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  <h1>Source code for pygraph.utils.utils</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="k">import</span> <span class="n">deepcopy</span>
<span class="c1">#from itertools import product</span>

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


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


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

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    G : NetworkX graph</span>
<span class="sd">        The graph to be tramsformed.</span>
<span class="sd">    edge_weight : string</span>
<span class="sd">        edge attribute corresponding to the edge weight.</span>

<span class="sd">    Return</span>
<span class="sd">    ------</span>
<span class="sd">    S : NetworkX graph</span>
<span class="sd">        The shortest-paths graph corresponding to G.</span>

<span class="sd">    Notes</span>
<span class="sd">    ------</span>
<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>

<span class="sd">    References</span>
<span class="sd">    ----------</span>
<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>
<span class="sd">    &quot;&quot;&quot;</span>
    <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>


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

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    G : NetworkX graph</span>
<span class="sd">        The graph to be tramsformed.</span>
<span class="sd">    edge_weight : string</span>
<span class="sd">        edge attribute corresponding to the edge weight. The default edge weight is bond_type.</span>

<span class="sd">    Return</span>
<span class="sd">    ------</span>
<span class="sd">    S : NetworkX graph</span>
<span class="sd">        The shortest-paths graph corresponding to G.</span>

<span class="sd">    References</span>
<span class="sd">    ----------</span>
<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>
<span class="sd">    &quot;&quot;&quot;</span>
    <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>
    <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>
    <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>
    <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>
    <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>
        <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>
            <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>
                <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>
    <span class="k">return</span> <span class="n">S</span></div>


<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>
    <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>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    G : NetworkX graph</span>
<span class="sd">        The graph to be tramsformed.</span>
<span class="sd">    node_label : string</span>
<span class="sd">        node attribute used as label. The default node label is &#39;atom&#39;.</span>
<span class="sd">    edge_label : string</span>
<span class="sd">        edge attribute used as label. The default edge label is &#39;bond_type&#39;.</span>

<span class="sd">    Return</span>
<span class="sd">    ------</span>
<span class="sd">    gt : NetworkX graph</span>
<span class="sd">        The transformed graph corresponding to G.</span>

<span class="sd">    References</span>
<span class="sd">    ----------</span>
<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>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># arrange all graphs in a list</span>
    <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>
    <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>
    <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>
    <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>
    <span class="k">for</span> <span class="n">edge</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">():</span>
        <span class="n">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>
        <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>
        <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>
        <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>
            <span class="n">edge_label</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="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>
        <span class="p">})</span>
        <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>
            <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>
                <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>
                <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>
                    <span class="n">edge_label</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="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>
                <span class="p">})</span>
    <span class="c1"># nx.draw_networkx(gt)</span>
    <span class="c1"># plt.show()</span>

    <span class="c1"># relabel nodes using consecutive integers for convenience of kernel calculation.</span>
    <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>
        <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>
    <span class="k">return</span> <span class="n">gt</span></div>


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

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    G1, G2 : NetworkX graph</span>
<span class="sd">        The original graphs.</span>
<span class="sd">    node_label : string</span>
<span class="sd">        node attribute used as label. The default node label is &#39;atom&#39;.</span>
<span class="sd">    edge_label : string</span>
<span class="sd">        edge attribute used as label. The default edge label is &#39;bond_type&#39;.</span>

<span class="sd">    Return</span>
<span class="sd">    ------</span>
<span class="sd">    gt : NetworkX graph</span>
<span class="sd">        The direct product graph of G1 and G2.</span>

<span class="sd">    Notes</span>
<span class="sd">    -----</span>
<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>

<span class="sd">    References</span>
<span class="sd">    ----------</span>
<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>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># arrange all graphs in a list</span>
    <span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
    <span class="c1"># G = G.to_directed()</span>
    <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>
    <span class="c1"># add nodes</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">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="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>
            <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>
            <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>
    <span class="c1"># add edges, faster for sparse graphs (no so many edges), which is the most case for now.</span>
    <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>
        <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>
                <span class="n">v1</span><span class="p">,</span> <span class="n">v2</span>
        <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>
                                                             <span class="n">v2</span><span class="p">][</span><span class="n">edge_label</span><span class="p">]:</span>
            <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>
            <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>
                <span class="n">edge_label</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">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="p">})</span>

    <span class="c1"># # add edges, faster for dense graphs (a lot of edges, complete graph would be super).</span>
    <span class="c1"># for u, v in product(gt, gt):</span>
    <span class="c1">#     if (u[0], v[0]) in G1.edges and (</span>
    <span class="c1">#             u[1], v[1]</span>
    <span class="c1">#     ) in G2.edges and G1.edges[u[0],</span>
    <span class="c1">#                                v[0]][edge_label] == G2.edges[u[1],</span>
    <span class="c1">#                                                              v[1]][edge_label]:</span>
    <span class="c1">#         gt.add_edge((u[0], u[1]), (v[0], v[1]))</span>
    <span class="c1">#         gt.edges[(u[0], u[1]), (v[0], v[1])].update({</span>
    <span class="c1">#             edge_label:</span>
    <span class="c1">#             G1.edges[u[0], v[0]][edge_label]</span>
    <span class="c1">#         })</span>

    <span class="c1"># relabel nodes using consecutive integers for convenience of kernel calculation.</span>
    <span class="c1"># gt = nx.convert_node_labels_to_integers(</span>
    <span class="c1">#     gt, first_label=0, label_attribute=&#39;label_orignal&#39;)</span>
    <span class="k">return</span> <span class="n">gt</span></div>


<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>
    <span class="sd">&quot;&quot;&quot;Deep copy a graph, including deep copy of all nodes, edges and </span>
<span class="sd">    attributes of the graph, nodes and edges.</span>
<span class="sd">    </span>
<span class="sd">    Note</span>
<span class="sd">    ----</span>
<span class="sd">    It is the same as the NetworkX function graph.copy(), as far as I know.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># add graph attributes.</span>
    <span class="n">labels</span> <span class="o">=</span> <span class="p">{}</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">G</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <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>
    <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">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>
    <span class="k">else</span><span class="p">:</span>
        <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>
        
    <span class="c1"># add nodes    </span>
    <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>
        <span class="n">labels</span> <span class="o">=</span> <span class="p">{}</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">attrs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <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>
        <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>
        
    <span class="c1"># add edges.</span>
    <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>
        <span class="n">labels</span> <span class="o">=</span> <span class="p">{}</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">attrs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <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>
        <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>
    
    <span class="k">return</span> <span class="n">G_copy</span></div>


<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>
    <span class="sd">&quot;&quot;&quot;Check if two graphs are identical, including: same nodes, edges, node</span>
<span class="sd">    labels/attributes, edge labels/attributes.</span>
<span class="sd">    </span>
<span class="sd">    Notes</span>
<span class="sd">    ----</span>
<span class="sd">    1. The type of graphs has to be the same.</span>
<span class="sd">    2. Global/Graph attributes are neglected as they may contain names for graphs.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># check nodes.</span>
    <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>
    <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>
    <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>
        <span class="k">return</span> <span class="kc">False</span>
    <span class="c1"># check edges.</span>
    <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>
    <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>
    <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>
        <span class="k">return</span> <span class="kc">False</span>
    <span class="c1"># check graph attributes.</span>
    
    <span class="k">return</span> <span class="kc">True</span></div>


<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>
    <span class="sd">&quot;&quot;&quot;Get node labels of dataset Gn.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">nl</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
    <span class="k">for</span> <span class="n">G</span> <span class="ow">in</span> <span class="n">Gn</span><span class="p">:</span>
        <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>
    <span class="k">return</span> <span class="n">nl</span></div>


<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>
    <span class="sd">&quot;&quot;&quot;Get edge labels of dataset Gn.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">el</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
    <span class="k">for</span> <span class="n">G</span> <span class="ow">in</span> <span class="n">Gn</span><span class="p">:</span>
        <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>
    <span class="k">return</span> <span class="n">el</span></div>
</pre></div>

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