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<h1 class="title toc-ignore">igraph (interfaz R)</h1>


<div id="TOC">
<ul>
<li><a href="#instalaci%C3%B3n" id="toc-instalación">Instalación</a></li>
<li><a href="#uso-de-igraph" id="toc-uso-de-igraph">Uso de
igraph</a></li>
<li><a href="#crear-un-grafo" id="toc-crear-un-grafo">Crear un
grafo</a></li>
<li><a href="#ids-de-v%C3%A9rtices-y-aristas" id="toc-ids-de-vértices-y-aristas">IDs de vértices y aristas</a></li>
<li><a href="#a%C3%B1adir-y-borrar-v%C3%A9rtices-y-aristas" id="toc-añadir-y-borrar-vértices-y-aristas">Añadir y borrar vértices y
aristas</a></li>
<li><a href="#construcci%C3%B3n-de-grafos" id="toc-construcción-de-grafos">Construcción de grafos</a></li>
<li><a href="#establecer-y-recuperar-atributos" id="toc-establecer-y-recuperar-atributos">Establecer y recuperar
atributos</a></li>
<li><a href="#propiedades-estructurales-de-los-grafos" id="toc-propiedades-estructurales-de-los-grafos">Propiedades
estructurales de los grafos</a></li>
<li><a href="#b%C3%BAsqueda-de-v%C3%A9rtices-y-aristas-basada-en-atributos" id="toc-búsqueda-de-vértices-y-aristas-basada-en-atributos">Búsqueda de
vértices y aristas basada en atributos</a>
<ul>
<li><a href="#selecci%C3%B3n-de-v%C3%A9rtices" id="toc-selección-de-vértices">Selección de vértices</a></li>
<li><a href="#selecci%C3%B3n-de-aristas" id="toc-selección-de-aristas">Selección de aristas</a></li>
</ul></li>
<li><a href="#tratar-un-grafo-como-una-matriz-de-adyacencia" id="toc-tratar-un-grafo-como-una-matriz-de-adyacencia">Tratar un grafo
como una matriz de adyacencia</a></li>
<li><a href="#dise%C3%B1os-y-graficaci%C3%B3n" id="toc-diseños-y-graficación">Diseños y graficación</a>
<ul>
<li><a href="#algoritmos-de-dise%C3%B1o" id="toc-algoritmos-de-diseño">Algoritmos de diseño</a></li>
<li><a href="#dibujar-un-grafo-utilizando-un-dise%C3%B1o" id="toc-dibujar-un-grafo-utilizando-un-diseño">Dibujar un grafo
utilizando un diseño</a></li>
<li><a href="#atributos-de-los-v%C3%A9rtices-para-graficar" id="toc-atributos-de-los-vértices-para-graficar">Atributos de los
vértices para graficar</a></li>
<li><a href="#atributos-de-las-aristas-para-graficar" id="toc-atributos-de-las-aristas-para-graficar">Atributos de las aristas
para graficar</a></li>
<li><a href="#argumentos-m%C3%A1s-comunes-de-plot" id="toc-argumentos-más-comunes-de-plot">Argumentos más comunes de
<code>plot()</code></a></li>
</ul></li>
<li><a href="#igraph-y-el-mundo-exterior" id="toc-igraph-y-el-mundo-exterior">igraph y el mundo exterior</a></li>
<li><a href="#d%C3%B3nde-ir-a-continuaci%C3%B3n" id="toc-dónde-ir-a-continuación">Dónde ir a continuación</a></li>
<li><a href="#informaci%C3%B3n-de-la-sesi%C3%B3n" id="toc-información-de-la-sesión">Información de la sesión</a></li>
</ul>
</div>

<p><code>igraph</code> es una biblioteca rápida y de código abierto para
el análisis de grafos o redes. El núcleo de ésta libreria se encuentra
escrito en C y contiene enlaces para lenguajes de alto nivel como <a href="https://r.igraph.org/">R</a>, <a href="https://python.igraph.org/">Python</a>, y <a href="http://szhorvat.net/pelican/igraphm-a-mathematica-interface-for-igraph.html">Mathematica</a>.
Esta viñeta pretende darte una visión general de las funciones
disponibles de <code>igraph</code> en R. Para obtener información
detallada de cada función, consulta <a href="https://r.igraph.org/reference/" class="uri">https://r.igraph.org/reference/</a>.</p>
<hr />
<p><strong>NOTA:</strong> A lo largo de este tutorial, utilizaremos las
palabras <code>grafo</code> y <code>red</code> como sinónimos, y también
<code>vértice</code> o <code>nodo</code> como sinónimos.</p>
<hr />
<div id="instalación" class="section level2">
<h2>Instalación</h2>
<p>Para instalar la librería desde CRAN, usa:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">&quot;igraph&quot;</span>)</span></code></pre></div>
<p>Encontrarás más información sobre dependencias, requisitos y
resolución de problemas sobre la instalación en la <a href="https://r.igraph.org/">página principal</a>.</p>
</div>
<div id="uso-de-igraph" class="section level2">
<h2>Uso de igraph</h2>
<p>Para utilizar <code>igraph</code> en tu código de R, primero debes
cargar la biblioteca:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&quot;igraph&quot;</span>)</span></code></pre></div>
<p>Ahora tienes todas las funciones de <code>igraph</code>
disponibles.</p>
</div>
<div id="crear-un-grafo" class="section level2">
<h2>Crear un grafo</h2>
<p><code>igraph</code> ofrece muchas formas de crear un grafo. La más
sencilla es con la función <code>make_empty_graph()</code>:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_empty_graph</span>()</span></code></pre></div>
<p>La forma más común de crear un grafo es con
<code>make_graph()</code>, que construye un grafo basado en especificar
las aristas. Por ejemplo, Para hacer un grafo con 10 nodos (numerados
<code>1</code> a <code>10</code>) y dos aristas que conecten los nodos
<code>1-2</code> y <code>1-5</code>:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_graph</span>(<span class="at">edges =</span> <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>, <span class="dv">1</span>,<span class="dv">5</span>), <span class="at">n=</span><span class="dv">10</span>, <span class="at">directed =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
<p>A partir de igraph 0.8.0, también puedes incluir literales mediante
la notación de fórmulas de igraph. En este caso, el primer término de la
fórmula tiene que empezar con un carácter <code>~</code>, como
comúnmente se usa en las fórmulas en R. Las expresiones constan de los
nombres de los vértices y los operadores de las aristas. El operador de
un arista es una secuencia de caracteres <code>-</code> y
<code>+</code>, el primero es para indicar propiamente las aristas y el
segundo para las puntas de flecha (dirección). Puedes utilizar tantos
caracteres <code>-</code> como quieras para “dibujarlas”. Si todos los
operadores de un arista están formados únicamente por caracteres
<code>-</code>, el grafo será no dirigido, mientras que un único
carácter <code>+</code> implica un grafo dirigido. Por ejemplo, para
crear el mismo grafo que antes:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_graph</span>(<span class="sc">~</span> <span class="dv">1</span><span class="sc">--</span><span class="dv">2</span>, <span class="dv">1</span><span class="sc">--</span><span class="dv">5</span>, <span class="dv">3</span>, <span class="dv">4</span>, <span class="dv">5</span>, <span class="dv">6</span>, <span class="dv">7</span>, <span class="dv">8</span>, <span class="dv">9</span>, <span class="dv">10</span>)</span></code></pre></div>
<p>Podemos imprimir el grafo para obtener un resumen de sus nodos y
aristas:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>g</span></code></pre></div>
<pre><code>## IGRAPH 5034d2d UN-- 10 2 -- 
## + attr: name (v/c)
## + edges from 5034d2d (vertex names):
## [1] 1--2 1--5</code></pre>
<p>Esto significa: grafo no dirigido (<strong>U</strong>ndirected) con
<strong>10</strong> vértices y <strong>2</strong> aristas, que se
enlistan en la última parte. Si el grafo tiene un atributo [nombre],
también se imprime.</p>
<hr />
<p><strong>NOTA</strong>: <code>summary()</code> no enlista las aristas,
lo cual es conveniente para grafos grandes con millones de aristas:</p>
<hr />
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="fu">summary</span>(g)</span></code></pre></div>
<pre><code>## IGRAPH 5034d2d UN-- 10 2 -- 
## + attr: name (v/c)</code></pre>
<p>También <code>make_graph()</code> puede crear algunos grafos
destacados con sólo especificar su nombre. Por ejemplo, puedes generar
el grafo que muestra la red social del club de kárate de Zachary, que
refleja la amistad entre los 34 miembros del club de una universidad de
los Estados Unidos en la década de los 70s:</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_graph</span>(<span class="st">&quot;Zachary&quot;</span>)</span></code></pre></div>
<p>Para observar un grafo puedes utilizar <code>plot()</code>:</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="fu">plot</span>(g)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>Más adelante en este tutorial se ofrece una descripción detallada de
las opciones para graficar un grafo.</p>
</div>
<div id="ids-de-vértices-y-aristas" class="section level2">
<h2>IDs de vértices y aristas</h2>
<p>Los vértices y las aristas tienen un identificador numérico en
igraph. Los ID de los vértices son siempre consecutivos y empiezan por
1. Para un grafo con “n” vértices, los ID de los vértices están siempre
entre 1 y “n”. Si alguna operación cambia el número de vértices en los
grafos, por ejemplo, se crea un subgrafo mediante
<code>induced_subgraph()</code>, entonces los vértices se vuelven a
enumerar para satisfacer este criterio.</p>
<p>Lo mismo ocurre con las aristas: los ID de las aristas están siempre
entre 1 y “m”, el número total de aristas del grafo.</p>
<hr />
<p><strong>NOTA</strong>: Si estás familiarizado con C o con la interfaz
<a href="https://python.igraph.org/en/stable/">Python</a> de
<code>igraph</code>, te habrás dado cuenta de que en esos lenguajes los
IDs de vértices y aristas empiezan por 0. En la interfaz de R, ambos
empiezan por 1, para mantener la coherencia con la convención de cada
lenguaje.</p>
<hr />
<p>Además de los IDs, a los vértices y aristas se les puede asignar un
nombre y otros atributos. Esto facilita su seguimiento cada vez que se
altera un grafo. Más adelante en este tutorial se muestran ejemplos de
cómo alterar estas características.</p>
</div>
<div id="añadir-y-borrar-vértices-y-aristas" class="section level2">
<h2>Añadir y borrar vértices y aristas</h2>
<p>Sigamos trabajando con el grafo del club de kárate. Para añadir uno o
más vértices a un grafo existente, utiliza
<code>add_vertices()</code>:</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">add_vertices</span>(g, <span class="dv">3</span>)</span></code></pre></div>
<p>Del mismo modo, para añadir aristas puedes utilizar
<code>add_edges()</code>:</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">add_edges</span>(g, <span class="at">edges =</span> <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">35</span>, <span class="dv">1</span>,<span class="dv">36</span>, <span class="dv">34</span>,<span class="dv">37</span>))</span></code></pre></div>
<p>Las aristas se añaden especificando el ID del vértice origen y el
vértice destino de cada arista. Con las instrucciones anteriores se
añaden tres aristas, una que conecta los vértices <code>1</code> y
<code>35</code>, otra que conecta los vértices <code>1</code> y
<code>36</code> y otra que conecta los vértices <code>34</code> y
<code>37</code>.</p>
<p>Además de las funciones <code>add_vertices()</code> y
<code>add_edges()</code>, se puede utilizar el operador “+” para añadir
vértices o aristas al grafo. La operación que se realice dependerá del
tipo de argumento del lado derecho:</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> g <span class="sc">+</span> <span class="fu">edges</span>(<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">35</span>, <span class="dv">1</span>,<span class="dv">36</span>, <span class="dv">34</span>,<span class="dv">37</span>))</span></code></pre></div>
<p>Puedes añadir un solo vértice/arista a la vez usando
<code>add_vertex()</code> y <code>add_edge()</code> (singular).</p>
<p><strong>Advertencia</strong>: Si necesitas añadir múltiples aristas a
un grafo, es mucho más eficiente usar <code>add_edges()</code> una vez
que utilizar repetidamente <code>add_edge()</code> con una nueva arista
a la vez. Lo mismo ocurre al eliminar aristas y vértices.</p>
<p>Si intentas añadir aristas a vértices con IDs no válidos (por
ejemplo, intentas añadir una arista al vértice <code>38</code> cuando el
grafo sólo tiene 37 vértices), <code>igraph</code> muestra un error:</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">add_edges</span>(g, <span class="at">edges =</span> <span class="fu">c</span>(<span class="dv">38</span>, <span class="dv">37</span>))</span></code></pre></div>
<pre><code>## Error in add_edges(g, edges = c(38, 37)): At vendor/cigraph/src/graph/type_indexededgelist.c:261 : Out-of-range vertex IDs when adding edges. Invalid vertex ID</code></pre>
<p>Añadamos más vértices y aristas a nuestro grafo. En
<code>igraph</code> podemos utilizar el paquete <code>magrittr</code>,
que proporciona un mecanismo para encadenar comandos con el operador
<code>%&gt;%</code>:</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> g <span class="sc">%&gt;%</span> </span>
<span id="cb17-2"><a href="#cb17-2" tabindex="-1"></a>  <span class="fu">add_edges</span>(<span class="at">edges =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">34</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb17-3"><a href="#cb17-3" tabindex="-1"></a>  <span class="fu">add_vertices</span>(<span class="dv">3</span>) <span class="sc">%&gt;%</span></span>
<span id="cb17-4"><a href="#cb17-4" tabindex="-1"></a>  <span class="fu">add_edges</span>(<span class="at">edges =</span> <span class="fu">c</span>(<span class="dv">38</span>, <span class="dv">39</span>, <span class="dv">39</span>, <span class="dv">40</span>, <span class="dv">40</span>, <span class="dv">38</span>, <span class="dv">40</span>, <span class="dv">37</span>))</span>
<span id="cb17-5"><a href="#cb17-5" tabindex="-1"></a>g</span></code></pre></div>
<pre><code>## IGRAPH 896c648 U--- 40 86 -- Zachary
## + attr: name (g/c)
## + edges from 896c648:
##  [1]  1-- 2  1-- 3  1-- 4  1-- 5  1-- 6  1-- 7  1-- 8  1-- 9  1--11  1--12
## [11]  1--13  1--14  1--18  1--20  1--22  1--32  2-- 3  2-- 4  2-- 8  2--14
## [21]  2--18  2--20  2--22  2--31  3-- 4  3-- 8  3--28  3--29  3--33  3--10
## [31]  3-- 9  3--14  4-- 8  4--13  4--14  5-- 7  5--11  6-- 7  6--11  6--17
## [41]  7--17  9--31  9--33  9--34 10--34 14--34 15--33 15--34 16--33 16--34
## [51] 19--33 19--34 20--34 21--33 21--34 23--33 23--34 24--26 24--28 24--33
## [61] 24--34 24--30 25--26 25--28 25--32 26--32 27--30 27--34 28--34 29--32
## [71] 29--34 30--33 30--34 31--33 31--34 32--33 32--34 33--34  1--35  1--36
## + ... omitted several edges</code></pre>
<p>Ahora tenemos un grafo no dirigido con 40 vértices y 89 aristas. Los
IDs de los vértices y aristas son siempre <em>contiguos</em>, así que si
borras un vértice, todos los vértices subsecuentes se vuelven a
enumerar. Cuando se re-numera un vértice, las aristas
<strong>no</strong> se vuelven a enumerar, pero sí sus vértices origen y
destino. Puedes usar <code>delete_vertices()</code> y
<code>delete_edges()</code> para realizar estas operaciones. Por
ejemplo, para borrar la arista que conecta los vértices
<code>1-34</code>, obtén su ID y luego bórrala:</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a>edge_id_para_borrar <span class="ot">&lt;-</span> <span class="fu">get_edge_ids</span>(g, <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">34</span>))</span>
<span id="cb19-2"><a href="#cb19-2" tabindex="-1"></a>edge_id_para_borrar</span></code></pre></div>
<pre><code>## [1] 82</code></pre>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">delete_edges</span>(g, edge_id_para_borrar)</span></code></pre></div>
<p>Por ejemplo, para crear un grafo con forma de anillo y para
partirlo:</p>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_ring</span>(<span class="dv">10</span>) <span class="sc">%&gt;%</span> <span class="fu">delete_edges</span>(<span class="st">&quot;10|1&quot;</span>)</span>
<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a><span class="fu">plot</span>(g)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>El ejemplo anterior muestra que también puedes referirte a las
aristas indicando los IDs de los vértices origen y destino, conectados
por el símbolo <code>|</code>. En el ejemplo, <code>&quot;10|1&quot;</code>
significa la arista que conecta el vértice <code>10</code> con el
vértice <code>1</code>. Por supuesto, también puedes usar los IDs de las
aristas directamente, o recuperarlos con la función
<code>get_edge_ids()</code>:</p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_ring</span>(<span class="dv">5</span>)</span>
<span id="cb23-2"><a href="#cb23-2" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">delete_edges</span>(g, <span class="fu">get_edge_ids</span>(g, <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">5</span>, <span class="dv">4</span>,<span class="dv">5</span>)))</span>
<span id="cb23-3"><a href="#cb23-3" tabindex="-1"></a><span class="fu">plot</span>(g)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>Veamos otro ejemplo, hagamos un grafo cordal. Recuerda que un grafo
es cordal (o triangulado) si cada uno de sus ciclos de cuatro o más
nodos tienen una “cuerda”, que es una arista que une dos nodos que no
son adyacentes en el ciclo. En primer lugar, vamos a crear el grafo
inicial utilizando <code>graph_from_literal()</code>:</p>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a>g1 <span class="ot">&lt;-</span> <span class="fu">graph_from_literal</span>(</span>
<span id="cb24-2"><a href="#cb24-2" tabindex="-1"></a>  A<span class="sc">-</span>B<span class="sc">:</span>C<span class="sc">:</span>I, </span>
<span id="cb24-3"><a href="#cb24-3" tabindex="-1"></a>  B<span class="sc">-</span>A<span class="sc">:</span>C<span class="sc">:</span>D, </span>
<span id="cb24-4"><a href="#cb24-4" tabindex="-1"></a>  C<span class="sc">-</span>A<span class="sc">:</span>B<span class="sc">:</span>E<span class="sc">:</span>H, </span>
<span id="cb24-5"><a href="#cb24-5" tabindex="-1"></a>  D<span class="sc">-</span>B<span class="sc">:</span>E<span class="sc">:</span>F,</span>
<span id="cb24-6"><a href="#cb24-6" tabindex="-1"></a>  E<span class="sc">-</span>C<span class="sc">:</span>D<span class="sc">:</span>F<span class="sc">:</span>H, </span>
<span id="cb24-7"><a href="#cb24-7" tabindex="-1"></a>  F<span class="sc">-</span>D<span class="sc">:</span>E<span class="sc">:</span>G, </span>
<span id="cb24-8"><a href="#cb24-8" tabindex="-1"></a>  G<span class="sc">-</span>F<span class="sc">:</span>H, </span>
<span id="cb24-9"><a href="#cb24-9" tabindex="-1"></a>  H<span class="sc">-</span>C<span class="sc">:</span>E<span class="sc">:</span>G<span class="sc">:</span>I,</span>
<span id="cb24-10"><a href="#cb24-10" tabindex="-1"></a>  I<span class="sc">-</span>A<span class="sc">:</span>H</span>
<span id="cb24-11"><a href="#cb24-11" tabindex="-1"></a>)</span>
<span id="cb24-12"><a href="#cb24-12" tabindex="-1"></a><span class="fu">plot</span>(g1)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>En este ejemplo, se ha utilizado el operador <code>:</code> para
definir conjuntos de vértices. Si el operador de un arista conecta dos
conjuntos de vértices, entonces cada vértice del primer conjunto estará
conectado a cada vértice del segundo conjunto. A continuación utilizamos
<code>is_chordal()</code> para evaluar si nuestro grafo es cordal y
buscar qué aristas faltan para rellenar el grafo:</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a><span class="fu">is_chordal</span>(g1, <span class="at">fillin=</span><span class="cn">TRUE</span>)</span></code></pre></div>
<pre><code>## $chordal
## [1] FALSE
## 
## $fillin
##  [1] 2 6 8 7 5 7 2 7 6 1 7 1
## 
## $newgraph
## NULL</code></pre>
<p>Luego, en una sola línea podemos añadir las aristas necesarias para
que el grafo inicial sea cordal:</p>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" tabindex="-1"></a>chordal_graph <span class="ot">&lt;-</span> <span class="fu">add_edges</span>(g1, <span class="fu">is_chordal</span>(g1, <span class="at">fillin=</span><span class="cn">TRUE</span>)<span class="sc">$</span>fillin)</span>
<span id="cb27-2"><a href="#cb27-2" tabindex="-1"></a><span class="fu">plot</span>(chordal_graph)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="construcción-de-grafos" class="section level2">
<h2>Construcción de grafos</h2>
<p>Además de <code>make_empty_graph()</code>, <code>make_graph()</code>
y <code>make_graph_from_literal()</code>, <code>igraph</code> incluye
muchas otras funciones para construir un grafo. Algunas son
<em>deterministas</em>, es decir, producen el mismo grafo cada vez, por
ejemplo <code>make_tree()</code>:</p>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a>graph1 <span class="ot">&lt;-</span> <span class="fu">make_tree</span>(<span class="dv">127</span>, <span class="dv">2</span>, <span class="at">mode =</span> <span class="st">&quot;undirected&quot;</span>)</span>
<span id="cb28-2"><a href="#cb28-2" tabindex="-1"></a><span class="fu">summary</span>(g)</span></code></pre></div>
<pre><code>## IGRAPH 48b2318 U--- 5 3 -- Ring graph
## + attr: name (g/c), mutual (g/l), circular (g/l)</code></pre>
<p>Esto genera un grafo regular en forma de árbol con 127 vértices, cada
vértice con dos hijos. No importa cuántas veces llames a
<code>make_tree()</code>, el grafo generado será siempre el mismo si
utilizas los mismos parámetros:</p>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a>graph2 <span class="ot">&lt;-</span> <span class="fu">make_tree</span>(<span class="dv">127</span>, <span class="dv">2</span>, <span class="at">mode =</span> <span class="st">&quot;undirected&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" tabindex="-1"></a><span class="fu">identical_graphs</span>(graph1, graph2)</span></code></pre></div>
<pre><code>## [1] TRUE</code></pre>
<p>Otras funciones son <em>estocásticas</em>, lo cual quiere decir que
producen un grafo diferente cada vez; por ejemplo,
<code>sample_grg()</code>:</p>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" tabindex="-1"></a>graph1 <span class="ot">&lt;-</span> <span class="fu">sample_grg</span>(<span class="dv">100</span>, <span class="fl">0.2</span>)</span>
<span id="cb33-2"><a href="#cb33-2" tabindex="-1"></a><span class="fu">summary</span>(graph1)</span></code></pre></div>
<pre><code>## IGRAPH f42d783 U--- 100 478 -- Geometric random graph
## + attr: name (g/c), radius (g/n), torus (g/l)</code></pre>
<p>Esto genera un grafo geométrico aleatorio: Se eligen <em>n</em>
puntos de forma aleatoria y uniforme dentro del espacio métrico, y los
pares de puntos más cercanos entre sí respecto a una distancia
predeterminada <em>d</em> se conectan mediante una arista. Si se generan
GRGs con los mismos parámetros, serán diferentes:</p>
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" tabindex="-1"></a>graph2 <span class="ot">&lt;-</span> <span class="fu">sample_grg</span>(<span class="dv">100</span>, <span class="fl">0.2</span>)</span>
<span id="cb35-2"><a href="#cb35-2" tabindex="-1"></a><span class="fu">identical_graphs</span>(graph1, graph2)</span></code></pre></div>
<pre><code>## [1] FALSE</code></pre>
<p>Una forma un poco más relajada de comprobar si los grafos son
equivalentes es mediante <code>isomorphic()</code>. Se dice que dos
grafos son isomorfos si tienen el mismo número de componentes (vértices
y aristas) y mantienen una correspondencia uno a uno entre vértices y
aristas, es decir, están conectados de la misma manera:</p>
<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" tabindex="-1"></a><span class="fu">isomorphic</span>(graph1, graph2)</span></code></pre></div>
<pre><code>## [1] FALSE</code></pre>
<p>Comprobar el isomorfismo puede llevar un tiempo en el caso de grafos
grandes (en este caso, la respuesta puede darse rápidamente comprobando
la secuencia de grados de los dos grafos).
<code>identical_graph()</code> es un criterio más estricto que
<code>isomorphic()</code>: los dos grafos deben tener la misma lista de
vértices y aristas, exactamente en el mismo orden, con la misma
direccionalidad, y los dos grafos también deben tener idénticos
atributos de grafo, vértice y arista.</p>
</div>
<div id="establecer-y-recuperar-atributos" class="section level2">
<h2>Establecer y recuperar atributos</h2>
<p>Además de los IDs, los vértices y aristas pueden tener
<em>atributos</em> como un nombre, coordenadas para graficar, metadatos
y pesos. El propio grafo también puede tener estos atributos (por
ejemplo, un nombre, que se mostrará en <code>summary</code>). En cierto
sentido, cada grafo, vértice y arista puede ser utilizado como un
espacio de nombres en R para almacenar y recuperar estos atributos.</p>
<p>Para demostrar el uso de los atributos, creemos una red social
sencilla:</p>
<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb39-1"><a href="#cb39-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_graph</span>(</span>
<span id="cb39-2"><a href="#cb39-2" tabindex="-1"></a>  <span class="sc">~</span> Alice<span class="sc">-</span>Boris<span class="sc">:</span>Himari<span class="sc">:</span>Moshe,</span>
<span id="cb39-3"><a href="#cb39-3" tabindex="-1"></a>  Himari<span class="sc">-</span>Alice<span class="sc">:</span>Nang<span class="sc">:</span>Moshe<span class="sc">:</span>Samira,</span>
<span id="cb39-4"><a href="#cb39-4" tabindex="-1"></a>  Ibrahim<span class="sc">-</span>Nang<span class="sc">:</span>Moshe, </span>
<span id="cb39-5"><a href="#cb39-5" tabindex="-1"></a>  Nang<span class="sc">-</span>Samira</span>
<span id="cb39-6"><a href="#cb39-6" tabindex="-1"></a>)</span></code></pre></div>
<p>Cada vértice representa a una persona, por lo que queremos almacenar
sus edades, géneros y el tipo de conexión entre dos personas
(<code>is_formal()</code> se refiere a si una conexión entre una persona
y otra es formal o informal, es decir, colegas o amigos). El operador
<code>$</code> es un atajo para obtener y establecer atributos de un
grafo. Es más corto y tan legible como <code>graph_attr()</code> y
<code>set_graph_attr()</code>.</p>
<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>age <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="dv">25</span>, <span class="dv">31</span>, <span class="dv">18</span>, <span class="dv">23</span>, <span class="dv">47</span>, <span class="dv">22</span>, <span class="dv">50</span>) </span>
<span id="cb40-2"><a href="#cb40-2" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>gender <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>)</span>
<span id="cb40-3"><a href="#cb40-3" tabindex="-1"></a><span class="fu">E</span>(g)<span class="sc">$</span>is_formal <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="cn">FALSE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">TRUE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">FALSE</span>)</span>
<span id="cb40-4"><a href="#cb40-4" tabindex="-1"></a><span class="fu">summary</span>(g)</span></code></pre></div>
<pre><code>## IGRAPH 965c8de UN-- 7 9 -- 
## + attr: name (v/c), age (v/n), gender (v/c), is_formal (e/l)</code></pre>
<p><code>V</code> y <code>E</code> son la forma estándar de obtener una
secuencia de todos los vértices y aristas respectivamente. Esto asigna
un atributo a <em>todos</em> los vértices/aristas a la vez. Otra forma
de generar nuestra red social es con el uso de
<code>set_vertex_attr()</code> y <code>set_edge_attr()</code> y el
operador <code>%&gt;%</code>:</p>
<div class="sourceCode" id="cb42"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_graph</span>(</span>
<span id="cb42-2"><a href="#cb42-2" tabindex="-1"></a>  <span class="sc">~</span> Alice<span class="sc">-</span>Boris<span class="sc">:</span>Himari<span class="sc">:</span>Moshe, </span>
<span id="cb42-3"><a href="#cb42-3" tabindex="-1"></a>  Himari<span class="sc">-</span>Alice<span class="sc">:</span>Nang<span class="sc">:</span>Moshe<span class="sc">:</span>Samira,</span>
<span id="cb42-4"><a href="#cb42-4" tabindex="-1"></a>  Ibrahim<span class="sc">-</span>Nang<span class="sc">:</span>Moshe, </span>
<span id="cb42-5"><a href="#cb42-5" tabindex="-1"></a>  Nang<span class="sc">-</span>Samira</span>
<span id="cb42-6"><a href="#cb42-6" tabindex="-1"></a>) <span class="sc">%&gt;%</span></span>
<span id="cb42-7"><a href="#cb42-7" tabindex="-1"></a>  <span class="fu">set_vertex_attr</span>(<span class="st">&quot;age&quot;</span>, <span class="at">value =</span> <span class="fu">c</span>(<span class="dv">25</span>, <span class="dv">31</span>, <span class="dv">18</span>, <span class="dv">23</span>, <span class="dv">47</span>, <span class="dv">22</span>, <span class="dv">50</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb42-8"><a href="#cb42-8" tabindex="-1"></a>  <span class="fu">set_vertex_attr</span>(<span class="st">&quot;gender&quot;</span>, <span class="at">value =</span> <span class="fu">c</span>(<span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb42-9"><a href="#cb42-9" tabindex="-1"></a>  <span class="fu">set_edge_attr</span>(<span class="st">&quot;is_formal&quot;</span>, <span class="at">value =</span> <span class="fu">c</span>(<span class="cn">FALSE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">TRUE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">FALSE</span>))</span>
<span id="cb42-10"><a href="#cb42-10" tabindex="-1"></a><span class="fu">summary</span>(g)</span></code></pre></div>
<p>Para asignar o modificar un atributo a un único vértice/arista:</p>
<div class="sourceCode" id="cb43"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" tabindex="-1"></a><span class="fu">E</span>(g)<span class="sc">$</span>is_formal</span></code></pre></div>
<pre><code>## [1] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE</code></pre>
<div class="sourceCode" id="cb45"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb45-1"><a href="#cb45-1" tabindex="-1"></a><span class="fu">E</span>(g)<span class="sc">$</span>is_formal[<span class="dv">1</span>] <span class="ot">&lt;-</span> <span class="cn">TRUE</span></span>
<span id="cb45-2"><a href="#cb45-2" tabindex="-1"></a><span class="fu">E</span>(g)<span class="sc">$</span>is_formal</span></code></pre></div>
<pre><code>## [1]  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE</code></pre>
<p>Los valores de los atributos pueden establecerse en cualquier objeto
de R, pero ten en cuenta que almacenar el grafo en algunos formatos
puede provocar la pérdida de valores en atributos complejos. Los
vértices, las aristas y el propio grafo pueden utilizarse para
establecer atributos, por ejemplo, para añadir una fecha al grafo:</p>
<div class="sourceCode" id="cb47"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb47-1"><a href="#cb47-1" tabindex="-1"></a>g<span class="sc">$</span>date <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;2022-02-11&quot;</span>)</span>
<span id="cb47-2"><a href="#cb47-2" tabindex="-1"></a><span class="fu">graph_attr</span>(g, <span class="st">&quot;date&quot;</span>)</span></code></pre></div>
<pre><code>## [1] &quot;2022-02-11&quot;</code></pre>
<p>Para recuperar atributos, también puedes utilizar
<code>graph_attr()</code>, <code>vertex_attr()</code> y
<code>edge_attr()</code>. Para encontrar el ID de un vértice puedes
utilizar la función <code>match()</code>:</p>
<div class="sourceCode" id="cb49"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb49-1"><a href="#cb49-1" tabindex="-1"></a><span class="fu">match</span>(<span class="fu">c</span>(<span class="st">&quot;Ibrahim&quot;</span>), <span class="fu">V</span>(g)<span class="sc">$</span>name)</span></code></pre></div>
<pre><code>## [1] 7</code></pre>
<p>Para asignar atributos a un subconjunto de vértices o aristas, puedes
utilizar:</p>
<div class="sourceCode" id="cb51"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb51-1"><a href="#cb51-1" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>name[<span class="dv">1</span><span class="sc">:</span><span class="dv">3</span>] <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Alejandra&quot;</span>, <span class="st">&quot;Bruno&quot;</span>, <span class="st">&quot;Carmina&quot;</span>)</span>
<span id="cb51-2"><a href="#cb51-2" tabindex="-1"></a><span class="fu">V</span>(g)</span></code></pre></div>
<pre><code>## + 7/7 vertices, named, from 965c8de:
## [1] Alejandra Bruno     Carmina   Moshe     Nang      Samira    Ibrahim</code></pre>
<p>Para eliminar atributos:</p>
<div class="sourceCode" id="cb53"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb53-1"><a href="#cb53-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">delete_vertex_attr</span>(g, <span class="st">&quot;gender&quot;</span>)</span>
<span id="cb53-2"><a href="#cb53-2" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>gender</span></code></pre></div>
<pre><code>## NULL</code></pre>
<p>Si quieres guardar un grafo en R con todos los atributos utiliza la
función estándar de R <code>dput</code> y recupéralo más tarde con
<code>dget</code>. También puedes simplemente guardar el espacio de
trabajo de R y restaurarlo más tarde.</p>
</div>
<div id="propiedades-estructurales-de-los-grafos" class="section level2">
<h2>Propiedades estructurales de los grafos</h2>
<p>igraph proporciona un amplio conjunto de métodos para calcular varias
propiedades estructurales de los grafos. Está más allá del alcance de
este tutorial documentar todos ellos, por lo que esta sección sólo
presentará algunos de ellos con fines ilustrativos. Trabajaremos con la
pequeña red social que construimos en la sección anterior.</p>
<p>Probablemente, la propiedad más sencilla en la que se puede pensar es
el “grado del vértice”. El grado de un vértice es igual al número de
aristas incidentes a él. En el caso de los grafos dirigidos, también
podemos definir el <code>grado de entrada</code> (el número de aristas
que apuntan hacia el vértice) y el <code>grado de salida</code> (el
número de aristas que se originan en el vértice). igraph es capaz de
calcularlos todos utilizando una sintaxis sencilla:</p>
<div class="sourceCode" id="cb55"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb55-1"><a href="#cb55-1" tabindex="-1"></a><span class="fu">degree</span>(g)</span></code></pre></div>
<pre><code>## Alejandra     Bruno   Carmina     Moshe      Nang    Samira   Ibrahim 
##         3         1         4         3         3         2         2</code></pre>
<p>Si el grafo fuera dirigido, podríamos calcular los grados de entrada
y salida por separado utilizando <code>degree(mode = &quot;in&quot;)</code> y
<code>degree(mode = &quot;out&quot;)</code>. También puedes pasar un único ID de
un vértice o una lista de IDs de los vértices a <code>degree()</code> si
quieres calcular los grados sólo para un subconjunto de vértices:</p>
<div class="sourceCode" id="cb57"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb57-1"><a href="#cb57-1" tabindex="-1"></a><span class="fu">degree</span>(g, <span class="dv">7</span>)</span></code></pre></div>
<pre><code>## Ibrahim 
##       2</code></pre>
<div class="sourceCode" id="cb59"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb59-1"><a href="#cb59-1" tabindex="-1"></a><span class="fu">degree</span>(g, <span class="at">v =</span> <span class="fu">c</span>(<span class="dv">3</span>,<span class="dv">4</span>,<span class="dv">5</span>))</span></code></pre></div>
<pre><code>## Carmina   Moshe    Nang 
##       4       3       3</code></pre>
<p>La mayoría de las funciones que aceptan los IDs de los vértices
también aceptan los “nombres” de los vértices (es decir, los valores del
atributo <code>name</code> del vértice) siempre que los nombres sean
únicos:</p>
<div class="sourceCode" id="cb61"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb61-1"><a href="#cb61-1" tabindex="-1"></a><span class="fu">degree</span>(g, <span class="at">v =</span> <span class="fu">c</span>(<span class="st">&quot;Carmina&quot;</span>, <span class="st">&quot;Moshe&quot;</span>, <span class="st">&quot;Nang&quot;</span>))</span></code></pre></div>
<pre><code>## Carmina   Moshe    Nang 
##       4       3       3</code></pre>
<p>También funciona para vértices individuales:</p>
<div class="sourceCode" id="cb63"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb63-1"><a href="#cb63-1" tabindex="-1"></a><span class="fu">degree</span>(g, <span class="st">&quot;Bruno&quot;</span>)</span></code></pre></div>
<pre><code>## Bruno 
##     1</code></pre>
<p>De igual manera, se utiliza una sintaxis similar para la mayoría de
las propiedades estructurales que igraph puede calcular. Para las
propiedades de los vértices, las funciones aceptan un ID, un nombre o
una lista de IDs o nombres (y si se omiten, el valor predeterminado es
el conjunto de todos los vértices). Para las propiedades de aristas, las
funciones aceptan un único ID o una lista de IDs.</p>
<hr />
<p><strong>NOTA:</strong> Para algunas mediciones, no tiene sentido
calcularlas sólo para unos pocos vértices o aristas en lugar de para
todo el grafo, ya que de todas formas llevaría el mismo tiempo. En este
caso, las funciones no aceptan IDs de vértices o aristas, pero se puede
restringir la lista resultante utilizando operaciones estándar. Un
ejemplo es la centralidad de vectores propios
(<code>evcent()</code>).</p>
<hr />
<p>Además del grado, igraph incluye funciones integradas para calcular
muchas otras propiedades de centralidad, como la intermediación de
vértices y aristas (<code>edge_betweenness()</code>) o el PageRank de
Google (<code>page_rank()</code>), por nombrar algunas. Aquí sólo
ilustraremos la intermediación de aristas:</p>
<div class="sourceCode" id="cb65"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb65-1"><a href="#cb65-1" tabindex="-1"></a><span class="fu">edge_betweenness</span>(g)</span></code></pre></div>
<pre><code>## [1] 6 6 4 3 4 4 4 2 3</code></pre>
<p>De este modo, ahora también podemos averiguar qué conexiones tienen
la mayor centralidad de intermediación:</p>
<div class="sourceCode" id="cb67"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb67-1"><a href="#cb67-1" tabindex="-1"></a>ebs <span class="ot">&lt;-</span> <span class="fu">edge_betweenness</span>(g)</span>
<span id="cb67-2"><a href="#cb67-2" tabindex="-1"></a><span class="fu">as_edgelist</span>(g)[ebs <span class="sc">==</span> <span class="fu">max</span>(ebs), ]</span></code></pre></div>
<pre><code>##      [,1]        [,2]     
## [1,] &quot;Alejandra&quot; &quot;Bruno&quot;  
## [2,] &quot;Alejandra&quot; &quot;Carmina&quot;</code></pre>
</div>
<div id="búsqueda-de-vértices-y-aristas-basada-en-atributos" class="section level2">
<h2>Búsqueda de vértices y aristas basada en atributos</h2>
<div id="selección-de-vértices" class="section level3">
<h3>Selección de vértices</h3>
<p>Tomando como ejemplo la red social anteriormente creada, te gustaría
averiguar quién tiene el mayor grado. Puedes hacerlo con las
herramientas presentadas hasta ahora y con la función
<code>which.max()</code>:</p>
<div class="sourceCode" id="cb69"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb69-1"><a href="#cb69-1" tabindex="-1"></a><span class="fu">which.max</span>(<span class="fu">degree</span>(g))</span></code></pre></div>
<pre><code>## Carmina 
##       3</code></pre>
<p>Otro ejemplo sería seleccionar sólo los vértices que tienen IDs
impares, utilizando la función <code>V()</code>:</p>
<div class="sourceCode" id="cb71"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb71-1"><a href="#cb71-1" tabindex="-1"></a>graph <span class="ot">&lt;-</span> <span class="fu">graph.full</span>(<span class="at">n=</span><span class="dv">10</span>)</span>
<span id="cb71-2"><a href="#cb71-2" tabindex="-1"></a>only_odd_vertices <span class="ot">&lt;-</span> <span class="fu">which</span>(<span class="fu">V</span>(graph)<span class="sc">%%</span><span class="dv">2</span><span class="sc">==</span><span class="dv">1</span>)</span>
<span id="cb71-3"><a href="#cb71-3" tabindex="-1"></a><span class="fu">length</span>(only_odd_vertices)</span></code></pre></div>
<pre><code>## [1] 5</code></pre>
<p>Por supuesto, es posible seleccionar vértices o aristas mediante
índices posicionales:</p>
<div class="sourceCode" id="cb73"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb73-1"><a href="#cb73-1" tabindex="-1"></a>seq <span class="ot">&lt;-</span> <span class="fu">V</span>(graph)[<span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">7</span>]</span>
<span id="cb73-2"><a href="#cb73-2" tabindex="-1"></a>seq</span></code></pre></div>
<pre><code>## + 3/10 vertices, from 6cad540:
## [1] 2 3 7</code></pre>
<div class="sourceCode" id="cb75"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb75-1"><a href="#cb75-1" tabindex="-1"></a>seq <span class="ot">&lt;-</span> seq[<span class="dv">1</span>, <span class="dv">3</span>]    <span class="co"># filtrar un conjunto de vértices existente</span></span>
<span id="cb75-2"><a href="#cb75-2" tabindex="-1"></a>seq</span></code></pre></div>
<pre><code>## + 2/10 vertices, from 6cad540:
## [1] 2 7</code></pre>
<p>Al seleccionar un vértice que no existe se produce un error:</p>
<div class="sourceCode" id="cb77"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb77-1"><a href="#cb77-1" tabindex="-1"></a>seq <span class="ot">&lt;-</span> <span class="fu">V</span>(graph)[<span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="st">&quot;foo&quot;</span>, <span class="fl">3.5</span>]</span></code></pre></div>
<pre><code>## Error in simple_vs_index(x, ii, na_ok): Unknown vertex selected</code></pre>
<p>Los nombres de los atributos también pueden utilizarse tal cual
dentro de los operadores de indexación (“[]”) de <code>V()</code> y
<code>E()</code>. Esto puede combinarse con la capacidad de R de
utilizar vectores booleanos para indexar y obtener expresiones muy
concisas y legibles para recuperar un subconjunto del set de vértices o
aristas de un grafo. Por ejemplo, el siguiente comando nos da los
nombres de los individuos menores de 30 años de nuestra red social:</p>
<div class="sourceCode" id="cb79"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb79-1"><a href="#cb79-1" tabindex="-1"></a><span class="fu">V</span>(g)[age <span class="sc">&lt;</span> <span class="dv">30</span>]<span class="sc">$</span>name</span></code></pre></div>
<pre><code>## [1] &quot;Alejandra&quot; &quot;Carmina&quot;   &quot;Moshe&quot;     &quot;Samira&quot;</code></pre>
<p>Por supuesto, <code>&lt;</code> no es el único operador booleano que
puede utilizarse para esto. Otras posibilidades son las siguientes:</p>
<table>
<colgroup>
<col width="13%" />
<col width="86%" />
</colgroup>
<thead>
<tr class="header">
<th>Operador</th>
<th>Significado</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>==</code></td>
<td>El valor del atributo/propiedad debe ser <em>igual</em> a</td>
</tr>
<tr class="even">
<td><code>!=</code></td>
<td>El valor del atributo/propiedad debe <em>no ser igual</em> a</td>
</tr>
<tr class="odd">
<td><code>&lt;</code></td>
<td>El valor del atributo/propiedad debe ser <em>menos</em> que</td>
</tr>
<tr class="even">
<td><code>&lt;=</code></td>
<td>El valor del atributo/propiedad debe ser <em>inferior o igual
a</em></td>
</tr>
<tr class="odd">
<td><code>&gt;</code></td>
<td>El valor del atributo/propiedad debe ser <em>mayor que</em></td>
</tr>
<tr class="even">
<td><code>&gt;=</code></td>
<td>El valor del atributo/propiedad debe ser <em>mayor o igual
a</em></td>
</tr>
<tr class="odd">
<td><code>%in%</code></td>
<td>El valor del atributo/propiedad debe estar <em>incluido en</em></td>
</tr>
</tbody>
</table>
<p>También puede crear un operador “no incluido en” a partir de
<code>%in%</code> utilizando el operador <code>Negate</code>:</p>
<div class="sourceCode" id="cb81"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb81-1"><a href="#cb81-1" tabindex="-1"></a><span class="st">`</span><span class="at">%notin%</span><span class="st">`</span> <span class="ot">&lt;-</span> <span class="fu">Negate</span>(<span class="st">`</span><span class="at">%in%</span><span class="st">`</span>)</span></code></pre></div>
<p>Si un atributo tiene el mismo nombre que una función de igraph, debes
tener cuidado ya que la sintaxis puede llegar a ser un poco confusa. Por
ejemplo, si hay un atributo llamado <code>degree</code> que representa
las notas de un examen para cada persona, no debe confundirse con la
función de igraph que calcula los grados de los vértices de una red:</p>
<div class="sourceCode" id="cb82"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb82-1"><a href="#cb82-1" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>degree <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;A&quot;</span>, <span class="st">&quot;B&quot;</span>, <span class="st">&quot;B+&quot;</span>, <span class="st">&quot;A+&quot;</span>, <span class="st">&quot;C&quot;</span>, <span class="st">&quot;A&quot;</span>, <span class="st">&quot;B&quot;</span>)</span>
<span id="cb82-2"><a href="#cb82-2" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>degree[<span class="fu">degree</span>(g) <span class="sc">==</span> <span class="dv">3</span>]</span></code></pre></div>
<pre><code>## [1] &quot;A&quot;  &quot;A+&quot; &quot;C&quot;</code></pre>
<div class="sourceCode" id="cb84"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb84-1"><a href="#cb84-1" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>name[<span class="fu">degree</span>(g) <span class="sc">==</span> <span class="dv">3</span>]</span></code></pre></div>
<pre><code>## [1] &quot;Alejandra&quot; &quot;Moshe&quot;     &quot;Nang&quot;</code></pre>
</div>
<div id="selección-de-aristas" class="section level3">
<h3>Selección de aristas</h3>
<p>Las aristas pueden seleccionarse basándose en atributos, igual que
los vértices. Como ya se ha mencionado, la forma estándar de obtener
aristas es <code>E</code>. Además, existen algunas propiedades
estructurales especiales para seleccionar aristas.</p>
<p>El uso de <code>.from()</code> permite filtrar la serie de aristas
desde los vértices de donde proceden. Por ejemplo, para seleccionar
todas las aristas procedentes de Carmina (cuyo ID de vértice es el
3):</p>
<div class="sourceCode" id="cb86"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb86-1"><a href="#cb86-1" tabindex="-1"></a><span class="fu">E</span>(g)[<span class="fu">.from</span>(<span class="dv">3</span>)]</span></code></pre></div>
<pre><code>## + 4/9 edges from 965c8de (vertex names):
## [1] Alejandra--Carmina Carmina  --Moshe   Carmina  --Nang    Carmina  --Samira</code></pre>
<p>Por supuesto, también funciona con nombres de vértices:</p>
<div class="sourceCode" id="cb88"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb88-1"><a href="#cb88-1" tabindex="-1"></a><span class="fu">E</span>(g)[<span class="fu">.from</span>(<span class="st">&quot;Carmina&quot;</span>)]</span></code></pre></div>
<pre><code>## + 4/9 edges from 965c8de (vertex names):
## [1] Alejandra--Carmina Carmina  --Moshe   Carmina  --Nang    Carmina  --Samira</code></pre>
<p>Al usar <code>.to()</code>, se filtran la serie de aristas en función
de los vértices de destino o diana. Esto es diferente de
<code>.from()</code> si el grafo es dirigido, mientras que da la misma
respuesta para grafos no dirigidos. Con <code>.inc()</code> sólo se
seleccionan las aristas que inciden en un único vértice o en al menos
uno de los vértices, independientemente de la dirección de las
aristas.</p>
<p>La expresión <code>%--%</code> es un operador especial que puede
utilizarse para seleccionar todas las aristas entre dos conjuntos de
vértices. Ignora las direcciones de las aristas en los grafos dirigidos.
Por ejemplo, la siguiente expresión selecciona todas las aristas entre
Carmina (su ID de vértice es el 3), Nang (su ID de vértice es el 5) y
Samira (su ID de vértice es el 6):</p>
<div class="sourceCode" id="cb90"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb90-1"><a href="#cb90-1" tabindex="-1"></a><span class="fu">E</span>(g) [ <span class="dv">3</span><span class="sc">:</span><span class="dv">5</span> <span class="sc">%--%</span> <span class="dv">5</span><span class="sc">:</span><span class="dv">6</span> ]</span></code></pre></div>
<pre><code>## + 3/9 edges from 965c8de (vertex names):
## [1] Carmina--Nang   Carmina--Samira Nang   --Samira</code></pre>
<p>Para que el operador <code>%--%</code> funcione con nombres, puedes
construir vectores de caracteres que contengan los nombres y luego
utilizar estos vectores como operandos. Por ejemplo, para seleccionar
todas las aristas que conectan a los hombres con las mujeres, podemos
hacer lo siguiente, luego de volver a añadir el atributo de género que
hemos eliminado anteriormente:</p>
<div class="sourceCode" id="cb92"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb92-1"><a href="#cb92-1" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>gender <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb93"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb93-1"><a href="#cb93-1" tabindex="-1"></a>men <span class="ot">&lt;-</span> <span class="fu">V</span>(g)[gender <span class="sc">==</span> <span class="st">&quot;m&quot;</span>]<span class="sc">$</span>name</span>
<span id="cb93-2"><a href="#cb93-2" tabindex="-1"></a>men</span></code></pre></div>
<pre><code>## [1] &quot;Bruno&quot;   &quot;Moshe&quot;   &quot;Nang&quot;    &quot;Ibrahim&quot;</code></pre>
<div class="sourceCode" id="cb95"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb95-1"><a href="#cb95-1" tabindex="-1"></a>women <span class="ot">&lt;-</span> <span class="fu">V</span>(g)[gender <span class="sc">==</span> <span class="st">&quot;f&quot;</span>]<span class="sc">$</span>name</span>
<span id="cb95-2"><a href="#cb95-2" tabindex="-1"></a>women</span></code></pre></div>
<pre><code>## [1] &quot;Alejandra&quot; &quot;Carmina&quot;   &quot;Samira&quot;</code></pre>
<div class="sourceCode" id="cb97"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb97-1"><a href="#cb97-1" tabindex="-1"></a><span class="fu">E</span>(g)[men <span class="sc">%--%</span> women]</span></code></pre></div>
<pre><code>## + 5/9 edges from 965c8de (vertex names):
## [1] Alejandra--Bruno  Alejandra--Moshe  Carmina  --Moshe  Carmina  --Nang  
## [5] Nang     --Samira</code></pre>
</div>
</div>
<div id="tratar-un-grafo-como-una-matriz-de-adyacencia" class="section level2">
<h2>Tratar un grafo como una matriz de adyacencia</h2>
<p>Una matriz de adyacencia es otra manera de representar un grafo. En
la matriz de adyacencia, las filas y columnas están indicadas por los
vértices del grafo y los elementos de la matriz indican el número de
aristas entre los vértices <em>i</em> y <em>j</em>. La matriz de
adyacencia del grafo de nuestra red social imaginaria es:</p>
<div class="sourceCode" id="cb99"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb99-1"><a href="#cb99-1" tabindex="-1"></a><span class="fu">as_adjacency_matrix</span>(g)</span></code></pre></div>
<pre><code>## 7 x 7 sparse Matrix of class &quot;dgCMatrix&quot;
##           Alejandra Bruno Carmina Moshe Nang Samira Ibrahim
## Alejandra         .     1       1     1    .      .       .
## Bruno             1     .       .     .    .      .       .
## Carmina           1     .       .     1    1      1       .
## Moshe             1     .       1     .    .      .       1
## Nang              .     .       1     .    .      1       1
## Samira            .     .       1     .    1      .       .
## Ibrahim           .     .       .     1    1      .       .</code></pre>
<p>Por ejemplo, Carmina (<code>1, 0, 0, 1, 1, 1, 0</code>) está
directamente conectada con Alejandra (que tiene el índice 1), Moshe
(índice 4), Nang (índice 5), Samira (índice 6) y , pero no con Bruno
(índice 2) ni con Ibrahim (índice 7).</p>
</div>
<div id="diseños-y-graficación" class="section level2">
<h2>Diseños y graficación</h2>
<p>Un grafo es un objeto matemático abstracto sin una representación
específica en el espacio 2D, 3D o cualquier espacio geométrico. Esto
significa que, cuando queremos visualizar un grafo, primero tenemos que
encontrar una correspondencia entre los vértices y las coordenadas en un
espacio bidimensional o tridimensional, preferiblemente de una manera
útil y/o agradable a la vista. Una rama separada de la teoría de grafos,
denominada dibujo de grafos, trata de resolver este problema mediante
varios algoritmos de diseño de grafos. igraph implementa varios
algoritmos de diseño y también es capaz de dibujarlos en la pantalla o
en cualquier formato de salida que soporte el propio R.</p>
<div id="algoritmos-de-diseño" class="section level3">
<h3>Algoritmos de diseño</h3>
<p>Las funciones de diseño en igraph siempre empiezan por
<code>layout</code>. La siguiente tabla las resume:</p>
<table>
<colgroup>
<col width="23%" />
<col width="76%" />
</colgroup>
<thead>
<tr class="header">
<th>Nombre del método</th>
<th>Descripción del algoritmo</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>layout_randomly</code></td>
<td>Coloca los vértices de forma totalmente aleatoria</td>
</tr>
<tr class="even">
<td><code>layout_in_circle</code></td>
<td>Disposición determinista que coloca los vértices en un círculo</td>
</tr>
<tr class="odd">
<td><code>layout_on_sphere</code></td>
<td>Disposición determinista que coloca los vértices de manera uniforme
en la superficie de una esfera</td>
</tr>
<tr class="even">
<td><code>layout_with_drl</code></td>
<td>El algoritmo DRL (<em>Distributed Recursive Layout</em>) para grafos
grandes</td>
</tr>
<tr class="odd">
<td><code>layout_with_fr</code></td>
<td>El algoritmo dirigido Fruchterman-Reingold</td>
</tr>
<tr class="even">
<td><code>layout_with_kk</code></td>
<td>El algoritmo dirigido Kamada-Kawai</td>
</tr>
<tr class="odd">
<td><code>layout_with_lgl</code></td>
<td>El algoritmo LGL (<em>Large Graph Layout</em>) para grafos
grandes</td>
</tr>
<tr class="even">
<td><code>layout_as_tree</code></td>
<td>Diseño de árbol de Reingold-Tilford, útil para grafos (casi)
arbóreos</td>
</tr>
<tr class="odd">
<td><code>layout_nicely</code></td>
<td>Algoritmo de diseño que elige automáticamente uno de los otros
algoritmos en función de determinadas propiedades del grafo</td>
</tr>
</tbody>
</table>
<p>Los algoritmos de diseño pueden ejecutarse directamente con un grafo
como primer argumento. Devolverán una matriz con dos columnas y tantas
filas como número de vértices del grafo; cada fila corresponderá a la
posición de un único vértice, ordenado según el ID del vértice. Algunos
algoritmos tienen una variante 3D; en este caso devuelven tres columnas
en lugar de 2.</p>
<div class="sourceCode" id="cb101"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb101-1"><a href="#cb101-1" tabindex="-1"></a>layout <span class="ot">&lt;-</span> <span class="fu">layout_with_kk</span>(g)</span></code></pre></div>
<p>Algunos algoritmos de diseño toman argumentos adicionales; por
ejemplo, cuando se diseña un grafo con la forma de un árbol, puede tener
sentido especificar qué vértice debe colocarse en la raíz del
diseño:</p>
<div class="sourceCode" id="cb102"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb102-1"><a href="#cb102-1" tabindex="-1"></a>layout <span class="ot">&lt;-</span> <span class="fu">layout_as_tree</span>(g, <span class="at">root =</span> <span class="dv">2</span>)</span></code></pre></div>
</div>
<div id="dibujar-un-grafo-utilizando-un-diseño" class="section level3">
<h3>Dibujar un grafo utilizando un diseño</h3>
<p>Podemos trazar nuestra red social imaginaria con el algoritmo de
diseño Kamada-Kawai de la siguiente manera:</p>
<div class="sourceCode" id="cb103"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb103-1"><a href="#cb103-1" tabindex="-1"></a>layout <span class="ot">&lt;-</span> <span class="fu">layout_with_kk</span>(g)</span></code></pre></div>
<div class="sourceCode" id="cb104"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb104-1"><a href="#cb104-1" tabindex="-1"></a><span class="fu">plot</span>(g, <span class="at">layout =</span> layout, <span class="at">main =</span> <span class="st">&quot;Red social con el algoritmo de diseño Kamada-Kawai&quot;</span>)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>Esto debería abrir una nueva ventana mostrando una representación
visual de la red. Recuerda que la ubicación exacta de los nodos puede
ser diferente en tu máquina, ya que la disposición no es
determinista.</p>
<p>El argumento <code>layout</code> también acepta funciones; en este
caso, la función será llamada con el grafo como su primer argumento.
Esto permite ingresar directamente el nombre de una función de diseño,
sin tener que crear una variable de diseño, como en el ejemplo
anterior:</p>
<div class="sourceCode" id="cb105"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb105-1"><a href="#cb105-1" tabindex="-1"></a><span class="fu">plot</span>(</span>
<span id="cb105-2"><a href="#cb105-2" tabindex="-1"></a>  g, </span>
<span id="cb105-3"><a href="#cb105-3" tabindex="-1"></a>  <span class="at">layout =</span> layout_with_fr,</span>
<span id="cb105-4"><a href="#cb105-4" tabindex="-1"></a>  <span class="at">main =</span> <span class="st">&quot;Red social con el algoritmo de disposición Fruchterman-Reingold&quot;</span></span>
<span id="cb105-5"><a href="#cb105-5" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>Para mejorar el aspecto visual, una adición trivial sería colorear
los vértices según el género. También deberíamos intentar colocar los
nombres ligeramente fuera de los vértices para mejorar la
legibilidad:</p>
<div class="sourceCode" id="cb106"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb106-1"><a href="#cb106-1" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>color <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(<span class="fu">V</span>(g)<span class="sc">$</span>gender <span class="sc">==</span> <span class="st">&quot;m&quot;</span>, <span class="st">&quot;yellow&quot;</span>, <span class="st">&quot;red&quot;</span>)</span>
<span id="cb106-2"><a href="#cb106-2" tabindex="-1"></a><span class="fu">plot</span>(</span>
<span id="cb106-3"><a href="#cb106-3" tabindex="-1"></a>  g, </span>
<span id="cb106-4"><a href="#cb106-4" tabindex="-1"></a>  <span class="at">layout =</span> layout, </span>
<span id="cb106-5"><a href="#cb106-5" tabindex="-1"></a>  <span class="at">vertex.label.dist =</span> <span class="fl">3.5</span>,</span>
<span id="cb106-6"><a href="#cb106-6" tabindex="-1"></a>  <span class="at">main =</span> <span class="st">&quot;Red social - con los géneros como colores&quot;</span></span>
<span id="cb106-7"><a href="#cb106-7" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>También puedes tratar el atributo <code>gender</code> como un factor
y proporcionar los colores como un argumento a <code>plot()</code>, que
tiene prioridad sobre el atributo <code>color</code> que se asigna de
manera estándar a los vértices. Los colores se asignan
automáticamente:</p>
<div class="sourceCode" id="cb107"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb107-1"><a href="#cb107-1" tabindex="-1"></a><span class="fu">plot</span>(</span>
<span id="cb107-2"><a href="#cb107-2" tabindex="-1"></a>  g, </span>
<span id="cb107-3"><a href="#cb107-3" tabindex="-1"></a>  <span class="at">layout =</span> layout, </span>
<span id="cb107-4"><a href="#cb107-4" tabindex="-1"></a>  <span class="at">vertex.label.dist =</span> <span class="fl">3.5</span>, </span>
<span id="cb107-5"><a href="#cb107-5" tabindex="-1"></a>  <span class="at">vertex.color =</span> <span class="fu">as.factor</span>(<span class="fu">V</span>(g)<span class="sc">$</span>gender))</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkAAAAJACAYAAABlmtk2AAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAJAoAMABAAAAAEAAAJAAAAAAC2FqgIAAEAASURBVHgB7J0HfFRV9sd/ISEhHQiBAIFQQ+9Neu+9qrh2XXXtq7iuq2t3LWtXdC1/14o0BQQLvfcWeofQQkIgQBqkkP89l52QQMrMZF4y897vfowz89599937vY/ML+eee45XjipgIQESIAESIAESIAELEShnobFyqCRAAiRAAiRAAiSgCVAA8UEgARIgARIgARKwHAEKIMtNOQdMAiRAAiRAAiRAAcRngARIgARIgARIwHIEKIAsN+UcMAmQAAmQAAmQAAUQnwESIAESIAESIAHLEaAAstyUc8AkQAIkQAIkQAIUQHwGSIAESIAESIAELEeAAshyU84BkwAJkAAJkAAJUADxGSABEiABEiABErAcAQogy005B0wCJEACJEACJEABxGeABEiABEiABEjAcgQogCw35RwwCZAACZAACZAABRCfARIgARIgARIgAcsRoACy3JRzwCRAAiRAAiRAAhRAfAZIgARIgARIgAQsR4ACyHJTzgGTAAmQAAmQAAlQAPEZIAESIAESIAESsBwBCiDLTTkHTAIkQAIkQAIkQAHEZ4AESIAESIAESMByBCiALDflHDAJkAAJkAAJkAAFEJ8BEiABEiABEiAByxGgALLclHPAJEACJEACJEACFEB8BkiABEiABEiABCxHgALIclPOAZMACZAACZAACVAA8RkgARIgARIgARKwHAEKIMtNOQdMAiRAAiRAAiRAAcRngARIgARIgARIwHIEKIAsN+UcMAmQAAmQAAmQAAUQnwESIAESIAESIAHLEaAAstyUc8AkQAIkQAIkQAIUQHwGSIAESIAESIAELEeAAshyU84BkwAJkAAJkAAJUADxGSABEiABEiABErAcAQogy005B0wCJEACJEACJEABxGeABEiABEiABEjAcgQogCw35RwwCZAACZAACZAABRCfARIgARIgARIgAcsRoACy3JRzwCRAAiRAAiRAAhRAfAZIgARIgARIgAQsR4ACyHJTzgGTAAmQAAmQAAlQAPEZIAESIAESIAESsBwBCiDLTTkHTAIkQAIkQAIkQAHEZ4AESIAESIAESMByBCiALDflHDAJkAAJkAAJkAAFEJ8BEiABEiABEiAByxGgALLclHPAJEACJEACJEACFEB8BkiABEiABEiABCxHgALIclPOAZMACZAACZAACVAA8RkgARIgARIgARKwHAEKIMtNOQdMAiRAAiRAAiRAAcRngARIgARIgARIwHIEKIAsN+UcMAmQAAmQAAmQAAUQnwESIAESIAESIAHLEaAAstyUc8AkQAIkQAIkQAIUQHwGSIAESIAESIAELEeAAshyU84BkwAJkAAJkAAJUADxGSABEiABEiABErAcAQogy005B0wCJEACJEACJEABxGeABEiABEiABEjAcgQogCw35RwwCZAACZAACZAABRCfARIgARIgARIgAcsRoACy3JRzwCRAAiRAAiRAAhRAfAZIgARIgARIgAQsR4ACyHJTzgGTAAmQAAmQAAlQAPEZIAESIAESIAESsBwBCiDLTTkHTAIkQAIkQAIkQAHEZ4AESIAESIAESMByBCiALDflHDAJkIAQOHr0HNLSMgiDBEjAogQogCw68Rw2CbgLgb17T+PRR3+Bl9ffERHxKi5dyiqwa0uWHNR1fH3/gTffXIbUVOfEi1z3yCNzEBX1Bk6evFDgvXiQBEjA/AQogMw/xxwhCbg1gUaNwvH220MQHOyL+PgUfPfdlgL7+9ln63WdNm1q4KmneiIw0LfAesUdlOvuv79TcdV4ngRIwOQEKIBMPsEcHgl4AgEfH2907VpHWWUq4t13VyInJydft2NjkxAQUB4hIRUQFOS48Ll8+XK+9ry9+asvHxB+IAELEuBvAQtOOodMAu5IwMenHB56qDN27kzAH3/sy9fFyZPX4i9/6ZzvmO3D5s0ncNtt0/DEE/MwYMCX+PrrTbZTWki99NIifPXVJtX2bDzzzB+55+TN0aPnMWHCDwgNfQETJ/6IvELp99/3YtKkX1G//lu48cYfcOZMar5r+YEESMCzCVAAefb8sfckYCoC99zTQS1tlcc776zMHVd6eqYSRfFo165m7jHbm/37EzF06H+V1WioWkYbig8+GI4HHpiFL7/coKssXHgA586l4+67O+D994cjKyvbdql+/fHHGHWvofj551sxZUoMFiw4oI+vW3cUv/22D2+9NQQbNz6ErVvjVLuz813LDyRAAp5NgALIs+ePvScBUxGoWNFfWXPaaiGybVucHpv4BE2c2LrAcb788mJ061YHYWGB+nzjxlXRvXtdPPvsfP05NvYcZs/ejcOHz0KWvUQI5S1icYqMDEWfPvURHh6IPXtO69OvvLJE+Rv5QQSSWKOqVw/GnDm78lmI8rbD9yRAAp5HgALI8+aMPSYBUxN4+OEuarcXcq1AM2bswPjxLQoc88aNx7U4yXuyU6daOHUqRf0kY8yYZnoZrFWr95V1aBXE4bqwEhpaIXdn2bp1x9CsWVU0bFhF/4h1adWqB1RbhV3N4yRAAp5GwMfTOsz+kgAJmJtAkyZV0b9/Q70kNXBgQ3TuXBvly3sXOOjMzMvYuzcx37lWrarrz5mZ2WpbfbBewhIfoUcfnastPJMnj8pXv6AP589fhL9/+QKX3Qqqz2MkQAKeR4AWIM+bM/aYBExJICPjqn/Oo492gXy+//5ZuO++joWOt3nzali//li+XWNHjiShYsUKemlr+fLDqFw5AHPn3qG32n/66TqcOHG+0PZsJxo0CFPLX9tsH/Xrnj0J2LfvyhJZvhP8QAIk4JEEKIA8ctrYaRIwFwEJfigOzbYyeHAjREdXUQ7OjdUSV4jtsF6iSkxMy/08aVIP5eR8US1PxeYe27DhuNoR1l0to3lh8eKDOHjwjD732GNdUa1akBJH/khJuRJE0fYqFVJSLuUev+OOdpg+fbtygl6Os2fTIM7U33yzRfWp8CW03A7wDQmQgEcQ8H5BFY/oKTtJAiRgSgIxMXHa0rN+/XG11TxNLTvVULF+/LQf0KhRTVGrVkUcO3ZOOzYvX34EiYmp+lz79pFqi3oY6tSppLe3h4UFYOXKIyq9RSZeeqm/dnpesyZWWX5WoFIlfyxdehg9e9ZDy5YReOWVxdiy5SRkmaxXr3qYOnUbvv9+Ky5cuIh+/RpAlt4OHDiD995biTfeWK5F1L//LcEa/Uw5BxwUCViRgJcKOEa3PivOPMdMAiYikJGRhd27T+tAimLhsZXs7MsoV85LR5gW649YhRwpSUnpkG34NWpctUI5cj3rkgAJuC8BCiD3nRv2jARIgARIgARIwCAC9AEyCCybJQESIAESIAEScF8CFEDuOzfsGQmQAAmQAAmQgEEEKIAMAstmSYAESIAESIAE3JcABZD7zg17RgIkQAIkQAIkYBABCiCDwLJZEiABEiABEiAB9yVAAeS+c8OekQAJkAAJkAAJGESAAsggsGyWBEiABEiABEjAfQlQALnv3LBnJEACJEACJEACBhGgADIILJslARIgARIgARJwXwIUQO47N+wZCZAACZAACZCAQQR8DGqXzZIACViMwOnTpzFjxgz8On8Rtm3fjsT4U0hPS0FAUAhqRNZGx/ZtMXzIIAwbNgyBgYEWo8PhkgAJuBsB5gJztxlhf0jAwwjExsbimeeex6xZsxHRti+CmvZAcO2m8K8SCe8KgchKT0baqcM4fygGqTuX4sy+jXjg/vvw9FOTULlyZQ8bLbtLAiRgFgIUQGaZSY6DBMqAwDfffouHH/srqve+DbUG3Qsf/6Bie3HxTByO/fIeLuxYip9nTEPXrl2LvYYVSIAESMDVBCiAXE2U7ZGARQg8/uRT+O8P09Dyye8QUK2Ow6M+u3sN1r86AT8ry9GokSMcvp4XkAAJkEBJCFAAlYQeryUBixJ44qmn8fnXP6DDi/PgG1zJaQrJx/dizT8G4ttvvsHEiROdbocXkgAJkICjBCiAHCXG+iRgcQKy7HX77bejz+SYEokfG8bko7sR8+ZNWLLgD7Rv3952mK8kQAIkYCgBCiBD8bJxEjAXAXF4btGmHVr/42cERtR12eAStixC4s+vYM+ObfD393dZu2yIBEiABAojwDhAhZHhcRIggesIyG6vGn1uc6n4kZtUbdMXqFIfn33++XX35AESIAESMIIALUBGUGWbJGBCAhLnp079aHR5Z41du70cRXD+8DbEfv4gjh85BC8vL0cvZ30SIAEScIgALUAO4WJlErAuAQlyGNGunyHiR6iG1m2JbJ8ArF271rqQOXISIIFSI0ABVGqoeSMS8GwCc/9YqIMcGjmKkGY9sGjRYiNvwbZJgARIQBOgAOKDQAIkYBeB7Sq9hUR4NrL412yCDVu3GXkLtk0CJEACmgBzgfFBIAESsIvA6VNxaFSlll11na3kHx6J41tPOns5ryMBUxJYsGA/3nlnJX7/fR9at66uwkXUhI+PNzIystC4cVU88kgX+Pnx69zRyScxR4mxPglYlMCli2nw9jN2i3o5X3+kpaVZlDCHTQIFE+jfvyEqVPDRAuj229visce66Yr79yeiTZsPsH37KXzzzYSCL+bRQglwCaxQNDxBAiSQl0AF/0BkXzJWnGRfSmWm+LzQ+Z4E/kcgJKSCfpd3h2TDhlWUNShSbRw4Sk5OEKAAcgIaLyEBKxKoUSsKqXGHDB26ZI2vX7eOofdg4yRgFgLJyZewe3cCbrqplR6SLIn9+GMMevX6DPPn78OAAV+iWbN3MW3aNtSs+Ro+/HC1rjd1agwaNXob99//s/68aNEB3HjjD3jyyXn47LP1qF//LX3+0KGzuaguXLiIhx6ajaee+hVjx36n36emZuSe98Q3FECeOGvsMwmUAYGOHdrh/KEYQ++cHrsN3W7oYOg92DgJeDKBjRuP4+uvN+HFFxeie/dPMXx4Ezz6aBc9pKysy0hKSseyZYfx3Xdb1fGuGDQoGhMmtES5cl4QwSTlxhtboW7dSjh7Nl1/Fr+ibdtOYd68vahTpyJWrLgP3t5eeO21Jfp8Tk4OBg/+Cl27RuHNN4dg6tSbsXnzSS2EdAUP/R8FkIdOHLtNAqVNYPjggUjdYdwWdfkle2bbEvTr16+0h8b7kYDHEPD19YYInRMnLuDixSwcP34esbHndP8DAnwxYkQT/X7cuOYYOrQx3n57qP5coUJ5FWD06jD9/a9+DgsL1IKoRYsIZTWKRo0aIejduz727UvUF8ybtwfr1h3D+PEt9GdxwL7rrvb444/9Siwdvtqoh72jAPKwCWN3SaCsCIwYMQJn9m3ExbNxhnQhcdtS1KhWFU2aXPkFbshN2CgJeDiBli2r4+67O6ilqjHK+fkxpKdnKivPV3pHmAxNnKWlVK4coF/t/Z/4FuUVSEFBvrh0KUtfvmHDcYSHB+qdZ7b2OnW6siN061Zjfh/Y7mPkKwWQkXTZNgmYiEBgYCDu+/O9OPbLB4aM6sSc9/D8P542pG02SgJmJFC+vDfGjGmO06dTMWvWLsOGmJl5GfHxKTh37sqSmdyoadOqShCVQ2ZmtmH3NbphCiCjCbN9EjARgb//7Smc37YISfs2uHRUxxf+FzVCfZVPwViXtsvGSMDsBGxWGlnSKq7I0pkzpXnzalAr1HoZzHa9LMFJe3LOUwsFkKfOHPtNAmVAICwsDD/PmIadH9yLlJMHXNKDUxt+w9HZ72GWatfb29slbbIREjAbgfj4ZD2kjIyrFpczZ1KVQ/RmREaGoEePuvnOp6RccXi2cZDgiUuWHNKO0Lt2xWv/nri4K21KnYsXM5GdfVUgicO0zbozdmxz1KtXGb/+utfWHGRZTJynJUaRpxbvF1Tx1M6z3yRAAqVPoHbt2ggPq4yPHxmLau0Hwy803OlOJGxegC3v3YNtMVvVL9h6TrfDC0nAzAT++GMf3nhjGY4cScKuXQnYtOmEigy9Ap9/vgGtWlXH9OkTUaVKIEQQvfTSYi1OZFmsWbOqqF49RKOR8x99tAaTJ6/V1pzq1YNx/vwlREQE4dix83j33ZUQQSQ7vc6cSVM7wJbi4MGz6NgxUm2JD1dCpwH+/e/lepfZhQuXMGVKjLr/GFSq5JivkTvNk5faeaEMWywkQAIk4BiBP+bPx+ix49H0gY8Q3qq3Yxer2scXfo2T8z7E5A/fV39pZqrtvMMRGhrqcDu8gARIwD4CEicoOzsHslwm1h3xIXK0HDiQqCy15dSuscqOXup29SmA3G5K2CES8BwC69evx9gbb4Z3RGPUHPYwQuo0L7Lz8vdW4vZlOKkcniOCfDD7p+moVasW9u7dq/5q3aC28I5ASMiVv1iLbIgnSYAESKCEBCiASgiQl5OA1Qmkp6fj00//g9ffelvtwQ1GaPNe8K/VBP4qcarkDstKT4FEeE6PjVFxfpYiIjwMLzz7d4wbNy6fz8/u3btVcLXNWgQFBwdbHSvHTwIkYDABCiCDAbN5ErACAbHsfPvtt8qfIELlJVqH9VtisEdZdXy8fRAYFKTTW3Tt1F4FWRtQZJyfXbt2YevWrVoEBanrWEiABEjAKAIUQEaRZbskYCECx48f10tYo0eP1qOWjO4zZ87Erbfe6jCFHTt2qABv27VPEEWQw/h4AQmQgJ0EuA3eTlCsRgIkUDiBffv2oWHDq9thL126BD8/v8IvKOJM8+bN1e6VZpg7dy5SU1OLqMlTJEACJOA8AQog59nxShIgAUVAdnDFxsaiQYMGuTxKIoCkkZYtW+qlsl9++QViTWIhARIgAVcToAByNVG2RwIWI3D48GEVa6S6ykFUIXfkFy9edNoCZGukVatWKv5II4gIEkdrFhIgARJwJQEKIFfSZFskYEEC+/fvR3R0dL6RiwUoryDKd9KBD23atNGWJRFBIqpYSIAESMBVBCiAXEWS7ZCABQmIj87p06cRFRWVb/QlXQLL21i7du1U0LW62ieIIigvGb4nARIoCQEKoJLQ47UkYHECYv2RFBbX5vBypQASxB06dNABE+fNmwdpm4UESIAESkqAAqikBHk9CViYQEHLX4LD1QJI2uzUqRNq1qwJiiChwUICJFBSAhRAJSXI60nAogQSExORlZWFatWqXUfAFU7Q1zWqDtxwww062OKvv/6KjIyMgqrwGAm4HYGEhAR88sknGDXuRkQ3a4WwatURWjkcNaPqoVvv/njun8+rBKeb3K7fZu8QAyGafYY5PhIwiMCaNWtUMsXyaN++/XV3ECuN7OKKjIy87pwrDqxcuVJlrD6DwYMHw9fX1xVNsg0ScDkBiY/19+eex++//Y7q7QcgsFFXBEVGwzckDOXKV1BpYi4gNe4Qzu9Zi6Qtf6BioB9efv5ZTJgw4bplZZd3jg2CAogPAQmQgMMELl++jO+++w4jR44sMIP7Tz/9hO7duyM8PNzhtu29YPny5Th37pwWQSLEWEjAnQi8/MprKj/eW6g99EHU6ne7zotXXP/O7FiJYz+ra8IC8MvPM1G1atXiLuH5EhDgElgJ4PFSErAqAUl9IVnbQ0NDC0RghA/QtTcSgSX3//333/VS3LXn+ZkEyoJAdnY2xoy/ER9/NxOdXluEOkPvt0v8SF/DmndDq2dn4UJkF720fOrUqbIYgmXuSQFkmanmQEnAdQTE+Tlv6otrWy4NAeTl5YUePXpAMsdTBF07A/xcVgTadeiE1XtPotXfp8OvouMWHHmu64x4GM3veVM7/cu/NRZjCFAAGcOVrZKAaQmI8/HRo0dRv379AscomeGlTmn45siXRc+ePREQEID58+fTElTgjPBgaRF48qmnsWvPHrR64huU8ymZb1pkr5vR9M7X0HfgYJw/f760hmCp+1AAWWq6OVgSKDkBSX0h29ELi/Rss/6IOCmNIvfp3bu3FlwLFiyALEGwkEBpE1i4cCG+/OZ79PxwkxI/rvFJi+x9Cyo064N77/9LaQ/HEvejALLENHOQJOA6Au6w/HXtaEQE9enTBz4+PqAIupYOPxtNQKyej/x1Eure/Dx8KgS69HZ1xjyFPxYuRkxMjEvbZWMABRCfAhIgAbsJpKSk6O3ntWvXLvQamwWo0AoGnShXrhz69u0LeZW/xmWnGgsJlAaBpUuX4kxqBqq1H+Ty23n7VkDNgX/Ga2+85fK2rd4gBZDVnwCOnwQcICDWn4JSX+RtwqggiHnvUdh7ET/9+vWD/EW+aNEiiqDCQPG4Swn899vvEdZlvEvbzNtY9e7jmAsvLxAXvacAchFINkMCViAgAujazO/XjlssQIX5B11b14jPIoL69++vHaIXL15MEWQEZLaZj8CChYsQ1rJ3vmOu/OAbVAmVakVj3bp1rmzW8m1RAFn+ESAAErCPgGR9l2WlglJf5G2hrJbA8vZBkrMOGDBA70aT5Qkuh+Wlw/euJJCcnIwzp+MRWL2eK5u9rq0KkU3pB3QdlZIdoAAqGT9eTQKWIVCc87MNhDsIIOmLTQSlp6dj2bJlelnM1ke+koCrCEhQ0IoRtWH0rkefsEgcOXrMVd1mO4oABRAfAxIggWIJiAXlwIEDRQY/tDXiLgJI+iO7wgYOHAhx3qYIss0QX11JIDU1Ve38CnBlkwW25a12lyWr55jFdQQogFzHki2RgGkJHDt2TKedkPQXxZWydIIuqG8igiRp6oULF7BixQpaggqCxGNOEwgMDETWxVSnr7f3wmx1j5CgIHurs54dBCiA7IDEKiRgdQL2Ln8Jp7J2gi5ormwiKCkpCZJJnoUEXEUgKioKZ08cMVxYZyUeRd06Ua7qNttRBCiA+BiQAAkUSUDSWogFqLDUF9de7E5LYHn7JhnjxRJ05swZrFq1Ku8pvicBpwlIGpbqtaKQcnyv023Yc2H6sR1o06aNPVVZx04CFEB2gmI1ErAqgUOHDiEyMhJ+fn52IXBXASSdl/xkQ4YMQXx8PNasWWPXeFiJBIojMKBfH5zZtqS4ak6fz7hwBudPHkaHDh2cboMXXk+AAuh6JjxCAiSQh8C+ffvscn62XeLOAkj6KCJo2LBhOHnyJNauXWvrNl9JwGkCo0cMR8KKqYYtg8Utn4rRo0eXSoJhpyF44IUUQB44aewyCZQWAYlxIn4zRaW+uLYv7i6ApL82ESRbmNevX3/tEPiZBOwiIM/P3Llzce7cOVQPC8Wpdb/YdZ0jlcTB+vj8L/D3p5505DLWtYOAjx11WIUESMCiBMT5WXx/JLqyPUX8hcTh2N769rRpVB1Z0hNLkHyBSX/bt29v1K3YrokISEgIWRbeunWrtvi0bt1a/xsRZ+hhYyagSoseKB9Y0WUjPjztNYweORxNmzZ1WZts6AoBCiA+CSRAAoUSkOUvybJub/EE60/esUjKDhFBc+bM0YHs2rVrl/c035NALoGsrCzs2bNHR2OWcBAdO3bMZxnt1q0bHn/oL/jk7dvQ6u/TUa68fT5zuTco4M2Oz/6KgMTdmPwTl2oLwFPiQxRAJUbIBkjAnAQSEhK0KKhatardA/Q0ASQDExE0fPhw/PLLL9oSxJ02dk+3JSpKXKsdO3Zg165diIiI0HnmCvs38fw/n8XWbduw6o2b0erJ70oUIPHogq9wfPl0nD17FhJriMX1BOyza7v+vmyRBEjAzQk46vwsw3G3IIj2Ivb399eWIBmzLG2wkID4v0m4hB9//BFpaWkYOXKkzi9XmPixEft5xjT0a98YG/85EGkJsbbDdr/mXM7G4RlvInn514iNjUWlSpXsvpYVHSNAAeQYL9YmAUsQED+HgwcPOrT7S8CIBagsM8GXZHIknossh8kyxzb1VzyLNQlInKhFixZh5syZkNhREyZMQI8ePXQkdHuJ/PDtN3jx6Sew+cXhODTzLWSkJBV7aY76Nxe/4Tdsem4AItMPYPuWTfmW2IptgBUcJsAlMIeR8QISMD+Bo0eP6r88g4ODHRqsJy6B5R2gLDWICLIthzVv3jzvab43MYETJ05o/x5ZcmrRogW6d+9eom3njzz8EEaPGokXXn4VP07qjmrNu6BCdBcE1WwEv5AwlPP1Q2bqBaSdOoSUfeuQsHk+6teri68nv6sDdhqdXNXEU2n30LxyVLG7NiuSAAlYgsCCBQtQq1YtNG7c2KHxbtmyBZmZmdpB1KEL3ayyJE8Vx2jZ4cPdN242OS7sjnz9yY6umJgYiJNzq1attNXT1bsYZTlNdhvOX7QE23ftQeLpBP3vJDg4BNHRDdGr6w0YMWIEGjRo4MLRsaniCFAAFUeI50nAYgTEivPDDz/glltucfgvYAksKP408kXi6UW+tMQSJE7RTZo0cfvhJCWlK6udf4H9vHQpCxs3HkfXrnUKPH/twZSUSzhyJAnNm0dce6rEn7OzL2P//kQlru13ri/xTa9pQMSO+HuJ8JGlT3leZRs7rS7XgDL5R/oAmXyCOTwScJSA+P6I9UeCBTpaPNUJuqBxyvKfLIdt2rQJe/cam+epoPs7ciwhIUU56H5Z4CUifDp3noyxY78v8HxBBz/+eK3ye/nM5ZGNt2w5qeItfYT77vu5oNsafkzE/ebNm7XAl/x2vXv31s7NderUofgxnL773YACyP3mhD0igTIlIMEPo6OjneqDJztBFzRgifciW+Q3bNigLQYF1XGHY19+uUFZeE5g6dJD13WnfftI3Hpr2+uOF3Xg8ce7Yvv2R10uCtq0qaF8a+qoYJml+9UjS5qrV6/GlClTcOHCBT2nAwcO1Nvai+LAc+YmQCdoc88vR0cCDhGQL4fz58/r5KcOXfi/yp7uBF3QmENDQ/M5Rrubn4YsKR06dFb5K1XHhx+uRq9e9a4bhggOL6/rDhd6wNfXBzVrhl53PjMzW+2M8r7uuD0HZGeh+NZ4e5ezO1K4jK1cOS+nhZg4NMsyl2wnF3+28ePHM6aOPZNlkToUQBaZaA6TBOwhINYf+YJ31gnUjAJIuFWsWFGLIHFkFT8RSQ/iLmXu3D0YNKgR2rWriYcemoOjR8+p7dPFp2L4/fe9arv3Qfz00061LFUTkyePRFhYIGQ57fvvt6r4NzFYt+5BPcwFC/arpaOTWozMmrVT1R2l/Gaqq+sP4LPP1qsl01BlNQzHG28s09ad3367E/XqVdbXij/RpEm/KUEVgri4ZCVI4pSfWHl9bv36Y/r6iIhg+Pl54/33V2PevNt1/z/4YLXyvaqqrDYxanzRePTRrnYjj4uL0/GcEhMT9Y6url27OrWka/cNWdEjCZSuHdIjEbHTJGAdAs4EP8xLx6wCSMYoAemGDh2ql1Jk55C7lBkztqsdRE2U03obFYPJB598UnzahHXrjuK33/bhrbeGqKWzh5RYiMMDD8zWQxKLy6lTydi5M15/Fgfq22+fjk6daikh0wMDB0bjpZcW6XNiddq27ZQSLXtRp05FrFhxn7LweOG115bk4hHfo379GuDZZ/vgo49GKGtMUq41SixTs2btUjukdisLVg386U+tER4ehBdfXKQskRdx221t8c47Q/HEE/NUjKms3DYLeiM7uo4cOaLam4Vly5ap/tTBxIkT9U4+Z/zZCroHj5mLAC1A5ppPjoYEnCZw6tQp9eXlrb6Awp1uw0xO0AVBqFy5MoYMGaK+8OdpK5l8yZZl2bfvtFquDNXLUrI0NXFia3zxxQY8/3xfJYauWFkK6t8rryzRFhyx8kipXj1YbfvfBVmmqlIlEC1bXt395evrjTvvbKd2w9XQdUNDK6i0EAn6vViM6tathKAgP+WEfcVvrHfv+sp/6JQ+v2LFYSxceADTp0/Un8V6NmRIYxw+fFZ/btu2Jho1qqKtRcOHN1G+OVd22/Xt20A9h1fSP8j9srNz9M6xgnalZWdnq3P7tcVHEtxK6AKZF+7o0oj5vyIIUAAVAYenSMBKBORLpGHDhk4PWbYWy5eOZIM3cwkLC9Mi6Ndff9Xjle3TZVU++WQd0tMz8a9/XbG4iJUkMTFNLxvdeWfh2e3XrTumxJLEvKmiu/7220P1a0FR4WROX311oBIYJ/H77/u0xSc1NSN3yHJe/ZdbgoJ8c601K1YcUctY4QgJqZB7PiCgfD5xIlarypUDcs/Lm/HjW0C29b/33kqVXuWK5SfvPaVORkaGzs+1fft2Jdqq6GjNNWpcEWlynoUEiiNg7t9UxY2e50mABDQB+StalnXGjh3rNBGzW3/ygpEv3MGDB6tlpN+U03GvMklZkJaWobbnn8Yzz/TO7Vr37nVV/qpY7QxdlACS5SXxwxG/IXvKK68sxsmTF/DxxyPx6afr1D2O2HOZvubkyWS9nT6vRSavYCqoIVmie+KJXzFjxi3ap+jvf/8jt5rk5ZJUJZKypHbt2npZUixzLCTgKAH6ADlKjPVJwIQEJPWFWDaCgoKcHp38RS5LEFYpslQ4aNAgtfV8KY4fP17qwxbn4DFjmqNbtzr5fu69tyO2bInDypVHCu1TgwZhysk5f76zPXsS1Fb/09dds3nzCTz33AK8/HL/fJab6yoWcCAqqpK25OzYccWfqIAqBR6aOHGq9gcS52hbSU5O0b4906ZN00t148aNQ58+fZT1iOLHxoivjhGgAHKMF2uTgCkJlNT5WaBYyQJkewgkM7jEk1m8eDEkl1RpFXH4lS3vo0Y1ve6WEya00Mfeemt57jlZJrt0KTv38x13tFN+OduVE/RynD2bpv10vvlmi97JlVvpf29kV5kUsSyJ1enXX/ciNTVTLUFlaSFy8WKm8tG5/L/aQHLyJZXm4cq9br65ld7d9fbbK/R5qSeC6sSJC6qNK8to0i/ZKWYrcq04Ycv9ZJxffbVan1qwYJnyawrEzTffjC5dVE6tEoh12734am0C3i+oYm0EHD0JWJuACJc1a9ZoHwpxgna2yJZjiSPkTlvEnR2LI9fJF3G1atWUiFioHcgdTSDryL2krgiOu+6agQULDqgM5X4qynNtHVvHdk6EzPz5+9XyWKLa+u2tHaRff30ZJLVFWFiAcmaujhtuqIUDB85oH5s33liOgwfP4N//HoLg4CsWPHFilu31srxWp04lJXr2qN1l6/Q1f/lLZ3zzzWbt6Cztvf/+Kr29vWvXKJw5k6Z2gC1V7Z1V+eAiIU7OTZtWxZtvLldb59fqQI1yj6ysy+pevnpLvDhtx8ae087c4uQscYJEEP3nP+tVxOZ1KixDptqaD+zena12unVDjRrFb/EXFiwkUBwB5gIrjhDPk4DJCezcuVP9xX0Kffv2LdFIxScjPj4ePXv2LFE7nnqxxJ6ZP3++2g01QO2qqu4RwxBHY7EO1agRkq+/33+/RW1bn692a/0t97jUs8XvcTQgouwuO3s2Xe8wEwFX2A41qSfO+FeSk3qprfdt1S6zusoSBO1o7Wx8qtxB8A0J5CFAJ+g8MPiWBKxIQL5w2rVrV+KhmzkGkD1wRPT069dPWWYWaBEUEXF1K7k915dFHUmeWlAC1fT0LB2EMG+fbOJHjjkaDVqEi2yvl1KQ+MnMzFQWnt3auVniLckSV2RkpK7P/5GAUQQogIwiy3ZJwAMISNoLWbaqWdO+3UBFDcnqAkjYCEdxzBVLkDhIi4+QpxQJUNinzxd4992hyqfpIB55pIvhXZcdXTt27NDiRwSPMJMddiwkUBoEuARWGpR5DxJwUwIbN25UDquZyo+kc4l7uHz5cv3l1bTp9Y65JW7cwxqQTONLlizRW+VLEliytIctaS6OHz+vtvbXQ0FBB13VHxHesswloRck9UrLli1VrKD8y3CuuhfbIYHCCNACVBgZHicBkxOQHTay+0t8VlxRxAJUocLVgHeuaNNT26hVq5aODyRxgiRytKdYNUaNamYo8tOnT+uIzSdPnkSzZs1w00038ZkxlDgbL4oABVBRdHiOBExMQByfy5cv77IvZy6B5X9YJEhfjx491A6qX3WwPomzZNUiFrGtW7fq5dZWrVqhd+/epo8YbtW59qRxUwB50myxryTgQgLi/BwdfSV/kyuapQC6nmIdlZNKLG2SO2zYsGGWCtonO7oOHjyol7qEgeTokhAJ3Ml1/XPCI2VDgAKobLjzriRQpgRsqS/Gjx/vsn5QABWM8so27qsiSHY5mblITjgJiSA+PuLX06lTJ8iSIAsJuBsBCiB3mxH2hwRKgUBsbKwO2hcYeGVrsituacVI0PZyq1evno6abLMEVaxovmB+Mv+yo0viSklSUvEt8yQHcHvnkvXMQ4ACyDxzyZGQgN0EXJH6Iu/NZLlDrEq+vr55D/N9HgKy20k4zZ07F8OHD1dRnEPznPXct8nJydrac+DAAYjQGzVqlGnG5rmzwp7bQ4ACyB5KrEMCJiIgf6lL1OKSRn7Oi4TWn7w0Cn8vPlfiD/PLL79gxIgRHr31W1KfyDKXODhL6IMJEyYgICCg8MHzDAm4GQEKIDebEHaHBIwmIH+pR0VF6R1grroXt8DbT7JRo0a5IkgsQZ4W/0aSvsqOrqSkJB2/R3a6yW5CFhLwNAIUQJ42Y+wvCZSQgCx/dezYsYSt5L+cDtD5eRT3qXHjxvmWw4xOoFpcf4o7L0t3hw8f1hYfcXKWrewNGzbkjq7iwPG8WxOgAHLr6WHnSMC1BM6dOwdJP+CK1Bd5e0YBlJeGfe9l2UiEhW05TLLKF1aOHj2qMqkvxc4dMYg7HqsywqcjOKQi6kc302K2W7duhgQUFLGzd+9enaNLHOYlZ5zEN/Ly8iqsqzxOAh5DgALIY6aKHSWBkhMQ648447r6C4wCyLm5ad68ee5ymPgE5d2VJ+Jo+vTpeOfNl3FYpYzo0sgX0VVS0SIYCAj2QnJ6DmKX+GDWN37Yd+ISRo8ejb8980+IdamkReZTdnPJrq5q1arp/GbyykICZiJAAWSm2eRYSKAIAuJ8K8EPBw8eXEQt505RADnHTa5q0aJFPkuQOBJv374dd/xpAsqln8QDPdPR65ZySrReUrUL+pV9CWdTcjB1/U/odsMsPPPsc3jokSec2pGXkpKirT0ilCV+kYgyM27Zd362eKWZCDAZqplmk2MhgSIISP6l1atXY9y4cUXUcu6UJFUVq5IskbA4R0Aci2W5Scpf7rsL/xiaidHtvR1q7IKyCr0wxxfxOfWxaOlq+Pv723X92bNntWOzLLWJBUlEWV5rlF2NsBIJeBiBgv6c8LAhsLskQAL2EJC/6l2Z+iLvPWUbPC0FeYk4/l5SRWzdshl//vO9mPaQD1rVdkz8yB1D/L3wzo2ZGPXedr0lXbaqF5WDTMIhiPCSeiJ6xJeIsZwcnzte4ZkEKIA8c97YaxJwiIA4sx45csTlu79snZAlMGaCt9Fw7nXVqlW486678dskXzSqXs65Rv531azHyuONeT4YNqgvlq1an0/UyFKoPAsifGTeRHhJ1GZvb8cFV4k6yYtJoIwJUACV8QTw9iRQGgTkC69q1aqGBaqjD1DJZjEhIQFjRw3Djw+WXPzYevK3oV54ctohfPDuW3jyb//QkbrFCijBC/38/LTwkWStrnaIt92fryTg7gQogNx9htg/EnABAVdnfr+2SxRA1xJx7PPLL/4TQ1pkomP9kll+rr3rP4dnYMAbr6Fug8Y6/EGVKlXQs2dPVK9e/dqq/EwCliNAAWS5KeeArUZA4v7Ex8ejf//+hg2dAsh5tOKA/O23X2PRpBzViGvj64hP0F3ds/H5px/ih6k/oXLlys53lFeSgMkIuPbPDZPB4XBIwAwEJPWFLHX4+Bj39w5zgTn/pEydOhW9m5ZH5SDXih9bjyZ0ANauW08fLRsQvpLA/whQAPFRIAGTE5DlL0lbYFQRp9rMzEztV2LUPczc7rxZU9G30UXDhlgx0AtNa/lh2bJlht2DDZOAJxKgAPLEWWOfScBOArK8kp6ejho1ath5hePVZPlLkmHSmdZxdnLFhk1b0KGesb+K20WmYf26dc51kFeRgEkJGPuvzqTQOCwS8BQCNudnI8WJCCBugXfuiUhNTUVKajqqhhiz/GXrVd0qKgr4nm22j3wlARJQBCiA+BiQgEkJ2FJfGLn8JejoAO38A5ScnIzgQF/nG7DzyqAKQEryBTtrsxoJWIMABZA15pmjtCABSX0heaUqVapk6OgpgJzHK/F40i9mOd+AnVemZwC+6l4sJEACVwlQAF1lwXckYCoCEvTOaOuPAKMAcv6xkfQhmVmXcTFDtsAbV04n56BGzSjjbsCWScADCVAAeeCkscskUBwBW+qLBg0aFFe1xOcpgJxHKL5ZDepGYn+8sQJof2IAmjRv5XxHeSUJmJAABZAJJ5VDIoHDhw/raL/2ZgMvCTEKIOfoiY+WLFM2VsJk5X7n2rD3qlX7stG7d297q7MeCViCgHGR0SyBj4MkAfckILu/GjVqVCqdkyCIQUFBpXIvT7/J5cuXIRnYDx06BBGpgYGBGDJ0JF59dhEe6JNpyPDWHshG5So1EB0dbUj7bJQEPJUABZCnzhz7TQKFEJDUF5JcUzJ8l0YRC5DkmGIpmICIHrH0iOg5opLSBgcHo27duhg1ahRCQkL0RR+99wb+2HYQA1u6PiP7J8v88fikZwvuHI+SgIUJUABZePI5dHMSEOuPfMEamfoiLzkugeWlceW9iJ7jx49r0RMbG4vQ0FDUq1cPo0eP1gLo2ivefOdj3DlxBHo1yYJfedfFBPp9WzbOZFbGrbfeeu0t+ZkELE+AAsjyjwABeDKBrKxsxMaeQ9WqQeqL9co2582bd2PgwB6lMqzs7MvYufMMWrUyPpZNqQyoBDfJzs7OJ3ok8agI0fbt2xe7RNinTx8MHDYOD/8wA/+5LcslUbVjjl7Gsz/74PcF00pNDJcAHy8lgVIn4KUc8YzdflDqQ+INScD8BJYsOYinn/4dUVGV0Lx5NZw+nYqYmDidkysqKgtTpjzqki/R4kguX34YPXt+hjVr7sYNNzQorrrpzstuu7yWHlkKFNEj1h6JweRIkXxqfXt1QXjWdrx1I0o0f7GJl9H7tQzMmTMbw4ePcKQbrEsCliFAC5BlppoDNQuBV19djNdeW4pZs25F//5Xk5wmJ19Cv34fK8fayiX68nSEU48edfHvf9dHy5bG5RpzpD+lUVdEz9GjR/Xy1rFjxxAeHq4FT+fOnVGSXXeST23hklWIqBaOYe9ewk+P5MDPx/HlsB3HL+Oer7zx2X8+ofgpjQeC9/BYAhRAHjt17LgVCWzadALPP78Qjz/eLZ/4ERaBgeUxfnyo+nIOzYdGlqnKlfMyTBSFhOQ4lQssMzNbJVF1vdNvvsG76INYZ/KKnoiICG3p6datm1NjL6xbvr6+SDh9Bg/cdzcGvTMTzw7NQN9m9jFKvpiDT5eWw/SNfvhh6kz1fPQv7DY8TgIkoAhQAPExIAEPIvDBB6uQnZ2Du+9uf12vT5w4gfr1K+Oeewbpc3FxF/DBB6vRpElVtSQWg0GDovHoo11x6lQyvvpqI+bN26ssSQNx++3TcMstrdG1ax189tl63HlnO6xaFYupU7ehc+famDx5FGbP3qVe1+LcuXR17Thd9+LFTEybFoN3341TW6yPqGNR+OSTdfj++6345z/74Oefd2LGjB3KHyla3f+mXAEmFqzIyFCsX39c9+W7725UlpPy142nrA9kZGQo/6pYvV1d2NpET48ePSApLIwq4rz++ZdfY/78W/DUXx/C2wvjMbplOro1UkETq3mhvPdVq9D5tBxsOHwZS/b64beYLIwZOwZbYl5XfCON6h7bJQHTEKAPkGmmkgOxAoEOHT7Cxo0nkJ7+krI85BcNixcvVs7QVZVPUHON4v77f9aWHxEwu3cnoEWL95Ca+hKSktJx330/Y+nSQ3j55f5amFSu7I9atSpqf54hQxrhww9HKMddX31Ns2bV8NRTPdCmTQ0llKbqthcuvEe1lYFFi/Zg5MgpWLz4HvTqVQ8rVhzRbUyY0AJvvjkY+/efUZaIL/H773dqIbRo0QHcdNMU5bP0nG6nefN38eyzfdQx94hSLDvaRPTIlnXZul6jRg29vBUVFWWo6Cnq2V2+fDl+/OEbLFk0H4ePxiEkoDz8/XyQnJ6JnBwvtG3dHENGTMDEW27RwS+LaovnSIAErhKgBegqC74jAbcnEBeXrJxry18nfmSJRr64u3TpkjuGvn0bKP+UQP05NLSCthzt35+oBFKEigpcD+JIff/9neDre/XXgIieG29sqb70K+vrxKoTEOCrrEdXgiqKUHn99aX6XKDKYh4dXVG/l/9JWod27Wrqz+PHt9AO2uKkLX3Yty9RC6CoqIp45pneuddIv0SclWUR0SNBCeXn1KlTqFmzprKk1YfszJIlqbIuYnGSHymy0+z06dM6/5rEE5KdZiwkQALOEbj6m8+563kVCZBAKRIQYSJWlrNn09SX39VdRvLlLdaKChUq5PZGRIhYe957byUu/i/juFhtpFSo4KN8hnzziZ/cC/O8CQryw+XLVzeKBgf7IjExNbdGZuaV9nIPFPBGfJMuXbqS8bxBgyp47LGu+PHHGBw7dl4tqV3UlqQCLjP0kESvtome+Ph4vWQkkZL79eun/JLyW9YM7YiDjXt7e+ulOAcvY3USIIECCDAXWAFQeIgE3JVAy5YRumuy5T1vKSjz+7p1R9UuoK/18tI993TIW93u98qoU2S5dKl4AZS3gZQU2an2hQoMWAGTJvVQS3ZXLFR56xj1Pj09Hbt27cLcuXOVT9IUiF9P48aNdZBAcRgWq487ix+juLBdErAqAVqArDrzHLdHEnjiie744osNahlqmVrGqq/HkJqaqqwyiWrJKUp/Pn/+ohYYEydOVSKju7IYBOez2rhy4JcuXXSouVdfXaLrDx58ZUnNoYudqCxpQWx5t86cOYPatWtrHylxEi6tSNlOdJuXkAAJlAIBCqBSgMxbkICrCNStWxmvvDJAOSX/hhdeWKi2xPdVjsb7taOu+ODILi7x45HlL9ntJbu57ruvE2bO3KG7kJycgYyMLPWTrRypM9XylmyRv2IIlvdZWVd+bP29dCk7d/lKjsl1sgvNVpKSriyHpaRcsQTJzjApsvXeVuScbHmXIlGrY2JOQSxB0j95Hx0drvuU1xfJdq0zrykpKXp5S4RPUlKSFoYtW7bUy1yyhMRCAiRAAkLA+wVViIIESMBzCHTpEoVOnWppK9B//7tJbSc/iA0b0tUW9A3a0XjcuBbw9i6nfWtEEP36615MnNhaiY2Tejt7tWrBWigdOpSk/XtatYpQvkDeeP/91bquiKAuXWqrdo/pbfRHjiRBdoKVL19OB2Dcvfu08j/yV87CIXjjjWVqWemcFlMdO9bS2+DFR0mEUq9eddU2+W3K32cbrgRpbIAGDcIwffp2fP75eu3MLdvsP/54rdq9FJzrQO3MTIjo2bNnj4pIvQZbtmzRzstNmzZF9+7dtTisWLFirtBzpn1eQwIkYD4C3AZvvjnliCxE4MCB4/jhh99x221jlaWjYm6sHRsCsfLYYuyIVUZ8emwWH1udkrwuW7YM1apV07409rYjFigRaPIjxdmAiBcuXMhd3pL3thQU4gzuyjHaOy7WIwES8CwCXALzrPlib0kgH4GEhKMYMqQ16tSplO+47YNN/Mhnm+CwnXPFqzOZ4K9d6nIkGvT58+e16JHlLfF9EtHTsWNHHf+GoscVM8o2SMA6BCiArDPXHKnJCIjPzoEDBzBq1KgyG5kzAsjRzp47dy5X9MhOrnr16qkI1Z216BG/JxYSIAEScIYABZAz1HgNCbgBAclCHhISon/KqjtGCaCzZ8/mih5JSSGiR/JuyXIbRU9ZzTbvSwLmIkABZK755GgsREB2f0nwvrIsElDQVXmxZJu6LG3Jj0Q8FtHTs2dPnd6DoqcsZ5n3JgFzEqAAMue8clQmJyBWEclO3rVr1zIdqViA8kafdrQzktZBIjKL6MnJydGip3fv3lr0ONoW65MACZCAIwQogByhxbok4CYERDBIzqqSiI+SDiUrK0uLFkcDCiYkJORaesRxWSw9koKiSpUqJe0SrycBEiABuwlQANmNihVJwH0IyPKXLet7WfXKXuuPWHYk35bN0iPpJkT0DBw4EGFhYWXVfd6XBEjA4gQogCz+AHD4nkdAgv6Jk7CkdSjLUpQDtIgeyawulioRPuInJKJn8ODBzGBelpPGe5MACeQSoADKRcE3JOAZBMT6I4k7yzqtw7UCSERPXFxcrugJCAjQcXqGDRsGicTMQgIkQALuRIACyJ1mg30hATsISOb3Xr162VHT2CoigHx9fSHb8cXSc+TIEZWHLEhbekaMGKESsoYa2wG2TgIkQAIlIEABVAJ4vJQEXE1Agv5JeomtKp/Vsdj9SEm+gMCgEETVi0abNm3RpEkT7Xgs8XDKqkgAxhMnTmDbtm0Qh2ZbcMLRo0cjODi4rLrF+5IACZCAQwQogBzCxcok4HoCsnS0cuVKvPnaC1i+YhXaNfBHk6ppqF/pMoICvJCanoOjK8rhnZkB2HggDZ1v6IhatWqphKVdSi0ooMTlEdFjs/RUqlRJ+/VIHKIePXq4HgpbJAESIAGDCTAZqsGA2TwJFEUgMTERd90+Eds2rcH9PS9iZFtvVPAtPL1DekYOZm++jE+X+aJtx+744qvvDNtJJaLn2LFjWvRIzKHKlSvr5S3JvxUYGKiyxa9XGeLLK8tUm6KGyHMkQAIk4JYEKIDcclrYKSsQ2L59O4YN7ofRLZPxUN/L8C5XuPC5lkdWdg4+XFQOs7cHY836LZAM6K4oEtsnr+gJDw/XjswiesSpOW9ZsWKFFl9NmzbNe5jvSYAESMAjCHAJzCOmiZ00GwFxHG7ZsiXenlgeo9t7q+HZL36EhY+3Fx4fkANcPqsDIu7YsQPNmjVzCpOIntjYWL1dXcRP1apVteiRJTZ/f/9C27x2F1ihFXmCBEiABNyQAAWQG04Ku2RuAidPntQ+PO/eUh4j24n4cb48PsgHDap6YUDfHti0dSciIiLsasyWSkN8esS3R5yqJU6PJBy1N7o0BZBdqFmJBEjATQlQALnpxLBb5iQg1pYxIwfj2VEVlPhxzRiHK7+hhJQ0jB01BMtXbSg0PpCIHrH0iOgREVa9enVt6ZGEo84kNKUAcs38sRUSIIGyIUABVDbceVeLEpgyZQqyLxzBnd3V8pWDy15FIbtLtffrJwcxbdo03HzzzblVRaRIfB6JxixBCsVXSCw9knBUYviUpLgyE3xJ+sFrSYAESMAZAnSCdoYaryEBJwk0aRiFfw6Iww0NSrb0VdDtV+7LxptLa2HDlp1a9IilR9JRREZGatEjqTNKKnry3verr77CLbfc4tI287bP9yRAAiRgJAFagIyky7ZJIA+BDRs2IDM9yRDxI7fpFu2N536Ox+uvv659eRo3bowBAwbA0Wztebpc6FsJhijLea4UVIXejCdIgARIwAACFEAGQGWTJFAQgV/nzUXfRpkFnXLZsX5NsyG76fv16+eyNgtqiP4/BVHhMRIgAU8iUM6TOsu+koAnE1i7ajHa1802dAgdVfsb1iwz9B7SOAWQ4Yh5AxIgAYMJUAAZDJjNk4CNwMGDh1Av3LF4P7Zr7X2NquKld3nZW9/ZehRAzpLjdSRAAu5CgALIXWaC/TA9gZTUNARVMFYABfl5ITkl1XCWFECGI+YNSIAEDCZAAWQwYDZPAjYCvipv1sVM2f5uXLmUlaPycxnv2sct8MbNIVsmARIoHQIUQKXDmXchARVtORynLxgL4nQyEFGtqrE3Ua2LBcjeiNGGd4Y3IAESIAEnCFAAOQGNl5CAMwSaNG2BvXGXnbnU7mv2qfabNG9ld31nK3IJzFlyvI4ESMBdCFAAuctMsB+mJ9Cn/xCsPhJo6DhXHwlCrz4DDb2HNE4BZDhi3oAESMBgAhQc2hlAAABAAElEQVRABgNm8yRgIzBy5Eis3HMJ59OM8QM6l5qD1XsvYsSIEbZbGvZKAWQYWjZMAiRQSgQogEoJNG9DApJwtI/KwfV/K4zZCfbFinKYOHEiQkJCDIdNJ2jDEfMGJEACBhMwfruIwQNg8yTg7gQkC/u2bduwc+dO3HrHPbj3rmUY1z4LtcJc9/dHbOJl/LjOB9u/erlUcMiYnMkgXyqd401IgARIwA4CFEB2QGIVEnCGgOTKEtETExMDSUQ6ZswYBAcH43RCPO5+eRJmPpSFYBfEBbqQnoMJk4E33npPZ3t3pq+OXkMLkKPEWJ8ESMDdCFAAuduMsD8eT0AShe7ZswebN29WW9+rYfjw4ahUqVLuuO67/wHEbN2Eu//vB/z37ssIUMELnS2pl3Iw7iNgws134J5773W2GYev4zZ4h5HxAhIgATcjQAHkZhPC7ngugZycHBw4cAAbN25EaGgoBg4ciPDw8AIH9PEnn+OmCWfQ/O+zsP5FP1QJdlwEnU7OwR1f+qD7gJH4aPJ/CryPEQdlnHSCNoIs2yQBEihNAhRApUmb9zItgcOHD2PDhg3aL6ZXr16oXr16kWP18vLC1Ok/o+O/38Sw117EY/2zMKGjF8pJKvdiSvblHExbl4P3FvrgmefUtY8/UcwVrj0t/j++vr6QMbCQAAmQgKcSoADy1Jljv92CwPHjx7F+/XqIVeSGG27Qvj6OdOyJJ59C7z798NRfH8KHr8dgfPvL6BF9GU1reKGC71WBcTEjBztO5GD53nL4abMPmrVojd8XfoC2bds6cjuX1KX1xyUY2QgJkEAZE6AAKuMJ4O09k0B8fLwWPmlpaejQoQPq1q3rtEVERMzCpauxZs0a/OvVl7BiyUHs3HMIlUIqILCCN1LTs5CUfAktmjZA/4HD8cfbt6N58+ZlBo4CqMzQ88YkQAIuJEAB5EKYbApISbmEzz/fgOeem4+KFf2VI/DDqFo1KBfN7Nm78PLLi3Hy5AU88UR3/ZN70gPenDlzRi91nT17Fu3atUPDhg3VspVrtrMHBARg0t+eQffu3SGO1AkJCYpnCgIDA7UztavuU1LMFEAlJcjrSYAE3IEABZA7zIKJ+hAU5IfHH++G3bsT8OWXG3HjjT9g4cJ74O19RSSMHNlUfbnnKEfh4x4lfs6dO6edm+Pi4tCmTRv0799fjcnbpTMnfkTt27fXbYrYiYiIcGn7rmqMW+BdRZLtkAAJlCUB1/zpWpYj4L3dkoA4yP7jH72xdOlhPP307/n6WLmyv9oW7p/vmHzIzr6sfWmuO2HnAfHDcXURC8yyZcswZ84cVKlSBTfffLNefnK1+ElNTYWIrBo1arh6CC5vTyxAzATvcqxskARIoJQJ0AJUysCtdLv77++EgwfP4N//XoGOHWth/PgWBQ4/Lu4CPvhgNZo0qYopU2IwaFA0Hn20K2Jjk/DRR2uUBekA/vvf8XjkkTnYvj0eb789BHfeecVSIg3+5z/rdJ3AQF8cO3ZOOSPXRp06lXDvvR0LvJ89B9PT07Flyxbs378fTZs2xU033aR3PtlzrTN1xPpTp04dly2nOdMHe6/hEpi9pFiPBEjAnQnQAuTOs2OCvv3nP6PRuHE47rprhl4WK2hIL764COfPX8Rtt7XFO+8MVUtj81ScmSxlDQlBZma2vm6JcgyePfs2leuqlVpim5vbzPHj5/Hmm8uVcLoJX301DqdPp2Lr1jh06lQrt44jb2SLt2xnnzZtmr5swoQJ2slZtn0bWUQAiSO1JxQKIE+YJfaRBEigOAIUQMUR4vkSERCfoBkzbtF+P2PGfIdktZvp2tK3bwNMmNBSHw4NraCWwnKU5SUR5ct7o3XrGkoMZeOxx7ppp2qxIp0/fwnx8cm6/g8/bEVQkC98fLz1Lqxeveop5+EUtGxZdByea/sgaSvE4jNlyhTIzq6xY8eiS5cu8Pe/fqnu2mtL+lmsTYmJiYiMjCxpU6VyPQVQqWDmTUiABAwmwCUwgwGzeaBZs2qYPHkk7rhjhvqZjocf7pIPi4iapKR0vPfeSly8mKXPpaZm5Ktj+yDLXFJs9cLDA7FjR7y2IIl4CgsLQI8e9ltSsrPFwrRbix/xvxk1apSO4my7X2m8HjlyRMcPcrVfkVF9pxO0UWTZLgmQQGkSoAWoNGlb+F63395OLYO1w08/7VQ+QcvzkVi37qjKl/W18rNphXvu6ZDvXHEfRo9uppe7XnttCU6cOI8VK47gwQc7F3eZ3ma+d+9eTJ06FRLMcMiQIejbt2+pix/pqCctf0l/6QQtFFhIgAQ8nQAtQJ4+g27af4ljI7u68paPPhqptpKfwLx5eyFLVbYyceJUTJrUXW37DlZLQam2w3a9Sqyhhx7qDLEYrVt3TAmam5UVKLDQa2WnmAgO8fORuDsieiRhaVkVERMSVFG21XtK4RKYp8wU+0kCJFAUAVqAiqLDc04TSExMw6lTV/x0bI34+5fH9OkTERx81aFYnJyl3qpVsXoL/MyZO3T15OQMZGRkqaWuTP1ZBJWUlJQrS2NynZTlyw/jxx9jlIBoiN696yNERU8urBw9elRZoH5STtJb0bVrV52lvSzFj/QzNjYWNWvWVP5O5QvrttsdpwByuylhh0iABJwgQAHkBDReUjgBiQT91FO/4o8/9uGvf52HlSuP5KscHR2uIkWPyT0mjs6y5f3HH7ehc+dP9Fb4Nm2qK6vObBV/R8TNNl333XdXaT+hTz5Zqz9//PFaLZCysi5j0aKDagfVm6hc+SUVn+Y5teX+43w7ziR44ezZs7F27VqdO2vMmDFu43DsactfAp8CSD+C/B8JkICHE/BSSwKujx7n4VDYfeMJnDuXrnd12e6Unp6pdlxdsYLI0pmKo2hXTByJESRb6Fu1isDZs+m4cOES9uxJUD+n8fzzXfRS1/nz53WE5QYNGjidr8vWT1e+ZmZm4ttvv8Wf/vQnQ2MMGdHnu+66y5XNsi0SIAESKHUC9AEqdeS8oRAQ3528xSZ+5JgtbUbe8wW9F6fnsWO/U1vmn8yXb6xhwyAVEPGAskL9oS0+jRs3tktMFXQPI4/JkpykuzA6xpArx0Drjytpsi0SIIGyJEABVJb0ee8SEahWLQjR0VUwYMCXGDq0sbIglVMZ2vepZTBZhuutYgi1UPGB3PcRl+WvevWuOoOXCEYpXUwBVEqgeRsSIAHDCbjvt4PhQ+cNPJ2ABD9cs+YB5Wd0UOXqWq+Wt5LxzDMdlNWntdtbVSTw4rFjx9CtWzePmgYKII+aLnaWBEigCAIUQEXA4Sn3JiAB+WJiYtROqj0qknQjZfFp7TFJOiX2kCRX9bSkogyC6N7/Jtg7EiAB+wlQANnPijXdhIA4D2/fvl3/yBLSuHHjEBhYeOwfN+l2vm544u4vGYBYgDxNtOUDzw8kQAIk8D8CFEB8FDyGgKSt2Llzp47jI3mzRo8ereL+hHhM/20dlZhGEv+nY0fns9Xb2irtVy6BlTZx3o8ESMAoAhRARpFluy4jIIJB0lZs2rRJ7faqimHDhqmYP5Vd1n5pN3TixAm1C66ix1mthBMFUGk/LbwfCZCAUQQogIwiy3ZLTEBCVB08eFClz9iookcHY+DAgQgPDy9xu2XdgKcufwk3EUCeaHUr6znn/UmABNyPAAWQ+80Je6QISIZ0ydclKSJ69OgBydRuhiKiTsYmWec9sdAJ2hNnjX0mARIoiAAFUEFUeKzMCMjykAgf2SYuPjJRUVFl1hcjbixpOcRh21OtKHSCNuKpYJskQAJlQYACqCyo857XEUhISFBBDNerZKcpOm1F/fr13SptxXUddvKAJy9/yZDpA+TkxPMyEiABtyNAAeR2U2KtDp09e1ZbfBITE9GuXTsV2TnaLdNWuGJWZPlLBJA4cXtqoQDy1Jljv0mABK4lQAF0LRGLfpYlp+XLl2PpsmXYumM3TsUnqGzrGQhV28wbRzdAj66dVcqJAS5zQr5w4YJ2bpYlLwlg2K9fP5UDzNvU9E+fPq0jVMsOME8tFECeOnPsNwmQwLUEKICuJWKxz5Ip/Z333sdHkz9BhbCaCGzcFQE1esK3kYpS7FsBqWkXsDzuEOZ//AP+/MBD6Na9O15+/lmnY9ikpqbq7exiCWnRogW6q/bE0dkK5dChQ6hbt67HDlXiMElIAqvMl8dOFDtOAiRgFwEKILswmbPSrFmzcNe996FSyz5oNmkqAqsXnJgzvFVvBeBuNLyUjhOrfsKA4aPx2MMP4W9PPKYSkObP6l4YqfT0dB3AUOL5NG3aFDfddBP8/PwKq27K4yL6xIrmqYXWH0+dOfabBEigIAIUQAVRscCxF158Ge9N/g+aPvwlQuu3tmvE3n7+qNXnFtToNgbfTXkJM2Z0xca1q4tMjSDLaNu2bdMRnBs0aKBydk1AQECAXfczU6UzZ87o4YSFhXnssCiAPHbq2HESIIECCFAAFQDF7Icmf/YF3pn8Odr/cw78KlZ1eLjevv5oePu/sP/HVxFasRKOxh5BtWrV8rUjPkU7duzQ4qd27doYO3YsgoKC8tWx0gdP3/0lc0UBZKUnlmMlAfMToAAy/xznG+GXX/4fHrzvXvT5dBt8gyrlO+foh4Y3/QPePuUxaNhIrF25TC9piY/I7t27sWXLFkRERGDEiBE67YOjbZutvvj/9OzZ06OHxSCIHj197DwJkMA1BCiArgFi5o+SQf3xSU+h5/vrSix+bJzqjXsKez9/DB8oJ+qRQ4fonV2VKlXCoEGDUKVKFVs1S7+eO3dO76iTPGaeXMQCxEzwnjyD7DsJkEBeAhRAeWmY/P0jf52EWiOU43KYa9NK1LvlJbz4VA8E+1fQKR7E8sNylYBt+cvLy+vqQQ98xyUwD5w0dpkESKBQAuUKPcMTpiIg1p8tMdsR2edPLh9X+YAQRPa7A+s3bdbLXi6/gYc3aBNAHj4M+gB5+gSy/yRAAvkIUADlw2HeD//95ltU6zYO5byNMfpV73mz2hU2E5mZmeaF6MTIkpOTdXqP6tWrO3G1e11CC5B7zQd7QwIkUDICFEAl4+cxV//y6x863o9RHa5QqRoCwyN1Pi+j7uGJ7Yr1p06dOqbIa0YnaE98AtlnEiCBwghQABVGxkTHZUv6kQN7EVqvlaGjCqrXVu/+MvQmHta4WZa/BDudoD3s4WN3SYAEiiRAAVQkHnOcjI2NRUh4DcOWv2yUfKpEYd/+g7aPln9NS0tDUlISatasaQoWXAIzxTRyECRAAv8jQAFkgUchJSUFvgHGByH08Q/EOeXzwnKFgFh/oqKiTJPdngKITzYJkICZCFAAmWk2CxlLYGAgMi+mFXLWdYezVa6wIHUvlisEzLT8JSOiAOKTTQIkYCYCFEBmms1CxiI7kM7FH0dOTk4hNVxzOOPsCURFujbGkGt6VvqtiMPw6dOnERkZWfo3N+COEuFbdvj5+voa0DqbJAESIIHSJ0ABVPrMS/2OYgEKr1YdaacOG3rvrLi9aN68uaH38JTGjxw5glq1asHHx5iwA6XNQZLaivjx9GCOpc2N9yMBEnBfAhRA7js3Lu1Zn969cGb7Mpe2mbcxWf6K37MJ3bt3z3vYsu+5/GXZqefASYAEPIQABZCHTFRJu3n7nybi7JoZJW2m0Ovj1s5Bj169ERISUmgdq5wQa8mpU6dQu3Zt0wyZ/j+mmUoOhARI4H8EKIAs8ij069cPQd7ZOB2zxOUjzrmcjRNzP8LfJ/3V5W17YoMSdkD8rsqXL++J3S+wzwyCWCAWHiQBEvBgAhRAHjx5jnRdfDfef/tNHP7hBWRnXHTk0mLrxs77FI3q18bJkychX/5WL7L8Va9ePVNhEAsQM8Gbako5GBKwPAEKIAs9AkOGDMGYYQOx++P7kaN29biiHFv8PU4v+S9mTp2CHj16YN26dZg3b54OAOiK9j2tDYm6ffz4cR3/x9P6XlR/uQRWFB2eIwES8EQCFECeOGsl6PMnH32AWgFZ2Dn5wRKLoMRty7Dz/57GquVLUaNGDb3le9y4cfrL/5dffsHKlSshSydWKkePHkVERAT8/PxMNWwKIFNNJwdDAiSgCFAAWewxkG3ZyxcvhE/Cbmx8YSiy0pyL3By3+mfs/+IxrF27Fo0aNcqlWK5cOb0VfsKECXrL9LRp07Bjxw5IHBkrFFn+kuSnZisUQGabUY6HBEiAAsiCz4CIoIP792Hi8P5Y93QvHF8+zW5rUMrxfdj94b24sOBjbFy3Bp06dSqQoPiLdO3aFcOHD9d+QTNmzNBLQwVWNsnB7OxsiAWobt26JhnR1WHQCfoqC74jARIwBwEvFR3Y2PDA5uBk2lGsX78eT/ztGWzbsQvVOo9CSJMuCK7VGH6h4fAq540slUIjNe4gkvasQ8r2BUhT75+e9CQefPAvCAgIsJuLBAZcs2YNKlWqhM6dOyM0NNTuaz2lojiAx8TEYMSIEZ7SZbv7+dtvv6FZs2am2tpv9+BZkQRIwJQEKIBMOa2OD2rv3r34YcqPmL94Gfbt3Y2k0/FQa1jwq+CPKtVqoG/vnhg7agQGDhzodDoEWQbbvn07tm7dqpfN2rZt63Rbjo/Q+CuWLl2KKlWqmDIa9qxZs7RwrVatmvEgeQcSIAESKAUCFEClANlTbyHGQdk+/9NPP6Fbt26oWrWqS4aSnp6ODRs2QKxCHTp0QOPGjT0+xYKIu2+++Qbjx4+HpB4xW5k6daoWvxUrVjTb0DgeEiABixKgD5BFJ96eYdvyPlWuXBlnz5615xK76vj7++st87Itf//+/Zg5cybi4uLsutZdK0kMJFnWM6P4EeZ0gnbXJ4/9IgEScJaAOTI1Ojt6XmcXAVcLINtNZblI/GUOHTqEJUuWIDw8HDfccAOCg4NtVTzm1Wy5v/KCF0sgBVBeInxPAiRgBgK0AJlhFg0eg1ECyNZtiZos2+bDwsK0NUiWxzIzM22n3f5VBIIZoz/bwMtcyM5BCXHAQgIkQAJmIcDfaGaZSQPHYbQAkq7LF6w4RYsPTXJyMsTnZN++ffCETYrx8fF6R5xZE8FyC7yB/7jYNAmQQJkRoAAqM/Sec2PZ7i5CJC0tzfBOiw9Nnz59MGDAAOzcuROy+yghIcHw+5bkBrKEZ8bYPzYmsvzFPGA2GnwlARIwCwEKILPMpMHjkOUpVzpCF9dd2XE2atQoHXtm/vz5WLx4MVJTU4u7rEzOm3n5S4CKAPL19S0TtrwpCZAACRhFgALIKLIma7c0lsGuRSa70KKjo3HjjTdqx+jp06dj8+bNkISj7lLEOiXLdxLg0ayFFiCzzizHRQLWJkABZO35t3v0ZSGAbJ0rX768jhc0duxYbYUS/yBZdnKHIrGMzLz8JYy5A8wdnjT2gQRIwNUEKIBcTdSk7ZWlALIhle3x/fr10z5CYgmaM2cOEhMTbafL5FWEmOxiM3OhADLz7HJsJGBdAhRA1p17h0YuSzxJSUlusSurevXqEGuQLI9Jjqply5ZBokuXdhGfKIkALfGMzFwogMw8uxwbCViXAAWQdefeoZHLMpTsBjt//rxD1xlVWfyDJIWG+Af5+flh2rRpOhGpZGQvrWL23V82jtwGbyPBVxIgATMRoAAy02waPBZ3WAa7doiyO0miR8uOMUmnIY7S4pdTGsUK/j/CkU7QpfE08R4kQAKlTYACqLSJe/D93FEA2XBKHq5BgwbppK3r16/HvHnzDN22L5YwWXazQnZ0LoHZnjK+kgAJmIkABZCZZtPgsZR2LCBnhhMZGYlx48YhKioKc+fOxcqVKyFLOK4uttxftoSxrm7fndqjAHKn2WBfSIAEXEWAAshVJC3QjjtbgPLil5xVzZs31/nFRKCIf9COHTu0w3LeeiV5bxX/H2FEAVSSJ4XXkgAJuCsBCiB3nRk37JfkupJozO4UiLAoTJK+oWvXrhg+fDhiY2MxY8YMHD9+vKhL7DqXkpKi85XJbjQrFDpBW2GWOUYSsB4BH+sNmSN2loBYVipWrKh9ayRVhacU2cI/dOhQLYJkSUzG0LlzZ4jfkDNFlr/q1KljiezoInbFiibRrllIgARIwEwEaAEy02yWwlg8ZRmsIBTiFzRhwgSI5UaSrK5ZswYZGRkFVS3ymM3/p8hKJjlJ649JJpLDIAESuI4ABdB1SHigKAKeLIBkXGLFatWqlRZCmZmZkLQau3fvtjvAY1pamraA1axZsyhMpjnHLfCmmUoOhARI4BoCtGtfA4QfiyYgAujYsWNFV/KAs/7+/ujRo4dOpbF69Wrs3LlT+wsV59cjsX9q164Nb29vDxhlybtIB+iSM2QLJEAC7kmAFiD3nBe37ZWnW4CuBStpLEaMGIG2bdtiyZIlWLBggXZwvrae7bOVlr9kzBRAtpnnKwmQgNkIUACZbUYNHk9gYKBeLpKlIDMVSWgq/kES6+inn36CBFOUJbK8RfxhEhISUKtWrbyHTf2eAsjU08vBkYClCVAAWXr6nRu82axANgqy00ksQRJIUbb7i3/Qvn37cv2DZCu9BFq00o4oewWQJIXdvz/RhtIlr0ePnkNaWuFO6rt2xePCBdcHuXRJ59kICZCA2xOgAHL7KXK/DppVANlIi5Wrd+/eGDBgAHbt2qV3jMXHx8Nqy1/C41oB9Mcf+1TKkf9TW+P/jkaN3sZnn63Hli0nlWP5B7jjjuk2hCV6TU3NwCOPzFHRvN/AyZMXCm2rd+/P8d57qwo9zxMkQAIkUBQBOkEXRYfnCiQgAkgEgdmLxDoaOXIkDhw4oH2DJPdXp06dzD7sfOOTZb+goKDcYwMHRiMgoDz++GM//vSn1vjznzvqc3361MfGjcdz65XkTWCgL+6/vxM+/HBNkc1s2fKIWrIMKLIOT5IACZBAYQRoASqMDI8XSsDsFqC8A5cggA0bNkSHDh0QHByMOXPmYPPmzR4TDTvvWJx5LxYgiaidt4SEXPlse5Vz3t5eeas4/D4zMzvfNd7exf9qqlEjBH5+/BsuHzh+IAESsJtA8b9l7G6KFa1CQARQUlJSrm+MFcZ99OhRtGnTBmPGjNFxgMQ/6ODBg6Yf+rVLYMUN+JtvNqN16/dVsMlX9fKY1D91Khn/+tcSdOv2KZYvP4y6dd/Ac8/N1029+upifP31Jjz22FyMHfsd0tPzO54fPXpeOaf/oKJ2v4CJE3/Mzee2enWssj79hKee+lW3s3fvaTz55Dw8/vhctWS5U/lyfYB27T7EqlVHEBMTp/y6vtN9euWVxbo+/0cCJEACFEB8BhwmUL58ebUMEoDz5887fK0nXiDpICSHmESSFitQv3790LdvX2zdulVbhBITXev8606MHBFAO3bEK3GYhm+/vRH9+zfEfff9DBEqUtauPYbt208pZifx1792R9OmVbFo0QHtw3P77e3w8ccjISJm9uxd+Yb/448xeOedofj551sxZUqMWoo8oM9HRoZi8eKDSEm54iRdoYIPli07rOZjt7JGlVP3+4vyUQrHrbdOw8KFB/Dll+OUX1EXJbwW6D7muwk/kAAJWJIABZAlp73kg7bSMpgEfgwPD8+3FBQREaGtQdHR0fjtt9/Ul+8yZb1ILzlYN2vBEQHUuHG4suR0Q4sWEfjiizHw9/fRVqCIiGDlVF5PWwzFt+fhh7vg5ptbK0FZEc880zt3xKGhFVRU7oTcz/LmoYc6q513oRAfo/DwQOzZc1qfr127ogpHcDWXW1RUJX3fJk3CVfLbJvD19VGvjZVwPY8nnuiuLEgV8OCDnfW1R44k6Vf+jwRIwNoEuIBu7fl3evQ2ASTxc8xeZPdXQeMU/6DGjRvrc+IXJMtirVu3Vl/ELZQVwhyRoh3JBRYU5Jv7KIgAad26hlomPKOPiYVGnJvluK00aFBFCaauECvPsWPnce7cRRV+oPBt7yJiijqvpkMnbrW1HxTkZ3urX239O3PGXDGs8g2SH0iABOwmQAuQ3ahYMS8BmwDKe8yM77OzsyH+P3Xq1Cl0eL6+vrjhhhu0RUh2x02bNg2SMsPTi8T2kfHL+JwpYo2pVMm/0EtTUi6p5cQvtHVm0qQeqFo1sNC6zpwoV84rnyASwcpCAiRAAjYCFEA2Enx1iIBVBNCJEyfUl3gl7fNUHKCQkBAMHDgQ3bt315Gk586dqx2mi7vOXc87Yv0paAzbt8ejY8fCo2a/+uoSfdngwY0KupzHSIAESMBQAhRAhuI1b+OhoaHKATXF9NvBnQl+KNGiJZp03bp1ISJo5cqVEDHhaaWgLfAyhvj4ZD2U+PiUfEPKyLi6lT0xMVX734jjsRQ5Jzu8xKpkK7Gx59QOrVPqObqkYi0l6vfJyRmqblauc7PNyVmukXp5P0vd5ORLtuZU0MZs9Txebf/ixSz9+dKlLF3H9pq3Tu7FfEMCJGA5At4vqGK5UXPAJSYgywkiDiRYoERONmORL+vly5frLPGOLgMJH2EjPkJxcXFaBIlfkCRf9ZSlGNnld+rUKT0G2/z+/vtevPHGcrXEl6SjNPv6eqsYSZE6OOL06TuwbdsplT4kUflDxeDzz8egRo1QHSDx9deX4dChJCWAclTU6AhV31cHMZw+fbuqt15f37lzbbUbbK1eNps7d4+OMC3xgXr1qqfa24bvv9+qU1/07Vsf8+btxVdfbYQILXG6FlH2+utLceLEBbRvX1N39+WXF6v0HGeU8zXUlviaajfZSixdekiJ0Uw1p1HIG8fINj6+kgAJWIeAV44q1hkuR+pKApI9vXr16vm+IF3Zflm3Jctf69at0749Je2LxE1avXq1zjHWuXNnj0ioKrnPdu/erVJfDLJ7+JKbSwRecHB+B+TCGhBrj2xblx8pInjKlzeHA3lhY+ZxEiAB9yBwdUuGe/SHvfAgAmb3A3Jm+auw6RM/oqFDh0JExapVq1CxYkXtOC2v7loc2QJvG4OjVpW8u8KkDYofG0m+kgAJGE2APkBGEzZx+2YWQGIYFQFU0Pb3kkypBFOcMGGCWhqqoYL+zcaaNWuUz0vhW79Lcq+SXltSJ+iS3p/XkwAJkICRBCiAjKRr8rbNLIBkO7vkwBJnb1eXcuXKoWXLlloIZWZm6vhBstTkbqvRIsz8/OxbynI1I7ZHAiRAAkYToAAymrCJ2xfnZ3EUNmMEZFcufxX2CPj7+6NHjx4YPHiwctbdj5kzZyrH4pOFVS/147QAlTpy3pAESKAUCVAAlSJsM94qLCwMZ85cifZrpvEZsfxVGB/ZGTZixAiVwLOt2qW0VOW7WqC2d1/Zal7YNaVxvLBt8KVxb96DBEiABIwmQAFkNGGTt2/GZTBJbirLVDK20izibyT+QSIqxRq0fv16tSsqf3b00uyPM07Qpdk/3osESIAESkKAAqgk9P6/vTsBr6q88zj+T3KTQEggEIhhS0LYQWBYFCkCUlnckC0WtCKPttqpWm21om3tlKo40GpdKjjqdJEij0NxKgztQBEKCpRKoSAVJUAgJIQ1i0nIvsx5jxMMYJJ7k/fee95zv+d5aJJ7z33P//2897G/55z3nJfP2iEhPz/fVRKZmZn2QwyD0SmPx2OfCVJB6Pz58/b8oIyMjKDMDyIABeMbwDERQCBQAgSgQEm79DhuPAMUyMtfjX0tYmJirBXUJ8qUKVPkwIED8u6771oP+zvd2O5+eZ0A5BdWGkUAAYcIEIAcMhCmlqECkHrIn9PuYGqpp+pLdXW1dOnSpaVNaP2cepr09OnT5corr7TnBm3evNlaDuLiJSi0HrBBY0yCboDBrwgg4DoBApDrhjSwHYqMjBR1N1NRUVFgD+ynowXi7i9fS1dPVu7bt6/MnTvXWr6hvT0/aPfu3X5dh00FWjX/iNvgfR0t9kcAAVMEeBK0KSPl4Drr7wTzxzNzAt1tNf9n7NixgT6sV8dT84NGjRplLz2yc+dOe37QNddcI7179/bq883tdOTIEdm7d69kZ2fbZ/XUrflqbIcPHx60OVHN1cz7CCCAQEsFWAuspXJ87oLArl277PWf1P85m7yps1hqrs28efOMWLBULVSqltVQwUiFNnU7va+bWu/spV++IstXvCXl1jpcHXsNEU9CsoRFx0hdRalU52VJQeY/JSY6UubP+7o89J0H7fXffD0O+yOAAAJOEyAAOW1EDKxHnTlQ/9SEXZO3ffv22Zfyxo0bZ0w31KWqgwcPigqhycnJ1srsV1krq8c0W796yvOPfvxv8up/vC7dxqdL4rVzJK5H/0Y/V5z9qZz+4G05ue0dWbJ4sdz3zXusdbsiG92fNxBAAAGnCxCAnD5CBtSnJg5v2LDBnqNiQLmNlqjO/qizWD169Gh0H6e+oQLNnj177DA0bNgwGTJkiLXC+pevqn7mzBm56dYZkufpLL3ueEqiO3h/5qii8IxkrnhSkiJKZNOG/xX1NHA2BBBAwEQBJkGbOGoOq1nN/VF3Jqm7p0zd1DN3CgsL7UVKTexDVFSUvbr8zJkz7dvlV61aJceOHbusK2qc+vYfKEVJ1lyiby/zKfyoxqLjE2Xgg69LpjXnPTY21pVPAb8MjRcQQMCVAgQgVw5rYDulnpocHx9vT5wN7JH1HU3d/ZWammo/AVpfq4FvSd0lNnXqVHuNMfUk6XXr1kn9gypVyEvq2k0Sr7tTet+2oFXFjXx8pfSd+bCMm3i9K9eCaxUOH0YAASMECEBGDJPzizT9gYhOvP29NaPevXt3SU9Pt+/eUiFo27Zt8rXb75SUqd+QPumPtabpC5/tPfv7UtV1qCx8ZvGF1/gFAQQQMEWAAGTKSDm8TpMDkFrNXq3/ZeLcn6a+FurM3ODBg2XOnDn2JOm/7tknPaZ9p6mP+Pxe2h0L5Y3fvGkv4urzh/kAAgggEEQBAlAQ8d106PpnAZnYJzVXRt1B1dikYRP71LBm9TDDFf+1WlJmLZBwT1TDt1r9u2qv5/RH5Ec/earVbdEAAgggEEgBAlAgtV18LJPPALnt8telXzO1mOqhQ4fliqtuvPQtLX8njblV1CMElCMbAgggYIoAAciUkXJ4nep26NraWuMmxKoFP9UDBdUZILdua9aulcSrb5aw8C+/Lb61/Q6P8EiS1f5a6zhsCCCAgCkCBCBTRsqAOk08C5SVlWXP/VFPU3brtvEvH0hc/zF+7V5sv9GyccsHfj0GjSOAAAI6BQhAOjVDvC0TA5DbL3+pr+ShQxkS21XPemGNfcXbdesjGRmHGnub1xFAAAHHCRCAHDck5hZkWgBSq53n5uZKSkqKueheVF5SXCSedu292LPlu3hi2os6DhsCCCBgigAByJSRMqBO0wLQ8ePH5YorrhD1FGU3b2FhYVJXU+PXLtbVWE8Bt47DhgACCJgiQAAyZaQMqLM+AKkFOk3Y1OWvtLQ0E0ptVY3xHROk6nxhq9po7sNVJYWSkOD9mmLNtcf7CCCAgL8FCED+Fg6h9tWZlLZt29orqju922rdsuzsbHv5C6fX2tr6Bg0cICU5B1vbTJOfL8k9JIMGNL6afJMf5k0EEEAgCAIEoCCgu/mQ9WeBnN7HnJwc6dKli7Rp08bppba6vskTx0vJwb+2up2mGig9uF0mf3VCU7vwHgIIIOAoAQKQo4bD/GJMCUDq8leqtfhpKGyzZs2SU39fL7VVFX7pbk1FmZzas1mmT5/ul/ZpFAEEEPCHAAHIH6oh3KYJAUg9sFE9/6dXr14hMVJdu3a1VoefIDmb3/JLf7Pfe1MmT55sn1HzywFoFAEEEPCDAAHID6ih3KQJAejEiRMSHx8v6unVobItWfSUZP9xqfbJ0JXFBXJi/Wvy7NMLQ4WSfiKAgEsECEAuGUindEMFi+LiYlGTjJ26hcrdXw39hw4dKnfPnycHX3tY6qwzYDq2WuvW949fuke+/a37ZNCgQTqapA0EEEAgYAIEoIBRh8aBwsPD7bMrBQUFjuywukVfrf4eKpe/Gg7CC8/9TPp08siB1x6S1j6qQH1+3/N3Sdd2YbL42WcaHobfEUAAASMECEBGDJNZRTr5MtjJkyftS19xcXFmoWqoVoXT9evWymf/3CoffG+MqDM4LdlqqyutMz/fkJ4xNbL7w53W8w95AGJLHPkMAggEV4AAFFx/Vx7dyQEoFNb+aupLFR0dLfl552TGjZNk709vkcJDe5ra/bL3CjJ2yT+sz00amiI73t8iKlSxIYAAAiYKuHcJbBNHwyU1qwC0f/9+x/VGXbZRAeiWW25xXG2BLCgiIkLeWv5bWblypTyy4EGJSuwlncbOkc5Dr5NIa02vSzf1FOlzH22VvO1vS/W54/Lyz5fI3LlzL92NvxFAAAGjBAhARg2XGcU69QzQ2bNn7XW/1ERtNpE77rhDbrvtNlm9erW88dvfyY7fPCFtO3SSuKRUCY9uKzXlpVJ86piUFxXIWOs2+h888aDMnj1bPB7+s8H3BwEEzBfgv2Tmj6HjehAbGys11uKb5eXljnrScmZmZkhOfm7qCxIZGSm33367/U+N2ZEjR+wlQkpKSkSNY3JysvTu3ZtLXU0h8h4CCBgpQAAycticX7Q6C5SXlyfdu3d3TLHq8teUKVMcU4/TClGXxvr162f/c1pt1IMAAgjoFmAGo25R2rMFnHYZ7Ny5c3ZdCQkJjBACCCCAAAJCAOJL4BcBpwWgUH32j18Gl0YRQAABFwgQgFwwiE7sgtMCEPN/nPgtoSYEEEAgeAIEoODZu/rIKgCpp0G39onDOpAKCwulsrJSEhMTdTRHGwgggAACLhAgALlgEJ3YhaioKPsOsKKioqCXV3/2hycWB30oKAABBBBwjAAByDFD4b5CnHIZjPk/7vtu0SMEEECgtQIEoNYK8vlGBZwQgNTK9OqZNl27dm20Tt5AAAEEEAg9AQJQ6I15wHrshACknv2TmprKgp0BG3UOhAACCJghQAAyY5yMrNIJAah+/o+RgBSNAAIIIOA3AQKQ32hpWK25pS5BVVdXBwWjtLRU1B1gTnoadVAgOCgCCCCAwGUCBKDLSHhBl0B4eLioEKRCSDA2dfkrJSWFdayCgc8xEUAAAYcLEIAcPkCml6cug6k1wYKxqQDUq1evYByaYyKAAAIIOFyAAOTwATK9vGDNA1Ir0Z89e1Z69OhhOiH1I4AAAgj4QYAA5AdUmvxCIFgBSD37p2fPnuLxeL4oht8QQAABBBD4fwECEF8FvwoEKwBx+cuvw0rjCCCAgPECBCDjh9DZHYiNjbXvAlOXpAK1qXW/Tp06JcnJyYE6JMdBAAEEEDBMgABk2ICZWG5CQoLk5+cHrPSsrCzp1q2bREZGBuyYHAgBBBBAwCwBApBZ42VktYG+DMblLyO/JhSNAAIIBFSAABRQ7tA8WCADkHroYk5Ojv38n9DUptcIIIAAAt4IEIC8UWKfVgmoABSoZwEdP35ckpKSJDo6ulU182EEEEAAAXcLEIDcPb6O6J0KQAUFBVJXV+f3erj85XdiDoAAAgi4QoAA5IphdHYnoqKipE2bNva6YP6stKamRtQZILX6OxsCCCCAAAJNCRCAmtLhPW0CgZgHpOb+qDvO2rZtq61uGkIAAQQQcKcAAcid4+q4XgUiAKnLX2lpaY7rOwUhgAACCDhPgADkvDFxZUX+DkC1tbWilr9g8VNXfn3oFAIIIKBdgACknZQGv0zA3wEoNzdXOnToIO3atfuyw/MaAggggAACFwkQgC7i4A9/CcTHx0tRUZGoicr+2Lj7yx+qtIkAAgi4V4AA5N6xdVTPwsPDRYUgdTu87k3dXs/8H92qtIcAAgi4W4AA5O7xdVTv/HUZTC18GhMTI+3bt3dUfykGAQQQQMC5AgQg546N6yrzVwDi8pfrvip0CAEEEPC7AAHI78QcoF7AnwGI29/rlfmJAAIIIOCNAAHIGyX20SLgjwB05swZ8Xg80rFjRy010ggCCCCAQGgIEIBCY5wd0cvY2FipqqqS8vJybfVw+UsbJQ0hgAACISVAAAqp4Q5+Z3WfBeLur+CPKRUggAACJgoQgEwcNYNr1hmA8vPzRT0BunPnzgaLUDoCCCCAQDAECEDBUA/hY6rFSlVw0bFlZmay9IUOSNpAAAEEQlCAABSCgx7MLus8A/Txx4etCdAJdneKiyusuUVVfulaTU2t/P3vOaIeuMiGAAIIIOAOAQKQO8bRmF7UB6DWhInjxwslPX25rFhxXFavPiITJrwmY8Ysk717T/rFYfv2LLnqqqXy6adn/dI+jSKAAAIIBF7AE/hDcsRQFoiKipLo6GgpLi5u8ZObH3porZSWlshPfvIVufbaa+0zM/PmrZLs7M/kmmv0644f30syMx+zLrd10t84LSKAAAIIBEWAM0BBYQ/tg9afBWqpwtGjBZKRcU66d0+2mwgLC5PnnrtJCgvLLmuyqkrP4qu+hB9dx7ysM7yAAAIIIKBNgACkjZKGvBVobQCaPXugZGWVybx5f5KTJ4vswyYlxcm99159oYRFizbLm2/ulu9+d53Mnr1Cyso+nx908OBZ+f73/yjf+946effdj2XEiJdl5Mhfyvbtx2TfvpPWpbUV0rXrInnmmc12W2pe0cqVe2XSpP+UrVszpbKyWt5+e59cd93r8uc/Z8iUKb+SwYNfsM9Cbdx4SJYs2Sovvrhdxo591W7vQkH8ggACCCDgKAECkKOGIzSKaW0Amjq1s4walWCFliwrfLxozQPafxHcpk2H7RAyf/5IWbp0uqjQs2bNAXufNm08VpA5KmvXfiIREeGyc+f90r9/FytMrZL33jssv/pVujz00Ffkxz/eaN2tVio1NXWSmBgrmzYdsW65r5Pq6lprRfsyu40VK/bKww+PlRtu6GcFoxqZP//3Mnp0T3nssfEydWo/eeqpTRfVxR8IIIAAAs4RIAA5ZyxCppLWBqDTp3Nk1ap0efbZKXL+fKV87Wsr7TMv9YApKfHywx9OrP9TOnRoI598csb+OyWlowwZkiQDB3aRadMGSlSUx/o5QHJyPpNHHx1n7/vAA2PsfY8dK5B27aJk2LCkC23FxETJrbcOtP9OT79Sbr55gDz//M1WOxFy990jZfjwbvZ76pgHDnx+zAsf5hcEEEAAAccIMAnaMUMROoWodbuKioqssys11lmYCJ86Xlpaaj9HKDm5p/zgB6n2mZb09LfkiSfWW5eu4uSuu0ZInz6drUtfY+1LVWpidGFhuR2U6g9kTRkSNW+ofouNja7/1f4ZGxtl/8zLK7V/NtxXvaDOIqmtU6cY+6f6H7XPokVTrTvRcmX9+gz56KNTFx3zwo78ggACCCDgCAHOADliGEKriPDwcOtMSwfrUlKBzx3fsuUj67NJF4LTiBHd5Q9/mGe38847/7R/lpRU2HN21FkYdTkqMbFdk8cJDw+7KBBdGnia/HCDN9W8oddf/1Aef3yCjBuXarXZ4E1+RQABBBBwlABngBw1HKFTTP1lMF+XscjIOGZdzhKZOfMLq2HDukpaWid7zo56ddGiv9hv3nhj/y928vNve/acsOcNnTv35EVhys+HpXkEEEAAgRYKcAaohXB8rHUC9QHIl1bUKvIRESXWAxAPyK5d2Rc+mp1dKGq+zowZg+3XsrIKrTuwTok6E3T48Dn79+LiSvsOLrVDRUWNPZm5voHy8mr774qKavul+p9qwrPa1FOm1VZSUmn/VBOeP//789fV7+rhjGpTE7NLSyvlT386aF0Cq7KPqdYrY0MAAQQQcJZAxEJrc1ZJVBMKApWVlVZoOC59+/b1urtHjhyRyMgw2bGjSLZty7KWpzgh6szLggXrZc6cobJw4SRRl7MSEmLk97/fL2+88aHExERaT4lOtu4G22nPEVK3sS9evEVOnCiy7iTrbh/76ac3y6FDedat7GLdEt9dfvGLbbJlS6a9tEbfvgmybNnf5MMPc+xb6dUdYy+9tN0KYDly9ux56y60RKvd9pKa2tEKPZ/Kq6/+zQpdeXL//WNk+fI9sn//KZk160r7jjOvO8qOCCCAAAJ+FwizliRggSO/M3OASwXUk6DXrFkjd95556VvNfr3+vXrrQnOfazJx93sCcjqTIs685OcHC+XTmRWQUfd5q7+qU09nDAy0rcJ140W0sQb6nlDbdtG2nsE6phNlMNbCCCAAAKNCHAJrBEYXvavQFxcnHV5qNK6HPXFZaSmjqj2zc3NtcJO8oW7r9Qt6YMGXXFZ+FHtqNvb68OP+jsQ4Ucdpz78BPKY6lhsCCCAAAK+CRCAfPNib40CCQkJkpeX51WL6nJZt27drGDz+S3qXn2InRBAAAEEEGhEgADUCAwv+1/Al4nQR48etRYj7eX/ojgCAggggEBICBCAQmKYndlJbwNQdXW19aTmHElJSXFmR6gKAQQQQMA4AQKQcUPmnoK9DUDZ2dnSpUsX6wnMbdzTeXqCAAIIIBBUAQJQUPlD++BqDlB+fr69knpTEuryV1paWlO78B4CCCCAAAI+CRCAfOJiZ50CakJzdHS09aDB4kabVeuFqQnQqampje7DGwgggAACCPgqQADyVYz9tQo0dxnsxIkT1m3vnawHGn6x8KjWAmgMAQQQQCAkBQhAITnszul0cwFIXf7i7I9zxotKEEAAAbcIEIDcMpKG9qOpAKTW0Dp27BjzfwwdW8pGAAEEnCxAAHLy6IRAbQ0D0KWrspw8eVLUE6NjY2NDQIIuIoAAAggEUsATyINxLATqBaqqqmTDhg2y9t3VsmXzn+Ubd98lpeWVEhEeLomdO8iAfn2kV78hMm3arfUf4ScCCCCAAALaBFgMVRslDXkjUFZWJsuWviLP/exZSe0scn2/Erk6LVzSEsOkXXSYVFTXSZ51U1jGqVrZfsQj//MPkX8ZPlL+/ecvyYgRI7w5BPsggAACCCDQrAABqFkidtAlcPjwYbnlxkmSEpcvj0yukH5JzV+Bramtk9Uf1siL70XJ/Hvuk8U/+4WEhYXpKol2EEAAAQRCVIAAFKIDH+hu79y5U6bdNFl+eFOVzBjpe4ApKquT25aGSXR8iuz96GNrpfeIQHeB4yGAAAIIuEiAAOSiwXRqVzIyMqR///7y5rciZVz/lgeX6po6uX+51cvOo2T9xq2EIKcOOHUhgAACBgg0fw3CgE5QonMF1IMMJ1w7Wp69zdOq8KN66IkIk1fni1Se2SePfPdB53aayhBAAAEEHC/AGSDHD5G5Barb2m+aOlGGtfmbfGuivn6UV9bJDS945I3l/y1TpkzR1zAtIYAAAgiEjABngEJmqAPf0a1bt0rGx7vl3gl1Wg/eJipMnrylUhY88mCzC6lqPTCNIYAAAgi4RoAA5JqhdF5Hnl/ytPzr+HIJD/d90nNzvZk0OEIqS06LCllsCCCAAAII+CpAAPJVjP29EigsLJT3P9gu04a3fNJzcwdKH14mb/3u183txvsIIIAAAghcJkAAuoyEF3QIbN++XYb3bivRkfrP/tTXN65/mGx+b2P9n/xEAAEEEEDAawECkNdU7OiLwL59+6R/5zJfPuLzvmldwiTn5FlRT5dmQwABBBBAwBcBApAvWuzrtcCJ7EzpHl/j9f4t2VHNLereOUbUoqlsCCCAAAII+CJAAPJFi329Fig9XyJto7zevcU7to2OkNLS0hZ/ng8igAACCISmAAEoNMfd772Oi4uX8xV+P4yUlNVIXFyc/w/EERBAAAEEXCVAAHLVcDqnM737DZRj+f49BVRlLY1xuqBMevTo4ZyOUwkCCCCAgBECBCAjhsm8IocPHy57cyL9Wvi+43UysG8qa4L5VZnGEUAAAXcKEIDcOa5B79Xo0aMl60ylnC3W+xTohh3bejBCbrh5RsOX+B0BBBBAAAGvBAhAXjGxk68C0dHRMmv2LHlnl6+f9G5/tTL8O7vDZN78u737AHshgAACCCDQQIAA1ACDX/UKPPb4k/LrbeFSUq7/LNBbO+tk+MirZdCgQXqLpjUEEEAAgZAQIACFxDAHp5MDBgyQWelz5el1eucC5RbUydJNHlny3MvB6RhHRQABBBAwXoAAZPwQOrsDL/1ymRw531Ne3qjnLFDh+Tq59ukKO/wMGTLE2Z2nOgQQQAABxwqE1VmbY6ujMFcI5ObmSq/UFLl3Yrg8ekPLM/fZojqZ/YrInDvvk+de4OyPK74cdAIBBBAIkkDL/98oSAVzWPMEunXrJoWfFcmW4z1k4RqPVFT7nrk/yq6Vma9EyDcfWED4Me8rQMUIIICA4wQ4A+S4IXFvQWrJinvmf122v79RHriuUmaMDG92tfhjZ2vltfejZGuGR5a99muZOXOme4HoGQIIIIBAwAQIQAGj5kD1Ajt27JBFP/2R7PjrThnbP0pG9SyVtMQwiWsTJhVVIudK6iTjdLjsONpWsvPq5NsPfEceefQxad++fX0T/EQAAQQQQKBVAgSgVvHx4dYInDlzRtavXy87tm2RzEOfyGdFRRLp8UhSUlcZOGSkTPzq9TJ+/HjxWK+xIYAAAgggoFOAAKRTk7YQQAABBBBAwAgBJkEbMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAA7rgWfQAAAqVJREFUAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgUIQDo1aQsBBBBAAAEEjBAgABkxTBSJAAIIIIAAAjoFCEA6NWkLAQQQQAABBIwQIAAZMUwUiQACCCCAAAI6BQhAOjVpCwEEEEAAAQSMECAAGTFMFIkAAggggAACOgX+D/j+lVa011QVAAAAAElFTkSuQmCC" /><!-- --></p>
<p>Como se vio anteriormente, con el argumento <code>vertex.color</code>
puedes especificar propiedades visuales para <code>plot</code> en lugar
de usar y/o manipular los atributos de vértices o aristas. El siguiente
gráfico muestra las relaciones formales con líneas gruesas y las
informales con líneas finas:</p>
<div class="sourceCode" id="cb108"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb108-1"><a href="#cb108-1" tabindex="-1"></a><span class="fu">plot</span>(</span>
<span id="cb108-2"><a href="#cb108-2" tabindex="-1"></a>  g,</span>
<span id="cb108-3"><a href="#cb108-3" tabindex="-1"></a>  <span class="at">layout =</span> layout,</span>
<span id="cb108-4"><a href="#cb108-4" tabindex="-1"></a>  <span class="at">vertex.label.dist =</span> <span class="fl">3.5</span>,</span>
<span id="cb108-5"><a href="#cb108-5" tabindex="-1"></a>  <span class="at">vertex.size =</span> <span class="dv">20</span>,</span>
<span id="cb108-6"><a href="#cb108-6" tabindex="-1"></a>  <span class="at">vertex.color =</span> <span class="fu">ifelse</span>(<span class="fu">V</span>(g)<span class="sc">$</span>gender <span class="sc">==</span> <span class="st">&quot;m&quot;</span>, <span class="st">&quot;yellow&quot;</span>, <span class="st">&quot;red&quot;</span>),</span>
<span id="cb108-7"><a href="#cb108-7" tabindex="-1"></a>  <span class="at">edge.width =</span> <span class="fu">ifelse</span>(<span class="fu">E</span>(g)<span class="sc">$</span>is_formal, <span class="dv">5</span>, <span class="dv">1</span>)</span>
<span id="cb108-8"><a href="#cb108-8" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
<p>Este último procedimiento es preferible si quieres modificar la
representación visual de tu grafo, pero no quieres hacer modificaciones
al grafo mismo.</p>
<p>En resumen, hay propiedades especiales de vértices y aristas que
corresponden a la representación visual del grafo. Estos atributos
pueden modificar la configuración predeterminada de igraph (es decir,
color, peso, nombre, forma, diseño, etc.). Las dos tablas siguientes
resumen los atributos visuales más utilizados para vértices y aristas,
respectivamente:</p>
</div>
<div id="atributos-de-los-vértices-para-graficar" class="section level3">
<h3>Atributos de los vértices para graficar</h3>
<table>
<colgroup>
<col width="23%" />
<col width="23%" />
<col width="53%" />
</colgroup>
<thead>
<tr class="header">
<th>Nombre del atributo</th>
<th>Argumento</th>
<th>Propósito</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>color</code></td>
<td><code>vertex.color</code></td>
<td>Color del vértice</td>
</tr>
<tr class="even">
<td><code>label</code></td>
<td><code>vertex.label</code></td>
<td>Etiqueta del vértice. Se convertirán en caracteres. Especifique NA
para omitir las etiquetas de los vértices. Las etiquetas de vértices por
defecto son los IDs de los vértices.</td>
</tr>
<tr class="odd">
<td><code>label.cex</code></td>
<td><code>vertex.label.cex</code></td>
<td>Tamaño de fuente de la etiqueta del vértice, interpretado como un
factor multiplicativo, de forma similar a la función <code>text</code>
de R</td>
</tr>
<tr class="even">
<td><code>label.color</code></td>
<td><code>vertex.label.color</code></td>
<td>Color de la etiqueta del vértice</td>
</tr>
<tr class="odd">
<td><code>label.degree</code></td>
<td><code>vertex.label.degree</code></td>
<td>Define la posición de las etiquetas de los vértices, en relación con
el centro de los mismos. Se interpreta como un ángulo en radianes, cero
significa ‘a la derecha’, y ‘pi’ significa a la izquierda, arriba es
-pi/2 y abajo es pi/2. El valor por defecto es -pi/4</td>
</tr>
<tr class="even">
<td><code>label.dist</code></td>
<td><code>vertex.label.dist</code></td>
<td>Distancia de la etiqueta del vértice desde el propio vértice, en
relación con el tamaño del vértice</td>
</tr>
<tr class="odd">
<td><code>label.family</code></td>
<td><code>vertex.label.family</code></td>
<td>Familia tipográfica del vértice, de forma similar a la función
<code>text</code> de R</td>
</tr>
<tr class="even">
<td><code>label.font</code></td>
<td><code>vertex.label.font</code></td>
<td>Fuente dentro de la familia de fuentes del vértice, de forma similar
a la función <code>text</code> de R</td>
</tr>
<tr class="odd">
<td><code>shape</code></td>
<td><code>vertex.shape</code></td>
<td>La forma del vértice, actualmente “circle”, “square”, “csquare”,
“rectangle”, “crectangle”, “vrectangle”, “pie” (consultar
<code>vertex.shape.pie</code>), ‘sphere’ y “none” son admitidos, y sólo
por el comando <code>plot.igraph</code></td>
</tr>
<tr class="even">
<td><code>size</code></td>
<td><code>vertex.size</code></td>
<td>El tamaño del vértice, un escalar numérico o vector, en este último
caso el tamaño de cada vértice puede ser diferente</td>
</tr>
</tbody>
</table>
</div>
<div id="atributos-de-las-aristas-para-graficar" class="section level3">
<h3>Atributos de las aristas para graficar</h3>
<table>
<colgroup>
<col width="34%" />
<col width="40%" />
<col width="25%" />
</colgroup>
<thead>
<tr class="header">
<th>Nombre del atributo</th>
<th>Argumento</th>
<th>Propósito</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>color</code></td>
<td><code>edge.color</code></td>
<td>Color de la arista</td>
</tr>
<tr class="even">
<td><code>curved</code></td>
<td><code>edge.curved</code></td>
<td>Un valor numérico especifica la curvatura de la arista; una
curvatura cero significa aristas rectas, valores negativos significan
que la arista se curva en el sentido de las agujas del reloj, valores
positivos lo contrario. TRUE significa curvatura 0.5, FALSE significa
curvatura cero</td>
</tr>
<tr class="odd">
<td><code>arrow.size</code></td>
<td><code>edge.arrow.size</code></td>
<td>Actualmente es una constante, por lo que es la misma para todas las
aristas. Si se presenta un vector, sólo se utiliza el primer elemento,
es decir, si se toma de un atributo de aristas, sólo se utiliza el
atributo de la primera arista para todas las flechas</td>
</tr>
<tr class="even">
<td><code>arrow.width</code></td>
<td><code>edge.arrow.width</code></td>
<td>El ancho de las flechas. Actualmente es una constante, por lo que es
la misma para todas las aristas</td>
</tr>
<tr class="odd">
<td><code>width</code></td>
<td><code>edge.width</code></td>
<td>Anchura del borde en píxeles</td>
</tr>
<tr class="even">
<td><code>label</code></td>
<td><code>edge.label</code></td>
<td>Si se especifica, añade una etiqueta al borde</td>
</tr>
<tr class="odd">
<td><code>label.cex</code></td>
<td><code>edge.label.cex</code></td>
<td>Tamaño de fuente de la etiqueta de la arista, interpretado como un
factor multiplicativo, de forma similar a la función <code>text</code>
de R</td>
</tr>
<tr class="even">
<td><code>label.color</code></td>
<td><code>edge.label.color</code></td>
<td>Color de la etiqueta de la arista</td>
</tr>
<tr class="odd">
<td><code>label.family</code></td>
<td><code>edge.label.family</code></td>
<td>Familia tipográfica de la arista, de forma similar a la función
<code>text</code> de R</td>
</tr>
<tr class="even">
<td><code>label.font</code></td>
<td><code>edge.label.font</code></td>
<td>Fuente dentro de la familia de fuentes de la arista, de forma
similar a la función <code>text</code> de R</td>
</tr>
</tbody>
</table>
</div>
<div id="argumentos-más-comunes-de-plot" class="section level3">
<h3>Argumentos más comunes de <code>plot()</code></h3>
<p>Estos parámetros pueden especificarse como argumentos de la función
<code>plot</code> para ajustar el aspecto general del gráfico.</p>
<table>
<colgroup>
<col width="44%" />
<col width="55%" />
</colgroup>
<thead>
<tr class="header">
<th>Argumento</th>
<th>Propósito</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>layout</code></td>
<td>El diseño que se va a utilizar. Puede ser una instancia de
<code>layout</code>, una lista de tuplas que contengan coordenadas X-Y,
o el nombre de un algoritmo de diseño. El valor por defecto es
<code>auto</code>, que selecciona un algoritmo de diseño automáticamente
basado en el tamaño y la conectividad del grafo.</td>
</tr>
<tr class="even">
<td><code>margin</code></td>
<td>La cantidad de espacio vacío debajo, encima, a la izquierda y a la
derecha del gráfico, es un vector numérico de longitud cuatro</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="igraph-y-el-mundo-exterior" class="section level2">
<h2>igraph y el mundo exterior</h2>
<p>Ningún módulo de grafos estaría completo sin algún tipo de
funcionalidad de importación/exportación que permita al paquete
comunicarse con programas y kits de herramientas externos. igraph no es
una excepción: proporciona funciones para leer los formatos de grafos
más comunes y para guardar grafos en archivos que obedezcan estas
especificaciones de formato. Las funciones principales para leer y
escribir de/a un fichero son <code>read_graph()</code> y
<code>write_graph()</code>, respectivamente. La siguiente tabla resume
los formatos que igraph puede leer o escribir:</p>
<table>
<colgroup>
<col width="23%" />
<col width="23%" />
<col width="26%" />
<col width="26%" />
</colgroup>
<thead>
<tr class="header">
<th>Formato</th>
<th>Nombre corto</th>
<th>Método de lectura</th>
<th>Método de escritura</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Lista de adyacencia (a.k.a. <a href="https://lgl.sourceforge.net/#FileFormat">LGL</a>)</td>
<td><code>lgl</code></td>
<td><code>read_graph(file, format = c(&quot;lgl&quot;))</code></td>
<td><code>write_graph(graph, file, format = c(&quot;lgl&quot;))</code></td>
</tr>
<tr class="even">
<td>Matriz de adyacencia</td>
<td><code>adjacency</code></td>
<td><code>graph_from_adjacency_matrix(adjmatrix, mode = c(&quot;directed&quot;, &quot;undirected&quot;, &quot;max&quot;, &quot;min&quot;, &quot;upper&quot;,&quot;lower&quot;, &quot;plus&quot;), weighted = NULL, diag = TRUE, add.colnames = NULL, add.rownames = NA)</code></td>
<td><code>as.matrix(graph, &quot;adjacency&quot;)</code></td>
</tr>
<tr class="odd">
<td>DIMACS</td>
<td><code>dimacs</code></td>
<td><code>read_graph(file, format = c(&quot;dimacs&quot;))</code></td>
<td><code>write_graph(graph, file, format = c(&quot;dimacs&quot;))</code></td>
</tr>
<tr class="even">
<td>Edge list</td>
<td><code>edgelist</code></td>
<td><code>read_graph(file, format = c(&quot;edgelist&quot;))</code></td>
<td><code>write_graph(graph, file, format = c(&quot;edgelist&quot;))</code></td>
</tr>
<tr class="odd">
<td><a href="https://www.graphviz.org">GraphViz</a></td>
<td><code>dot</code></td>
<td>not supported yet</td>
<td><code>write_graph(graph, file, format = c(&quot;dot&quot;))</code></td>
</tr>
<tr class="even">
<td>GML</td>
<td><code>gml</code></td>
<td><code>read_graph(file, format = c(&quot;gml&quot;))</code></td>
<td><code>write_graph(graph, file, format = c(&quot;gml&quot;))</code></td>
</tr>
<tr class="odd">
<td>GraphML</td>
<td><code>graphml</code></td>
<td><code>read_graph(file, format = c(&quot;graphml&quot;))</code></td>
<td><code>write_graph(graph, file, format = c(&quot;graphml&quot;))</code></td>
</tr>
<tr class="even">
<td>LEDA</td>
<td><code>leda</code></td>
<td>not supported yet</td>
<td><code>write_graph(graph, file, format = c(&quot;leda&quot;))</code></td>
</tr>
<tr class="odd">
<td>Labeled edgelist (a.k.a. <a href="https://lgl.sourceforge.net/#FileFormat">NCOL</a>)</td>
<td><code>ncol</code></td>
<td><code>read_graph(file, format = c(&quot;ncol&quot;))</code></td>
<td><code>write_graph(graph, file, format = c(&quot;ncol&quot;))</code></td>
</tr>
<tr class="even">
<td><a href="http://mrvar.fdv.uni-lj.si/pajek/">Pajek</a> format</td>
<td><code>pajek</code></td>
<td><code>read_graph(file, format = c(&quot;pajek&quot;))</code></td>
<td><code>write_graph(graph, file, format = c(&quot;pajek&quot;))</code></td>
</tr>
</tbody>
</table>
<hr />
<p><strong>NOTA:</strong> La mayoría de los formatos tienen sus propias
limitaciones; por ejemplo, no todos pueden almacenar atributos. Tu mejor
opción es probablemente GraphML o GML si quieres guardar los grafos de
igraph en un formato que pueda ser leído desde un paquete externo y
quieres preservar los atributos numéricos y de cadena. <em>Edge
list</em> y NCOL también están bien si no tienes atributos (aunque NCOL
admite nombres de vértices y pesos de aristas).</p>
<hr />
</div>
<div id="dónde-ir-a-continuación" class="section level2">
<h2>Dónde ir a continuación</h2>
<p>Este tutorial es una breve introducción a <code>igraph</code> en R.
Esperamos que hayas disfrutado de su lectura y que te resulte útil para
tus propios análisis de redes.</p>
<p>Para una descripción detallada de funciones específicas, consulta <a href="https://r.igraph.org/reference/" class="uri">https://r.igraph.org/reference/</a>. Si tienes preguntas
sobre cómo utilizar <code>igraph</code>, visita nuestro <a href="https://igraph.discourse.group">Foro</a>. Para informar de un
error, abre una <a href="https://github.com/igraph/rigraph/issues">incidencia en
Github</a>. Por favor, no hagas preguntas de uso en Github directamente,
ya que está pensado para desarrolladores y no para usuarios.</p>
</div>
<div id="información-de-la-sesión" class="section level2">
<h2>Información de la sesión</h2>
<p>En favor de la reproducibilidad, la información de la sesión para el
código anterior es la siguiente:</p>
<div class="sourceCode" id="cb109"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb109-1"><a href="#cb109-1" tabindex="-1"></a><span class="fu">sessionInfo</span>()</span></code></pre></div>
<pre><code>## R version 4.4.2 (2024-10-31)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sequoia 15.2
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: Europe/Zurich
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] igraph_2.1.4
## 
## loaded via a namespace (and not attached):
##  [1] digest_0.6.37     R6_2.5.1          fastmap_1.2.0     Matrix_1.5-4.1   
##  [5] xfun_0.50         lattice_0.22-6    magrittr_2.0.3    glue_1.8.0       
##  [9] cachem_1.1.0      knitr_1.49        pkgconfig_2.0.3   htmltools_0.5.8.1
## [13] rmarkdown_2.29    lifecycle_1.0.4   cli_3.6.3         grid_4.4.2       
## [17] vctrs_0.6.5       sass_0.4.9        jquerylib_0.1.4   compiler_4.4.2   
## [21] rstudioapi_0.17.1 tools_4.4.2       pillar_1.10.1     evaluate_1.0.3   
## [25] bslib_0.8.0       yaml_2.3.10       crayon_1.5.3      rlang_1.1.5      
## [29] jsonlite_1.8.9</code></pre>
</div>



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