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  147.   <h1>Source code for pygraph.utils.utils</h1><div class="highlight"><pre>

  148. <span></span><span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>

  149. <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

  150. <span class="kn">from</span> <span class="nn">copy</span> <span class="k">import</span> <span class="n">deepcopy</span>

  151. <span class="c1">#from itertools import product</span>

  152. 

  153. <span class="c1"># from tqdm import tqdm</span>

  154. 

  155. 

  156. <div class="viewcode-block" id="getSPLengths"><a class="viewcode-back" href="../../../pygraph.utils.html#pygraph.utils.utils.getSPLengths">[docs]</a><span class="k">def</span> <span class="nf">getSPLengths</span><span class="p">(</span><span class="n">G1</span><span class="p">):</span>

  157.     <span class="n">sp</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">shortest_path</span><span class="p">(</span><span class="n">G1</span><span class="p">)</span>

  158.     <span class="n">distances</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">G1</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">(),</span> <span class="n">G1</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">()))</span>

  159.     <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">sp</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>

  160.         <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">sp</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>

  161.             <span class="n">distances</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="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">sp</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="o">-</span> <span class="mi">1</span>

  162.     <span class="k">return</span> <span class="n">distances</span></div>

  163. 

  164. 

  165. <div class="viewcode-block" id="getSPGraph"><a class="viewcode-back" href="../../../pygraph.utils.html#pygraph.utils.utils.getSPGraph">[docs]</a><span class="k">def</span> <span class="nf">getSPGraph</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">edge_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>

  166.     <span class="sd">&quot;&quot;&quot;Transform graph G to its corresponding shortest-paths graph.</span>

  167. 

  168. <span class="sd">    Parameters</span>

  169. <span class="sd">    ----------</span>

  170. <span class="sd">    G : NetworkX graph</span>

  171. <span class="sd">        The graph to be tramsformed.</span>

  172. <span class="sd">    edge_weight : string</span>

  173. <span class="sd">        edge attribute corresponding to the edge weight.</span>

  174. 

  175. <span class="sd">    Return</span>

  176. <span class="sd">    ------</span>

  177. <span class="sd">    S : NetworkX graph</span>

  178. <span class="sd">        The shortest-paths graph corresponding to G.</span>

  179. 

  180. <span class="sd">    Notes</span>

  181. <span class="sd">    ------</span>

  182. <span class="sd">    For an input graph G, its corresponding shortest-paths graph S contains the same set of nodes as G, while there exists an edge between all nodes in S which are connected by a walk in G. Every edge in S between two nodes is labeled by the shortest distance between these two nodes.</span>

  183. 

  184. <span class="sd">    References</span>

  185. <span class="sd">    ----------</span>

  186. <span class="sd">    [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.</span>

  187. <span class="sd">    &quot;&quot;&quot;</span>

  188.     <span class="k">return</span> <span class="n">floydTransformation</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">edge_weight</span><span class="o">=</span><span class="n">edge_weight</span><span class="p">)</span></div>

  189. 

  190. 

  191. <div class="viewcode-block" id="floydTransformation"><a class="viewcode-back" href="../../../pygraph.utils.html#pygraph.utils.utils.floydTransformation">[docs]</a><span class="k">def</span> <span class="nf">floydTransformation</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">edge_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>

  192.     <span class="sd">&quot;&quot;&quot;Transform graph G to its corresponding shortest-paths graph using Floyd-transformation.</span>

  193. 

  194. <span class="sd">    Parameters</span>

  195. <span class="sd">    ----------</span>

  196. <span class="sd">    G : NetworkX graph</span>

  197. <span class="sd">        The graph to be tramsformed.</span>

  198. <span class="sd">    edge_weight : string</span>

  199. <span class="sd">        edge attribute corresponding to the edge weight. The default edge weight is bond_type.</span>

  200. 

  201. <span class="sd">    Return</span>

  202. <span class="sd">    ------</span>

  203. <span class="sd">    S : NetworkX graph</span>

  204. <span class="sd">        The shortest-paths graph corresponding to G.</span>

  205. 

  206. <span class="sd">    References</span>

  207. <span class="sd">    ----------</span>

  208. <span class="sd">    [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.</span>

  209. <span class="sd">    &quot;&quot;&quot;</span>

  210.     <span class="n">spMatrix</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">floyd_warshall_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="n">edge_weight</span><span class="p">)</span>

  211.     <span class="n">S</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>

  212.     <span class="n">S</span><span class="o">.</span><span class="n">add_nodes_from</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">data</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>

  213.     <span class="n">ns</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>

  214.     <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="mi">0</span><span class="p">,</span> <span class="n">G</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">()):</span>

  215.         <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">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">G</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">()):</span>

  216.             <span class="k">if</span> <span class="n">spMatrix</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="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">:</span>

  217.                 <span class="n">S</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">ns</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">cost</span><span class="o">=</span><span class="n">spMatrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">])</span>

  218.     <span class="k">return</span> <span class="n">S</span></div>

  219. 

  220. 

  221. <div class="viewcode-block" id="untotterTransformation"><a class="viewcode-back" href="../../../pygraph.utils.html#pygraph.utils.utils.untotterTransformation">[docs]</a><span class="k">def</span> <span class="nf">untotterTransformation</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">node_label</span><span class="p">,</span> <span class="n">edge_label</span><span class="p">):</span>

  222.     <span class="sd">&quot;&quot;&quot;Transform graph G according to Mahé et al.&#39;s work to filter out tottering patterns of marginalized kernel and tree pattern kernel.</span>

  223. 

  224. <span class="sd">    Parameters</span>

  225. <span class="sd">    ----------</span>

  226. <span class="sd">    G : NetworkX graph</span>

  227. <span class="sd">        The graph to be tramsformed.</span>

  228. <span class="sd">    node_label : string</span>

  229. <span class="sd">        node attribute used as label. The default node label is &#39;atom&#39;.</span>

  230. <span class="sd">    edge_label : string</span>

  231. <span class="sd">        edge attribute used as label. The default edge label is &#39;bond_type&#39;.</span>

  232. 

  233. <span class="sd">    Return</span>

  234. <span class="sd">    ------</span>

  235. <span class="sd">    gt : NetworkX graph</span>

  236. <span class="sd">        The transformed graph corresponding to G.</span>

  237. 

  238. <span class="sd">    References</span>

  239. <span class="sd">    ----------</span>

  240. <span class="sd">    [1] Pierre Mahé, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and Jean-Philippe Vert. Extensions of marginalized graph kernels. In Proceedings of the twenty-first international conference on Machine learning, page 70. ACM, 2004.</span>

  241. <span class="sd">    &quot;&quot;&quot;</span>

  242.     <span class="c1"># arrange all graphs in a list</span>

  243.     <span class="n">G</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">to_directed</span><span class="p">()</span>

  244.     <span class="n">gt</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>

  245.     <span class="n">gt</span><span class="o">.</span><span class="n">graph</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">graph</span>

  246.     <span class="n">gt</span><span class="o">.</span><span class="n">add_nodes_from</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">data</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>

  247.     <span class="k">for</span> <span class="n">edge</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">():</span>

  248.         <span class="n">gt</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="n">edge</span><span class="p">)</span>

  249.         <span class="n">gt</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="n">edge</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">node_label</span><span class="p">:</span> <span class="n">G</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="n">edge</span><span class="p">[</span><span class="mi">1</span><span class="p">]][</span><span class="n">node_label</span><span class="p">]})</span>

  250.         <span class="n">gt</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">edge</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">edge</span><span class="p">)</span>

  251.         <span class="n">gt</span><span class="o">.</span><span class="n">edges</span><span class="p">[</span><span class="n">edge</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">edge</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">({</span>

  252.             <span class="n">edge_label</span><span class="p">:</span>

  253.             <span class="n">G</span><span class="p">[</span><span class="n">edge</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">edge</span><span class="p">[</span><span class="mi">1</span><span class="p">]][</span><span class="n">edge_label</span><span class="p">]</span>

  254.         <span class="p">})</span>

  255.         <span class="k">for</span> <span class="n">neighbor</span> <span class="ow">in</span> <span class="n">G</span><span class="p">[</span><span class="n">edge</span><span class="p">[</span><span class="mi">1</span><span class="p">]]:</span>

  256.             <span class="k">if</span> <span class="n">neighbor</span> <span class="o">!=</span> <span class="n">edge</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>

  257.                 <span class="n">gt</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">edge</span><span class="p">,</span> <span class="p">(</span><span class="n">edge</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">neighbor</span><span class="p">))</span>

  258.                 <span class="n">gt</span><span class="o">.</span><span class="n">edges</span><span class="p">[</span><span class="n">edge</span><span class="p">,</span> <span class="p">(</span><span class="n">edge</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">neighbor</span><span class="p">)]</span><span class="o">.</span><span class="n">update</span><span class="p">({</span>

  259.                     <span class="n">edge_label</span><span class="p">:</span>

  260.                     <span class="n">G</span><span class="p">[</span><span class="n">edge</span><span class="p">[</span><span class="mi">1</span><span class="p">]][</span><span class="n">neighbor</span><span class="p">][</span><span class="n">edge_label</span><span class="p">]</span>

  261.                 <span class="p">})</span>

  262.     <span class="c1"># nx.draw_networkx(gt)</span>

  263.     <span class="c1"># plt.show()</span>

  264. 

  265.     <span class="c1"># relabel nodes using consecutive integers for convenience of kernel calculation.</span>

  266.     <span class="n">gt</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">convert_node_labels_to_integers</span><span class="p">(</span>

  267.         <span class="n">gt</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">label_attribute</span><span class="o">=</span><span class="s1">&#39;label_orignal&#39;</span><span class="p">)</span>

  268.     <span class="k">return</span> <span class="n">gt</span></div>

  269. 

  270. 

  271. <div class="viewcode-block" id="direct_product"><a class="viewcode-back" href="../../../pygraph.utils.html#pygraph.utils.utils.direct_product">[docs]</a><span class="k">def</span> <span class="nf">direct_product</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="p">,</span> <span class="n">edge_label</span><span class="p">):</span>

  272.     <span class="sd">&quot;&quot;&quot;Return the direct/tensor product of directed graphs G1 and G2.</span>

  273. 

  274. <span class="sd">    Parameters</span>

  275. <span class="sd">    ----------</span>

  276. <span class="sd">    G1, G2 : NetworkX graph</span>

  277. <span class="sd">        The original graphs.</span>

  278. <span class="sd">    node_label : string</span>

  279. <span class="sd">        node attribute used as label. The default node label is &#39;atom&#39;.</span>

  280. <span class="sd">    edge_label : string</span>

  281. <span class="sd">        edge attribute used as label. The default edge label is &#39;bond_type&#39;.</span>

  282. 

  283. <span class="sd">    Return</span>

  284. <span class="sd">    ------</span>

  285. <span class="sd">    gt : NetworkX graph</span>

  286. <span class="sd">        The direct product graph of G1 and G2.</span>

  287. 

  288. <span class="sd">    Notes</span>

  289. <span class="sd">    -----</span>

  290. <span class="sd">    This method differs from networkx.tensor_product in that this method only adds nodes and edges in G1 and G2 that have the same labels to the direct product graph.</span>

  291. 

  292. <span class="sd">    References</span>

  293. <span class="sd">    ----------</span>

  294. <span class="sd">    [1] Thomas Gärtner, Peter Flach, and Stefan Wrobel. On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, pages 129–143, 2003.</span>

  295. <span class="sd">    &quot;&quot;&quot;</span>

  296.     <span class="c1"># arrange all graphs in a list</span>

  297.     <span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>

  298.     <span class="c1"># G = G.to_directed()</span>

  299.     <span class="n">gt</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">()</span>

  300.     <span class="c1"># add nodes</span>

  301.     <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">product</span><span class="p">(</span><span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">):</span>

  302.         <span class="k">if</span> <span class="n">G1</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="n">node_label</span><span class="p">]</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">v</span><span class="p">][</span><span class="n">node_label</span><span class="p">]:</span>

  303.             <span class="n">gt</span><span class="o">.</span><span class="n">add_node</span><span class="p">((</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">))</span>

  304.             <span class="n">gt</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="n">v</span><span class="p">)]</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">node_label</span><span class="p">:</span> <span class="n">G1</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="n">node_label</span><span class="p">]})</span>

  305.     <span class="c1"># add edges, faster for sparse graphs (no so many edges), which is the most case for now.</span>

  306.     <span class="k">for</span> <span class="p">(</span><span class="n">u1</span><span class="p">,</span> <span class="n">v1</span><span class="p">),</span> <span class="p">(</span><span class="n">u2</span><span class="p">,</span> <span class="n">v2</span><span class="p">)</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="n">G1</span><span class="o">.</span><span class="n">edges</span><span class="p">,</span> <span class="n">G2</span><span class="o">.</span><span class="n">edges</span><span class="p">):</span>

  307.         <span class="k">if</span> <span class="p">(</span><span class="n">u1</span><span class="p">,</span> <span class="n">u2</span><span class="p">)</span> <span class="ow">in</span> <span class="n">gt</span> <span class="ow">and</span> <span class="p">(</span>

  308.                 <span class="n">v1</span><span class="p">,</span> <span class="n">v2</span>

  309.         <span class="p">)</span> <span class="ow">in</span> <span class="n">gt</span> <span class="ow">and</span> <span class="n">G1</span><span class="o">.</span><span class="n">edges</span><span class="p">[</span><span class="n">u1</span><span class="p">,</span> <span class="n">v1</span><span class="p">][</span><span class="n">edge_label</span><span class="p">]</span> <span class="o">==</span> <span class="n">G2</span><span class="o">.</span><span class="n">edges</span><span class="p">[</span><span class="n">u2</span><span class="p">,</span>

  310.                                                              <span class="n">v2</span><span class="p">][</span><span class="n">edge_label</span><span class="p">]:</span>

  311.             <span class="n">gt</span><span class="o">.</span><span class="n">add_edge</span><span class="p">((</span><span class="n">u1</span><span class="p">,</span> <span class="n">u2</span><span class="p">),</span> <span class="p">(</span><span class="n">v1</span><span class="p">,</span> <span class="n">v2</span><span class="p">))</span>

  312.             <span class="n">gt</span><span class="o">.</span><span class="n">edges</span><span class="p">[(</span><span class="n">u1</span><span class="p">,</span> <span class="n">u2</span><span class="p">),</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="o">.</span><span class="n">update</span><span class="p">({</span>

  313.                 <span class="n">edge_label</span><span class="p">:</span>

  314.                 <span class="n">G1</span><span class="o">.</span><span class="n">edges</span><span class="p">[</span><span class="n">u1</span><span class="p">,</span> <span class="n">v1</span><span class="p">][</span><span class="n">edge_label</span><span class="p">]</span>

  315.             <span class="p">})</span>

  316. 

  317.     <span class="c1"># # add edges, faster for dense graphs (a lot of edges, complete graph would be super).</span>

  318.     <span class="c1"># for u, v in product(gt, gt):</span>

  319.     <span class="c1">#     if (u[0], v[0]) in G1.edges and (</span>

  320.     <span class="c1">#             u[1], v[1]</span>

  321.     <span class="c1">#     ) in G2.edges and G1.edges[u[0],</span>

  322.     <span class="c1">#                                v[0]][edge_label] == G2.edges[u[1],</span>

  323.     <span class="c1">#                                                              v[1]][edge_label]:</span>

  324.     <span class="c1">#         gt.add_edge((u[0], u[1]), (v[0], v[1]))</span>

  325.     <span class="c1">#         gt.edges[(u[0], u[1]), (v[0], v[1])].update({</span>

  326.     <span class="c1">#             edge_label:</span>

  327.     <span class="c1">#             G1.edges[u[0], v[0]][edge_label]</span>

  328.     <span class="c1">#         })</span>

  329. 

  330.     <span class="c1"># relabel nodes using consecutive integers for convenience of kernel calculation.</span>

  331.     <span class="c1"># gt = nx.convert_node_labels_to_integers(</span>

  332.     <span class="c1">#     gt, first_label=0, label_attribute=&#39;label_orignal&#39;)</span>

  333.     <span class="k">return</span> <span class="n">gt</span></div>

  334. 

  335. 

  336. <div class="viewcode-block" id="graph_deepcopy"><a class="viewcode-back" href="../../../pygraph.utils.html#pygraph.utils.utils.graph_deepcopy">[docs]</a><span class="k">def</span> <span class="nf">graph_deepcopy</span><span class="p">(</span><span class="n">G</span><span class="p">):</span>

  337.     <span class="sd">&quot;&quot;&quot;Deep copy a graph, including deep copy of all nodes, edges and </span>

  338. <span class="sd">    attributes of the graph, nodes and edges.</span>

  339. <span class="sd">    </span>

  340. <span class="sd">    Note</span>

  341. <span class="sd">    ----</span>

  342. <span class="sd">    It is the same as the NetworkX function graph.copy(), as far as I know.</span>

  343. <span class="sd">    &quot;&quot;&quot;</span>

  344.     <span class="c1"># add graph attributes.</span>

  345.     <span class="n">labels</span> <span class="o">=</span> <span class="p">{}</span>

  346.     <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">G</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>

  347.         <span class="n">labels</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>

  348.     <span class="k">if</span> <span class="n">G</span><span class="o">.</span><span class="n">is_directed</span><span class="p">():</span>

  349.         <span class="n">G_copy</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="o">**</span><span class="n">labels</span><span class="p">)</span>

  350.     <span class="k">else</span><span class="p">:</span>

  351.         <span class="n">G_copy</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="o">**</span><span class="n">labels</span><span class="p">)</span>

  352.         

  353.     <span class="c1"># add nodes    </span>

  354.     <span class="k">for</span> <span class="n">nd</span><span class="p">,</span> <span class="n">attrs</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="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>

  355.         <span class="n">labels</span> <span class="o">=</span> <span class="p">{}</span>

  356.         <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">attrs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>

  357.             <span class="n">labels</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>

  358.         <span class="n">G_copy</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="n">nd</span><span class="p">,</span> <span class="o">**</span><span class="n">labels</span><span class="p">)</span>

  359.         

  360.     <span class="c1"># add edges.</span>

  361.     <span class="k">for</span> <span class="n">nd1</span><span class="p">,</span> <span class="n">nd2</span><span class="p">,</span> <span class="n">attrs</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>

  362.         <span class="n">labels</span> <span class="o">=</span> <span class="p">{}</span>

  363.         <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">attrs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>

  364.             <span class="n">labels</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>

  365.         <span class="n">G_copy</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">nd1</span><span class="p">,</span> <span class="n">nd2</span><span class="p">,</span> <span class="o">**</span><span class="n">labels</span><span class="p">)</span>

  366.     

  367.     <span class="k">return</span> <span class="n">G_copy</span></div>

  368. 

  369. 

  370. <div class="viewcode-block" id="graph_isIdentical"><a class="viewcode-back" href="../../../pygraph.utils.html#pygraph.utils.utils.graph_isIdentical">[docs]</a><span class="k">def</span> <span class="nf">graph_isIdentical</span><span class="p">(</span><span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">):</span>

  371.     <span class="sd">&quot;&quot;&quot;Check if two graphs are identical, including: same nodes, edges, node</span>

  372. <span class="sd">    labels/attributes, edge labels/attributes.</span>

  373. <span class="sd">    </span>

  374. <span class="sd">    Notes</span>

  375. <span class="sd">    ----</span>

  376. <span class="sd">    1. The type of graphs has to be the same.</span>

  377. <span class="sd">    2. Global/Graph attributes are neglected as they may contain names for graphs.</span>

  378. <span class="sd">    &quot;&quot;&quot;</span>

  379.     <span class="c1"># check nodes.</span>

  380.     <span class="n">nlist1</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">G1</span><span class="o">.</span><span class="n">nodes</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>

  381.     <span class="n">nlist2</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">G2</span><span class="o">.</span><span class="n">nodes</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>

  382.     <span class="k">if</span> <span class="ow">not</span> <span class="n">nlist1</span> <span class="o">==</span> <span class="n">nlist2</span><span class="p">:</span>

  383.         <span class="k">return</span> <span class="kc">False</span>

  384.     <span class="c1"># check edges.</span>

  385.     <span class="n">elist1</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">G1</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>

  386.     <span class="n">elist2</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">G2</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>

  387.     <span class="k">if</span> <span class="ow">not</span> <span class="n">elist1</span> <span class="o">==</span> <span class="n">elist2</span><span class="p">:</span>

  388.         <span class="k">return</span> <span class="kc">False</span>

  389.     <span class="c1"># check graph attributes.</span>

  390.     

  391.     <span class="k">return</span> <span class="kc">True</span></div>

  392. 

  393. 

  394. <div class="viewcode-block" id="get_node_labels"><a class="viewcode-back" href="../../../pygraph.utils.html#pygraph.utils.utils.get_node_labels">[docs]</a><span class="k">def</span> <span class="nf">get_node_labels</span><span class="p">(</span><span class="n">Gn</span><span class="p">,</span> <span class="n">node_label</span><span class="p">):</span>

  395.     <span class="sd">&quot;&quot;&quot;Get node labels of dataset Gn.</span>

  396. <span class="sd">    &quot;&quot;&quot;</span>

  397.     <span class="n">nl</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>

  398.     <span class="k">for</span> <span class="n">G</span> <span class="ow">in</span> <span class="n">Gn</span><span class="p">:</span>

  399.         <span class="n">nl</span> <span class="o">=</span> <span class="n">nl</span> <span class="o">|</span> <span class="nb">set</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">get_node_attributes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">node_label</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>

  400.     <span class="k">return</span> <span class="n">nl</span></div>

  401. 

  402. 

  403. <div class="viewcode-block" id="get_edge_labels"><a class="viewcode-back" href="../../../pygraph.utils.html#pygraph.utils.utils.get_edge_labels">[docs]</a><span class="k">def</span> <span class="nf">get_edge_labels</span><span class="p">(</span><span class="n">Gn</span><span class="p">,</span> <span class="n">edge_label</span><span class="p">):</span>

  404.     <span class="sd">&quot;&quot;&quot;Get edge labels of dataset Gn.</span>

  405. <span class="sd">    &quot;&quot;&quot;</span>

  406.     <span class="n">el</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>

  407.     <span class="k">for</span> <span class="n">G</span> <span class="ow">in</span> <span class="n">Gn</span><span class="p">:</span>

  408.         <span class="n">el</span> <span class="o">=</span> <span class="n">el</span> <span class="o">|</span> <span class="nb">set</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">get_edge_attributes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">edge_label</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>

  409.     <span class="k">return</span> <span class="n">el</span></div>

  410. </pre></div>

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