">extended_barabasi_albert_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns an extended Barabási–Albert model graph.</span> +<span class="w"> </span><span class="sd">"""Returns an extended Barabási–Albert model graph.</span> <span class="sd"> An extended Barabási–Albert model graph is a random graph constructed</span> <span class="sd"> using preferential attachment. The extended model allows new edges,</span> @@ -1410,7 +1410,7 @@ <div class="viewcode-block" id="powerlaw_cluster_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.powerlaw_cluster_graph.html#networkx.generators.random_graphs.powerlaw_cluster_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">powerlaw_cluster_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Holme and Kim algorithm for growing graphs with powerlaw</span> +<span class="w"> </span><span class="sd">"""Holme and Kim algorithm for growing graphs with powerlaw</span> <span class="sd"> degree distribution and approximate average clustering.</span> <span class="sd"> Parameters</span> @@ -1499,7 +1499,7 @@ <div class="viewcode-block" id="random_lobster"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.random_lobster.html#networkx.generators.random_graphs.random_lobster">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_lobster</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random lobster graph.</span> +<span class="w"> </span><span class="sd">"""Returns a random lobster graph.</span> <span class="sd"> A lobster is a tree that reduces to a caterpillar when pruning all</span> <span class="sd"> leaf nodes. A caterpillar is a tree that reduces to a path graph</span> @@ -1549,7 +1549,7 @@ <div class="viewcode-block" id="random_shell_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.random_shell_graph.html#networkx.generators.random_graphs.random_shell_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_shell_graph</span><span class="p">(</span><span class="n">constructor</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random shell graph for the constructor given.</span> +<span class="w"> </span><span class="sd">"""Returns a random shell graph for the constructor given.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1606,7 +1606,7 @@ <div class="viewcode-block" id="random_powerlaw_tree"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.random_powerlaw_tree.html#networkx.generators.random_graphs.random_powerlaw_tree">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_powerlaw_tree</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tries</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span> - <span class="sd">"""Returns a tree with a power law degree distribution.</span> +<span class="w"> </span><span class="sd">"""Returns a tree with a power law degree distribution.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1642,7 +1642,7 @@ <div class="viewcode-block" id="random_powerlaw_tree_sequence"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.random_powerlaw_tree_sequence.html#networkx.generators.random_graphs.random_powerlaw_tree_sequence">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_powerlaw_tree_sequence</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tries</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span> - <span class="sd">"""Returns a degree sequence for a tree with a power law distribution.</span> +<span class="w"> </span><span class="sd">"""Returns a degree sequence for a tree with a power law distribution.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1698,7 +1698,7 @@ <div class="viewcode-block" id="random_kernel_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.random_graphs.random_kernel_graph.html#networkx.generators.random_graphs.random_kernel_graph">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_kernel_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">kernel_integral</span><span class="p">,</span> <span class="n">kernel_root</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns an random graph based on the specified kernel.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns an random graph based on the specified kernel.</span> <span class="sd"> The algorithm chooses each of the $[n(n-1)]/2$ possible edges with</span> <span class="sd"> probability specified by a kernel $\kappa(x,y)$ [1]_. The kernel</span> @@ -1828,7 +1828,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/small.html b/_modules/networkx/generators/small.html index 763acdfd..b26088ee 100644 --- a/_modules/networkx/generators/small.html +++ b/_modules/networkx/generators/small.html @@ -505,7 +505,7 @@ <span class="k">def</span> <span class="nf">_raise_on_directed</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> A decorator which inspects the `create_using` argument and raises a</span> <span class="sd"> NetworkX exception when `create_using` is a DiGraph (class or instance) for</span> <span class="sd"> graph generators that do not support directed outputs.</span> @@ -523,7 +523,7 @@ <div class="viewcode-block" id="LCF_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.LCF_graph.html#networkx.generators.small.LCF_graph">[docs]</a><span class="k">def</span> <span class="nf">LCF_graph</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">shift_list</span><span class="p">,</span> <span class="n">repeats</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return the cubic graph specified in LCF notation.</span> <span class="sd"> LCF notation (LCF=Lederberg-Coxeter-Fruchte) is a compressed</span> @@ -589,7 +589,7 @@ <div class="viewcode-block" id="bull_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.bull_graph.html#networkx.generators.small.bull_graph">[docs]</a><span class="nd">@_raise_on_directed</span> <span class="k">def</span> <span class="nf">bull_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the Bull Graph</span> <span class="sd"> The Bull Graph has 5 nodes and 5 edges. It is a planar undirected</span> @@ -622,7 +622,7 @@ <div class="viewcode-block" id="chvatal_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.chvatal_graph.html#networkx.generators.small.chvatal_graph">[docs]</a><span class="nd">@_raise_on_directed</span> <span class="k">def</span> <span class="nf">chvatal_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the Chvátal Graph</span> <span class="sd"> The Chvátal Graph is an undirected graph with 12 nodes and 24 edges [1]_.</span> @@ -666,7 +666,7 @@ <div class="viewcode-block" id="cubical_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.cubical_graph.html#networkx.generators.small.cubical_graph">[docs]</a><span class="nd">@_raise_on_directed</span> <span class="k">def</span> <span class="nf">cubical_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the 3-regular Platonic Cubical Graph</span> <span class="sd"> The skeleton of the cube (the nodes and edges) form a graph, with 8</span> @@ -708,7 +708,7 @@ <div class="viewcode-block" id="desargues_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.desargues_graph.html#networkx.generators.small.desargues_graph">[docs]</a><span class="k">def</span> <span class="nf">desargues_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the Desargues Graph</span> <span class="sd"> The Desargues Graph is a non-planar, distance-transitive cubic graph</span> @@ -738,7 +738,7 @@ <div class="viewcode-block" id="diamond_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.diamond_graph.html#networkx.generators.small.diamond_graph">[docs]</a><span class="nd">@_raise_on_directed</span> <span class="k">def</span> <span class="nf">diamond_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the Diamond graph</span> <span class="sd"> The Diamond Graph is planar undirected graph with 4 nodes and 5 edges.</span> @@ -766,7 +766,7 @@ <div class="viewcode-block" id="dodecahedral_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.dodecahedral_graph.html#networkx.generators.small.dodecahedral_graph">[docs]</a><span class="k">def</span> <span class="nf">dodecahedral_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the Platonic Dodecahedral graph.</span> <span class="sd"> The dodecahedral graph has 20 nodes and 30 edges. The skeleton of the</span> @@ -796,7 +796,7 @@ <div class="viewcode-block" id="frucht_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.frucht_graph.html#networkx.generators.small.frucht_graph">[docs]</a><span class="k">def</span> <span class="nf">frucht_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the Frucht Graph.</span> <span class="sd"> The Frucht Graph is the smallest cubical graph whose</span> @@ -842,7 +842,7 @@ <div class="viewcode-block" id="heawood_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.heawood_graph.html#networkx.generators.small.heawood_graph">[docs]</a><span class="k">def</span> <span class="nf">heawood_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the Heawood Graph, a (3,6) cage.</span> <span class="sd"> The Heawood Graph is an undirected graph with 14 nodes and 21 edges,</span> @@ -875,7 +875,7 @@ <div class="viewcode-block" id="hoffman_singleton_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.hoffman_singleton_graph.html#networkx.generators.small.hoffman_singleton_graph">[docs]</a><span class="k">def</span> <span class="nf">hoffman_singleton_graph</span><span class="p">():</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the Hoffman-Singleton Graph.</span> <span class="sd"> The Hoffman–Singleton graph is a symmetrical undirected graph</span> @@ -918,7 +918,7 @@ <div class="viewcode-block" id="house_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.house_graph.html#networkx.generators.small.house_graph">[docs]</a><span class="nd">@_raise_on_directed</span> <span class="k">def</span> <span class="nf">house_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the House graph (square with triangle on top)</span> <span class="sd"> The house graph is a simple undirected graph with</span> @@ -948,7 +948,7 @@ <div class="viewcode-block" id="house_x_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.house_x_graph.html#networkx.generators.small.house_x_graph">[docs]</a><span class="nd">@_raise_on_directed</span> <span class="k">def</span> <span class="nf">house_x_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the House graph with a cross inside the house square.</span> <span class="sd"> The House X-graph is the House graph plus the two edges connecting diagonally</span> @@ -977,7 +977,7 @@ <div class="viewcode-block" id="icosahedral_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.icosahedral_graph.html#networkx.generators.small.icosahedral_graph">[docs]</a><span class="nd">@_raise_on_directed</span> <span class="k">def</span> <span class="nf">icosahedral_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the Platonic Icosahedral graph.</span> <span class="sd"> The icosahedral graph has 12 nodes and 30 edges. It is a Platonic graph</span> @@ -1019,7 +1019,7 @@ <div class="viewcode-block" id="krackhardt_kite_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.krackhardt_kite_graph.html#networkx.generators.small.krackhardt_kite_graph">[docs]</a><span class="nd">@_raise_on_directed</span> <span class="k">def</span> <span class="nf">krackhardt_kite_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the Krackhardt Kite Social Network.</span> <span class="sd"> A 10 actor social network introduced by David Krackhardt</span> @@ -1068,7 +1068,7 @@ <div class="viewcode-block" id="moebius_kantor_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.moebius_kantor_graph.html#networkx.generators.small.moebius_kantor_graph">[docs]</a><span class="k">def</span> <span class="nf">moebius_kantor_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the Moebius-Kantor graph.</span> <span class="sd"> The Möbius-Kantor graph is the cubic symmetric graph on 16 nodes.</span> @@ -1097,7 +1097,7 @@ <div class="viewcode-block" id="octahedral_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.octahedral_graph.html#networkx.generators.small.octahedral_graph">[docs]</a><span class="nd">@_raise_on_directed</span> <span class="k">def</span> <span class="nf">octahedral_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the Platonic Octahedral graph.</span> <span class="sd"> The octahedral graph is the 6-node 12-edge Platonic graph having the</span> @@ -1131,7 +1131,7 @@ <div class="viewcode-block" id="pappus_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.pappus_graph.html#networkx.generators.small.pappus_graph">[docs]</a><span class="k">def</span> <span class="nf">pappus_graph</span><span class="p">():</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the Pappus graph.</span> <span class="sd"> The Pappus graph is a cubic symmetric distance-regular graph with 18 nodes</span> @@ -1154,7 +1154,7 @@ <div class="viewcode-block" id="petersen_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.petersen_graph.html#networkx.generators.small.petersen_graph">[docs]</a><span class="nd">@_raise_on_directed</span> <span class="k">def</span> <span class="nf">petersen_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the Petersen graph.</span> <span class="sd"> The Peterson graph is a cubic, undirected graph with 10 nodes and 15 edges [1]_.</span> @@ -1197,7 +1197,7 @@ <div class="viewcode-block" id="sedgewick_maze_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.sedgewick_maze_graph.html#networkx.generators.small.sedgewick_maze_graph">[docs]</a><span class="k">def</span> <span class="nf">sedgewick_maze_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return a small maze with a cycle.</span> <span class="sd"> This is the maze used in Sedgewick, 3rd Edition, Part 5, Graph</span> @@ -1229,7 +1229,7 @@ <div class="viewcode-block" id="tetrahedral_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.tetrahedral_graph.html#networkx.generators.small.tetrahedral_graph">[docs]</a><span class="k">def</span> <span class="nf">tetrahedral_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the 3-regular Platonic Tetrahedral graph.</span> <span class="sd"> Tetrahedral graph has 4 nodes and 6 edges. It is a</span> @@ -1258,7 +1258,7 @@ <div class="viewcode-block" id="truncated_cube_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.truncated_cube_graph.html#networkx.generators.small.truncated_cube_graph">[docs]</a><span class="nd">@_raise_on_directed</span> <span class="k">def</span> <span class="nf">truncated_cube_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the skeleton of the truncated cube.</span> <span class="sd"> The truncated cube is an Archimedean solid with 14 regular</span> @@ -1315,7 +1315,7 @@ <div class="viewcode-block" id="truncated_tetrahedron_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.truncated_tetrahedron_graph.html#networkx.generators.small.truncated_tetrahedron_graph">[docs]</a><span class="k">def</span> <span class="nf">truncated_tetrahedron_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the skeleton of the truncated Platonic tetrahedron.</span> <span class="sd"> The truncated tetrahedron is an Archimedean solid with 4 regular hexagonal faces,</span> @@ -1345,7 +1345,7 @@ <div class="viewcode-block" id="tutte_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.small.tutte_graph.html#networkx.generators.small.tutte_graph">[docs]</a><span class="nd">@_raise_on_directed</span> <span class="k">def</span> <span class="nf">tutte_graph</span><span class="p">(</span><span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the Tutte graph.</span> <span class="sd"> The Tutte graph is a cubic polyhedral, non-Hamiltonian graph. It has</span> @@ -1467,7 +1467,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/social.html b/_modules/networkx/generators/social.html index 651fcbca..6238bdba 100644 --- a/_modules/networkx/generators/social.html +++ b/_modules/networkx/generators/social.html @@ -475,7 +475,7 @@ <div class="viewcode-block" id="karate_club_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.social.karate_club_graph.html#networkx.generators.social.karate_club_graph">[docs]</a><span class="k">def</span> <span class="nf">karate_club_graph</span><span class="p">():</span> - <span class="sd">"""Returns Zachary's Karate Club graph.</span> +<span class="w"> </span><span class="sd">"""Returns Zachary's Karate Club graph.</span> <span class="sd"> Each node in the returned graph has a node attribute 'club' that</span> <span class="sd"> indicates the name of the club to which the member represented by that node</span> @@ -556,7 +556,7 @@ <div class="viewcode-block" id="davis_southern_women_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.social.davis_southern_women_graph.html#networkx.generators.social.davis_southern_women_graph">[docs]</a><span class="k">def</span> <span class="nf">davis_southern_women_graph</span><span class="p">():</span> - <span class="sd">"""Returns Davis Southern women social network.</span> +<span class="w"> </span><span class="sd">"""Returns Davis Southern women social network.</span> <span class="sd"> This is a bipartite graph.</span> @@ -706,7 +706,7 @@ <div class="viewcode-block" id="florentine_families_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.social.florentine_families_graph.html#networkx.generators.social.florentine_families_graph">[docs]</a><span class="k">def</span> <span class="nf">florentine_families_graph</span><span class="p">():</span> - <span class="sd">"""Returns Florentine families graph.</span> +<span class="w"> </span><span class="sd">"""Returns Florentine families graph.</span> <span class="sd"> References</span> <span class="sd"> ----------</span> @@ -739,7 +739,7 @@ <div class="viewcode-block" id="les_miserables_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.social.les_miserables_graph.html#networkx.generators.social.les_miserables_graph">[docs]</a><span class="k">def</span> <span class="nf">les_miserables_graph</span><span class="p">():</span> - <span class="sd">"""Returns coappearance network of characters in the novel Les Miserables.</span> +<span class="w"> </span><span class="sd">"""Returns coappearance network of characters in the novel Les Miserables.</span> <span class="sd"> References</span> <span class="sd"> ----------</span> @@ -1054,7 +1054,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/spectral_graph_forge.html b/_modules/networkx/generators/spectral_graph_forge.html index 333cfd96..554b3749 100644 --- a/_modules/networkx/generators/spectral_graph_forge.html +++ b/_modules/networkx/generators/spectral_graph_forge.html @@ -472,7 +472,7 @@ <div class="viewcode-block" id="spectral_graph_forge"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.spectral_graph_forge.spectral_graph_forge.html#networkx.generators.spectral_graph_forge.spectral_graph_forge">[docs]</a><span class="nd">@np_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">spectral_graph_forge</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="s2">"identity"</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random simple graph with spectrum resembling that of `G`</span> +<span class="w"> </span><span class="sd">"""Returns a random simple graph with spectrum resembling that of `G`</span> <span class="sd"> This algorithm, called Spectral Graph Forge (SGF), computes the</span> <span class="sd"> eigenvectors of a given graph adjacency matrix, filters them and</span> @@ -633,7 +633,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/stochastic.html b/_modules/networkx/generators/stochastic.html index d4fb321e..8b3649d6 100644 --- a/_modules/networkx/generators/stochastic.html +++ b/_modules/networkx/generators/stochastic.html @@ -474,7 +474,7 @@ <div class="viewcode-block" id="stochastic_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.stochastic.stochastic_graph.html#networkx.generators.stochastic.stochastic_graph">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">stochastic_graph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns a right-stochastic representation of directed graph `G`.</span> +<span class="w"> </span><span class="sd">"""Returns a right-stochastic representation of directed graph `G`.</span> <span class="sd"> A right-stochastic graph is a weighted digraph in which for each</span> <span class="sd"> node, the sum of the weights of all the out-edges of that node is</span> @@ -561,7 +561,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/sudoku.html b/_modules/networkx/generators/sudoku.html index 397c8a51..53574921 100644 --- a/_modules/networkx/generators/sudoku.html +++ b/_modules/networkx/generators/sudoku.html @@ -511,7 +511,7 @@ <div class="viewcode-block" id="sudoku_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.sudoku.sudoku_graph.html#networkx.generators.sudoku.sudoku_graph">[docs]</a><span class="k">def</span> <span class="nf">sudoku_graph</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span> - <span class="sd">"""Returns the n-Sudoku graph. The default value of n is 3.</span> +<span class="w"> </span><span class="sd">"""Returns the n-Sudoku graph. The default value of n is 3.</span> <span class="sd"> The n-Sudoku graph is a graph with n^4 vertices, corresponding to the</span> <span class="sd"> cells of an n^2 by n^2 grid. Two distinct vertices are adjacent if and</span> @@ -642,7 +642,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/trees.html b/_modules/networkx/generators/trees.html index d12fe040..295a7333 100644 --- a/_modules/networkx/generators/trees.html +++ b/_modules/networkx/generators/trees.html @@ -471,7 +471,7 @@ <div class="viewcode-block" id="prefix_tree"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.trees.prefix_tree.html#networkx.generators.trees.prefix_tree">[docs]</a><span class="k">def</span> <span class="nf">prefix_tree</span><span class="p">(</span><span class="n">paths</span><span class="p">):</span> - <span class="sd">"""Creates a directed prefix tree from a list of paths.</span> +<span class="w"> </span><span class="sd">"""Creates a directed prefix tree from a list of paths.</span> <span class="sd"> Usually the paths are described as strings or lists of integers.</span> @@ -603,7 +603,7 @@ <span class="k">def</span> <span class="nf">prefix_tree_recursive</span><span class="p">(</span><span class="n">paths</span><span class="p">):</span> - <span class="sd">"""Recursively creates a directed prefix tree from a list of paths.</span> +<span class="w"> </span><span class="sd">"""Recursively creates a directed prefix tree from a list of paths.</span> <span class="sd"> The original recursive version of prefix_tree for comparison. It is</span> <span class="sd"> the same algorithm but the recursion is unrolled onto a stack.</span> @@ -697,7 +697,7 @@ <span class="sd"> """</span> <span class="k">def</span> <span class="nf">_helper</span><span class="p">(</span><span class="n">paths</span><span class="p">,</span> <span class="n">root</span><span class="p">,</span> <span class="n">tree</span><span class="p">):</span> - <span class="sd">"""Recursively create a trie from the given list of paths.</span> +<span class="w"> </span><span class="sd">"""Recursively create a trie from the given list of paths.</span> <span class="sd"> `paths` is a list of paths, each of which is itself a list of</span> <span class="sd"> nodes, relative to the given `root` (but not including it). This</span> @@ -751,7 +751,7 @@ <span class="c1">#</span> <div class="viewcode-block" id="random_tree"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.trees.random_tree.html#networkx.generators.trees.random_tree">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_tree</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a uniformly random tree on `n` nodes.</span> +<span class="w"> </span><span class="sd">"""Returns a uniformly random tree on `n` nodes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -886,7 +886,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/generators/triads.html b/_modules/networkx/generators/triads.html index 00376007..3e461b77 100644 --- a/_modules/networkx/generators/triads.html +++ b/_modules/networkx/generators/triads.html @@ -496,7 +496,7 @@ <div class="viewcode-block" id="triad_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.triads.triad_graph.html#networkx.generators.triads.triad_graph">[docs]</a><span class="k">def</span> <span class="nf">triad_graph</span><span class="p">(</span><span class="n">triad_name</span><span class="p">):</span> - <span class="sd">"""Returns the triad graph with the given name.</span> +<span class="w"> </span><span class="sd">"""Returns the triad graph with the given name.</span> <span class="sd"> Each string in the following tuple is a valid triad name::</span> @@ -587,7 +587,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/linalg/algebraicconnectivity.html b/_modules/networkx/linalg/algebraicconnectivity.html index c125b7b5..e377b40f 100644 --- a/_modules/networkx/linalg/algebraicconnectivity.html +++ b/_modules/networkx/linalg/algebraicconnectivity.html @@ -477,7 +477,7 @@ <span class="k">class</span> <span class="nc">_PCGSolver</span><span class="p">:</span> - <span class="sd">"""Preconditioned conjugate gradient method.</span> +<span class="w"> </span><span class="sd">"""Preconditioned conjugate gradient method.</span> <span class="sd"> To solve Ax = b:</span> <span class="sd"> M = A.diagonal() # or some other preconditioner</span> @@ -535,7 +535,7 @@ <span class="k">class</span> <span class="nc">_LUSolver</span><span class="p">:</span> - <span class="sd">"""LU factorization.</span> +<span class="w"> </span><span class="sd">"""LU factorization.</span> <span class="sd"> To solve Ax = b:</span> <span class="sd"> solver = _LUSolver(A)</span> @@ -567,7 +567,7 @@ <span class="k">def</span> <span class="nf">_preprocess_graph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> - <span class="sd">"""Compute edge weights and eliminate zero-weight edges."""</span> +<span class="w"> </span><span class="sd">"""Compute edge weights and eliminate zero-weight edges."""</span> <span class="k">if</span> <span class="n">G</span><span class="o">.</span><span class="n">is_directed</span><span class="p">():</span> <span class="n">H</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">MultiGraph</span><span class="p">()</span> <span class="n">H</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> @@ -593,7 +593,7 @@ <span class="k">def</span> <span class="nf">_rcm_estimate</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="p">):</span> - <span class="sd">"""Estimate the Fiedler vector using the reverse Cuthill-McKee ordering."""</span> +<span class="w"> </span><span class="sd">"""Estimate the Fiedler vector using the reverse Cuthill-McKee ordering."""</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="n">G</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">subgraph</span><span class="p">(</span><span class="n">nodelist</span><span class="p">)</span> @@ -608,7 +608,7 @@ <span class="k">def</span> <span class="nf">_tracemin_fiedler</span><span class="p">(</span><span class="n">L</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">normalized</span><span class="p">,</span> <span class="n">tol</span><span class="p">,</span> <span class="n">method</span><span class="p">):</span> - <span class="sd">"""Compute the Fiedler vector of L using the TraceMIN-Fiedler algorithm.</span> +<span class="w"> </span><span class="sd">"""Compute the Fiedler vector of L using the TraceMIN-Fiedler algorithm.</span> <span class="sd"> The Fiedler vector of a connected undirected graph is the eigenvector</span> <span class="sd"> corresponding to the second smallest eigenvalue of the Laplacian matrix</span> @@ -661,7 +661,7 @@ <span class="k">if</span> <span class="n">normalized</span><span class="p">:</span> <span class="k">def</span> <span class="nf">project</span><span class="p">(</span><span class="n">X</span><span class="p">):</span> - <span class="sd">"""Make X orthogonal to the nullspace of L."""</span> +<span class="w"> </span><span class="sd">"""Make X orthogonal to the nullspace of L."""</span> <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">j</span><span class="p">]</span> <span class="o">-=</span> <span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="n">j</span><span class="p">]</span> <span class="o">@</span> <span class="n">e</span><span class="p">)</span> <span class="o">*</span> <span class="n">e</span> @@ -669,7 +669,7 @@ <span class="k">else</span><span class="p">:</span> <span class="k">def</span> <span class="nf">project</span><span class="p">(</span><span class="n">X</span><span class="p">):</span> - <span class="sd">"""Make X orthogonal to the nullspace of L."""</span> +<span class="w"> </span><span class="sd">"""Make X orthogonal to the nullspace of L."""</span> <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">j</span><span class="p">]</span> <span class="o">-=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="n">n</span> @@ -719,7 +719,7 @@ <span class="k">def</span> <span class="nf">_get_fiedler_func</span><span class="p">(</span><span class="n">method</span><span class="p">):</span> - <span class="sd">"""Returns a function that solves the Fiedler eigenvalue problem."""</span> +<span class="w"> </span><span class="sd">"""Returns a function that solves the Fiedler eigenvalue problem."""</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">"tracemin"</span><span class="p">:</span> <span class="c1"># old style keyword <v2.1</span> @@ -780,7 +780,7 @@ <span class="k">def</span> <span class="nf">algebraic_connectivity</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"tracemin_pcg"</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the algebraic connectivity of an undirected graph.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the algebraic connectivity of an undirected graph.</span> <span class="sd"> The algebraic connectivity of a connected undirected graph is the second</span> <span class="sd"> smallest eigenvalue of its Laplacian matrix.</span> @@ -875,7 +875,7 @@ <span class="k">def</span> <span class="nf">fiedler_vector</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"tracemin_pcg"</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Returns the Fiedler vector of a connected undirected graph.</span> +<span class="w"> </span><span class="sd">"""Returns the Fiedler vector of a connected undirected graph.</span> <span class="sd"> The Fiedler vector of a connected undirected graph is the eigenvector</span> <span class="sd"> corresponding to the second smallest eigenvalue of the Laplacian matrix</span> @@ -971,7 +971,7 @@ <span class="k">def</span> <span class="nf">spectral_ordering</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"tracemin_pcg"</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Compute the spectral_ordering of a graph.</span> +<span class="w"> </span><span class="sd">"""Compute the spectral_ordering of a graph.</span> <span class="sd"> The spectral ordering of a graph is an ordering of its nodes where nodes</span> <span class="sd"> in the same weakly connected components appear contiguous and ordered by</span> @@ -1099,7 +1099,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/linalg/attrmatrix.html b/_modules/networkx/linalg/attrmatrix.html index 122721bd..83f6faa6 100644 --- a/_modules/networkx/linalg/attrmatrix.html +++ b/_modules/networkx/linalg/attrmatrix.html @@ -469,7 +469,7 @@ <span class="k">def</span> <span class="nf">_node_value</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">node_attr</span><span class="p">):</span> - <span class="sd">"""Returns a function that returns a value from G.nodes[u].</span> +<span class="w"> </span><span class="sd">"""Returns a function that returns a value from G.nodes[u].</span> <span class="sd"> We return a function expecting a node as its sole argument. Then, in the</span> <span class="sd"> simplest scenario, the returned function will return G.nodes[u][node_attr].</span> @@ -514,7 +514,7 @@ <span class="k">def</span> <span class="nf">_edge_value</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">edge_attr</span><span class="p">):</span> - <span class="sd">"""Returns a function that returns a value from G[u][v].</span> +<span class="w"> </span><span class="sd">"""Returns a function that returns a value from G[u][v].</span> <span class="sd"> Suppose there exists an edge between u and v. Then we return a function</span> <span class="sd"> expecting u and v as arguments. For Graph and DiGraph, G[u][v] is</span> @@ -613,7 +613,7 @@ <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns the attribute matrix using attributes from `G` as a numpy array.</span> +<span class="w"> </span><span class="sd">"""Returns the attribute matrix using attributes from `G` as a numpy array.</span> <span class="sd"> If only `G` is passed in, then the adjacency matrix is constructed.</span> @@ -770,7 +770,7 @@ <div class="viewcode-block" id="attr_sparse_matrix"><a class="viewcode-back" href="../../../reference/generated/networkx.linalg.attrmatrix.attr_sparse_matrix.html#networkx.linalg.attrmatrix.attr_sparse_matrix">[docs]</a><span class="k">def</span> <span class="nf">attr_sparse_matrix</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">edge_attr</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">node_attr</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">normalized</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">rc_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Returns a SciPy sparse array using attributes from G.</span> +<span class="w"> </span><span class="sd">"""Returns a SciPy sparse array using attributes from G.</span> <span class="sd"> If only `G` is passed in, then the adjacency matrix is constructed.</span> @@ -974,7 +974,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/linalg/bethehessianmatrix.html b/_modules/networkx/linalg/bethehessianmatrix.html index 82d35e80..8403f92d 100644 --- a/_modules/networkx/linalg/bethehessianmatrix.html +++ b/_modules/networkx/linalg/bethehessianmatrix.html @@ -471,7 +471,7 @@ <div class="viewcode-block" id="bethe_hessian_matrix"><a class="viewcode-back" href="../../../reference/generated/networkx.linalg.bethehessianmatrix.bethe_hessian_matrix.html#networkx.linalg.bethehessianmatrix.bethe_hessian_matrix">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">bethe_hessian_matrix</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the Bethe Hessian matrix of G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the Bethe Hessian matrix of G.</span> <span class="sd"> The Bethe Hessian is a family of matrices parametrized by r, defined as</span> <span class="sd"> H(r) = (r^2 - 1) I - r A + D where A is the adjacency matrix, D is the</span> @@ -590,7 +590,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/linalg/graphmatrix.html b/_modules/networkx/linalg/graphmatrix.html index b5db4379..52a089d7 100644 --- a/_modules/networkx/linalg/graphmatrix.html +++ b/_modules/networkx/linalg/graphmatrix.html @@ -470,7 +470,7 @@ <div class="viewcode-block" id="incidence_matrix"><a class="viewcode-back" href="../../../reference/generated/networkx.linalg.graphmatrix.incidence_matrix.html#networkx.linalg.graphmatrix.incidence_matrix">[docs]</a><span class="k">def</span> <span class="nf">incidence_matrix</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">edgelist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">oriented</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns incidence matrix of G.</span> +<span class="w"> </span><span class="sd">"""Returns incidence matrix of G.</span> <span class="sd"> The incidence matrix assigns each row to a node and each column to an edge.</span> <span class="sd"> For a standard incidence matrix a 1 appears wherever a row's node is</span> @@ -560,7 +560,7 @@ <div class="viewcode-block" id="adjacency_matrix"><a class="viewcode-back" href="../../../reference/generated/networkx.linalg.graphmatrix.adjacency_matrix.html#networkx.linalg.graphmatrix.adjacency_matrix">[docs]</a><span class="k">def</span> <span class="nf">adjacency_matrix</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns adjacency matrix of G.</span> +<span class="w"> </span><span class="sd">"""Returns adjacency matrix of G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -669,7 +669,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/linalg/laplacianmatrix.html b/_modules/networkx/linalg/laplacianmatrix.html index 7300abf2..e2051e81 100644 --- a/_modules/networkx/linalg/laplacianmatrix.html +++ b/_modules/networkx/linalg/laplacianmatrix.html @@ -477,7 +477,7 @@ <div class="viewcode-block" id="laplacian_matrix"><a class="viewcode-back" href="../../../reference/generated/networkx.linalg.laplacianmatrix.laplacian_matrix.html#networkx.linalg.laplacianmatrix.laplacian_matrix">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">laplacian_matrix</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns the Laplacian matrix of G.</span> +<span class="w"> </span><span class="sd">"""Returns the Laplacian matrix of G.</span> <span class="sd"> The graph Laplacian is the matrix L = D - A, where</span> <span class="sd"> A is the adjacency matrix and D is the diagonal matrix of node degrees.</span> @@ -539,7 +539,7 @@ <div class="viewcode-block" id="normalized_laplacian_matrix"><a class="viewcode-back" href="../../../reference/generated/networkx.linalg.laplacianmatrix.normalized_laplacian_matrix.html#networkx.linalg.laplacianmatrix.normalized_laplacian_matrix">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">normalized_laplacian_matrix</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the normalized Laplacian matrix of G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the normalized Laplacian matrix of G.</span> <span class="sd"> The normalized graph Laplacian is the matrix</span> @@ -610,7 +610,7 @@ <span class="k">def</span> <span class="nf">total_spanning_tree_weight</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Returns the total weight of all spanning trees of `G`.</span> <span class="sd"> Kirchoff's Tree Matrix Theorem states that the determinant of any cofactor of the</span> @@ -650,7 +650,7 @@ <span class="k">def</span> <span class="nf">directed_laplacian_matrix</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">walk_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.95</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the directed Laplacian matrix of G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the directed Laplacian matrix of G.</span> <span class="sd"> The graph directed Laplacian is the matrix</span> @@ -739,7 +739,7 @@ <span class="k">def</span> <span class="nf">directed_combinatorial_laplacian_matrix</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">walk_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.95</span> <span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Return the directed combinatorial Laplacian matrix of G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Return the directed combinatorial Laplacian matrix of G.</span> <span class="sd"> The graph directed combinatorial Laplacian is the matrix</span> @@ -814,7 +814,7 @@ <span class="k">def</span> <span class="nf">_transition_matrix</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">walk_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.95</span><span class="p">):</span> - <span class="sd">"""Returns the transition matrix of G.</span> +<span class="w"> </span><span class="sd">"""Returns the transition matrix of G.</span> <span class="sd"> This is a row stochastic giving the transition probabilities while</span> <span class="sd"> performing a random walk on the graph. Depending on the value of walk_type,</span> @@ -941,7 +941,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/linalg/modularitymatrix.html b/_modules/networkx/linalg/modularitymatrix.html index a2aea089..e4ecc2e5 100644 --- a/_modules/networkx/linalg/modularitymatrix.html +++ b/_modules/networkx/linalg/modularitymatrix.html @@ -472,7 +472,7 @@ <div class="viewcode-block" id="modularity_matrix"><a class="viewcode-back" href="../../../reference/generated/networkx.linalg.modularitymatrix.modularity_matrix.html#networkx.linalg.modularitymatrix.modularity_matrix">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">modularity_matrix</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns the modularity matrix of G.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the modularity matrix of G.</span> <span class="sd"> The modularity matrix is the matrix B = A - <A>, where A is the adjacency</span> <span class="sd"> matrix and <A> is the average adjacency matrix, assuming that the graph</span> @@ -540,7 +540,7 @@ <div class="viewcode-block" id="directed_modularity_matrix"><a class="viewcode-back" href="../../../reference/generated/networkx.linalg.modularitymatrix.directed_modularity_matrix.html#networkx.linalg.modularitymatrix.directed_modularity_matrix">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"undirected"</span><span class="p">)</span> <span class="nd">@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">directed_modularity_matrix</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodelist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns the directed modularity matrix of G.</span> +<span class="w"> </span><span class="sd">"""Returns the directed modularity matrix of G.</span> <span class="sd"> The modularity matrix is the matrix B = A - <A>, where A is the adjacency</span> <span class="sd"> matrix and <A> is the expected adjacency matrix, assuming that the graph</span> @@ -676,7 +676,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/linalg/spectrum.html b/_modules/networkx/linalg/spectrum.html index bbde9bc1..049aaaf9 100644 --- a/_modules/networkx/linalg/spectrum.html +++ b/_modules/networkx/linalg/spectrum.html @@ -476,7 +476,7 @@ <div class="viewcode-block" id="laplacian_spectrum"><a class="viewcode-back" href="../../../reference/generated/networkx.linalg.spectrum.laplacian_spectrum.html#networkx.linalg.spectrum.laplacian_spectrum">[docs]</a><span class="k">def</span> <span class="nf">laplacian_spectrum</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns eigenvalues of the Laplacian of G</span> +<span class="w"> </span><span class="sd">"""Returns eigenvalues of the Laplacian of G</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -520,7 +520,7 @@ <div class="viewcode-block" id="normalized_laplacian_spectrum"><a class="viewcode-back" href="../../../reference/generated/networkx.linalg.spectrum.normalized_laplacian_spectrum.html#networkx.linalg.spectrum.normalized_laplacian_spectrum">[docs]</a><span class="k">def</span> <span class="nf">normalized_laplacian_spectrum</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Return eigenvalues of the normalized Laplacian of G</span> +<span class="w"> </span><span class="sd">"""Return eigenvalues of the normalized Laplacian of G</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -554,7 +554,7 @@ <div class="viewcode-block" id="adjacency_spectrum"><a class="viewcode-back" href="../../../reference/generated/networkx.linalg.spectrum.adjacency_spectrum.html#networkx.linalg.spectrum.adjacency_spectrum">[docs]</a><span class="k">def</span> <span class="nf">adjacency_spectrum</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">):</span> - <span class="sd">"""Returns eigenvalues of the adjacency matrix of G.</span> +<span class="w"> </span><span class="sd">"""Returns eigenvalues of the adjacency matrix of G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -586,7 +586,7 @@ <div class="viewcode-block" id="modularity_spectrum"><a class="viewcode-back" href="../../../reference/generated/networkx.linalg.spectrum.modularity_spectrum.html#networkx.linalg.spectrum.modularity_spectrum">[docs]</a><span class="k">def</span> <span class="nf">modularity_spectrum</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Returns eigenvalues of the modularity matrix of G.</span> +<span class="w"> </span><span class="sd">"""Returns eigenvalues of the modularity matrix of G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -617,7 +617,7 @@ <div class="viewcode-block" id="bethe_hessian_spectrum"><a class="viewcode-back" href="../../../reference/generated/networkx.linalg.spectrum.bethe_hessian_spectrum.html#networkx.linalg.spectrum.bethe_hessian_spectrum">[docs]</a><span class="k">def</span> <span class="nf">bethe_hessian_spectrum</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns eigenvalues of the Bethe Hessian matrix of G.</span> +<span class="w"> </span><span class="sd">"""Returns eigenvalues of the Bethe Hessian matrix of G.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -697,7 +697,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/readwrite/adjlist.html b/_modules/networkx/readwrite/adjlist.html index 0ba8f3c3..3b97ea74 100644 --- a/_modules/networkx/readwrite/adjlist.html +++ b/_modules/networkx/readwrite/adjlist.html @@ -492,7 +492,7 @@ <div class="viewcode-block" id="generate_adjlist"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.adjlist.generate_adjlist.html#networkx.readwrite.adjlist.generate_adjlist">[docs]</a><span class="k">def</span> <span class="nf">generate_adjlist</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s2">" "</span><span class="p">):</span> - <span class="sd">"""Generate a single line of the graph G in adjacency list format.</span> +<span class="w"> </span><span class="sd">"""Generate a single line of the graph G in adjacency list format.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -551,7 +551,7 @@ <div class="viewcode-block" id="write_adjlist"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.adjlist.write_adjlist.html#networkx.readwrite.adjlist.write_adjlist">[docs]</a><span class="nd">@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">"wb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">write_adjlist</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">comments</span><span class="o">=</span><span class="s2">"#"</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s2">" "</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">):</span> - <span class="sd">"""Write graph G in single-line adjacency-list format to path.</span> +<span class="w"> </span><span class="sd">"""Write graph G in single-line adjacency-list format to path.</span> <span class="sd"> Parameters</span> @@ -616,7 +616,7 @@ <div class="viewcode-block" id="parse_adjlist"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.adjlist.parse_adjlist.html#networkx.readwrite.adjlist.parse_adjlist">[docs]</a><span class="k">def</span> <span class="nf">parse_adjlist</span><span class="p">(</span> <span class="n">lines</span><span class="p">,</span> <span class="n">comments</span><span class="o">=</span><span class="s2">"#"</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nodetype</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Parse lines of a graph adjacency list representation.</span> +<span class="w"> </span><span class="sd">"""Parse lines of a graph adjacency list representation.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -694,7 +694,7 @@ <span class="n">nodetype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Read graph in adjacency list format from path.</span> +<span class="w"> </span><span class="sd">"""Read graph in adjacency list format from path.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -820,7 +820,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/readwrite/edgelist.html b/_modules/networkx/readwrite/edgelist.html index 2deb1ebe..eaa43342 100644 --- a/_modules/networkx/readwrite/edgelist.html +++ b/_modules/networkx/readwrite/edgelist.html @@ -504,7 +504,7 @@ <div class="viewcode-block" id="generate_edgelist"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.edgelist.generate_edgelist.html#networkx.readwrite.edgelist.generate_edgelist">[docs]</a><span class="k">def</span> <span class="nf">generate_edgelist</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s2">" "</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Generate a single line of the graph G in edge list format.</span> +<span class="w"> </span><span class="sd">"""Generate a single line of the graph G in edge list format.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -588,7 +588,7 @@ <div class="viewcode-block" id="write_edgelist"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.edgelist.write_edgelist.html#networkx.readwrite.edgelist.write_edgelist">[docs]</a><span class="nd">@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">"wb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">write_edgelist</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">comments</span><span class="o">=</span><span class="s2">"#"</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s2">" "</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">):</span> - <span class="sd">"""Write graph as a list of edges.</span> +<span class="w"> </span><span class="sd">"""Write graph as a list of edges.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -639,7 +639,7 @@ <div class="viewcode-block" id="parse_edgelist"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.edgelist.parse_edgelist.html#networkx.readwrite.edgelist.parse_edgelist">[docs]</a><span class="k">def</span> <span class="nf">parse_edgelist</span><span class="p">(</span> <span class="n">lines</span><span class="p">,</span> <span class="n">comments</span><span class="o">=</span><span class="s2">"#"</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nodetype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">True</span> <span class="p">):</span> - <span class="sd">"""Parse lines of an edge list representation of a graph.</span> +<span class="w"> </span><span class="sd">"""Parse lines of an edge list representation of a graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -770,7 +770,7 @@ <span class="n">edgetype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Read a graph from a list of edges.</span> +<span class="w"> </span><span class="sd">"""Read a graph from a list of edges.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -847,7 +847,7 @@ <div class="viewcode-block" id="write_weighted_edgelist"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.edgelist.write_weighted_edgelist.html#networkx.readwrite.edgelist.write_weighted_edgelist">[docs]</a><span class="k">def</span> <span class="nf">write_weighted_edgelist</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">comments</span><span class="o">=</span><span class="s2">"#"</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s2">" "</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">):</span> - <span class="sd">"""Write graph G as a list of edges with numeric weights.</span> +<span class="w"> </span><span class="sd">"""Write graph G as a list of edges with numeric weights.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -894,7 +894,7 @@ <span class="n">nodetype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Read a graph as list of edges with numeric weights.</span> +<span class="w"> </span><span class="sd">"""Read a graph as list of edges with numeric weights.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -998,7 +998,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/readwrite/gexf.html b/_modules/networkx/readwrite/gexf.html index 931347ac..5b64cc5a 100644 --- a/_modules/networkx/readwrite/gexf.html +++ b/_modules/networkx/readwrite/gexf.html @@ -497,7 +497,7 @@ <div class="viewcode-block" id="write_gexf"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.gexf.write_gexf.html#networkx.readwrite.gexf.write_gexf">[docs]</a><span class="nd">@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">"wb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">write_gexf</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">,</span> <span class="n">prettyprint</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">version</span><span class="o">=</span><span class="s2">"1.2draft"</span><span class="p">):</span> - <span class="sd">"""Write G in GEXF format to path.</span> +<span class="w"> </span><span class="sd">"""Write G in GEXF format to path.</span> <span class="sd"> "GEXF (Graph Exchange XML Format) is a language for describing</span> <span class="sd"> complex networks structures, their associated data and dynamics" [1]_.</span> @@ -551,7 +551,7 @@ <div class="viewcode-block" id="generate_gexf"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.gexf.generate_gexf.html#networkx.readwrite.gexf.generate_gexf">[docs]</a><span class="k">def</span> <span class="nf">generate_gexf</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">,</span> <span class="n">prettyprint</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">version</span><span class="o">=</span><span class="s2">"1.2draft"</span><span class="p">):</span> - <span class="sd">"""Generate lines of GEXF format representation of G.</span> +<span class="w"> </span><span class="sd">"""Generate lines of GEXF format representation of G.</span> <span class="sd"> "GEXF (Graph Exchange XML Format) is a language for describing</span> <span class="sd"> complex networks structures, their associated data and dynamics" [1]_.</span> @@ -597,7 +597,7 @@ <div class="viewcode-block" id="read_gexf"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.gexf.read_gexf.html#networkx.readwrite.gexf.read_gexf">[docs]</a><span class="nd">@open_file</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"rb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">read_gexf</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">node_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">relabel</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">version</span><span class="o">=</span><span class="s2">"1.2draft"</span><span class="p">):</span> - <span class="sd">"""Read graph in GEXF format from path.</span> +<span class="w"> </span><span class="sd">"""Read graph in GEXF format from path.</span> <span class="sd"> "GEXF (Graph Exchange XML Format) is a language for describing</span> <span class="sd"> complex networks structures, their associated data and dynamics" [1]_.</span> @@ -1472,7 +1472,7 @@ <div class="viewcode-block" id="relabel_gexf_graph"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.gexf.relabel_gexf_graph.html#networkx.readwrite.gexf.relabel_gexf_graph">[docs]</a><span class="k">def</span> <span class="nf">relabel_gexf_graph</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Relabel graph using "label" node keyword for node label.</span> +<span class="w"> </span><span class="sd">"""Relabel graph using "label" node keyword for node label.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1572,7 +1572,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/readwrite/gml.html b/_modules/networkx/readwrite/gml.html index 2859e368..673c9714 100644 --- a/_modules/networkx/readwrite/gml.html +++ b/_modules/networkx/readwrite/gml.html @@ -507,7 +507,7 @@ <span class="k">def</span> <span class="nf">escape</span><span class="p">(</span><span class="n">text</span><span class="p">):</span> - <span class="sd">"""Use XML character references to escape characters.</span> +<span class="w"> </span><span class="sd">"""Use XML character references to escape characters.</span> <span class="sd"> Use XML character references for unprintable or non-ASCII</span> <span class="sd"> characters, double quotes and ampersands in a string</span> @@ -522,7 +522,7 @@ <span class="k">def</span> <span class="nf">unescape</span><span class="p">(</span><span class="n">text</span><span class="p">):</span> - <span class="sd">"""Replace XML character references with the referenced characters"""</span> +<span class="w"> </span><span class="sd">"""Replace XML character references with the referenced characters"""</span> <span class="k">def</span> <span class="nf">fixup</span><span class="p">(</span><span class="n">m</span><span class="p">):</span> <span class="n">text</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> @@ -547,7 +547,7 @@ <div class="viewcode-block" id="literal_destringizer"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.gml.literal_destringizer.html#networkx.readwrite.gml.literal_destringizer">[docs]</a><span class="k">def</span> <span class="nf">literal_destringizer</span><span class="p">(</span><span class="n">rep</span><span class="p">):</span> - <span class="sd">"""Convert a Python literal to the value it represents.</span> +<span class="w"> </span><span class="sd">"""Convert a Python literal to the value it represents.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -578,7 +578,7 @@ <div class="viewcode-block" id="read_gml"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.gml.read_gml.html#networkx.readwrite.gml.read_gml">[docs]</a><span class="nd">@open_file</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"rb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">read_gml</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"label"</span><span class="p">,</span> <span class="n">destringizer</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Read graph in GML format from `path`.</span> +<span class="w"> </span><span class="sd">"""Read graph in GML format from `path`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -660,7 +660,7 @@ <div class="viewcode-block" id="parse_gml"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.gml.parse_gml.html#networkx.readwrite.gml.parse_gml">[docs]</a><span class="k">def</span> <span class="nf">parse_gml</span><span class="p">(</span><span class="n">lines</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"label"</span><span class="p">,</span> <span class="n">destringizer</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Parse GML graph from a string or iterable.</span> +<span class="w"> </span><span class="sd">"""Parse GML graph from a string or iterable.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -737,7 +737,7 @@ <span class="k">class</span> <span class="nc">Pattern</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span> - <span class="sd">"""encodes the index of each token-matching pattern in `tokenize`."""</span> +<span class="w"> </span><span class="sd">"""encodes the index of each token-matching pattern in `tokenize`."""</span> <span class="n">KEYS</span> <span class="o">=</span> <span class="mi">0</span> <span class="n">REALS</span> <span class="o">=</span> <span class="mi">1</span> @@ -759,7 +759,7 @@ <span class="k">def</span> <span class="nf">parse_gml_lines</span><span class="p">(</span><span class="n">lines</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">destringizer</span><span class="p">):</span> - <span class="sd">"""Parse GML `lines` into a graph."""</span> +<span class="w"> </span><span class="sd">"""Parse GML `lines` into a graph."""</span> <span class="k">def</span> <span class="nf">tokenize</span><span class="p">():</span> <span class="n">patterns</span> <span class="o">=</span> <span class="p">[</span> @@ -780,7 +780,7 @@ <span class="k">while</span> <span class="n">pos</span> <span class="o"><</span> <span class="n">length</span><span class="p">:</span> <span class="n">match</span> <span class="o">=</span> <span class="n">tokens</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="n">line</span><span class="p">,</span> <span class="n">pos</span><span class="p">)</span> <span class="k">if</span> <span class="n">match</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> - <span class="n">m</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"cannot tokenize </span><span class="si">{</span><span class="n">line</span><span class="p">[</span><span class="n">pos</span><span class="p">:]</span><span class="si">}</span><span class="s2"> at (</span><span class="si">{</span><span class="n">lineno</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">pos</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">)"</span> + <span class="n">m</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"cannot tokenize </span><span class="si">{</span><span class="n">line</span><span class="p">[</span><span class="n">pos</span><span class="p">:]</span><span class="si">}</span><span class="s2"> at (</span><span class="si">{</span><span class="n">lineno</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">pos</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">)"</span> <span class="k">raise</span> <span class="n">NetworkXError</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">patterns</span><span class="p">)):</span> <span class="n">group</span> <span class="o">=</span> <span class="n">match</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> @@ -960,7 +960,7 @@ <div class="viewcode-block" id="literal_stringizer"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.gml.literal_stringizer.html#networkx.readwrite.gml.literal_stringizer">[docs]</a><span class="k">def</span> <span class="nf">literal_stringizer</span><span class="p">(</span><span class="n">value</span><span class="p">):</span> - <span class="sd">"""Convert a `value` to a Python literal in GML representation.</span> +<span class="w"> </span><span class="sd">"""Convert a `value` to a Python literal in GML representation.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1068,7 +1068,7 @@ <div class="viewcode-block" id="generate_gml"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.gml.generate_gml.html#networkx.readwrite.gml.generate_gml">[docs]</a><span class="k">def</span> <span class="nf">generate_gml</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">stringizer</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Generate a single entry of the graph `G` in GML format.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Generate a single entry of the graph `G` in GML format.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1258,7 +1258,7 @@ <div class="viewcode-block" id="write_gml"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.gml.write_gml.html#networkx.readwrite.gml.write_gml">[docs]</a><span class="nd">@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">"wb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">write_gml</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">stringizer</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Write a graph `G` in GML format to the file or file handle `path`.</span> +<span class="w"> </span><span class="sd">"""Write a graph `G` in GML format to the file or file handle `path`.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1370,7 +1370,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/readwrite/graph6.html b/_modules/networkx/readwrite/graph6.html index e3d2727a..8c22affc 100644 --- a/_modules/networkx/readwrite/graph6.html +++ b/_modules/networkx/readwrite/graph6.html @@ -483,7 +483,7 @@ <span class="k">def</span> <span class="nf">_generate_graph6_bytes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">header</span><span class="p">):</span> - <span class="sd">"""Yield bytes in the graph6 encoding of a graph.</span> +<span class="w"> </span><span class="sd">"""Yield bytes in the graph6 encoding of a graph.</span> <span class="sd"> `G` is an undirected simple graph. `nodes` is the list of nodes for</span> <span class="sd"> which the node-induced subgraph will be encoded; if `nodes` is the</span> @@ -524,7 +524,7 @@ <div class="viewcode-block" id="from_graph6_bytes"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.graph6.from_graph6_bytes.html#networkx.readwrite.graph6.from_graph6_bytes">[docs]</a><span class="k">def</span> <span class="nf">from_graph6_bytes</span><span class="p">(</span><span class="n">bytes_in</span><span class="p">):</span> - <span class="sd">"""Read a simple undirected graph in graph6 format from bytes.</span> +<span class="w"> </span><span class="sd">"""Read a simple undirected graph in graph6 format from bytes.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -562,7 +562,7 @@ <span class="sd"> """</span> <span class="k">def</span> <span class="nf">bits</span><span class="p">():</span> - <span class="sd">"""Returns sequence of individual bits from 6-bit-per-value</span> +<span class="w"> </span><span class="sd">"""Returns sequence of individual bits from 6-bit-per-value</span> <span class="sd"> list of data values."""</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]:</span> @@ -579,7 +579,7 @@ <span class="n">nd</span> <span class="o">=</span> <span class="p">(</span><span class="n">n</span> <span class="o">*</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">5</span><span class="p">)</span> <span class="o">//</span> <span class="mi">6</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">!=</span> <span class="n">nd</span><span class="p">:</span> <span class="k">raise</span> <span class="n">NetworkXError</span><span class="p">(</span> - <span class="sa">f</span><span class="s2">"Expected </span><span class="si">{</span><span class="n">n</span> <span class="o">*</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="si">}</span><span class="s2"> bits but got </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="mi">6</span><span class="si">}</span><span class="s2"> in graph6"</span> + <span class="sa">f</span><span class="s2">"Expected </span><span class="si">{</span><span class="n">n</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="p">(</span><span class="n">n</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">//</span><span class="w"> </span><span class="mi">2</span><span class="si">}</span><span class="s2"> bits but got </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">6</span><span class="si">}</span><span class="s2"> in graph6"</span> <span class="p">)</span> <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span> @@ -594,7 +594,7 @@ <div class="viewcode-block" id="to_graph6_bytes"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.graph6.to_graph6_bytes.html#networkx.readwrite.graph6.to_graph6_bytes">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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_graph6_bytes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Convert a simple undirected graph to bytes in graph6 format.</span> +<span class="w"> </span><span class="sd">"""Convert a simple undirected graph to bytes in graph6 format.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -647,7 +647,7 @@ <div class="viewcode-block" id="read_graph6"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.graph6.read_graph6.html#networkx.readwrite.graph6.read_graph6">[docs]</a><span class="nd">@open_file</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"rb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">read_graph6</span><span class="p">(</span><span class="n">path</span><span class="p">):</span> - <span class="sd">"""Read simple undirected graphs in graph6 format from path.</span> +<span class="w"> </span><span class="sd">"""Read simple undirected graphs in graph6 format from path.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -712,7 +712,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"multigraph"</span><span class="p">)</span> <span class="nd">@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">"wb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">write_graph6</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Write a simple undirected graph to a path in graph6 format.</span> +<span class="w"> </span><span class="sd">"""Write a simple undirected graph to a path in graph6 format.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -772,7 +772,7 @@ <span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">write_graph6_file</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Write a simple undirected graph to a file-like object in graph6 format.</span> +<span class="w"> </span><span class="sd">"""Write a simple undirected graph to a file-like object in graph6 format.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -835,7 +835,7 @@ <span class="k">def</span> <span class="nf">data_to_n</span><span class="p">(</span><span class="n">data</span><span class="p">):</span> - <span class="sd">"""Read initial one-, four- or eight-unit value from graph6</span> +<span class="w"> </span><span class="sd">"""Read initial one-, four- or eight-unit value from graph6</span> <span class="sd"> integer sequence.</span> <span class="sd"> Return (value, rest of seq.)"""</span> @@ -855,7 +855,7 @@ <span class="k">def</span> <span class="nf">n_to_data</span><span class="p">(</span><span class="n">n</span><span class="p">):</span> - <span class="sd">"""Convert an integer to one-, four- or eight-unit graph6 sequence.</span> +<span class="w"> </span><span class="sd">"""Convert an integer to one-, four- or eight-unit graph6 sequence.</span> <span class="sd"> This function is undefined if `n` is not in ``range(2 ** 36)``.</span> @@ -926,7 +926,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/readwrite/graphml.html b/_modules/networkx/readwrite/graphml.html index c19cfda7..2d4de0ce 100644 --- a/_modules/networkx/readwrite/graphml.html +++ b/_modules/networkx/readwrite/graphml.html @@ -531,7 +531,7 @@ <span class="n">named_key_ids</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">edge_id_from_attribute</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Write G in GraphML XML format to path</span> +<span class="w"> </span><span class="sd">"""Write G in GraphML XML format to path</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -586,7 +586,7 @@ <span class="n">named_key_ids</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">edge_id_from_attribute</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Write G in GraphML XML format to path</span> +<span class="w"> </span><span class="sd">"""Write G in GraphML XML format to path</span> <span class="sd"> This function uses the LXML framework and should be faster than</span> <span class="sd"> the version using the xml library.</span> @@ -655,7 +655,7 @@ <span class="n">named_key_ids</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">edge_id_from_attribute</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Generate GraphML lines for G</span> +<span class="w"> </span><span class="sd">"""Generate GraphML lines for G</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -697,7 +697,7 @@ <div class="viewcode-block" id="read_graphml"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.graphml.read_graphml.html#networkx.readwrite.graphml.read_graphml">[docs]</a><span class="nd">@open_file</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"rb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">read_graphml</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">node_type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span> <span class="n">edge_key_type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">force_multigraph</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> - <span class="sd">"""Read graph in GraphML format from path.</span> +<span class="w"> </span><span class="sd">"""Read graph in GraphML format from path.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -771,7 +771,7 @@ <div class="viewcode-block" id="parse_graphml"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.graphml.parse_graphml.html#networkx.readwrite.graphml.parse_graphml">[docs]</a><span class="k">def</span> <span class="nf">parse_graphml</span><span class="p">(</span> <span class="n">graphml_string</span><span class="p">,</span> <span class="n">node_type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span> <span class="n">edge_key_type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">force_multigraph</span><span class="o">=</span><span class="kc">False</span> <span class="p">):</span> - <span class="sd">"""Read graph in GraphML format from string.</span> +<span class="w"> </span><span class="sd">"""Read graph in GraphML format from string.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -910,7 +910,7 @@ <span class="p">}</span> <span class="k">def</span> <span class="nf">get_xml_type</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span> - <span class="sd">"""Wrapper around the xml_type dict that raises a more informative</span> +<span class="w"> </span><span class="sd">"""Wrapper around the xml_type dict that raises a more informative</span> <span class="sd"> exception message when a user attempts to use data of a type not</span> <span class="sd"> supported by GraphML."""</span> <span class="k">try</span><span class="p">:</span> @@ -965,7 +965,7 @@ <span class="k">return</span> <span class="n">s</span> <span class="k">def</span> <span class="nf">attr_type</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">scope</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span> - <span class="sd">"""Infer the attribute type of data named name. Currently this only</span> +<span class="w"> </span><span class="sd">"""Infer the attribute type of data named name. Currently this only</span> <span class="sd"> supports inference of numeric types.</span> <span class="sd"> If self.infer_numeric_types is false, type is used. Otherwise, pick the</span> @@ -1016,7 +1016,7 @@ <span class="k">return</span> <span class="n">new_id</span> <span class="k">def</span> <span class="nf">add_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">element_type</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">scope</span><span class="o">=</span><span class="s2">"all"</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Make a data element for an edge or a node. Keep a log of the</span> <span class="sd"> type in the keys table.</span> <span class="sd"> """</span> @@ -1030,7 +1030,7 @@ <span class="k">return</span> <span class="n">data_element</span> <span class="k">def</span> <span class="nf">add_attributes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scope</span><span class="p">,</span> <span class="n">xml_obj</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">default</span><span class="p">):</span> - <span class="sd">"""Appends attribute data to edges or nodes, and stores type information</span> +<span class="w"> </span><span class="sd">"""Appends attribute data to edges or nodes, and stores type information</span> <span class="sd"> to be added later. See add_graph_element.</span> <span class="sd"> """</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> @@ -1077,7 +1077,7 @@ <span class="n">graph_element</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">edge_element</span><span class="p">)</span> <span class="k">def</span> <span class="nf">add_graph_element</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Serialize graph G in GraphML to the stream.</span> <span class="sd"> """</span> <span class="k">if</span> <span class="n">G</span><span class="o">.</span><span class="n">is_directed</span><span class="p">():</span> @@ -1116,7 +1116,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">xml</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">graph_element</span><span class="p">)</span> <span class="k">def</span> <span class="nf">add_graphs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph_list</span><span class="p">):</span> - <span class="sd">"""Add many graphs to this GraphML document."""</span> +<span class="w"> </span><span class="sd">"""Add many graphs to this GraphML document."""</span> <span class="k">for</span> <span class="n">G</span> <span class="ow">in</span> <span class="n">graph_list</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">add_graph_element</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> @@ -1146,7 +1146,7 @@ <span class="k">class</span> <span class="nc">IncrementalElement</span><span class="p">:</span> - <span class="sd">"""Wrapper for _IncrementalWriter providing an Element like interface.</span> +<span class="w"> </span><span class="sd">"""Wrapper for _IncrementalWriter providing an Element like interface.</span> <span class="sd"> This wrapper does not intend to be a complete implementation but rather to</span> <span class="sd"> deal with those calls used in GraphMLWriter.</span> @@ -1208,7 +1208,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">add_graph_element</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span> <span class="k">def</span> <span class="nf">add_graph_element</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Serialize graph G in GraphML to the stream.</span> <span class="sd"> """</span> <span class="k">if</span> <span class="n">G</span><span class="o">.</span><span class="n">is_directed</span><span class="p">():</span> @@ -1278,7 +1278,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">add_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">incremental_writer</span><span class="p">)</span> <span class="c1"># adds attributes too</span> <span class="k">def</span> <span class="nf">add_attributes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scope</span><span class="p">,</span> <span class="n">xml_obj</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">default</span><span class="p">):</span> - <span class="sd">"""Appends attribute data."""</span> +<span class="w"> </span><span class="sd">"""Appends attribute data."""</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> <span class="n">data_element</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">add_data</span><span class="p">(</span> <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">attr_type</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">),</span> <span class="n">scope</span><span class="p">,</span> <span class="n">v</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="p">),</span> <span class="n">scope</span><span class="p">,</span> <span class="n">default</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> @@ -1298,7 +1298,7 @@ <span class="k">class</span> <span class="nc">GraphMLReader</span><span class="p">(</span><span class="n">GraphML</span><span class="p">):</span> - <span class="sd">"""Read a GraphML document. Produces NetworkX graph objects."""</span> +<span class="w"> </span><span class="sd">"""Read a GraphML document. Produces NetworkX graph objects."""</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node_type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span> <span class="n">edge_key_type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">force_multigraph</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> <span class="bp">self</span><span class="o">.</span><span class="n">construct_types</span><span class="p">()</span> @@ -1363,7 +1363,7 @@ <span class="k">return</span> <span class="n">G</span> <span class="k">def</span> <span class="nf">add_node</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G</span><span class="p">,</span> <span class="n">node_xml</span><span class="p">,</span> <span class="n">graphml_keys</span><span class="p">,</span> <span class="n">defaults</span><span class="p">):</span> - <span class="sd">"""Add a node to the graph."""</span> +<span class="w"> </span><span class="sd">"""Add a node to the graph."""</span> <span class="c1"># warn on finding unsupported ports tag</span> <span class="n">ports</span> <span class="o">=</span> <span class="n">node_xml</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">{{</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">NS_GRAPHML</span><span class="si">}</span><span class="se">}}</span><span class="s2">port"</span><span class="p">)</span> <span class="k">if</span> <span class="n">ports</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> @@ -1379,7 +1379,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">make_graph</span><span class="p">(</span><span class="n">graph_xml</span><span class="p">,</span> <span class="n">graphml_keys</span><span class="p">,</span> <span class="n">defaults</span><span class="p">,</span> <span class="n">G</span><span class="p">)</span> <span class="k">def</span> <span class="nf">add_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G</span><span class="p">,</span> <span class="n">edge_element</span><span class="p">,</span> <span class="n">graphml_keys</span><span class="p">):</span> - <span class="sd">"""Add an edge to the graph."""</span> +<span class="w"> </span><span class="sd">"""Add an edge to the graph."""</span> <span class="c1"># warn on finding unsupported ports tag</span> <span class="n">ports</span> <span class="o">=</span> <span class="n">edge_element</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">{{</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">NS_GRAPHML</span><span class="si">}</span><span class="se">}}</span><span class="s2">port"</span><span class="p">)</span> <span class="k">if</span> <span class="n">ports</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> @@ -1420,7 +1420,7 @@ <span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">([(</span><span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">edge_id</span><span class="p">,</span> <span class="n">data</span><span class="p">)])</span> <span class="k">def</span> <span class="nf">decode_data_elements</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graphml_keys</span><span class="p">,</span> <span class="n">obj_xml</span><span class="p">):</span> - <span class="sd">"""Use the key information to decode the data XML if present."""</span> +<span class="w"> </span><span class="sd">"""Use the key information to decode the data XML if present."""</span> <span class="n">data</span> <span class="o">=</span> <span class="p">{}</span> <span class="k">for</span> <span class="n">data_element</span> <span class="ow">in</span> <span class="n">obj_xml</span><span class="o">.</span><span class="n">findall</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">{{</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">NS_GRAPHML</span><span class="si">}</span><span class="se">}}</span><span class="s2">data"</span><span class="p">):</span> <span class="n">key</span> <span class="o">=</span> <span class="n">data_element</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"key"</span><span class="p">)</span> @@ -1478,7 +1478,7 @@ <span class="k">return</span> <span class="n">data</span> <span class="k">def</span> <span class="nf">find_graphml_keys</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph_element</span><span class="p">):</span> - <span class="sd">"""Extracts all the keys and key defaults from the xml."""</span> +<span class="w"> </span><span class="sd">"""Extracts all the keys and key defaults from the xml."""</span> <span class="n">graphml_keys</span> <span class="o">=</span> <span class="p">{}</span> <span class="n">graphml_key_defaults</span> <span class="o">=</span> <span class="p">{}</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">graph_element</span><span class="o">.</span><span class="n">findall</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">{{</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">NS_GRAPHML</span><span class="si">}</span><span class="se">}}</span><span class="s2">key"</span><span class="p">):</span> @@ -1562,7 +1562,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/readwrite/json_graph/adjacency.html b/_modules/networkx/readwrite/json_graph/adjacency.html index 7258c8d8..2a77623f 100644 --- a/_modules/networkx/readwrite/json_graph/adjacency.html +++ b/_modules/networkx/readwrite/json_graph/adjacency.html @@ -471,7 +471,7 @@ <div class="viewcode-block" id="adjacency_data"><a class="viewcode-back" href="../../../../reference/readwrite/generated/networkx.readwrite.json_graph.adjacency_data.html#networkx.readwrite.json_graph.adjacency_data">[docs]</a><span class="k">def</span> <span class="nf">adjacency_data</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">attrs</span><span class="o">=</span><span class="n">_attrs</span><span class="p">):</span> - <span class="sd">"""Returns data in adjacency format that is suitable for JSON serialization</span> +<span class="w"> </span><span class="sd">"""Returns data in adjacency format that is suitable for JSON serialization</span> <span class="sd"> and use in Javascript documents.</span> <span class="sd"> Parameters</span> @@ -547,7 +547,7 @@ <div class="viewcode-block" id="adjacency_graph"><a class="viewcode-back" href="../../../../reference/readwrite/generated/networkx.readwrite.json_graph.adjacency_graph.html#networkx.readwrite.json_graph.adjacency_graph">[docs]</a><span class="k">def</span> <span class="nf">adjacency_graph</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">directed</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">multigraph</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">attrs</span><span class="o">=</span><span class="n">_attrs</span><span class="p">):</span> - <span class="sd">"""Returns graph from adjacency data format.</span> +<span class="w"> </span><span class="sd">"""Returns graph from adjacency data format.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -669,7 +669,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/readwrite/json_graph/cytoscape.html b/_modules/networkx/readwrite/json_graph/cytoscape.html index c54f4f9a..aa278968 100644 --- a/_modules/networkx/readwrite/json_graph/cytoscape.html +++ b/_modules/networkx/readwrite/json_graph/cytoscape.html @@ -467,7 +467,7 @@ <div class="viewcode-block" id="cytoscape_data"><a class="viewcode-back" href="../../../../reference/readwrite/generated/networkx.readwrite.json_graph.cytoscape_data.html#networkx.readwrite.json_graph.cytoscape_data">[docs]</a><span class="k">def</span> <span class="nf">cytoscape_data</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"name"</span><span class="p">,</span> <span class="n">ident</span><span class="o">=</span><span class="s2">"id"</span><span class="p">):</span> - <span class="sd">"""Returns data in Cytoscape JSON format (cyjs).</span> +<span class="w"> </span><span class="sd">"""Returns data in Cytoscape JSON format (cyjs).</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -544,7 +544,7 @@ <div class="viewcode-block" id="cytoscape_graph"><a class="viewcode-back" href="../../../../reference/readwrite/generated/networkx.readwrite.json_graph.cytoscape_graph.html#networkx.readwrite.json_graph.cytoscape_graph">[docs]</a><span class="k">def</span> <span class="nf">cytoscape_graph</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"name"</span><span class="p">,</span> <span class="n">ident</span><span class="o">=</span><span class="s2">"id"</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Create a NetworkX graph from a dictionary in cytoscape JSON format.</span> <span class="sd"> Parameters</span> @@ -685,7 +685,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/readwrite/json_graph/node_link.html b/_modules/networkx/readwrite/json_graph/node_link.html index 1e56e9f1..681057c3 100644 --- a/_modules/networkx/readwrite/json_graph/node_link.html +++ b/_modules/networkx/readwrite/json_graph/node_link.html @@ -472,7 +472,7 @@ <span class="k">def</span> <span class="nf">_to_tuple</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> - <span class="sd">"""Converts lists to tuples, including nested lists.</span> +<span class="w"> </span><span class="sd">"""Converts lists to tuples, including nested lists.</span> <span class="sd"> All other non-list inputs are passed through unmodified. This function is</span> <span class="sd"> intended to be used to convert potentially nested lists from json files</span> @@ -498,7 +498,7 @@ <span class="n">key</span><span class="o">=</span><span class="s2">"key"</span><span class="p">,</span> <span class="n">link</span><span class="o">=</span><span class="s2">"links"</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns data in node-link format that is suitable for JSON serialization</span> +<span class="w"> </span><span class="sd">"""Returns data in node-link format that is suitable for JSON serialization</span> <span class="sd"> and use in Javascript documents.</span> <span class="sd"> Parameters</span> @@ -653,7 +653,7 @@ <span class="n">key</span><span class="o">=</span><span class="s2">"key"</span><span class="p">,</span> <span class="n">link</span><span class="o">=</span><span class="s2">"links"</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Returns graph from node-link data format.</span> +<span class="w"> </span><span class="sd">"""Returns graph from node-link data format.</span> <span class="sd"> Useful for de-serialization from JSON.</span> <span class="sd"> Parameters</span> @@ -845,7 +845,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/readwrite/json_graph/tree.html b/_modules/networkx/readwrite/json_graph/tree.html index 8e67fb6e..3f234250 100644 --- a/_modules/networkx/readwrite/json_graph/tree.html +++ b/_modules/networkx/readwrite/json_graph/tree.html @@ -469,7 +469,7 @@ <div class="viewcode-block" id="tree_data"><a class="viewcode-back" href="../../../../reference/readwrite/generated/networkx.readwrite.json_graph.tree_data.html#networkx.readwrite.json_graph.tree_data">[docs]</a><span class="k">def</span> <span class="nf">tree_data</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">root</span><span class="p">,</span> <span class="n">ident</span><span class="o">=</span><span class="s2">"id"</span><span class="p">,</span> <span class="n">children</span><span class="o">=</span><span class="s2">"children"</span><span class="p">):</span> - <span class="sd">"""Returns data in tree format that is suitable for JSON serialization</span> +<span class="w"> </span><span class="sd">"""Returns data in tree format that is suitable for JSON serialization</span> <span class="sd"> and use in Javascript documents.</span> <span class="sd"> Parameters</span> @@ -549,7 +549,7 @@ <div class="viewcode-block" id="tree_graph"><a class="viewcode-back" href="../../../../reference/readwrite/generated/networkx.readwrite.json_graph.tree_graph.html#networkx.readwrite.json_graph.tree_graph">[docs]</a><span class="k">def</span> <span class="nf">tree_graph</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ident</span><span class="o">=</span><span class="s2">"id"</span><span class="p">,</span> <span class="n">children</span><span class="o">=</span><span class="s2">"children"</span><span class="p">):</span> - <span class="sd">"""Returns graph from tree data format.</span> +<span class="w"> </span><span class="sd">"""Returns graph from tree data format.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -650,7 +650,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/readwrite/leda.html b/_modules/networkx/readwrite/leda.html index 13b69cac..9bf80df0 100644 --- a/_modules/networkx/readwrite/leda.html +++ b/_modules/networkx/readwrite/leda.html @@ -483,7 +483,7 @@ <div class="viewcode-block" id="read_leda"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.leda.read_leda.html#networkx.readwrite.leda.read_leda">[docs]</a><span class="nd">@open_file</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"rb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">read_leda</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"UTF-8"</span><span class="p">):</span> - <span class="sd">"""Read graph in LEDA format from path.</span> +<span class="w"> </span><span class="sd">"""Read graph in LEDA format from path.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -509,7 +509,7 @@ <div class="viewcode-block" id="parse_leda"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.leda.parse_leda.html#networkx.readwrite.leda.parse_leda">[docs]</a><span class="k">def</span> <span class="nf">parse_leda</span><span class="p">(</span><span class="n">lines</span><span class="p">):</span> - <span class="sd">"""Read graph in LEDA format from string or iterable.</span> +<span class="w"> </span><span class="sd">"""Read graph in LEDA format from string or iterable.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -618,7 +618,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/readwrite/multiline_adjlist.html b/_modules/networkx/readwrite/multiline_adjlist.html index f30bb23b..10e68a3c 100644 --- a/_modules/networkx/readwrite/multiline_adjlist.html +++ b/_modules/networkx/readwrite/multiline_adjlist.html @@ -500,7 +500,7 @@ <div class="viewcode-block" id="generate_multiline_adjlist"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.multiline_adjlist.generate_multiline_adjlist.html#networkx.readwrite.multiline_adjlist.generate_multiline_adjlist">[docs]</a><span class="k">def</span> <span class="nf">generate_multiline_adjlist</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s2">" "</span><span class="p">):</span> - <span class="sd">"""Generate a single line of the graph G in multiline adjacency list format.</span> +<span class="w"> </span><span class="sd">"""Generate a single line of the graph G in multiline adjacency list format.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -598,7 +598,7 @@ <div class="viewcode-block" id="write_multiline_adjlist"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.multiline_adjlist.write_multiline_adjlist.html#networkx.readwrite.multiline_adjlist.write_multiline_adjlist">[docs]</a><span class="nd">@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">"wb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">write_multiline_adjlist</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s2">" "</span><span class="p">,</span> <span class="n">comments</span><span class="o">=</span><span class="s2">"#"</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">):</span> - <span class="sd">"""Write the graph G in multiline adjacency list format to path</span> +<span class="w"> </span><span class="sd">"""Write the graph G in multiline adjacency list format to path</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -657,7 +657,7 @@ <div class="viewcode-block" id="parse_multiline_adjlist"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.multiline_adjlist.parse_multiline_adjlist.html#networkx.readwrite.multiline_adjlist.parse_multiline_adjlist">[docs]</a><span class="k">def</span> <span class="nf">parse_multiline_adjlist</span><span class="p">(</span> <span class="n">lines</span><span class="p">,</span> <span class="n">comments</span><span class="o">=</span><span class="s2">"#"</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nodetype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">edgetype</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Parse lines of a multiline adjacency list representation of a graph.</span> +<span class="w"> </span><span class="sd">"""Parse lines of a multiline adjacency list representation of a graph.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -772,7 +772,7 @@ <span class="n">edgetype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">,</span> <span class="p">):</span> - <span class="sd">"""Read graph in multi-line adjacency list format from path.</span> +<span class="w"> </span><span class="sd">"""Read graph in multi-line adjacency list format from path.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -903,7 +903,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/readwrite/pajek.html b/_modules/networkx/readwrite/pajek.html index 657fdc1f..81b03f30 100644 --- a/_modules/networkx/readwrite/pajek.html +++ b/_modules/networkx/readwrite/pajek.html @@ -486,7 +486,7 @@ <div class="viewcode-block" id="generate_pajek"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.pajek.generate_pajek.html#networkx.readwrite.pajek.generate_pajek">[docs]</a><span class="k">def</span> <span class="nf">generate_pajek</span><span class="p">(</span><span class="n">G</span><span class="p">):</span> - <span class="sd">"""Generate lines in Pajek graph format.</span> +<span class="w"> </span><span class="sd">"""Generate lines in Pajek graph format.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -536,7 +536,7 @@ <span class="n">s</span> <span class="o">+=</span> <span class="sa">f</span><span class="s2">" </span><span class="si">{</span><span class="n">make_qstr</span><span class="p">(</span><span class="n">k</span><span class="p">)</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">make_qstr</span><span class="p">(</span><span class="n">v</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span> <span class="k">else</span><span class="p">:</span> <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span> - <span class="sa">f</span><span class="s2">"Node attribute </span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2"> is not processed. </span><span class="si">{</span><span class="p">(</span><span class="s1">'Empty attribute'</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">else</span> <span class="s1">'Non-string attribute'</span><span class="p">)</span><span class="si">}</span><span class="s2">."</span> + <span class="sa">f</span><span class="s2">"Node attribute </span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2"> is not processed. </span><span class="si">{</span><span class="p">(</span><span class="s1">'Empty attribute'</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span><span class="w"> </span><span class="nb">str</span><span class="p">)</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="s1">'Non-string attribute'</span><span class="p">)</span><span class="si">}</span><span class="s2">."</span> <span class="p">)</span> <span class="k">yield</span> <span class="n">s</span> @@ -554,14 +554,14 @@ <span class="n">s</span> <span class="o">+=</span> <span class="sa">f</span><span class="s2">" </span><span class="si">{</span><span class="n">make_qstr</span><span class="p">(</span><span class="n">k</span><span class="p">)</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">make_qstr</span><span class="p">(</span><span class="n">v</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span> <span class="k">else</span><span class="p">:</span> <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span> - <span class="sa">f</span><span class="s2">"Edge attribute </span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2"> is not processed. </span><span class="si">{</span><span class="p">(</span><span class="s1">'Empty attribute'</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">else</span> <span class="s1">'Non-string attribute'</span><span class="p">)</span><span class="si">}</span><span class="s2">."</span> + <span class="sa">f</span><span class="s2">"Edge attribute </span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2"> is not processed. </span><span class="si">{</span><span class="p">(</span><span class="s1">'Empty attribute'</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span><span class="w"> </span><span class="nb">str</span><span class="p">)</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="s1">'Non-string attribute'</span><span class="p">)</span><span class="si">}</span><span class="s2">."</span> <span class="p">)</span> <span class="k">yield</span> <span class="n">s</span></div> <div class="viewcode-block" id="write_pajek"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.pajek.write_pajek.html#networkx.readwrite.pajek.write_pajek">[docs]</a><span class="nd">@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">"wb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">write_pajek</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"UTF-8"</span><span class="p">):</span> - <span class="sd">"""Write graph in Pajek format to path.</span> +<span class="w"> </span><span class="sd">"""Write graph in Pajek format to path.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -594,7 +594,7 @@ <div class="viewcode-block" id="read_pajek"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.pajek.read_pajek.html#networkx.readwrite.pajek.read_pajek">[docs]</a><span class="nd">@open_file</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"rb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">read_pajek</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"UTF-8"</span><span class="p">):</span> - <span class="sd">"""Read graph in Pajek format from path.</span> +<span class="w"> </span><span class="sd">"""Read graph in Pajek format from path.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -626,7 +626,7 @@ <div class="viewcode-block" id="parse_pajek"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.pajek.parse_pajek.html#networkx.readwrite.pajek.parse_pajek">[docs]</a><span class="k">def</span> <span class="nf">parse_pajek</span><span class="p">(</span><span class="n">lines</span><span class="p">):</span> - <span class="sd">"""Parse Pajek format graph from string or iterable.</span> +<span class="w"> </span><span class="sd">"""Parse Pajek format graph from string or iterable.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -737,7 +737,7 @@ <span class="k">def</span> <span class="nf">make_qstr</span><span class="p">(</span><span class="n">t</span><span class="p">):</span> - <span class="sd">"""Returns the string representation of t.</span> +<span class="w"> </span><span class="sd">"""Returns the string representation of t.</span> <span class="sd"> Add outer double-quotes if the string has a space.</span> <span class="sd"> """</span> <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span> @@ -796,7 +796,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/readwrite/sparse6.html b/_modules/networkx/readwrite/sparse6.html index 4f2e7a69..586dd651 100644 --- a/_modules/networkx/readwrite/sparse6.html +++ b/_modules/networkx/readwrite/sparse6.html @@ -483,7 +483,7 @@ <span class="k">def</span> <span class="nf">_generate_sparse6_bytes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">header</span><span class="p">):</span> - <span class="sd">"""Yield bytes in the sparse6 encoding of a graph.</span> +<span class="w"> </span><span class="sd">"""Yield bytes in the sparse6 encoding of a graph.</span> <span class="sd"> `G` is an undirected simple graph. `nodes` is the list of nodes for</span> <span class="sd"> which the node-induced subgraph will be encoded; if `nodes` is the</span> @@ -519,7 +519,7 @@ <span class="n">k</span> <span class="o">+=</span> <span class="mi">1</span> <span class="k">def</span> <span class="nf">enc</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> - <span class="sd">"""Big endian k-bit encoding of x"""</span> +<span class="w"> </span><span class="sd">"""Big endian k-bit encoding of x"""</span> <span class="k">return</span> <span class="p">[</span><span class="mi">1</span> <span class="k">if</span> <span class="p">(</span><span class="n">x</span> <span class="o">&</span> <span class="mi">1</span> <span class="o"><<</span> <span class="p">(</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">i</span><span class="p">))</span> <span class="k">else</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">k</span><span class="p">)]</span> <span class="n">edges</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">((</span><span class="nb">max</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">),</span> <span class="nb">min</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">))</span> <span class="k">for</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">())</span> @@ -565,7 +565,7 @@ <div class="viewcode-block" id="from_sparse6_bytes"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.sparse6.from_sparse6_bytes.html#networkx.readwrite.sparse6.from_sparse6_bytes">[docs]</a><span class="k">def</span> <span class="nf">from_sparse6_bytes</span><span class="p">(</span><span class="n">string</span><span class="p">):</span> - <span class="sd">"""Read an undirected graph in sparse6 format from string.</span> +<span class="w"> </span><span class="sd">"""Read an undirected graph in sparse6 format from string.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -609,7 +609,7 @@ <span class="n">k</span> <span class="o">+=</span> <span class="mi">1</span> <span class="k">def</span> <span class="nf">parseData</span><span class="p">():</span> - <span class="sd">"""Returns stream of pairs b[i], x[i] for sparse6 format."""</span> +<span class="w"> </span><span class="sd">"""Returns stream of pairs b[i], x[i] for sparse6 format."""</span> <span class="n">chunks</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="n">d</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># partial data word</span> <span class="n">dLen</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># how many unparsed bits are left in d</span> @@ -662,7 +662,7 @@ <div class="viewcode-block" id="to_sparse6_bytes"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.sparse6.to_sparse6_bytes.html#networkx.readwrite.sparse6.to_sparse6_bytes">[docs]</a><span class="k">def</span> <span class="nf">to_sparse6_bytes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Convert an undirected graph to bytes in sparse6 format.</span> +<span class="w"> </span><span class="sd">"""Convert an undirected graph to bytes in sparse6 format.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -713,7 +713,7 @@ <div class="viewcode-block" id="read_sparse6"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.sparse6.read_sparse6.html#networkx.readwrite.sparse6.read_sparse6">[docs]</a><span class="nd">@open_file</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"rb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">read_sparse6</span><span class="p">(</span><span class="n">path</span><span class="p">):</span> - <span class="sd">"""Read an undirected graph in sparse6 format from path.</span> +<span class="w"> </span><span class="sd">"""Read an undirected graph in sparse6 format from path.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -777,7 +777,7 @@ <div class="viewcode-block" id="write_sparse6"><a class="viewcode-back" href="../../../reference/readwrite/generated/networkx.readwrite.sparse6.write_sparse6.html#networkx.readwrite.sparse6.write_sparse6">[docs]</a><span class="nd">@not_implemented_for</span><span class="p">(</span><span class="s2">"directed"</span><span class="p">)</span> <span class="nd">@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">"wb"</span><span class="p">)</span> <span class="k">def</span> <span class="nf">write_sparse6</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Write graph G to given path in sparse6 format.</span> +<span class="w"> </span><span class="sd">"""Write graph G to given path in sparse6 format.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -886,7 +886,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/relabel.html b/_modules/networkx/relabel.html index 636eacb3..6b65465e 100644 --- a/_modules/networkx/relabel.html +++ b/_modules/networkx/relabel.html @@ -467,7 +467,7 @@ <div class="viewcode-block" id="relabel_nodes"><a class="viewcode-back" href="../../reference/generated/networkx.relabel.relabel_nodes.html#networkx.relabel.relabel_nodes">[docs]</a><span class="k">def</span> <span class="nf">relabel_nodes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">mapping</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> - <span class="sd">"""Relabel the nodes of the graph G according to a given mapping.</span> +<span class="w"> </span><span class="sd">"""Relabel the nodes of the graph G according to a given mapping.</span> <span class="sd"> The original node ordering may not be preserved if `copy` is `False` and the</span> <span class="sd"> mapping includes overlap between old and new labels.</span> @@ -686,7 +686,7 @@ <div class="viewcode-block" id="convert_node_labels_to_integers"><a class="viewcode-back" href="../../reference/generated/networkx.relabel.convert_node_labels_to_integers.html#networkx.relabel.convert_node_labels_to_integers">[docs]</a><span class="k">def</span> <span class="nf">convert_node_labels_to_integers</span><span class="p">(</span> <span class="n">G</span><span class="p">,</span> <span class="n">first_label</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ordering</span><span class="o">=</span><span class="s2">"default"</span><span class="p">,</span> <span class="n">label_attribute</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span> - <span class="sd">"""Returns a copy of the graph G with the nodes relabeled using</span> +<span class="w"> </span><span class="sd">"""Returns a copy of the graph G with the nodes relabeled using</span> <span class="sd"> consecutive integers.</span> <span class="sd"> Parameters</span> @@ -793,7 +793,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/utils/decorators.html b/_modules/networkx/utils/decorators.html index 09fcad82..f2bddfee 100644 --- a/_modules/networkx/utils/decorators.html +++ b/_modules/networkx/utils/decorators.html @@ -486,7 +486,7 @@ <div class="viewcode-block" id="not_implemented_for"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.decorators.not_implemented_for.html#networkx.utils.decorators.not_implemented_for">[docs]</a><span class="k">def</span> <span class="nf">not_implemented_for</span><span class="p">(</span><span class="o">*</span><span class="n">graph_types</span><span class="p">):</span> - <span class="sd">"""Decorator to mark algorithms as not implemented</span> +<span class="w"> </span><span class="sd">"""Decorator to mark algorithms as not implemented</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -564,7 +564,7 @@ <div class="viewcode-block" id="open_file"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.decorators.open_file.html#networkx.utils.decorators.open_file">[docs]</a><span class="k">def</span> <span class="nf">open_file</span><span class="p">(</span><span class="n">path_arg</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"r"</span><span class="p">):</span> - <span class="sd">"""Decorator to ensure clean opening and closing of files.</span> +<span class="w"> </span><span class="sd">"""Decorator to ensure clean opening and closing of files.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -656,7 +656,7 @@ <div class="viewcode-block" id="nodes_or_number"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.decorators.nodes_or_number.html#networkx.utils.decorators.nodes_or_number">[docs]</a><span class="k">def</span> <span class="nf">nodes_or_number</span><span class="p">(</span><span class="n">which_args</span><span class="p">):</span> - <span class="sd">"""Decorator to allow number of nodes or container of nodes.</span> +<span class="w"> </span><span class="sd">"""Decorator to allow number of nodes or container of nodes.</span> <span class="sd"> With this decorator, the specified argument can be either a number or a container</span> <span class="sd"> of nodes. If it is a number, the nodes used are `range(n)`.</span> @@ -720,7 +720,7 @@ <div class="viewcode-block" id="np_random_state"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.decorators.np_random_state.html#networkx.utils.decorators.np_random_state">[docs]</a><span class="k">def</span> <span class="nf">np_random_state</span><span class="p">(</span><span class="n">random_state_argument</span><span class="p">):</span> - <span class="sd">"""Decorator to generate a `numpy.random.RandomState` instance.</span> +<span class="w"> </span><span class="sd">"""Decorator to generate a `numpy.random.RandomState` instance.</span> <span class="sd"> The decorator processes the argument indicated by `random_state_argument`</span> <span class="sd"> using :func:`nx.utils.create_random_state`.</span> @@ -764,7 +764,7 @@ <div class="viewcode-block" id="py_random_state"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.decorators.py_random_state.html#networkx.utils.decorators.py_random_state">[docs]</a><span class="k">def</span> <span class="nf">py_random_state</span><span class="p">(</span><span class="n">random_state_argument</span><span class="p">):</span> - <span class="sd">"""Decorator to generate a random.Random instance (or equiv).</span> +<span class="w"> </span><span class="sd">"""Decorator to generate a random.Random instance (or equiv).</span> <span class="sd"> The decorator processes the argument indicated by `random_state_argument`</span> <span class="sd"> using :func:`nx.utils.create_py_random_state`.</span> @@ -817,7 +817,7 @@ <div class="viewcode-block" id="argmap"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.decorators.argmap.html#networkx.utils.decorators.argmap">[docs]</a><span class="k">class</span> <span class="nc">argmap</span><span class="p">:</span> - <span class="sd">"""A decorator to apply a map to arguments before calling the function</span> +<span class="w"> </span><span class="sd">"""A decorator to apply a map to arguments before calling the function</span> <span class="sd"> This class provides a decorator that maps (transforms) arguments of the function</span> <span class="sd"> before the function is called. Thus for example, we have similar code</span> @@ -1159,7 +1159,7 @@ <span class="nd">@staticmethod</span> <span class="k">def</span> <span class="nf">_lazy_compile</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> - <span class="sd">"""Compile the source of a wrapped function</span> +<span class="w"> </span><span class="sd">"""Compile the source of a wrapped function</span> <span class="sd"> Assemble and compile the decorated function, and intrusively replace its</span> <span class="sd"> code with the compiled version's. The thinly wrapped function becomes</span> @@ -1205,7 +1205,7 @@ <span class="k">return</span> <span class="n">func</span> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">):</span> - <span class="sd">"""Construct a lazily decorated wrapper of f.</span> +<span class="w"> </span><span class="sd">"""Construct a lazily decorated wrapper of f.</span> <span class="sd"> The decorated function will be compiled when it is called for the first time,</span> <span class="sd"> and it will replace its own __code__ object so subsequent calls are fast.</span> @@ -1270,7 +1270,7 @@ <span class="nd">@classmethod</span> <span class="k">def</span> <span class="nf">_count</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span> - <span class="sd">"""Maintain a globally-unique identifier for function names and "file" names</span> +<span class="w"> </span><span class="sd">"""Maintain a globally-unique identifier for function names and "file" names</span> <span class="sd"> Note that this counter is a class method reporting a class variable</span> <span class="sd"> so the count is unique within a Python session. It could differ from</span> @@ -1293,7 +1293,7 @@ <span class="nd">@classmethod</span> <span class="k">def</span> <span class="nf">_name</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">f</span><span class="p">):</span> - <span class="sd">"""Mangle the name of a function to be unique but somewhat human-readable</span> +<span class="w"> </span><span class="sd">"""Mangle the name of a function to be unique but somewhat human-readable</span> <span class="sd"> The names are unique within a Python session and set using `_count`.</span> @@ -1312,7 +1312,7 @@ <span class="k">return</span> <span class="sa">f</span><span class="s2">"argmap_</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="bp">cls</span><span class="o">.</span><span class="n">_count</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span> <div class="viewcode-block" id="argmap.compile"><a class="viewcode-back" href="../../../reference/generated/generated/networkx.utils.decorators.argmap.compile.html#networkx.utils.decorators.argmap.compile">[docs]</a> <span class="k">def</span> <span class="nf">compile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">):</span> - <span class="sd">"""Compile the decorated function.</span> +<span class="w"> </span><span class="sd">"""Compile the decorated function.</span> <span class="sd"> Called once for a given decorated function -- collects the code from all</span> <span class="sd"> argmap decorators in the stack, and compiles the decorated function.</span> @@ -1360,7 +1360,7 @@ <span class="k">return</span> <span class="n">func</span></div> <div class="viewcode-block" id="argmap.assemble"><a class="viewcode-back" href="../../../reference/generated/generated/networkx.utils.decorators.argmap.assemble.html#networkx.utils.decorators.argmap.assemble">[docs]</a> <span class="k">def</span> <span class="nf">assemble</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">):</span> - <span class="sd">"""Collects components of the source for the decorated function wrapping f.</span> +<span class="w"> </span><span class="sd">"""Collects components of the source for the decorated function wrapping f.</span> <span class="sd"> If `f` has multiple argmap decorators, we recursively assemble the stack of</span> <span class="sd"> decorators into a single flattened function.</span> @@ -1472,7 +1472,7 @@ <span class="sa">f</span><span class="s2">"index </span><span class="si">{</span><span class="n">arg</span><span class="si">}</span><span class="s2"> not a parameter index and this function doesn't have args"</span> <span class="p">)</span> <span class="n">mutable_args</span> <span class="o">=</span> <span class="kc">True</span> - <span class="k">return</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">sig</span><span class="o">.</span><span class="n">args</span><span class="si">}</span><span class="s2">[</span><span class="si">{</span><span class="n">arg</span> <span class="o">-</span> <span class="n">sig</span><span class="o">.</span><span class="n">n_positional</span><span class="si">}</span><span class="s2">]"</span> + <span class="k">return</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">sig</span><span class="o">.</span><span class="n">args</span><span class="si">}</span><span class="s2">[</span><span class="si">{</span><span class="n">arg</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">sig</span><span class="o">.</span><span class="n">n_positional</span><span class="si">}</span><span class="s2">]"</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_finally</span><span class="p">:</span> <span class="c1"># here's where we handle try_finally decorators. Such a decorator</span> @@ -1503,7 +1503,7 @@ <div class="viewcode-block" id="argmap.signature"><a class="viewcode-back" href="../../../reference/generated/generated/networkx.utils.decorators.argmap.signature.html#networkx.utils.decorators.argmap.signature">[docs]</a> <span class="nd">@classmethod</span> <span class="k">def</span> <span class="nf">signature</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">f</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Construct a Signature object describing `f`</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Construct a Signature object describing `f`</span> <span class="sd"> Compute a Signature so that we can write a function wrapping f with</span> <span class="sd"> the same signature and call-type.</span> @@ -1603,7 +1603,7 @@ <span class="nd">@staticmethod</span> <span class="k">def</span> <span class="nf">_flatten</span><span class="p">(</span><span class="n">nestlist</span><span class="p">,</span> <span class="n">visited</span><span class="p">):</span> - <span class="sd">"""flattens a recursive list of lists that doesn't have cyclic references</span> +<span class="w"> </span><span class="sd">"""flattens a recursive list of lists that doesn't have cyclic references</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -1633,7 +1633,7 @@ <span class="nd">@staticmethod</span> <span class="k">def</span> <span class="nf">_indent</span><span class="p">(</span><span class="o">*</span><span class="n">lines</span><span class="p">):</span> - <span class="sd">"""Indent list of code lines to make executable Python code</span> +<span class="w"> </span><span class="sd">"""Indent list of code lines to make executable Python code</span> <span class="sd"> Indents a tree-recursive list of strings, following the rule that one</span> <span class="sd"> space is added to the tab after a line that ends in a colon, and one is</span> @@ -1721,7 +1721,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/utils/mapped_queue.html b/_modules/networkx/utils/mapped_queue.html index 912d7dbb..b5bf4ed7 100644 --- a/_modules/networkx/utils/mapped_queue.html +++ b/_modules/networkx/utils/mapped_queue.html @@ -470,7 +470,7 @@ <span class="k">class</span> <span class="nc">_HeapElement</span><span class="p">:</span> - <span class="sd">"""This proxy class separates the heap element from its priority.</span> +<span class="w"> </span><span class="sd">"""This proxy class separates the heap element from its priority.</span> <span class="sd"> The idea is that using a 2-tuple (priority, element) works</span> <span class="sd"> for sorting, but not for dict lookup because priorities are</span> @@ -553,7 +553,7 @@ <div class="viewcode-block" id="MappedQueue"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.mapped_queue.MappedQueue.html#networkx.utils.mapped_queue.MappedQueue">[docs]</a><span class="k">class</span> <span class="nc">MappedQueue</span><span class="p">:</span> - <span class="sd">"""The MappedQueue class implements a min-heap with removal and update-priority.</span> +<span class="w"> </span><span class="sd">"""The MappedQueue class implements a min-heap with removal and update-priority.</span> <span class="sd"> The min heap uses heapq as well as custom written _siftup and _siftdown</span> <span class="sd"> methods to allow the heap positions to be tracked by an additional dict</span> @@ -615,7 +615,7 @@ <span class="sd"> """</span> <div class="viewcode-block" id="MappedQueue.__init__"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.mapped_queue.MappedQueue.html#networkx.utils.mapped_queue.MappedQueue.__init__">[docs]</a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Priority queue class with updatable priorities."""</span> +<span class="w"> </span><span class="sd">"""Priority queue class with updatable priorities."""</span> <span class="k">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">heap</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span> <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> @@ -626,7 +626,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">_heapify</span><span class="p">()</span></div> <span class="k">def</span> <span class="nf">_heapify</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Restore heap invariant and recalculate map."""</span> +<span class="w"> </span><span class="sd">"""Restore heap invariant and recalculate map."""</span> <span class="n">heapq</span><span class="o">.</span><span class="n">heapify</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">heap</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="p">{</span><span class="n">elt</span><span class="p">:</span> <span class="n">pos</span> <span class="k">for</span> <span class="n">pos</span><span class="p">,</span> <span class="n">elt</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">heap</span><span class="p">)}</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">heap</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">position</span><span class="p">):</span> @@ -636,7 +636,7 @@ <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">heap</span><span class="p">)</span> <div class="viewcode-block" id="MappedQueue.push"><a class="viewcode-back" href="../../../reference/generated/generated/networkx.utils.mapped_queue.MappedQueue.push.html#networkx.utils.mapped_queue.MappedQueue.push">[docs]</a> <span class="k">def</span> <span class="nf">push</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">elt</span><span class="p">,</span> <span class="n">priority</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Add an element to the queue."""</span> +<span class="w"> </span><span class="sd">"""Add an element to the queue."""</span> <span class="k">if</span> <span class="n">priority</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="n">elt</span> <span class="o">=</span> <span class="n">_HeapElement</span><span class="p">(</span><span class="n">priority</span><span class="p">,</span> <span class="n">elt</span><span class="p">)</span> <span class="c1"># If element is already in queue, do nothing</span> @@ -651,7 +651,7 @@ <span class="k">return</span> <span class="kc">True</span></div> <div class="viewcode-block" id="MappedQueue.pop"><a class="viewcode-back" href="../../../reference/generated/generated/networkx.utils.mapped_queue.MappedQueue.pop.html#networkx.utils.mapped_queue.MappedQueue.pop">[docs]</a> <span class="k">def</span> <span class="nf">pop</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Remove and return the smallest element in the queue."""</span> +<span class="w"> </span><span class="sd">"""Remove and return the smallest element in the queue."""</span> <span class="c1"># Remove smallest element</span> <span class="n">elt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">heap</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">position</span><span class="p">[</span><span class="n">elt</span><span class="p">]</span> @@ -669,7 +669,7 @@ <span class="k">return</span> <span class="n">elt</span></div> <div class="viewcode-block" id="MappedQueue.update"><a class="viewcode-back" href="../../../reference/generated/generated/networkx.utils.mapped_queue.MappedQueue.update.html#networkx.utils.mapped_queue.MappedQueue.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">elt</span><span class="p">,</span> <span class="n">new</span><span class="p">,</span> <span class="n">priority</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Replace an element in the queue with a new one."""</span> +<span class="w"> </span><span class="sd">"""Replace an element in the queue with a new one."""</span> <span class="k">if</span> <span class="n">priority</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="n">new</span> <span class="o">=</span> <span class="n">_HeapElement</span><span class="p">(</span><span class="n">priority</span><span class="p">,</span> <span class="n">new</span><span class="p">)</span> <span class="c1"># Replace</span> @@ -681,7 +681,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">_siftup</span><span class="p">(</span><span class="n">pos</span><span class="p">)</span></div> <div class="viewcode-block" id="MappedQueue.remove"><a class="viewcode-back" href="../../../reference/generated/generated/networkx.utils.mapped_queue.MappedQueue.remove.html#networkx.utils.mapped_queue.MappedQueue.remove">[docs]</a> <span class="k">def</span> <span class="nf">remove</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">elt</span><span class="p">):</span> - <span class="sd">"""Remove an element from the queue."""</span> +<span class="w"> </span><span class="sd">"""Remove an element from the queue."""</span> <span class="c1"># Find and remove element</span> <span class="k">try</span><span class="p">:</span> <span class="n">pos</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">position</span><span class="p">[</span><span class="n">elt</span><span class="p">]</span> @@ -701,7 +701,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">_siftup</span><span class="p">(</span><span class="n">pos</span><span class="p">)</span></div> <span class="k">def</span> <span class="nf">_siftup</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pos</span><span class="p">):</span> - <span class="sd">"""Move smaller child up until hitting a leaf.</span> +<span class="w"> </span><span class="sd">"""Move smaller child up until hitting a leaf.</span> <span class="sd"> Built to mimic code for heapq._siftup</span> <span class="sd"> only updating position dict too.</span> @@ -740,7 +740,7 @@ <span class="n">position</span><span class="p">[</span><span class="n">newitem</span><span class="p">]</span> <span class="o">=</span> <span class="n">pos</span> <span class="k">def</span> <span class="nf">_siftdown</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start_pos</span><span class="p">,</span> <span class="n">pos</span><span class="p">):</span> - <span class="sd">"""Restore invariant. keep swapping with parent until smaller.</span> +<span class="w"> </span><span class="sd">"""Restore invariant. keep swapping with parent until smaller.</span> <span class="sd"> Built to mimic code for heapq._siftdown</span> <span class="sd"> only updating position dict too.</span> @@ -810,7 +810,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/utils/misc.html b/_modules/networkx/utils/misc.html index f0318b02..cd739238 100644 --- a/_modules/networkx/utils/misc.html +++ b/_modules/networkx/utils/misc.html @@ -505,7 +505,7 @@ <div class="viewcode-block" id="flatten"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.misc.flatten.html#networkx.utils.misc.flatten">[docs]</a><span class="k">def</span> <span class="nf">flatten</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">result</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Return flattened version of (possibly nested) iterable object."""</span> +<span class="w"> </span><span class="sd">"""Return flattened version of (possibly nested) iterable object."""</span> <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="p">(</span><span class="n">Iterable</span><span class="p">,</span> <span class="n">Sized</span><span class="p">))</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span> <span class="k">return</span> <span class="n">obj</span> <span class="k">if</span> <span class="n">result</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> @@ -519,7 +519,7 @@ <div class="viewcode-block" id="make_list_of_ints"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.misc.make_list_of_ints.html#networkx.utils.misc.make_list_of_ints">[docs]</a><span class="k">def</span> <span class="nf">make_list_of_ints</span><span class="p">(</span><span class="n">sequence</span><span class="p">):</span> - <span class="sd">"""Return list of ints from sequence of integral numbers.</span> +<span class="w"> </span><span class="sd">"""Return list of ints from sequence of integral numbers.</span> <span class="sd"> All elements of the sequence must satisfy int(element) == element</span> <span class="sd"> or a ValueError is raised. Sequence is iterated through once.</span> @@ -555,7 +555,7 @@ <div class="viewcode-block" id="dict_to_numpy_array"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.misc.dict_to_numpy_array.html#networkx.utils.misc.dict_to_numpy_array">[docs]</a><span class="k">def</span> <span class="nf">dict_to_numpy_array</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">mapping</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Convert a dictionary of dictionaries to a numpy array</span> +<span class="w"> </span><span class="sd">"""Convert a dictionary of dictionaries to a numpy array</span> <span class="sd"> with optional mapping."""</span> <span class="k">try</span><span class="p">:</span> <span class="k">return</span> <span class="n">_dict_to_numpy_array2</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">mapping</span><span class="p">)</span> @@ -566,7 +566,7 @@ <span class="k">def</span> <span class="nf">_dict_to_numpy_array2</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">mapping</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Convert a dictionary of dictionaries to a 2d numpy array</span> +<span class="w"> </span><span class="sd">"""Convert a dictionary of dictionaries to a 2d numpy array</span> <span class="sd"> with optional mapping.</span> <span class="sd"> """</span> @@ -589,7 +589,7 @@ <span class="k">def</span> <span class="nf">_dict_to_numpy_array1</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">mapping</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Convert a dictionary of numbers to a 1d numpy array with optional mapping."""</span> +<span class="w"> </span><span class="sd">"""Convert a dictionary of numbers to a 1d numpy array with optional mapping."""</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="k">if</span> <span class="n">mapping</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> @@ -604,7 +604,7 @@ <div class="viewcode-block" id="arbitrary_element"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.misc.arbitrary_element.html#networkx.utils.misc.arbitrary_element">[docs]</a><span class="k">def</span> <span class="nf">arbitrary_element</span><span class="p">(</span><span class="n">iterable</span><span class="p">):</span> - <span class="sd">"""Returns an arbitrary element of `iterable` without removing it.</span> +<span class="w"> </span><span class="sd">"""Returns an arbitrary element of `iterable` without removing it.</span> <span class="sd"> This is most useful for "peeking" at an arbitrary element of a set,</span> <span class="sd"> but can be used for any list, dictionary, etc., as well.</span> @@ -683,7 +683,7 @@ <div class="viewcode-block" id="groups"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.misc.groups.html#networkx.utils.misc.groups">[docs]</a><span class="k">def</span> <span class="nf">groups</span><span class="p">(</span><span class="n">many_to_one</span><span class="p">):</span> - <span class="sd">"""Converts a many-to-one mapping into a one-to-many mapping.</span> +<span class="w"> </span><span class="sd">"""Converts a many-to-one mapping into a one-to-many mapping.</span> <span class="sd"> `many_to_one` must be a dictionary whose keys and values are all</span> <span class="sd"> :term:`hashable`.</span> @@ -705,7 +705,7 @@ <div class="viewcode-block" id="create_random_state"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.misc.create_random_state.html#networkx.utils.misc.create_random_state">[docs]</a><span class="k">def</span> <span class="nf">create_random_state</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a numpy.random.RandomState or numpy.random.Generator instance</span> +<span class="w"> </span><span class="sd">"""Returns a numpy.random.RandomState or numpy.random.Generator instance</span> <span class="sd"> depending on input.</span> <span class="sd"> Parameters</span> @@ -811,7 +811,7 @@ <div class="viewcode-block" id="create_py_random_state"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.misc.create_py_random_state.html#networkx.utils.misc.create_py_random_state">[docs]</a><span class="k">def</span> <span class="nf">create_py_random_state</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a random.Random instance depending on input.</span> +<span class="w"> </span><span class="sd">"""Returns a random.Random instance depending on input.</span> <span class="sd"> Parameters</span> <span class="sd"> ----------</span> @@ -851,7 +851,7 @@ <div class="viewcode-block" id="nodes_equal"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.misc.nodes_equal.html#networkx.utils.misc.nodes_equal">[docs]</a><span class="k">def</span> <span class="nf">nodes_equal</span><span class="p">(</span><span class="n">nodes1</span><span class="p">,</span> <span class="n">nodes2</span><span class="p">):</span> - <span class="sd">"""Check if nodes are equal.</span> +<span class="w"> </span><span class="sd">"""Check if nodes are equal.</span> <span class="sd"> Equality here means equal as Python objects.</span> <span class="sd"> Node data must match if included.</span> @@ -878,7 +878,7 @@ <div class="viewcode-block" id="edges_equal"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.misc.edges_equal.html#networkx.utils.misc.edges_equal">[docs]</a><span class="k">def</span> <span class="nf">edges_equal</span><span class="p">(</span><span class="n">edges1</span><span class="p">,</span> <span class="n">edges2</span><span class="p">):</span> - <span class="sd">"""Check if edges are equal.</span> +<span class="w"> </span><span class="sd">"""Check if edges are equal.</span> <span class="sd"> Equality here means equal as Python objects.</span> <span class="sd"> Edge data must match if included.</span> @@ -933,7 +933,7 @@ <div class="viewcode-block" id="graphs_equal"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.misc.graphs_equal.html#networkx.utils.misc.graphs_equal">[docs]</a><span class="k">def</span> <span class="nf">graphs_equal</span><span class="p">(</span><span class="n">graph1</span><span class="p">,</span> <span class="n">graph2</span><span class="p">):</span> - <span class="sd">"""Check if graphs are equal.</span> +<span class="w"> </span><span class="sd">"""Check if graphs are equal.</span> <span class="sd"> Equality here means equal as Python objects (not isomorphism).</span> <span class="sd"> Node, edge and graph data must match.</span> @@ -1003,7 +1003,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/utils/random_sequence.html b/_modules/networkx/utils/random_sequence.html index 55fde26a..2bdf5609 100644 --- a/_modules/networkx/utils/random_sequence.html +++ b/_modules/networkx/utils/random_sequence.html @@ -486,7 +486,7 @@ <div class="viewcode-block" id="powerlaw_sequence"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.random_sequence.powerlaw_sequence.html#networkx.utils.random_sequence.powerlaw_sequence">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">powerlaw_sequence</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">exponent</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return sample sequence of length n from a power law distribution.</span> <span class="sd"> """</span> <span class="k">return</span> <span class="p">[</span><span class="n">seed</span><span class="o">.</span><span class="n">paretovariate</span><span class="p">(</span><span class="n">exponent</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)]</span></div> @@ -494,7 +494,7 @@ <div class="viewcode-block" id="zipf_rv"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.random_sequence.zipf_rv.html#networkx.utils.random_sequence.zipf_rv">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">zipf_rv</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">xmin</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sa">r</span><span class="sd">"""Returns a random value chosen from the Zipf distribution.</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns a random value chosen from the Zipf distribution.</span> <span class="sd"> The return value is an integer drawn from the probability distribution</span> @@ -558,7 +558,7 @@ <div class="viewcode-block" id="cumulative_distribution"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.random_sequence.cumulative_distribution.html#networkx.utils.random_sequence.cumulative_distribution">[docs]</a><span class="k">def</span> <span class="nf">cumulative_distribution</span><span class="p">(</span><span class="n">distribution</span><span class="p">):</span> - <span class="sd">"""Returns normalized cumulative distribution from discrete distribution."""</span> +<span class="w"> </span><span class="sd">"""Returns normalized cumulative distribution from discrete distribution."""</span> <span class="n">cdf</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">]</span> <span class="n">psum</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">distribution</span><span class="p">)</span> @@ -569,7 +569,7 @@ <div class="viewcode-block" id="discrete_sequence"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.random_sequence.discrete_sequence.html#networkx.utils.random_sequence.discrete_sequence">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">def</span> <span class="nf">discrete_sequence</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">distribution</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cdistribution</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""</span> +<span class="w"> </span><span class="sd">"""</span> <span class="sd"> Return sample sequence of length n from a given discrete distribution</span> <span class="sd"> or discrete cumulative distribution.</span> @@ -601,7 +601,7 @@ <div class="viewcode-block" id="random_weighted_sample"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.random_sequence.random_weighted_sample.html#networkx.utils.random_sequence.random_weighted_sample">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">def</span> <span class="nf">random_weighted_sample</span><span class="p">(</span><span class="n">mapping</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns k items without replacement from a weighted sample.</span> +<span class="w"> </span><span class="sd">"""Returns k items without replacement from a weighted sample.</span> <span class="sd"> The input is a dictionary of items with weights as values.</span> <span class="sd"> """</span> @@ -615,7 +615,7 @@ <div class="viewcode-block" id="weighted_choice"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.random_sequence.weighted_choice.html#networkx.utils.random_sequence.weighted_choice">[docs]</a><span class="nd">@py_random_state</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="k">def</span> <span class="nf">weighted_choice</span><span class="p">(</span><span class="n">mapping</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Returns a single element from a weighted sample.</span> +<span class="w"> </span><span class="sd">"""Returns a single element from a weighted sample.</span> <span class="sd"> The input is a dictionary of items with weights as values.</span> <span class="sd"> """</span> @@ -676,7 +676,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/utils/rcm.html b/_modules/networkx/utils/rcm.html index 6607f6ed..6137ccc1 100644 --- a/_modules/networkx/utils/rcm.html +++ b/_modules/networkx/utils/rcm.html @@ -475,7 +475,7 @@ <div class="viewcode-block" id="cuthill_mckee_ordering"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.rcm.cuthill_mckee_ordering.html#networkx.utils.rcm.cuthill_mckee_ordering">[docs]</a><span class="k">def</span> <span class="nf">cuthill_mckee_ordering</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">heuristic</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generate an ordering (permutation) of the graph nodes to make</span> +<span class="w"> </span><span class="sd">"""Generate an ordering (permutation) of the graph nodes to make</span> <span class="sd"> a sparse matrix.</span> <span class="sd"> Uses the Cuthill-McKee heuristic (based on breadth-first search) [1]_.</span> @@ -532,7 +532,7 @@ <div class="viewcode-block" id="reverse_cuthill_mckee_ordering"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.rcm.reverse_cuthill_mckee_ordering.html#networkx.utils.rcm.reverse_cuthill_mckee_ordering">[docs]</a><span class="k">def</span> <span class="nf">reverse_cuthill_mckee_ordering</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">heuristic</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Generate an ordering (permutation) of the graph nodes to make</span> +<span class="w"> </span><span class="sd">"""Generate an ordering (permutation) of the graph nodes to make</span> <span class="sd"> a sparse matrix.</span> <span class="sd"> Uses the reverse Cuthill-McKee heuristic (based on breadth-first search)</span> @@ -670,7 +670,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> diff --git a/_modules/networkx/utils/union_find.html b/_modules/networkx/utils/union_find.html index 9b5c176a..5c46b002 100644 --- a/_modules/networkx/utils/union_find.html +++ b/_modules/networkx/utils/union_find.html @@ -469,7 +469,7 @@ <span class="k">class</span> <span class="nc">UnionFind</span><span class="p">:</span> - <span class="sd">"""Union-find data structure.</span> +<span class="w"> </span><span class="sd">"""Union-find data structure.</span> <span class="sd"> Each unionFind instance X maintains a family of disjoint sets of</span> <span class="sd"> hashable objects, supporting the following two methods:</span> @@ -492,7 +492,7 @@ <span class="sd"> """</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">elements</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> - <span class="sd">"""Create a new empty union-find structure.</span> +<span class="w"> </span><span class="sd">"""Create a new empty union-find structure.</span> <span class="sd"> If *elements* is an iterable, this structure will be initialized</span> <span class="sd"> with the discrete partition on the given set of elements.</span> @@ -507,7 +507,7 @@ <span class="bp">self</span><span class="o">.</span><span class="n">parents</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span> <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">object</span><span class="p">):</span> - <span class="sd">"""Find and return the name of the set containing the object."""</span> +<span class="w"> </span><span class="sd">"""Find and return the name of the set containing the object."""</span> <span class="c1"># check for previously unknown object</span> <span class="k">if</span> <span class="nb">object</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parents</span><span class="p">:</span> @@ -529,11 +529,11 @@ <span class="k">return</span> <span class="n">root</span> <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Iterate through all items ever found or unioned by this structure."""</span> +<span class="w"> </span><span class="sd">"""Iterate through all items ever found or unioned by this structure."""</span> <span class="k">return</span> <span class="nb">iter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parents</span><span class="p">)</span> <span class="k">def</span> <span class="nf">to_sets</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> - <span class="sd">"""Iterates over the sets stored in this structure.</span> +<span class="w"> </span><span class="sd">"""Iterates over the sets stored in this structure.</span> <span class="sd"> For example::</span> @@ -552,7 +552,7 @@ <span class="k">yield from</span> <span class="n">groups</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parents</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">()</span> <div class="viewcode-block" id="UnionFind.union"><a class="viewcode-back" href="../../../reference/generated/networkx.utils.union_find.UnionFind.union.html#networkx.utils.union_find.UnionFind.union">[docs]</a> <span class="k">def</span> <span class="nf">union</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">objects</span><span class="p">):</span> - <span class="sd">"""Find the sets containing the objects and merge them all."""</span> +<span class="w"> </span><span class="sd">"""Find the sets containing the objects and merge them all."""</span> <span class="c1"># Find the heaviest root according to its weight.</span> <span class="n">roots</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span> <span class="nb">sorted</span><span class="p">(</span> @@ -618,7 +618,7 @@ <p class="copyright"> - © Copyright 2004-2022, NetworkX Developers.<br> + © Copyright 2004-2023, NetworkX Developers.<br> </p> |