Add typing to functions

brandones · May 29, 2024 · 6be7bc7 · 6be7bc7
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diff --git a/docs/html/api.html b/docs/html/api.html
@@ -35,9 +35,9 @@
 <span id="api"></span><h1>API<a class="headerlink" href="#module-graphpca" title="Link to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="graphpca.draw_graph">
-<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">draw_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#graphpca.draw_graph" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">draw_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Graph</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">DiGraph</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#graphpca.draw_graph" title="Link to this definition">¶</a></dt>
 <dd><p>Draws the input graph on two axes with lines between the nodes</p>
-<p>Positions of the nodes are determined with reduce_graph, of course.</p>
+<p>Positions of the nodes are determined with reduce_graph.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>nx_graph</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">nx.Graph</span></code> or <code class="xref py py-class docutils literal notranslate"><span class="pre">nx.DiGraph</span></code>) – The graph to be plotted</p>
@@ -47,7 +47,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="graphpca.reduce_graph">
-<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">reduce_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dim</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#graphpca.reduce_graph" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">reduce_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Graph</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">DiGraph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dim</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ndarray</span></span></span><a class="headerlink" href="#graphpca.reduce_graph" title="Link to this definition">¶</a></dt>
 <dd><p>Run PCA on the ETCD of the input NetworkX graph</p>
 <p>The best algorithm and parameters for doing so are selected dynamically,
 based on the size of the graph. A graph G with number of nodes n &lt; 50 will
@@ -67,7 +67,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="graphpca.reduce_graph_efficiently">
-<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">reduce_graph_efficiently</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_supernode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigendecomp_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'smart'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#graphpca.reduce_graph_efficiently" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">reduce_graph_efficiently</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Graph</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">DiGraph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dim</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_supernode</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigendecomp_strategy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'smart'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'sparse'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'exact'</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'smart'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ndarray</span></span></span><a class="headerlink" href="#graphpca.reduce_graph_efficiently" title="Link to this definition">¶</a></dt>
 <dd><p>Run PCA on the ETCD of the input NetworkX graph</p>
 <p>We skip calculating the actual ETCD for efficiency. The ETCD is given by
 the Moore-Penrose pseudoinverse of the Laplacian of the input graph. The
@@ -120,7 +120,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="graphpca.reduce_graph_naively">
-<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">reduce_graph_naively</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigendecomp_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'exact'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#graphpca.reduce_graph_naively" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">reduce_graph_naively</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Graph</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">DiGraph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dim</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigendecomp_strategy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'exact'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'sparse'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'smart'</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'exact'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ndarray</span></span></span><a class="headerlink" href="#graphpca.reduce_graph_naively" title="Link to this definition">¶</a></dt>
 <dd><p>Run PCA on the ETCD of a NetworkX graph using a slow but precise method</p>
 <p>This is the method that calculates the actual ETCD. It calculates the
 Moore-Penrose pseudoinverse of the Laplacian of the input graph. We return

diff --git a/docs/html/index.html b/docs/html/index.html
@@ -35,9 +35,9 @@
 <span id="api"></span><h1>API<a class="headerlink" href="#module-graphpca" title="Link to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="graphpca.draw_graph">
-<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">draw_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#graphpca.draw_graph" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">draw_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Graph</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">DiGraph</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#graphpca.draw_graph" title="Link to this definition">¶</a></dt>
 <dd><p>Draws the input graph on two axes with lines between the nodes</p>
-<p>Positions of the nodes are determined with reduce_graph, of course.</p>
+<p>Positions of the nodes are determined with reduce_graph.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>nx_graph</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">nx.Graph</span></code> or <code class="xref py py-class docutils literal notranslate"><span class="pre">nx.DiGraph</span></code>) – The graph to be plotted</p>
@@ -47,7 +47,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="graphpca.reduce_graph">
-<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">reduce_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dim</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#graphpca.reduce_graph" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">reduce_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Graph</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">DiGraph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dim</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ndarray</span></span></span><a class="headerlink" href="#graphpca.reduce_graph" title="Link to this definition">¶</a></dt>
 <dd><p>Run PCA on the ETCD of the input NetworkX graph</p>
 <p>The best algorithm and parameters for doing so are selected dynamically,
 based on the size of the graph. A graph G with number of nodes n &lt; 50 will
@@ -67,7 +67,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="graphpca.reduce_graph_efficiently">
-<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">reduce_graph_efficiently</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_supernode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigendecomp_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'smart'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#graphpca.reduce_graph_efficiently" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">reduce_graph_efficiently</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Graph</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">DiGraph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dim</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_supernode</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigendecomp_strategy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'smart'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'sparse'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'exact'</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'smart'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ndarray</span></span></span><a class="headerlink" href="#graphpca.reduce_graph_efficiently" title="Link to this definition">¶</a></dt>
 <dd><p>Run PCA on the ETCD of the input NetworkX graph</p>
 <p>We skip calculating the actual ETCD for efficiency. The ETCD is given by
 the Moore-Penrose pseudoinverse of the Laplacian of the input graph. The
@@ -120,7 +120,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="graphpca.reduce_graph_naively">
-<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">reduce_graph_naively</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigendecomp_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'exact'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#graphpca.reduce_graph_naively" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">graphpca.</span></span><span class="sig-name descname"><span class="pre">reduce_graph_naively</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nx_graph</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Graph</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">DiGraph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dim</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigendecomp_strategy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'exact'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'sparse'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'smart'</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'exact'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ndarray</span></span></span><a class="headerlink" href="#graphpca.reduce_graph_naively" title="Link to this definition">¶</a></dt>
 <dd><p>Run PCA on the ETCD of a NetworkX graph using a slow but precise method</p>
 <p>This is the method that calculates the actual ETCD. It calculates the
 Moore-Penrose pseudoinverse of the Laplacian of the input graph. We return