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1816 lines
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</head>
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<body>
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<h1 class="title toc-ignore">Extending ggplot2</h1>
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<p>This vignette documents the official extension mechanism provided in
|
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ggplot2 2.0.0. This vignette is a high-level adjunct to the low-level
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details found in <code>?Stat</code>, <code>?Geom</code> and
|
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<code>?theme</code>. You’ll learn how to extend ggplot2 by creating a
|
||
new stat, geom, or theme.</p>
|
||
<p>As you read this document, you’ll see many things that will make you
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scratch your head and wonder why on earth is it designed this way?
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||
Mostly it’s historical accident - I wasn’t a terribly good R programmer
|
||
when I started writing ggplot2 and I made a lot of questionable
|
||
decisions. We cleaned up as many of those issues as possible in the
|
||
2.0.0 release, but some fixes simply weren’t worth the effort.</p>
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<div id="ggproto" class="section level2">
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||
<h2>ggproto</h2>
|
||
<p>All ggplot2 objects are built using the ggproto system of object
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||
oriented programming. This OO system is used only in one place: ggplot2.
|
||
This is mostly historical accident: ggplot2 started off using <a href="https://cran.r-project.org/package=proto">proto</a> because I
|
||
needed mutable objects. This was well before the creation of (the
|
||
briefly lived) <a href="http://vita.had.co.nz/papers/mutatr.html">mutatr</a>, reference
|
||
classes and R6: proto was the only game in town.</p>
|
||
<p>But why ggproto? Well when we turned to add an official extension
|
||
mechanism to ggplot2, we found a major problem that caused problems when
|
||
proto objects were extended in a different package (methods were
|
||
evaluated in ggplot2, not the package where the extension was added). We
|
||
tried converting to R6, but it was a poor fit for the needs of ggplot2.
|
||
We could’ve modified proto, but that would’ve first involved
|
||
understanding exactly how proto worked, and secondly making sure that
|
||
the changes didn’t affect other users of proto.</p>
|
||
<p>It’s strange to say, but this is a case where inventing a new OO
|
||
system was actually the right answer to the problem! Fortunately Winston
|
||
is now very good at creating OO systems, so it only took him a day to
|
||
come up with ggproto: it maintains all the features of proto that
|
||
ggplot2 needs, while allowing cross package inheritance to work.</p>
|
||
<p>Here’s a quick demo of ggproto in action:</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>A <span class="ot"><-</span> <span class="fu">ggproto</span>(<span class="st">"A"</span>, <span class="cn">NULL</span>,</span>
|
||
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a> <span class="at">x =</span> <span class="dv">1</span>,</span>
|
||
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a> <span class="at">inc =</span> <span class="cf">function</span>(self) {</span>
|
||
<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a> self<span class="sc">$</span>x <span class="ot"><-</span> self<span class="sc">$</span>x <span class="sc">+</span> <span class="dv">1</span></span>
|
||
<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a> }</span>
|
||
<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a>)</span>
|
||
<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a>A<span class="sc">$</span>x</span>
|
||
<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a><span class="co">#> [1] 1</span></span>
|
||
<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a>A<span class="sc">$</span><span class="fu">inc</span>()</span>
|
||
<span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a>A<span class="sc">$</span>x</span>
|
||
<span id="cb1-11"><a href="#cb1-11" tabindex="-1"></a><span class="co">#> [1] 2</span></span>
|
||
<span id="cb1-12"><a href="#cb1-12" tabindex="-1"></a>A<span class="sc">$</span><span class="fu">inc</span>()</span>
|
||
<span id="cb1-13"><a href="#cb1-13" tabindex="-1"></a>A<span class="sc">$</span><span class="fu">inc</span>()</span>
|
||
<span id="cb1-14"><a href="#cb1-14" tabindex="-1"></a>A<span class="sc">$</span>x</span>
|
||
<span id="cb1-15"><a href="#cb1-15" tabindex="-1"></a><span class="co">#> [1] 4</span></span></code></pre></div>
|
||
<p>The majority of ggplot2 classes are immutable and static: the methods
|
||
neither use nor modify state in the class. They’re mostly used as a
|
||
convenient way of bundling related methods together.</p>
|
||
<p>To create a new geom or stat, you will just create a new ggproto that
|
||
inherits from <code>Stat</code>, <code>Geom</code> and override the
|
||
methods described below.</p>
|
||
</div>
|
||
<div id="creating-a-new-stat" class="section level2">
|
||
<h2>Creating a new stat</h2>
|
||
<div id="the-simplest-stat" class="section level3">
|
||
<h3>The simplest stat</h3>
|
||
<p>We’ll start by creating a very simple stat: one that gives the convex
|
||
hull (the <em>c</em> hull) of a set of points. First we create a new
|
||
ggproto object that inherits from <code>Stat</code>:</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>StatChull <span class="ot"><-</span> <span class="fu">ggproto</span>(<span class="st">"StatChull"</span>, Stat,</span>
|
||
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a> <span class="at">compute_group =</span> <span class="cf">function</span>(data, scales) {</span>
|
||
<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a> data[<span class="fu">chull</span>(data<span class="sc">$</span>x, data<span class="sc">$</span>y), , drop <span class="ot">=</span> <span class="cn">FALSE</span>]</span>
|
||
<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a> },</span>
|
||
<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a> </span>
|
||
<span id="cb2-6"><a href="#cb2-6" tabindex="-1"></a> <span class="at">required_aes =</span> <span class="fu">c</span>(<span class="st">"x"</span>, <span class="st">"y"</span>)</span>
|
||
<span id="cb2-7"><a href="#cb2-7" tabindex="-1"></a>)</span></code></pre></div>
|
||
<p>The two most important components are the
|
||
<code>compute_group()</code> method (which does the computation), and
|
||
the <code>required_aes</code> field, which lists which aesthetics must
|
||
be present in order for the stat to work.</p>
|
||
<p>Next we write a layer function. Unfortunately, due to an early design
|
||
mistake I called these either <code>stat_()</code> or
|
||
<code>geom_()</code>. A better decision would have been to call them
|
||
<code>layer_()</code> functions: that’s a more accurate description
|
||
because every layer involves a stat <em>and</em> a geom.</p>
|
||
<p>All layer functions follow the same form - you specify defaults in
|
||
the function arguments and then call the <code>layer()</code> function,
|
||
sending <code>...</code> into the <code>params</code> argument. The
|
||
arguments in <code>...</code> will either be arguments for the geom (if
|
||
you’re making a stat wrapper), arguments for the stat (if you’re making
|
||
a geom wrapper), or aesthetics to be set. <code>layer()</code> takes
|
||
care of teasing the different parameters apart and making sure they’re
|
||
stored in the right place:</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>stat_chull <span class="ot"><-</span> <span class="cf">function</span>(<span class="at">mapping =</span> <span class="cn">NULL</span>, <span class="at">data =</span> <span class="cn">NULL</span>, <span class="at">geom =</span> <span class="st">"polygon"</span>,</span>
|
||
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a> <span class="at">position =</span> <span class="st">"identity"</span>, <span class="at">na.rm =</span> <span class="cn">FALSE</span>, <span class="at">show.legend =</span> <span class="cn">NA</span>, </span>
|
||
<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a> <span class="at">inherit.aes =</span> <span class="cn">TRUE</span>, ...) {</span>
|
||
<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a> <span class="fu">layer</span>(</span>
|
||
<span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a> <span class="at">stat =</span> StatChull, <span class="at">data =</span> data, <span class="at">mapping =</span> mapping, <span class="at">geom =</span> geom, </span>
|
||
<span id="cb3-6"><a href="#cb3-6" tabindex="-1"></a> <span class="at">position =</span> position, <span class="at">show.legend =</span> show.legend, <span class="at">inherit.aes =</span> inherit.aes,</span>
|
||
<span id="cb3-7"><a href="#cb3-7" tabindex="-1"></a> <span class="at">params =</span> <span class="fu">list</span>(<span class="at">na.rm =</span> na.rm, ...)</span>
|
||
<span id="cb3-8"><a href="#cb3-8" tabindex="-1"></a> )</span>
|
||
<span id="cb3-9"><a href="#cb3-9" tabindex="-1"></a>}</span></code></pre></div>
|
||
<p>(Note that if you’re writing this in your own package, you’ll either
|
||
need to call <code>ggplot2::layer()</code> explicitly, or import the
|
||
<code>layer()</code> function into your package namespace.)</p>
|
||
<p>Once we have a layer function we can try our new stat:</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><span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, hwy)) <span class="sc">+</span> </span>
|
||
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a> <span class="fu">geom_point</span>() <span class="sc">+</span> </span>
|
||
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a> <span class="fu">stat_chull</span>(<span class="at">fill =</span> <span class="cn">NA</span>, <span class="at">colour =</span> <span class="st">"black"</span>)</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" alt="Scatterplot of engine displacement versus highway miles per gallon, for 234 cars. The convex hull of all the points is marked by a polygon with no fill." style="display: block; margin: auto;" /></p>
|
||
<p>(We’ll see later how to change the defaults of the geom so that you
|
||
don’t need to specify <code>fill = NA</code> every time.)</p>
|
||
<p>Once we’ve written this basic object, ggplot2 gives a lot for free.
|
||
For example, ggplot2 automatically preserves aesthetics that are
|
||
constant within each group:</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><span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, hwy, <span class="at">colour =</span> drv)) <span class="sc">+</span> </span>
|
||
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a> <span class="fu">geom_point</span>() <span class="sc">+</span> </span>
|
||
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a> <span class="fu">stat_chull</span>(<span class="at">fill =</span> <span class="cn">NA</span>)</span></code></pre></div>
|
||
<p><img 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" alt="Scatterplot of engine displacement versus highway miles per gallon, for 234 cars. The convex hulls of points, grouped and coloured by three types of drive train, are marked by polygons with no fill but the outline matches the colours of the points." style="display: block; margin: auto;" /></p>
|
||
<p>We can also override the default geom to display the convex hull in a
|
||
different way:</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><span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, hwy)) <span class="sc">+</span> </span>
|
||
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a> <span class="fu">stat_chull</span>(<span class="at">geom =</span> <span class="st">"point"</span>, <span class="at">size =</span> <span class="dv">4</span>, <span class="at">colour =</span> <span class="st">"red"</span>) <span class="sc">+</span></span>
|
||
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a> <span class="fu">geom_point</span>()</span></code></pre></div>
|
||
<p><img 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" alt="Scatterplot of engine displacement versus highway miles per gallon, for 234 cars. The points that are part of the convex hull of all points are marked with a red outline." style="display: block; margin: auto;" /></p>
|
||
</div>
|
||
<div id="stat-parameters" class="section level3">
|
||
<h3>Stat parameters</h3>
|
||
<p>A more complex stat will do some computation. Let’s implement a
|
||
simple version of <code>geom_smooth()</code> that adds a line of best
|
||
fit to a plot. We create a <code>StatLm</code> that inherits from
|
||
<code>Stat</code> and a layer function, <code>stat_lm()</code>:</p>
|
||
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a>StatLm <span class="ot"><-</span> <span class="fu">ggproto</span>(<span class="st">"StatLm"</span>, Stat, </span>
|
||
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a> <span class="at">required_aes =</span> <span class="fu">c</span>(<span class="st">"x"</span>, <span class="st">"y"</span>),</span>
|
||
<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a> </span>
|
||
<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a> <span class="at">compute_group =</span> <span class="cf">function</span>(data, scales) {</span>
|
||
<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a> rng <span class="ot"><-</span> <span class="fu">range</span>(data<span class="sc">$</span>x, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
|
||
<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a> grid <span class="ot"><-</span> <span class="fu">data.frame</span>(<span class="at">x =</span> rng)</span>
|
||
<span id="cb7-7"><a href="#cb7-7" tabindex="-1"></a> </span>
|
||
<span id="cb7-8"><a href="#cb7-8" tabindex="-1"></a> mod <span class="ot"><-</span> <span class="fu">lm</span>(y <span class="sc">~</span> x, <span class="at">data =</span> data)</span>
|
||
<span id="cb7-9"><a href="#cb7-9" tabindex="-1"></a> grid<span class="sc">$</span>y <span class="ot"><-</span> <span class="fu">predict</span>(mod, <span class="at">newdata =</span> grid)</span>
|
||
<span id="cb7-10"><a href="#cb7-10" tabindex="-1"></a> </span>
|
||
<span id="cb7-11"><a href="#cb7-11" tabindex="-1"></a> grid</span>
|
||
<span id="cb7-12"><a href="#cb7-12" tabindex="-1"></a> }</span>
|
||
<span id="cb7-13"><a href="#cb7-13" tabindex="-1"></a>)</span>
|
||
<span id="cb7-14"><a href="#cb7-14" tabindex="-1"></a></span>
|
||
<span id="cb7-15"><a href="#cb7-15" tabindex="-1"></a>stat_lm <span class="ot"><-</span> <span class="cf">function</span>(<span class="at">mapping =</span> <span class="cn">NULL</span>, <span class="at">data =</span> <span class="cn">NULL</span>, <span class="at">geom =</span> <span class="st">"line"</span>,</span>
|
||
<span id="cb7-16"><a href="#cb7-16" tabindex="-1"></a> <span class="at">position =</span> <span class="st">"identity"</span>, <span class="at">na.rm =</span> <span class="cn">FALSE</span>, <span class="at">show.legend =</span> <span class="cn">NA</span>, </span>
|
||
<span id="cb7-17"><a href="#cb7-17" tabindex="-1"></a> <span class="at">inherit.aes =</span> <span class="cn">TRUE</span>, ...) {</span>
|
||
<span id="cb7-18"><a href="#cb7-18" tabindex="-1"></a> <span class="fu">layer</span>(</span>
|
||
<span id="cb7-19"><a href="#cb7-19" tabindex="-1"></a> <span class="at">stat =</span> StatLm, <span class="at">data =</span> data, <span class="at">mapping =</span> mapping, <span class="at">geom =</span> geom, </span>
|
||
<span id="cb7-20"><a href="#cb7-20" tabindex="-1"></a> <span class="at">position =</span> position, <span class="at">show.legend =</span> show.legend, <span class="at">inherit.aes =</span> inherit.aes,</span>
|
||
<span id="cb7-21"><a href="#cb7-21" tabindex="-1"></a> <span class="at">params =</span> <span class="fu">list</span>(<span class="at">na.rm =</span> na.rm, ...)</span>
|
||
<span id="cb7-22"><a href="#cb7-22" tabindex="-1"></a> )</span>
|
||
<span id="cb7-23"><a href="#cb7-23" tabindex="-1"></a>}</span>
|
||
<span id="cb7-24"><a href="#cb7-24" tabindex="-1"></a></span>
|
||
<span id="cb7-25"><a href="#cb7-25" tabindex="-1"></a><span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, hwy)) <span class="sc">+</span> </span>
|
||
<span id="cb7-26"><a href="#cb7-26" tabindex="-1"></a> <span class="fu">geom_point</span>() <span class="sc">+</span> </span>
|
||
<span id="cb7-27"><a href="#cb7-27" tabindex="-1"></a> <span class="fu">stat_lm</span>()</span></code></pre></div>
|
||
<p><img 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" alt="Scatterplot of engine displacement versus highway miles per gallon, for 234 cars. A straight line with a negative slope passes through the cloud of points." style="display: block; margin: auto;" /></p>
|
||
<p><code>StatLm</code> is inflexible because it has no parameters. We
|
||
might want to allow the user to control the model formula and the number
|
||
of points used to generate the grid. To do so, we add arguments to the
|
||
<code>compute_group()</code> method and our wrapper function:</p>
|
||
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>StatLm <span class="ot"><-</span> <span class="fu">ggproto</span>(<span class="st">"StatLm"</span>, Stat, </span>
|
||
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a> <span class="at">required_aes =</span> <span class="fu">c</span>(<span class="st">"x"</span>, <span class="st">"y"</span>),</span>
|
||
<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a> </span>
|
||
<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a> <span class="at">compute_group =</span> <span class="cf">function</span>(data, scales, params, <span class="at">n =</span> <span class="dv">100</span>, <span class="at">formula =</span> y <span class="sc">~</span> x) {</span>
|
||
<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a> rng <span class="ot"><-</span> <span class="fu">range</span>(data<span class="sc">$</span>x, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
|
||
<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a> grid <span class="ot"><-</span> <span class="fu">data.frame</span>(<span class="at">x =</span> <span class="fu">seq</span>(rng[<span class="dv">1</span>], rng[<span class="dv">2</span>], <span class="at">length =</span> n))</span>
|
||
<span id="cb8-7"><a href="#cb8-7" tabindex="-1"></a> </span>
|
||
<span id="cb8-8"><a href="#cb8-8" tabindex="-1"></a> mod <span class="ot"><-</span> <span class="fu">lm</span>(formula, <span class="at">data =</span> data)</span>
|
||
<span id="cb8-9"><a href="#cb8-9" tabindex="-1"></a> grid<span class="sc">$</span>y <span class="ot"><-</span> <span class="fu">predict</span>(mod, <span class="at">newdata =</span> grid)</span>
|
||
<span id="cb8-10"><a href="#cb8-10" tabindex="-1"></a> </span>
|
||
<span id="cb8-11"><a href="#cb8-11" tabindex="-1"></a> grid</span>
|
||
<span id="cb8-12"><a href="#cb8-12" tabindex="-1"></a> }</span>
|
||
<span id="cb8-13"><a href="#cb8-13" tabindex="-1"></a>)</span>
|
||
<span id="cb8-14"><a href="#cb8-14" tabindex="-1"></a></span>
|
||
<span id="cb8-15"><a href="#cb8-15" tabindex="-1"></a>stat_lm <span class="ot"><-</span> <span class="cf">function</span>(<span class="at">mapping =</span> <span class="cn">NULL</span>, <span class="at">data =</span> <span class="cn">NULL</span>, <span class="at">geom =</span> <span class="st">"line"</span>,</span>
|
||
<span id="cb8-16"><a href="#cb8-16" tabindex="-1"></a> <span class="at">position =</span> <span class="st">"identity"</span>, <span class="at">na.rm =</span> <span class="cn">FALSE</span>, <span class="at">show.legend =</span> <span class="cn">NA</span>, </span>
|
||
<span id="cb8-17"><a href="#cb8-17" tabindex="-1"></a> <span class="at">inherit.aes =</span> <span class="cn">TRUE</span>, <span class="at">n =</span> <span class="dv">50</span>, <span class="at">formula =</span> y <span class="sc">~</span> x, </span>
|
||
<span id="cb8-18"><a href="#cb8-18" tabindex="-1"></a> ...) {</span>
|
||
<span id="cb8-19"><a href="#cb8-19" tabindex="-1"></a> <span class="fu">layer</span>(</span>
|
||
<span id="cb8-20"><a href="#cb8-20" tabindex="-1"></a> <span class="at">stat =</span> StatLm, <span class="at">data =</span> data, <span class="at">mapping =</span> mapping, <span class="at">geom =</span> geom, </span>
|
||
<span id="cb8-21"><a href="#cb8-21" tabindex="-1"></a> <span class="at">position =</span> position, <span class="at">show.legend =</span> show.legend, <span class="at">inherit.aes =</span> inherit.aes,</span>
|
||
<span id="cb8-22"><a href="#cb8-22" tabindex="-1"></a> <span class="at">params =</span> <span class="fu">list</span>(<span class="at">n =</span> n, <span class="at">formula =</span> formula, <span class="at">na.rm =</span> na.rm, ...)</span>
|
||
<span id="cb8-23"><a href="#cb8-23" tabindex="-1"></a> )</span>
|
||
<span id="cb8-24"><a href="#cb8-24" tabindex="-1"></a>}</span>
|
||
<span id="cb8-25"><a href="#cb8-25" tabindex="-1"></a></span>
|
||
<span id="cb8-26"><a href="#cb8-26" tabindex="-1"></a><span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, hwy)) <span class="sc">+</span> </span>
|
||
<span id="cb8-27"><a href="#cb8-27" tabindex="-1"></a> <span class="fu">geom_point</span>() <span class="sc">+</span> </span>
|
||
<span id="cb8-28"><a href="#cb8-28" tabindex="-1"></a> <span class="fu">stat_lm</span>(<span class="at">formula =</span> y <span class="sc">~</span> <span class="fu">poly</span>(x, <span class="dv">10</span>)) <span class="sc">+</span> </span>
|
||
<span id="cb8-29"><a href="#cb8-29" tabindex="-1"></a> <span class="fu">stat_lm</span>(<span class="at">formula =</span> y <span class="sc">~</span> <span class="fu">poly</span>(x, <span class="dv">10</span>), <span class="at">geom =</span> <span class="st">"point"</span>, <span class="at">colour =</span> <span class="st">"red"</span>, <span class="at">n =</span> <span class="dv">20</span>)</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" alt="Scatterplot of engine displacement versus highway miles per gallon, for 234 cars. A wobbly line follows the point cloud over the horizontal direction. 20 points are placed on top of the line with constant horizontal intervals." style="display: block; margin: auto;" /></p>
|
||
<p>Note that we don’t <em>have</em> to explicitly include the new
|
||
parameters in the arguments for the layer, <code>...</code> will get
|
||
passed to the right place anyway. But you’ll need to document them
|
||
somewhere so the user knows about them. Here’s a brief example. Note
|
||
<code>@inheritParams ggplot2::stat_identity</code>: that will
|
||
automatically inherit documentation for all the parameters also defined
|
||
for <code>stat_identity()</code>.</p>
|
||
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="co">#' @export</span></span>
|
||
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="co">#' @inheritParams ggplot2::stat_identity</span></span>
|
||
<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a><span class="co">#' @param formula The modelling formula passed to \code{lm}. Should only </span></span>
|
||
<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a><span class="co">#' involve \code{y} and \code{x}</span></span>
|
||
<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a><span class="co">#' @param n Number of points used for interpolation.</span></span>
|
||
<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a>stat_lm <span class="ot"><-</span> <span class="cf">function</span>(<span class="at">mapping =</span> <span class="cn">NULL</span>, <span class="at">data =</span> <span class="cn">NULL</span>, <span class="at">geom =</span> <span class="st">"line"</span>,</span>
|
||
<span id="cb9-7"><a href="#cb9-7" tabindex="-1"></a> <span class="at">position =</span> <span class="st">"identity"</span>, <span class="at">na.rm =</span> <span class="cn">FALSE</span>, <span class="at">show.legend =</span> <span class="cn">NA</span>, </span>
|
||
<span id="cb9-8"><a href="#cb9-8" tabindex="-1"></a> <span class="at">inherit.aes =</span> <span class="cn">TRUE</span>, <span class="at">n =</span> <span class="dv">50</span>, <span class="at">formula =</span> y <span class="sc">~</span> x, </span>
|
||
<span id="cb9-9"><a href="#cb9-9" tabindex="-1"></a> ...) {</span>
|
||
<span id="cb9-10"><a href="#cb9-10" tabindex="-1"></a> <span class="fu">layer</span>(</span>
|
||
<span id="cb9-11"><a href="#cb9-11" tabindex="-1"></a> <span class="at">stat =</span> StatLm, <span class="at">data =</span> data, <span class="at">mapping =</span> mapping, <span class="at">geom =</span> geom, </span>
|
||
<span id="cb9-12"><a href="#cb9-12" tabindex="-1"></a> <span class="at">position =</span> position, <span class="at">show.legend =</span> show.legend, <span class="at">inherit.aes =</span> inherit.aes,</span>
|
||
<span id="cb9-13"><a href="#cb9-13" tabindex="-1"></a> <span class="at">params =</span> <span class="fu">list</span>(<span class="at">n =</span> n, <span class="at">formula =</span> formula, <span class="at">na.rm =</span> na.rm, ...)</span>
|
||
<span id="cb9-14"><a href="#cb9-14" tabindex="-1"></a> )</span>
|
||
<span id="cb9-15"><a href="#cb9-15" tabindex="-1"></a>}</span></code></pre></div>
|
||
<p><code>stat_lm()</code> must be exported if you want other people to
|
||
use it. You could also consider exporting <code>StatLm</code> if you
|
||
want people to extend the underlying object; this should be done with
|
||
care.</p>
|
||
</div>
|
||
<div id="picking-defaults" class="section level3">
|
||
<h3>Picking defaults</h3>
|
||
<p>Sometimes you have calculations that should be performed once for the
|
||
complete dataset, not once for each group. This is useful for picking
|
||
sensible default values. For example, if we want to do a density
|
||
estimate, it’s reasonable to pick one bandwidth for the whole plot. The
|
||
following Stat creates a variation of the <code>stat_density()</code>
|
||
that picks one bandwidth for all groups by choosing the mean of the
|
||
“best” bandwidth for each group (I have no theoretical justification for
|
||
this, but it doesn’t seem unreasonable).</p>
|
||
<p>To do this we override the <code>setup_params()</code> method. It’s
|
||
passed the data and a list of params, and returns an updated list.</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>StatDensityCommon <span class="ot"><-</span> <span class="fu">ggproto</span>(<span class="st">"StatDensityCommon"</span>, Stat, </span>
|
||
<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a> <span class="at">required_aes =</span> <span class="st">"x"</span>,</span>
|
||
<span id="cb10-3"><a href="#cb10-3" tabindex="-1"></a> </span>
|
||
<span id="cb10-4"><a href="#cb10-4" tabindex="-1"></a> <span class="at">setup_params =</span> <span class="cf">function</span>(data, params) {</span>
|
||
<span id="cb10-5"><a href="#cb10-5" tabindex="-1"></a> <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(params<span class="sc">$</span>bandwidth))</span>
|
||
<span id="cb10-6"><a href="#cb10-6" tabindex="-1"></a> <span class="fu">return</span>(params)</span>
|
||
<span id="cb10-7"><a href="#cb10-7" tabindex="-1"></a> </span>
|
||
<span id="cb10-8"><a href="#cb10-8" tabindex="-1"></a> xs <span class="ot"><-</span> <span class="fu">split</span>(data<span class="sc">$</span>x, data<span class="sc">$</span>group)</span>
|
||
<span id="cb10-9"><a href="#cb10-9" tabindex="-1"></a> bws <span class="ot"><-</span> <span class="fu">vapply</span>(xs, bw.nrd0, <span class="fu">numeric</span>(<span class="dv">1</span>))</span>
|
||
<span id="cb10-10"><a href="#cb10-10" tabindex="-1"></a> bw <span class="ot"><-</span> <span class="fu">mean</span>(bws)</span>
|
||
<span id="cb10-11"><a href="#cb10-11" tabindex="-1"></a> <span class="fu">message</span>(<span class="st">"Picking bandwidth of "</span>, <span class="fu">signif</span>(bw, <span class="dv">3</span>))</span>
|
||
<span id="cb10-12"><a href="#cb10-12" tabindex="-1"></a> </span>
|
||
<span id="cb10-13"><a href="#cb10-13" tabindex="-1"></a> params<span class="sc">$</span>bandwidth <span class="ot"><-</span> bw</span>
|
||
<span id="cb10-14"><a href="#cb10-14" tabindex="-1"></a> params</span>
|
||
<span id="cb10-15"><a href="#cb10-15" tabindex="-1"></a> },</span>
|
||
<span id="cb10-16"><a href="#cb10-16" tabindex="-1"></a> </span>
|
||
<span id="cb10-17"><a href="#cb10-17" tabindex="-1"></a> <span class="at">compute_group =</span> <span class="cf">function</span>(data, scales, <span class="at">bandwidth =</span> <span class="dv">1</span>) {</span>
|
||
<span id="cb10-18"><a href="#cb10-18" tabindex="-1"></a> d <span class="ot"><-</span> <span class="fu">density</span>(data<span class="sc">$</span>x, <span class="at">bw =</span> bandwidth)</span>
|
||
<span id="cb10-19"><a href="#cb10-19" tabindex="-1"></a> <span class="fu">data.frame</span>(<span class="at">x =</span> d<span class="sc">$</span>x, <span class="at">y =</span> d<span class="sc">$</span>y)</span>
|
||
<span id="cb10-20"><a href="#cb10-20" tabindex="-1"></a> } </span>
|
||
<span id="cb10-21"><a href="#cb10-21" tabindex="-1"></a>)</span>
|
||
<span id="cb10-22"><a href="#cb10-22" tabindex="-1"></a></span>
|
||
<span id="cb10-23"><a href="#cb10-23" tabindex="-1"></a>stat_density_common <span class="ot"><-</span> <span class="cf">function</span>(<span class="at">mapping =</span> <span class="cn">NULL</span>, <span class="at">data =</span> <span class="cn">NULL</span>, <span class="at">geom =</span> <span class="st">"line"</span>,</span>
|
||
<span id="cb10-24"><a href="#cb10-24" tabindex="-1"></a> <span class="at">position =</span> <span class="st">"identity"</span>, <span class="at">na.rm =</span> <span class="cn">FALSE</span>, <span class="at">show.legend =</span> <span class="cn">NA</span>, </span>
|
||
<span id="cb10-25"><a href="#cb10-25" tabindex="-1"></a> <span class="at">inherit.aes =</span> <span class="cn">TRUE</span>, <span class="at">bandwidth =</span> <span class="cn">NULL</span>,</span>
|
||
<span id="cb10-26"><a href="#cb10-26" tabindex="-1"></a> ...) {</span>
|
||
<span id="cb10-27"><a href="#cb10-27" tabindex="-1"></a> <span class="fu">layer</span>(</span>
|
||
<span id="cb10-28"><a href="#cb10-28" tabindex="-1"></a> <span class="at">stat =</span> StatDensityCommon, <span class="at">data =</span> data, <span class="at">mapping =</span> mapping, <span class="at">geom =</span> geom, </span>
|
||
<span id="cb10-29"><a href="#cb10-29" tabindex="-1"></a> <span class="at">position =</span> position, <span class="at">show.legend =</span> show.legend, <span class="at">inherit.aes =</span> inherit.aes,</span>
|
||
<span id="cb10-30"><a href="#cb10-30" tabindex="-1"></a> <span class="at">params =</span> <span class="fu">list</span>(<span class="at">bandwidth =</span> bandwidth, <span class="at">na.rm =</span> na.rm, ...)</span>
|
||
<span id="cb10-31"><a href="#cb10-31" tabindex="-1"></a> )</span>
|
||
<span id="cb10-32"><a href="#cb10-32" tabindex="-1"></a>}</span>
|
||
<span id="cb10-33"><a href="#cb10-33" tabindex="-1"></a></span>
|
||
<span id="cb10-34"><a href="#cb10-34" tabindex="-1"></a><span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, <span class="at">colour =</span> drv)) <span class="sc">+</span> </span>
|
||
<span id="cb10-35"><a href="#cb10-35" tabindex="-1"></a> <span class="fu">stat_density_common</span>()</span>
|
||
<span id="cb10-36"><a href="#cb10-36" tabindex="-1"></a><span class="co">#> Picking bandwidth of 0.345</span></span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" alt="A line plot showing three kernel density estimates of engine displacement, coloured for three types of drive trains. The lines are a little bit wobbly." style="display: block; margin: auto;" /></p>
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<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>
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<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a><span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, <span class="at">colour =</span> drv)) <span class="sc">+</span> </span>
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<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a> <span class="fu">stat_density_common</span>(<span class="at">bandwidth =</span> <span class="fl">0.5</span>)</span></code></pre></div>
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<p><img 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" alt="A line plot showing three kernel density estimates of engine displacement, coloured for three types of drive trains. The lines are fairly smooth." style="display: block; margin: auto;" /></p>
|
||
<p>I recommend using <code>NULL</code> as a default value. If you pick
|
||
important parameters automatically, it’s a good idea to
|
||
<code>message()</code> to the user (and when printing a floating point
|
||
parameter, using <code>signif()</code> to show only a few significant
|
||
digits).</p>
|
||
</div>
|
||
<div id="variable-names-and-default-aesthetics" class="section level3">
|
||
<h3>Variable names and default aesthetics</h3>
|
||
<p>This stat illustrates another important point. If we want to make
|
||
this stat usable with other geoms, we should return a variable called
|
||
<code>density</code> instead of <code>y</code>. Then we can set up the
|
||
<code>default_aes</code> to automatically map <code>density</code> to
|
||
<code>y</code>, which allows the user to override it to use with
|
||
different geoms:</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>StatDensityCommon <span class="ot"><-</span> <span class="fu">ggproto</span>(<span class="st">"StatDensity2"</span>, Stat, </span>
|
||
<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a> <span class="at">required_aes =</span> <span class="st">"x"</span>,</span>
|
||
<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a> <span class="at">default_aes =</span> <span class="fu">aes</span>(<span class="at">y =</span> <span class="fu">after_stat</span>(density)),</span>
|
||
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a></span>
|
||
<span id="cb12-5"><a href="#cb12-5" tabindex="-1"></a> <span class="at">compute_group =</span> <span class="cf">function</span>(data, scales, <span class="at">bandwidth =</span> <span class="dv">1</span>) {</span>
|
||
<span id="cb12-6"><a href="#cb12-6" tabindex="-1"></a> d <span class="ot"><-</span> <span class="fu">density</span>(data<span class="sc">$</span>x, <span class="at">bw =</span> bandwidth)</span>
|
||
<span id="cb12-7"><a href="#cb12-7" tabindex="-1"></a> <span class="fu">data.frame</span>(<span class="at">x =</span> d<span class="sc">$</span>x, <span class="at">density =</span> d<span class="sc">$</span>y)</span>
|
||
<span id="cb12-8"><a href="#cb12-8" tabindex="-1"></a> } </span>
|
||
<span id="cb12-9"><a href="#cb12-9" tabindex="-1"></a>)</span>
|
||
<span id="cb12-10"><a href="#cb12-10" tabindex="-1"></a></span>
|
||
<span id="cb12-11"><a href="#cb12-11" tabindex="-1"></a><span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, drv, <span class="at">colour =</span> <span class="fu">after_stat</span>(density))) <span class="sc">+</span> </span>
|
||
<span id="cb12-12"><a href="#cb12-12" tabindex="-1"></a> <span class="fu">stat_density_common</span>(<span class="at">bandwidth =</span> <span class="dv">1</span>, <span class="at">geom =</span> <span class="st">"point"</span>)</span></code></pre></div>
|
||
<p><img 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" alt="A plot showing the engine displacement versus three types of drive trains. Every drive train is represented by a series of densely packed points that imitate a horizontal line, and their colour intensity indicates the kernel density estimate of the displacement." style="display: block; margin: auto;" /></p>
|
||
<p>However, using this stat with the area geom doesn’t work quite right.
|
||
The areas don’t stack on top of each other:</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><span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, <span class="at">fill =</span> drv)) <span class="sc">+</span> </span>
|
||
<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a> <span class="fu">stat_density_common</span>(<span class="at">bandwidth =</span> <span class="dv">1</span>, <span class="at">geom =</span> <span class="st">"area"</span>, <span class="at">position =</span> <span class="st">"stack"</span>)</span></code></pre></div>
|
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<p><img 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" alt="An area plot showing the kernel density estimates of engine displacement. Three areas are shown that indicate the estimates for three types of drive trains separately. All areas are floored to the x-axis and overlap one another." style="display: block; margin: auto;" /></p>
|
||
<p>This is because each density is computed independently, and the
|
||
estimated <code>x</code>s don’t line up. We can resolve that issue by
|
||
computing the range of the data once in <code>setup_params()</code>.</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>StatDensityCommon <span class="ot"><-</span> <span class="fu">ggproto</span>(<span class="st">"StatDensityCommon"</span>, Stat, </span>
|
||
<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a> <span class="at">required_aes =</span> <span class="st">"x"</span>,</span>
|
||
<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a> <span class="at">default_aes =</span> <span class="fu">aes</span>(<span class="at">y =</span> <span class="fu">after_stat</span>(density)),</span>
|
||
<span id="cb14-4"><a href="#cb14-4" tabindex="-1"></a></span>
|
||
<span id="cb14-5"><a href="#cb14-5" tabindex="-1"></a> <span class="at">setup_params =</span> <span class="cf">function</span>(data, params) {</span>
|
||
<span id="cb14-6"><a href="#cb14-6" tabindex="-1"></a> min <span class="ot"><-</span> <span class="fu">min</span>(data<span class="sc">$</span>x) <span class="sc">-</span> <span class="dv">3</span> <span class="sc">*</span> params<span class="sc">$</span>bandwidth</span>
|
||
<span id="cb14-7"><a href="#cb14-7" tabindex="-1"></a> max <span class="ot"><-</span> <span class="fu">max</span>(data<span class="sc">$</span>x) <span class="sc">+</span> <span class="dv">3</span> <span class="sc">*</span> params<span class="sc">$</span>bandwidth</span>
|
||
<span id="cb14-8"><a href="#cb14-8" tabindex="-1"></a> </span>
|
||
<span id="cb14-9"><a href="#cb14-9" tabindex="-1"></a> <span class="fu">list</span>(</span>
|
||
<span id="cb14-10"><a href="#cb14-10" tabindex="-1"></a> <span class="at">bandwidth =</span> params<span class="sc">$</span>bandwidth,</span>
|
||
<span id="cb14-11"><a href="#cb14-11" tabindex="-1"></a> <span class="at">min =</span> min,</span>
|
||
<span id="cb14-12"><a href="#cb14-12" tabindex="-1"></a> <span class="at">max =</span> max,</span>
|
||
<span id="cb14-13"><a href="#cb14-13" tabindex="-1"></a> <span class="at">na.rm =</span> params<span class="sc">$</span>na.rm</span>
|
||
<span id="cb14-14"><a href="#cb14-14" tabindex="-1"></a> )</span>
|
||
<span id="cb14-15"><a href="#cb14-15" tabindex="-1"></a> },</span>
|
||
<span id="cb14-16"><a href="#cb14-16" tabindex="-1"></a> </span>
|
||
<span id="cb14-17"><a href="#cb14-17" tabindex="-1"></a> <span class="at">compute_group =</span> <span class="cf">function</span>(data, scales, min, max, <span class="at">bandwidth =</span> <span class="dv">1</span>) {</span>
|
||
<span id="cb14-18"><a href="#cb14-18" tabindex="-1"></a> d <span class="ot"><-</span> <span class="fu">density</span>(data<span class="sc">$</span>x, <span class="at">bw =</span> bandwidth, <span class="at">from =</span> min, <span class="at">to =</span> max)</span>
|
||
<span id="cb14-19"><a href="#cb14-19" tabindex="-1"></a> <span class="fu">data.frame</span>(<span class="at">x =</span> d<span class="sc">$</span>x, <span class="at">density =</span> d<span class="sc">$</span>y)</span>
|
||
<span id="cb14-20"><a href="#cb14-20" tabindex="-1"></a> } </span>
|
||
<span id="cb14-21"><a href="#cb14-21" tabindex="-1"></a>)</span>
|
||
<span id="cb14-22"><a href="#cb14-22" tabindex="-1"></a></span>
|
||
<span id="cb14-23"><a href="#cb14-23" tabindex="-1"></a><span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, <span class="at">fill =</span> drv)) <span class="sc">+</span> </span>
|
||
<span id="cb14-24"><a href="#cb14-24" tabindex="-1"></a> <span class="fu">stat_density_common</span>(<span class="at">bandwidth =</span> <span class="dv">1</span>, <span class="at">geom =</span> <span class="st">"area"</span>, <span class="at">position =</span> <span class="st">"stack"</span>)</span></code></pre></div>
|
||
<p><img 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" alt="A stacked area plot showing kernel density estimates of engine displacement. Three areas are shown that indicate the estimates for three types of drive trains separately. The areas are stacked on top of one another and show no overlap." style="display: block; margin: auto;" /></p>
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<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a><span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, drv, <span class="at">fill =</span> <span class="fu">after_stat</span>(density))) <span class="sc">+</span> </span>
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<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a> <span class="fu">stat_density_common</span>(<span class="at">bandwidth =</span> <span class="dv">1</span>, <span class="at">geom =</span> <span class="st">"raster"</span>)</span></code></pre></div>
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<p><img 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" alt="A heatmap showing the density of engine displacement for three types of drive trains. The heatmap has three rows for the drive trains, but are continuous in the horizontal direction. The fill intensity of the heatmap shows the kernel density estimates." style="display: block; margin: auto;" /></p>
|
||
</div>
|
||
<div id="exercises" class="section level3">
|
||
<h3>Exercises</h3>
|
||
<ol style="list-style-type: decimal">
|
||
<li><p>Extend <code>stat_chull</code> to compute the alpha hull, as from
|
||
the <a href="https://cran.r-project.org/package=alphahull">alphahull</a>
|
||
package. Your new stat should take an <code>alpha</code>
|
||
argument.</p></li>
|
||
<li><p>Modify the final version of <code>StatDensityCommon</code> to
|
||
allow the user to specify the <code>min</code> and <code>max</code>
|
||
parameters. You’ll need to modify both the layer function and the
|
||
<code>compute_group()</code> method.</p>
|
||
<p>Note: be careful when adding parameters to a layer function. The
|
||
following names <em>col</em>, <em>color</em>, <em>pch</em>,
|
||
<em>cex</em>, <em>lty</em>, <em>lwd</em>, <em>srt</em>, <em>adj</em>,
|
||
<em>bg</em>, <em>fg</em>, <em>min</em>, and <em>max</em> are
|
||
intentionally renamed to accommodate base graphical parameter names. For
|
||
example, a value passed as <em>min</em> to a layer appears as
|
||
<em>ymin</em> in the <code>setup_params</code> list of params. It is
|
||
recommended you avoid using these names for layer parameters.</p></li>
|
||
<li><p>Compare and contrast <code>StatLm</code> to
|
||
<code>ggplot2::StatSmooth</code>. What key differences make
|
||
<code>StatSmooth</code> more complex than <code>StatLm</code>?</p></li>
|
||
</ol>
|
||
</div>
|
||
</div>
|
||
<div id="creating-a-new-geom" class="section level2">
|
||
<h2>Creating a new geom</h2>
|
||
<p>It’s harder to create a new geom than a new stat because you also
|
||
need to know some grid. ggplot2 is built on top of grid, so you’ll need
|
||
to know the basics of drawing with grid. If you’re serious about adding
|
||
a new geom, I’d recommend buying <a href="https://www.amazon.com/dp/B00I60M26G/ref=cm_sw_su_dp">R
|
||
graphics</a> by Paul Murrell. It tells you everything you need to know
|
||
about drawing with grid.</p>
|
||
<div id="a-simple-geom" class="section level3">
|
||
<h3>A simple geom</h3>
|
||
<p>It’s easiest to start with a simple example. The code below is a
|
||
simplified version of <code>geom_point()</code>:</p>
|
||
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a>GeomSimplePoint <span class="ot"><-</span> <span class="fu">ggproto</span>(<span class="st">"GeomSimplePoint"</span>, Geom,</span>
|
||
<span id="cb16-2"><a href="#cb16-2" tabindex="-1"></a> <span class="at">required_aes =</span> <span class="fu">c</span>(<span class="st">"x"</span>, <span class="st">"y"</span>),</span>
|
||
<span id="cb16-3"><a href="#cb16-3" tabindex="-1"></a> <span class="at">default_aes =</span> <span class="fu">aes</span>(<span class="at">shape =</span> <span class="dv">19</span>, <span class="at">colour =</span> <span class="st">"black"</span>),</span>
|
||
<span id="cb16-4"><a href="#cb16-4" tabindex="-1"></a> <span class="at">draw_key =</span> draw_key_point,</span>
|
||
<span id="cb16-5"><a href="#cb16-5" tabindex="-1"></a></span>
|
||
<span id="cb16-6"><a href="#cb16-6" tabindex="-1"></a> <span class="at">draw_panel =</span> <span class="cf">function</span>(data, panel_params, coord) {</span>
|
||
<span id="cb16-7"><a href="#cb16-7" tabindex="-1"></a> coords <span class="ot"><-</span> coord<span class="sc">$</span><span class="fu">transform</span>(data, panel_params)</span>
|
||
<span id="cb16-8"><a href="#cb16-8" tabindex="-1"></a> grid<span class="sc">::</span><span class="fu">pointsGrob</span>(</span>
|
||
<span id="cb16-9"><a href="#cb16-9" tabindex="-1"></a> coords<span class="sc">$</span>x, coords<span class="sc">$</span>y,</span>
|
||
<span id="cb16-10"><a href="#cb16-10" tabindex="-1"></a> <span class="at">pch =</span> coords<span class="sc">$</span>shape,</span>
|
||
<span id="cb16-11"><a href="#cb16-11" tabindex="-1"></a> <span class="at">gp =</span> grid<span class="sc">::</span><span class="fu">gpar</span>(<span class="at">col =</span> coords<span class="sc">$</span>colour)</span>
|
||
<span id="cb16-12"><a href="#cb16-12" tabindex="-1"></a> )</span>
|
||
<span id="cb16-13"><a href="#cb16-13" tabindex="-1"></a> }</span>
|
||
<span id="cb16-14"><a href="#cb16-14" tabindex="-1"></a>)</span>
|
||
<span id="cb16-15"><a href="#cb16-15" tabindex="-1"></a></span>
|
||
<span id="cb16-16"><a href="#cb16-16" tabindex="-1"></a>geom_simple_point <span class="ot"><-</span> <span class="cf">function</span>(<span class="at">mapping =</span> <span class="cn">NULL</span>, <span class="at">data =</span> <span class="cn">NULL</span>, <span class="at">stat =</span> <span class="st">"identity"</span>,</span>
|
||
<span id="cb16-17"><a href="#cb16-17" tabindex="-1"></a> <span class="at">position =</span> <span class="st">"identity"</span>, <span class="at">na.rm =</span> <span class="cn">FALSE</span>, <span class="at">show.legend =</span> <span class="cn">NA</span>, </span>
|
||
<span id="cb16-18"><a href="#cb16-18" tabindex="-1"></a> <span class="at">inherit.aes =</span> <span class="cn">TRUE</span>, ...) {</span>
|
||
<span id="cb16-19"><a href="#cb16-19" tabindex="-1"></a> <span class="fu">layer</span>(</span>
|
||
<span id="cb16-20"><a href="#cb16-20" tabindex="-1"></a> <span class="at">geom =</span> GeomSimplePoint, <span class="at">mapping =</span> mapping, <span class="at">data =</span> data, <span class="at">stat =</span> stat, </span>
|
||
<span id="cb16-21"><a href="#cb16-21" tabindex="-1"></a> <span class="at">position =</span> position, <span class="at">show.legend =</span> show.legend, <span class="at">inherit.aes =</span> inherit.aes,</span>
|
||
<span id="cb16-22"><a href="#cb16-22" tabindex="-1"></a> <span class="at">params =</span> <span class="fu">list</span>(<span class="at">na.rm =</span> na.rm, ...)</span>
|
||
<span id="cb16-23"><a href="#cb16-23" tabindex="-1"></a> )</span>
|
||
<span id="cb16-24"><a href="#cb16-24" tabindex="-1"></a>}</span>
|
||
<span id="cb16-25"><a href="#cb16-25" tabindex="-1"></a></span>
|
||
<span id="cb16-26"><a href="#cb16-26" tabindex="-1"></a><span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, hwy)) <span class="sc">+</span> </span>
|
||
<span id="cb16-27"><a href="#cb16-27" tabindex="-1"></a> <span class="fu">geom_simple_point</span>()</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" alt="Scatterplot of engine displacement versus highway miles per gallon, for 234 cars. The points are larger than the default." style="display: block; margin: auto;" /></p>
|
||
<p>This is very similar to defining a new stat. You always need to
|
||
provide fields/methods for the four pieces shown above:</p>
|
||
<ul>
|
||
<li><p><code>required_aes</code> is a character vector which lists all
|
||
the aesthetics that the user must provide.</p></li>
|
||
<li><p><code>default_aes</code> lists the aesthetics that have default
|
||
values.</p></li>
|
||
<li><p><code>draw_key</code> provides the function used to draw the key
|
||
in the legend. You can see a list of all the build in key functions in
|
||
<code>?draw_key</code></p></li>
|
||
<li><p><code>draw_panel()</code> is where the magic happens. This
|
||
function takes three arguments and returns a grid grob. It is called
|
||
once for each panel. It’s the most complicated part and is described in
|
||
more detail below.</p></li>
|
||
</ul>
|
||
<p><code>draw_panel()</code> has three arguments:</p>
|
||
<ul>
|
||
<li><p><code>data</code>: a data frame with one column for each
|
||
aesthetic.</p></li>
|
||
<li><p><code>panel_params</code>: a list of per-panel parameters
|
||
generated by the coord. You should consider this an opaque data
|
||
structure: don’t look inside it, just pass along to <code>coord</code>
|
||
methods.</p></li>
|
||
<li><p><code>coord</code>: an object describing the coordinate
|
||
system.</p></li>
|
||
</ul>
|
||
<p>You need to use <code>panel_params</code> and <code>coord</code>
|
||
together to transform the data
|
||
<code>coords <- coord$transform(data, panel_params)</code>. This
|
||
creates a data frame where position variables are scaled to the range
|
||
0–1. You then take this data and call a grid grob function.
|
||
(Transforming for non-Cartesian coordinate systems is quite complex -
|
||
you’re best off transforming your data to the form accepted by an
|
||
existing ggplot2 geom and passing it.)</p>
|
||
</div>
|
||
<div id="collective-geoms" class="section level3">
|
||
<h3>Collective geoms</h3>
|
||
<p>Overriding <code>draw_panel()</code> is most appropriate if there is
|
||
one graphic element per row. In other cases, you want graphic element
|
||
per group. For example, take polygons: each row gives one vertex of a
|
||
polygon. In this case, you should instead override
|
||
<code>draw_group()</code>.</p>
|
||
<p>The following code makes a simplified version of
|
||
<code>GeomPolygon</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>GeomSimplePolygon <span class="ot"><-</span> <span class="fu">ggproto</span>(<span class="st">"GeomPolygon"</span>, Geom,</span>
|
||
<span id="cb17-2"><a href="#cb17-2" tabindex="-1"></a> <span class="at">required_aes =</span> <span class="fu">c</span>(<span class="st">"x"</span>, <span class="st">"y"</span>),</span>
|
||
<span id="cb17-3"><a href="#cb17-3" tabindex="-1"></a> </span>
|
||
<span id="cb17-4"><a href="#cb17-4" tabindex="-1"></a> <span class="at">default_aes =</span> <span class="fu">aes</span>(</span>
|
||
<span id="cb17-5"><a href="#cb17-5" tabindex="-1"></a> <span class="at">colour =</span> <span class="cn">NA</span>, <span class="at">fill =</span> <span class="st">"grey20"</span>, <span class="at">linewidth =</span> <span class="fl">0.5</span>,</span>
|
||
<span id="cb17-6"><a href="#cb17-6" tabindex="-1"></a> <span class="at">linetype =</span> <span class="dv">1</span>, <span class="at">alpha =</span> <span class="dv">1</span></span>
|
||
<span id="cb17-7"><a href="#cb17-7" tabindex="-1"></a> ),</span>
|
||
<span id="cb17-8"><a href="#cb17-8" tabindex="-1"></a></span>
|
||
<span id="cb17-9"><a href="#cb17-9" tabindex="-1"></a> <span class="at">draw_key =</span> draw_key_polygon,</span>
|
||
<span id="cb17-10"><a href="#cb17-10" tabindex="-1"></a></span>
|
||
<span id="cb17-11"><a href="#cb17-11" tabindex="-1"></a> <span class="at">draw_group =</span> <span class="cf">function</span>(data, panel_params, coord) {</span>
|
||
<span id="cb17-12"><a href="#cb17-12" tabindex="-1"></a> n <span class="ot"><-</span> <span class="fu">nrow</span>(data)</span>
|
||
<span id="cb17-13"><a href="#cb17-13" tabindex="-1"></a> <span class="cf">if</span> (n <span class="sc"><=</span> <span class="dv">2</span>) <span class="fu">return</span>(grid<span class="sc">::</span><span class="fu">nullGrob</span>())</span>
|
||
<span id="cb17-14"><a href="#cb17-14" tabindex="-1"></a></span>
|
||
<span id="cb17-15"><a href="#cb17-15" tabindex="-1"></a> coords <span class="ot"><-</span> coord<span class="sc">$</span><span class="fu">transform</span>(data, panel_params)</span>
|
||
<span id="cb17-16"><a href="#cb17-16" tabindex="-1"></a> <span class="co"># A polygon can only have a single colour, fill, etc, so take from first row</span></span>
|
||
<span id="cb17-17"><a href="#cb17-17" tabindex="-1"></a> first_row <span class="ot"><-</span> coords[<span class="dv">1</span>, , drop <span class="ot">=</span> <span class="cn">FALSE</span>]</span>
|
||
<span id="cb17-18"><a href="#cb17-18" tabindex="-1"></a></span>
|
||
<span id="cb17-19"><a href="#cb17-19" tabindex="-1"></a> grid<span class="sc">::</span><span class="fu">polygonGrob</span>(</span>
|
||
<span id="cb17-20"><a href="#cb17-20" tabindex="-1"></a> coords<span class="sc">$</span>x, coords<span class="sc">$</span>y, </span>
|
||
<span id="cb17-21"><a href="#cb17-21" tabindex="-1"></a> <span class="at">default.units =</span> <span class="st">"native"</span>,</span>
|
||
<span id="cb17-22"><a href="#cb17-22" tabindex="-1"></a> <span class="at">gp =</span> grid<span class="sc">::</span><span class="fu">gpar</span>(</span>
|
||
<span id="cb17-23"><a href="#cb17-23" tabindex="-1"></a> <span class="at">col =</span> first_row<span class="sc">$</span>colour,</span>
|
||
<span id="cb17-24"><a href="#cb17-24" tabindex="-1"></a> <span class="at">fill =</span> scales<span class="sc">::</span><span class="fu">alpha</span>(first_row<span class="sc">$</span>fill, first_row<span class="sc">$</span>alpha),</span>
|
||
<span id="cb17-25"><a href="#cb17-25" tabindex="-1"></a> <span class="at">lwd =</span> first_row<span class="sc">$</span>linewidth <span class="sc">*</span> .pt,</span>
|
||
<span id="cb17-26"><a href="#cb17-26" tabindex="-1"></a> <span class="at">lty =</span> first_row<span class="sc">$</span>linetype</span>
|
||
<span id="cb17-27"><a href="#cb17-27" tabindex="-1"></a> )</span>
|
||
<span id="cb17-28"><a href="#cb17-28" tabindex="-1"></a> )</span>
|
||
<span id="cb17-29"><a href="#cb17-29" tabindex="-1"></a> }</span>
|
||
<span id="cb17-30"><a href="#cb17-30" tabindex="-1"></a>)</span>
|
||
<span id="cb17-31"><a href="#cb17-31" tabindex="-1"></a>geom_simple_polygon <span class="ot"><-</span> <span class="cf">function</span>(<span class="at">mapping =</span> <span class="cn">NULL</span>, <span class="at">data =</span> <span class="cn">NULL</span>, <span class="at">stat =</span> <span class="st">"chull"</span>,</span>
|
||
<span id="cb17-32"><a href="#cb17-32" tabindex="-1"></a> <span class="at">position =</span> <span class="st">"identity"</span>, <span class="at">na.rm =</span> <span class="cn">FALSE</span>, <span class="at">show.legend =</span> <span class="cn">NA</span>, </span>
|
||
<span id="cb17-33"><a href="#cb17-33" tabindex="-1"></a> <span class="at">inherit.aes =</span> <span class="cn">TRUE</span>, ...) {</span>
|
||
<span id="cb17-34"><a href="#cb17-34" tabindex="-1"></a> <span class="fu">layer</span>(</span>
|
||
<span id="cb17-35"><a href="#cb17-35" tabindex="-1"></a> <span class="at">geom =</span> GeomSimplePolygon, <span class="at">mapping =</span> mapping, <span class="at">data =</span> data, <span class="at">stat =</span> stat, </span>
|
||
<span id="cb17-36"><a href="#cb17-36" tabindex="-1"></a> <span class="at">position =</span> position, <span class="at">show.legend =</span> show.legend, <span class="at">inherit.aes =</span> inherit.aes,</span>
|
||
<span id="cb17-37"><a href="#cb17-37" tabindex="-1"></a> <span class="at">params =</span> <span class="fu">list</span>(<span class="at">na.rm =</span> na.rm, ...)</span>
|
||
<span id="cb17-38"><a href="#cb17-38" tabindex="-1"></a> )</span>
|
||
<span id="cb17-39"><a href="#cb17-39" tabindex="-1"></a>}</span>
|
||
<span id="cb17-40"><a href="#cb17-40" tabindex="-1"></a></span>
|
||
<span id="cb17-41"><a href="#cb17-41" tabindex="-1"></a><span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, hwy)) <span class="sc">+</span> </span>
|
||
<span id="cb17-42"><a href="#cb17-42" tabindex="-1"></a> <span class="fu">geom_point</span>() <span class="sc">+</span> </span>
|
||
<span id="cb17-43"><a href="#cb17-43" tabindex="-1"></a> <span class="fu">geom_simple_polygon</span>(<span class="fu">aes</span>(<span class="at">colour =</span> class), <span class="at">fill =</span> <span class="cn">NA</span>)</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAKgCAYAAABEPM/FAAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+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/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAKgoAMABAAAAAEAAAKgAAAAAMw48TsAAEAASURBVHgB7J0HfJvV9fd/2rI85JG9h0MSsid7k0ESdiDs0kJpoaUtUEopLS2lg1FGWX1bKPwLhIY9QhIgkDBTyCaDTGfvxLa8ZEvWeO95HDmyLduSLcmP7N/lE/ToPveee+73PpKO7zjHEFQJTCRAAiRAAiRAAiRAAiSQJALGJLXDZkiABEiABEiABEiABEhAI0ADlA8CCZAACZAACZAACZBAUgnQAE0qbjZGAiRAAiRAAiRAAiRAA5TPAAmQAAmQAAmQAAmQQFIJ0ABNKm42RgIkQAIkQAIkQAIkQAOUzwAJkAAJkAAJkAAJkEBSCZiT2locGispKYHf749ZktVqhdfrjbleMiuYTCZYLBZUVVUls9mY2zIYDBBdfT5fzHWTWUFYiq6pMO7iDS0QCCQTT8xt2Ww2Tcfq6uqY6yazgtls1r4j9O5hzm63Q1i25PssmTxT4bvTaDRC9PR4PND7uKcCT/kMyb9E/BbJb4fT6UzmI8y2dEog5QxQMSZaYvg4HA6UlZXpdBhq1BId5Z8Y2XpOIUNZvuz1nOQHXoxQvY97Wlqa9qOpd56ZmZmaMa93PcVQlh9OPRt28odRbm4uXC6XZjTp+XOUCt+dYtSJnhUVFS36fUgm/1TgKcanfH8m4rdIfj+YSEAIcAmezwEJkAAJkAAJkAAJkEBSCdAATSpuNkYCJEACJEACJEACJEADlM8ACZAACZAACZAACZBAUgnQAE0qbjZGAiRAAiRAAiRAAiRAA5TPAAmQAAmQAAmQAAmQQFIJ0ABNKm42RgIkQAIkQAIkQAIkQAOUzwAJkAAJkAAJkAAJkEBSCdAATSpuNkYCJEACJEACJEACJJByjujF4bD8izWJ81txAKznFOqX3vWUqCPi4F3veoozZdFV73oKS0miq56T6CdM9c4z5ERb7xFxZKzlMy9O6fWcUuG7M+TcXJyn6z2iWCrwDD2Xev+s6/lzQ92aJ5ByBqhEN2nJD4vUaUkEpeYRxq9E6EtU73qGDBG96yk/RKKr3vWUcU+F51N0TBU95XtC74aIfHOIjnp/PlNhzEPfwjLueo6AJXqmAk/5I05SIp5Nvf/BpXWc/0sKgZQ0QFvyoZAver3HBA996PWupxhMMmundz1DIS71rmfIANW7nvLDKT/uetczFWKsh36E5btM7zxT4bsz9GtZXV2dEKMpJD8er6nAU77f5fOeiGczNNESD5aUkdoE9L3ml9psqT0JkAAJkAAJkAAJkEAEAjRAI0BhFgmQAAmQAAmQAAmQQOII0ABNHFtKJgESIAESIAESIAESiECABmgEKMwiARIgARIgARIgARJIHAEaoIljS8kkQAIkQAIkQAIkQAIRCNAAjQCFWSRAAiRAAiRAAiRAAokjQAM0cWwpmQRIgARIgARIgARIIAIBGqARoDCLBEiABEiABEiABEggcQRogCaOLSWTAAmQAAmQAAmQAAlEIEADNAIUZpEACZAACZAACZAACSSOAA3QxLGlZBIgARIgARIgARIggQgEaIBGgMIsEiABEiABEiABEiCBxBGgAZo4tnUkL1myBN/73vdwww03YPfu3XXu8Q0JkAAJkAAJkAAJdCQCNECTMNovvPACZs6ciYULF2LBggU44YQT8OWXXyahZTZBAiRAAiRAAiRAAvojQAM0wWNy4MAB3HPPPVorVrMVRqNJu77mmmvg9XoT3DrFkwAJkAAJkAAJkID+CNAATfCYrFmzBgaDQWvlrz+eiwd+9J52Lcanz+dLcOsUTwIkQAIkQAIkQAL6I0ADNMFj0qdPHwSDQa0Vq3kU0mzTMGnCT7T3NEATDJ/iSYAESIAESIAEdEnArEut2pFSQ4YMwTnnnIPPP12ietUVJuMBTD/xQYw7vTeysrLaUU/ZFRIgARIgARIgARKIjgBnQKPj1KpSL730En552x81GblZP4fVUowRXW9DMNAqsaxMAiRAAiRAAiRAAilJgAZokobtkulXai11HdAZXXN+gKJ9Fmz8OiNJrbMZEiABEiABEiABEtAPARqgSRqL8iK/aimAzvnjkZm9AV17zMWGJRlwHeQuiCQNAZshARIgARIgARLQCQEaoEkaiAoXYDbthtWRi06DrkaG8YfIyPZi6fvZ8PMwfJJGgc2QAAmQAAmQAAnogQAN0CSNQoXLpAzQHTBZncgZcImaDHUhf8QrKC82Y91nPIyUpGFgMyRAAiRAAiRAAjogQAM0SYPgLrPDogxQszJAbRm9kdnjTFQXPYPjTy3D1hUOHN5lTZImbIYESIAESIAESIAE2pYADdAk8a8sz4DZrGZAbU6txbz8WagsXIPeA/+H3O7VWD7fCW9VkpRhMyRAAiRAAiRAAiTQhgRogCYBvrfKoPZ52pT7pV0wmuxai1k9z9aW44u2vYrx013wuE1YOj8tCdqwCRIgARIgARIgARJoWwI0QJPAX/Z/SrKlHaltzWiyIXfg5XDtfB/pTjdGnl2K7Wus2La2tggvSIAESIAESIAESKBdEqABmoRhrSipcbVkTyup01rugEvh95bAtWMuBox2o/vAaix6Faiq4LDUAcU3JEACJEACJEAC7YoALZ0kDKfMgBoM1bA5quu0lpYzBOmdx6Nw6xwt/+QLK1XceGDlBzX7ROsU5hsSIAESIAESIAESaCcEaIAmYSDdJSZYrfthPnoAKbzJvPwrUHF4OTxlu+DICuLMmcD+Aju2f8v9oOGceE0CJEACJEACJNB+CNAATcJYVigD1GLZrR06qt+cs89UdTApDYVbXtZuDRoD9D7ejTWLsxDaO1q/Dt+TAAmQAAmQAAmQQCoToAGahNGTPaAW03Y1A5rdoDWTJUM5pr8YRdvfQTAg4TqB0eeWKn+hQSybn60tyTeoxAwSIAESIAESIAESSGECNEATPHiyp1OW4E3GLRFnQKV5WYb3VR5C0c4FmjZWexDjp7lQuMeCTd+kJ1hDiicBEiABEiABEiCB5BKgAZpg3lXlRgT8BpiwqVED1JE3ErasfBzcWLMMLyp17efFwLFubPgqEyWHa07RJ1hViicBEiABEiABEiCBpBCgAZpgzLL/U5IswZsiLMGHmu80+Fq49nwGT/mBUBaGn1GqDib5sez9bGXE1mbzggRIgARIgARIgARSmgAN0AQPn/uoD1AJw2m2ZjXaWk7f85WrJgN2rXmutozZAkyY4ULpETPWf5FZm88LEiABEiABEiABEkhlAjRAEzx6MgNqtvjUHlBXo0vwooLZnou8ARdg3/pX6mgkceKHnFyOzUvTcUTtCWUiARIgARIgARIggVQnQAM0wSMorpTSMsq1VkzWhqfgw5vvOuQauF0FKNv/RXg2hpxUjuyuPiyflw2f11DnHt+QAAmQAAmQAAmQQKoRoAGa4BGTGVCbo1hrxRTBEX1481ndT4Y9s3dtZKTQPaMapQkzilFZbsLqTxpfxg+V5ysJkAAJkAAJkAAJ6JkADdAEj464YLLbC7VWTJamjUeDwYg+o3+Ikt0fq5nOunHjs/L8GHFWKXaudahISbYEa03xJEACJEACJEACJJA4AjRAE8cWgYDyAVqmwnDaDsBodqiIR9ZmW+tx/FXKIb0XRQWvNyg7cIwbnft6sGKBEx43l+IbAGIGCZAACZAACZBAShCgAZrAYXKXKhdMQYOKAx85DGekptOyeiOr59ko3vZWg9vqkDzGn+eC32fAyg+dDe4zgwRIgARIgARIgARSgQAN0ASOklsdQJJkNu9s8gR8fRUkMlJl8XeoOLyy/i3lFzSAMZNLsG9LGnasTWtwnxkkQAIkQAIkQAIkoHcCNEATOEIhJ/RmFESMA99Y01k9z1RO63MbHEYKle9zfBV6HleJb9WBJHcphzDEha8kQAIkQAIkQAKpQYDWSwLHqcJlhi3dj6D/cEwzoAajRYsP79q1AAFfVUQNx0wpgckSxPL52ZB480wkQAIkQAIkQAIkkCoEaIAmcKRkBjTd6YdfnWg3WWPbs5k74BIEqstQvP2diBra0oIYN7UEh3fZsGV5esQyzCQBEiABEiABEiABPRKgAZrAUQkZoOJSKVYD1O7MR0bXk1BY8GqjGnYf6MGA0RVY/3kmSgvNjZbjDRIgARIgARIgARLQEwEaoAkcDfEBmu70aTOg5hhnQEWt3PxZcB9ZharSbY1qOeLMMqRl+rHs/WwE/I0W4w0SIAESIAESIAES0A0BGqAJGgpfNZSvTpM6tV6tltLL1aGi2JbgRa3s3lOU/9B0FG5+uVEtzdYgJkx3wXXQjO++ymy0HG+QAAmQAAmQAAmQgF4I0ABN0Ei4S2qWxO3ppVoLsS7BSyWjOQ25Ay5F8Y73lHN6X6Oa5vWsxpATy7Hpm3QU7bM0Wo43SIAESIAESIAESEAPBGiAJmgUKo76ALWnHQ3Dac1uUUtigPqqjkBOxDeVhp5SDmcnH5bNy4bMvjKRAAmQAAmQAAmQgF4J0ABN0MhoPkANQZith7QWWrIHVCo6Oo1CWu6IRn2ChtQ3Kp/3E2a4lF9QE9YsbjrmfKgOX0mABEiABEiABEigLQjQAE0QdTFAHepwUNDn0lpoyR7QkGp56jBS+YEl8FbsD2VFfHV29mH46WXYvjodB7Y3H3c+ohBmkgAJkAAJkAAJkECCCdAATRBg2QOanq18gHpKtBZMLVyCl8rZfc+HOKcv3PpKs9rmj69Ap14erFiQDW+VCh7PRAIkQAIkQAIkQAI6I0ADNEEDIntAHcoJvfgAlWSytPyEulmdoM/udz6Kt72toh41HfbIoGzO8dNLVLsGrPoo9pP3CcJBsSRAAiRAAiRAAiRQS4AGaC2K+F6EnNBLFCSjMj4NskmzFSkv/wq1BL8HZfsWNytFoi+NPrcUezamYdd6e7PlWYAESIAESIAESIAEkkmABmgCaHsrDWoG0tgqJ/T11UrvPB7WjN5qGb7xyEjhdfoOr0T3/Cqs/tiJyjIOczgbXpMACZAACZAACbQtAVomCeBfcdQHaM0eUFeLnNDXV8ug1tbzBl2D0r2L4PMU178d8f3YKSVq5jWI5Wo/KBMJkAAJkAAJkAAJ6IUADdAEjITmgknJlaXwlsSBb0yl3P4XqT2gfjUL+lpjRerk29MDGDe1BId22LB1haPOPb4hARIgARIgARIggbYiQAM0AeTlAJLRFIRNGYCyB9TcihPw4epZHF3h7DVZHUZ6Izy7yesegzzoN8KNdZ9loayodftQm2yIN0mABEiABEiABEggSgI0QKMEFUuxmgNIPsiJdDFAWxKGs7H2xCdoVckWlB9c2liRBvkjzy6FzeHXoiQFAw1uM4MESIAESIAESIAEkkqABmgCcLtdNT5ARbTfo/aAWuPnDimz+2kw2zs3GxkpvFsWWxDjp5WgeL8FG/6XEX6L1yRAAiRAAiRAAiSQdAI0QBOAXIuCpPZ/StL2gCo/nvFKBqNZHUa6EiW7P4C/uiJqsZ37eHHcxApsXJKB4gOWqOuxIAmQAAmQAAmQAAnEmwAN0DgTFT/xEo9dDiAF/F4E/VVqBjS+p9BzB1yCgM+N4u1vx6T98aeWITPPh2XvO+H3xVSVhUmABEiABEiABEggbgRogMYNZY2gqnKjMjwNNWE4vTVx4M1xXIKXVmyZ/ZDR7dSYluGlnskMTJjuQrnaIrD20yzJYiIBEiABEiABEiCBpBOgARpn5MdcMPm0A0giXvaAHj58GHPmzMEbb7wBt9vd6lYlMlJl0TpUujbHJCu7qw/D1ExowUoHDu20xlSXhUmABEiABEiABEggHgRogMaDYpiMCjW7KEniwPu9pdr1rn1FGD16NO69917cfffdyM/Px4EDB7R7Lf2fs/e5Kr58Fgo3vxSzCNkLmtujGsvnZ6Pao47qM5EACZAACZAACZBAEgnQAI0zbJkBtdgCsNqDKmJRzRL81df9SDmQD6K8vBwVFTUHh6677jotr6XNG0125A68DMU75mp7TWORY1CjLkvx3ioDVi2M3wGpWHRgWRIgARIgARIggY5LgAZonMferQxQmf2UJD5AJRnVTGX9tGXLFni93vrZMb3PUYeR/GqfqWvX/JjqSeGMHD9GKf+gu79Lw55N9pjrswIJkAAJkAAJkAAJtJQADdCWkmukXsgJvdyuMUCNcJV6GpT2eDytmgEVgY7cYXB0GoOirXMayI8mo/+oSnQbUIVVHzkhh6eYSIAESIAESIAESCAZBGh1xJmy7AFNzz7qA1RzQp+FG2/8Iez2Y7OMVqsVTz75ZJ28lqohh5HKD34NT/nuFokYq2LFQ7mOWvEBl+JbBJCVSIAESIAESIAEYiZAAzRmZI1XCCi7s1LNJIoPUEmhMJy/+c1v8MADD2DkyJGYOHEinn/+eVx66aWNC4rhTnafaTCo/aCFW16JodaxomkZAYyZUoID2+zYtspx7AavSIAESIAESIAESCBBBGqObCdIeEcTKw7oETTU2QNqttU4ob/88ssh/+KdTNZM5PS/UHNK3330nSr+fOx/U/QaXIX9w9xY82kmuvT3IOPoDG68daU8EiABEiABEiABEhACsVsr5NYogVofoNk1YYbkgFA848A31nDewFmodh9A6Z5PGivSbP6oc0phVTHjl8/LVntTmy3OAiRAAiRAAiRAAiTQYgI0QFuMrmFFOQEvKT0rtAe0JCkGaHrnsbBlDYg5MlJ4D8Rt1PhpLhTutWDT1+nht3hNAiRAAiRAAiRAAnElQAM0jjjlAJI93a8cxNcIFUf0pqNL8HFsJqKovEFXo3TfZ6iuOhLxfjSZXfp5kT/Oje++yoTrEHdnRMOMZUiABEiABEiABGInQAM0dmaN1pAl+JAPUCkkh5DiHQe+scZlH6ikoi0tc8kUkjv89FJtD+iy97Phr9lJELrFVxIgARIgARIgARKICwEaoHHBWCOkwmWqdcEkOTWn4Bs6oY9jk7WiLPZOyO4zFUXb36rNa8mFzN5KlKSyQjPWf5HZEhGsQwIkQAIkQAIkQAJNEqAB2iSe2G6GO6EP+CoRDHiTsgc0pKX4BPWUbkPZgSWhrBa95nSvxtBTyrFlWTqO7D66n6BFkliJBEiABEiABEiABBoSoAHakEmLcnxeA7yVagb0qA9Q39EwnMnaAypKZ3Q7GRZHN3UY6dUW9SG80uATy5HTrRrL5mej2mMIv8VrEiABEiABEiABEmgVARqgrcJ3rHLIBVNoD6hfRUGSlKw9oNKW+ADNy78KJbs/VMv/ZZLV4mRUT4YsxVdVmPDtJ8nZRtBiZVmRBEiABEiABEggpQjQAI3TcIUM0FAYzpo48EjqErx0JWfAxQj6q1C07c1W9ywzz4+RZ5Zi5zoH9m2xtVoeBZAACZAACZAACZCAEEi6Abp48WKUldWdndu0aRM++ugjHDnSchdCbT2cmg9QQxBpmcfCcIpOJmtNJKRk6WfL6I3MHmcpA/SNuDQ5YIwbXfp6sPJDJzzupD8ucekDhZAACZAACZAACeiLQFItis8//xz33nsvioqKaik89thjePjhh7Fq1SrccMMN2LVrV+29VLoQH6AO5YBelq4l+VQUJEkmm1N7Teb/8vJnobJoHdxHvm11swa1/VMc1Af8Bs0IbbVACiABEiABEiABEujwBJJmgMrs5vPPP4/s7GMzgjt27MAXX3yBf/3rX7jrrrtw5ZVXYvbs2Sk5KDUn4GtmP6UDfk+J2pRphsnsSHp/snqerc28Fha0/jCSKJ+WGcDoSSVqGd6OHWvSkt4fNkgCJEACJEACJNC+CCQl3E1QBRd/4IEH8NOf/hQPPvhgLcFt27Zh5MiRatawxg4eO3Ys5s2bV3tfLgoKCiBL9KEk5dPTYw8VKW3Y7faQmLi/VpaakdfDX9uGIVABiz279n00DZrNNcPRej3t6DL4Khza9DIGnvIXGM2t7/dxY4FD271Ys8iJPkPKYM5SUZ8SyDMaXs2VMZlM6mCWQfd6WiwWyGdE70k+Q/KMpsK422w2BAIBvSNFKox9or874zFIoe9Oq9WqPaPxkJkoGanAU57LVPjuTNQYUW5yCCTFAH3jjTfQu3dvjB8/vk6v9u/fD6fz2BJ1VlYWCgsL65SRvaGPP/54bd5bb72FXr161b6P5UK+nBKVKtSE5+DxZuTk1BzW2YdKWNPy1PucmJtsSZ36jZjH/xD71z6DygML0WvEdfVvt+j9pCuBVx4CvpmbhYt/At0bIqFOxoNnSFZHfxWjXow7vSe9G8khfvLHdEv+oA7VT9ZrIr8749mH8N+TeMqNt6xU4ZmI706v1xtvnJSXogQSboBu374dCxYswD/+8Y8GiOTHzO8/tmzt8/mQllZ3iff666/HrFmzautWV1fj4MGDte+jvZAvppISZSUmIHkqDcpXZhe14bNE6ValtVDm2q/ep8ekq/RdjPCW9K9ht/KQ3mUitq34JyxdpjS83cKccVOt+OK1HHz9gQcDxtbsc22hqIRXE5YyMxK+5zjhjbagATGWZAbU4/G0oHbyquTl5UF+POofIkyeBtG1lJGRAbfbrfsZ0K5du2rfSVVVNd8Z0fUu+aUS+d0Zr97IjF1ubq52kDX8NyVe8uMpJxV4OhwO7Q+jw4cPx7PrmiyZAe7cuXPc5VJg6hFIuAH6ySefYOfOnTj//PM1OpWVlbjxxhvx5z//WXsI16xZU0tNDIXu3bvXvpcLMcrCjVL5QIih2pKUqCW58qKaaEFpWdW1P3o+tQfUZHXWvo9G39AybLz0lMNIu5bcgcqSbbBl9otGhWbLdOlXhfyxbqz6JA25vYzI6tSysWi2oTgUiDfPOKgUUYToKf/iNe4RG4lDZqroKV3VO09Z3kwFPTUl1f9S4dkkz9Botf5VPj+SEjHuoWe/9VpSQqoTSPghJDE2xQiVpXT5161bNzz33HOYOHEiJkyYgHXr1mH37t2aUTl37lwtP9Wg1vcBKvr71Sl4MUDbMjl7T1X7Px04sjm+B7tGnlWuIj4FsfT9bHU6vi17yLZJgARIgARIgARSkUDCDdCmoMgS6U033aTNiF577bXa0t5VV13VVBVd3hMD1GgOwuY4duhBQnG2tQFqsqQjp//FKN7xjopLH7+ZSrPaSnvazCqUHDbjuy8zdTkmVIoESIAESIAESEC/BBK+BF+/66+//nqdrBkzZmDKlCna/jfZu5WKqcYFk0+dGjymvURCSrYT+mOtH7vKy78ChVtmo2TPQmT3Oe/YjVZedekdwBAVL37j1xnonl+FvJ7VrZTI6iRAAiRAAiRAAh2FQJvOgIYgywbyVDU+pQ/ihD7dWXct2u8tTWoc+BDL+q+OvBGwOwcpI3RO/Vutfj/05HI4O/uwbF62crwfZn23WjIFkAAJkAAJkAAJtGcCujBAUx2whOEMN0D9XhVqNOhvkyhIkVjmHae2Nxz4AtXu2L0HRJIXyjOagIkzXKgsM2HN4qxQNl9JgARIgARIgARIoEkCNECbxNP8TTks6C41wZF9bAZUlt8ltfUe0JD2Of3OV9sDTCjcGv9ZUDkFP/yMUmz/1oED2/TvHzLEhK8kQAIkQAIkQAJtR4AGaCvZV5YbtTjp6c5jh3zkAJIkPewBFT3Mthy1/3Maira9mZCIO/nj3OjU24MVC5zwKp+oTCRAAiRAAiRAAiTQFAEaoE3RieKe26XWoVWquwRfY4Ca29gNU7j6ueowkrd8F8r2fxGeHZdrOXw1floJfNUGrPyobV1PxaVDFEICJEACJEACJJBQAjRAW4m3oqTGkYAj7BCS3pbgpYsZXU+ENb0XihKwDC/yxQAffW4p9m5Kw851daNZyX0mEiABEiABEiABEggRoAEaItHCV3HBZLEFYLXXRI4QMX4VBUmSyaafgzkSfSJv0FXKHdMnkChNiUh9h1eix6AqfPtJFtxlfLQSwZgySYAESIAESKA9EKCV0MpRrFBL8OlhB5BEnE9FQTKYbDCa7K2UHt/q4pQ+GPCiqOC1+AoOkzZ2SonqdxAr5mer/aZhN3hJAiRAAiRAAiRAAkcJ0ABt5aNQ44Lp2AEkEVfjhF5/eyGt6d2R1esc7TBSK7vdaHWJBjV2agkO7bRh6wpHo+V4gwRIgARIgARIoOMSoAHayrGXPaDh+z9FnBigZmt2KyUnprpERqpybUTF4RWJaUBJ7ZHvQb+Rbqz7PAtlhTWHtBLWGAWTAAmQAAmQAAmkHAEaoK0YsoBy/Vmp9jqGn4AXcX6PSzdO6Ot3L6vHmTDbOymfoK/WvxXX9yPPKoU93a9FSQoE4iqawkiABEiABEiABFKcAA3QVgygOKAHDBH2gEoceP0twUtXDUYzcvNnwbVrPgK+ylb0vumqFlsQE6a5UHzAgg1fZTRdmHdJgARIgARIgAQ6FAEaoK0YbjkBL8kR5oRe3ut5CV70yx1wKQLV5Sja/o68TVjq1Lsag0+owKavM1C035KwdiiYBEiABEiABEggtQjQAG3FeFW4xAdoMPISvE5nQKW79qwByi/oSconaGKX4aWt408tQ2aeT1uK91dLDhMJkAAJkAAJkEBHJ0ADtBVPgJyAt2cEYKrxRV8rye8t1e0SfEjJPLUM7y5cjaqSraGshLwa1STxxBkuiLuqNZ/qxy9qQjpLoSRAAiRAAiRAAlERoAEaFabIhWQJvv4BpGAwAH+1MkBt+jwFH+qJs/dUGC2ZOLJldigrYa/OLj4MO60M21Y5cHCHNWHtUDAJkAAJkAAJkEBqEKAB2opxklm9hvs/yzSJZqu+Z/uMZrvaC3oJire/q5zTJ35t/LiJFcjrWa05qPdWqeDxTCRAAiRAAiRAAh2WAA3QVgy9+ACtPwPqV1GQJOn1FHx4d3MHzFQuo4rUifgF4dkJuVaRQDF+ugtejwGrFurTQ0BCOk6hJEACJEACJEACDQjQAG2AJLoMn9cAb6XyAVovDKecgJeUCgaoI28E0vJGJtwnaIhohmI1+pxS7NmQhj0b9RWmNKQjX0mABEiABEiABBJPgAZoCxmHXDA1nAE9aoDqfA9oqNt5A2eh/MASeCv2hbIS+tpvZCW6DazCyo+cqCzn45dQ2BROAiRAAiRAAjolQAughQMj+z8l1Q/D6VNRkCSZdeyGSVPw6P+y+81QzumtKNzySnh2Qq/HqVjxsiS/YgGX4hMKmsJJgARIgARIQKcEaIC2cGBkBtRgCMKRqeJxhqVUWoIXtcVQzul3Poq2vQU5wZ+MZE8PYOyUEhzcbkfBSkcymmQbJEACJEACJEACOiJAA7SFg+FWB5AcWX41e1hXgBigRnO6FvKy7h39vsvNvwLV7n0o3ftp0pTseVwV+g53Y+1nmSgvrplNTlrjbIgESIAESIAESKBNCdQzn9pUl5RqXGZAHfUOIEkHZAk+FQ4ghcPO6DIe1oy+KjLSnPDshF+PUgeSrPagFiUpSZOvCe8TGyABEiABEiABEmieAA3Q5hlFLCF7QOsfQJKCMgOaagao6N3puKtRum8xfFVF8jYpyWILYvw0F4r2WbBRxYtnIgESIAESIAES6BgEaIC2cJxroiD5GtSWMJzmFDkBH658Tv+LtD2ghQWvhWcn/LpLXy8Gja/AhiUZcB2sF9M04a2zARIgARIgARIggbYgQAO0BdQ9boMKt6l8gDrrHkASUeKIPhVnQC1pXeDsNQlFBW+0gEjrqgw7vQwZOT4sfT8b/oY2feuEszYJkAAJkAAJkIDuCNAAbcGQSAQkSZH3gMoSvL7DcDbW5bxBV8BTuhXlB79prEhC8k0K5wQVJam82Ix1n6Umu4SAoVASIAESIAESaKcEaIC2YGDd6gCSpMgzoCUpuQQv/cnsdhrMaia0MMmHkaTtnG4+HH9KGbaucODwLqtkMZEACZAACZAACbRTAjRAWzCwcgDJZA5C/FnWT6m6BC/9MBhNyFMumUp2faC2GJTX71rC3w8+oQI53auxfL4T1SpmPBMJkAAJkAAJkED7JEADtAXjqrlgcjbcrBgM+BDwuVNyD2gIQ+6AmQj4K1G8/e1QVtJexaeqLMV73Cas/phL8UkDz4ZIgARIgARIIMkEaIC2ALjsAU2P4AP0WBSk7BZI1UcVW2YfZHY/rU2W4YVAZq4fI88qxa71DuzdbNMHFGpBAiRAAiRAAiQQVwI0QFuAszEfoD7lA1SS2ZbaMc5lGb6yaD0qize2gE7rqwwY40bXfh6s/NCJqgo+oq0nSgkkQAIkQAIkoC8C/HWPcTyCQcBd2ogTehUFSVIqumEKx5DV61ytD0e2zA7PTur1uPNcCAYMmhGa1IbZGAmQAAmQAAmQQMIJ0ACNEXFlmVEzjBwR9oAeW4JP7RlQo8mG3IGXwbXjPbUf1BMjofgUT8sMYMzkEuzfasf2b9PiI5RSSIAESIAESIAEdEGABmiMwxDyAdpe94CGcOQOuFQLK+raOT+UlfTX3kOr0HtoJdYszoIc/GIiARIgARIgARJoHwRogMY4jk35AA3tATVZM2OUqr/iaTlD4eg0ts0OI4WIjJ6k/Kpag1g2L1uFCg3l8pUESIAESIAESCCVCdAAjXH05ACSxR6AxdbQGvJ7VBQkSxYM4k+oHaS8/FmoOPQNPOW726w3VnsQ49V+0MI9Fmz+Jr3N9GDDJEACJEACJEAC8SPQPiyl+PFoVpIsBUeKgCQVZQ+oKcVPwIcDyO47HUZTGgo3t91hJNGna38vBo5147uvMlFyuCYMarievCYBEiABEiABEkgtAjRAYxwvzQdohANIIsbndaX8CfhwHCZLBnL6X6g5pQ8G/OG3kn49/IxSOLL8WPZ+tjoYlfTm2SAJkAAJkAAJkEAcCdAAjRGmWy3BRzqAJGK0GVBrap+Ar48jVy3DV1ceROneT+rfSup7s0VFSZrhQskRM9Z/kfp7bJMKj42RAAmQAAmQgM4I0ACNYUBk5q2y3AiHM/IUnBigZlvqRkGKhCK90xjYsvLb/DCS6Jar4sQPPakcm5em44jaE8pEAiRAAiRAAiSQmgRogMYwbjWugAyN7wFVjuhT3Ql9JBydBl2F0n2fqZnQQ5FuJzVvyMnlyO7qw3J1Kt7nNSS1bTZGAiRAAiRAAiQQHwI0QGPgeMwFky9iLXHD1B4NUNkHChjULOirEfudzEyjemJlKb6y3IRvP8lKZtNsiwRIgARIgARIIE4EaIDGALLGCX2wwRK8z+fDE088garyI/j0ixWQ9+0pme15yO5zHoq2vamLbmXl+TDizFLsWOvA/gKbLnSiEiRAAiRAAiRAAtEToAEaPSstGo89IwBTPU9Aw4cPx1NPPgqzKYBPPv0affr0gdvtjkGy/ouKT1Bv2Q6UHViiC2XFLVPnPh6s+MAJj5tL8boYFCpBAiRAAiRAAlESoAEaJSgpJk7o6/sAff7551FRUQGL0atJKi33w2q14tFHH41Bsv6LZnQ7GRZHD10cRhJaBmVzjp/mgr/agFUftS/PA/p/GqghCZAACZAACbSOAA3QGPjJHtD07LrL6/v374ff74fDXiOoogrwer3YtWtXDJL1X1SiO+UNuhIluz+Ez1OsC4UdWQGMUaE6925Ow851abrQiUqQAAmQAAmQAAk0T4AGaPOMaktUuMwNZkAHDx6MtLQ0GI+uAku8cqM6KTNixIjaeu3lInfAJQj6vWov6Nu66VKfYVXoeVwlVn+cBXcpH2fdDAwVIQESIAESIIEmCPAXuwk44beqPQZ4qxr6AJ05cybECA0lm92G3Nxc3HrrraGsdvNqTe+JrJ5n4sjW13TVpzFTlPcBSxDL52dD/gBgIgESIAESIAES0DcBGqBRjs8xF0wNndDPnz8f99zzW03SNddcg9WrV0cpNfWK5eZfgcqi9Sg7tFI3ytvSghg3tQSHd9mwdXm6bvSiIiRAAiRAAiRAApEJ0ACNzKVBbo0TejTYAxoqOHnyJO1yypSp2hJ8KL+9vTp7nq2iPeXiwIaXdNW17gM96D+qAus+z0RpYT03BbrSlMqQAAmQAAmQAAnQAI3yGRAD1GAMIk25YerIyWC0QFwyHd76NgI+deJKR2nkWWVqfPxY9n42Ah17mHQ0KlSFBEiABEiABBoSoAHakEnEHDmA5MjyKyM04u0OlZmXfxn83jIU73xPV/02W4MYP90F10EzNnyZoSvdqAwJkAAJkAAJkMAxAjSnjrFo8kpzweRsuP+zyUrt9GZa9iBkdT8JhVvm6K6HnXpVY/CJFdj4TQYO7ebjrbsBokIkQAIkQAIkoAjwFzrKx0CW4B00QGtpdRt6LdxHVqKqdFttnl4ujj+lDM5OPnz+uhXVNfEB9KIa9SABEiABEiABElAEaIBG+RiIAVrfCX2UVdtlsU4DZsBoTlezoP/VXf+MJmDCDBfKXQZ8/T4fcd0NEBUiARIgARLo8AT46xzFI+BxG1XIR2MDJ/RRVG23RUyWdOT0vxjF299GMFA3OpQeOu3s7MO4ydX47msTDm636kEl6kACJEACJEACJHCUAA3QKB6FWhdMXIKvQytP+QT1VR1R4Tk/qpOvlzfDT/ahe/8Ali/IVkEEjoaq0oty1IMESIAESIAEOjABGqBRDH6FS63pqsQ9oHVhOfKGw549GIVb9XcYSTQVjwVnzfLD5zVg1UfOusrzHQmQAAmQAAmQQJsRoAEaBXqZATVZArCn07lkfVydBl2NsgNfwus+UP+WLt5n5gKjzinFno1p2P2dXRc6UQkSIAESIAES6OgEaIBG8QS4S8zc/9kIp+x+F8BgMKNIp7Ogona/EZXonl+FVQudqCzjI9/IUDKbBEiABEiABJJGgL/GUaCWJfj0JvZ/LlmyBJMmTdYkXX7ZZViwYEEUUttHEbMtG9l9p6Go4A0Eg0HddmrslBItktUKtR+UiQRIgARIgARIoG0J0ACNgn9TPkB37tyJmTNnwuv11Eq64YYbsGjRotr37f1CDiN5K/agbP/nuu2qbJ8QI/TgDhu2rnToVk8qRgIkQAIkQAIdgQAN0GZGWSb13KWN+wC9/fbbI0r43e9+FzG/PWamdzkB1ozeuj2MFGLe8zgP+o5wY92nWSgrqjlYFrrHVxIgARIgARIggeQRoAHaDGvZMxgMGBpdgq+srIwoobH8iIVTPNNgMCBv0FUo3fMJfJ5iXfdm1NmlsDn8WDYvW42rrlWlciRAAiRAAiTQbgnQAG1maCtcZq1EY3tAr7vuuogSrr766oj57TUzt/8lag+oT9sLquc+WmxBjJ9WguL9Fmz4X4aeVaVuJEACJEACJNBuCdAAbWZoQ07oG/MBesUVV+DKK6+sI2XSpEm444476uS19zcWR1dk9TxHGaCv676rnft4MWhCBTYqA7T4QM0fGLpXmgqSAAmQAAmQQDsiQAO0mcF0Kx+gVnsAMnPWWHrkkUfw/L+f127ff//9+M9//tNY0XadL4eRqko2o+LwCt33c9hpZcjI8WHZ+9nw6y+SqO75UUESIAESIAESaA0BGqDN0NNOwGc3b6EMzB+oSRo8ZEgzEtvv7aweZ8Bs76z7w0gyAiY18TlxhgvlaovFWnUoiYkESIAESIAESCB5BGiANsNa9oA2tv+zmaod7rbBaEZe/iy4ds5Ts4pu3fc/u6sPx59ahgLllunQTqvu9aWCJEACJEACJNBeCNAAbWYkZQaUBmgzkMJu5w64FAFlfBZvfycsV7+XgydWILdHNZbPz0a1x6BfRakZCZAACZAACbQjAjRAmxhM2RtYVW6kAdoEo/q3bFn9kdH1JBWa89X6t3T53qA+AROmu+CtMqhQnVyK1+UgUSkSIAESIIF2R4AGaBNDKg7oAQMcUewBbUJMh7slh5Hchd+iyrUlJfqekePHyLPKsPs7B/ZutqeEzlSSBEiABEiABFKZAA3QJkYv5IKJS/BNQIpwy9l7CkyWLBzZMjvCXX1mDRjtRtf+VVj5oRNVFfxY6HOUqBUJkAAJkEB7IcBf2iZG0q05oQ/CkeVvohRv1SdgNNuRM+ASFO94FwG/t/5t3b4fd14JoLxtrfjAqVsdqRgJkAAJkAAJtAcCNECbGEWZAbVnBDSXPU0U460IBHIHXga/CstZsmtBhLv6zEpTYz1mcgkOFNixbbVDn0pSKxIgARIgARJoBwRogDYxiNoJ+GzOfjaBqNFbjtxhSMsbicKC1DiMFOpIryFV6DPMjTWLM5WPUNkDzEQCJEACJEACJBBvAjRAmyBa44KpeSf0TYjo0LfkMFL5gf/BW74npTiMOqcUVhX5avm8bBXfPqVUp7IkQAIkQAIkkBIEaIA2MUyyBzTaA0hr1qzRJK1ataoJiR3rVk7fGTCYrCkRGSl8ZKz2IMZNc6FwrwWbvkkPv8VrEiABEiABEiCBOBCgAdoIRHFK7q1SPkCjWIK//fbbceutt2qS/vqXv2DKlCmNSO1Y2SZrFnL6XYCibW+qmcRASnW+az8v8se58d2XmSg5pOJ2MpEACZAACZAACcSNAA3QRlCGXDA5nE3vAf3Pf/6DOXPm1JGydu1a3HfffXXyOuobWYavdu9H6d7FKYdg+OmlyFB/gCx9P1ud5k859akwCZAACZAACeiWQMpN7dhsNqSlpcUM1Gw2IzMzM+p6RbtrDqB07WlHeqat0Xqvv/56xHvvvvsu/va3v0W811im6CgpFj0bk5XIfIPBAIvFEpWemZlnYo9zIEp3vIleQy9KpFoNZIuORqMxKj0bVD6accZlXsx7Ng2bv87DhKmJcSkVGnfRV89JWFqt1lbxTEb/hGN6erqaddf/Bl673Y7Q+CeDTUvaiPW7syVttLaOyVTzfe1wOHQ/7qnAUz5D8j2fiN+iQCC1VsNa+2yyfuMEUs4Ara6uRkseYPlAVVZWNk6i3p3iQ2kwGIMwWNyqXr2bYW/FII6U5IcllvZEhtRpSb1I7ScyT77s5csp2v7lDboKe1f8FaVFu2FJ65RI1erIFj3lyz5aPetUPvomPQ8YdkoQ675wKEf1bnTuUx2pWKvyQs+Qx+NplZxEV5Zn0+fztYpnonUU+TLuVVVVLfqeSIZ+oTYyMjLg9Xqh93GP9bsz1L9kvoqOMjEhLP1+fS9XpAJPGTv5Y7M1352Njb/8djCRgBBIOQNUjE/5EYw1yWxILPXKilUITuWA3i8B4ZtIDz74IE4//fQGJV544YWY2hMBIcM6Fj0bNJyEDGEZyzg4+16APSv+gkOb/4uuw25OgoY1TYRmwFrLc9AJpdi7xYKv52bg3OuPwKJOyMczyQ9SrM9nPNuPVlas4x6t3HiXk2dTjBA9GyKhH+FYPkfx5hStvFR4NmV2XpKMeWs/79FyaWm5VOApz2Wi9AzNVreUH+u1HwLcA9rIWNa4YGr+L+n8/HwsWrRILfXWLAGJMfHss89i8ODBjUjueNmWtM5w9p6MooI3UrLz8ts2YbpLheg04dtPslKyD1SaBEiABEiABPREgAZoI6NRoZyQR3MCXqoPGTIEixcv0iTNfuUVTJ8+vRGpHTdbDiN5SgtQfvDrlISQmefHyDNLsXOdA/u2Rt52kZIdo9IkQAIkQAIk0AYEaIA2At1daoLD2fTyeyNVmR2BQGa3U2FxdEs5n6DhXRkwxo0ufT1YqWLFe9z86ISz4TUJkAAJkAAJxEKAv6IRaFVVGOGvVj5Am3HBFKEqsxohYFBbFHIHzoJr1wfwe8saKaXvbNk7Lw7qA34DVn7o1Ley1I4ESIAESIAEdEyABmiEwXGX1OznjHYJPoIIZkUgkDtwJoL+KhRvfzvC3dTIcmQGMGZSCfZtsWPH2tjdgaVGL6klCZAACZAACSSWAA3QCHxDTug5AxoBTiuybBm9kdn9dBQWRPad2grRSa3a+/gq9BxcqR1ICj0rSVWAjZEACZAACZBAihOgARphAMWoMFkCsDnoMDcCnlZlyWGkyqK1cBeua5Wctq48dnIJzJYgls93Knclba0N2ycBEiABEiCB1CJAAzTCeFW4zNz/GYFLPLKyep0DkzU7pQ8jCQdrWhDjzivBkd02bFmWHg80lEECJEACJEACHYYADdAIQy17QLn/MwKYOGQZTTZ1GGkmXDvfU4d5quIgse1EdBvgwYAxFVj/RSZKj6RcTIe2A8eWSYAESIAEOjwBGqARHoFondBHqMqsKAjkDrxMnYQvVUbo/ChK67vIiDPKkJbpx9L3s5VBrW9dqR0JkAAJkAAJ6IUADdB6IxFU2z7pA7QelDi/TcseDEensSm/DC9YzNagFiWp5LAZ332ZGWdSFEcCJEACJEAC7ZMADdB64+ouMyEYMHAPaD0u8X6bN+gKVBxaCk/ZzniLTrq8vJ7VGHJiOTYtTUfhPkvS22eDJEACJEACJJBqBGiA1huxlvoAfe+99zRJc+e+p05F81h0PawN3mb3mQ6j2YHCLa80uJeKGUNPLoezsw/L1FK8z6s81jORAAmQAAmQAAk0SoAGaD00Ib+OjqzoN/SdfvrpePLJpzRJr8x+Bf3790d1dXU9yXwbTsBkSUdOvwtRtP0tNeMcPetwGXq6VoGeMHGGC5VqBn3NYi7F62lsqAsJkAAJkID+CNAArTcmFS6TcrETgMUW3SzmnDlzUFBQAK/Xo0ny+/0wqJiNTz/9dD3JfFufQG7+LPgqD6Nk78f1b6Xk+6xOPgw/vQzbv03HgW22lOwDlSYBEiABEiCBZBCgAVqPcs0JeF+93MbfivFZf8nd4/Hgu+++a7wS72gE0juNht05CEVb57QbIvnjK9CptwcrFjjhreRSfLsZWHaEBEiABEggrgRogNbD6S4xw+GMfkm4X79+SEurGxPcbDZj0KBB9STzbSQCeYOuQum+z1DtPhTpdsrlqclvjJ9WAl+1ASs/cqac/lSYBEiABEiABJJBgAZoPcraDGh29AboVVddhby8PG3ZXURZrVb4fD7ccccd9STzbSQCOf0vUuxMKj78q5Fup2ReuvoDZvS5pdi7KQ271tf94yQlO0SlSYAESIAESCDOBGiAhgH1q5X3qnJjTC6YZL/n0qVL8dOf3qpJuujii7F9+3YYjUQbhrbRS7MtB84+56Go4PUGWxkarZQCN/oOr0SPQVVY/XEW3GV8FlJgyKgiCZAACZBAEgnwlzEMdo0LJvEBGv0e0FD1mTMv1S5nzpwJm40HUEJconnNGzgL3vJdKD+wJJriKVNm7JQSGE1BrJifrYzrlFGbipIACZAACZBAwgnQAA1DXKH2f0qKZQ9oWHVetpBARreTYU3v1S4iI4UjsDkCECP00E4bClY6wm/xmgRIgARIgAQ6NAEaoGHDX+MDNBjTEnxYdV62kIBsYxCXTCW7P4LPU9JCKfqs1mOQB/1GurH2syyUFSpnoUwkQAIkQAIkQAKgARr2EMgSfFpGQC2bhmXyMikEcgfMVA7pvSje9mZS2ktmIyPPKoU93Y9l87IRCCSzZbZFAiRAAiRAAvokQAM0bFzECb0jhhPwYVV52UoC1vTuyOp5ljoN/3orJemvugQ1mDDNheIDFmxckqE/BakRCZAACZAACSSZAA3QMOCyB7QlB5DCRPCyFQRkGb7KtQEVR1a3Qoo+q3bqXY3jTqjAxv9loHi/RZ9KUisSIAESIAESSBIBGqBhoGP1ARpWlZdxIODseTbMtrx2FRkpHMvxp5QhM8+nLcX7q8Pv8JoESIAESIAEOhYBGqBHx7vaY0B1VWw+QDvWo5L43hqMFuQOvAzFO99HwFeZ+AaT3IJJOVmYMMOFcrXVY+2nWUlunc2RAAmQAAmQgH4I0AA9Ohay/1MSl+CPAmmjFzFAA9XlKN7xXhtpkNhms7v4MOy0MhSscuDQDmtiG6N0EiABEiABEtApARqgRwemxgWT8gHKQ0ht+qjanQOR3uUE5RO0/YTmrA/0uIkVyOtZjeXKQb23SgWPZyIBEiABEiCBDkaABujRAZcDSAZjUHPD1MGeAd11Ny//CriPrERVyVbd6RYPhZTbU4yf7oJXbftYvdAZD5GUQQIkQAIkQAIpRaDDG6Berxd33HEHnn1mDo64tuGzzz6NeQAPHjyI2267Xav305/8BHv37o1ZBiscI5DdZyqMlox2FxnpWA+BDDXTPursUuzekIad63kqPpwNr0mABEiABNo/gQ5vgI4bNw6vvfYaMuw9cai4AN/73vfwwQcfRD3ybrcbY8aMwapVq7Q6hw4dwimnnILdu3dHLYMF6xIwmtOQ0/9i5ZT+beWcvv0eF+8/qhLdBlThm3lpqCzjUnzdp4DvSIAESIAE2jOBDm2ALlmyBGVlZfD7/cjL6o/Cku2orq7GvffeG/WYP/PMM7BarQgGj4W48fl8ePTRR6OWwYINCeQNvFyF5SzUwnM2vNt+csZNLYGYnl/PTW8/nWJPSIAESEBnBDweD+677z5ODuloXDq0AVpeXg6Hw6ENR0ZaZ5S5D2rXpaWlUQ9RRUUFZBk/PAVUvMVYZITX5XUNAUfeCKTlDmvXy/DSU7sK/XrC+ZXYt9WinYzn+JMACZAACcSfQFVVFf7whz/QAI0/2hZL7NAG6Pjx41Vs7mMzl0JRZjNlWT7aNGnSpIhFL7jggoj5zIyegBxGKtv/JbwV+6KvlIIl+wytRv+RHuUbNBPlxTXuwFKwG1SZBEiABEiABKIm0KEN0NzcXLz+ek3scaPRAJvdhrFjx+LFF1+MGuDJJ5+M3/72t7Xl09LS8OMf/xgXXnhhbR4vWkYgu98FyjOBtV27ZAqRGT+1ElZ7UIuSFLabI3SbryRAAiRAAlEQOHLkCB544AGcf/75+PWvf40VK1Y0Wmvu3Ln4wQ9+gHPOOQfXXXcdFixYUFvW5XLhnnvuweTJkzFr1iw899xzaqtdsPb+f/7zH1x00UWYNm0a7r77bhQVFdXe40V0BDq0ASqIRowYgXXr1iE9PQMXqAf2rbfegskU2yzULbfcgpdfnq0R/+P9f4xpD2l0w9QxS5mtTmT3nYaibW/U2WPbHmmI8Tl+mgtF+yzY+E1Ge+wi+0QCJEACCSUgW+LOO+88vPTSS5g6dSpk36ccCt6+fXuDdp9++mlcddVVGDBggHb4WA4UizG5bNkyrew111yDxYsXa2UmTJiAO++8UzNs5aZMUt1222047bTTNON00aJFOPfccxu0wYymCajggEwyE2q1WtC7d28Fo7xFQHr37oWNq4G+ffu1qD4rRSYgy/DF299WS/GfI6vHmZELtZPcLn29yB9fgQ1fZaC7Oh2f3dXXTnrGbpAACZBA4gn8+9//hrhF3Lp1q7adTlqUMxoff/wxLr/88joKiMeaRx55BDfddJOWL8Zo586d8fXXX0MMTjmk/Ne//hXXX3+9dn/IkCHagWV589VXX0G28N1+++0wKMfOYoi+++67msFrs9m08vxf8wRogDbPiCXakEB6l4mwZvRF4ZY57d4AFczDTy/DwW02bSn+7OuOQOLHM5EACZAACTRPQNwhijEoZzlCSWY6JZWUlISytFc5Eb9//35t1XPjxo349ttvUVlZCTmsJElcMsrqpsymysyobKsbNmyYdu+KK67QZlrz8/O1ezNmzMCtt94Ks5lf2BqgKP/X4Zfgo+TEYm1EQP66zBt0JUr3fgJfVfvfYyMG54QZJSgrMmP955ltRJ3NkgAJkEDqEZDZz/T06FzaPf744+jfv782yymzoTNnzkSXLl1qO/3YY49ps5qDBw/GE088geHDh2t7SqXAWWedhdWrV0MM0S+//FJb7j/hhBMg+0aZoidAAzR6VizZRgRy+1+i7QGVvaAdIeV0q8bQU8qxZXk6Du869pd8R+g7+0gCJEACLSUg+zk3bdpUp7r45P7Tn/5UJ09mOu+66y48/PDD2p5PMUYvueQSiCEqnnFkP+gLL7wAMSplWX/fvn2aCyeRJTOkH374oXbo6M9//rMWhGblypVYs2aNll+nIb52Xgw5AABAAElEQVRpkgAN0Cbx8KYeCFgcXeDseQ4KC17TgzpJ0WHICeXI6V6N5fOdqFYx45lIgARIgASaJnDDDTdoM5IyeykHkmSW8v7779f2a4bXlKXyvLw8HDhwoNbg/NnPfqbt4RQDU7zZSJAZOUUvs5pisB4+fBg9e/aE3W7XluuvvfZabNmyRTsZL3IkAM3AgQPDm+F1MwRogDYDiLf1QSBXHUbylGxF+aHl+lAowVoY1CdzwnQXPG4TVn+cleDWKJ4ESIAEUp+AhMWWE+oy4ykGpvjjFsNSTsSHJ4vFgoceeghz5szRlt27deuGjIwMbUld9pHK1q+nnnoKBQUFmtEpB5XlINObb76piRGZEydOxEknnYTMzEztJPzf//73BoZueJu8bkiAO2YbMmGODglk9TgD5rQuKNo6BxldxutQw/irlJnrx4izSrF6oRM9j6tCj0Ge+DdCiSRAAiTQjghcffXVmuukvXv3asajGJOSnE5nHT+e4mZJ/u3ZswdigNY/QCTL759++ikkYqLMgMoJ+VCSWdD//ve/2uyptNOrVy/NaA3d52t0BDgDGh0nlmpjAgajCRIf3rVzHvzVLXOV1cZdaFHzA8e40aWfBys+cKKqgh/XFkFkJRIggQ5FQIzOaI1CKVff+AyHJTOj4cZn+D2j0ai5bwwZueH3eN08Af6iNc+IJXRCIHfgZQj4K1G84z2daJQcNcaf50IwYMDKD53JaZCtkAAJkAAJkECCCdAATTBgio8fAVtmX2R0O1mF5pwTP6EpICktM4Axk0uwf6sd29ekpYDGVJEESIAESIAEmiZAA1TxEdcLssdjzZq1TdPiXY2AbMyWjdoS5izZSSIjVRauQaWrrquNZOuR7PZ6D61Cr6HqGV2UhYqS2ELFJltXtkcCJEACJEACzRHo8AaohM8aPXq08vtVqZ1yk/0gErqLqSGBYDCoRYaYPHmydrpQnPjKBu5kJmfvyTBZs1RkpFeS2awu2hozqQRmaxDL58lmel2oRCVIgARIgARIoEUEOrQBumvXLtx88811wIkTWnGvwNSQwC9+8QssWLBAOxUYivhw3nnnaf7WGpZOTI7RZEfOgEtVfPh31H7Q5M/AJqZX0Um12oOQ/aBH9tiweWl00T6ik8xSJEACJEACJJBcAh3aAH3kkUci0pYleaaGBBYuXKgtu/eebMCYXxnhPA7w+/1YunRpw8IJzJHT8H5vCUp2LUhgK/oU3bW/FwPHVOC7LzNRcphe1PQ5StSKBEiABEigOQId+hdMfHkxRU9AYux2mlqCARcb4XEFccL9JhStrkCZZX/0QuJQMi1nKBx5o9RhpFeR0/+iOEhMLRHDzyzFwR02LHs/G2dfdwTKQxUTCZAACXR4AocWfYJE7E8KKkf0XceM7fB84w2gQxugf/jDH/Dyyy/XcU4rgMUBLVNdAoGgH5P+2Av7Mg5g6+sBbHsziF5nGdH/Uj/eDv4aBzZ9g4v63I4uaX3rVkzQO4mMtOebu+Ep3w1bRu8EtaJPsWZLTZSkxbPzsF7NhI44o0yfilIrEiABEkgigbQXX4AhARvkvSeeBNAAjftIdmgDVOK9Ll++HOPGjasFO2zYMLz99tu173kBeJXvzWc23oL9GSsxcP8MfPTGu7BarRgUPAVPnfMkvil9A/P2PI1vDr+HM7tdjfP73Ipsa9eEosvpOwN7l9+nuWTqMfrOhLalR+G5Paox9KRybFiSgR75VcjrWa1HNakTCZAACSSVgOecSaieEL9zHI5n/19S9e9IjXVoA1QGunv37ti3bx/mPpmL22+/Dcef+sOONP7N9rW8uhiPr/8+dpavw0+H/hPjTpuKP1z5LzgcDpSV1cy8Tc26CWd2vxoL9vwTH+59Fl8cfBWTetyAab1+jHRLdrNttKSAyZqJnH4XoLjgDXQfeTskUlJHS0OUAbq/wI5l87Jx7vVHtBPyHY0B+0sCJEAC4QSCaQ4Es3PCs1p1HTR1eDOpVfyaqtyhDyE1BYb3gCLPfvxlzaXY596CX46YjXGdpjaKxW5Kx8V9b8ffJixRxug1+Gjvc7hz2SmYt/sZeNQMaiJSXv4sVFceROm+xYkQr3uZYnNPmOFCZZkJ3yr/oEwkQAIkQAIkkCoEaICmykglWc+9FZvxp9UXwu0rw29GvYnBzuj2xWZYcnDVgN/jwQlfYHynaXhzx0P4lTJEF+17Eb5AfJeJ0zuPgy1roHYYKcl4dNNcVp4PI9ShpB1rHGo21KYbvagICZAACZAACTRFgAZoU3Q66L0tpcu1mU+byYHfjXoHvdKHxEwi19YdPzjuYfxl3CIMck7AiwX34NfLz8CSQ28hEAzELK+xChIZqXTvIjUTerixIu0+f+BYNzr38WDFB054Kg3tvr/sIAmQAAmQQOoToAGa+mMY1x6sLvwYD629El3T+uGeUW8jz96zVfK7OQZoe0fvG7NAyeyPf236OX63cjJWFS5sldxQ5dwBl6hLA4oKXg9ldbhXg7I5x09zwV9twKoPnR2u/+wwCZAACZBA6hGgAZp6Y5Ywjb848Cr+/t0NGOo8CXeNeA2ynB6v1DdjOO5U+0jvHvk60kwZqp0f4P7VF2CD63+tasJsz0O2Cs/ZkQ1QAejICmDMpFLs3ZyGnevSWsWUlUmABEiABGInsGbNGrzxxhsJjQ5YWZmYMxWx97b1NWiAtp5hu5Dw/u6n8e8tv8RJXS7Gz4c9D5spMUbMYOeJ+O3od/Dz4/8NT6ASD669HI+suxY7yta0mKP4BPWUbUfZgdYZsy1WQCcV+wyrRM/jKrH64yy4S/nR1smwUA0SIIEOQGDKlCm4++67NdeOo0aNwkcffRT3XofCYcddcBsJ5K9UG4HXS7NB5bR3dsG9eGPHA5ja80f44XGPwWRIvNuJMXmT8ccxH6r2HseByu34w+rpeHrDzdjvLogZTWb3U2FxdFezoK/GXLe9VRgzuQQmSxDLF2QnIiBIe8PF/pAACZBAqwn873//w969ezFv3jw88MAD2r/wUN/bt2/Hpk2b4PP56rRVVVWFdevWobS0tE6+vDl48CBkRjXk7lBmPr/77jutrMfj0crL73dBQQF2795dp35hYSGqq6uxbdu2Ovl6e5N4S0NvPaY+tQR8Aa/ak/kLLD0yF1f2vxdTeiXXB6rRYMQpXS/FCZ0vwKcHZmPurifxmxVn47Sus3Bh318gz9ajVtemLgxKjrhkOrj+H+g14Y8wWTuuSyKbI4hxKlzqkjdzsXWFA4PGu5tCx3skQAIkQAKtJCDRE7/55ptaKUeOHKldhp81a5ZmJObl5WHHjh3azGjfvn2xaNEiXHXVVRg+fDhWrFgBMVh/8IMfaEbqzJkzsWvXLkidZcuWaREbxVhdvXo1RLbUHz16NKZOnaoZmi6XCyNHjtSC6BjUoQCRKe8l0M67776LU089tVY3PV1wBlRPo5FEXSp95Xh0/XVYUbgAPxr8RNKNz/Cumo0WnNvjejw84Utc2u9OLC+cj7uWnYY52+5HWXVReNFGr3MHzETQ70XR9ncaLdNRbnQf6EH/UW6s+ywLpYX8G7OjjDv7SQIk0DYEjEYj0tPTtcb37NmDv/zlL/jtb3+L4uJibVb0888/x4cffog777xTC3wjBaXM7Nmz8fHHH2Pjxo148MEHtbDgGzZsgNPpxMqVK7Fw4ULcc889ePHFFyFG6cSJEzW5Z511Fl577TWMGTNGK7dlyxbNEJWZ2FC68MILNWNVr8an6Mlfp9BodaDXUu8Rbd/lgcptuG3Y/2F4zhm66L1V7Tud0funOKv7tZivHNgv3PeCNjMqWwOm9Pwh0swZjeppzeiFzO6na8vwnQdf12i5jnJj5FmlOLTDimXvZ+Osa49AfT8ykQAJkAAJJJCALJHPmDEDv//977XZSWlKZin79euH6dOn4+KLL8ZJJ52Ew4cP48svv9QMy5deeknTSIzVr7/+Wrv/q1/9SpsRlRlPMSqHDh3aQOvXX6/x/HL99ddr9w4dOqQdgDr55JO196effjpkNlTPiQaoTkZHptf/7//+D7LP4/jjj8cZZyTGKDxUuQMPr7sGVf4y/Hrka+ifOaoOAVkCkKUBs9mMd955R5vKDy8g+1FefvlleL1enHbaaRg7dmz47bhcp5uduKz/3Zjc80a8s+sxzN39JD5Wxuj5fX6mjNNrYDXaI7aTN+gK7Pj8ZrgL18KRNyJimY6SabYGMX66C5+9kocNX2Vg2GnlHaXr7CcJkAAJJJ2ALJVfdNFFePLJJ3HJJeIesCbJqXiZ1Xz77bfx85//XFtGv/nmm2G1WnHjjTfCYrFoBX/84x8jPz8fixcvxtVXX41f/vKXuPXWW7Xf2blz54bE1XmVg0/yOxxKOTnHPNdkZDQ+YRMq39avNEDbegRU+36/H/JXi2xEFkM0EAjgJz/5iTb1Hk/1JJ67nDi3GtOUj8930E355QxPI0aMgGxeliQG5uTJk/HXv/4V3/ve97S8kpISnHjiido92QQtSwaPPvoorrjiCu1+vP/ntHbG9/L/osWUf2vH39SS/B/xgYo3f3HfO3Bq18tgNKhYlGEpq+c5MNlyVGSkOR3eABUsnXpVY/AJFdj0dQa653uQ2z2+kajC0POSBEiABDosgX379mkzn7IsHj55JAeJZLlcjNPf/OY3mrEps5qdO3eG7BvduXMnrrnmGm1Z/pxzztGMT5nxPPfcc3H77bdrtoAs1cuBIkl2u12bpJJr2Vsqy/pyMl6M2EsvvVSbYR08eLDcTonEhTkdDJM8QEVFRXC73doDJyo9//zz2t6OeKm3vvhL/HXNTIhR99vRbzcwPp955pla4zO8TXErEUqyfCAzoDJLK0aypHvvvVfbLB0qk4jXzvY++NGQJ/CnsQsh/kSf33In7l6hPtRH5mt7ZkJtGk1WyF5Q1465CPiqQtkd+vX4U8uQ1cmnLcX7aH926GeBnScBEkgMgccffxyyBH722WfDZDJp/3r06IGuXbtqBua4ceO0/ZuyyimGqKT77rtPm2SSw0Iy2XPbbbehW7duuPbaa7F+/XrIEros14tBKSfdJUmezJ4+99xzuPzyy1FeXq4t74vbJ5vNhiuvvFIrlyr/4wyoDkZK3DDIjGN4kk3NMm0fjyXupYfn4p8qAtGgrHHK/+bzai9lZnhT2rW4j2guyb6VkOEZKit/eW3evBl9+vQJZSXstWf6YPxi2AvYWrpScxv19IYfoW/6cG25fnjO6Vq7eQMvx+ENz8K1ax5y8n6QMF1SRbBRTRJPmOHCohc7Ye2nWZqz+lTRnXqSAAmQQCoQeOihhyD/IiUxOGUiRyaZ5FR7KMmqp8yAyu+qzIiGUu/evbVT8bIamZubq+3jlNVGST/72c9w0003acv3YiN88MEH2sqpLOfL7Ggo7d+/P3Sp61fOgOpgeHr16qUOidQdioqKCnTp0qXV2i3c+zz+sfEnGJN7Lu4Y/nJE41MaGTKk+Xjv8tdc/SQbpyPl1y8Xz/f5WWO1/at3DH9J+3D+bd3Vanb3MmWYroA9exDSO4/TluHj2WYqy3J29qk9oGXYtsqBg9utqdwV6k4CJEACKUdADgOFG5/hHQg3PsPzpXykQ0RiaIbbC1lZWXWMz3AZer+ua/XoXdt2qp84rpWZxdBDJbOKMqU+adKkVvX4zR0PYfa23+NMdXDnlqH/DxajrVF54oNMlg7qp9AJO8l/4okntNuhD4XoK5utZe9oW6QROWfi96Pn4ZYhz6DUexh/+vYi/H39DfD0PR0Vh5bB7YrdqX1b9CMZbQ6aUIE8tSdUHNR7q/R9MjIZPNgGCZAACZBA2xKgAdq2/LXWe/bsqS1ji58v2Ygs/sMWLFjQYs0CQT/+vfmX2unxi/rcph3kEafvzSWJphA+mynGp2yADiU5nS8bqM8//3xtr4sYrU899VTodpu8ijE8sfP5+PO4T/D9QQ9iZ/laPHL4EXzY2Yh1a2sM5jZRTGeNijeOCdNc8HkNWLXQqTPtqA4JkAAJkEBHI8A9oDoZcXGZ8K9//UtzQNua/RtefyWe2XgLvi1ahOvzH1Czn1fH1MNVq1Y1WV62BTz77LNwOBy1IcKarJCkm3Ii/oxuV+HkLpfik33/wXvbH8KDrhdw3moLJnX5EbKtrd/OkKSuJKyZ9Gw/Rp1TihVqFrRHfhV6D+VBrYTBpmASIIE2IWCoKINB7auMVzLUC58ZL7mUQ0f07eoZqKh24bH116tZwHX46dB/Ylynqe2qf9F0RrYZTO11EyZaRuDVpbPwyc4XsVD9E5+i5/X6McTHaEdO/UZUYt9muzYL2qmXF2mZNd4MOjIT9p0ESKD9ELAtXgT5x6R/ApwB1f8YRaVhkWc/5DCOy3MQvxwxG4OdJ0RVr70Wyu16Es41DcdZgS5Y2aMfPtzzLBbtexHTe/9Ehf38Pmwq6lJHTWNVrPiFz3fSZkJPvTy6UKcdlRX7TQIkkDoEDl46PiHKWvL6IDchkju2UBqg7WD897m34G9rr0ZA/Xf3qDfQO71h2K520M2Yu9B92HXY+uVvMHPcA2pW9Ed4e+cjyn3Tg/ho73O4sM8vcHq3KyFx6DtasqcHMHZKCb5+JxcFKx0YONbd0RCwvyRAAu2QwJZNym91MP6rOnmDrkFfXNAOibVtl2iAti3/Vrcurodk2T3TkotfDp+NTvZerZbZXgR0Oe5yFCy5F0VbX0O3kT/DDcf9DdN73YI3dj6IFwvuwYK9/8QlfX+JEzpfqKIqNX9Iq71wkX70PM6DvsPdmm/QLv08yMz1t6fusS8kQAIdlECXYTergCTHQmG2FsO2xTe0VgTrN0KABmgjYFIhe3Xhx3h6481qxnMIbh/2IjIsx+LApoL+idbRYs9B1/yLUFTwGrqOuFXzqdbNMUDbH7tDnZZ/ffsDykH/z/D+7qdxWb9fY3TeuYlWSVfy5UDS4V1WLJ+XjTOvLkQHs8F1NRZUhgRIID4ELPbOsDsHxUeYkmJQEfaYEkOgY037JIZhm0j94sCreOK7GzHEeSLuGvEqjc9GRqHH8OvgrdiD8gNf1SnRL2ME7lR7ZX894jXYTel4/Lvv4/7VF2KD6391yrXnNxZbEOOnlaBovwUbVbx4JhIgARIgARJIFgEaoMkiHcd2ZMbu31t+iRO7XKSFprSZHHGU3r5E5fQ6A9b0Xo1GRhqSfRJ+N/pd/Oz45+Dxu/Hg2svxyLprITOkHSF17uOFOKnfsCQDxQe4INIRxpx9JAESIAE9EKABqodRiFKHYDCI2QX3anHQp/b8EX543GMwGWg0NIVPC4GWfwVKdn8En8fVaNGxeVPwx7EfKqaP40DlNvxh1TQ8s+EW7He3/2hKEqYzI8eHZWop3u9rFBFvkAAJkAAJkEDcCNAAjRvKxAryBbz4f5t+ioX7XsAV/X+HKwb8NmKc2MRqkZrScwbORDDoQ9G2N5vsgBxEOqXrpfjruE9xzcD7sankG/xmxdl4YcuvUOjZ12TdVL5pUn/DTJzhQnmxGes+y0rlrlB3EiABEiCBFCGQNAP00KFD+OCDD7B9+/YGaDZt2oSPPvoIR44caXCPGUClrxyPrv8elh+Zj5sG/11ztE4u0ROwOrohq8dZ2mGkaGqJa6Zze1yPhyZ8qZ2SX6a437XsNMzZdj/Kqtun38zsrj4cf2oZtq5w4NBObrqP5jlhGRIgARJIJQKVlZW6UjcpBuj777+PO++8E3v27NHinM+bN68WwmOPPYaHH34YEgLyhhtuwK5du2rvpcqFy+XC73//B03de3/3O5SUlMSs+tq1a7X46gMGDMCtt96qZuyCmoxS7xE8sOYyFJSu1PZ7ntwlfu4l6ivpUyHHLrroIvTp0wd9+/bFa6+9Vr9Iyr7Py5+FKtcmVBxpOtRoeAfFWf35fW7FwxO+UpGUbsCi/S/izmUn452dj2l/FISXbQ/XgydWILdHNZbPz0a1RwWPZyIBEiABEmgXBKqrqzF8+HBd9SXhBqgYUjK7ed999+HGG2/EL37xC7z66qsahB07duCLL77QYqDfdddduPLKKzF79mxdAWpOGa/Xi+OPPx6ff/65VnTT5s0YNWoUZMY32rR3715MnToVK1eu1GaI586di3PPPRcH3Dvwp28vRpF3H+4a+SpG5JwZrcgWlRsyZAiWLl0KMUTlYZWxeuSRR1okS2+VsnqeDbNyz1G4dU7Mqkn4zsv7/0bNiH6Fk9QfAHN3P6EZoh/t/TeqA56Y5em1grhhmjDdBW+VAas/5lK8XseJepEACeiTQFlZGXbu3NlAObEHdu/eXSe/sLBQey/li4pqVtbkd3fr1q215eS3WCa0PB4PZKU4EKjrZN/v92vlNyu7Q8qGJ7kn+fIqSVaYxeY6fPhweLE2vU74CRY5BPLEE09onRS4X331Ffr166e937ZtG0aOHAmjscYOHjt2LMJnR6XQmjVrsHr1aq28/O/MM8+E0xl7PG+TyQSHo/HT4qKn2WJpskytEmEXTz31FKxWq3owvCrXpIIwqHhEAQMeffTR2n6HFY94ecstt9TOeEoBMWqLjbvw+2XTkOXIxv0nLECP9PyIdeOV+fLLL8PtbhgR5/HHH8fv1KxueJLxsrSAVbiMZFybzWbt2QqNe9chV2L/+n9j0GkPwGRJj1kFh6Mfbs5+HJfm34Y5W/+C/267Dx/u+xcuH/hrnNHzCuXM3hSzTKkgLCWFPgfamzb6n3xExk/x4Jv3Heg/LIg+xx/7UhP9hGmIZxup2GyzomNaWlqDL+tmK7ZBAfnukO8ePafmvjv1oLvoKMlut+t+3FOBZ+i5TMRnPbS6p4fnJp463HPPPXjuuecwYsQIyKroJ598goqKCnz/+9/XjD8x/OTee++9B3kGZKLq1FNPxf79+7XJJ/mdfemll5CVlQUxZGVVeNmyZdrEndhOsjoqK6Uff/wxhg4divXr1+Oyyy5D7969NRmyvP7NN98gNzcXX375JS6++GKMHj1aM3xlpVlsKzFgr7vuOsgkl3xPtnVKmgalpaW4+uqrIZCeffZZrd8CPtyYFPChvwpCYJYsWQIxgkJp/PjxGvDQ+1heRX5jSX4DbDabGnxbY0Ui5ssDJgZjeJK/RIqLi7UHKTy/sWthE55yhwHD7qyCLZCHx8/9Cnlp3cNvJ+Ra/sqSH8L6Xw7y11Nj3ORLKhVSSP8B427C3tVPoGLvB+g18vstVj0rawR+2+2/2FGyDs+vvQf/WP8zzN31FK4ffj9O6Xlxiw0KMZr0kMafDezfGsQ38xwYoJ5FR+YxrWTMU2HcU0FHoSpjrpdxPzbKDa/+P3tXAV9l1cb/6+5gyTYYPRgwumt0d4iAKCWKICWlIkqKKCIIioJICkpJSCMl3TG6x7p7+85z+N5xt91tt3e3nYffuPe+7znPOef/1vM+KV1Deffo1xZr6+KRz7a44KmNeZIwVdKIBD4SPinGhYT2OXPmgFwPSeirUKEC9u3bxzWUrVu35oJp27ZtOQQkgI4dOxbLly/Hhx9+yAVJZ2dnNGrUiCvrSDlx48YN/kdC54oVKzBy5EhucT1y5Ag+/vhj7rpIzOrWrct5k1BKf2vXrkWHDh24ELt06VL8+OOPvP+ePXv0Bn6dCaB0IpPkT6ZqAnD79u38LUBSDxMiJLjlvhmPGjUK9CcRvUW8fPlS+qnwp4ODAxcK8+uQmemKhPhExjs+vyZyt9erV+//bxJvNEXUsGXLlgrPk07Ke/fu8fWXaWiA6mMNEH0nC5Oa/4C0GAO8jFF+vXInW8DGpk2byt1LGoXceEtv8PSWps9ELzd0Ab8JbrOGdZkGuH9hJYxdO6g9dXM4Y4z/j2jrep6nxppzqh8owX1vVlUpwKGZwvzpnCfBPzk5WeE+2m4Y0MoQ/6x2wZ61qWjcK4oPRzdGetnK/cKk7bkoy5/uNfRiKHtvUZaHttvTy56bmxs3r+lbYEDutRd278zdvih+0wuHk5MTv9ZzmyKLYj4FjVkc8LSysgIJ86GhoQUtRaV99PxwdXVVqa++diLNY3BwcLZ1aMaMGXyqpNVcvXo1/04ax169emHDhg2QBFDpuUvaTbIA0z2WyMPDA+Sa58usxZUqVeIaT9reuXNnTJgwgd/bRo8eDRImaQyyEoeEhPBnCJn66dlMcgURCaYkjOrjfea17ZtPUzv/0QPr9OnTnDnddJs3bw57e3uuPnZxccn2faAG5Afh7q59bZ8mV0qC5tChQ7NZksDWpUsX9O/fP3tbYV+mTp0KwsKngxFqfGiIiIuGGOCwADUq1y6sq8b2+/v7Y9GiRTn4kfAm6/6QY2cx/eFYvh8SWSBScnSIxlbgbxuEqTW28HKoxHTRtUE8cOwuCxwrrmRhnYnabWPw8p45HlzWD81sccVSzFsgIBAo2QiQ4Cir2SUTPAmQnp6eOSykJATKviDJauxzK9/kIRYfH88ttSTEjxs3jgdwk4aVrMSkNSVFho2NDVfuyVozSYsq+1se76LYpnUBlIQYUhtTcAsROdKSmZ2irEkyv3btGvdRoINCfgmkUSxuNHv2bPy44kc+7SlMmCRVtzJEb0bTtvZDpaFAhYwWWNLlAAb0HagMC420pSAwSpVFrhLkl0rHShsmGI1MVkUm9mU7MP9P5uqhQjBSYUPWcGyJT2vuxpjKPyA69RULIOvGy6U+TbhVWFe93O9VORneVZNw+ZAt4qNV82/Vy4WJSQkEBAICAQ0i0LVrVx5QLbkQfvLJJzzYum/fvjywmuQbEh63bt2Khg0bKjUyPYcvX77M+5D2tAWLgyEisz+Z4Mmnk9wHyV+UxiEFHwmjJE8RHTp0iJvpqQ1RbpdBvrGI/tO6CZ60nhRNTULoypUrOVAUEU8aP6IRI0ZwJ1tynCWhdOBA3QtemsC+cpXKuHWX+W5WY05zSlBmVgZ+DZmKY6Eb0af8ZLwb9BX3A1GChUabUlAYpcUqqWRobA4Hv26IfLAN7rWmwNBIs36sdL7Xc+mCOs4dcTx0E7Y/YkFcF9ry6PkePhPgYl62WEFbs00Mwh+74ByrkuTzQbGaupisQEAgIBDQCQLkVkG+nGRJ9PPzgy8znVPgD7kC/fnnnyhfvjwXDrt165bDpVCRyZEWlZRDpMEktzJJsKTUlhMnTsS3337L4w7IGitF0JMLQL9+/bgvKlllqQ1pTcnnlKzMpBEtU6aMIsNrtY0BW9TrhJNaHeY1c3oDkFU5S0OS6prSDMjbJ7WRPskHVFaFLW0v7LMwv5udS11RvlYiS8atnA+oNG5yTAhu7QxG+TbrYePWSNpc4GdqRhJ+uPU+LkcexBD/r9Cx/Hv8BKPgLH2m4usD+hrVxMhruPN3Z/g0+R4Ovp21CjWlaTrw/FfsfrIMSRlxaOH2Fs8tam/6xgdKH31AZUEJfWiKfzc7ol6HTFRrnCx8QGXBUfG75ANKpjp99M2SXVZh907ZtkX1XfIBVfX5oMt5Fwc8i6sP6J5FFvCsPQMuVd7R2CG9ubMNrF0boF73nwvlSdpFkmXIDC5LdJ1TcJKywZGnTp3igu358+d5DAudO7JEUe3EmxR48og0suQbLUuU7UYb2Q1kx1D0u9ZN8LITyU/AJDN9fvtk+5ek7wlp0Vh4bSCuRx3D2Cor0MJ9UElanl6vxdIxABbsTxtm+NwLNzE0QwevkSyZ/Ul09h6LE6+2YPLZxixoaT4S0pUvWJCbvy5+l/FNRfnaiTi33xCRL1m6CEECAYGAQEAgkAcBEjBzC5/UiMziygqfuZnnFj5pP6XGy0/4pP25hU/api/CJ81FpwIoDSiIBVulvMCXV3rhWcIdTKy+DkHO6kdkC1yVQ8DJvz/iX55AavxT5Tqq2NrC2Bo9fD7mgmgLt0HY+3QlS2bfmGlGf0BKRt78qyoOo7Vu1ZvHwoa9ZB/bwvIsvs5rrLWxBGOBgEBAICAQACpWrIj58+eXWCiEAKrjQ/s8MQRzLnVDItN+fRL4ByrZNdDxDMRwhICDb1cYMP/PiHubdQqIjYkjBpb/DPPrHkeQU3uuCf3weF388+RXpGfqb348I5Yrv1X/DESHGeD68ZzmJZ0CKAYTCAgEBAKlBAHSYFJVxJJKWg9CKqnAqbKuu7Hn8c31oSAhZGLA73A291KFjeijAQSMTG1hX7YTIu9tgVuNj5gTt27fxZzMPDC84iJ09BqNv54sws+3JmGH+VL09JmI+i7dWFUl3c5HEUhdvbMQ2CINlw5Zwd0/Gc5e+iswK7Ie0UYgIBAoeQhkpMUiNUFzcRRZmTlzfJc8xIpuRUIA1RH2l1ig0Q83R8HTsjImBKzhQqiOhhbD5IMAmeGjWDR83POjsPVsmU8r7W52tyyPCTV/xb2YS/j99uf48faH3Czf23cKajrp35tvYPM0PLwBnGVR8cHDwmFsqrMYRu0eCMFdICAQKBEIvLyyBPSnUXJrolF2gtlrBIQAqoMz4XjoZvxyZzKqOTRlAUc/wswo/5r0OpiOGOL/CFiXqQczGz8WjLSpyARQ6WCUsw3EpOrrcTP6FK+qtOTGMPjbBLGqSlNQ2V65vHEST218GrJ0oHU7sTrHa1xw6aAt6nQoHoFU2sBC8BQICAT0CwGD+pPB8hVpflK2lTTPU3CEEEC1fBLsYul3/ng4D41cezKT69cwMhCQaxlypdg7Mi3oi0sLkZ4cAWPznOkqlGKkocZVmLA5s+Z2XIjYh60PF2Le1b6o7tACvXwn8zKfGhpGLTa2Thmo3iIWlw/YwaNCMjz8U9TiJzoLBAQCAgFNILDkxVxkIVMTrHLwaGkwGPUgMtXkAEUDP4Q0pAEQ5bGg9Krr73+Gf56vRnvPkejnN50ni5XXVmwrOgQcy/VkAugCRN7fCteqI4puIrlGru3UDjUdg3Hq1Tb8+WgxPrvYEfWcu3AfUTfLcrla6/4n5cx9EWKOC3vt4PROGMwstaB10P2yxIgCAYFAMUegvecINGB+9Jqi75nrnCDtICAEUIZrRkYGKKErJWhVlR4/fsy7Prh/H1Vd62DVnfE4E7YD/f1mor2XYoLNlClTcPXqVdAnlSlVhW7dusUT9QcEBKjSnWNBifCpbJdUrSo3ozNnzvCkuFRVQZH6tbn769NvEwsX2Hm14Wb4ohJAKXk2nX+5y55SIFLjMr15UNKRl+uw8/FSTDvfCk3d+qJb2fFwNHMvMihZwScEdYzGgdUuuLDPHg17RBXZXMTAAgGBgEBAQsCRBXj62tSQfqr9aWpkrjYPwUA+AqVeAH316hWoXuuwliewk9Vzt9h5BUuXLpWPVj5bhw8fjmvn9+DHiaaYOnMKbAbPhrV/GkZU+pab3vPplmOzh4dH9m8q10UlMakuu6KUnJzM67++fPkyu8uVK1fg7Oyc/buwL1Q1gbB48uQJr1vbsWNHrFq1KofmlgTbyMjIbFbbt29XWVjOZlLEX5z8++H+4XcQ/+osq3ihmuCvyhLomNG5Q1UuqHoGHSuq7yvV7JV4GhuaoI3HMDQt0x/7n/2EPU9X4GToNrT2GIpO3mOKLKDN0iYTNYNjcHaXAx5etYBv9SRpyuJTICAQEAgIBAQCBSKgf7leCpyuZnfSQ79mzZq4c+cOr7NK3Ldu3crrpio6Egloe/bs4c0TDbPg8J4BTDwTYXSotkrCpzQuCY9ffvml9LPQz+rVq0NW+KQOjRopVhKU2lI5VOJx8+ZNLnzStr///jtHXfjAwMAcwie1IWGZhPjiTDbuzWFiUUYnlZFkcWrQoAGOHDnCS1vSufj8+XOMHj1atkmO72ZGFryM58K6JxDs+Q4OvVjDktk3wvbHS5CUrloJ2RwDqPCjbNVkeFZKwmUWkJQYW6pvJyqgJ7oIBAQCAoHSi0CpfmKcPXuW116XDr8R0zQRrVu3TtpU6OeiRYt4GwNTYJNbJozsgXOzM7H3p7OF9i2swQ8//FBYE76fNGkJCQl52sbHx4PM5YrQxYsX85iAqd/69euzu5OpWB4tXrxY3uZis82AhXY7+vdF9KPdyEiN08m8SYscExOT/eJDg5Lf8PHjx7NfAPKbiJWJPfr6TcOCuv+yl5xe2PH4W0w+1xj7nq4C1Z7XNdVqGwNjkyyemkkbAai6Xo8YTyAgEBAICAS0j0CpFkCpjqqREcsrw+j+8xNoUO0dWJjZcz9IRaEnHpxYDIYR+8tiAXjpeWVBRdnlaGdAjnZqkrS+wtjQOoyN83pkpKcXnoRX0TEKm0NR7ncs3xdZGcmIerhDJ9MgvOXVBqaXiexzqpCZ2JuWwdv+X2JenaMIcGiOjQ++wJSzTXHs5UZkZumuXqaZRRaCWDqm8CdmCDlnVcisxW6BgEBAICAQEAiU8lrwderU4RpQEvS2Hv0IZqbW6NViMUaMUCxoiE6gCRMm8PMoixWF6fnKEPQZNMMQoz4eyrcr8l9+gqaiNWDNzc1RoUKFPEMRX1qjIkSuCBR0JCv8EN+hQ4dmd6e6tPJoxowZ8jYXq21m1t6wdmusMzO8vb09mjZtChOT11p3CSzy/bW0VC5PrIt5WYys9B1m194Hb+uqWB0yCdPPt8a58D05NKzSGNr4dCuXAr+aCbh+zAax4XlfZLQxpuApEBAICASKEwKnT5/GpUuX8kyZrF8rV64s8H6dX988zIrRhlKtASUNFPlv0sGPT36OfWc/R70qQ9G70wcKH8L33nsvW0izzjBA1M9ZMLMwRUxj5tuXGq4Qn2fPnuVp17x5cwwapHjesYMHD8LR0TGbDwW03LjBStYoSKT93LlzJ29NQhEJnySIT5w4MZsD+SvKBjWRsLpv375iHwkvLZAqIyVFXkVS1E1pk1Y/ly9fjkqVKnEh1MrKCj179gQFdalK3lZVML7ar5gR+BdsTV3w/c0R+PxSZ1yPOq4qS6X61WgRBwubDG6Kz9SdAlapOYrGAgGBgECgqBAglzh5AihlQRk5cmSBAmh+fYtqLZoY14AJX8UqgR/5ISpiFs4NjoODA08dlHu79Ds2NhZGhsY4vbUsMtIM0GZYGKjqi6L0OOQkIs8MhG2NJTD3D8DcK71gZ+qKT6pvAfnsKUIkDD969AgUfV62bFlFuuRpExUVxVP6ODk55dmn6Ia4uDjumpCfJu7p06dcUyobua8ob122s7Oz48JdeLhiLwKZGSm4vrU+HHy7wqvebJ1NlTTVdAPKTxOu6kSuRB5myewX4FHCNVS2a8iqKk2Fv21tVdnxl4/U1FQeNJUfk4hnJjiy3gmV6icgoJlu/Glzz4XSWZFPNKVX01eiY+3m5obo6GgkJel39oDC7p36gDEpE+iep+rzQZdrKA540guxtbU1QkNDNQ4NuWy5urpqnC8xbL/ZFAPKzUJbz+Ea40+p7yrZNcDUpr/ky5NkErrnEGYhISEoX748f/bQ85gCTOlaJ6I2dO1Lz1baR898Pz8/7pJF9yxS7FBQMKVUpHNaes5KfelcJ76yREojsqoRkRvX3bt3uQyRO7WfbB99+K6wBpQEtJJMdKCsrC1Ru1004qKMcOuUtVLLdXV14e1d2IXlYemPiQHrEcm0ql9fH4zkDMWcQnv16oXPP/9cZeGTJkA3N3WET+JhY2OTfYHQ79zk4+PDNXe5txf334ZGZqDE9FEP/0Im8wfVFZG2WRv5VGs4tsRntf7G6MrLEJX6EnMud8N3N97Fs4TbWluak2caKtePx+0zVoh4ntO9QGuDCsYCAYGAQKAIEaCA5hYtWvDMM+S2RgLlggUL0KxZM/43c+ZMPrtZs2ZhyZIl/Ptff/3Fhct3330XZPGUiBQ8lO6Q8oG3a9cO/fv3z9GXhMs+ffrwv379+qFatWoYMmQIb3Po0CH4+vrio48+Aj2nV69eLbHVy0+FBVDy/6MckZSaR581C+qi7OCWjgp1EnDrtDViI5RQgeYa2Me6GiYErMVT9rBfcn0YUjN1J9Dkmor4qQQCjiwnaEZqLGIe71Wil/42pbft+i5dMTfoMIb6z8eDuMuYcSEYK29/hLDkx1qZeJXG8bBzSce53fZIZz7RggQCAgGBQElHgFze/vjjD5CvJslLBw4c4IVlSCikdI2yRBrOt99+m+f6PnbsWI64E5KxKOUhfVKOaC8vrxyZUSpXrgxyh6O/adOmca0ruXMRffXVV/j999/52KRBpTgSfTZyKyyAbtmyhafpGTBgAAdk0qRJuH79uiymJeZ7VfYAtbDOwMV9duzgqb4sf9sgjKu6Gndjz2PZjZFIzxRPY9XR1E1PC/tKsHSqySsj6WZE3YxiaGCEFu4DMb/ucZbCaTquRB7C1HMtsO7eLMSkyk+vperMyHWlXudolhfUCFcO26rKRvQTCAgEBALFBgHSPHp7e/P5litXjmsv6QeZ0MmVQdZ1kPJtk9VLChLu3LlztgtW27ZtefpECkgli+iwYcO4kJkbiGvXrvH4k23btvExyP2ECpmsXbuWbycNKpnqSSDWV1JYAKWI3Z9++oknO//mm294wvKgoCBeBWfZsmU8p6G+LlLZeRmbZrEKL7EIf2qGh1cslO2eo301hyZ4v8pyXI06ih9vf8jS47A8TYL0GgEKRooPPYWUuEd6PU9VJmdqaI4OXiOxsO5JdPZ+H/+GbmbJ7BtzX9GE9BhVWMrtY+ucjmrMB/TBJSu8vG8mt43YKBAQCAgESgoC5P8pS+RaRURWqNxEfpykBSXffyLyi5Uy0JAge+/ePZCcRQJkw4YNcfny5RwsqGhJly5dsGLFCl5MR9pJfMmkP2rUKP63Y8cOuRlypPZF/amwACpNlKR28kkgXwZaKEV0TZ48GZ6enjximkAtCeRePgVelZNw9YgtkhOUhikHBLWc2rKynEtYWpzd+CVksl6rxHNMvJT+sPftzALQLEqcFlT2cFoYW6OHz8dYUOcEmrsNYOU9f+SC6O4nPyA1QzNBMeTK4uydgvN77ZCalPcmLDsf8V0gIBAQCJQWBCj7CQmoUhVFMt1Lro1r1qzBmDFj0Lp1a1AxGjK5P3z4MBsaKjDTqVMnngKSNKcSURrF+vXr86AmqrJHgcykPZXVvEpt9eVTKcmKnF8/++wzLlFTmUcCZePGjVxKP3r0KMiplvaXFAps/TrwisoMqksNXLtjiP88HA/dhPX3P1OXneivRQSMTKxh79sFkff/YIUF9DeSWhMQ2Jo6YVD5z7lpvrZTO/zxcD4TRJuwMp+/qe0yQi/+dTrGID3VABf222liuoKHQEAgIBAo9giQVvTPP//E2LFjeRARmdGlyHiKtXn8+DH3AyVBlQKaKBhJol9//ZX7ln777bfc5E9m/ypVqvDdZLKfPn06yHxPpvzx48dnR+BL/fXpU+GM0a1atcLhw4f5Qin35eDBg+Hu7p69FjLH9+jRA+SXUFLI3CoT1VvE4sI+e/gEJIGSbatD5IOXnBHPK9aYGlqgj99UddiJvlpEgMzwkfc2I/bZIdh5B2txJP1g7WTmgXcrfo2OXqOxjaVuWnt3GteK9vKZiHosiMnQQKl31exFWdlloGabWJz72x6PbySDascLEggIBAQCJQkBMpNTwJBE5K4oEQmbUiDQ119/LW3m2kpS4lFZZtkc3mRl3rt3LxITE3k/SolFJNuXBFd5RIpBSutE/qCkEdV3UlgArV27NubOnctBy29RH374YbYUn1+b4rbdt0YSHl23xMX9tgh+JxzkH6oOtfcagZTMRPz56GtYGtuik/cYddiJvlpCwMqlNszs/BFxb1OpEEAlGCmF2NiqK/Ew7gq2PJyHFbc/wK6ny3gO0TbO/aRmSn3Sy9vzEHNc+scOLt6pLFm98INWCkDRWCAgECixCMgKn7KLlDSistsU/V4chE9ai8JqDQpCqlq1aoHrJ1WwujkoCxygCHaSGZFygybFG+HGvzmdjFWdTreyH6Gd53vsAT8Xh56vVZWN6KdlBEgLGvvsMNISX2l5JP1j72tTA5Oqr8eU6pthxrT1S64PxcSjLXE94oRKk63dLob51WZxTag6mSVUGlx0EggIBAQCAgG9Q0BhAZTSLlEZRqrSQzVLX758qXeL0daEbJ0yULlhPELOWyHqpcJK4wKnQ9UaWrgNxNp703EydFuBbcXOokHA0a8ni2A0ZL6gW4pmAnowahX7hphZcwc+qLIKSWmxmHm6I76+NhiP4pVztTGzzGQvcjF49cgM9y5Y6sHKxBQEAgIBgYBAoCgRUFiaun37Ni5cuIBdu3bxpKoUpVWvXj10794dvXv3BuW9KslUuUE8nt604P6grQaHQ0WXuBwQve0/l/mEJmLVnfEwyjLBwrFreJlDch6WrcGeo1MR/6AyX+Q8TdkOyO+lQoUKRTwj7Q1vbO7IzO9tWTT8ZrhWG5MnnQaVpaToRYpKpIoXFK2oLFGZUArgIx+h4OBgUPlQTROVcKMAQZovjUGJjZWlIOf2CK40EP88+A0bbn2JTy924AnuezAfUTcLP4XYeVRIgW/1RFw9aosyvimwYS92ggQCAgGBgCYRSGHP1Pi0nKUq1eGfmSXuU+rgV1BflWvBU+mpjz/+GMePH+cloTZv3lzQOBrbp2qtX03U3w17YopjGxxRvWUcKtbNWV4zOSYEt3YGo3yb9bBxa6TwejOy0rH0+nu4EPYPLs7PRMSV110p+o2CvvSJqGY1uWJQrXhK7UC/f/nllxwRevo0X2Vrwcube9yL47h3cHCe40rCHGFBedoofQZhQdUounXrJo+N3G2UVYIEV8ofRwIoCYonTpzgUY9yO6iwkeZHY1DNYWmeJIzSy6OyRBYQWndkdASOvFyHnY+XIi4tEs3c+qErcytxNHsTlJgf77QUAxz4xRmkEW3xVgTLfZdfS9W3i1rwqmMnr6cm7p3y+Gpym6gFr0k0geJcCz4Lmvcxb+k+uMBa8JpFv/RwU1gDSoIfCURUVor+6OFJof4khJIGtDQQBVBQUBL5gnpWTAZF+KpLRgbGWNHvBCqMAWpONMT5LzMRzUp1k8b5u+++AwV26QtRloNXr17lyCv2wQcfcA2ebEYEfZmvJuZh7dYYJlaeiLy7KceLxcCBA7kbimzeW7oWatasyWvwFjY2CXIkGBKRBlWiQYMGcTxNTEykTWp9SkKyFIVJzCg3HAm69vb2KvE2NjRBG49haFqmP/Y9W4W9LIfoidCtaO0xlCe3tzZxyJeviVkW6nSK4S9yt05Zg6qOCRIICAQEAppAoHO5xchi/zRNvpY+mmYp+DEEFBZAqbZpbGwsevbsidmzZ4PSMrm6upY6ECkt04u7Ziyi1xaNe2tGzR8bFY9Li4Cg6YaoPdUQ52ZnIvYBeCoGfRJAKTdZ7qS2xsbGPCdZSRVAyQfUqXxfhF77AZ4pMTA2e20iDwkJ4W4IshcACY1Xr15VSAAl07uNjQ3XJsvyoO0xMTHc31p2u6rfSVstK3xKfOgFUioDJ21T9tOMJevvWvZDtHZ/G7uf/oADz39hmtHfeaUlCrIzN3qdPiQ3X3qRq1gvAbdOWsOdpTZzcC8ZxStyr1P8FggIBHSLwNywaKb/1LwA2tvJHV10u5RSMZrCAiglN929ezd27twJevieO3eOZ+onLY6Up6o0IGZqngVKUP/fTgc8uWkO7yrq5zWkMlwZKRm4MDcTdT5lQigTRM9+lql3Aj6li4iOjs5xmOk3mWZLMjmW74OXV5Yg6uFfcKk0hC+Vsj2QVUCWSHBUNAsEmd1ltacSH9KGUh44TRFpOXPPk8zyis5TkXlYmdiz+vLT0NZzOLY/WoIdj7/lwmgX73Fo6T4IJoZmedhUbRLHS3Se3W2P1kPCYKQZhW+eccQGgYBAoHQh0N+5KtrYay4mZeajI6ULQB2uVmEPLAqKIRN8REQEvvzyS+5PNnXqVJ5AlYrelyYiobOMXzIuH7JFarL6JQYpvypROquASCb4VFaSu84MQyxY/hnfri//USJcqV4tzYkE5yZNmoByxJZkMrXygI1Hc0SEbMxe5uLFi7O/0xfSBFMxBgrMUoTIT5FK2Er1gqU+dC5o8oVu4cKF/DhJ/OmzX79+GvUzlXjbm5bBkApzMTfoMKrZN8MGVvFryrlmrPrXZuR25Ddir751O0cjPtqIByVJPMSnQEAgIBBQBwEvM1vUtnbT2J+5ocJ6OnWmXSr7KiyASuiQCZYCGihLP5nkyexIpsTSRrXaxvISg1QrXl166623uHaZhJj0eAM8WeUMjzLeWHB1ACJTXqjLXmP9qc7sqVOnePBNYGAgL/mlq+AzjS1CRUZO/v2QHH0Tif+PEiNfT7ICNG7cGAEBAaA0ZTt27FCK+6hRo3gQF/lpkiXh119/xZAhrzWsSjEqoDFlVPjnn3/4PGnO8+fPxzfffFNAD/V3uVr4YlTlpZhdax+8LSvj5zsfY/r51jgXvicHc3vXdFRjmlBKy/TqoWmOfeKHQEAgIBAQCJRsBBQW7UkzQymYzpw5w03DXbp0wbJly7gvaG4tTsmG7PXqKACpapN4XD1sA59qibBWUw6tVasWyP+PIrdfvHiBsOTH+PJyTyaE9se0Gltha6ofZm4qNkCph6hKA/kXlhay82rD/D+dWEqmTbB0qsGX7eHhgS1b1MsR2rx5c7Rv3577aVKKK20QpYdSd56qzMvbugrGB6xBSOw5bHkwD9/fHAFf6xro4zsV1RyacpYV6yfgxT1znNtjjzbDwkAuLoIEAgIBgYBAoOQjoLAG9O+//0bLli25BuzZs2f48ccfeVL60ih8SqdFhaAE2JdJZ7lB7ZCZob4pXuJLny7mZTG5+gaez2zhtYFISGd2eUFFhoABi/x2KNeT+YFuRyb5SghSGIEKtnUwLfAPTKi2lpvi6Xyef6Uf7sVeZLlVmbtJx2juynLpwOsAL4UZi4YCAYGAQEAgUGwRUFgApXyfc+bMgaenJ27duoWbN29m/5FAWhqJktFTdZe4KGPcveitcQg8LCvwcojhyU+xmFWfSc7ImXtU4wMKhgUiQGb4zLR4RD/aXWA7sVM+AjUcW+LzWnswuvIyRKa+wBeXu+K7G+8hxvQGAlvF4skNCzy9bS6/s9gqEBAICAQEAiUKAYUFUIqAp4haEkCpJrzs3/jx40sUKMosxsEtDaQJvXexLFLTKyrTVaG2PtYBmBCwFk8SbuHb6+8gNVM7ZlqFJlPKG5nb+cPKpQ4zw78JRirlkCi9fAOm8qzv0hVfBR3CUP/5eBB3CTMuBOOA+Sg4+kbjIrMmJMcrfFtSenzRQSAgEBAIFBUCp0+fxqVLlwocnnJtb9u2rcA2JWWnwnf6kSNH8oo3pAl98OBBjj/yBS3NRL6gZpapCIv+gfnyaR4JMmGOq/oz96VbdnMU0jNF3kTNo6wYRyf//kgIO4fkmHuKdRCt5CJABRhauDNTfN3jPIXTpcgD2OxcEymZiTj9t+bSUMkdXGwUCAgEBAJFgADF0CgigFK569JACgmgFO1OZnZKPUNpd3x9fXP8ubi4lAas8l2jsWkWApqGIDm1KZ7d9c23nTo7KGhjTJUfcDXyCFbeHsd86TRfbkyd+ZWWvvY+nWBoYi20oBo64KaG5ixx/SgsqnsSweV64bLfKEQ8tMXWg8eRmB6roVEEG4GAQEAgoD0EKDsQ5YGmcsfkoijleKacyy9fvswe+N1330Xfvn35b0ppSfTkyRNQkReJKGiUqiBSpiHZKnm0XzanM5VupsInz58/l7ryQjHSPMhNkmQ3fSaFBFDKWUhCpyxI+ryoopiba9koWJlvwZ3z1ZGcoBCsSk+ztlM7vFfpG5wN34VfQ6bIrXCjNFPRQSkEDI0t4ODbFVH3tyFLaKKVwq6gxhbGNujpOxGTOkxFRtlTSL/YCbOODcLfT5cjNUMEfRWEndgnEBAIFC0CZ8+eRYsWLdCoUSMMHTqU51lesGABT69HKfZmzpzJROKiDQAAQABJREFUJzhr1iwsWbKEf6dUhpSTuU+fPjx/9EcffcS3U3o/qjRJedeDg4OzF0ZjUNYUopUrV/L0f1QgiDLokIWaiNpQsDjl5h4zZgyX2w4cOMD36eN/BUpK//33HweBgKAa1QTU8uXLsX///uzttO/69ev6uDadz8nZ7mNQFbDLB9XMyVTAzBu69sAQ/7k4FrqRJfr+vICWYpe2ECAzfHpKBGKe6u+Fra21a5uvrakTevTwg5WlCereX48t9xdg8rmmOPxiHTKy0rU9vOAvEBAICARUQuDGjRs8RSH5eVLpchL8SEN56NAhrFq1Si7PKlWqgNqfOHGCp7XMzHxj2aT0fKT0o7LJRGvXruXCLZVWptzOBw8e5Lmnyaz/008/Qep78eJFrF+/nstoVCwov7HlTkjHGwvMA/rOO+/kES5Jqs5NJJiWloTkudcu+9vYKBQVg67ixukg+AQkwY3VudYGtWDlDZMy4rHpwRyYGVmil+9kbQwjeOaDAOUBNbevwnOC2pftkE8rsVlVBEzMslCvUxyOb6qA0biFMzYfYM3dT5g2dAV6+UxkQUzdWPomlr9JkEBAICAQ0BMEyEpMebKJypUrBxIuiShfdGhoKDeP8w0y/3Xu3Jn/or4kWJLZXSKqNDh48GD8/vvvmDZtGhduL1y4wO99JFRu3bqVa1PJp5SEz9TU1Gxe1apV498rVKjABVGJp759FqgBPX/+PJKSkgr9I4AEvUbAs8JDOHml4uJ+W6RrMVaog9dIdC87ATufLMUe9mAWpFsEnCr0R9zzo0hNeON/o9sZlOzRXH1S4R+UiKf/+WCw02p8WnMXy43rjRW3P8DMi21xKfJgyQZArE4gIBAoVghYW1vnmK+UI72gl2UHB4fsPlTmmoRQWRo2bBjXZu7bt4+XenZ3d+dCKpnYr1y5wouYkLaTSOrr6OiYzaKgsbMbFeGXAgVQMzMzXquagCzoj8pxCnqNAClmKDdoUrwRbvyr3RKl3X3Go63nu0wT+iUOvfhNHAIdIuDg2x0GhqaIvKdeJSQdTrnYDRXQPBbW9uk4u9seZS0CeWGGKdU3gQKXllwfyiqF9cDtmDPFbl1iwgIBgYBAQBEEKlWqBCcnJ3z22WcgYZSIAsIpgGnRokW8GNCRI0f4dgqEKm5UoABa3BajL/O1dUpH5QbxCDlnhejQAr0csqdM6a3Kly/PS1z27Nkze3thXwaW+xTN3Qbgt7vTMfv3MTw/K520X331VWFd5e5v06YNz/VatmxZyEsFQdF5ZFKgwDRyui6uFB0djXHjxoFKylIdd2UvXmMzOxjYN8St08tYerK23H9HFSzozbZ79+6gY06+QNog8hEiNxky92zcWHxymBqxS6duZ1boIcIY14+/fpmrYt8Is2ruxAdVVrHqYLGYe6U3K9LwNh7FCz90bZw7gqdAQCBQtAiQK+TDhw/5s4pmQmZ1el4EBQXxIKd///2Xm/wlX9Gina1yoxswtW1Ona9y/XXemtIQKCss0CRJ1U0pEbRFyTEhuLUzGOXbrIeNWyNksJeRA7+6wNgkC60GhzNtWf4j04lDkXKyRFrlR48eyW7K9zulZBq7pQXiXe/j8jeZCDv7umnjxo2VqgFOfiiSH4k02PDhw/HFF1/wn1R+9fPPP5d28U/SjN+/fz/HNn35YWdnB8IxPDw8x5RSUlJ4lKKxsTE/l8jXhtJn3Lt3DxYWFjna5veD1vz+0OaYO8II01el4WJIFhfIJXNIfv1kt1MkI73ZytLcuXMxZMgQ2U1qfSfn9oEDB2anBSFmI0aMyDOuIoM4Ozvz80PXqT1unrRm1gRrNBsQCRfv135ONF8670++2oq/Hi1GeMpTnuC+B/MRrVgmEJSihI6pvhKZxtzc3EAvQuTmpM+k7XunJtZuamrKNUWqPh80MQdFeRQHPK2srEAmZfJd1DTR/dbV1VXTbDk/zz1zMMGzPga6BGiMf6+bfyDI2h1L6/XWGE9NMKIUTfQMk0z9muCpax4FiEW6nkrJGo+0N2SKJw3o3fNWBS6uU6dOefZTHjFJ8MuzM9eGuNg4bJ9wF+EXgcBxhnCs/roBCR8UmacILV26NI/wSf1+/vnn7O6S8OlkYo+m9kF8e3JyMubNm5fdpjh8Ie2wJHzSfElQoYt4xQrFfWn79++Py3cz8CIiC+3qvb6MKH3GqVOnFIIgLi5OrhA4e/bsHI7oCjEroBHlnJNy0knN1q1bx9+opd/6/knWBKo4dm63HdJSDLKna8je6pqU6YN5dY5iULnPcTP6JKada4nlV8chMuVFdjvxRSAgEBAIlDQE6AWhOAufdDyEAKrFs5K0Nb41knCdaW8SY/OHmoQ4eSSbwFbefmkbRcAxZRDXfkbdAmpNMoR9pdd7Fc3dStWtFKWRHn2wv+YqHK29Bk3taiusqVWUv7bbUamz3Fp0OgbKvO2TFpXo/vMsVPB6fWxJqJWSCxe2BhJAbWzy+giTdoC0d5oief7Z5Nstm9BYU2Npiw9ZD+p2jkZKopHcFGfGzBc32PMdLKx7At19JuDE822YeKYRNt3/EvFp2rN6aGu9gq9AQCAgECgNCOQvFZWG1etgjdVbxHIz/MX9dvmORikb5BElo1WESJAhoYLSJF5amIk4ZrmvNdUQNn7swV23riIsuE9JYQ0lYYbKKCay5OCWLBhkf62f8I3VeBg90GLIf2ETU3I/uSaQyS431a9fP/emfH83aNAAbk5GaFDVAHtOvzb1kjm1evX/q5/z7fl6BzmWkx9tbiKzir29fe7NKv+m+eSOhCRXFPIjKk5k45gBupYeXbPE8xAzuVOnlGRdy36IFa2usOC84Tjw4ldMPNsIOx5/i+QMzQn1cgcXGwUCAgGBgEBAKQSEAKoUXMo3NjXPQmDrWLy8b46nt8zlMti+fXue7a1bt0avXr3ybJe3gTRvUh7WDKaYuzA3E0mvgOZf2SLZPEJelzzbqMSqvPFOnjyZ3XbPnj3Z3+OZAFrvXH98Y7ARrukOsJkeAaul0TB8of+ReG+99RZq1KjB10KCu6WlJTp06MASoPfIXl9hXygCsWdTIDnNAAcvmYBSaHz77bfw8fEprGv2uJK/KPlb0R8RuU1Igj7foOZ/5EJBbt60Rsm/dcuWLRoVctWcosLdy9VKhKtvCi7ssyuw2pi1iQP6l5+BBXWOowHLGbr98RJMOtsY+5+tRnrmGx9ShQcWDQUCAoFig0A6MwemZKZr7C+LqssI0goCIghJQ7DmDkLKzfbfLQ6IfmWCtsPDQEJpbiIzOiWbJe0UldIiH0NliUz2FMRCPn+D3+uPHRmzeML6aTW2wtVCMcGI/ANXr17NhRUKOvLy8soxDdLQ/fP2BnQ3bYZf2518nRoiPQtmBxNh/lcCDGJZQtyWFkjqYY0spiEsSsovCEmaE0Wgv3jxAhTxr6i2WeqblhyOG382xquMhrif0JAHkUmJh6U2inxGRkbyShl0/OmlgzSjmiYKcqGkxeQ2QKXdaL2qUFEFIcnONSnOEP+sdoEzc29p1FO+eZ00y7JBSK+SHmLro0U4E7YdTmae3Ezf2LUXDA2K7vwUQUiyR1X97yIISX0MZTkU5yAk5pAmuxSNfO/tVEXvgpA0srAiZiIEUA0dgMIE0IQYI/zzswvKVkviwUnyhiUtFQlNJBRpgqJYIMaXl0mLmoXpgdvgYOauCbaw/CMBpkzgjF7ukpNfShbM/06A2W4miKZlIaWdFZI7WyHLtmgU7YUJoDknr9yv5xfnI+zWalTrcRLG5uoJjaSZJC1lfr7Ays1Me631QQCl1T25YY7/djkgqH0097HOveLcAqi0/0n8TfzxcB4uRx2Cu4U/erMKYkHOHaTdOv0UAqhm4RYCqGbxLK4C6JfnT0AbAmglSxv0rRKoWZAFNyiWpFIApTYCVnYZqNokDleP2DAhNBHOXtr3mSSBc3L1DfjqSi8suDoAnzBNKNXa1hqZGSCZaT5T2lrCfDsTRPezvwOJSO7CBNH2loBF0Qiiml5vRmoswu+sA9WEV1f41PTcSgM/76rJeH43CZcP2cKFVUyia0sR8raugvEBa3An5iwXRJfeHAE/60AmiE5FNYcmirAQbQQCAgE9RmBpSDkmgGqe+jLdTd8qmudb2jkKAVSHZ0CFOgl4fMMCF/baoc2wcBjqwAJIpvdJAet5wu5F1wZiSo3NsDLOPyBKE3BkWRkiaaANkjsyv8Ot8TDfFg+zvQmvhdPWTBA1eZNKRxPj6ZpH+O21yExn/ohVR+h6aDHe/xGo1TaGm+IpNRPlB6UKZIpSRbu6mBa4FVciDzNBdD4WXhuAKnaN0MdvKsrZ1FKUjWgnEBAI6CECvZmw2MpZcxObfUdzvASnnAgIATQnHlr9RelkgtrH4NBvTqyCjjWqNo7X6ngSc0+riphY/XfMv9KPV40hrShFDGubsuyNkDicBYwwM7zF5nhY/BYH810JSOpjjdSmLOG7oRJSg7YnqyD/zPRkZnr/BY5+PWBq5algL9FM0wiQH3VQh2ic2OKEkLNWqFhP+Sj3Go4tUd2hBfMN3YE/mY/o7EtdUdupPXr5TAJdM4IEAgKB4odAOfZoa/ymHLraC7DUgaJI7UkWUwYlwyZajMCnhNr+QQm4zQTQuAjdndm+1tUxvtoaPEm4wepoD0NaJguX1xFlljFGwgf2iJ3nhIyyJrD6MRa2k8JhcpblPy1ehbgQcW8T0lMi4FptpI7QE8Pkh4CbXyrK1UrgZTpjwlR7lyZfzAau3fBVncMY6j8P9+MuYcaFNlh1ezzCk5/mN7TYLhAQCAgEBAJqIiAEUDUBVKV7tSbMJG2ZiQssN6gu5S8yPX5Y9WeExJ7DspujkEGJQ3VImd4miJ/sgNjPHJFpbwjrb6JhMyMCxtd0Jwyrs9wsltrj1Y2VsPNuD3O74pVHU51163Nfyg1qaZuBs7vtkamYK6jc5VBu2xbug7Cg7nH09ZuGS5EHMOVcM/x+bxZiU3OWcpXLQGwUCAgEBAICAaUQEAKoUnBpprGxaRbIhy38iRkeXmWmaB1SgEMzjKnyA/d/W3l7HK+nrcPh+VAZFU0RP9MJcZMcKEAfNl9FwfqLSBiF6HeOxqiH25GW8Axlqo3WNWRivHwQMDZhxRY6RYM0oDf+zVtZKp9u+W42ZcUVOniNwqK6J9HJawyOvdzEk9lvfbgQiemx+fYTOwQCAgGBgEBAOQSEAKocXhpr7V4+BZ6VknD1MEsWn6Dbw1DbqR3erbQY/4XtxK8hUzW2JmUZpdcyQ9yXTogfawfD6AzYfhoJq8VRMHyi/QwBys6V0iSFXl8Oa7cmsHQW6TiUxU+b7R090kD14m//Z4WIZ0wi1QBZGNugp+9EXt6zuVt/7Hm6giez//vpcqSyIgyCBAICAYGAQEA9BHQr+ag31xLXuyarkERE6WR0TY1ce+Jt/69wLHQD1t//XNfDvxmP+eClNbJA7EJnJAy3hfH9NNhOjYDlihgYvtKti8CbSeX9FvNkH1Ji7qJMwJi8O8WWIkegSiNWwtQ1nZvi0zSoSLc1dcag8rMxv84x1HIMxpYH8zD5XFMcfrFO5y4sRQ6ymIBAQCCgNAJU8S42Nn/ryatXr7Bt2zal+ZaEDkIALcKjaG6diYDmcXh604KV6pRf31qb02vp/hb6+U1nJQp/wraHi7Q5VOG8WUR8KkvRFPONC5IG2MDkYjJsPw6HxZpYGDDtaFHTK6b9tHQKhI1bo6KeihhfDgKU0oxM8UlxRvjvb1M5LdTb5GTuya0GXwYdQHmb2lhz9xN8cq4lTr/azosIqMdd9BYICARKKgIbN24sUAC9f/8+vvjii5K6/ALXJQTQAuFRfCeVvyRKTVVO/eIXmAgnz1Rc3G+LkDsPcOrUKVC5S1WJ3rQKetvKzZf83bqVHY8dT75lZsYf+W4qY5iYmJi7afbv8PBwVu5TuXVmdy7sC8sRmsLSNl0Zn4gHdWJheiQRdh+FwXxzHAwSlEsxfOfOHVy/fj3fEWNiYkDlSwvDK+7FCSRGXGbaz/fz5aXODiqRSX/5EZVnffbsmVqVkmitERER+Q2h0HY6J/S5WpOtczp7oYvFnXMmeHFX80IogeRhWQEfVF2JWTV3wtncCytuj8Wsi+1wOfKQQhiWxkZUCpauMSo3q89E53dcXJw+T1HMTU8RSE9PB91j6T5++/btHOf6L7/8And39+yZ0zn26NGj7N+5v9D9np7BJAfQtSMRXUPSc4Lu5eQWdvfuXbXkBYl3UX0KAVQDyF+5cgWDBr3FOb01aBCOHz+uMFdKoF2b5QaNi87Elx8fQKNGjVCxYkX89ddfCvOghnQBNG/eHJUrV+Z/3t7eCp+YPXwmoK3HcGx6MAf9Pm2CgIAA+Pv7Y9asWXnm4OvrizVr1yI6OgYeHh44c+ZMnjbqbmjQoAEatmqMavNboOyhVrji8YTnD7VlgqjZTiacs5KfBRG9DFAN+1q1avE/middqLJEde6rVq2K2rVrc7y2bNkiuzvH99Dry2Bm5w9br+Ac29X9QfN8//33UaFCBT7f7t278+Moy3fKlCmoVq0a6tati3LlyuHkyZOyuxX63q5dO1Cd+urVq/NjRjc4ZYgezAMGDICbmxuoHOcHH3ygTHedtvUPSoSbH4uK/9sGKUns4tISlbOpyauMTa6+EcYGZvjm+hBW9rYnq7L0n5ZGLJ5s16xZw8+7wMBAfo7TC58+0jfffMPve3SN0L1DCKL6eJT0d05nz57lz246f8aNGwd6/t68eZNPuGbNmnjw4AH/Pn36dP5sHT58OOrUqcOFVtlVbd68GfT8IwGT7v3Lly/P3j106NBsUz1dT3RfHzlyJJcXvv/+++x2xemLEEDVPFp0Q23fvj1CQ9/cWN966y2QUKoovTu6J/b/Nw8ta42Hl8vrSixjxozB+fPnFWWBoKAghISEZLfPyMhA/fr1s38X9qWvzww8PZgJ81YP4VDntXZz3bp1WLZsWXZXEkpza3h79Oihsdr1NNDo0aPx+PHj7DEj0qLR4NduODToEdKCzGGxMR5248N4LXqkyxdEfXx8cryBErNmzZpl86QXhM8//zyH6ZRuGvJeHBLDLyP+5UkW+T6GVdvRrEBD5w29aNDDjo7XpUuX+M1LmijdjH777TfpJ//s3bt3juOcY6ecH/RScvXq1Rx76tWrlwefHA1y/aCXmmPHjmVrP3ft2oW5c+fmaqUfP+kQNe2Vgox0A1zcp92KX7TiqvaN8WmtnfigykokpEfzsreLr72NR/H5a971Ayntz+Lff//Fp59+yq0pkuamVatWeR662p9JwSOsWrUKS5cu5ec33d9Is0TXZu57XcFcxN7SjsCNGzewfft27N27FzNnzuTCoSwmdD389NNPXBg9cOAASOFA91KJ/vjjD8yePRuHDh1C2bJlpc35fjZp0gQHDx7kz42JEydyK1m+jfV0h2rZm/V0MUUxrQ0bNsDYmGB8EzBDmi168//6668VmhKdmMZGZ1G7Yj8MCl6NlTu7ISruMSZNmsRPxsKY0I0yLCwsTzPSdJ07d46/aeXZmWsDCT9PNlrByDwBAe8bIINpGcPOJWP16tVcS0fN8zPLk9CorMY21/DZP+kClkfj5kzkAmJyV1ZVaVMcLH+OhflOVlWprzXSGpgrVFVpzpw5mDFjBj777DN5Q2DhwoVo2rRpjn2h15axikdecPDtmmO7uj/IXEMvDPSwk4iO4/79+znOlpaW/GYk7ZP9XLJkSY4XA9l9ub/LvpRI++g40jj0kC2MyI3B1NQ0x7EnYYIE408++aSw7kWy39qepTkLjsd/u2zx+HoKylZ7Y8bS1oSCnDugFssucfLVVvz1aDE+vdge9V26oafPxyhj4aetYfWa76JFi7gQV9HWFtNrVucvcCbsXpm+YhnMPb00OndDQ1b+18wUpuzcNM58c00pMkiVE8exsn4d3vQFuza+u36LC8mk1WrcuLEiLEQbgQAqVarELU0ERefOnTFhwgSuWJCgIWthcHAw6N5ORM8iotOnT4P8QElxRVpPT09Pvr2w/0j5Q+Tq6sotffSsV7RvYbx1tV8IoGoiTZorWSFCYif5hEq/C/tMz0jBuv1DMbLbTkx/+wb2nvkCT2J3FNaN7y/It4pM84oQrcPYyATXvs+CkZkBAj8yxIX5mUh6UvjDW9m1KjKf3G2kdWR6sKpK4x1gxKLlLTbEwfr7GGRsZz6r/W1AaZ0KImmeEq/cbQkDWUqOCUHM0/3wqjsbBoaavVRoLAsLizymPkkDQ/PI77jmnqfsnBX5Tudrfhjk7k9tzczMcgig1EbWNyl3H3347Vs9GU9vm+DiP7Zw9k5hyeq1739oyGrtNinTBw2Y4ElR8jufLMUnYbvQjKVx6lb2IziYuekDNDqbg3SONXR1YSmtfHAi9BW7jgxhxl7QDRKYK40GifhmpbBrlHjLvNQpMoQTE1xTKCExoxbMzWR4xQpY8+ARjJgwKkggoAoC5L9J900jI6Ps7uS+JD2DaGN0dDT39aTvJJSSNpOUAn369OHuIGRxk64halNQbAjtkwRbaltcSLNP1eKyag3Os1u3bli8eHEejsOGDcuzLb8Nfn5+XC3/8OVpfPFrJXRtMg9dGn/FNG/T8epRKlx9Cg74MTc3535LT58+zTME+ZMoQuQvaW1tDdKaXlmSiVpTDVFrkhFczr/pT5oweWYpWTO9ImMV1IbeIsmJOzeRdlKWMsqxqkrTHWF8I4Wb5W0WRiG9ggmSmCBKF668lwIyuxONGjUKH3/8sSw7/j33ttBry2Fs5gRH/7552qq7wdHRkfufkmlbmivNu0yZMrCysuLsBzF/YnnYfvjhhwoPT+bzW7du5WmviPaTOpF/Kvl+yvqN0o1V1qUhD3M92VC7XQz+We2Cc3vs0bRvJDsvdDMxY0NTBHu+wwXPfc9W8eC+E0wz2sZ9KDp5j4G1CSvAUApoyJAhuHDhQvZKu/5zCKksEIlMlUn29tnbNfGF7k22Tk7cEiT70FaE917m/yndX+xMTPBRQFVWrKMSzI8fRppBFlKbMvcdM2ZlESQQKAABem5dvnwZ5J9JltEWLVrkaN21a1duNSL/Tid2rpIFie6vFPdB91jyCaVnFPl6klaU2khBtOTqR9eSrFyxadMm7l997do1HtRE/YsbCR9QNY8YnUCUZkEi0mqRQzAJdIrSiRMnst9eElMisfHgCIQaL4WNrRmOb3LCfzvtWXqZgg/VkSNHuKlUGpOESTKrK0p0A9+zZw9vbmFqhVvfmcIswQlJTU/jYfxrH8KHDx/mYUc3bl9f3zzbVd1w+PBh2DKTnSyNHTsW5O8ij9KrsmT2s1ky+/H2MEjKgg2rqBQ+5iICrSvlaE7+NRJRQA05gROR0Ed/dDMg/zSJUuOfgiofuVQZDkMj7Tx8yL3Bnj2I6QWC1kw3LFk/VHJYb9mypTQlkJmRBFIKnlKUyB+JzgWJ6PykTAuv3Uakrfl/0ph//vknb0CCMfHq2LEjKLJT38nMMgtBLMAv7JEZ7p5/bfbS5ZzNjCzRtew4nsyehM8DL37lyex3PP4WKRklX7tGmhzZgDWKBD569Cg/53V5HAoba/z48dwfj9plsmvx97gEvBz9AdKD6sD00AFYzf8KJsePcu1qYbzE/tKLAJm/6dlCAZ/79u3DypUrc4Dh4OAAepZRLAUFv4aGhoJiPWSJftPzYN68eaAXOJIN6H5PwmvDhg1lm/J9NFaHDh24DEL8ixsxa4WS9ooiXiH5Oir7hktTpoMjq8XR9DKiXl7FowNd4N5wFcqUVy1amrRhFC1HfiIUuU1H5v4lS1w/boMsZkGs2iQO5WsnMkEk/9mTdoF89JQRgGW5kXmXNKkkqFg5mmH+lb6ITHmBaYFbeQoaahsy6ySqPvRA+FIH2NlpJ9CDnLTJ/7BLly48olB2jvl+Z75fpv8mw3xrPAzDMnDfKwzHfG6i+/vyNZgvXrzgb44kQNMbqCw9/W8WIh/8iWo9TsLI1EZ2l8a/U3oNugzzw5L8g54/f86j4VW9ydB5Qak9KFiNhEpViPpTXzo39JlImKe5Sq4K5/fa4fENC7QeEsa0ZDndLHS5jqiUl9j+eAkr77kRVib2eCtgJpo49UVaStHNSZH1q3vvTDl8EM779iB85ucw+792X5FxlWlDL9CkMVL1+UBjUV/yjyZBQnpBM4iMgNk/+2B86SKybGyRGtyWBUPWZT7nql1DNI66eBIPbZP0sklCkqaJzNLkt6gNctv4AhPLA4M06GLc4yxQhz3mvm/unu+U6aWehEsKHCY5o6D7NFkR6RltY6PYc4XOSxcXlxxjU6YGepmjc56eG6REKY4kTPAaOmoWFq+1ZJZq3GDJLEonEwlGRHROla+VCC8q2XnEFldYxaSHVyxRMzgWLt7yzfLKaMfkLZ1uDhRFLtHEgPUssrcnFlwdgGk1tsLVwgeBNQJhGpaINC0JnzR2mzZt+J80D4U+KZl9MwukNjKH2cFElNthhPKnXJBiHoOk7tbIcnrjj0P8SCMjm59NGiMtORwR9zYx7ee7Whc+aUwyu5MAml+OTUq/RH/qkLrnBY1N5wXdPAvLm6rOPLXRN7BVLNOCmvIqSS3filBHdlBreuQDOrTCPFZrfiT+fPQ1ll/8CFvMF6F72Qlo5NoL5ENaEsna6rUGnlw39JlyP+RprlmOTkjuNxCGzVvCjAnR5lu3wPToEaS0bY/06jVe36T1eVFibjpHoCDhkyZDL0v0pyjJOy+lvmRBK85UMu94xfmIyJk7mRLrdIxBC/bwpGfUsQ1O7GFqh+R47R8+W1Mnlu+Qch2acCE0imlD9Z6MWTL7dlbI+qkcDAa7wuRUMuwmhMFiPUtmH1d4MErYzdV8iS6Vh+n9UsUEC0fA2JRdP6xKUvRLE9w6+cYdofCe2mlBUfGjqyzDD23Pw9u6Mn66MwEzLrTB+fDXLjDaGVVwVQeBTDd3JA15BwljPkAm07BbrP8NlkuXwOhOXn91dcYRfYsnApS7e/78+TqbPOWxzm2109ngGhxI+xKMBidb2lk5eaQxM2I4AlvHsEov5tj3kwvuXrDk5nltYuNo5s6TbqdlpnAhtNj4r5kbwnCQK2KXMC0oE0jN9ie8rqr0J4vATZYviGakxiH8zm9wKt8PJubO2oRV8NYhAs5eaahUPwG3Tlkj8oWJDkfOf6jy9oGYWnsjtyxYGztg6c0R+PxiF1yP+jf/TmJPkSKQWdYHSSNGI/Gd97j203L1Klj8+AMMHz0s0nmJwYsWATKFk9VOV9SpU6fsYFVdjamNcYQAqg1UtciTzPJU7aXtu2HwqJCMywdscXCNMyKeafeh6mrhy4XQuLRIluvwT5a0RL4Ap8Wlq8w6y5rlCBxow+vMk3nefBtLZj+OVVXal8ACC16nX5GYk/CZmZ4I16ojpE3is4QgQD7UNqxc59nd9sh4XTlXL1ZW0a4e97EeX20NMrJSsfDaAPai1x/34xQPItSLhZSiSWRUrITEseOQNGgwDOLjYLX8e1isWQ3Dl8XAQlSKjpNYqn4jIHxA9fv45Ds7c6tM1O0UA6olf/EfOxz53Qk+1ZNQvXkczCy1Ixx6WlXExIB1uH9qLxLTWF3aDBtQpG9xoSwHIyQOZ64LnVky+83xsFgbx0t8JvWxQWoTc2RmpSDs1mqWdL4bTK016MVeXAAq4fM0ZC7A9TpH8xe2K8ynuhbzpdYnCnRshRoOLXEmbAe2PVqE2Ze6IMipA3r5TsoOANSn+Zb6uTBtQHr1QKRXqw6T82dhemA/LL9djPSatZDSph3zOXcq9RAVBQAL7gEL2Z+miFQUFIQkSPMICAFU85jqlCOZFtsws/zd81a4ccIaz++YI6BZHPxqJvIgJk1PxtemBguaMUfG9XR8e+MdkNbGxFC/gwtyY5BZhiWz/8AeRt1YMvuNTHuxIgbmO+IR2uQ80lkAUpmA0bm7iN8lBAE7l3RUaxqHa0dtuAWhjK/8YL6iWi5FszZw7Ya6Lp1w/OUm/MWi5qefb4PGLEipu88EOJuLF6OiOjb5jssi4tPq1kdarSDmb34CpkcOweryJaTVa4DU1m149Hy+fcUOjSIw2dOQFfDQKEvOzJ9VVxOkeQSEAKp5THXOkQKTKtRNgHeVJFw+bMs1og9YtHyt4Bg4Mr9RTZOTmScMjaNxO+Y/LLs5Gh9UXQkjg+J3KmWUZcnsJzuyQIJUJojGwmNzTdi5bEVmzbJIF2+8mj5t9IZfxXoJeHHPHOf+tkfwO2EwNde/hwtdTy3cB7Ho+J48f+juJz/gVNhfaOX+Frp4fwhbU+GfrDcnlDQRVmY0rWlzJnjWh+nxYzA9dpRrRlMbN0Uqi6Jn+cukluJTSwiY/snSO2UxPzUNk2lN5q4VqGGmgh2Kn9QgDlq+CJhbZ6J+l2iU42Z5Wxxe58RM9Emo1iwWZhaafchStZcxlX9gAugorLz9EUZW+q7YppHJqGiKx28dQ/xff6DKy/kw/SoKaVVNWVUla2T4K54uI98DI3boFQLkR123YzQO/OrMX9bomtFXMjWyQEev0axE5CBeUWn/s59YHtFNaOv5Lts+ChbGiuUS1Nf1lch5sapJqW1YvtCGjWHKcqCa/suE0dMnkdqiFUsRJ7+gRonEoYgW5ROQCHf/FI2NTnEWgrSDgBBAtYNrkXJ1KZuKNsPCEXLWCjdZ2pmnt82Zb2gsfGskadQsH+TcHu9WXIyVd8bBnPmCDquwoEjXrerglIPz1XVWdrOGC6sz74VUlrbJ4o942M6KRGpdMyT1sUaml3aDvFSdu+inGgJW9hmowfKDXthrD08WzOdVOVk1RjrqZWlsy31Bgz2GsRrz3+Hvp8tx6MVadPZ+H63dh4AEVUH6hUAWywmd0rkrL+Vp+s9+mLI8oib/Hkdml65AAMshynIuC9I8AnauafCsqLnr+frxok/dpnmU9IOjiILXj+Og8VkwtySedqYdi5Z39UnBhX32OPybE6I0nIKmUZmeeNv/Kxx9uQEb7s/W+Dp0wTD26T9IjglBmWrv89QqaY0sELvIGQnv2ML4Xhpsp0TAkvmJGoal62I6YgwdIeDHXsjcyyfjwn67Qkvd6mhKhQ5DpvdB5Wdjfp1jqOUYjM0P5mLyuWY48uJ3FkEvzs9CASyCBll29kjp3RcJEyYjw88PmRt+h9XX82F84Tyr/akFh8UiWKMYUiCgCgJGnzFSpWNR9aFSaZkqXLRUPjC/SjOaWMv1K6eBiL8RmlwFnn61VWJpYmLC68DGx7M8lRoiE7Msrt1x9kzF8xAL3D7D3soTDeHEfhupoP+muW3/YgN8UlzxBw7z8pB+NqwyEtPAUI1rMvRXsc9Zs1aVpSxatAjbtm3jpR9VqQJEx5rKeV67dg2Ojo4c19zz+OKLL7B8+XJ4G2yDo6s/PGpNftOE2WkzypmwaFaWZ9XCAGbHkljEPAvsYonsM/wYcCzHqCaI8Dxy5AhCQkJ4YmE6BzRNVL2IasITFlQCz9JStcwF1I/KW1IZOX0mqriTlpbGq0sVNk8Xn1Re7jaKJakvW01zWpPCxqVgI2tra35PUqW0MGlEazu3Qz2XzniRdA97nq3AmVfbYWPiBE/LSrw0H1VU279/Py83S/WnVS3Xp+690+jZMxjfvIHUVm3UKmFZEKZUwY3OT1WfD/RMoWvk0qVLvFY9lXPVODGNaDqrImdZvwHSH9yHGasvb3z9Kktsb4csF+2UplR1DVK1Hippq2micr5U6lMb9N8+VlnOLwWUM1tTdO+iJazsMlChpnDH0hSmEh8VRBCpq/iUEJg3bx72/LUK344FPv30U7j9dgK//PKLtFsvPl1ZtG+bYWG4w8zylIz76S0LVG8RC58Axc3y4eHhqFGjBmb6jka6RwbGjRvHhTyqykB+asnpCazW9TewMLJGey/V82hWqVIFMTExHLf169ejZ8+e+P777xXGMS4uDq1bt+b1wOnBQryobm6FChWyeVDlChL+AssbwM7UBHN+jMLaDtm733wxZVWVulhzQdR8ZwLM9ybC7EgSkjtaIqUTq7ZkqbogSgJCs2bNQEInzZNKXFItYXnlQd9MSLlv9AChBMnR0dFcIKMxDh06hMqVKyvHqIS2pnRmtdvF4PRfjqAHDZW+LU7kYVkBH1ZdxXOG/vFwHlbcHsvM8z+gWnI3TBm4gL940QsIvZDdvXtX5ZeP4oSJsnOlFwAqg/z06VPela6RrVu3omFD9V+k5c3FgJLZDx8Bo/v3YLb3b1iu/QUZbFtK+47spZcVMhckECglCKj+9CwlABW2TNJefffdd+wGn5Td9ODBg9i0aVP2b335QnkQKzdIQNvhYXD2SsH5PfY8f2h0qGLvISR85qadO3dyzQFt7+k7EcEe72Djgy+4STB3W0V+k7ApCZ9Se9KE7tq1S/pZ6GfdunXx/PlzREVFZfPq27cvkpJeHyP6LmmZ+7YywuPQLBz4Lx4jR47Mn7eFIZL7smT2VFWphQVIGLWlZPa7mIYgVfkAL3roBQUFcSGZhEOpvvrAgQO5ljH/iSi3hwRcerASptIY/fr14+Mqx6nktvasmMJexBJxlWWQiIssnn555Wxq8pK5kwI2ICvDEHsy5iJwWjoM3WO58Ekawo8//rjkHkQ1VkbXHFkg6PqQrpG3334br169UoNr4V1J2ExkpT0T3x7G7iEpsFy5HBY/r4Ths9eCcOEcRAuBQPFGQDHJQ4/WaG5uzny3lX9IkJbJzk7zuXVOnDjB0WEKLE7dmhghKTWDawZHjFBOC2jM0ngQaWOenPH//yMY3Idm4emdZJzeZYpDa51RuT4TiILTWEoa2ZaKff/pp59AQg3R6FrfINMoDWvufgIHG2c08+yrGJP/tyITmDzavXs3Bg0aJG9Xnm3yzHCkBSKNY61atbiWkTqR9rNWBUN8vfG179zx48cLx55OoQ8dkDUgDQa/hfP0TRZ7EpE1iKXFCWY7WR16RYiEQjJD5TZx0XYSTsltQBMUFhaWR6Al/o8fP0aDBg2UGoKuOzLNafv8VGpSchrTtU5mPgouU5Sa9gD+XJqFi/uc0GlECuuvaE/12pF5mzDVFDWy64SYm4aY+efb8O6RgnqzjXBtWSaeH8vgVgBVjp26984stkY6EmTWNmDHRhtEx5uI3BqUOe7U58aNG9xlg75LRMfkzp07Oawm0j51P/Pg2bARsphZHmf/g/Gu7TBeugSoHQSDLt1h4Oam7nAq9adnEblsqHK+FDagKi50hfEsjvt///13dO3aFTY2xTOTBSl06P6lDhU7AZT8z8gPTVmihyeZZjVN5HdEQvHziGT88nc6ujQ2wpIPTBCReA4PL6+Bg08nGBoplqhdehhpY57y1m3nDmaWB/cLvXXKCvevGCKwVTzTBinnC0fCkuycB/t9hfjkGHx7cSQyUw0RxHzVFCXCUp6vLuEsO0ZB/KitpMmQ2pE2lG6qxIMeLoZZSZjQzxg3H2Xi8MXXbw+Ev6JjgAT196xh2NEM5iyZvcn3ocjYEo6UfrYs/QrbSbl+CiF55zEJpHSDVngehYxBD/3IyMgcrWSxyLGjkB+EG/lWampuhQyn8m66oUs4KsOkTodkHN3ggLP7M1G1seZ933LPhc5TOtflne+52yryOyUjEWfDdmNn1o9c+ExPzkLoqSxEXH8jiKty7NS9dxqzNdIdkFsd/v+Srch6lGlDQh3dO+i4y7uuCuJFQlbua4QsBtL9oqC+quzLF8+q1VjkaGUYnzkFkwP/wGD2LKTXrYe0Nm2RZe+gylAq96FzU1vrp5cFelEo7TRp0iQ0atSoWAqg9BwICAjAvXvqlZzS0Xu+5k41erulB7Syf6r2K2ycd999l9/4jIyMseVIJobNS8fcdenw9PLDo38/wtU/6uPZhQVIjnta6JylN/fCxtTkfgPDTFRuGIdgZpanpPX/7bLDod8cEBVKFSVy4ty8eXO5B3LlypU52pK6472KS1DdsQWW3RiFqxFHc+zPzVf295Il7O1fDi1cuFBhHuSHm1ur1LlzZ5QtW5bzIP/csT2NYMXkxIUb0lkJztcD7tmzR+ExpDmnuxshfrw9Yuc4IdOZBUJ8FwWryWEwvJBUIC8SksaMGcPPHdnlTpgwgb9VSvzV/Zw9e3YOLEir0apVK67ZUZY3nZ/auo6UnUtB7VWdp7N3CirUSWAVxawQ8Tzv+V/QmMruk651TeB5J/ocfr49ER+cqslz8lpbWyHzaCWcZi/C11dkISWCKeaZ4EfnvbLzpPbqzlFaqypjK9pHGkOVuc6dOzeHVY2w8mPR6mQhUHR8ZdoVOEd2faY2aISEyZ8gtV0HGF+9AosFc2Gy4y9ksZdnZcZRp62Epzo88usr8Za975WE7ySUXb16lbt/Seuhly7J9Yu2kWJENoiTsCD3j4gIdpHmInpZfPToUa6t4K4hT548ybFd6k/tpZcpmg/5fUtEli96saLxb9++zc8laR990osbtSfNP7WVpdxzoXiQhw8fgixs6lCxE0DVWaw2+pIgQYEjjRs35jethiz58Nc/nUbljttQqdNe2Hu3RdjNn3HjryZ4cHQU4l6e0sY01OZJUX6NekahUa9IJMcb4iBL0n35kA3SUt5o8jZs2IDBgwdnj0Uaw/v372f/lv1ibGiC96usgL9tECvZORwhsedkd+f7vW3bttixYwcXmkhT4Ovri+vXr3OTar6dcu0YMGAAfv75Z1CgEf3NmDEDJCRLVNHlKZrXNMKKHZl4yZSDJKzSmBQhripRxHz8dKYJns40FWYGsFkQBZvPIlj0b/6lHidOnIg5c+bwgCB6m1y8eDFomyaJfGrJ1ENR0ITl5MmTsW7dOk0OUaJ4VWNlbK0d0nF2tz0yct6D9WqdMalhPDH9J+daYs7lbrgUcZBVSXob84KOYnrgn1g74wB6d+8HT09PVK1aFRTMV79+fb1ag75Mhvyk6eWzfPny8Pb2xnvvvYcjzLe/SIndkyhxffzkaUht0hQmTCtqNf8rXm8eKcpZqIp0HaVkcHKdonv4lClT0K5dO/Tv35+vnH5TphWJhg4dyrO7SL/J/5i20bm3YMGbPNrTp0/n9+zhw4ejTp06XHCkuAbi3aFDBy5vdOrUKVvbHxgYyMck32UfHx/Oq3bt2hgyZAgPHCbh8uzZs1zjWr16dR5ATOf6zZs3+VToGUvb33//ffTu3RsUCCwJsvLm8uWXX3IBlsbLLaxKa1Pk04BJ4G/sM4r0KOI2JHGrsmAHB6bVY2bYoqD01BhE3t2E8Du/IzX+Eczs/OFSaRgc/LrDyORNOgoye5A5iHwVi5LowXv7tDUzzVszn9BM1GgZC++qb256ln8kwPRgIqKXuxQ6zeSMBCy8OgDPE+9iao3N8LEOKLSPphoQlmSao7c1iVLiHuP27g6w9WgO32Y/SJs1/mlyliWz3xwPo2fpSKvJktn3ZVWVfOX7v5EgT5ehpkyxGl/M/xk6OzuDfGlzuzdoazxV+ZLbgSqmWGk8Cso79JszytdOYC4pmnfboXFIE+3G/PsoAE1WQyLNQd4n5fm8GnkEx0I3MoHzAOdRw6EVmrn1R6BjK1aJTHnfeHnj5N6m7r3T5L8zMN+2BXFz5pEqNjd7jfyml0gnJyeukVHl+aCRSSjIRBU8DeJi2T33AEz+O40s5mqQ2rI1r7SkLTzJP53M5KGhoQquSvFmpFhQ52W/oJG+n5DFn1cV6mgum8X+n53h4p2KjkPePKtzz4EUHJT2b/PmzfweOW3aNHz22WdcICXhkixbRKQQ6NOnD0hJ4uHhgVGjRmHWrFl4xlKVUbpB0oaSf36vXr3w4MEDnrWClBSkkac4CHJ3o4wwdI5TphcSDklp4+XlhalTp2Ls2LFc4P3www+5HEH3bDLzU6YeehbSd/J5JgFzxYoV/MX02LFjWLZsGbfGkcBLRIG8pKygjCzy5kLrIOWbutea0IByuLX7n7GpHVyrjkCVbkfg12I1TC098fS/6bi+jZnnz81Gcqx8LaJ2Z5U/d+ZNgKpN4rlZ3r4MmeUdmG+cI2LDlX94mBtZYUK1tXA298Kia4OYIBqS/8Ba3pOVmYFHJz6CkakNvOrP1epoaXXNETvfCQkjbVlUazpspkXAamk0DF/osVpNq4gUH+b2ZdKZD2gc7p6zQthjzQUIqYrAi8R7LOH8V5hwph6W3BiGl0n30cdvKr6pdxbjqv2MWk7BWhM+VZ2z6KdZBLJsbJHSvScSJk5BBvMTNft7F6wWMtM8C1xiqijNDia4KY0ACYFnzpzh2sbPP/8cw4YNU8jPlbSNRGSpIA0kWVOJT3BwcHbKNLLgUQDu6dOnuXaS2pObCAmGZJWUqGnTpvwrCbKk/SThk4gEXRJwiSpVqsSFT/pObmnnzp3jWtTRo0fzF+KZM2eiS5cuXBAmhUh+c6H+miDlJQpNjFpKefCoQq9WsGN/KXGPEH57DSLv/YGwW6th494cnoEjYBvQU2/QsWblChv3jmIJ7M1w+aAtr51NPnL/Y+864KOqsvc3mUxLZtJ7p4SEXkLvIEhTEVkVO+Kuq2tn7e4q61pX1/K37aqrYkcEC0VpIr33DgECBNJ7mT7zP+cNA0lIyCSZSSbJPfwe0967797vTWa+OeU7/cmr2RDzVwThsR5f46W90/GvfTdTiHABwtUJDRnCLftm73sblfm70Gnc1/BVcTm7h82H8rlG+cE0TAPVykqofyTppi355L3QQD+NqnVDPOOx8vCq2sXwKYMqkJWulkLx42flgRs6NKdx5GBb3mLyds6j9JVt1OpWi4FhV5O380YpraU55yLO5T0I2ENCYbjxZviMGgMVtfZUL/gOyjWrYZwwERZu70medWHNjwCnN3FBDutNOzVkWVWFv/Oregmd8n/OGTrVG/gxEz7WquYIE+dvOo2jJBzRYZLKESinceSk6thVC7s4qlaf8Vy4aQd7pB944AEpf3XWrFmYSSkB/Jijckxia5uLu1RahAe0vqvkoddVukTE9n8W3advQdzAF2HW5+Dwr7dgzUddkXvwQ1iMxR46c8OHjUk2StqhKQPLkb7DH8d3ahqcH8ctBB/v+S2pFPni1b0zUGTMbvhEmnBERd4O5Ox/FxHd74EuyjMC03VOj6SZjBP9SUM0DIbpWiio13zgI3nQfF0mdVaq8zjxQoshIKNPxgFXFcNkkGH3yoBmmwfnSv/v6KN4cHNf/O/Yo5DRvz92eQNvD9qJWV3+Jchns10J7z6RLSoa+jtmoeLe+6mTUgA0X30Bv3ffhvzoEe+eeBud3dy5c6WiUg6Lv//++1JePxfpcFoI51eyZWdnY+fOndUQ+OKLL6RcSpYfZHLJeaQszcTk1VlY9NRTT0m64qxfzfn8TDqZPDqJbrUB63nAxUd79uyR9mLv6ejRo6X769evl3SCOaeTSSnni/J56pqLs8i3KiGu59S1viw8oLXC0nxP+vhqENblFmkzF+9GwdHPcW7Xq8ja828pRzSsy23wC2m+vMm6Vi6n9MXuI8uRQJ2TDO/YYM6SYd28EPQZVwJdqGuyWCGqaCKh3+DFPewJvQlPkydUp3CP3mVd8+bnreZyUiR4CJrgroju7cjFudz+HnuN2ncayPNpHE/SXT9XkAeDNsqltUwzwzKFtOCE88Jj0DdmYG2wFb1Gl2HXikDEJBvAgvWesGJTDjbkLJByO3P0JxGkjJIaOrC3M1LTwROnFGO2EQRsiUnQ332vRDzZI+r3yUewdOwkdVWyUXclYc2DAJNDJnRcDMSezD59+kgFQxxW5/A8FwKyh7Jmd63Dhw9LYXEmm6ynzcY5wpzLyYWjnPvJ3tU333xT8oL+8MMPUsESk8OpU6dKOaQNWSF7UTn/lL2bXCPBjWTYWBKKC2DffvttyWs7ZswYqSK+rrmw13T48OFSjijnlEZGRjZkGhf2FUVIF6Bo+TvOIqTTx3dTwdIXKEj/FhZDPvzD+yMs5Q4EJUyEjKrLW9q4CEm+rBLz+nWHocIHXQZUIHVoGXxdnNrZiiMUjv8DQlWxUmES97X2hDmLkHb8dBuKT/+ClMlLoA7s5IlTNWpMWZEVmgXlFELTA/4+FJb3h3Es9WlXeCcTbS9FSDUv5vr5VMCYrQSH4rl1pzuMC4pO23di8dGPsDNvuZTD2SdkHEYQ6ewVPNqrcjobUzRTFSNRhFQVDQfB8EhBLJEKlm1SLf8VPvlUrNu1G4XmJ4G9pQ01UYR0ETFXipCce3MTFCZ3NXvdc/F0eHjtRbv8XuCCHs7rrGrsXWTJJH6tqnFInrmC0wtZ9bXL3d+0aZNEbDnPlM/Jf9dVjWWzeOzawut1zYXXy3NprIkQfGOR8+BxCr9IRPd5FN2mbUTCsLdgt1nIg/cAFS0NRfbet2CuzPXg2V0bmguVuKVnMpFP7i+//OMInD2qcungWP8Uygn9CrmGU3hj/+1gAW1PWfaR71F08gcp3cGbyCev1x4sR+UfA2F6Jw7WXhpo5pYhcHYelGuJkDrFST0FjBjXZQTSJpXQlwqw89em5w2frTiKb0+8gIe39Mc/NkxHrj4DMzr8DW8N2o4Hun2IPiFXeBX5dBkksWPLI8D5hr16o2L2Y5Tqcz18zp2F39tvQD3vG8gKL9WZbPkJt70ZMBmrST55lXWRT36NiWBN8snPM8GsST75+aCgoAaTTz6uqtUkn/wa56PWRj75tbrm0hTyyeNWp9z8jDCvQcBHrkQISTXxVlmwH3lHPqU8xg+Qve9dBCVOJimnO8g7mtZi8/VV2tFzVJnUR3v3ykBs/jEEkR0o/DCulLQULx+WT9L1our4uVJlPOuEPtL9Myh8XCOwri6Yxf8P//YQAuLGIyz5ZlcPa/b97NEKmGeHo/IqNbX2LIP/f0ooRE8CxjeSDitV0wtrWQQ0Whv6XVmCLT8H4+QeDTr0ph8IDTC9pRxb83/G2ux5OF62Exq5DoMjpuLabn9BlLyLyzJMDTil2LU9I0BEwjxgEMx90yjffAOUq1fBf88umKndp2nsOHBFvbD2hwBrYr/66qtetXBBQL3qctQ9Gb/QHkgc+m/Epv0NBce+Qf6xr1Cc8TPUQV0RnjoTwUlT4ePbMmQlgHJAR95YiDOH1Ni7OgArPglHFypYSh1MOpiXCct3CRyIB7t9jDcP3In3D/0F93f7L+RUpOQOs9ttOLzyHmqDqkbCYO/6o6trfdYEErN/PITyuUwSEdW+WUz5XL7Q30R6a93dS87rmoN4vnYE4lINpAahp+YMAYhINMGfFCLqsyMlW7COSOfW/EUw2QxIDRwsdQgbEDYZKl8/RIU6dEDrG0e8LhBoFAIU0jWPGAXzwEEUVVkD5bq1UGzfRqocI0idYwyo5VqjhhUHtU4EuCBq3LhxXjV593zbe9WS2vZkfFXBiOzxF6mauyRzhSTldGbzEzi380WEdp6B0ORbodI1v8QRox7f1YDoTkZqZUgi9iRkf/qABr3JGxrTue7ijR7Bo/CX1Pfw7qF78NGRR3B3ytsUgmx6Zkju/vdRcm4D+k77iYSbPV/o5M53nbWLEuXPhkKxywA1idnrXiyCuZsS+hkkZt+55XUp3bnW1jRWn/ElyDsTLkkzjbq5oFbFmyJjFjbkUkEREc9cQwZClNGYEHs3RkTegAiNKAppTde7zcxVRcL14yfAPHS45A1VriMySp2VmISa6DmKr7aZpYqFtC4EBAFtXdfrwmxlRNKC4idIm6EkXSKi3Gkp9+BHCIgdKxUt6aJHSBVtFw5qhjsclu81pgxJPfVUPRyATQtDENWJwvJXlNbpNUoLmyRJzXx09GHSO/TDzOSmeSwrC/Yii3JlY3vfi9DEK6p1QmoGCNx2CvBbMe4AAEAASURBVHNftdRFiWWbNN+XI+DZQpgGUFel63WwxYk/XbcB7eJASrUd/ScXY/13ITi61R+sFcpmsZmxu3AleTu/xd6i3yUvPovD39rpefAPLHf8oHJximI3gUCdCNipu5HxqmtgGj5SaumppKp5xYZ1MF0xXgrZkyBknce2phf2rwnAgXXVC3eaMn+rWSZ1QmrKGOLY2hEQVfC149Iiz3JCb1NacbLcUOGJBURGv4CxNB1KXZKUJxrScTp1/3Et72fhwoWSvhgnP3NHh5ryCtweLOP5bRhr7If51+2+0JmhLsBOH6Cw/O8BMBt8kEIh+ZRB5XjiyUelfrgs0svtvv785z9Lh/927nN8fvwZ9JJdhf/MWiXJWXAbs9WrV9c1/CXPWy2VOLpkCoxmGz5dS1X6RqvUI5dlJdxtGzduxMMPPyzJY3BogyUsGmMuteK02sl7oYfmx3LIimwwjdTAcJ0/bOHNQ0S5VzZLgKipDSDLdSQktIyX3RV8m9qKs75zsC7oid1+6HH9NuywfkoSSt+j3FKEeP+u5Om8EUMjroNWUb3CtOaYjWnFWXOM5nosquDdi3RT8XTXbGR5uVLFPFfO26nFo3E8idn37oPT1Nf83//+t9R/nCWEuF+5O82TrTjXzSuSCgbdOV8eKzDagt6jaq9id/e52tN4goB60dVuKgGtupSyrPVUtDQXpWdXUXWbCsFEQsNSbocmqEvV3ardf+KJJ/Dll19KMhL8BclyEsuWLZNahPGOp06dArf7ejr+T7grejoSN47DTOqa8NJLL1Ubp+YDs1GGg+t1SN/ph9LKTHy17B4czFh6YTduCca9dNm+3v8ylhe9j/T5Npz4nkqPyXguzlZi0hOX+e80pSMUHF+IB94y4gS1wHTanDlzcPfddzsfNvmWxXwnTZpUbZwk0mtjUtpQc4mAOgc12SX9UPWiCsj0dklT1DDVH/ZAz3kvXnvtNUmHzjkFvmUyOmjQoKpPec19TxLQSkspNmctRfZP02BEKbb3GY9BUVMwkognF9a5aoKAuoqUa/txlW5b7gXvGgqN28uHCKdq2VL4HjsKY2gYZi78EUvPZEoC6Twi9/3m/uPuMk8SUP3YXZA5vjbcNV1pHOM4DQKfSXXrmGIwUQXfZt8Duujh1N5zOEwVZ8kj+jmRsu+oeOlLaCOHSEQ0MO5K0hS9SFq4QwJ3ZWCTx4VBHuXw4Nzz/stUOfeK9PyDzz0Mnz4d4KMMgcxXDmX/ZPxwfCe67V2HpKR68tsGUKsxdTHyl4bi3muXYF/BCizN+DfJ0JzA8uwjWHx6H8lLBOK551YhbHAnRM8gSZEYOwr22WEuA6b/YzYeeeRhaR51/VeetxO52duxZG8cMqNtoPS7C/bGygWInjCYJC+CLjzXlDszaT68fjZ7mR7mY2fBnS9YjJiFfj1mSuqqdDWJ2V9BYvaLK6D+pRIq8owaJvvBOIWIqF/T82erzj2TvpxYBLmmsVAyd8toD8Y/xA6XbMI6aou5LX8pzFRQ1Lf3YcRt/gB/Mh9Bn84Nq4pvD5iJNbYeBGxxcdDfdTfkx9OR/sbr+GbUcGzOzcMzO3Zha14+li9fLvUM79+/f6tYlHGYmjy57iva5I51wjyDQPPE7zwzdzGqCwgo/WMR0+8pRPV+BEVUNc9kNGPtvVD4RVH3pdukwiVfdShycnIkPTIWqA164nooOjnYG3+1PnhiueNMD08Bl/No1nWBbI8aIU/eJj3/hu0IcIK2+syfdvgD0JP06a44Ng5PUJ7cuYBzOBR5EG9kHkZxIQny/n0GeZaADB5rIqCjje0kbRfmIT1Tx39JNwFJkOZZc4/nirYCRTWfbdzjgGfpPFXMVloJ47ajWFN5FldbTdCShJZHjYim4Qad1OJTvbAc7BFVLa+EYSqR0ytJGJiIqjuMBZRZG66wsLDacM42cdWebGMPCoznpPA6E888w2mpccLkuHukMHuYOg4HZRU4tDEQcckWhMVd7N3cxmAQy2mrCJiM8KG/bx/67PfJdWwBcscP2MER4fho+BD0/mGR1Cucvx9ai1k7KijPVeO26ap/KnfbWGKg6ggIAlodjzb7iOWIQjvdIG3cFz2PiCiL2mfvfZs0Ra9CQvhkqfcrA1Axfx2CnrwBZR/9ipCMYsrX/EHC5YEH7sd26qJQGXI7bMHhyLvzDel5zhuNiYmV7l/uv6zsLEy79lr8Rjut9/VH7/hr0Cd+KkYVjZYIqV+4AT+s+Te2Hv8eBeUZ0ERRg6AYGTSUepMyMBHhHXREBE6RcL2j+IPPxbl2EeokaErOQltJOaY9HsVrc/6Ho7tPUw/O6rNZsWJlrcK+1fdy7dHESRNRTGSdTR4RBNWgFNpSsTU2CGP3fYE0bQxGBSZgZEACYlTuS4ivOTt7AHVQmhlAxQX+UM8vg+abMqiXVkD/By1VudKHsLxpRJRzPblDRk1TKC6jr1Vz51b02GIzYWfBcqpi/xYHitcSfAqkhU3EzM6voFvQ8GpFfalDy5F1QoXtS4Iw7s58cAGeMIGA1yGg10sEU56b6yCa5wmnrLjoQvdfG9Uf2CKjcAQ++GD7ThwsKsbuAsePzpKSEqktpNetS0yo1SMgckC96BK6MwfUlWWZqc1nwdGvKTT/Fcz6HBhk8Xj3m5NYv488j/+cCXlMCJb2uRUxYRHScOwN4163czrdhzsjp0k5oC+88AJmzZrlyumkfbiw6b///W+1/f/5j1cxbsjdyDysRk6Gkjo/+eB0znbsPPod9qQvhMmeB+6Z67QycyHOVR5Dtv44sitP4lTub8iqPIpShS9xTkfep42KdvQ5MlRmAeVnbZgxaRbGpl2FaE0nBCjDnEM1+vbEiRNSL9yqA4wdOxYvfPwu1pScxtrSU9hTkQsb7OisDpaI6KjARHT3C6eq6OqksEE5oFVPWMt9H8p71cwrg3K7EdZIOfQ3aGEerOZE2lr2du2pFStW4I477pA8IUw8OYdrzZo19KMjxrUBmnmvxuSAni4/KIXYN+YuRIWlGAn+3cG92IeET4O/ou60jdICOVbNDUdCNz3SJpa4vFKRA+oyVC7tKHJA6U+8vMzhzaTioqpeTZ+yiyFkW0AAbBGRjo36d9vCI+hzgn/pc3iKAkT0o7p79+5QqVTg9ov8PuVC0QcffNCl6+DKTp7OAdXfSpGhSY71uDKf+vYJeIzbmipFDmh9QDXidUFAGwGapw5pbgLqXAe3+iw5s0wqWqrI3QqTVY398iH4T4/huCWiJx6KGejcVeracmLOFvTJSsKmB/PRmLygJUuWSIVL3H7sqaeewsSJ5+PsdBYuWMo44IOfvz6IuNAh8JWrEBRlQlyKAbFdDJd0WNIXHcTRX6hbFHl3Ywc+L3lIsyiv9CR1jtpyeDUqFXmw6SpQbrvYik4t9ycimowovw4SIY3SdEQUEdNITRJUJAPlqpWWluIvf/kLyugDfurUqZcQ8RKLAetLz2BN6WlsLs2kOZgRTM0C2CvK22BdLHXFUZAetEYq+DIYDK6eut795CfM4NwlxUETLAkkZj+DxOz7ND4vin8A/Pbbb1JLtmnTpkkFH/VOooV2cJWAVliou1HuT1ib8y0yyvfB3zcIg8OvJeI5A4na7i7PPn2HH/asCsTQ6woRfRnN26oDCgJaFY2m329PBFRG/bqdIXMfyauZDTl5NWXk6WSzE2m0U3tHJ9G0RkRcIJwgTdD6jInn3LlzJRWSAQMGYPDgwfUd0qDXBQFtEFxtemdBQL3o8rYUAa0Kgb7osKQpWnTyR8wPG4xfw4bgk/Bk9Iwbc2E3v+8roFxVieIPPCtLwWT03DG15BnNPaWCzSqrRkb9AqgP/dKribzZkDJ5ySWdoFjSij12+fn5Ur/5LMljSl5TIqhMUrMrjyPHkAGD9WKODwuHR/l1IkLqIKVRGiKp9DhUFdukHt1mmuPO8izJO7qOCOlZE5FD0nIdQKH68WGdMTIoEcF294e1fQ8YqatSOXyPm2HponAQ0dTG5aeGhYVJXhEm3t5slyOgXFB0sHiD5O3ckf8LLHYThdZHSN7OfqETGtUOlvvEryNt0NI8X4yflQeVX/2heEFA3fsOanMElNJeZEWFkidTXsOjKSOCyGanlps26m5ji4iijUgmezTZu0leTfrgaxLA3M9cq9VKtQFNGqiWgwUBrQWUdvqUyAFtpxe+rmVrglMRP/hlRPd7En9Kn4/NlFf5UsYqPLvrRUlTNLjDdXUd6vbnFSo79ZnXS1tVMsqSTiw27K/LgNrnWnSfMPES8llzMuzZTNL2lLaarxUZs4mUnnSE9M8T0/1Fa/F71lcUQnckknIuYCSTUfKUMil1klR+XJ/mI5+PyeYg8njy9jiGIF1fSGF6CtVTuP75k79ToJ7yXDWhDu8o5Y5204RVyzesOWdXH3MLz7J/qqDYSmL286mr0vOFkrg9h+atSU37knJ1Dt6wX4HhLJHO77CetnxjJsJU8ZgSfx9GUJg9VNW0VALObmCB+pXUgnbnskAMmVbsDUsWc2gNCFit8MmnQqCq+Zl5VBBE6U4yiyOdyE6RIlt4uEQuLV27OUgmE84wcgC0EfH41nCpxBzdj4AgoO7HtE2M6KsMpLy2P+KZ4gzMzliJTVSb3m/r33Fu16vomv0qwuxpzbrOmmT0+FYKs+8sRqF5DtZ+51vNM6oNrlF9VM9Mg1VR4K1r0JBqe1rtFuToM2hjj6kj35TzTo+VbkOpOf/Cvn6+gRIxlcip30XPaYQ6EUp57dWYnTUh4G1WZB/o6a/w98KTWFVwHF/m7cNHObsQ5qvBSMoZ5VD9QF0MEe2m/amaB1JXpf4qKNfpoV5QAd3TBdSaTw39dC1s0U0b+wIQXnbHbDNSQdEyqaDoYPF6+JIeblroRMyKeh1dA4e6heA7l+yns6HPuBJq0xmMU/uN0o8m52viViAAs5lIpaMIyEpFPerTp4l0ZsOngFq6ni/ysyspysPEMjqW0mX6wUqeTPZq2oNJe4S8ncIEAm0Ngbb5zdPWrlILrmd0UBJGEQn6mgjR1Kv+DEP6N9AfOgSLoQOOr3pcavkZEDuGvsyb8QOSipIsOdcjuVsyEkb0RVa6BplH1JLYPXtGnTmjqWkyhFJ+fWNNLvNFjF9naetbYxC9pYxIabpUBCWRUyKppyr2Y2v+YphsF3Uhw1RxUn5plERMO17IOQ1WRV9o0Rii0GBaRFdMCuhAx1qxrfyc5BnlUP3CgsNQyeSS55Sr6ofTtQhXuJ6nWm3aPjKqjPeDaZgGqpWVUP9YgYDN+TCN0UA/TUvdUC7qwlY7rpU9OFGyB8tOcoeihaikPM8kbS/c1ukFDI64Fn6+AR5bTUJ3A6WM6MGdksITjPALuFQ9wGMnFwN7BwKUx+3Mz7xQdU4SRzKSMXOWAXJLTBl5NK0dOsE8aMh5jyYRzaC6i928Y3FiFpdDwEw/MjhXnhsiOAs0y8vLpaJNzvNn4/QlLvCykueb1UU4zcFpXOQbTu+L9mSCgLanq93ItT4WOwTTD3+Pj8pz8GT/v0Nzohjys6VUOZ+Lk7/fBaV/nCRuz4VAvirPf4ie2fQYVcqbkTDsTSgpp/6SMP0FMko9fONsiOzkX2sBUyPhkA7T+OrQUddX2mqOw/qRnF/KuaZSpT6F93cXrESB8SyF2h2kRCFTIfJ8EVScrgvlmXZGmCIekeoOGBYQL21PYRiO6gsob/SUlDv6zzPrpVNxeN4h8ZSIFL/Qmqev/7EvidlPpL7QozUk2UREdAnl9K7VwzjBH4ZrSMxe24w/JuqfrUt7lJuLsDnvR8nbebriIKVFhGBYxHSpoIhbZDaX9Z1QIoXity8NwogbiXQ4WUdzTUCcp1kQkFVUUH4meTBr5Gf6VMmPtlE7Y87JtKR0vZifSR7OoPgElJ2XcGuWyYqTeBwBbthxxRVXgFtHnzlzRlIS+Pbbb8HdBfm52bNnS3PgzoHXX389OE+dFWQ2bdokPc9NPVhp5ODBgx6fqzedQBBQb7oaXjoX1rH8U1RfvJe1HVNDUpDmo6GcSw1Sr/oV5TlbpKIlDs1n7fk3gpOmSmTUL6SHR1bD+qWlZ1cjaeQHUJKYflWrGaYvyqRw6EHFJZ7R2qrpq47T1PucU8hb9+AR1YZijckcKdf0YhEUk9RDJRvBBMppOkWoVATFIX3OO02jIqgp8d3hI4/AJipk4rzRT3P24IPsnYhU+F/IG+WCJmWV7lbO8eq8VZOY/XUO4XoWW1YtIzF7Ki4zkKaoYRJ5Wel1bzYbFXZxaJ01OznUbrWb0SN4FB5NeQLdtaOoJV/ze3RVGjv6kRzTxgUhSN/uj+QBFzVrvRlLMbfaEZBRuNzp0byYp0nEs7JSOoBzt6WK83Aimr37SoRTqjqn0DnUtaff1H4m8WxrRmDp0qWSROF3330nFWo+/fTTYO9nXcbKL9waOj09XdJY/fzzz8HktL2ZIKDt7Yo3cr23hffEosJjeClzA77HFRdG0UYOovaeg2CuzEE+tfosOPYNCqntp394mhSeD0qYRC0/3VPsYig+hnM7X5Qkl3jcyxmT0eR+VnQb5IOsswWOavoLntGLYXpPk9Gqc/T1USLWP0Xaqj7P4ZkyUxEyig5IXtOs897T42W7sCn3B5jtRml3GYlEh6sTEE+ktK+2E0rliciwKPBbyQnMLzgk5YkOoSInzhsdQRuH9l0x9njqbwmgdp7+0Cygrkq0qZYREZ1GXlJq+QnymHqTcVei9TnzpaKiQvI2h1Ou7TUJD2F45PUIodSGy1XBN8c6ojsZ0aF3Bfav1SGygxEBYY5ikuY4tzhHIxDginMSZZeIJskZVQudGx1/e1xxbg8JBZNL86DBVBRE1ebndTRJm6wRJxWHtCUErrzySrz44ovo1asXrr76atx5553Vwus118pKALfddhu++uorMFn9/vvvsXPnzpq7tfnHgoC2+UvsngUqyLP2ZNxQ3Hv8FxyszEdfVBf6VfhFIrr3XxHZ4wGUnF4qdVo6tf5BnFWHIzT5ZoQl3wKFH8mDNNJs1N7y1IaHoKD2SLH9n2vQKDU9o5K0UwuT0ZoL0JLYeeeANGmr+hrLBnHVtkN0n0P6jm13wVIw+WLjGu4I3yRY1QOwl0KAqylkz5QxWaXFmKAOuCIoWSp44n0vZ/ZgOSr/GAjD1UREvyuHZm4ZVBSeN/xBR63t6tcPvNzYTX3NRP3XWTaJ22KyjJLSR43+YZOpLeYMpAYOdmtBUVPnysf3GlMGlg7bRl2SxtyaT95rd4wqxmgSAlxxTkU/UvGPs+pcakGZe7HinIjBhYrzlNTz+ZnnK86pGl2YQKA2BJKSknD8+HGpQceCBQswZMgQrFu3TvpcspxXM+DjqnpFmaRec801SEtLk7bo6Ojahm7Tz4m/qDZ9ed27OJYQmhDUEdvKzqGPvXOtg/tQD/TgDtdKWyWJwecfnYvcA/9Bzv73EJQwUfKKaiMG1Hrs5Z7M2v0a9MVHkDxhAeQUdm6stQYyWnVtrBcZro6Xtp7Bo6u+RAVLBuRQJyhnEZQj33QFTlNubo4sGlmWFHxoKMZ/c/bBD3ok+xopnB+AwVTsFOffWRqTC61qmi3SFxUPBEE+lcTsqbWn/3+oavfnctj/REL2A5qXiGaU7cUaEopnwfhKayk6aPtIbTEHhV8DzsP1VuO2nAOmFOP3r0OpX7wW3UfUHY7z1jW02nlJFeckbcRyRlLbyfMtKEnu6GLFuVLSy7RFRsPSq49038oV5+TlFBXnrfbKt9jEWbh/w4YN+PDDD6Vc0O3btyMjI0MqSDpw4IA0r+zsbMnLycSTLSUlRXp9zpw5UkMW6cl29t+l3z7tDACx3IYhMDt2MH6z70IFFQHVZ36hPZAw5DXE9HsaBenfUtvPr5B+ajHUQV0RnjpTyhf1oc5A9VlZ1nrkHfoIUeRh9Q/rU9/uLr/e2shozYWxFzBe21Xaar5WasqXvKWnKtOxhX4w7NWbcMisw55SH3xWchQ6y1IEWtLRUWFAgsZRqR99vlKfu0IFKqlKN0GB8idCID9iIjH7Mtj+QdIxySr43uAH1hf1lHE+LLfE5A5FmRWHoaOCItbrHBl54yXpC56agzvGDY01I2VQBQ5v1iKKwvKhMfX/zbjjvO1mDCNXnDvIpZw8m/rCAqjOnIaaK865OwCZndJbpHaTiYkwDxjo8Ggy0QykYklRIdZu3iqeXugNN9yAb775RsoD5Y52ffr0wYQJE9CzZ09weL5bt25SSJ49o1WN21g/+eSTUti+6vPt5b7ohORFV9obOiG5Asfxz08iaa0P9r2lQh9t9UKgyx3PHYtKM1dKLT/LszdArgygfM4bEdblNqh0CbUeajEW4fDiCVBpE9B5/DzKJ3U9llm1E1Ktg9fx5AXRewrT52ac78AUaUZcqt7t1fQ8BU+04qxtaRzO31eRg18L92M9kdJMM/V6prr8UBQTIT0ChXE71Odblqrl2guSUU4JqX5ZaQidp4b8pBnm7krobyQx+87uyX/jgqL9RWukEPvOguWw2a3oRR7fEdQWs0/IONLwdD2PuKVzQKtiT6pa+O2LMFjNMlwxMw++55chOiFVRame+1RxLnUD4nC55NGkW75PBUJOs2t18I2Ph5E0My0k0O7oChQBu85zslvOczf0NpjaZHLPdW820Qnp4tVpSC/4SipO489Zxq+qtUeJparrr+u+8IDWhYx4vlYEONRg/SwHSUnXY9aaz7F27P3QalzTpWSt0MD4K6XNUJJO1fOfU9HS1+Td/BisJRqWcgd00SPxyy+/SPIVrJn2zG2kgdnDD4nD3moQ+ax18i4+Wa9n1INk1MUpNmi3d955B++99x74w7FLly4Svo+Td/MctQPlinruyLS9PBQW38GIUqjQTemDOFk+lOZ0Set0V8EKGGyOELL9SmDM4Wtw2957EPZsOM70zEL2NZUI6EhSXOq4OtuVZmVl4a677pJkRrjXNFeLDh8+HLn6DKmYiIuKikzZJEOVhGmJf5UklLg5gLvtgw8+wJtvvin1uU4kr9iqVauk/vbuPo9zPP69xKH43z4Pw77VAeh7pXe3MXXO2123ixYtwu7338W/evdE3759sZry4oLq0LuU0d/7xYpzJpnk3WTCWXH+vUeTYq1MSdqoZ+8L+ZlWkjpS0vM60l8szyON4Co5d+5ahxhHIOAKAuxEqs3am75nbRjU9pwgoLWhIp6rFYF58+aB81Uej54Fe6UR9phgTPz341j79P9R2lTDJHvUgZ0RN/B5RPd9HIUnFkpk9MRvdwAkX7RkQSb0FRaM7euDgakyvPeDBW9cFQT3+NpqXVqdT7Z2Mvraa6+BSReHhdiOHTuGW2+9FXwtY5Q6zAjvLm0VVOS1qeyspDm6ofQMfrNSL2j5AAwLnobN//kAZbt2Qa2rhH+MDGfjF2PPhEMYlzsak7Zci7QXO+C3lMV4Y+Bn8IlQO4T3qVLfISPVEUGUj5qWNlBKyGfvADszZ79/E8bJuuOc7QAVFGkwMOwqKcyeEjiozmvR1Bfef/99vP766xewOE3daDh09uOPPzZ16MseHxhuoRzQMuz7PQDRyQZEdXD08r7sQW3gxT179uC+++7DLUmJ0mqKi4sxevRobF66BP5lRDbP52fKpUIgEms//x61U2icczG5K5B5wAAKoVepOCcRb2ECAYFA20BAhOC96Dp6ewi+R48eKKT8qr8n3Ys/xkxHz7T/g2ZMLzxv7Yqrh41pMpJlWRuw9Iv70DmqCGZSruEUrY37bPhgsUYivjfffHODztHYELwrJ3FnmN6TIfiEhIRLPEIcov7hhx/QtWvXWpdqI5K4l0L17BldkXcMZ+162KmC2LT/FMq/XA3zgVOS1BF3/bAYTLAtyUbILz7wMfpgz4DdWNR/PtJ9dldrV2omOcyKs3YYiwFKDYavnwy2bB3uGvE3Ip9cUHSxI0itk2rAk3WF4FkQWq+/2KWKh2RvnDN3qwGnaPCuBCnWfhuC8iJfjL8zDypylERFRYFJWc05NXhwDx/Q2JAxy9Hs2LEDd3TuhHeHDsLewiJSedBRNyqH38POFeehYVVE2s8TTe5xfn4fV5emJCkk7kDDoU5v94A2Fk9XsXDHfiIEfxHFhoTgLx4l7rmCgPCAuoKS2EdCgPPW2LJNedS1JwiqL7cBw7tjgSobV0uvNO0/XfQwzP09BmczcjFliBxJUeT9/JGS6Hxs1ErZuwo4WotnlL+Ya/tCvhyePnSdObeXtzEFGsx48M8wD+sC7R+Gw7jtmERAnaTJV01+6ekJKJ9kkzoq9V2ahr670iQh++KJdpyTncA3S/6Hldt/hoo8geowGc6spOd/t6FHXApGXd+wHxVNeYcpFIpLyB57ZI3ntR6bMnZ9x/KfTv/J1CXp0zDsWhGIwVMv5i/Wd2xrfd2J6/6iYmzNy8eJsjIsPpeFfpOnYDh1g5EqzomEChMICATaJwKCgLbP696oVXMLsf/+979YWrAWbyc/hcmqgfjuf8uw9+Frsak0E0MC4ho1btWDbrrpJsr/3I3PfiHiecEqMX78+AuPvO2OS2Q0hQqYUgzQBlddl+dXMnnyZCxcuFDqO+w8G+fW1uX9dO7jvE1NTUWQSYZ8pS9slQbof90u5UxyVWc186OuStfrYLzSD+ofKqBeVIHIFTIEXpuMW9P+ivdmLzi/u6M6mR/c9tRt1Ybw9AP2yLG3k3swO62ECll69+7tfOjRW/9AK3pfUYodvwThdLKRPKAePV2LD85/y8888wx2UIX6Fb8svzCfnR9/Ant44zWBLwwk7ggEakGANYzVP1HIxU0mK6PPi9qDRW46Q/sdRoTgvejae3sInr+4WTiXOzZsHPgVimxlCHmnN54z7kGRxYD5qdMb1gqyDuwfffRRfP3111J4lHNLufCJxXobap4MwbsylzrD9DXIqCdD8OzpHDp0KHIo306r1YLDfz/99BPCwsJcWYK0z/Gcs/jDyZ9gWbYLlm/XS8Uk3DrO9zJhUp88K9Tfl0G53kCSNz7Y3ycbQ/8xBQFBAZLHkeVHuAOIJ6yuEDx7gseOHYsTJ05IKQS8H+d/cii8OW3jwmDkn1Hilid9YCEFAqc3uTnn0JBzNSVk/NBDD2H+/PnS3zJ3f+HOL9wtxt0mQvDuRbS1huCL3jlKlWruxYJHs3ZSIGxKB/cP3M5HFATUi94A3k5AnVBxx4eI3+WIX6NG8YcROGIrwi1Hf5T6xf85qp9ztybdcoFIAXlOOnfuDJ2ucYLjLU1AqwJwOTLaiRxw7Bl1FgpVPc5d97n4iKvPuQqeQ9ENsfeytuOL3H34OGQUQuRqxMTEuHy4z1kLNPOIiG43whwuw9HBRbAO80d8QrzLYzR0x7oIqHMcxoKxZiFoJi7NbcZKH6z4JAwR8XKMvLGY5lI9L7W551Pf+ZpCQHlsFuTmXFf+W+YfQZ4wQUDdi2prJaDuRUGM5mkERAje0wi3wfGZxPiF+kK26hQUu41IGRxKldTd8GnOHkwJpup2VdO197h4hre2YnWG6TfoqGe4DCHRFsQkyz0Wpk9OTm4UlFwdPy//IK4N7YLRfQZKJJZD+K6aLZa6Ks0OhuG4o6tS90VBsOzxhf4mIyy9W6aiubFYuLrm+vZT+dnQb2IpNpEn9Og2JRJ6ejcBrW899b2elJRU3y7idYGAQKAdIiAIaDu86G5ZcrwS1hg5lNvIszVYg3ui0rC86ARezdyEdzpNcMsp2uogNclo/ikdTh1U4KBERgMQxDqjNcL0LYXFd/mHoLeacXt408KmHMIq/1sIfA8YqatSOXSvFsGSQkVBN+pgSW1+L2RL4ek8byzlgHYdCOxcoUFwLOER0ry5wc55iFuBgEBAINBSCDRMvLGlZinO65UImPqrJQ8oLHbSjFTir9Smc0PZGawqPumV8/XGSTEZ7djbjNEzKnDV/TlUKV0MjdYqkdFlH0Vg1dwwHNnsT/I9zV8tbKI2Pl/n7cfE4E6IUTUuDaIm5tzCs+yfJBj+cBBk5Tboni+E/2tFkJ/yLpWDmvP2xOMR0wC1vw3bFpO4+sW6KE+cSowpEBAICAS8DgFBQL3ukrSeCZkHqCHT28mrZZImPYGIykBtDF4/u1nymrWelXjHTJ2e0aHTi7yCjP5UeAQFFj1mRri/Stw8UI3SV8NQcXcA5GfM0D1VAP93i+GTTQKw7cSUamDotEoUZStweJNnciPbCZRimQIBgUArRECE4FvhRfOWKVs7kjRPsA+F4Q0X8vmejBuKG44sxH9zduLhGM91tfEWDDw1DycZTeyhh7OA6Sz1pm+uML2VerPPzd2LUQEJ6KQJ9swyfWQwjfaDabgGqhWVknRKwOZ8mMZooL9OC3tw83t9PbPQukeNTLQieUAFDm/UIrqTEcFR7c8TXDc64hWBgECgLSMgPKBt+ep6em2krm0iL6hihxGwObQvktRBuCOiF77O3Y8ThiJPz6BdjO8ko83pGV1G+bznTOWYFdnH8xj7ymCc5I+St8JgIOKp3GhA4MN50HxTJoXpPT+Blj0Dt+nUhVooFB8Ia/txALcs6OLsAgGBQIsjIAhoi1+C1j0Bc38VfEpskKdf9NzcRaQlXOmPl85saN2L88LZNwcZ5e5An+buwQBtNHr6N6NguJrE7ImAlrwdLgnaq36tQOBDeVD/WA4Y2m6SpJziUAOmFKO82Bf71zRdQcIL37ZiSgIBgYBA4BIEBAG9BBLxREMQsHRVwuYvk8LwzuPUPr54PHYIdlZkY3HhMefT4tbNCHiKjK6hHvDHyXvdLN7PWjCxa32gvyWAPKLhMA1Wk6B9OXlE86FaRt1NqOCtLVpQpAXdhpchfYcfck+1P1WAtnhNxZoEAgKByyMgCOjl8RGv1oeAXAZzPxUUJDRe1UYFJkr5g2+d24oyS/XXqu4n7rsHAXeS0c9Iz7WbJgyDdLHumVwjR+Ec0Mo/BaL09TBYuimhmVuGgNl5UK4l3czzKR+NHNorD0sZWIGQGDO2Lw2S8n69cpJiUgIBgYBAwE0ICALqJiDb8zBmkmOS51ghP30xDM94PBY3BJUkZP4uddIR1nwINIWMbi87h72Vubgz0v2V741FwBZFYvYPBqHs5VCwsL3/f0oQ8EQ+/egxNHZIrzxORp/GHIo3GWTYtUKE4r3yIolJCQQEAm5DQFTBuw3K9juQuZcKdooaKkiU3ppwsc1jjFIntedkAjo1NAXd/Bz9x7kN4o4dO8B93gcOHAjuEe1us1qtWLt2LTifUerc5Ofn7lNI4/3vf/9DUVERrrvuOnTs2NEj52jKoExG47tVILNkJQKD6H7wSOSdDKyzmv4Tyv1MUgViTGBSU07rkWOtiSRm/wSJ2R82Se09tW8Uw0IC9/oZWrC+qCtmI8HNjz/+GBUVFbjlllsQEdGMOa4uTJBbsvYaU4ZdywOpM5aRGhK0LZLtAgRiF4GAQKCdICAIaDu50B5dporC8ERC2SNlmF5dz/DW8J5YRHmgL51Zj8+7TEVRYSHGjx8vtXS0WCzgto4HDx5EUFCQ26ao1+txzTXXICsriwS+bVIf6q1btyIuLs5t5+Bxe/ToIY3Ng77xxht46623cMMNN7jtHO4YyGg0YurUqTh9+rRExktKSrB582YMuCoR546pUVXaSR2uhywoETP7auFDCgfeatw5qey5UCh2GqCeT12EXiyCuYdS6qrEHZfqssrKSvTq1Qt8y/baa69h3rx5GDFiRF2HtMjzHftUIitdhV3LAhEWa4Ja23YLsFoEYHFSgYBAwCsQECF4r7gMrX8SHIb3PWWBT171loIKHzmejBuGg/p8zM87iJ49eyI7OxuFRESZfMqI6Nxxxx1uBWDIkCE4fPiwdI7i4mJpbPZQmkwOwXx3nIw9t86xneM9/PDDOHbMu4quxo4dKxF8niuTT7Zp06bBYqsEa4w6pZ049Jvlm49Rx0fB/N2gFu3A5MSzvltzPzXKXgpFxX2B0vsu4O8kZv8WSX+dqT3nmH8wOMmnc+ybbroJ+fn5zodec5s20XGtdvwa6DVzEhMRCAgEBALuREAQUHei2Y7H4kIkO72basvLG6iLwcSgTpQLug3amHAJJf/b5iBozo9QT74b+7JIgqacpHbcZEwoOARf1Xj8o0ePVn2qSffZu1rTmEx/8803NZ9u0cdnzpwBe5qrGnuI2evsNA7T2zpl4YOen8F60zopD7FmO9DD1A60pMB5hBfdshbtMI1UqFQxKwC+R83wue8U1B8UXfJjiFM/ahqnaPz44481n27xx+z17DuhBNkn1Dix2zPpIy2+SDEBgYBAoF0jIELw7fryu2/xLJ3DkkwK6orEouI1bXbsIKwtOQXlraOBf82Hb1JPyOO7wr9TX8hufQ4zD9lwBXHTKyhNtEv1KH7Noep97Ef5njUJLXsA+Xl3ma+vL8zm6kVXTGYCA73LY6XVai/x1LIn1N+/+jX6jLoeBcrVuDa6MzSxeiR0d3RgykpXI/OwGoc26HBgrQyh0T6ISrZKuYmcr+g1RmoMpnHUVWmkBoG/W6GYXwDFukoYx/vBMNUf9kC55G3na1TV+HFwsIc6PVU9USPuc/7nuW6V2Ltah4hEI7wK70asRxwiEBAICASqIiCfQ1b1CW+/zyE0zr9rqGk0GtTmAWnoOJ7cX6FQQK1WX0KePHnOxozNxUM815ohbRmJhatW6aUvfVBeaFXzkyugoW1LiBH2Q2fh02UcrKcPQf/KDIzoHI2ITl2xOAf4+hywiG5zKIrqR7VJEVRb0tB0RMZw06ZNF7yg7JkcNGgQ7r333qpTatJ9fg/yOWra/Pnzaz5V72PGkq2mp7LeA13YQafTYd26dRew4GvHoehHHnnkwtE5pgo8f3otZlLle1XpJRZID4ywUBGTAZ3TKhDTgULeFOE+sUeN9O1aylNUSxXban8blJrqxO7C4M19h4ioqm8QykcrYSepJvVKPdS/Us4n6YfmBpRi94E91WbE2H/44YfVnmuOB/ye5B8H/Jl0uesekWjCqX2sDapCUk/K0a3+Z9UcU0Vr+OzkQkb+gdnY74dmAfL8SVoDnkqlErxxsZ67jT+Dav4Advc5xHitAwFBQL3oOrV2AmoLkkO9lH4gRMth7XBpMQhXwbPIuax3EkwlCVDCitnDkzHn7lswIQK4lWqEuuscTW+W5QHfEhmdT1smyT5St0ZEERklflGvpaWlITQ0FLt375a+5G+//Xa8++67kges3oNd3GHo0KHSFzOTOyYTCQkJUnEPk9+GmicJaO/evRETEwMuwuIvPq78ZsLFXwJO+yBrB9JJeP7lxDFQUROB2ozJaHwnP8obNSG+RyECwizQl8qRQeTI28ioSqWCGRaYuylgpL7yMj0R0V8qMdk+BN17dMfSI7/RO8+G1NRU6ZqxN7u5zVUCKv0ICDfjyGYqDPO1Iyyuute9OebdGgiTIKDufScIAupePMVotSMgoxCUl7guap9gzWfz8vIu6zGoub/zMYfZWC7Hm41/wXMIt7b8Qm+at/PDvqys7JJp6f6WDxuFOyseqz2subciFzOP/Yxo8wyk+mnx7+6XDCE9wQ1vdlD90CqqD1lNWy7VD/mTR3RUKDCWwvTDQhwe0tqPdjzLWDK588Yik6rz5i94/jNsCQ99kcWAKQe+wYzw7ngwZmDVaV1yPywsTPJ6c/GY08xGmeQJzTyiRs5JFWxWGYIizYhN0bdYmD4gIEDy3FTNA+biOPX3ZVCuN8AeRJ2WSK3BNErj2i8a52LdeMsENCoqSkqP4Jzc+mzPqgAc3+WHsbfnI4i80s1preGzkwkT/+hs7PeDwLM6AuyhZA99Tg6Fo9xs/P3hbfJnbl6iGM5FBJr/p7+LExO7tU4EzAOodeJCKiji3t3U27um9aLe4teFpmLB6XJ0kLyFtb8F2eM5iDgsb08nA/uI8/xGRJQJ6dJcQEmvDyESyjmjI4mUBl/qcK15avG4FgS+ydtPvkDg5vAetbxa/1NcwMT5otVyRomMOnJGA1qcjDpXYAunrkr3BsFwtQWa78rg/3Ep1IsroL9BC/Mg8lq3RGzbOTkXbnuMLJUI/rbFQRIJZc+oMIGAQEAg0JoREB9jrfnqeeHcTSTHpJlXDsVuE8zUx7s2eyC6P344XYKjei6rjqxtl0ue60mNYXh7qCNwklL6VlGInsnos0cAprlpJCPKntExREajaz/tJWO29ycqqEvVvPyDuCakC8IUTS/Qag1k1BZHXZVmB8OQTmL235ZD+38lsCQSEZ2hg6U35Xh4qVH6tKROsPrLUBxYp5PE6r10qmJaAgGBgEDAJQQEAXUJJrGTqwhwq0RrjBxKqoavi4AG+qoRp7Qhw5SDjaWZGBrQMIH4DsSV/pjo2LhYiYkoe0dfTwdepa2b1uEZndrBiq4hwjVa17X7Pv8wKqxm3BHRq65dGv28t5NRa2clyv9GXZX2G4mIlkH3KonZp3BXJR2sKdTWywstONqM1KHl5F3WIrqzEeHx7tO19cLliikJBAQCbRwBQUDb+AVuieWxF1S9wlF5LFUP1TKJUIUGxTYVXs1cj/mp06EkwfrGWCQ5rW6OdWwlVJ+xmpyqTEY/PA28k1GOjtpKjKEwPntHucDJyyOtjYGgUceYbFZ8lbcPE4I7IlZFwHjQvJmMWnqoUPYCdfHaYoBmfhkC/lEIU18VDBSa59af3mapQ8qRfVyF7UsCMe7OfDC2wgQCAgGBQGtEQBDQ1njVvHzO5v4qaH6ugO8B02XCmjKkakKxw1SGT3J2457otCavKpD4wrVRjq2SJCp3GfywPMeK784Z8b8zJOlEji0morxxyJ7zTNur/VR4FPkWPe6M6N2sEHgrGeU8UPMAFZRrSbaJcph1TxWQwL0ahj9oYYv0no9JFi8YcFUxVn0Wjj2/BaD/pJJmvX7iZAIBgYBAwF0IeM8nq7tWJMZpcQS4H7ct2AdK6g1/ubw6rVyFm0K7g0XQp4QkI15FSZ5uMtYQnRyjwNREP2TlGrGVKurZM7qSNpZ30tE7fzTli3IR02DykGoa54B102ybdxir3YbPCfNRAQnorKFKrhYyryOjPiRmP5rE7IdroCIPvvqnCig35cM0VgP9NC3swd7xJtGFWNFzNGmargxETGcDYpIpD0WYQEAgIBBoZQgIAtrKLlirmC7FuTkMr9xKrQ9nUYjwMnHve6L6YXnRCQrFb8S7nSZ6ZHkK8hqxbBNvf6Pp7Cl1kFEmpCx6z8X6/Bp7RkfSbYD3RV7disvy4hM4S57nF0n301usPjIaGGFGXGozSTuRa5y7ebGGqHoJEdGlRETXUIOFif5URU9dlajrV0tbx76VOHdMjZ3LAhEamw+VX8Obc7T0GsT5BQICgfaNgCCg7fv6e2z1HM7kPFD5MTOsXeou6vCXK/HX2MF48tRvWFl8EuOCOnhsTjwwc+E+1C2Tt9mdgGPlF4uYnjlMspC0z4DzOaNcUc+dmNqSsd7opzl7kKaNBktieaN5DRmlXyYG0gs1TvCD+sdyqH6tgGplpURCDROpEq4WmbHmwpPfx/0nF2PFJ+HY8Wsghl7n3RrHzYWLOI9AQCDQehAQBLT1XKtWNVPuC2/zl0nV8PrLEFBe1JVUCLOw4DBeP7sJw3RxUsvO5lpsshbg7Z4k4Cw5bFneiT2jLx8DXqKtF2UFcJiew/VJTVcqaq5l1XmedaVnpK5H73X0jLe5zhM38gVvIKPs8dTfGgDDZH9oFpSToD2TUSKi15GXdCy9KVoomVijs6Hv+BJsXRxMHak0UqvORsIsDhMICAQEAs2OgCCgzQ55Ozkh9cw096Pq4u1G6G+pf81PxQ3D9UcW4D/ZO/FI7KD6D/DAHrGkH3p7vGMrIIWb36miniWe3j0JvHkC6Excw1nE1NWzheMeWJ1jSC746qoJw5AGSl95bEINGNhVMpqaJoPcA1qw9hASs/9TIAxXERGdXw7NZ2VQLSYiej11VaKCJVAOaXNbfDcDzqXrwZ2SwhNM8A+k6jthAgGBgECgFSAgCGgruEitdYpmygNVrTPA54wZtvjLJ1YmqgMxkyqyPyWCxMLonTQUB29BC6WsgenRjq2COh+uLXR4Rr8865B44r707BnljcP5rvSob6nlnDp1Ch9//DHOEWnee2Ui/pV0RUtNxW3nvZSMqpB5RHO+AxO3A1XU2g40MzMTH3zwASorK3HddddhxIgRDZ6TLZrE7B8Mgvwas6Qh6v9BCdSk+qC/kboq0Xu+uY29oCs+Dcf2pYEYOaPwcinXzT01cT6BgEBAIFAnAoKA1gmNeKGpCJh7qWAn3qncZoShHgLK55oV2RtLi47h5cwN+Dj5qqae3m3H+9NfySRKl+TNRLUemyndjsP03BL0KyKkQfT6GCKi7B3linply9eoXFh7eno6xo0bB4vFgsB/3AJ5Zj5yDm4k4VTP5tpemEAz3HGQUQO1A6XmB9SbvvhsENL3yC60A3UWMMm0xzHx6hESFtwnft68eXjhhRcwa9asRs3SmqRA+ZMkZn+YuirNK4P2jWJYSAFCP0MLS/fmSx5WauxIm1SMDfNDcWybP7oMrGjUesRBAgGBgECgORGQzyFrzhM29VzsubDZGl7xqdFoYDBQkp8Xm0KhgJr6o5eXl3vxLCnSSGKEPFeTqZ5OLJQbJz9phi8VIpnGVU+g/CnbIX00oUodjK/MB/HKAHyauwcxSi1SSCe0KcZYyuUUNqX3jLuMPZ2k7ITRRDZvpwZOg1hPlAjnevKQfpcFfJkJHKHvfytV20cTB3GFjDKWbEwS3W0DBgyA0WiEvGMUAu6agLL/LcP6r3/E9ddfD61W26DT+fn5gYkbj+etxj3SoxOViE4uQ8e+5QgIM0NfLkfGXj/kHI1G18Qp0KiCUa7PQ4WhANu3b8e1116LgIDGS4DZwuSSfJO1gy989xEZpbC871ETrNQVrC7pJhlVETH+/JnkjuuuDabrUuFDBFRLskwGqP0b/hlZ1zVtDZ+d/HfO78/Gfj/UtXZPPN8a8FQqleCtosL9P2b4+8Pf398T0IoxWxkC9HEtTCDgOQQ4JOn/nxL45FlhC69fR3FEYAJGBybirXNbMTogkfQ6m8+T1FAUOOWPBe15e7wzcKjsYkX9cipm4toU9ohymH4UcWkO6ze3MdFh080cB2tuMfS/7YEmMAhnz55FZGRkc0+nWc9X0zN6y7S/oVv8NZg06DlcM+wlFJVlIiNnLY7uoGsT5AONtmmkzdyPxOypi5JyIxFAKlQK+BuJ2Q9UQX+9Dtyi1tPWc0wpck+psG1JEMbelo9GNhfz9DTF+AIBgYBAQELA85+KAuh2jQAXItnJQ6ggUXrWVnTFHosdgumH5uOdrG14On64K4d4xT5cmMTb/R2A03pHRT0XMf3jKMk/0Qw5V5TJKMs7xWmaZ8rx8fHICJFBlZaM4pfnkWvWhsLCQsTFkfu2HRmT0RLrVny46H9Qk5c9JWEcusRfgVS6LTrSBUuPEEkPNSMi0YSIJEefdT6mwUaE3zRMA9Ngyn/+jboq/UBE9HESsx9JERiSdGJvqafMlxzp/acU4/evQnFwvQ49RtEvImECAYGAQMBLERAheC+6MG0uBM/YKmXwPWiC/JwFplEXw/C1heCdl0JH2qA+FI6fS916hgfEI0LhGnF1Hu+89UQI3jl2fbfcFrQvEc7rqJDpD7Qx4TxDpHQhh+kpb3Q1EVOutA+kn4CR/p4LwacNHoTFHW2wZORQ+H25NO1XXnkFQ4cOrW8Jl7zeGkLwPGmVSgWz2QzWPK1qvOZPP/0UdliQVXAQBzOWYsy1oZg+qysCwy2wWmTUZ11N4Xp/HN3qj+yTKlSWyKWiHo3WCnpLum7kHueOYMbxfrCr6QfA70RGSdBeVmaDtQNdb9IQdWcI3jkxP5JmokZXOLJFi0gi0n4BTfPq8ritIWQsQvDOd4B7bkUI3j04ilEuj4DwgF4eH/GqGxBgUXrN52WQldKXY4Br3+K3RvTE4kIqSDqzAZ93mUqE1BFKdsN0mn2IcMoiuDHGsZVSmufaAkcR02dngA9OAfEaA8ZH+mAUhfJZd9Sdaj4bg/RQRodg1CEzFLffLuU7Dh48uNkx8IYTdurUCQcOHMDbb78t5bZNnToVw4ezh90maWgm9aRfCGSl+b4UylYiN0OF9B3+OLxJB7mvHWFxDu9oRKIRgREW16rN6QeY8RqSaaIcaNWiCqhJP1S1mroqkZST/c5w6Xzu/q/r0HJkn1BLofhxM/Phq6xOxN19PjGeQEAgIBBoDAKCgDYGNXFMgxDgtpyauWVQ7DRIxRquHKwgd9OTcUPx5+NL8X3BIdwQ1s2Vw7x+nwD6i7uKUi95M5Bk4yaqqF9T5IMFZ634JAMIIeeYU2t0IBFSbiPaWMsyleMz6no0I7w7Hp39x8YO06aOCw4ORn11lwFhFipesqBzWqXkTSzMUki5lUxK96/V0XMBUGpspLtpvBCy1wZdXn/T7kddlW7UUTtP6qr0A3VUos5KxhUHIL+O3OSj6aITUXWXce7nAArFr5obhr2rdeg3gXrPChMICAQEAl6GgCCgXnZB2uJ07KFyWDv6QkFyTKbRF8Pw9a11gC4Gk4M7492s7RgX2IHIWTMlTtY3MTe9riaiwPJNk+OVMFNu5oYcoyR8z+H57ylUr6XXR1K+KBNS7lXvR48bYtxZSuOjwJ8j+zXkMLFvFQQ47B4aa5a2rpS1YDED+ZkO7ygX/Jw9wrqfMgp1Wyh3lDyk5B1lQfi6qtDtgXLoZwbARB7QoEUW+M4tROBP1GmJ8kNNo+j97SZBWSbQPUaWEQENoKp4I6I6GqusStwVCAgEBAItj4AgoC1/DdrFDMwD1FAvLCe3H+WkNaCH9sMxA7G25BTePLcF/0wc3Wax8qW4+yCqmOftqc7AfqofYa1RLmJivVF2kA0hEspFTExKgx1po3XisaXsLFYTbnPiR3q1kkCdC/DSF7jQJ6qDSdqAMhj1MuQREWUyyh5SlnsCZZkGUE5pJJFRJqUcuq8ZBreF+0L5dBxKrvaDbG4+/D8uhXoxidnfQGL2g4jUuiHlpHP/CmQdV2HHL4EYNysPKtILFSYQEAgIBLwFAUFAveVKtPF5SGH4eeVQ7DE5vmBdXG+Ywg9/ie6Pf5E379rQFKRpqaKnjRtzj56UC8rbQx2BkyRjyj3qmYw+e4RyRGn9LP3EnlGuqI+u0XzHTFUo/8rchO5+4bg6JLmNo9Wyy2NSF5dqkDaeSQUVLeVmkIeUCOnpgxoc266l4iU7QmK4wp5D9kaERJspp9Qxbzt5vyv/GgxDOumHflsO7f+VwJJIRHSGDpbeTZMg4/dR/8nFWPFJOHYtD8TgqcUtC5Y4u0BAICAQqIKAIKBVwBB3PYcA6yBao+XUFYm61bCHpwF2fVhX/Fx4VOqQ9G3KdaSv2YTEyAac11t27UBOtT8mOjaK0ktElL2jr6UDr9LWTevwjDIh7UiCAV/n7sdJYzG+oOItpw6ot6ylrc+De7F36K2XNi7CL83zRQ6R0TzyjnJl/aENVNCk4PxREzr3IBWEaB+oSLrL2lmJ8r9RV6V9Rqmrku7VIphTuauSDtYujReQ5Sr4PuNKqU1nEE4fcHSLauvXQKxPICAQaB0ICALaOq5Tm5glh+FVK8idZ+FQoOtFF3IinE/FDcMdx37Gl7n7MJNadrZXiySn2M2xjq2Y8hHXUEU9e0Y/PA28k0FyT2obcgnbUSFp6KbxTJV1e8W+oetmDyRXy/PWZUAFbFSnJBU0UXU9e0g3LqL6eypoUvn5I5z0RzlkH54oh+UFFRRbDNDML0PAnEKYSEvXQKF5a0I9eRd1TDCxhx7njqmwe2Ugwoj4slSTMIGAQEArBcQ9AABAAElEQVQg0NIICALa0legHZ3f1F8F9c8V8D1golU3LLzY0z8C00NT8WHOLkwM7oQoatXZ3i2I+MjUKMdWSeRmQyHwxqk8mCpT8Ns5Ja4kYuqsqOeQPXdmEtZyCHB1elicWdq6j6hASHAUju4pR+YxSJJPmYccBU3+QVTQxOH6uwKRcKYYukVl0D1JXZWGqWH4A4nZRzb8Y7vfhBIpFM+e0BE3FLojxbTlgBRnFggIBNoEAg3/JGsTyxaLaAkEWJjbFuwDJXVFQlrDCCjP937KBV1VfBKvn92M1zuMa4kleO05uUI+wi8HOb6L8ETqUCT6dpOKmFYSCf32HHX5ob/00ZQvykVMQ6jQiSvwhbUsAkr6E4jrYkFoPOuPlsFAvdw5VM8h+xwSwT+5xx9bEIyQfib0Ls9F4q5CBGymrkpjNNBP09bZZ762Van87EibWIKNC0NwfKefJDFV237iOYGAQEAg0FwICALaXEiL80iVvVyMpNxKBLRfw8LwDF+grxoPxwzCnDNrsaH0DIZRlyRhDgRslHD4SuZGdFYH48bwVEnNh6Wb/kYw7yl1VNRz3uiiHIcIAb/G3tGRdBvQuMiugN7NCLB0U3w3g7Tx0OVFVNBEZJSLmjaUxWJdpxj0ys9Fz99zoVttQOlIigLMIOkmnWs50dGdjUjqVYl9awKkdqMBoeQ2FyYQEAgIBFoIAUFAWwj49npaM4fhKQ805ZwZuUkNL67gqu4fCo/gVSJb81OnQ+Uj3sL8XmKx/iP6AnzUeQqRz4uEhPMQuQc9b7M7AcfKLxYxPXOYZCfp2AHkEXVW1Ec03DHdXt/KHl+3NtgKbXAlOvYhQXz6IVGcwx2a/LHyWGfE7yhC6u/5sK6twMmuoagY64+wZBLQD6VWW5ex3mNLaQyl1CVpzK0F8Ln4VrnMUeIlgYBAQCDgfgSa7eOnqKgIK1euRFZW1iWrOHLkCJYvX478fHLRCGvTCFi6KWHzJ83LY40Txuaq7qepIIm7/HxCXX6EETGxGPB+1g5MotzY+mSqkslpdk8S8F1/0hcdBDxCpNRINSkvUx7i+M3ArTuBT08Dp6hWTJj3IMA/JIKjLEgZVIHBt5Yi6jU5Tj8ZjSK6oF0O5KHr+5nIfAX45d0wIpeBOLWfwvRll368sx4pd0kqzlbg8EZ6MwgTCAgEBAIthECzuI9++uknfP/99xg5cqR0m5qaiocfflha8ptvvin1Z05OTsZ7772Hd955BwkJCS0EhzitpxGw2K2Yf+oXDLRegXuMa7D0D4/h0KFDDTptsiYEN1F7yc9y92BKSGckqMi9146NO0WZCdeHogc2CIVYqnm5Lc6xFVBd2O/nK+rfywDeOgl0Jvkn9owO8a/EdQO7wWTi4jHqXR8fjy1btkj3xX8tgwDriAb3pF8OPf1QlqWEijREh27LRL+SbOwpisT2/aSAQKw1u/Awjp5ZhY7dFbh39jVQqrmnvRldBlbg0EZ/PP6PG3AkYx369u2Lb775BnK5SA5umSsqzioQaH8IXPoT2c0YWK1WfPHFF3j++efxpz/9CW+88YbkCWWPaEZGBtatW4cPP/wQTzzxBG666SZ89dVXbp6BGM6bEEhMTMTC7BXoYFAgVa9GSUkJUlJSGjzFP0f1QxDlhHIovj3b4cp8/FBwGHdTu80IJYmANtJCKRtiOmn8v9+TpJ2o5eQrXR2aol+eBe485Aft6+vhf9scKFIH4UzmWfTr16+RZxKHuRsBW7Qv9I8EoeylUPh29MHgQ2cxfPNPOL56Dk5lb0HPjlMR53s3fn47Ar99Hor9a3Q4fm4tcovTMTntLZioO9nWrVtx4403untqYjyBgEBAIFAnAh73gPIv6rlz58Lf3/HlaDAYUF5eDiamJ06cQK9evSgPycGD+UttyZIl1Sa7efNmbNiw4cJz06dPR1gYuWUaaL6+vtDpSPHZi43nyObt8+QwuEKhaPA8f/zxR8pls2NF4SbofWy4qjQUW2m9ZWVlUmpGly5dpPW78h9fyb93GoMHDy/BekMWJoVfeizPkd9b3o6n87rzfBtijOVrx5cgUROEuzsOhpJ1ftxgjO0NlBd6A4Xnn33+Bby3cjuUAyZBPXw6/CbfDVtpAcp3LMPWSg2Gh8mhclP/cjdMXRqCceTPG8bH202tVsN5/Zs8V/rxgJ4h+P6FrxGzXIGXg6dia/4+/H3rNByWlaFr4njMvvdNqbreapiMCJLmYrtm+MtYsOZhHDx4EPv378eQIUMcL5z/vzV8djo9t35+fl5/3VsDnvw3xJ/znvjstNmEDm21P7B2/MDjBJSxdZJPfuO9/fbbmDhxokQiOR80MPBi+DQgIAAFBQXVLsexY8fAxMVpU6ZMgUZDlZ8NNCYijTmugadp0u7OrjXePk9eJM/V+aHv6qKd+b96mwEL/bPwYG4sNo1+Cr/8/rI0XkPXfV1Cb3yfdxAvn1yLCTFd4e9LbrwqxnPkraHjVhmiWe46r3tDCeh3Z/diV1kWvuh3IwL9PZPPV1JAuqK7VklbuewxKFIGSmRURYT0T9v08CNx0SuilJgcq8IV0UroFB4PqtR7TRrz3qx3UA/toFQqpR9z7hw+XX0Ot+9+HhNDhmNOh/uwrM+HWFW4Gc8efxdT7pKR+D11R+o6HrEhg5GSMA46PxKTJWPc2DFQ8++lNXx2OvFjQu/t1hrwdH4m1XwvuANbs5k6aAgTCBACMvISNIubwGg0SmF4Pt2cOXPAH7zffvstMjMz8eijj0oXg+8/9NBDWLBgQZ0XJy8vDxbL5Ss9azs4ODgYHPb3ZuNf70zInUTNW+fKxJPnyp7Lhhh7vp1eTqVcjUUTlmCAKRjXKRfj64//2JChLux72liC6w8vwI1h3TE7lqpqqhhjyaTO24vb+EOe/y44OuCqlVtNmHZoPlig/40O4109rMH7nTlzBoMGVcfVOciqI+ckrVGWd0qnoiUWuh9EnlPWGmXNUQ7rt4TxD9mKigqJTLXE+V05J3/BR0VFobi4GHq93pVDXN6Hr9ngwYMveAJnREzC3zvcg46aeJgGqqC/XoeHXn9MysdnwlnVDh8+DMavqrWGz07+PgkNDUVjvx+qrtfT91sDnuw00mq1yMnJcTsc/P0RERHh9nHFgK0PgWZxV1RWVkokk935//znPyXyyVCFh4ejsJDat5w3vh8dTYlowtokAvyB9vHHH0trM1kNmPbrFOz3KcA8y9X4Yq2ZvjAbvmwuQJoZ0Rvf5O1Huv7ie6nhI7WuI/6TvQNlREL/GjPYoxPngqNXXqHy6hq2fft2dKVY/X0dgAUDSF+U6p/uT6Le5+TceP4okdBNwMzdwBeZQKZ7+VWNmYiHNRHga/bZZ59deHpe3q8YefhOlN7uB9+jZgQ8no8PUv6O7hFdpB9oQUFBEnlbv379JeTzwiDijkBAICAQcDMCzRKCf+6556RCk/vvv7/a9AcMGCCF5PkXOxPPRYsWYeDAhlXyVhtQPPB6BCZPnoy9e/fi6aeflr78Ip6JgfHfVtz6WSHesofi/hG+aGgUdxb1hl9alI6XMzfg485XSaFErweiCRM8YSjCPEo9uCuyD2JVxAI9bLfffjuGDh2Kl156SfJ8v/XWW5fkLSZQVsydJF7BW56RhO8LHOL3b50AXj8OpFAKOFfUs3eUpaCEeRaB8ePHY9u2bVI0iX/433nnnWBfZ8lYHVS/VkC9qAKbu3+FUz1LcahXHroN6SnIp2cviRhdICAQqIGAx0PwLLFz9913S6d15pXwg3fffVcqQFq8eLEkvRQSEgKukH7hhRcu+XKrOufGhlhaQ9ijrYfgq17HqvdlpTbIni2AodyOf9wWgqeH+VI+Z9U96r+/vvQ0HjyxHHPiR+KaUEdBUlsNwd+TvhRnjKVY0PUPUDejED8X/7EUU2lpaf0X5PwepZQts/Y8Gd1IDmo91R/EUZoeE1EmpL0o2utDoXt3WnsPwbuCpazSBtXPRESXUe4EmWGKHwyT6VeC36VBsdbw2SlC8K5cddf3ESF417ESezYegQZ+zTf8RF27dpWkluo68qqrrsKECRPAOaIcohXW/hCwB9CX3rMh0D1biKe+LsJj9hD8c6i8QTmEwwMSMCYwEW+d24rRdBvgq2qTQK4oPoGt5efwetK4ZiWfjQUzgD5hrop0bAZywW2iNOxVlDP6QzYwN5PyRKnwf8x5MjqQqrIb6v1u7Lza+3F2IpqGGToYJ/lBvZCI6E8VUC2vhGGqFsbxJACrdPOvgvYOuFi/QEAgcAkCl/7cvWQXzz/BhSKCfHoeZ28+gz1EDvPfgxHhY8dL3xXiwQ02nGlg7uCjsUNgsFvwTtY2b15qo+emt1nw5tktGKiNwdigpEaP01IHqkklisnmC6nAatIa/bAXdV8ivXT2kP5lHxUubQSePASsyAMqq9fGtNSU2/x57YFy6O8MQOkbYTD3UUHzdRkCZ+dBuZo8o9ZGJGW3ecTEAgUCAgF3IeAVBNRdixHjtG4EbJG+MDwTgniLDW/9UIj7NttwqAGF9tFKLQmy98VCEmY/UEkspo3ZJzm7kW+uxONx1XUaW+MynRXzTyUDy6mO6qu+pDsaA+l6P3oQGLUBeHA/8BN5SouFaovHL7Et3BeVfwlC6auhsHQgHdWPShHwWD4Um0mZoTHVgR6fsTiBQEAg0NoREAS0tV/BNjZ/Wxx9ET4Vgk4VVvxncRHu227DlgaoZ90S0RNJqiC8dGYDbG3oizOTcj4/z92LmyN6oKOatI7akJEiEXpQLuhDHYlwUg3iD/2BPyc6ipmePUJeU/KM/nEP8PVZINt1pao2hFDzLcUWp0DFX4NR+nwIbBSV0P5fMfBABnz3UGWZMIGAQEAg4EYEBAF1I5hiKPcgYCUPTMXjwehaYMbc5cV4eLcdy3JdG1sh88HT8cP+n73zgG+q7P74L03bpOlu2XvvJXuJOPAvQ8EXRUAEHPiiIoqAouJWQEEFFBDZDvRlCOIWBUFkKXvK3qu7Tds0aZP/ObemtiWFNk3Sm+Sczwea3Nz7jO9zk5w8Z+FQZjw+v0B5gHxEpp7fggitDiNoh9fXpQ7FwjxCCugXbYCfOgDj6+XOeOox4P+2AYN2APNPAyfSfZ1E2c0vp14wjBNjkDaBfuzQt0T420kIeyMB2iPmshuU9CwEhIBPERAF1KeW03cmk9MwWNmJaX7ejC9/S8YLB2zKDlhxZtgmrDJ6R9fDuyf/oJRA3q+l/JF6Fr/TvzFVOiBUG1wcBD5zTiWKmB9clRTOlrl+o683pPzBFF82lxTQu/+iMpJUy3UGpXraR4H5PrThrZr1y25BsGfWhvGpKASkWBHxaiJCpyVBe0b8IlSzSDIQIeClBNweBe+lXGTYKiCQ3VyH9Cej0HJGMlbqU9BPE4kEswZP1r7+4J6u0h4bKTXTm0fX4aVK3uszabHm4J1zW9AytCJ6RlNxdj+WKIqY71sp9x8HKf1BaZ24CtOyC8DCs0AF0s05yOnOGtlopreBLPsiLiJg6aCHpZ0OwRszEbLSiPDnE2DurIfpnjCw77aIEBACQqCkBOSTo6TE5HyPErC00yNjZCRazUnB1zoN7iIlNJ6sgC83ALTX0DBigwwYW5tqYR9bh15htcC7ot4on8TtwzlzKt6udYvPJ9gvyfoYKKKeI+j5n4Vyi/5Jroqc3ukXij3734UMSsNFgUxUDpTzjXYiKzJH4IuUkgAlbDV3N8DcJQS6XzKgX21EMAUpZd0SAlO/MNiiBXIpCcvlQsCvCIgC6lfL7Z2TNXelMjsmG1otTMV3Bg3uRASSyAL4TuNrKxb3V2mFlZcPKgFJXzb6D9g/1JvksjkdCyjy/Z7YxmhkIE1KxCEBzh3aOSb330SKqj+WY8C3lMPrlys2fHOZ7hF6vQu9zonvu5FSysqpSCkIBGkof2gosm4Ogf47UkS/pxyiGzKRdUcoTH1CYQvzrvdZKUjIpUJACJSCgPZVklJc7/FLua681UpbHiWUkBD6lW5Sdwgt50PV6/UwGo0lnJ1nTw8ICFDKaHJVHE9JTp0g2GgHtNq36fi/cja8o9dhK+168Q6XroiNlxBi2SKyCuac2aYkbb8hjOy3HpYTJ07gu+++A9+31apVc9g7rztLdjaVDconb5z9HayEvlv72knnr1y5ghUrVuDSpUuoV69evhZc9zAxMVGpXrZz507ccIN6A6E4or52VAjahWdjUBUbbqP7I5Lw7iEf0WUXc2vT/3wsDn/tO4h65SMQbSAfx0LCNdGXLl0Ki8WC2rWL4e9R6PriPOWqcJz7mD+TCq97ca4vzjmZmZnYs2ePUrmKq1g5K0V+dlIurewmwbQDaoDGYoOOqirp1mZAQ+lDOZUTONeWh0Sr1SplYp39fvDQMJVuiuTpyUFcpy+uLMX/0tNd70PP3x9caUlECMhegNwDXkMgi3ZXNCYrGlPllh/+E4B+AaEYRoHuH1FC84pX6xHKvFpGVkb/2EaYd3mX4kNZiXKFekpmzpyJt99+W+nORhEyzZs3x08//VSs7ncYL2Jt8km8WK0rIgMpEqcI+fnnn5U639w+S1RUFPbv3w/+kHeVbN68Gffcc09ecy+++KLSB5fPVbtw3Xn+N7IWcCrVjDsnvo+DTW/C0Qbt8dM+DeoFppPPaKiyO1qTCgANHDgQGzduzJtW3bp1r1nJLe9ElT04ffo0/vOf/4CV0JSUFDRp0gQ//vgjWFFztdjCA5A5JAIm2hUN+coI/XIj1Zunqkp3U1UlMs97UhF19dykPSEgBNxHwHXfUu4bo7QsBPIImO4Jh+kOA+p/lYZv0zJgpE3DB3ZdOyXPqMrtoNcEglMZeUp++eUXTJkyhSKzbco/7nffvn144YUXrjuEHJsVU85tRqOQWNwd27DI80+ePInhw4fntc8nJicn49577y3ympK+wLvx+ZVP+/Xt2rWzP/Savz07tETCihlIee0/SHisJYzzxuPQjm348JQNd/1J5vkfaGe0YkcE1mqeN6fjx4/jqaeeynvuDQ94F7BTp064ePGicj/wPXjo0CG89tprbh2+LVaLjBGRSJ1aDtmNghGymJLZj41H8CYqaWbN/YHk1gFI40JACHgVAVFAvWq5ZLBMIPMBqmHdPQS1PkvF6iyKyqW7mHdC96Q45sN14cdUbY/1KaexiSLjPSELFy502M3q1asdHs9/8H/xB3HclITnqnVGANuUi5AlS5Y4fGX7dspN5CKZP3++w5Z4Z83bhHcC7WJLTYBp/VKkvvMARp5aircbk/J+eAdCej6C6Mk/IWbmNoT0fRKaiFjFhcJ+nTf85fWPjIwsMFR2W1q5cmWBY+56Yq0ciHRK25Q2KRbWylqEzk5BxIQEBO1QtwuUu3hIu0JACDgmIAqoYy5yVM0ESCnLeCQCnBqm6vwULLeZUJMsfSP2AhsSHA+8T3R9tKJURm9TSqMsqqnubtHpHPsE2P09i+o/0ZKJjy7txJ0x9ZXUS0Wdx8fZX9iRsH+hq8SXfLUcceHdwaiQYNxRgSzFn76AhEebIYWUUsvh7Qjt/wxiZ+2AfsR72E8+pN4ifO8VNVdPziGnVhCMEyiZ/UsxsIWQz+u7yQh/OQGBB6WqkifXQfoSAmolIAqoWldGxnVtApQSJv3xSFha6lBhVjKWaLPQNgoYsx9YTfXDCwt/Ib9QrQsumY0UWb6n8Msuf15UbN+sWbOu2dfMi38qJvUnyW3gejJ69GglGKzweWPHji18yOnnI0aMQGDg1a7i1atXd7rNsrqwV69eV3XN94XdZWHq1KkUPWOGedevSJv9JBKeaIOMr6YjpsPtuJ/cPLgCE0fVm0seA3lVv+480LFjR9SoUaPAurFSWtQ96c6xcNvZjYOR9losjGOjoMmyIfxNqqo0JRHaE5LM3t3spX0hoGYCooCqeXVkbNcmQFG2bOrLbhCM2OnJmB1sRq+KwCt/AwscWNrrhcRgUPlmWHJlD85k/WuOvXYnzr1as2ZNrFu3Lm8nioOCXnnlFXTt2rXIBvenX8GaxCMYWakNylEe0+uJwWAAR6XbA0tYmRoyZIjLfRYPHjxYIKiJA3O2bdt2veGp7vV58+ahR48eeeOqUqUKeG52ufXWW5H/B4ItLRGj6gdj/U16TG2Sm/Jr4mHKPUquxDNPAhdValHm++Drr7/OU0I5MI39PwcMGGCfapn8tbTRI3VyrPLDMeBSDsInJiCUikwEXHC/RaJMJiydCgEhcE0CGjJBeZV3eFxcnFNpS6Kjo5GUlHRNGGX9IisU7LvFwQNqFnvKk7S0NHUMkyLjwydRecDz2Uil+tXTrEFYcg4YXjsYb94QjoSEf+3yGTkW/OfwctTRR2N23Z6qGD+nZWEfvXv3/g+Z5B7wP8pZGqjCnKWcyodTb6WmqtseHRERoaSPycmhckkulmPpUErCfk87oVm0E8qVlwZWAdpHl6wjVhIrVaqkBAmp3Z/WLZ+d2ZS2aV2mksxeQyU+zTeFILM/JbOnQCZnhFMGxcbGwtnvB2f6dPYat/B0djBFXMeuN5wm7PJlutFdLPz9UaEC+byI+D0B2QH1+1vABwBQpnHjc9HIKa9FOJn2xumzMa4usPikGU9sT1Mq5dhnadAGYVzVTtiadh4/J52wHy7zvyuuHMTBzHg8W62TKpXPMgekkgHUo/SFXIVrbSfgGbrHjpJCyr7H/SiK/n9UEpRLhIoUgwBZL7JuNyDl/fIwDQhD0J8mRI6JQ8inqdCkqtzHoRjTk1OEgBC4PgFRQK/PSM7wAgK2UFJCn49WqrCEv5WIYcHZmN46BN+dJyV0H5Cez8p3W1RtdAqvincvbEV6jueS6ReFMTU7C9PPbgGPqwONS0T9BMLJLfaBasAactX9sBlQheLBJh8FbiPz/NvHKOdohvrnoIoRUnEJU98wpE4nRbRXKHS/ZiDy6Tjoqd48mQNUMUQZhBAQAu4hIAqoe7hKq2VAwBapRdoLFHFLuythbyWhn16DT7pEYC9ZjB+iuKOEfLompzhKzjZhLkWcl7XMOLNFMb2PqdKhrIci/ZeQAFnScSOV95zdHPi2PXA3FdviQKW+tCP6GO2MclYGSYF5faj8A9I0MBwppIhm3UglPqnOPCuiOirzCbNXeYldf7JyhhAQAgoBUUDlRvApAuxDZnwxWikNqJl4Dt10AVjQCrhMmV+GUiTzuX/SV9bQReLBCi3xRdwBHM1MLDMGRzIT8OXl/Xi0ShtU9mCVpjKbsA93XC0EGF8P+KUj8FJ9IJ5+8IymrAy9t5E7yFnKMypB39ddfVuUFpkPRiD13XKwtNAh5PM0RD4Th+D1tKUsmvx1+ckJQsCbCIgC6k2rJWMtFgFrxUBlJ5S2OGF9/hSaaa1YQkoo76Nw1aRD/8ROPVixpaL0TTr3R4FqQsXqxEUnvUN5SavowvFQldYualGaKWsCeoqjuYcCk5a3BRa2BJpGAB9Q1DxHz79KGRrs919Zj1PN/VsrBCLjiSikTolFNuUTDZ1HVZXGxyNoK6Ue8K64WTVjlrEJgTIlIApomeKXzt1FwFotELY3KFflRTPC3k5CzYBcJbRccK45fhslRNAFBCrVhvakX1bSH7lrLEW1+0PSMexMv4QXandDcABpLSI+R6AN5aadRimcfiTviuF0O24kk/xA8voYutOGVaczKUBOzMvXWnRr9SCkj4tG6msxsNLuaNhMSmb/IiWz30smDREhIAS8moAooF69fDL4axKor0fAmzWhPW1B2HvJKB9gw0LaCW0SBjxOgUk/XQG6RFTHLZG1MOPCdqSQT6inhNNBTT+/HV3Cq+Pm6Nqe6lb6KSMC5XXAE7TMP5F5fnIjsibTOP67JRk3/WbCnFPAFdGnrrkyOZSP1UgVldImUL4r8rsNn0LJ7N9IgPZIPsfua7YgLwoBIaA2AqKAqm1FZDwuJaBpFgrjM9EIPGRWkl6Ha2yY0wLoToEjzx0CvjgPSsvUESZbDj64+JdL+75WY/Mu70JSjgnjq5FGIuI3BILoE5eLJXzWWoNf/68culHqsEXkH3rHVuDZg8BO99ZH8HrO2eQXmvYmVVWiAhQByVZEvJqI0GlJ0JwWRdTrF1cm4HcERAH1uyX3vwnzl1b66CgE7cpC6OwUBJM3KFe2ubcyMIVS5iw/H4b/VmyNVQmHwdWI3C2nTSn4PG4/hpZvDg6GEvFPAs2jgzCleTDW0m+QUbQ7up98kx/cTf6j9DvoK6pFkSk5RR3fGJR6wNKBqipNLYf0EREIPGVByLhLML91CprL+fKtOb5ajgoBIaASAqKAqmQhZBjuJWBpp0fGY5EI2mKCYUEqqJQ8XqSE4iNrAvPPAMeTm6OWLgockJRjc2/+wXfOb0ZMoB4PVyR/ABG/JxAZRH7JNXLTOE1vCsTQ89eOUNAS7Yq+dxw4+0/mBr8HVRgAvYnNN+cmszcPjYL1zzSEPn0JIYtSoEkW7b0wLnkuBNRGgNIpiwgB/yBg7kp5ckw2hC5MhS1Eg8whEXisFlCeApPeOqpBi8g+2Gv7AsvjD2FgedIE3CDrk09hC1VhmlLzFoRQVSZXyO+//47ffvsNXIJy5MiR0OnI4dDFsn//fqW2fWBgIAYPHgyuL15S+f7775Xa9VyG75FHHilQX744bWVnZ2PRokVKucUWLVqgT58+xbnMa87hH0Vc2pP/cSJ7dg9ZQTuhn1BZ2RtjKHiJahR0ZhdIOs8Vcvr0aTz77LPIyMjAfffdhyFDhpS42aNHj2LNmjUICgpS7gsu15pfuKwwt83lWzt37ozZs2fnf9k1j4M0yL4zAhEDaiB18SnovkmDbkMmTHeEIuvOUHCOUREhIATUR0BqwatoTaQWvGsXIzKSdjzpizE+Pr5Aw7pvjDB8YUTmf0JhuidceW0dnfIc+eCFBacgW/89VjXti3JBhgLXlfZJFtV57394BaV+Cse8er3zmuNa8DZKLWMylTwI6p133sHMmTOVWvJcD5trtR84cABcb9pVwgoGK7YBAQFgBZT72Lx5M2rVqlXsLvj6n376CVlZWcqaWCwWHD9+HDz34gjzqVOnDriONCtMLD169MCSJUuuutydteCv6szJA8WtBc+lPddcyi3zeYKmXV0PDCJF9K5KFIhTiu2DY8eOoVu3bgVGP2jQILz77rsFjvGTomqXb9iwAXwNrwnfG7ym69atQ6NGjZQ2uMZ93bp1C7TH78lDh8j52sWSvxZ8TqIZ+jVG6NZmwEbKKSuhrIyCqi6pQYriqYax2ccgteDtJOSvOwnIT0N30pW2VUkg684wZN4dipCv0qH7jiqtkNxCGzdzKTgpKzsCRmNPTDpDzngulsVX9uKyOR3PUS16VwgrmtOnT1eUT26PFUNWBsaOHeuK5pU2EhISFOWTn1itVqUPfjxixAj+Uyzh3VlWYln5ZGFFhRWGyZMnF+t6PmnKlCnKdXblk49t2rQJP/zwAz/0WTFQdi7e+VxFJT8/pvuTa9FPI7M85xR9k8z0XIveGbnllluuuuyLL77An3/+edVxRweMRqOifPJrOTk5ytrw44ceeijvMe9SF5aUlBS8/PLLhQ+79LktIkCxbqS8Vx6W9nrol/1TVeln0uCzJe2VS2FLY0KgFAREAS0FPLnUewmY7g2nXREDDFRpJXhd7o5aa7IqL2mlgUETil/Pt8CaK64LSLpgTsPiy3swoFwT1Ashe6oLhHcQebcvv7AywIqpq+Ts2bMOd1NPnDhR7C6OHDmiKJz5L2Bleffu4iv5u3btylO07e2wQstt+4t0oE3t6c2AHzrk7oL+Qrv2HLD0MGFcG1cy3Yrvk8LCu7Jbt24tfNjh88uXLzu8L67Qe4Z/qLDk/7GQv5EdO3bkf+q2x1wVLePRSKROK4fsRsEIWUzJ7MfFI3hTplRVcht1aVgIFJ+AKKDFZyVn+hiBzAfCkXVTiBKUFLSZvpRI6oeRubN1ICWGt+GVw1HYkZL7ZVraqb97fisM5PM5snKb0jaVd33FihXzvuztB1mJcMY/03594b/sr8k7loWF/TGLK5UrV1bM7vnP553aGjVq5D90zcc1a9a8qg2+oFIlskX7mVQiM/xTdYCfKXr+jYZAOumS4w4CPUl3nHcaSChGRiJ2TSks7ObQsCE1WAyJiYlxeF/kVzod9cFN81p6UqyVA5FOaZvS3oqFtZJWyYQR8XwCgnaU3OXFk+OWvoSArxMQBdTXV1jmVzQBUtYyKI0Lm+k4PZP9C6kaBSh90CIL1oBkPLont3pN0Y1c/5WtaeewPuU0Rlduh3AtRTy5SDp06KD4QdqbY6WOfTQ///xz+6FS/61SpQrGjBlToB1WLL755psCx6715M477yygbPIYeQdu2rRp17qswGsTJ05UFB6+loUDrVj55AAXf5Vg+vRmX9Av6TfNZzcAbWkHfy4poLeTIvriIWBfatFkZs2addWL7Lt5++23X3Xc0QH2Y3zllVcKvMT3xfLly/OC4Bz557KvqFsCkQqMxPGTnNpBME6gZPYTY2DTaxD2LlVVeoWqKh0shsbuuEk5KgSEQCkIaF8lKcX1Hr+Uf2HbTTwl6ZyDHZwJ8ihJH6U9lz/A9Xo9+SAaS9uUW6/nLxEeK5tR1SzMkpWy/LsyV42XlFBLWx0CT2RD/006susHgetQV9MZEGfbi8PGAPxwKRyV6QurEe2OllQslNJpzImfqb0ITKjWmSKYrw6EYJYsJdlVtI+jd+/e4B1GVuhat26tRInzzqgrpV27drjhhhuU912rVq0wY8YMNG3atERdDBs2DGFhYYopnqOh2d+QnxdXeC1HjRqF5GSqaFW+PAYMGIC5c+c65MnKKe/a8o6eWoXvA54/fyY5s+6F51VRB9xWHuhPuW3Zb5RN8p+ez/3xxIpqbYqn0+a79Ro0aAC+dzhoiP1xObPB4sWLCzerPC/qs5N9PLt27aq8v/gx/6Dge8UutWrVQv/+/bFs2TJlLZo0aQJ2pXCH8PucgziL8/1gpeT/nL4pp1YggvZQwNK36Qg8akZOVSrfG03w3CxF8XRztyVqnu8J/peenl6i64pzMn9/cJCTiBCQKHgV3QMSBe/axSgqCt5hL2Ybwt5JQuBxC9Kej0ZOg2CkZWeh76EVCDLdjEsZVfBU7dx8jQ6vL+LgJxR4xGU+P2nQF00NpCE4EP5CcjYK3kFzbjvEKXb4Rwen1FGz+FIUvLOcc0j35swOnMppRwoQRRvHrJzeWwX0Y6pkrXpD1DYrS7GxsUqKrhIp9FYbgjeboF9hRMAVCqaiBPeZ94bBWqUUKQaug9cbeEoU/HUWUV52CQExwbsEozTi9QSCNTCOi0JOtUBFEdVSdZXwQB2eqdoeVwK/x63lUzHjJPA2VU4q7sZanCUDH1/ahX6xDYtUPr2em0xAlQR4t7MH/d5Z2IpyibbN3R1dSspor23AmAPAtiRVDtvzg+Jk9pQfmAOVModTVaXDZkSMj4dhHiWzT7g6UMvzA5QehYDvEhAF1HfXVmZWUgL6ABifi4a1nBZhkxMRcD4bfWLqo01YJRzB97QDmgP+Ep9A/nWWYsQm8c6nlkyto8j3U0QIlBWB+mTtfKkBmeUp+9dYSst5LB14dC/Q708KuKP7mXON+r0EUr7Q26mq0vTyMA0IQ9B2EyKfiUPIZ6nQpBbjze73AAWAECg5AVFAS85MrvBhArYwUkLJBM9/wyeREnolG89X66Lk7zQF7cKkRgCnv3li37W/uHcbL+H7pGN4vFJbRFPZTREhUNYEOHH9kGqU2J5+D81qDlSj23Iy7ejfRjlFpxzNrb5U1mMs8/4pWb2pbxhSSRHN6hkK3S8ZiHw6DvqV5JefKYpoma+PDMCnCIgC6lPLKZNxBQFbpBZpL1CkLMUjhE1KQj1TBO6v0AycSL5ZZApmNgP2khvkQ5R/0VHKG64lP4XqvdfXx+CecqSxiggBFRHgOLiuMcCHpIR+2x74D/mGfkspb/vSjuh/aWf0N/qBRa6Rfi1cvjNzYLiyI5p1IyWzX/1PMvvvafvY4udw/PrOkMm7koAooK6kKW35DAFOYm18MQYaCk4KfysRjxlaIYZ2Mief+wNd6Mt7fkvgUhYwjIJ6z2UWnPbKhMM4kpmI5yjqXauRt1hBOvJMTQSqUSXUcWSW/4Vyir5MZnqqYomnDgC9yVd08Vkg+eoUsGoavtvHYovSIvNBSmb/bjlYWugQQoUrIsfEIfg3Kl7h71q62+lLB75OQL4dfX2FZX5OE7BWDKSd0GjFB6zcO2l4PqYTthsv4Kek42hGBYgWU4AHRxsPJSX08D+Zs5KzTZh9cQfuiKqL1uQ7KiIEvIGAnnb7OUp+OQUsLaIfV3x/f0BBd1zyc9zOdBxK84ZZuG+MnJot44kopE6JRXatIIR+TFWVKFgpaBslsy9uVKL7hictCwGvJCAKqFcumwzaUwSs1XKTV2sv56DP3AjcrKuB985vo+ozZtSi3IqfUALwGMotz+b47Ulk1rz4F1nocvA0Rc+LCAFvJMAlaac2AX7sADxIxarWX7Zg4M7cH1o/kKm+OAF43jjv4ozZWj0I6eOikfpqDKy0Oxo2g5LZT6Rk9nvJHCIiBIRAiQiIAloiXHKyPxLIqUNK6PhocGqmGStbISPLgjmXcutZl6cE4ItoJ5ST1I/cZ8NKssuPqHgDKgRJomV/vFd8ac58bz9eC9j6f5GY0jh3ZpwB4v+2ArNPAVf8WOfiPMHGl6iqEmXNAFlBwqckIeyNRGgpob2IEBACxSMgCmjxOMlZfk4guxF94YyJRtjfVnz1Yzcsv3yI/DwTFCocXTynOSWy112ENeMWBFkoukNECPgIgSDKldmzQu5u/5etgRtjc/1D7yBFdPxBYGeyj0zUiWlkt9QpNeaNo6MQkJyDiFcSETotCQFn/dx51gmWcon/ERAF1P/WXGbsJAH+skl/Mgr1D4Zg1g9tMfn0H0oFI27u55SjSAv+nr6c0/HO8QDFf87JbuQyIaBaAo3DgdcaUk5RCloaXQfYn0pm+j3APX8BKy9SpiJ/zClKaQUsHfVInVoO6SMomT1ZSiImJMAwO1lJ46baxZSBCYEyJiAKaBkvgHTvXQQs7fXIGBmJ2w5QPfKvK6Le8D6oWrc2Xtr7A7pH1sCHzcIwsiZFyZ8Bnt2Vjjbt2qN69eqoRXWx9+6lHDcqlA0bNqBmzZqoUqUKqlWrhoMHaVsrn3Cd+Q4dOii1obl+OddkL6lcunQJ7du3V9rnvv78k3L+lIFMnTpVWQue56233nrVCBYsWKBwYBb8b8WKFVedU9oD8fHx6NixI7h+eYUKFbB58+YSN3nq1Cn069dPWZcBAwYgM7NQKoZitPjUU0/lzbVRo5KlC4sMAoZXB74jP9EZTYFY8oN+/QgFLdGu6LTjwNl/hrNy5UrUqVNHWfeWLVsqpVyLMbQSnXLu3Dn07dsXdevWVZi4o355sQbEVZWoxnzK++WReX841ZnPQsTYeIQsomT2tDu6f/9+3HHHHahXrx6GDRsGi0V2SYvFVU7yWQJSC15FSyu14F27GCWqBV/Crjc+9zX6nu2Aj1scxkva5Qi5qRkSHpuFMzv2Ky0tPW3BlBMBMO/8BakzH6PIDYqWJVm2bBm6du2qPLb/V5a14M+cOaMoQ/ax2P+uWbMGbdtSSDRJjRo1ULi+dqdOncDKRXGE68ezAl5YPvnkE9x2222FD5f6eVG14KdMmYKZM2cWaL9y5crYsWOHcmzPnj3o2bNngdf5yUsvvYTHHqM1dIFYrVZFGSvc1Lx589C7d+/Chx0+T05ORpMmTaDRaJQd+MDAQISHh2P79u3gGt7FkfHjx+Pzzz8vcGpQUBBOnz5d4Bg/KW7t8lMZwJdUWWnN5dwiDc21ydj41uOw7Pktr03u4+jRo8qPmbyDpXiQmpoKVp7tLFipZwbMgu+DMhVKXK//Lh367zNgo3QZ044txHtnFyMl2wjmwPfe77//rjwu03E66FxqwTuAIodcTkB2QF2OVBr0BwIDP30ML12aSyUNG+FF/X1IX74J2RcTMWbMGGX6v74+kqqpPIrgFjch6sUvoQmPUY47s3voTp59+vRx2PyDDz6oHF+yZMlVyie/sGXLFofXOTpoZ1L4taeffrrwIbc+L6x8cmcXL17E6tWrlX579erlsP833njD4XFnDj777LMOLxs7dqzD444OskIcEBCQ5/7BPw5MJhMWL17s6HSHxworn3wS78ixku6scFaICfUpp2gn4Pl6wO6zcYiasBQx7/8B/S33K81yHzx+VwmvDSudtn9SIfFufVZWFlihL3MJCYDpntxk9qvNv+Gp6kNwqMO3GF1tiMI6MTFR+UFa5uOUAQiBMiIgCmgZgZduvZ/AtMNzMTXoO4ze0gAL992FyMAw8JcKS0pKCsx//oCUyYOgrdoA0W9+D22NxsjIoG0iFQkrLo7Efvz8edrSKqXwLpUjccZs7Kid0hzjnbPLl2nLjsSuxJSmvetdy/eFI7HzdvRa4WN8j/FOan7h64tqO/9513tsZ3G98671uoFyit5XFUh7vgeS37wX2ReOI3zEVATWz91R5x1cV0lSUhJY6cwvrIC6so/8bTvz2BYRgEnxC9BsW19K35aJCTUfoQIVWuWHndFodKZJuUYI+AQBUUB9YhllEp4mwCZPlpfXvoRhB5/HzRHtsLnN53i6z0jlOPvnsVgOb0PSxF6wWbIQ/do3aDp4tHJcLf8Vdgewj6tLly7KwyFDhtgPOf3XzqJwA+xX6kkpX778Vd2x0mnfBWZfXUcSFRXl6LBTx4pi0bp162K3x6Z6NrvnF57HTTfdlP/QNR/r9XqHrw8dOtThcWcOtmjRApYDfyB12nDkXD4FQ78nlWbuvvtuZ5pzeA37VBZmwSc68u912ICHDvKaNQtvgGr6injhxHQqYJGj7FoX9f7z0LCkGyFQpgREAS1T/NK5txL4448/8oa+7MpP6LzjfgSH63HzN7UR/GsGWHGz+zda6cs3mZRQHNmGEzePwken1FM8ZeHChVf5yrHPn92cy/6fkydPzpur/QH72BVX+vfvj7vuuqvA6eyfu3Tp0gLH3P1k7dq1V3Uxa9YsVK1K23Uk27Ztu+p1PlA4KMvhScU8yIrIoEGDCpzNCu5XX31V4Ni1ngwePDhP2WQ/R/Ydf/7552H/0XCta+2vbdy40f4w7+/o0aNxww035D0v7QOeE/s6wmZFxppZCL7hNtz9xLNKIE5p27Zff8899+T57fJ9y76L7PLRvXt3+ymq+PvUyNGY1XgiNiXvxKqM9eAfAO+++y6aNqUILhEh4KcEJAhJRQsvQUiuXQx3BiHZR/r4448rZvcRI0bg1htvgWFBCnS/m2CmtCyckuXHDT9j06ZNSiT8o4/+V0nPtOAs0J1yKU5qBITSRlZZBiHZ5/H+++8rwTisgDjyR+QAHfah5LG+9dZbYLYllV9++QXr1q1TFL4nnniipJcX+/yigpC4AfaX5Eh4doXg3b769clpsZBwVPnhw4eVwCkOxnKHsALI/2JiYsD3kDOydetWxX2A58BBSc4IB1exGZuVz86dOztsorhBSA4vpoPTp0/HhSvx2PJ/L6Nz+WC87dxQi2peOc73J5uzY2NjlaCka55cBi+GfJYK3U8Z+LX/ScTpU5T7jqP21SoShKTWlfGtcYkCqqL1FAXUtYvhCQXU0YiDN2bCQKlXrNEBSH8qCjk1aRcon6yNo8jqw0AVsoLOaAY0iAlR/A9L4geYrzmPPSxXrpySRqcon06PDeQ6HV1LAb3OpR57mX1PK1WqpPgqqsEX9loTL60Cam97Cf3wmn4C+KY9UC3EftQ1f4ODgxXlMy4uzmHQnGt6ca4VrqAW/mICTHeHKkFJruLp3GiKd5UooMXjJGeVjoCY4EvHT64WAlcRMHcLQeobtMVJwRjhLycg+LeCgUc9yBWRa8hz0u5BO4E/4gsGUVzVoBwQAj5A4N4quTv+i0gR9Rux2mCYlwJrJS1MfcP8ZtoyUSFQHAKigBaHkpwjBEpIwFotEKlvloOlnR6hH6fCMIsif03/Ri43oO+ipW1ya8g//JcZS05nl7AHOV0IeBcBjo4fTO62X18C4v2kZLruxwxoT2Yj4xFyWQnSeNeCyWiFgJsJiALqZsDSvB8T0GmQPioK6Y9GIHi7CRETEwrUiI4my/xHLYCB1bWYdDgbE8ksn/WvjurH4GTqvkqAFdBA0sM+8YNd0ACybIQsN1J1pBBkN6ZSUSJCQAgUICAKaAEc8kQIuJ6AubuBdkPJJG+DooSyj6hd+Mv41abBeLNpIH64QnW1dwOXs+yvyl8h4FsEouhHV//KwHKqG5/q45v+hoWpsNGP0MxBuSnbfGslZTZCoPQERAEtPUNpQQhcl4C1ehBS34rNNcl/lALDHDLJZ5FG+o/cSyb7hS2BS5QXfhBVhtzjOF+5/XT5KwS8lsBQSrdqpp3+L8577RSuO/CgrZkI2p2FjGHhsIXJ1+x1gckJfklA3hl+uewy6TIhoKeoeDLJZwwnk/xWNsnHI+D8v9tALclN7AvyC61E0fEP7QFW0S6RiBDwNQIVdcCdFckH+lxuIJ6vzU+TboVhSRosLYJh6eTicH9fgyXz8WsCooD69fLL5MuCQNbtBqS9TiZ5Cn5X/EI3/luOj7+cF7UC7qBI+VePAG8fo9yV/26UlsVwpU8h4HICw2kXNIV+e33lgz+yQpamQZNJyfcfjnA5N2lQCPgSAVFAfWk1ZS5eQ4Bzgyom+VY6BL8fh6BZ8WSXzNU0dfSufKsxMKYO8CWZKUfuBZItXjM1GagQuC6BWgbgNvqRtYR2QS0+FHgXeNiM4PWZyLw3HNbyBculXheKnCAE/IyAKKB+tuAyXRURCMlNVG95OAbaDUZEvERR8hf+NcnzLtHs5sBh2iBlv9Aj/26UqmgSMhQh4ByBh+n+5oC7by87d73qriJThWF+ChWeCERWT9KwRYSAELgmAVFAr4lHXhQC7ieQ0ycSWZMpNJiCkiKoYkrQ5n+j5DvFkF9oa0BPORSH7gK4ipJI6Qjs2rULGzZsKLJizqVLl/D777/j9OnTpevIzVfz+HgeV65Q+gQn5cKFCzhx4gRycsgfxMPSmILDu0STywmlZKJ87V4v+q/TEXAxBxkjyJk7QHJ+ev2CygTcTkAUULcjlg6EwPUJ2OrqkDaJouQpcCHswxSELKIw+H9M8tUpjuGzG4AO9GU97iAw5xRldPKBL+zrU3HtGVarFR06dEDv3r0xaNAg1KhRAydPnizQyXvvvYc2bdpg4MCB6NSpE5577rkCr6vlyZNPPqmMb/DgwWjVqhXmzp1boqHZ6AZ65pln0K1bN9xxxx2oXr06EhISStSGK05+uAZwmn5v/UoeKN4sbLnQf21E1h0G5NShXFMiQkAIXJeAKKDXRSQnCAHPELAZyCQ/JhoZ94dDty4T4a+QSf5Srkk+lNzJpjcFHqEv7I9oY+6pA0D6v9Z6zwzQy3th5fPs2YIZ0Lt3746MjNxSqWvXrsW0adNIubcp/3i6n376KZYsWaKqmS9cuBArV65UxsRjZXnttdewfv165XFx/nv22WexevVqZe5GY65vByuiJhPlAfOgtIkCWlKszoIzHuzU1V3RGhgWpMAWGUC+n1Ju09V4pT3fJSAKqO+urczMSwlk9Q5F2qux0KT/Y5KnlE0sGrLqPVkbmNYE2J4EDCGT/Jl/rfVeOlvPDfv8+asTT2ZnZ2PVqlXKIKZPn+5wMB9//LHD42V18IMPPnDY9Zw5cxwed3Tw22+/vUrZZEWc3RM8LbwLeoh04M2Jnu7ZNf0F/0Y5Pw9ZkPEQadKUak1ECAiB4hGQd0vxOMlZQsCjBHLqBuWa5JuQSX5mMkKWpOblY+pB0cOfkkmey3YO3um9X9weBUqdaViDLyS8g6jXU+JVksBAx1HLRR0v1JTHngYFOTbxBgcXv9yjwXB1kIzFYoFWS87GHpZu5OdcLxSY74W7oJoUKrdJaZfMHfSw3JB7H3kYn3QnBLyWgCigXrt0MnBfJ8AVVNLHkkl+UBh0v2TkmuQv59rd65OlbykFJzWmv0/sI4WU0tmIXJtAly5dHJ5w9913K8eL8vecPHmyw+vK6uC4ceMcdv3KK684PO7o4IgRI/IUb349ICAAbIpv27ato9Pdeox/F7BryY4UYC/9zvImMXySppTY5YpHIkJACJSMgCigJeMlZwsBjxPIujMMaS/HICDFiogXKEr+z1yTPNfVntMCGFCFzPLHgRcO5e6KenyAXtLhsmXL0KQJ+S/8I1FRUdi9e7eifPGhzp0746OPPrK/rBx//fXXleN5B1XwYMCAAXj++efzRsI7tOwXWr9+/bxj13swcuRIPPTQQ8ppvHPasWNHHD58OI/F9a539eu3065+VdpA9KZd0MA9WQjeYlJqvduiPL9z7Oo1kPaEgKcJaMgElevF7umenewvLi6uyPQp12oyOjoaSUnkOKdiYbNYZGQkLl5Ud3kQNtPxWNPS6Ne/ioVZsrkyPl7dIbYhISFK0Mv1AkA0RitCZ6coNaZNvQzIHEi7LoG5ZuXVl4A3qXJSfTJlTm8GcEUlV0u5cuVgNpuRmqrubaqIiAikp6cXmVooJYW22kj4/nAkvBOYlZWl3DvcljuE3QEqVaqE5ORkZGY658jL8+DIfp1Op7wfnRknzzO/G4KjNjz12bn8At3DR4EVtAnL93FJhJXo2NhYOPv9UJK+lHM5Zdr4eNhiA5Qfh4qDdjEb8RTPYg7H4WmhoaEICwvD5cuuT9LK3x8VKlRw2K8c9C8CsgPqX+sts/ViAmySN46PQuYAMsn/SCb51xIREJebv7FfJWAhlfC8Ys5NWr87V8fy4tm6b+iseBalfHKv/MXLyoy7lE9XzYznwMqMI3/O4vbByqvdB7a417jrvL50D5cjN9aFZ9zVg+vaDVmRhoCkHKQ/Qj9iHPgWu64naUkI+C4BUUB9d21lZr5IgL7sTP3CYJxIJvnEHIS/EI+gHbkm+Ra0WcdJ6yuTKfPhPb5ZZ9sXl1TmlEsgmL6NHqgG/Eh59c85tynsEZTaUxbovs+AqW8orFUdB655ZCDSiRDwcgKigHr5Asrw/ZNAdqNgpE4uB46WD3uXouQpEhc5NlQg0/si2gntSRau18gkP4VMmlQhUEQIeAWBe6kgGOe8XVwwXat6xk4lmwzzyPWhkpYUUIoAFBECQsBpAqKAOo1OLhQCZUvAFkEm+eeikdmfTPLfpSP89URoEnLAO0lvNgKeqQP8j/zq/ku7oUmWsh2r9C4EikOAlc9BFFT3Nfk0x5M7idpE91MGtCezkfEwmd6Drk7rpbbxyniEgJoJiAKq5tWRsQmB6xFgkzwpoMYXySRP/qARz8cjcFeWctWw6rlR8kfSKV/oDuBvSvYtIgTUTmAwmeG5lLraUosFxFPOz2VGmLuHIJvy84oIASFQOgKigJaOn1wtBFRBgL8QUyfHIqcWmeSnJkH/PzLJk7mwY3RuvlADZYkZugv4OU4Vw5VBCIEiCURTerH/kCl+Ge3ep6qo3GzIolTYdBpkDpacn0UunrwgBEpAQBTQEsCSU4WAmgnYIrUwToiG6e5Q6NekI+wNMslToFL1ENpNouCkTqSMjj8IzDpJubPFL1TNS+n3YxtGu6BmqvT1v/PqQBFE5XCDybKQMTQcnI1CRAgIgdITkHdS6RlKC0JAPQTIdmm6NxzGF6KhvfiPSX5vFngH9P2mwKNUcebjM8BTBwCjinaX1ANQRqIGApUok0OfisDnpIBm5mYaK7NhaTKsMHySCkuLYFg60685ESEgBFxCQBRQl2CURoSAughkN9UhdQqZ5KuRSX4KmeQpbyFVncATtYH3Zm/uogAAKvVJREFUqBjQ9iRgCJnkT2eoa9wyGiFgJ/Ag+TAnU/DcV2VclyPkC3rvkBKa8bB7ihLY5yt/hYC/ERAF1N9WXObrNwS4PKDxRTLJ30Um+VVkkn8rCZrkHNxKZQ/ZJM8mzvt3ApsS/QaJTNSLCNQyALeWA5acAyx0r5aFaP82I/jXTGTeEw5recn5WRZrIH36LgF5R/nu2srMhIASTmyikp0cpBQ6KxkRExKQPioS9ZvplKT148gndNQ+YEwdgKPmi5JTp07h888/B9cdv/POO0tVfaeoPjxxnMtsrly5Uim1eeutt6Ju3bpXdbt27VrwfKtUqYLevXtf9bpaDkycOBHnz59Hq1at8NRTT101LK7tvm7dOnBZxUGDBoHLVeaXjIwMLFu2TCkFyiwaNGiQ/2WXPM7Ozsby5cuVMsjt2rUD/yuJPEIuIwPpR9Kgdz9F9JGNGDVqFFq2bFmSJpw/lxLohs5PQU7NQGRR6VsRISAEXEtAasG7lmepWpNa8KXCd9XFXKrQl2rBXzXBEh7QUOnA0JnJCDxiUVI3mfqFIofSOE07TpHy5GvXi5LXv0I6iJ78RfPL77//jvvuuw9cs55rmLPisnPnTqWWef7z1PK4qFrwXHe9a9euivLJihHXQV+6dCm6d++eN/Thw4dj06ZN4Ne57n2LFi3w448/5r3uqgelrQVfq1YtZXz28VSrVg3bt2+3P8Xq1avxxBNPKEpnQECAomQePHgQUVFRyjlpaWkKC15LO4vFixfj9ttvz2vD/sDZ2uVcY75bt25KPXFmyf/Gjx+PMWPG2Ju+7t89e/Zg4MYkBMRWRdL47kr03PTp0zFgwIAC17qjFrz+KyP0K41Ie4NcWeoEFeivNE+c5VmaPkt6rdSCLykxOd8ZAmKCd4aaXCMEvJCALZpM8i/FIKs3meRXGBE2OQmBqTl4rh7wekNgLaVoGr4buJybRlSZISttrHyyZGZmKsonK0+PPPKIcsyb/uvRowd4Punp6YryyWN//PHHkZCQoEzjq6++ws8//6zMkZUllr///husmKlJhg4dWkD55LGdO3cOkydPVoZ54cIFZV6sALKSzevGSuiTTz6ZN40+ffogMTGxAIvRo0fj0iXKAO8imTBhgjIu3nW28/zggw/ASmVxhBXjnj17ImP1TARWrY/gdj2Vy1iBjY+PL04TTp8TcDEb+tVGZP2fwaXKp9MDkguFgA8S8DoTPO8SarWFtmiKsTC8E8a/PNUs9nmpfZysgPBY2RyrZuHx8Rev2nnyGFl4h9Ej8kQM0N6IoKkXEPUCRSNNqILhLULRqlI2RmwzYhCZPOd2CEP72EAcPXoUMTExirJiHxsrNgcOHFAtV153/sfjzC9JSUmwWgs6EzJ7Vtjq1auHffvIF6GQsAL3119/lWjXrlAT13zKO016PYV8l0B276ZfCQ7kzz//VNZk27ZtV60Zz3vXrl15a3blyhXk5BQML2dmrMg2bty4QOvOfnbyLjnzyy/8ucFKffd8u875X8//mHdnWQIiK8CWbYa++yCYt3+vHOPd3L59+yqPlXP+eQ/x7nfhdc87qSQPJp8GogOhf7Q69CGu3adxlmdJhl/ac3md+HPeHZ+dhe+70o5VrvdeAurWIBxwtZuMHLx0zUP8RuIvIDWL3QSv9nHyhxOPlc14aha7CV7tPFnx5C9Nk8nkOZz1AM2kGISRSV474QxMA8JQk4KVPm+lwRhK0TRwUxom0DkddLqrlAgeJCsWauValAmeFWm7UmMHzbuArADyXNg8zY/zrwMrZeXLl3f5XO0meN6N5R3KkgiP09EOIB/nefD9ZN9xzN8uK9v2NWMWKSkp+V9WfmTYWeR/wdnPztjYWEWJya8QMtuwsLC8ceTvp/DjtefSET35ZwTWagbz/k1I/3KScgq3Z5+r/Rq7CT41NVVxKbAfd+Zv8G8ZCN2XAeO4KFhMxMjFb0tneTozF2evsZvg7feLs+04uo6/P7h9ESHg2p92wlMICAGvIWCL1SLtZTLJ32Ggyklkkn87CRWyrFjYKtcf9M2jwGdZNfDA8AcLzImVMg7k8TZ5//33CwyZlcD+/fujfv36yvH//ve/ivLJX5AsPE82A7MpWU1SlEvA/PnzlWGy32phX06eEwcc2aUwC1ZOe/XqhWbNmtlPKfXft99+W/lhxZxZWEnkwC7u51qym3S+B2mTd9zxUJQjRTl50kCkvDUAOWcOKZe1b99e8c29VhvOvqZJoXKbn6fB3EEPS+uS7Uw726dcJwT8lYD2VRJvmjzvYBQ2oxVn/LwrkH9nozjXePocNs3wDgT7TKlZ+MuKx+pol0VN42aW/MVbeNdLTWPksTBLFlZ2PC6UuD67pY5KeAZCtzYDut9oN65eELo30iKM7CMLzpBrQPMb8XDH+jClJqFhw4Zg5YWVALWKjnZtLRbLVabYGjVqKArn8ePHUa5cOTz99NMFlEtWOPkY+4RyG126dFEiuN2xW8NKGe8E8mdSSdedd9AeeughrFmzRjGjN2rUCBs2bCjgwsFKHu8S8o4gvz537lw0adIkb8mqVq2qBPIcO3YMvFPJ0eUvv/xy3uv5Hzj72cn9P/zww+A+eFeafYkXLFig7Irmb9/++Ah97L12BJhOlbp0tDUykQLi3rghHLXDA7FlyxblfXL//ffj448/tl+S95ff52yVcfb7wd5Q6PxUpYCDcTy5a7nY9G7vw1me9us98Zd/LPA/3qF3tfD3hzveU64ep7TnfgISBe9+xsXuwW6Cv3jxYrGvKYsT7R/23mKCd2SuLAtuRfXJX0geN8E7GExAXG6UvPakBZn3hSGrTyi2JWvAqZpCaVNwcdcoNDBYFaXGweWqOVSUCV41A6SB2E3wHBRVUhO8p+fhbpPxWfrNM/sU8MMVoKIOGFkTuKsSoM3dOC3WdO0m+Li4uBIr9PYOAvdkIZysAOkPRcB8m8F+2OV/3c3TFQO2m+AvX77siuYKtMHfHxUqVChwTJ74JwExwfvnusushcBVBKzlyST/Cpnk6cvX8AWZ5Kcmo2OgVckXGkq7oX1/S8a3F6g0jYgQcAGBOIpPeovcPPr9CWwh9/xxdYFvaGP97solUz5dMBRyarbBsJD8RxuQZedWDwUDumTg0ogQ8F4CooB679rJyIWA6wkEapA5PALGMVHQHjEj4vl41Dxrxqc3ADdXDMaTOzLxAZlIrQUDzF0/DmnRZwmk0m+YGSeo1vt24DvaYBtByea/70ClYauRn2gZfSOFUL7PgMQcpD8SydvTPsteJiYE1ESA9jVEhIAQEAIFCVja6ZFGFWBCZyQj/I1EBA4Kx7wHYjF1fxpmkGL6N/nrTaFsPewnKiIEikMgk7I+fX6eXDnO0IYjZcO6ryrAlY6icl2gi9OEW87RnrJA9306TH1DYa0mN7RbIEujQsABAXm3OYAih4SAEKBdzgqBSHstFoZPUmH4LA3WY2coSKcCagWZ8QIFJHMd+RkUNM01u0WEQFEEuI77SnJr//g0kEy7n+zf+VitXH/Poq7x2HHayjdQuU1rBS1M/cI81q10JASEAOX4FQhCQAgIgSIJkEk+46FIGEdHAXvTETD6NHokm/FZa4raJzM8K6GbcgsJFdmEvOCfBNhN41sysfclH8/Jx4A2dAutolLwrzZUifJJy6L7KQOBJ7KR8UgEpaMQ07t/3qky67IiIApoWZGXfoWAFxGwdNQjYBZFiVA4fPhriWi6KR1LSQltRt/bo/aTWfWsF01Ghup2Ar9Rpcx7/wJePEw75BTT8yXdK1ObADVVtFuuSaCcn8uo3OZNIchuQuH3IkJACHiUgJjgPYpbOhMC3ktAU1UH63vVkT3zPAxL0lDloBlzHo3EtIsBeP8EcIgKY71Gu1t6Stkk4p8EtlM0+0wKUttH90JL+nGysGXuzqcaaRgWpcKmo6C7+8PVODwZkxDweQKyA+rzSywTFAIuJBAUgIwRZJIfFYmgvWZEvZiA5wMseIMUz3W06zWMKthcMrmwP2nKKwgcJIVz5F6KaKd/JvL5nNEU+IQyJ7DZXY0StM2E4J1ZyHwgHLYw+RpU4xrJmHyfgLzzfH+NZYZCwOUELJ1DkDopVtlBCn81AfdS7Wwu4ZlgBgaRX+jOFJd3KQ2qkMDJDMrfSYUKeM05ofykRsDyNkD3cioc7D9D0mRYaQc/FZYWwTB3If8AESEgBMqEgCigZYJdOhUC3k/AWpmi5N+IVb7E2ZzZcVEyvmxsRVUqoT1iD7DigvfPUWbgmADvcr/6N/AfCjDaRT82XqgPrKYAo94V1Z9GM+SLNLASmkEVj0SEgBAoOwKigJYde+lZCHg/gWCKkv9vJNIfI5P87izUeT0Bn0Zb0IcUkTeoys2bVNub0/CIeJ7Ahx9+iFtvvRV9+/bFqVOnXDKARNrhnkoR7ZxE/hdyuRhVm5LJU/Wi+6pQELmKv03mzp2L2267DS/e/QyCf81AZv8wJc2YS6BII0JACDhFQIKQnMImFwkBIZCfgPlGiiSuE4Sw6cmIJpP85GERaNDAgHePA8fITPseRUDHBOe/Qh67k0D//v2xfft25ORQ9neSrl27Yvny5ejUqZNT3aZnk0/nudx/NkqvNJSqFg2vAUR4wTfIAw88gPXr1yPAFoDP2r6OfcYjOBkGdMNNTrGQi4SAEHANARX/ZnXNBKUVISAEPEPAWjUQqW+SSb5TCELnp+LRVcmY18CKE+m5PoIcJS/ifgK7du3Cli1b8pRP7tFqtWLcuHEl7pwrFs07ZkKvbcD8M8CdtLP9XQdgdB3vUD4PHDiAX3/9FXoE4/Xao9DQUAuP/f0Gxj03vsQs5AIhIARcS8ALfr+6dsLSmhAQAm4kQGltMkZGUl7FYBgWpOBmKnO4cmQUHksJUiLkOU1Tzwpu7F+aRkJCAqKiopCcnFyARnw82cyLKTm0y/n1JWDOKSDOnInetGZcvaiaF8XsaKi2e9jGbKy5YRZuDGtN6cF0mHZmEXamHURIthdNpJhrJqcJAW8jIAqot62YjFcIeAEBczcyydcORBjVkq//ZgJWDI/A2AoGTDgEHKU68uw7GCCFZ9yyks2aNSuw+8mdBAYGonZtgn4dYfP6WtJTPzwJnM4Eboql+u2U0LNCTup1rlTHywEnzdBvz0DQThMCT2ajpaYidgcmYsrp+fguYSP2px+FVqtF1apUiF5ECAiBMiUgCmiZ4pfOhYDvErBWD1JM8oYFqYiel4o5Xc2YeVskPjirwd9klp/SGAiXTyCX3wCVKlXC7Nmzwb6PBoNBUbgiIyOxatWqa/b1R2JuEvnD9AOhHeXvfJNSKrWgQPHoCC2SKMG8KsVsQyAVRAjebYRp50WExluU1GCWZsFIv80AS2sdzu8/jrfvXaCwCA8PR1hYGH788UdVTkcGJQT8iYB8/PvTastchYCnCegpcf0TUchumAHDp6l45qQFNwyNxqiUQKWO/MxmVKpRReUZPY3HXf1x9PumTZuwYcMGhIaGKpHwOp3OYXd7KI3SDNrx3EF/m1BwzpzmQOcYh6eq4qAmOUfJuBBEieSD9mZBQ5H51hgttF2ikEYJ8LMa09davrruXbp0UXximYVer8ddd92l/FXFZGQQQsCPCYgC6seLL1MXAp4iYKbdqJz6QQglk/yt78bjh/sjMSQoRFFCeSf0RjL1iriWQJ06dcD/ihJ2hfjgFLAhIbde+zTKVHBbOXXm8dSesZBZnRRO+qc9ZlGmlFMrEKY7w5RdTm3DUBhiY5ETFwdkU8h+IalZsyaGDh1a6Kg8FQJCoCwJiAJalvSlbyHgRwRyapJJ/q1YhJI5vvaiFPzYzYzH2kXgyf0ajCb3xIcorY+I+wmcI9/O2aeA768AFWlT9NUGwF2VAK2afHKzybR+yIygHVkI3mFCQIIVtiDSLZvqlATyljY62KK1ebD+fZR3SB4IASGgcgKigKp8gWR4QsCnCIQEIH00meQbpSPsszQsJpP8B3dH4f2TgfibduRepSj5ENEm3LLk8WSqnnsa+Opiru/t2Lq5CeSDVZKMT5Nq/ce0bkLQHjM0WTZYowJgaaUDK5yW5qQtU+EDESEgBHyDgCigvrGOMgsh4FUEsm4PRXb9YMUk//TcBLS9NwIPakJwinbnppMfX2Uq5yniGgKpZLFedBZYej53l/MR2mnmRPKhKvj0DzifrUSsB7Np/YgFGorCz65BpvWeuQFEOXVp21MjSqdr7gRpRQioi4AKPoLUBURGIwSEgGcI5NQOQtqkWBg+TsGNn6Xg9xvN6N8sAoN2avAu+SO2oUhsEecJZFIRJFY6F1ECeU4ofx9lHnqYlM9o0unKTNi0/neuaV3x57ySAxt9C3He2Mxh4TC30cMWK1vgZbY+0rEQ8CABUUA9CFu6EgJCoCABm4FM8k9HI/uHdFRamob1py148vYoPLo3EM/VAwZQjXGRkhGwkLLJZnY2tyfT7if7d3ISefb3LAvRGMm0vic3gChoN0WtZ5JpPYJM6y1J6RwcDksLqtFK2RJEhIAQ8C8CooD613rLbIWAKglk9SSTfINck/xHnyVgSZ8IvGILUfxCJ5AiGiT6yXXXzUrmaw4s4gCj8yagR3lK+F+rbNJcBVxk0zornZQQ/nCuaT2HSrVm9TDATLk5c+rRNqxUIrjumsoJQsCXCYgC6surK3MTAl5EgP390iaTSf6jFDy4IgVtO1twT044jqeTSZ78QmNpo0zEMYHfqHrRB5TL81gG5fCMhuLC0Djc8bluOUrab+DfnCqJAogocl17iUzrZEnPbkS7nENol7OtDtby8nXjFvbSqBDwUgLyieClCyfDFgK+SMAWSib5sWSS/y4dzb5Mw5/nzBjYLQqDsgIxg5RQjypVXgD4Tyr3PpMUz71UKZMqZmJBS6Cth3xnNRlWBFIieFY42bQekE6m9TANmdZ1yBxA+TlbkM2fXCxEhIAQEAKOCIgC6oiKHBMCQqBMCWT1ZpN8EMJmJuOblQl4g0p4DrPo8VpDoGeFMh2aKjo/mJa747mZSmTWC6VKRqScd6ck8u6WgMtkWt+Vq3QGHqZUSRTolFNZC3P3EEoIr6eKV2Jad/caSPtCwFcIiALqKysp8xACPkYgh9I0pU4uB8OcFLz6TTK6tDNgpCUcf9fKTVzvjy6Ep8jE/uEpYG0cUJVSVb1F9dp7kULuNhbsWHowAyEb0nJN65Q2yUabmqxoZg4k0zrl57RWkq8RH3vryXSEgEcIyCeHRzBLJ0JACDhDwBZGJvlxlLj+m3TctsyI3y+SX2i3SBxJDwSX8Izwk0+wSxRU9BFFtX99CYghX9gX6gH/qeym4KxMilrfR6mSOIhoF3WcZkOwQYNsMq2b+oUqieHZVUJECAgBIVAaAn7y8V0aRHKtEBACZUqAEpFn3RVGu27BqPRBMn5blYCnu1Mt+Uw9ZjQDahvKdHRu7TyJ0igtOAN8Sfk89RTUM6o2MJjyebq6WlRAXI6ibLLSGXjgH9N6RTKtdwmB/qZySKmWpbJanW7FLo0LASHgAQKigHoAsnQhBIRA6QnkkAKaOqkcQmcnY/YPyfjisgHDMsPxRjMNbootfftqaiE9G/jkXO4/toJz5aLhlETeZTu+Nhu0xzlqnXY5Wek8Q6Z1KjiUU59M6xxARP6cVkqbxKKPJifTJKrjKSIEhIAQcCEBUUBdCFOaEgJCwL0EbJTA3PhcNPSr0zFwhRFtLlkw1BiFo8214BKT3i5mSiL/vwvAfDK3GynAh83s/60JlHNFCiqqrR60P1fh5Mj1AKq9bguhqPXmwUjvRab1G3SwhYtp3dvvIRm/EPAWAqKAestKyTiFgBDIJUAmedPdZJKnHJP1yCS/7qt4PJ4QifFt9XidouQpG5HXSQ7tcrJ/J/t5XiFrNwcWPV4LqBZSuqloEsm0TjucXGs98ABVISKTfk45Mq131NMup04pgYlA2voUEQJCQAh4mIAooB4GLt0JASHgGgLZjTlKPhahs1Kw6MdkfHwpFA8aw/BxZysoF7tXiI1M4RzRzknkT2dCcSWY1RyoT1Zvp4RN66fyVSE6+Y9pnZL8s9LOSmdOjbIsBu/UrOQiISAEfJCAKKA+uKgyJSHgLwRskVoYJ5BJflU6Hv3KiA6XzRiRYsNzHQLRWuVboesvZuF1yql5gJLIt40E3qSUSi2cGbOZqhBR4FDwP1WIApLJtK77x7ROpS8V0zpxEhECQkAIqImAKKBqWg0ZixAQAiUnQEkwTf3ZJB+EZh+kYMWX8XjySiSOdtfhPooYV5vsSaHqRads+Cs5EU0jNJhDO56dY0o2Sk0yR63/48+5j0zrFCNkjQlQ8nKa21BC+KbkNBokpvWSUZWzhYAQ8CQBUUA9SVv6EgJCwG0EspvqYJwSi/A5Riz+LgkfXAjF633C8HxDDYJUEFtzND3X1L4hAahFvp3zO0fhxnATTCbKtVkM0Z6hqHUue0n+nBzBzpJTOxCmO8m0Tgnhc2qJab0YGOUUISAEVEJAFFCVLIQMQwgIgdITsEWRqfnNajAvuYQnV6Vh2yUzxveLwksdtIh1RSS5E0M8R76ds08B318BKlB59FcaAP0our1q5RAkJ1PEUVFiIdP6wdyE8GxeD0gg0zrpmJZmOmQ9HKH4c9qixbReFD45LgSEgLoJiAKq7vWR0QkBIVBSAlpKXH9fBMwNyQ+UouTfW5yAiRcpcX1PHZqEl7Qx58+PJ7P4xxTVvvIiEEZ64ti6wH1VgGDajdVQJL8j0VBqpKDdvMtpQtAeSghPqZOsUWRapxRJHEBkaU4abLDjax21J8eEgBAQAmolIAqoWldGxiUEhECpCGSTspbzdjnoZyRj1uokfHg2FCcHh6F3ZfcqcKmURH4xVS/6nKoXcY12zk/KieRDi/i0DTjHUesmBJN5XXvMAg2lZMquSab1XhRARAnhc+rQhUUorKUCJBcLASEgBMqQQBEfiWU4IulaCAgBIeAiAmyiNr8cA+uXRjz5bTr+oMT1c4dEYkQLraIcuqgbpZlMShy/lJTORWcBEz3m3c5HKIl8dGHXzGwyrf9thmXFOQRvSoL+MqVKok/i7CbByBxGO7fkz2mLFdO6K9dG2hICQkB9BEQBVd+ayIiEgBBwJQHahsweHA4jKXhtPkxGg5kJmNUvEsPIJO+K0pYWql70FZnZP6Zdz0Qyu/etBIwkxbOS/t9JaIx20zqZ1/dQ1HqmDTlRgbDSDmfG4EClGhH0KoiU+nfI8kgICAEh4FYCooC6Fa80LgSEgFoIZLfKNclr30vGi18mYeGpMLR+MBR1wpwzyXON9h8osGjWKeA8BbL3KA+MqkUR7obcGQdcJNO6ErVuoh3PXNN6TjUyrVNuzmxKlVSua3WYUlNgyaQoJREhIASEgJ8REAXUzxZcpisE/JkAm7YDXo9B/KdpeORnI/44Z8bmkVHoXLtku4+/xVNKpVPAMUqt1JnKLr3bBGhsYNM6pUriACJOlXQpBzaypHPFpsyh4UoQkbV87kcuByFp2EFURAgIASHgpwREAfXThZdpCwG/JUBR8sHDIxBHydpbzUmB8a14/DAkCj27Xz9P01/JwIyTwF6qXsRVi5Y0sKLdKTKrf0L/KHo9IJ2i1mlH1UK7rZkDSOlsQW0aSqbc+u26yMSFgBDwKwKigPrVcstkhYAQsBMIaqeHpWYQLNOSMWBeIlYfDUMnMsk/NGQQDh06hJo1a2LNmjXK6YfSqHoRKZ6bk4Bu5mx8S/k7G/+epQQTaSjgKKcKBTvdzFHrOmQ3oKgj2d20Y5a/QkAICAGHBEQBdYhFDgoBIeAXBCpoETYpBsfmp+Ge9Ub8ui8RB/+OQ3xcHOLoX/XWXTHo09+RsN+Cu89l4aNzJkRdJtM6bWpmNyTT+qB/TOuV5KPUL+4XmaQQEAIuIyCfmi5DKQ0JASHglQQCNag4MgKj1n+Mt1Lux7Y2n+OxpMWIqdkavcPb4ZaplxFD0e1WA0XTt6Ryn/dS3Xn6awsV07pXrrcMWggIAVUQEAVUFcsggxACQqCsCSz6Yzp+jfkJX3Scj691w4FLwIkrcfjs3Pd4cPZjyo4nyH9URAgIASEgBEpPQBTQ0jOUFoSAEPABAlqtFqfiD6Dbjzfj3vbPYMfhNfg7cT8CAgLwQJOnfWCGMgUhIASEgHoIiA1JPWshIxECQqAMCXzxxRdK75ZsE5ZunqQon3zg22+/LcNRSddCQAgIAd8kIAqob66rzEoICIESEujcuTM2btxY4KqpU6eiVatWBY7JEyEgBISAECg9ATHBl56htCAEhICPEKhXrx4uXLjgI7ORaQgBISAE1EtAdkDVuzYyMiEgBISAEBACQkAI+CQBUUB9clllUkJACAgBISAEhIAQUC8BUUDVuzYyMiEgBISAEBACQkAI+CQBUUB9clllUkJACAgBISAEhIAQUC8BUUDVuzYyMiEgBISAEBACQkAI+CQBUUB9clllUkJACAgBISAEhIAQUC8BUUDVuzYyMiEgBISAEBACQkAI+CQBUUB9clllUkJACAgBISAEhIAQUC8BUUDVuzYyMiEgBISAEBACQkAI+CQBUUB9clllUkJACAgBISAEhIAQUC8BUUDVuzYyMiEgBISAEBACQkAI+CQBUUB9clllUkJACAgBISAEhIAQUC8BUUDVuzYyMiEgBISAEBACQkAI+CQBUUB9clllUkJACAgBISAEhIAQUC8BUUDVuzYyMiEgBISAEBACQkAI+CQBUUB9clllUkJACAgBISAEhIAQUC8BUUDVuzYyMiEgBISAEBACQkAI+CQBUUB9clllUkJACAgBISAEhIAQUC8BUUDVuzYyMiEgBISAEBACQkAI+CQBUUB9clllUkJACAgBISAEhIAQUC8BUUDVuzYyMiEgBISAEBACQkAI+CQBUUB9clllUkJACAgBISAEhIAQUC8BUUDVuzYyMiEgBISAEBACQkAI+CQBUUB9clllUkJACAgBISAEhIAQUC8BjY1EvcPzr5EtW7YMr7zyCvbv3w+tVutfk3fDbCdOnIhDhw5h5cqVbmjd/5q866670KZNG+Ue9b/Zu3bGmZmZaN26NSZPnox+/fq5tnE/bG3btm0YPnw4vv32W9StW9cPCbh2yvPmzcNHH32EHTt2uLZhaU0I5CMgO6D5YJT1Q6vVCv4nvwlcsxLMMicnxzWNSSsKS2YqUnoC/B63v99L35q0YOcpn52uuRfk3nQNR2nl2gREAb02H3lVCAgBISAEhIAQEAJCwMUEAl3cnjRXCgI1atTAHXfcgYAA+V1QCox5lzZv3hzR0dF5z+VB6QjceOONqFOnTukakasVAoGBgcp7vWrVqkLEBQRiY2MVnmFhYS5oTZrg93mPHj0EhBBwKwHxAXUrXmlcCAgBISAEhIAQEAJCoDAB2WorTESeCwEhIASEgBAQAkJACLiVgJjg3Yq3ZI0fO3YMZ8+eRceOHRESElKyi+XsAgRMJhM4MjY0NBStWrUCmzxFSk9g/fr1aNu2LcLDw0vfmJ+2YDQalUwX+afP73kR5wkcOHAA586dQ/v27cXtxnmMyn2ZlpZWoIVy5cqhfv36BY7JEyHgCgJigncFRRe08cwzzyiplziFCH/Jjx07VvkwdUHTftdEXFwcRo4ciVtuuQVnzpxRoo2nTp3qdxxcPeGNGzfixRdfxGeffYaaNWu6unm/aY/f3x9++CHq1auXN+e3334777E8KBkBThe0a9cu5YfRjz/+iBdeeEFJF1ayVuRsJjBr1izlM9NOY9++fYov6JgxY+yH5K8QcBkB2RZyGUrnG+K8n6w0ffrpp0ojDRs2xJdffikKqJNIf/31V/Tv3x+DBw9WUgdx/srTp0+L0uQkT74sPj4eCxcuRFRUVClakUuZwNGjR8H35LBhwwRIKQlcuHAB/H5ftGgROACJ89QePHhQFFAnuT7xxBN5V/Ku8ssvv4yHH34475g8EAKuJCA+oK6k6WRbTZo0wccff5x3dUpKCtiELOIcgYEDByrKJ1/NOyOcC7R8+fLONSZXKXlpp0yZglGjRkGv1wuRUhJgBZSVpaVLl+LPP/+UvL+l4MmJ0jnbBf+A591PNhUPGTKkFC3KpUwgKysLkyZNwnPPPYeIiAiBIgTcQkAUULdgLVmjnHbJ7vN55coVZSd06NChJWtEzr6KAFeVYtMRszQYDFe9LgeKR2DFihWoXr26YuIs3hVy1rUIsAK6detW6HQ6fPLJJxg/fvy1TpfXrkGAFc9Tp06BfyDxjh3vKjNbkdIR+OWXXxAZGSlWuNJhlKuvQ0BM8NcB5MmXT548qfzifPDBB5VAJE/27Yt9vfbaa3j00UcVf9rKlSvj5ptv9sVpunVOfE/+8MMPmDNnjlv78afG7a4M/MPzzjvvRN++fZUAmmrVqvkTBpfMlSv2JCYmKuV2uXxxixYtsGrVKvn8LCXdNWvW4J577illK3K5ELg2AdkBvTYfj73KNct5t459cPr06eOxfn2xI94JSUhIUKbGib67d++Ov/76yxen6vY5sX8d+8+yonT77bfj0qVLeOSRR7B9+3a39+2LHZjNZoWnvdhEcHAwKlasqHD1xfm6e07sWsM+86x8sjRo0EDxAXV3v77cPgdunj9/Xvnc9OV5ytzKnoAooGW/BkqAx7PPPgs2Gd90000qGJF3D4H96ubPn69Mgn1pt2zZUiDi2Ltn59nRs7LJSujPP/+s/KtUqZLCltPdiJScQFBQEN577708MzH/8OQfS5wqTKTkBDp16gRmePnyZeVivlfZJ1TEeQKHDx9Go0aNwPeqiBBwJwExwbuTbjHbXrZsGZKTk/HUU0/lXRETE4PVq1fnPZcHxSfApiNOa/PQQw8pAUhcQpKjjkWEQFkT0Gg0ePrpp5WgQw48ZB/GCRMmSJ5aJxemQoUKePzxx5V/HNjF+X7feecdJ1uTy5gA+9RKyV25FzxBQPKAeoKy9FEmBDIyMpSobbu5s0wGIZ0KgSIIcLYLjjBmpVSkdAQ40wUn+OfAGREhIAS8g4AooN6xTjJKISAEhIAQEAJCQAj4DAHxAfWZpZSJCAEhIASEgBAQAkLAOwiIAuod6ySjFAJCQAgIASEgBISAzxAQBdRnllImIgSEgBAQAkJACAgB7yAgCqh3rJOMUggIASEgBISAEBACPkNAFFCfWUqZiBBQN4F169Zh0aJFyiA5apkrVXGlpdLKzp07ldyapW1HrhcCQkAICAHPERAF1HOspSch4NcEWAHlMpQsrIC++eabLlFAd+zYIQqoX99ZMnkhIAS8kYAkovfGVZMxCwEvJ8AlKC0Wi5fPQoYvBISAEBACzhIQBdRZcnKdEBAC1yRw4sQJLFiwALt371ZKzPKup12ys7Px2GOPYezYsUrZP64ENnXqVHAZ1ejoaPTo0QMPP/ywkqR969atWLt2Lbp06aJUEGLltX///ujbt6+9OfkrBISAEBACXkZATPBetmAyXCHgDQQSExNx2223gc3uffr0wapVqzBz5sy8oVutVqWm/Pnz55VjQ4YMwfr16zF48GC0a9cO48ePx5QpU5TXjh49imnTpmHo0KHgGvRcj57P++KLL/LakwdCQAgIASHgXQRkB9S71ktGKwS8gsD7778Prs29efNmZReTdzs7duxY5Nj5vMmTJ2P48OHKOY0aNVL8RO0XpKamKgpnr169lENarRajR4/GoEGD7KfIXyEgBISAEPAiArID6kWLJUMVAt5CgM3u3bt3L1Dn/I477ihy+MOGDcPjjz+Orl27YtKkSahdu3YBE7tOp1Paszdw++23Iz4+HmfPnrUfkr9CQAgIASHgRQREAfWixZKhCgFvIcA+nfl9PnncQUFBRQ6fd0y//vprNGzYUDHVN2vWDBMmTMg7v3z58jAYDHnPY2JilMdGozHvmDwQAkJACAgB7yEgCqj3rJWMVAh4DYHWrVvj559/LjDeX3/9tcBz+5OMjAwlP2iHDh2UoKULFy7g1VdfVVIrmUwm5bRz586BfUHt8ssvvyA0NFRRWO3H5K8QEAJCQAh4DwFRQL1nrWSkQsBrCLBv5pkzZzBjxgxkZmZi2bJl+OOPPxyOPyQkBLNnz1Z2PHnnlM+Pi4tD1apVodfr8655/fXXceXKFWzbtk1RVNlfNCBAPsLyAMkDISAEhIAXEZBPby9aLBmqEPAWAhxwxFWPOHo9KioK48aNU6LYHY1fo9Hgww8/xPHjxxWlk83rvMO5cuXKvNMjIyORlZWFGjVq4Oabb0bbtm0xffr0vNflgRAQAkJACHgXAY2NxLuGLKMVAkLAmwhwqqUqVaoUCEgqavzs08k7oOzzaZdPP/0UY8aMUYKOEhISFF9Q3jUVEQJCQAgIAe8lIGmYvHftZORCwCsIsCm9uMKpm/hfURIbG1vUS3JcCAgBISAEvIiAmOC9aLFkqELAHwmwH2j+HVF/ZCBzFgJCQAj4GgExwfvaisp8hIAQEAJCQAgIASGgcgKyA6ryBZLhCQEhIASEgBAQAkLA1wiIAuprKyrzEQJCQAgIASEgBISAygmIAqryBZLhCQEhIASEgBAQAkLA1wiIAuprKyrzEQJCQAgIASEgBISAygmIAqryBZLhCQEhIASEgBAQAkLA1wj8/0YboMMtRkf9MxoCoyEwGgKjITAaAqMhMBoCgzwEALY3UdrbPnjhAAAAAElFTkSuQmCC" alt="Scatterplot of engine displacement versus highway miles per gallon, for 234 cars. The convex hulls of points, grouped by 7 types of cars, are displayed as multiple polygons with no fill, but the outer line is coloured by the type." style="display: block; margin: auto;" /></p>
|
||
<p>There are a few things to note here:</p>
|
||
<ul>
|
||
<li><p>We override <code>draw_group()</code> instead of
|
||
<code>draw_panel()</code> because we want one polygon per group, not one
|
||
polygon per row.</p></li>
|
||
<li><p>If the data contains two or fewer points, there’s no point trying
|
||
to draw a polygon, so we return a <code>nullGrob()</code>. This is the
|
||
graphical equivalent of <code>NULL</code>: it’s a grob that doesn’t draw
|
||
anything and doesn’t take up any space.</p></li>
|
||
<li><p>Note the units: <code>x</code> and <code>y</code> should always
|
||
be drawn in “native” units. (The default units for
|
||
<code>pointGrob()</code> is a native, so we didn’t need to change it
|
||
there). <code>lwd</code> is measured in points, but ggplot2 uses mm, so
|
||
we need to multiply it by the adjustment factor
|
||
<code>.pt</code>.</p></li>
|
||
</ul>
|
||
<p>You might want to compare this to the real <code>GeomPolygon</code>.
|
||
You’ll see it overrides <code>draw_panel()</code> because it uses some
|
||
tricks to make <code>polygonGrob()</code> produce multiple polygons in
|
||
one call. This is considerably more complicated, but gives better
|
||
performance.</p>
|
||
</div>
|
||
<div id="inheriting-from-an-existing-geom" class="section level3">
|
||
<h3>Inheriting from an existing Geom</h3>
|
||
<p>Sometimes you just want to make a small modification to an existing
|
||
geom. In this case, rather than inheriting from <code>Geom</code> you
|
||
can inherit from an existing subclass. For example, we might want to
|
||
change the defaults for <code>GeomPolygon</code> to work better with
|
||
<code>StatChull</code>:</p>
|
||
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a>GeomPolygonHollow <span class="ot"><-</span> <span class="fu">ggproto</span>(<span class="st">"GeomPolygonHollow"</span>, GeomPolygon,</span>
|
||
<span id="cb18-2"><a href="#cb18-2" tabindex="-1"></a> <span class="at">default_aes =</span> <span class="fu">aes</span>(<span class="at">colour =</span> <span class="st">"black"</span>, <span class="at">fill =</span> <span class="cn">NA</span>, <span class="at">linewidth =</span> <span class="fl">0.5</span>, <span class="at">linetype =</span> <span class="dv">1</span>,</span>
|
||
<span id="cb18-3"><a href="#cb18-3" tabindex="-1"></a> <span class="at">alpha =</span> <span class="cn">NA</span>)</span>
|
||
<span id="cb18-4"><a href="#cb18-4" tabindex="-1"></a> )</span>
|
||
<span id="cb18-5"><a href="#cb18-5" tabindex="-1"></a>geom_chull <span class="ot"><-</span> <span class="cf">function</span>(<span class="at">mapping =</span> <span class="cn">NULL</span>, <span class="at">data =</span> <span class="cn">NULL</span>, </span>
|
||
<span id="cb18-6"><a href="#cb18-6" tabindex="-1"></a> <span class="at">position =</span> <span class="st">"identity"</span>, <span class="at">na.rm =</span> <span class="cn">FALSE</span>, <span class="at">show.legend =</span> <span class="cn">NA</span>, </span>
|
||
<span id="cb18-7"><a href="#cb18-7" tabindex="-1"></a> <span class="at">inherit.aes =</span> <span class="cn">TRUE</span>, ...) {</span>
|
||
<span id="cb18-8"><a href="#cb18-8" tabindex="-1"></a> <span class="fu">layer</span>(</span>
|
||
<span id="cb18-9"><a href="#cb18-9" tabindex="-1"></a> <span class="at">stat =</span> StatChull, <span class="at">geom =</span> GeomPolygonHollow, <span class="at">data =</span> data, <span class="at">mapping =</span> mapping,</span>
|
||
<span id="cb18-10"><a href="#cb18-10" tabindex="-1"></a> <span class="at">position =</span> position, <span class="at">show.legend =</span> show.legend, <span class="at">inherit.aes =</span> inherit.aes,</span>
|
||
<span id="cb18-11"><a href="#cb18-11" tabindex="-1"></a> <span class="at">params =</span> <span class="fu">list</span>(<span class="at">na.rm =</span> na.rm, ...)</span>
|
||
<span id="cb18-12"><a href="#cb18-12" tabindex="-1"></a> )</span>
|
||
<span id="cb18-13"><a href="#cb18-13" tabindex="-1"></a>}</span>
|
||
<span id="cb18-14"><a href="#cb18-14" tabindex="-1"></a></span>
|
||
<span id="cb18-15"><a href="#cb18-15" tabindex="-1"></a><span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, hwy)) <span class="sc">+</span> </span>
|
||
<span id="cb18-16"><a href="#cb18-16" tabindex="-1"></a> <span class="fu">geom_point</span>() <span class="sc">+</span> </span>
|
||
<span id="cb18-17"><a href="#cb18-17" tabindex="-1"></a> <span class="fu">geom_chull</span>()</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" alt="Scatterplot of engine displacement versus highway miles per gallon, for 234 cars. The convex hull of all the points is marked by a polygon with no fill." style="display: block; margin: auto;" /></p>
|
||
<p>This doesn’t allow you to use different geoms with the stat, but that
|
||
seems appropriate here since the convex hull is primarily a polygonal
|
||
feature.</p>
|
||
</div>
|
||
<div id="exercises-1" class="section level3">
|
||
<h3>Exercises</h3>
|
||
<ol style="list-style-type: decimal">
|
||
<li><p>Compare and contrast <code>GeomPoint</code> with
|
||
<code>GeomSimplePoint</code>.</p></li>
|
||
<li><p>Compare and contrast <code>GeomPolygon</code> with
|
||
<code>GeomSimplePolygon</code>.</p></li>
|
||
</ol>
|
||
</div>
|
||
</div>
|
||
<div id="geoms-and-stats-with-multiple-orientation" class="section level2">
|
||
<h2>Geoms and Stats with multiple orientation</h2>
|
||
<p>Some layers have a specific orientation. <code>geom_bar()</code>
|
||
e.g. have the bars along one axis, <code>geom_line()</code> will sort
|
||
the input by one axis, etc. The original approach to using these geoms
|
||
in the other orientation was to add <code>coord_flip()</code> to the
|
||
plot to switch the position of the x and y axes. Following ggplot2 v3.3
|
||
all the geoms will natively work in both orientations without
|
||
<code>coord_flip()</code>. The mechanism is that the layer will try to
|
||
guess the orientation from the mapped data, or take direction from the
|
||
user using the <code>orientation</code> parameter. To replicate this
|
||
functionality in new stats and geoms there’s a few steps to take. We
|
||
will look at the boxplot layer as an example instead of creating a new
|
||
one from scratch.</p>
|
||
<div id="omnidirectional-stats" class="section level3">
|
||
<h3>Omnidirectional stats</h3>
|
||
<p>The actual guessing of orientation will happen in
|
||
<code>setup_params()</code> using the <code>has_flipped_aes()</code>
|
||
helper:</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>StatBoxplot<span class="sc">$</span>setup_params</span>
|
||
<span id="cb19-2"><a href="#cb19-2" tabindex="-1"></a><span class="co">#> <ggproto method></span></span>
|
||
<span id="cb19-3"><a href="#cb19-3" tabindex="-1"></a><span class="co">#> <Wrapper function></span></span>
|
||
<span id="cb19-4"><a href="#cb19-4" tabindex="-1"></a><span class="co">#> function (...) </span></span>
|
||
<span id="cb19-5"><a href="#cb19-5" tabindex="-1"></a><span class="co">#> setup_params(..., self = self)</span></span>
|
||
<span id="cb19-6"><a href="#cb19-6" tabindex="-1"></a><span class="co">#> </span></span>
|
||
<span id="cb19-7"><a href="#cb19-7" tabindex="-1"></a><span class="co">#> <Inner function (f)></span></span>
|
||
<span id="cb19-8"><a href="#cb19-8" tabindex="-1"></a><span class="co">#> function (self, data, params) </span></span>
|
||
<span id="cb19-9"><a href="#cb19-9" tabindex="-1"></a><span class="co">#> {</span></span>
|
||
<span id="cb19-10"><a href="#cb19-10" tabindex="-1"></a><span class="co">#> params$flipped_aes <- has_flipped_aes(data, params, main_is_orthogonal = TRUE, </span></span>
|
||
<span id="cb19-11"><a href="#cb19-11" tabindex="-1"></a><span class="co">#> group_has_equal = TRUE, main_is_optional = TRUE)</span></span>
|
||
<span id="cb19-12"><a href="#cb19-12" tabindex="-1"></a><span class="co">#> data <- flip_data(data, params$flipped_aes)</span></span>
|
||
<span id="cb19-13"><a href="#cb19-13" tabindex="-1"></a><span class="co">#> has_x <- !(is.null(data$x) && is.null(params$x))</span></span>
|
||
<span id="cb19-14"><a href="#cb19-14" tabindex="-1"></a><span class="co">#> has_y <- !(is.null(data$y) && is.null(params$y))</span></span>
|
||
<span id="cb19-15"><a href="#cb19-15" tabindex="-1"></a><span class="co">#> if (!has_x && !has_y) {</span></span>
|
||
<span id="cb19-16"><a href="#cb19-16" tabindex="-1"></a><span class="co">#> cli::cli_abort("{.fn {snake_class(self)}} requires an {.field x} or {.field y} aesthetic.")</span></span>
|
||
<span id="cb19-17"><a href="#cb19-17" tabindex="-1"></a><span class="co">#> }</span></span>
|
||
<span id="cb19-18"><a href="#cb19-18" tabindex="-1"></a><span class="co">#> params$width <- params$width %||% (resolution(data$x %||% </span></span>
|
||
<span id="cb19-19"><a href="#cb19-19" tabindex="-1"></a><span class="co">#> 0, discrete = TRUE) * 0.75)</span></span>
|
||
<span id="cb19-20"><a href="#cb19-20" tabindex="-1"></a><span class="co">#> if (!is_mapped_discrete(data$x) && is.double(data$x) && !has_groups(data) && </span></span>
|
||
<span id="cb19-21"><a href="#cb19-21" tabindex="-1"></a><span class="co">#> any(data$x != data$x[1L])) {</span></span>
|
||
<span id="cb19-22"><a href="#cb19-22" tabindex="-1"></a><span class="co">#> cli::cli_warn(c("Continuous {.field {flipped_names(params$flipped_aes)$x}} aesthetic", </span></span>
|
||
<span id="cb19-23"><a href="#cb19-23" tabindex="-1"></a><span class="co">#> i = "did you forget {.code aes(group = ...)}?"))</span></span>
|
||
<span id="cb19-24"><a href="#cb19-24" tabindex="-1"></a><span class="co">#> }</span></span>
|
||
<span id="cb19-25"><a href="#cb19-25" tabindex="-1"></a><span class="co">#> params</span></span>
|
||
<span id="cb19-26"><a href="#cb19-26" tabindex="-1"></a><span class="co">#> }</span></span></code></pre></div>
|
||
<p>Following this is a call to <code>flip_data()</code> which will make
|
||
sure the data is in horizontal orientation. The rest of the code can
|
||
then simply assume that the data is in a specific orientation. The same
|
||
thing happens in <code>setup_data()</code>:</p>
|
||
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" tabindex="-1"></a>StatBoxplot<span class="sc">$</span>setup_data</span>
|
||
<span id="cb20-2"><a href="#cb20-2" tabindex="-1"></a><span class="co">#> <ggproto method></span></span>
|
||
<span id="cb20-3"><a href="#cb20-3" tabindex="-1"></a><span class="co">#> <Wrapper function></span></span>
|
||
<span id="cb20-4"><a href="#cb20-4" tabindex="-1"></a><span class="co">#> function (...) </span></span>
|
||
<span id="cb20-5"><a href="#cb20-5" tabindex="-1"></a><span class="co">#> setup_data(..., self = self)</span></span>
|
||
<span id="cb20-6"><a href="#cb20-6" tabindex="-1"></a><span class="co">#> </span></span>
|
||
<span id="cb20-7"><a href="#cb20-7" tabindex="-1"></a><span class="co">#> <Inner function (f)></span></span>
|
||
<span id="cb20-8"><a href="#cb20-8" tabindex="-1"></a><span class="co">#> function (self, data, params) </span></span>
|
||
<span id="cb20-9"><a href="#cb20-9" tabindex="-1"></a><span class="co">#> {</span></span>
|
||
<span id="cb20-10"><a href="#cb20-10" tabindex="-1"></a><span class="co">#> data <- flip_data(data, params$flipped_aes)</span></span>
|
||
<span id="cb20-11"><a href="#cb20-11" tabindex="-1"></a><span class="co">#> data$x <- data$x %||% 0</span></span>
|
||
<span id="cb20-12"><a href="#cb20-12" tabindex="-1"></a><span class="co">#> data <- remove_missing(data, na.rm = params$na.rm, vars = "x", </span></span>
|
||
<span id="cb20-13"><a href="#cb20-13" tabindex="-1"></a><span class="co">#> name = "stat_boxplot")</span></span>
|
||
<span id="cb20-14"><a href="#cb20-14" tabindex="-1"></a><span class="co">#> flip_data(data, params$flipped_aes)</span></span>
|
||
<span id="cb20-15"><a href="#cb20-15" tabindex="-1"></a><span class="co">#> }</span></span></code></pre></div>
|
||
<p>The data is flipped (if needed), manipulated, and flipped back as it
|
||
is returned.</p>
|
||
<p>During the computation, this sandwiching between
|
||
<code>flip_data()</code> is used as well, but right before the data is
|
||
returned it will also get a <code>flipped_aes</code> column denoting if
|
||
the data is flipped or not. This allows the stat to communicate to the
|
||
geom that orientation has already been determined.</p>
|
||
</div>
|
||
<div id="omnidirecitonal-geoms" class="section level3">
|
||
<h3>Omnidirecitonal geoms</h3>
|
||
<p>The setup for geoms is pretty much the same, with a few twists.
|
||
<code>has_flipped_aes()</code> is also used in
|
||
<code>setup_params()</code>, where it will usually be picked up from the
|
||
<code>flipped_aes</code> column given by the stat. In
|
||
<code>setup_data()</code> you will often see that
|
||
<code>flipped_aes</code> is reassigned, to make sure it exist prior to
|
||
position adjustment. This is needed if the geom is used together with a
|
||
stat that doesn’t handle orientation (often
|
||
<code>stat_identity()</code>):</p>
|
||
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a>GeomBoxplot<span class="sc">$</span>setup_data</span>
|
||
<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a><span class="co">#> <ggproto method></span></span>
|
||
<span id="cb21-3"><a href="#cb21-3" tabindex="-1"></a><span class="co">#> <Wrapper function></span></span>
|
||
<span id="cb21-4"><a href="#cb21-4" tabindex="-1"></a><span class="co">#> function (...) </span></span>
|
||
<span id="cb21-5"><a href="#cb21-5" tabindex="-1"></a><span class="co">#> setup_data(...)</span></span>
|
||
<span id="cb21-6"><a href="#cb21-6" tabindex="-1"></a><span class="co">#> </span></span>
|
||
<span id="cb21-7"><a href="#cb21-7" tabindex="-1"></a><span class="co">#> <Inner function (f)></span></span>
|
||
<span id="cb21-8"><a href="#cb21-8" tabindex="-1"></a><span class="co">#> function (data, params) </span></span>
|
||
<span id="cb21-9"><a href="#cb21-9" tabindex="-1"></a><span class="co">#> {</span></span>
|
||
<span id="cb21-10"><a href="#cb21-10" tabindex="-1"></a><span class="co">#> data$flipped_aes <- params$flipped_aes</span></span>
|
||
<span id="cb21-11"><a href="#cb21-11" tabindex="-1"></a><span class="co">#> data <- flip_data(data, params$flipped_aes)</span></span>
|
||
<span id="cb21-12"><a href="#cb21-12" tabindex="-1"></a><span class="co">#> data$width <- data$width %||% params$width %||% (resolution(data$x, </span></span>
|
||
<span id="cb21-13"><a href="#cb21-13" tabindex="-1"></a><span class="co">#> FALSE, TRUE) * 0.9)</span></span>
|
||
<span id="cb21-14"><a href="#cb21-14" tabindex="-1"></a><span class="co">#> if (isFALSE(params$outliers)) {</span></span>
|
||
<span id="cb21-15"><a href="#cb21-15" tabindex="-1"></a><span class="co">#> data$outliers <- NULL</span></span>
|
||
<span id="cb21-16"><a href="#cb21-16" tabindex="-1"></a><span class="co">#> }</span></span>
|
||
<span id="cb21-17"><a href="#cb21-17" tabindex="-1"></a><span class="co">#> if (!is.null(data$outliers)) {</span></span>
|
||
<span id="cb21-18"><a href="#cb21-18" tabindex="-1"></a><span class="co">#> suppressWarnings({</span></span>
|
||
<span id="cb21-19"><a href="#cb21-19" tabindex="-1"></a><span class="co">#> out_min <- vapply(data$outliers, min, numeric(1))</span></span>
|
||
<span id="cb21-20"><a href="#cb21-20" tabindex="-1"></a><span class="co">#> out_max <- vapply(data$outliers, max, numeric(1))</span></span>
|
||
<span id="cb21-21"><a href="#cb21-21" tabindex="-1"></a><span class="co">#> })</span></span>
|
||
<span id="cb21-22"><a href="#cb21-22" tabindex="-1"></a><span class="co">#> data$ymin_final <- pmin(out_min, data$ymin)</span></span>
|
||
<span id="cb21-23"><a href="#cb21-23" tabindex="-1"></a><span class="co">#> data$ymax_final <- pmax(out_max, data$ymax)</span></span>
|
||
<span id="cb21-24"><a href="#cb21-24" tabindex="-1"></a><span class="co">#> }</span></span>
|
||
<span id="cb21-25"><a href="#cb21-25" tabindex="-1"></a><span class="co">#> if (is.null(params) || is.null(params$varwidth) || !params$varwidth || </span></span>
|
||
<span id="cb21-26"><a href="#cb21-26" tabindex="-1"></a><span class="co">#> is.null(data$relvarwidth)) {</span></span>
|
||
<span id="cb21-27"><a href="#cb21-27" tabindex="-1"></a><span class="co">#> data$xmin <- data$x - data$width/2</span></span>
|
||
<span id="cb21-28"><a href="#cb21-28" tabindex="-1"></a><span class="co">#> data$xmax <- data$x + data$width/2</span></span>
|
||
<span id="cb21-29"><a href="#cb21-29" tabindex="-1"></a><span class="co">#> }</span></span>
|
||
<span id="cb21-30"><a href="#cb21-30" tabindex="-1"></a><span class="co">#> else {</span></span>
|
||
<span id="cb21-31"><a href="#cb21-31" tabindex="-1"></a><span class="co">#> data$relvarwidth <- data$relvarwidth/max(data$relvarwidth)</span></span>
|
||
<span id="cb21-32"><a href="#cb21-32" tabindex="-1"></a><span class="co">#> data$xmin <- data$x - data$relvarwidth * data$width/2</span></span>
|
||
<span id="cb21-33"><a href="#cb21-33" tabindex="-1"></a><span class="co">#> data$xmax <- data$x + data$relvarwidth * data$width/2</span></span>
|
||
<span id="cb21-34"><a href="#cb21-34" tabindex="-1"></a><span class="co">#> }</span></span>
|
||
<span id="cb21-35"><a href="#cb21-35" tabindex="-1"></a><span class="co">#> data$width <- NULL</span></span>
|
||
<span id="cb21-36"><a href="#cb21-36" tabindex="-1"></a><span class="co">#> if (!is.null(data$relvarwidth)) </span></span>
|
||
<span id="cb21-37"><a href="#cb21-37" tabindex="-1"></a><span class="co">#> data$relvarwidth <- NULL</span></span>
|
||
<span id="cb21-38"><a href="#cb21-38" tabindex="-1"></a><span class="co">#> flip_data(data, params$flipped_aes)</span></span>
|
||
<span id="cb21-39"><a href="#cb21-39" tabindex="-1"></a><span class="co">#> }</span></span></code></pre></div>
|
||
<p>In the <code>draw_*()</code> method you will once again sandwich any
|
||
data manipulation between <code>flip_data()</code> calls. It is
|
||
important to make sure that the data is flipped back prior to creating
|
||
the grob or calling draw methods from other geoms.</p>
|
||
</div>
|
||
<div id="dealing-with-required-aesthetics" class="section level3">
|
||
<h3>Dealing with required aesthetics</h3>
|
||
<p>Omnidirectional layers usually have two different sets of required
|
||
aesthetics. Which set is used is often how it knows the orientation. To
|
||
handle this gracefully the <code>required_aes</code> field of
|
||
<code>Stat</code> and <code>Geom</code> classes understands the
|
||
<code>|</code> (or) operator. Looking at <code>GeomBoxplot</code> we can
|
||
see how it is used:</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>GeomBoxplot<span class="sc">$</span>required_aes</span>
|
||
<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a><span class="co">#> [1] "x|y" "lower|xlower" "upper|xupper" "middle|xmiddle"</span></span>
|
||
<span id="cb22-3"><a href="#cb22-3" tabindex="-1"></a><span class="co">#> [5] "ymin|xmin" "ymax|xmax"</span></span></code></pre></div>
|
||
<p>This tells ggplot2 that either all the aesthetics before
|
||
<code>|</code> are required or all the aesthetics after are
|
||
required.</p>
|
||
</div>
|
||
<div id="ambiguous-layers" class="section level3">
|
||
<h3>Ambiguous layers</h3>
|
||
<p>Some layers will not have a clear interpretation of their data in
|
||
terms of orientation. A classic example is <code>geom_line()</code>
|
||
which just by convention runs along the x-axis. There is nothing in the
|
||
data itself that indicates that. For these geoms the user must indicate
|
||
a flipped orientation by setting <code>orientation = "y"</code>. The
|
||
stat or geom will then call <code>has_flipped_aes()</code> with
|
||
<code>ambiguous = TRUE</code> to cancel any guessing based on data
|
||
format. As an example we can see the <code>setup_params()</code> method
|
||
of <code>GeomLine</code>:</p>
|
||
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a>GeomLine<span class="sc">$</span>setup_params</span>
|
||
<span id="cb23-2"><a href="#cb23-2" tabindex="-1"></a><span class="co">#> <ggproto method></span></span>
|
||
<span id="cb23-3"><a href="#cb23-3" tabindex="-1"></a><span class="co">#> <Wrapper function></span></span>
|
||
<span id="cb23-4"><a href="#cb23-4" tabindex="-1"></a><span class="co">#> function (...) </span></span>
|
||
<span id="cb23-5"><a href="#cb23-5" tabindex="-1"></a><span class="co">#> setup_params(...)</span></span>
|
||
<span id="cb23-6"><a href="#cb23-6" tabindex="-1"></a><span class="co">#> </span></span>
|
||
<span id="cb23-7"><a href="#cb23-7" tabindex="-1"></a><span class="co">#> <Inner function (f)></span></span>
|
||
<span id="cb23-8"><a href="#cb23-8" tabindex="-1"></a><span class="co">#> function (data, params) </span></span>
|
||
<span id="cb23-9"><a href="#cb23-9" tabindex="-1"></a><span class="co">#> {</span></span>
|
||
<span id="cb23-10"><a href="#cb23-10" tabindex="-1"></a><span class="co">#> params$flipped_aes <- has_flipped_aes(data, params, ambiguous = TRUE)</span></span>
|
||
<span id="cb23-11"><a href="#cb23-11" tabindex="-1"></a><span class="co">#> params</span></span>
|
||
<span id="cb23-12"><a href="#cb23-12" tabindex="-1"></a><span class="co">#> }</span></span></code></pre></div>
|
||
</div>
|
||
</div>
|
||
<div id="creating-your-own-theme" class="section level2">
|
||
<h2>Creating your own theme</h2>
|
||
<p>If you’re going to create your own complete theme, there are a few
|
||
things you need to know:</p>
|
||
<ul>
|
||
<li>Overriding existing elements, rather than modifying them</li>
|
||
<li>The four global elements that affect (almost) every other theme
|
||
element</li>
|
||
<li>Complete vs. incomplete elements</li>
|
||
</ul>
|
||
<div id="overriding-elements" class="section level3">
|
||
<h3>Overriding elements</h3>
|
||
<p>By default, when you add a new theme element, it inherits values from
|
||
the existing theme. For example, the following code sets the key colour
|
||
to red, but it inherits the existing fill colour:</p>
|
||
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a><span class="fu">theme_grey</span>()<span class="sc">$</span>legend.key</span>
|
||
<span id="cb24-2"><a href="#cb24-2" tabindex="-1"></a><span class="co">#> NULL</span></span>
|
||
<span id="cb24-3"><a href="#cb24-3" tabindex="-1"></a></span>
|
||
<span id="cb24-4"><a href="#cb24-4" tabindex="-1"></a>new_theme <span class="ot"><-</span> <span class="fu">theme_grey</span>() <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">legend.key =</span> <span class="fu">element_rect</span>(<span class="at">colour =</span> <span class="st">"red"</span>))</span>
|
||
<span id="cb24-5"><a href="#cb24-5" tabindex="-1"></a>new_theme<span class="sc">$</span>legend.key</span>
|
||
<span id="cb24-6"><a href="#cb24-6" tabindex="-1"></a><span class="co">#> List of 5</span></span>
|
||
<span id="cb24-7"><a href="#cb24-7" tabindex="-1"></a><span class="co">#> $ fill : NULL</span></span>
|
||
<span id="cb24-8"><a href="#cb24-8" tabindex="-1"></a><span class="co">#> $ colour : chr "red"</span></span>
|
||
<span id="cb24-9"><a href="#cb24-9" tabindex="-1"></a><span class="co">#> $ linewidth : NULL</span></span>
|
||
<span id="cb24-10"><a href="#cb24-10" tabindex="-1"></a><span class="co">#> $ linetype : NULL</span></span>
|
||
<span id="cb24-11"><a href="#cb24-11" tabindex="-1"></a><span class="co">#> $ inherit.blank: logi FALSE</span></span>
|
||
<span id="cb24-12"><a href="#cb24-12" tabindex="-1"></a><span class="co">#> - attr(*, "class")= chr [1:2] "element_rect" "element"</span></span></code></pre></div>
|
||
<p>To override it completely, use <code>%+replace%</code> instead of
|
||
<code>+</code>:</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>new_theme <span class="ot"><-</span> <span class="fu">theme_grey</span>() <span class="sc">%+replace%</span> <span class="fu">theme</span>(<span class="at">legend.key =</span> <span class="fu">element_rect</span>(<span class="at">colour =</span> <span class="st">"red"</span>))</span>
|
||
<span id="cb25-2"><a href="#cb25-2" tabindex="-1"></a>new_theme<span class="sc">$</span>legend.key</span>
|
||
<span id="cb25-3"><a href="#cb25-3" tabindex="-1"></a><span class="co">#> List of 5</span></span>
|
||
<span id="cb25-4"><a href="#cb25-4" tabindex="-1"></a><span class="co">#> $ fill : NULL</span></span>
|
||
<span id="cb25-5"><a href="#cb25-5" tabindex="-1"></a><span class="co">#> $ colour : chr "red"</span></span>
|
||
<span id="cb25-6"><a href="#cb25-6" tabindex="-1"></a><span class="co">#> $ linewidth : NULL</span></span>
|
||
<span id="cb25-7"><a href="#cb25-7" tabindex="-1"></a><span class="co">#> $ linetype : NULL</span></span>
|
||
<span id="cb25-8"><a href="#cb25-8" tabindex="-1"></a><span class="co">#> $ inherit.blank: logi FALSE</span></span>
|
||
<span id="cb25-9"><a href="#cb25-9" tabindex="-1"></a><span class="co">#> - attr(*, "class")= chr [1:2] "element_rect" "element"</span></span></code></pre></div>
|
||
</div>
|
||
<div id="global-elements" class="section level3">
|
||
<h3>Global elements</h3>
|
||
<p>There are four elements that affect the global appearance of the
|
||
plot:</p>
|
||
<table>
|
||
<colgroup>
|
||
<col width="23%" />
|
||
<col width="33%" />
|
||
<col width="42%" />
|
||
</colgroup>
|
||
<thead>
|
||
<tr class="header">
|
||
<th>Element</th>
|
||
<th>Theme function</th>
|
||
<th>Description</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="odd">
|
||
<td>line</td>
|
||
<td><code>element_line()</code></td>
|
||
<td>all line elements</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>rect</td>
|
||
<td><code>element_rect()</code></td>
|
||
<td>all rectangular elements</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>text</td>
|
||
<td><code>element_text()</code></td>
|
||
<td>all text</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>title</td>
|
||
<td><code>element_text()</code></td>
|
||
<td>all text in title elements (plot, axes & legend)</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>These set default properties that are inherited by more specific
|
||
settings. These are most useful for setting an overall “background”
|
||
colour and overall font settings (e.g. family and size).</p>
|
||
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a>df <span class="ot"><-</span> <span class="fu">data.frame</span>(<span class="at">x =</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">3</span>, <span class="at">y =</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">3</span>)</span>
|
||
<span id="cb26-2"><a href="#cb26-2" tabindex="-1"></a>base <span class="ot"><-</span> <span class="fu">ggplot</span>(df, <span class="fu">aes</span>(x, y)) <span class="sc">+</span> </span>
|
||
<span id="cb26-3"><a href="#cb26-3" tabindex="-1"></a> <span class="fu">geom_point</span>() <span class="sc">+</span> </span>
|
||
<span id="cb26-4"><a href="#cb26-4" tabindex="-1"></a> <span class="fu">theme_minimal</span>()</span>
|
||
<span id="cb26-5"><a href="#cb26-5" tabindex="-1"></a></span>
|
||
<span id="cb26-6"><a href="#cb26-6" tabindex="-1"></a>base</span></code></pre></div>
|
||
<p><img 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" alt="Scatterplot of three observations arranged diagonally. The axis titles 'x' and 'y' are coloured in black" style="display: block; margin: auto;" /></p>
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<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" tabindex="-1"></a>base <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">text =</span> <span class="fu">element_text</span>(<span class="at">colour =</span> <span class="st">"red"</span>))</span></code></pre></div>
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<p><img 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" alt="Scatterplot of three observations arranged diagonally. The axis titles 'x' and 'y' are coloured in red" style="display: block; margin: auto;" /></p>
|
||
<p>You should generally start creating a theme by modifying these
|
||
values.</p>
|
||
</div>
|
||
<div id="complete-vs-incomplete" class="section level3">
|
||
<h3>Complete vs incomplete</h3>
|
||
<p>It is useful to understand the difference between complete and
|
||
incomplete theme objects. A <em>complete</em> theme object is one
|
||
produced by calling a theme function with the attribute
|
||
<code>complete = TRUE</code>.</p>
|
||
<p>Theme functions <code>theme_grey()</code> and <code>theme_bw()</code>
|
||
are examples of complete theme functions. Calls to <code>theme()</code>
|
||
produce <em>incomplete</em> theme objects, since they represent (local)
|
||
modifications to a theme object rather than returning a complete theme
|
||
object per se. When adding an incomplete theme to a complete one, the
|
||
result is a complete theme.</p>
|
||
<p>Complete and incomplete themes behave somewhat differently when added
|
||
to a ggplot object:</p>
|
||
<ul>
|
||
<li><p>Adding an incomplete theme augments the current theme object,
|
||
replacing only those properties of elements defined in the call to
|
||
<code>theme()</code>.</p></li>
|
||
<li><p>Adding a complete theme wipes away the existing theme and applies
|
||
the new theme.</p></li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
<div id="creating-a-new-faceting" class="section level2">
|
||
<h2>Creating a new faceting</h2>
|
||
<p>One of the more daunting exercises in ggplot2 extensions is to create
|
||
a new faceting system. The reason for this is that when creating new
|
||
facetings you take on the responsibility of how (almost) everything is
|
||
drawn on the screen, and many do not have experience with directly using
|
||
<a href="https://cran.r-project.org/package=gtable">gtable</a> and <a href="https://cran.r-project.org/package=grid">grid</a> upon which the
|
||
ggplot2 rendering is built. If you decide to venture into faceting
|
||
extensions, it is highly recommended to gain proficiency with the
|
||
above-mentioned packages.</p>
|
||
<p>The <code>Facet</code> class in ggplot2 is very powerful as it takes
|
||
on responsibility of a wide range of tasks. The main tasks of a
|
||
<code>Facet</code> object are:</p>
|
||
<ul>
|
||
<li><p>Define a layout; that is, a partitioning of the data into
|
||
different plot areas (panels) as well as which panels share position
|
||
scales.</p></li>
|
||
<li><p>Map plot data into the correct panels, potentially duplicating
|
||
data if it should exist in multiple panels (e.g. margins in
|
||
<code>facet_grid()</code>).</p></li>
|
||
<li><p>Assemble all panels into a final gtable, adding axes, strips and
|
||
decorations in the process.</p></li>
|
||
</ul>
|
||
<p>Apart from these three tasks, for which functionality must be
|
||
implemented, there are a couple of additional extension points where
|
||
sensible defaults have been provided. These can generally be ignored,
|
||
but adventurous developers can override them for even more control:</p>
|
||
<ul>
|
||
<li><p>Initialization and training of positional scales for each
|
||
panel.</p></li>
|
||
<li><p>Decoration in front of and behind each panel.</p></li>
|
||
<li><p>Drawing of axis labels</p></li>
|
||
</ul>
|
||
<p>To show how a new faceting class is created we will start simple and
|
||
go through each of the required methods in turn to build up
|
||
<code>facet_duplicate()</code> that simply duplicate our plot into two
|
||
panels. After this we will tinker with it a bit to show some of the more
|
||
powerful possibilities.</p>
|
||
<div id="creating-a-layout-specification" class="section level3">
|
||
<h3>Creating a layout specification</h3>
|
||
<p>A layout in the context of facets is a <code>data.frame</code> that
|
||
defines a mapping between data and the panels it should reside in as
|
||
well as which positional scales should be used. The output should at
|
||
least contain the columns <code>PANEL</code>, <code>SCALE_X</code>, and
|
||
<code>SCALE_Y</code>, but will often contain more to help assign data to
|
||
the correct panel (<code>facet_grid()</code> will e.g. also return the
|
||
faceting variables associated with each panel). Let’s make a function
|
||
that defines a duplicate layout:</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>layout <span class="ot"><-</span> <span class="cf">function</span>(data, params) {</span>
|
||
<span id="cb28-2"><a href="#cb28-2" tabindex="-1"></a> <span class="fu">data.frame</span>(<span class="at">PANEL =</span> <span class="fu">c</span>(1L, 2L), <span class="at">SCALE_X =</span> 1L, <span class="at">SCALE_Y =</span> 1L)</span>
|
||
<span id="cb28-3"><a href="#cb28-3" tabindex="-1"></a>}</span></code></pre></div>
|
||
<p>This is quite simple as the faceting should just define two panels
|
||
irrespectively of the input data and parameters.</p>
|
||
</div>
|
||
<div id="mapping-data-into-panels" class="section level3">
|
||
<h3>Mapping data into panels</h3>
|
||
<p>In order for ggplot2 to know which data should go where it needs the
|
||
data to be assigned to a panel. The purpose of the mapping step is to
|
||
assign a <code>PANEL</code> column to the layer data identifying which
|
||
panel it belongs to.</p>
|
||
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" tabindex="-1"></a>mapping <span class="ot"><-</span> <span class="cf">function</span>(data, layout, params) {</span>
|
||
<span id="cb29-2"><a href="#cb29-2" tabindex="-1"></a> <span class="cf">if</span> (<span class="fu">is.null</span>(data) <span class="sc">||</span> <span class="fu">nrow</span>(data) <span class="sc">==</span> <span class="dv">0</span>) {</span>
|
||
<span id="cb29-3"><a href="#cb29-3" tabindex="-1"></a> <span class="fu">return</span>(<span class="fu">cbind</span>(data, <span class="at">PANEL =</span> <span class="fu">integer</span>(<span class="dv">0</span>)))</span>
|
||
<span id="cb29-4"><a href="#cb29-4" tabindex="-1"></a> }</span>
|
||
<span id="cb29-5"><a href="#cb29-5" tabindex="-1"></a> <span class="fu">rbind</span>(</span>
|
||
<span id="cb29-6"><a href="#cb29-6" tabindex="-1"></a> <span class="fu">cbind</span>(data, <span class="at">PANEL =</span> 1L),</span>
|
||
<span id="cb29-7"><a href="#cb29-7" tabindex="-1"></a> <span class="fu">cbind</span>(data, <span class="at">PANEL =</span> 2L)</span>
|
||
<span id="cb29-8"><a href="#cb29-8" tabindex="-1"></a> )</span>
|
||
<span id="cb29-9"><a href="#cb29-9" tabindex="-1"></a>}</span></code></pre></div>
|
||
<p>here we first investigate whether we have gotten an empty
|
||
<code>data.frame</code> and if not we duplicate the data and assign the
|
||
original data to the first panel and the new data to the second
|
||
panel.</p>
|
||
</div>
|
||
<div id="laying-out-the-panels" class="section level3">
|
||
<h3>Laying out the panels</h3>
|
||
<p>While the two functions above have been deceivingly simple, this last
|
||
one is going to take some more work. Our goal is to draw two panels
|
||
beside (or above) each other with axes etc.</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>render <span class="ot"><-</span> <span class="cf">function</span>(panels, layout, x_scales, y_scales, ranges, coord, data,</span>
|
||
<span id="cb30-2"><a href="#cb30-2" tabindex="-1"></a> theme, params) {</span>
|
||
<span id="cb30-3"><a href="#cb30-3" tabindex="-1"></a> <span class="co"># Place panels according to settings</span></span>
|
||
<span id="cb30-4"><a href="#cb30-4" tabindex="-1"></a> <span class="cf">if</span> (params<span class="sc">$</span>horizontal) {</span>
|
||
<span id="cb30-5"><a href="#cb30-5" tabindex="-1"></a> <span class="co"># Put panels in matrix and convert to a gtable</span></span>
|
||
<span id="cb30-6"><a href="#cb30-6" tabindex="-1"></a> panels <span class="ot"><-</span> <span class="fu">matrix</span>(panels, <span class="at">ncol =</span> <span class="dv">2</span>)</span>
|
||
<span id="cb30-7"><a href="#cb30-7" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_matrix</span>(<span class="st">"layout"</span>, panels, </span>
|
||
<span id="cb30-8"><a href="#cb30-8" tabindex="-1"></a> <span class="at">widths =</span> <span class="fu">unit</span>(<span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">1</span>), <span class="st">"null"</span>), <span class="at">heights =</span> <span class="fu">unit</span>(<span class="dv">1</span>, <span class="st">"null"</span>), <span class="at">clip =</span> <span class="st">"on"</span>)</span>
|
||
<span id="cb30-9"><a href="#cb30-9" tabindex="-1"></a> <span class="co"># Add spacing according to theme</span></span>
|
||
<span id="cb30-10"><a href="#cb30-10" tabindex="-1"></a> panel_spacing <span class="ot"><-</span> <span class="cf">if</span> (<span class="fu">is.null</span>(theme<span class="sc">$</span>panel.spacing.x)) {</span>
|
||
<span id="cb30-11"><a href="#cb30-11" tabindex="-1"></a> theme<span class="sc">$</span>panel.spacing</span>
|
||
<span id="cb30-12"><a href="#cb30-12" tabindex="-1"></a> } <span class="cf">else</span> {</span>
|
||
<span id="cb30-13"><a href="#cb30-13" tabindex="-1"></a> theme<span class="sc">$</span>panel.spacing.x</span>
|
||
<span id="cb30-14"><a href="#cb30-14" tabindex="-1"></a> }</span>
|
||
<span id="cb30-15"><a href="#cb30-15" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_col_space</span>(panel_table, panel_spacing)</span>
|
||
<span id="cb30-16"><a href="#cb30-16" tabindex="-1"></a> } <span class="cf">else</span> {</span>
|
||
<span id="cb30-17"><a href="#cb30-17" tabindex="-1"></a> panels <span class="ot"><-</span> <span class="fu">matrix</span>(panels, <span class="at">ncol =</span> <span class="dv">1</span>)</span>
|
||
<span id="cb30-18"><a href="#cb30-18" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_matrix</span>(<span class="st">"layout"</span>, panels, </span>
|
||
<span id="cb30-19"><a href="#cb30-19" tabindex="-1"></a> <span class="at">widths =</span> <span class="fu">unit</span>(<span class="dv">1</span>, <span class="st">"null"</span>), <span class="at">heights =</span> <span class="fu">unit</span>(<span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">1</span>), <span class="st">"null"</span>), <span class="at">clip =</span> <span class="st">"on"</span>)</span>
|
||
<span id="cb30-20"><a href="#cb30-20" tabindex="-1"></a> panel_spacing <span class="ot"><-</span> <span class="cf">if</span> (<span class="fu">is.null</span>(theme<span class="sc">$</span>panel.spacing.y)) {</span>
|
||
<span id="cb30-21"><a href="#cb30-21" tabindex="-1"></a> theme<span class="sc">$</span>panel.spacing</span>
|
||
<span id="cb30-22"><a href="#cb30-22" tabindex="-1"></a> } <span class="cf">else</span> {</span>
|
||
<span id="cb30-23"><a href="#cb30-23" tabindex="-1"></a> theme<span class="sc">$</span>panel.spacing.y</span>
|
||
<span id="cb30-24"><a href="#cb30-24" tabindex="-1"></a> }</span>
|
||
<span id="cb30-25"><a href="#cb30-25" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_row_space</span>(panel_table, panel_spacing)</span>
|
||
<span id="cb30-26"><a href="#cb30-26" tabindex="-1"></a> }</span>
|
||
<span id="cb30-27"><a href="#cb30-27" tabindex="-1"></a> <span class="co"># Name panel grobs so they can be found later</span></span>
|
||
<span id="cb30-28"><a href="#cb30-28" tabindex="-1"></a> panel_table<span class="sc">$</span>layout<span class="sc">$</span>name <span class="ot"><-</span> <span class="fu">paste0</span>(<span class="st">"panel-"</span>, <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">2</span>))</span>
|
||
<span id="cb30-29"><a href="#cb30-29" tabindex="-1"></a> </span>
|
||
<span id="cb30-30"><a href="#cb30-30" tabindex="-1"></a> <span class="co"># Construct the axes</span></span>
|
||
<span id="cb30-31"><a href="#cb30-31" tabindex="-1"></a> axes <span class="ot"><-</span> <span class="fu">render_axes</span>(ranges[<span class="dv">1</span>], ranges[<span class="dv">1</span>], coord, theme, </span>
|
||
<span id="cb30-32"><a href="#cb30-32" tabindex="-1"></a> <span class="at">transpose =</span> <span class="cn">TRUE</span>)</span>
|
||
<span id="cb30-33"><a href="#cb30-33" tabindex="-1"></a></span>
|
||
<span id="cb30-34"><a href="#cb30-34" tabindex="-1"></a> <span class="co"># Add axes around each panel</span></span>
|
||
<span id="cb30-35"><a href="#cb30-35" tabindex="-1"></a> panel_pos_h <span class="ot"><-</span> <span class="fu">panel_cols</span>(panel_table)<span class="sc">$</span>l</span>
|
||
<span id="cb30-36"><a href="#cb30-36" tabindex="-1"></a> panel_pos_v <span class="ot"><-</span> <span class="fu">panel_rows</span>(panel_table)<span class="sc">$</span>t</span>
|
||
<span id="cb30-37"><a href="#cb30-37" tabindex="-1"></a> axis_width_l <span class="ot"><-</span> <span class="fu">unit</span>(grid<span class="sc">::</span><span class="fu">convertWidth</span>(</span>
|
||
<span id="cb30-38"><a href="#cb30-38" tabindex="-1"></a> grid<span class="sc">::</span><span class="fu">grobWidth</span>(axes<span class="sc">$</span>y<span class="sc">$</span>left[[<span class="dv">1</span>]]), <span class="st">"cm"</span>, <span class="cn">TRUE</span>), <span class="st">"cm"</span>)</span>
|
||
<span id="cb30-39"><a href="#cb30-39" tabindex="-1"></a> axis_width_r <span class="ot"><-</span> <span class="fu">unit</span>(grid<span class="sc">::</span><span class="fu">convertWidth</span>(</span>
|
||
<span id="cb30-40"><a href="#cb30-40" tabindex="-1"></a> grid<span class="sc">::</span><span class="fu">grobWidth</span>(axes<span class="sc">$</span>y<span class="sc">$</span>right[[<span class="dv">1</span>]]), <span class="st">"cm"</span>, <span class="cn">TRUE</span>), <span class="st">"cm"</span>)</span>
|
||
<span id="cb30-41"><a href="#cb30-41" tabindex="-1"></a> <span class="do">## We do it reverse so we don't change the position of panels when we add axes</span></span>
|
||
<span id="cb30-42"><a href="#cb30-42" tabindex="-1"></a> <span class="cf">for</span> (i <span class="cf">in</span> <span class="fu">rev</span>(panel_pos_h)) {</span>
|
||
<span id="cb30-43"><a href="#cb30-43" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_cols</span>(panel_table, axis_width_r, i)</span>
|
||
<span id="cb30-44"><a href="#cb30-44" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_grob</span>(panel_table, </span>
|
||
<span id="cb30-45"><a href="#cb30-45" tabindex="-1"></a> <span class="fu">rep</span>(axes<span class="sc">$</span>y<span class="sc">$</span>right, <span class="fu">length</span>(panel_pos_v)), <span class="at">t =</span> panel_pos_v, <span class="at">l =</span> i <span class="sc">+</span> <span class="dv">1</span>, </span>
|
||
<span id="cb30-46"><a href="#cb30-46" tabindex="-1"></a> <span class="at">clip =</span> <span class="st">"off"</span>)</span>
|
||
<span id="cb30-47"><a href="#cb30-47" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_cols</span>(panel_table, axis_width_l, i <span class="sc">-</span> <span class="dv">1</span>)</span>
|
||
<span id="cb30-48"><a href="#cb30-48" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_grob</span>(panel_table, </span>
|
||
<span id="cb30-49"><a href="#cb30-49" tabindex="-1"></a> <span class="fu">rep</span>(axes<span class="sc">$</span>y<span class="sc">$</span>left, <span class="fu">length</span>(panel_pos_v)), <span class="at">t =</span> panel_pos_v, <span class="at">l =</span> i, </span>
|
||
<span id="cb30-50"><a href="#cb30-50" tabindex="-1"></a> <span class="at">clip =</span> <span class="st">"off"</span>)</span>
|
||
<span id="cb30-51"><a href="#cb30-51" tabindex="-1"></a> }</span>
|
||
<span id="cb30-52"><a href="#cb30-52" tabindex="-1"></a> <span class="do">## Recalculate as gtable has changed</span></span>
|
||
<span id="cb30-53"><a href="#cb30-53" tabindex="-1"></a> panel_pos_h <span class="ot"><-</span> <span class="fu">panel_cols</span>(panel_table)<span class="sc">$</span>l</span>
|
||
<span id="cb30-54"><a href="#cb30-54" tabindex="-1"></a> panel_pos_v <span class="ot"><-</span> <span class="fu">panel_rows</span>(panel_table)<span class="sc">$</span>t</span>
|
||
<span id="cb30-55"><a href="#cb30-55" tabindex="-1"></a> axis_height_t <span class="ot"><-</span> <span class="fu">unit</span>(grid<span class="sc">::</span><span class="fu">convertHeight</span>(</span>
|
||
<span id="cb30-56"><a href="#cb30-56" tabindex="-1"></a> grid<span class="sc">::</span><span class="fu">grobHeight</span>(axes<span class="sc">$</span>x<span class="sc">$</span>top[[<span class="dv">1</span>]]), <span class="st">"cm"</span>, <span class="cn">TRUE</span>), <span class="st">"cm"</span>)</span>
|
||
<span id="cb30-57"><a href="#cb30-57" tabindex="-1"></a> axis_height_b <span class="ot"><-</span> <span class="fu">unit</span>(grid<span class="sc">::</span><span class="fu">convertHeight</span>(</span>
|
||
<span id="cb30-58"><a href="#cb30-58" tabindex="-1"></a> grid<span class="sc">::</span><span class="fu">grobHeight</span>(axes<span class="sc">$</span>x<span class="sc">$</span>bottom[[<span class="dv">1</span>]]), <span class="st">"cm"</span>, <span class="cn">TRUE</span>), <span class="st">"cm"</span>)</span>
|
||
<span id="cb30-59"><a href="#cb30-59" tabindex="-1"></a> <span class="cf">for</span> (i <span class="cf">in</span> <span class="fu">rev</span>(panel_pos_v)) {</span>
|
||
<span id="cb30-60"><a href="#cb30-60" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_rows</span>(panel_table, axis_height_b, i)</span>
|
||
<span id="cb30-61"><a href="#cb30-61" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_grob</span>(panel_table, </span>
|
||
<span id="cb30-62"><a href="#cb30-62" tabindex="-1"></a> <span class="fu">rep</span>(axes<span class="sc">$</span>x<span class="sc">$</span>bottom, <span class="fu">length</span>(panel_pos_h)), <span class="at">t =</span> i <span class="sc">+</span> <span class="dv">1</span>, <span class="at">l =</span> panel_pos_h, </span>
|
||
<span id="cb30-63"><a href="#cb30-63" tabindex="-1"></a> <span class="at">clip =</span> <span class="st">"off"</span>)</span>
|
||
<span id="cb30-64"><a href="#cb30-64" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_rows</span>(panel_table, axis_height_t, i <span class="sc">-</span> <span class="dv">1</span>)</span>
|
||
<span id="cb30-65"><a href="#cb30-65" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_grob</span>(panel_table, </span>
|
||
<span id="cb30-66"><a href="#cb30-66" tabindex="-1"></a> <span class="fu">rep</span>(axes<span class="sc">$</span>x<span class="sc">$</span>top, <span class="fu">length</span>(panel_pos_h)), <span class="at">t =</span> i, <span class="at">l =</span> panel_pos_h, </span>
|
||
<span id="cb30-67"><a href="#cb30-67" tabindex="-1"></a> <span class="at">clip =</span> <span class="st">"off"</span>)</span>
|
||
<span id="cb30-68"><a href="#cb30-68" tabindex="-1"></a> }</span>
|
||
<span id="cb30-69"><a href="#cb30-69" tabindex="-1"></a> panel_table</span>
|
||
<span id="cb30-70"><a href="#cb30-70" tabindex="-1"></a>}</span></code></pre></div>
|
||
</div>
|
||
<div id="assembling-the-facet-class" class="section level3">
|
||
<h3>Assembling the Facet class</h3>
|
||
<p>Usually all methods are defined within the class definition in the
|
||
same way as is done for <code>Geom</code> and <code>Stat</code>. Here we
|
||
have split it out so we could go through each in turn. All that remains
|
||
is to assign our functions to the correct methods as well as making a
|
||
constructor</p>
|
||
<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="co"># Constructor: shrink is required to govern whether scales are trained on </span></span>
|
||
<span id="cb31-2"><a href="#cb31-2" tabindex="-1"></a><span class="co"># Stat-transformed data or not.</span></span>
|
||
<span id="cb31-3"><a href="#cb31-3" tabindex="-1"></a>facet_duplicate <span class="ot"><-</span> <span class="cf">function</span>(<span class="at">horizontal =</span> <span class="cn">TRUE</span>, <span class="at">shrink =</span> <span class="cn">TRUE</span>) {</span>
|
||
<span id="cb31-4"><a href="#cb31-4" tabindex="-1"></a> <span class="fu">ggproto</span>(<span class="cn">NULL</span>, FacetDuplicate,</span>
|
||
<span id="cb31-5"><a href="#cb31-5" tabindex="-1"></a> <span class="at">shrink =</span> shrink,</span>
|
||
<span id="cb31-6"><a href="#cb31-6" tabindex="-1"></a> <span class="at">params =</span> <span class="fu">list</span>(</span>
|
||
<span id="cb31-7"><a href="#cb31-7" tabindex="-1"></a> <span class="at">horizontal =</span> horizontal</span>
|
||
<span id="cb31-8"><a href="#cb31-8" tabindex="-1"></a> )</span>
|
||
<span id="cb31-9"><a href="#cb31-9" tabindex="-1"></a> )</span>
|
||
<span id="cb31-10"><a href="#cb31-10" tabindex="-1"></a>}</span>
|
||
<span id="cb31-11"><a href="#cb31-11" tabindex="-1"></a></span>
|
||
<span id="cb31-12"><a href="#cb31-12" tabindex="-1"></a>FacetDuplicate <span class="ot"><-</span> <span class="fu">ggproto</span>(<span class="st">"FacetDuplicate"</span>, Facet,</span>
|
||
<span id="cb31-13"><a href="#cb31-13" tabindex="-1"></a> <span class="at">compute_layout =</span> layout,</span>
|
||
<span id="cb31-14"><a href="#cb31-14" tabindex="-1"></a> <span class="at">map_data =</span> mapping,</span>
|
||
<span id="cb31-15"><a href="#cb31-15" tabindex="-1"></a> <span class="at">draw_panels =</span> render</span>
|
||
<span id="cb31-16"><a href="#cb31-16" tabindex="-1"></a>)</span></code></pre></div>
|
||
<p>Now with everything assembled, lets test it out:</p>
|
||
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" tabindex="-1"></a>p <span class="ot"><-</span> <span class="fu">ggplot</span>(mtcars, <span class="fu">aes</span>(<span class="at">x =</span> hp, <span class="at">y =</span> mpg)) <span class="sc">+</span> <span class="fu">geom_point</span>()</span>
|
||
<span id="cb32-2"><a href="#cb32-2" tabindex="-1"></a>p</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" alt="Scatterplot showing horsepower against miles per gallon for 32 cars." style="display: block; margin: auto;" /></p>
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<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" tabindex="-1"></a>p <span class="sc">+</span> <span class="fu">facet_duplicate</span>()</span></code></pre></div>
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<p><img 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" alt="Scatterplot with two panels showing horsepower against miles per gallon for 32 cars. The left and right panels are identical." style="display: block; margin: auto;" /></p>
|
||
</div>
|
||
<div id="doing-more-with-facets" class="section level3">
|
||
<h3>Doing more with facets</h3>
|
||
<p>The example above was pretty useless and we’ll now try to expand on
|
||
it to add some actual usability. We are going to make a faceting that
|
||
adds panels with y-transformed axes:</p>
|
||
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" tabindex="-1"></a><span class="fu">library</span>(scales)</span>
|
||
<span id="cb34-2"><a href="#cb34-2" tabindex="-1"></a></span>
|
||
<span id="cb34-3"><a href="#cb34-3" tabindex="-1"></a>facet_trans <span class="ot"><-</span> <span class="cf">function</span>(trans, <span class="at">horizontal =</span> <span class="cn">TRUE</span>, <span class="at">shrink =</span> <span class="cn">TRUE</span>) {</span>
|
||
<span id="cb34-4"><a href="#cb34-4" tabindex="-1"></a> <span class="fu">ggproto</span>(<span class="cn">NULL</span>, FacetTrans,</span>
|
||
<span id="cb34-5"><a href="#cb34-5" tabindex="-1"></a> <span class="at">shrink =</span> shrink,</span>
|
||
<span id="cb34-6"><a href="#cb34-6" tabindex="-1"></a> <span class="at">params =</span> <span class="fu">list</span>(</span>
|
||
<span id="cb34-7"><a href="#cb34-7" tabindex="-1"></a> <span class="at">trans =</span> scales<span class="sc">::</span><span class="fu">as.transform</span>(trans),</span>
|
||
<span id="cb34-8"><a href="#cb34-8" tabindex="-1"></a> <span class="at">horizontal =</span> horizontal</span>
|
||
<span id="cb34-9"><a href="#cb34-9" tabindex="-1"></a> )</span>
|
||
<span id="cb34-10"><a href="#cb34-10" tabindex="-1"></a> )</span>
|
||
<span id="cb34-11"><a href="#cb34-11" tabindex="-1"></a>}</span>
|
||
<span id="cb34-12"><a href="#cb34-12" tabindex="-1"></a></span>
|
||
<span id="cb34-13"><a href="#cb34-13" tabindex="-1"></a>FacetTrans <span class="ot"><-</span> <span class="fu">ggproto</span>(<span class="st">"FacetTrans"</span>, Facet,</span>
|
||
<span id="cb34-14"><a href="#cb34-14" tabindex="-1"></a> <span class="co"># Almost as before but we want different y-scales for each panel</span></span>
|
||
<span id="cb34-15"><a href="#cb34-15" tabindex="-1"></a> <span class="at">compute_layout =</span> <span class="cf">function</span>(data, params) {</span>
|
||
<span id="cb34-16"><a href="#cb34-16" tabindex="-1"></a> <span class="fu">data.frame</span>(<span class="at">PANEL =</span> <span class="fu">c</span>(1L, 2L), <span class="at">SCALE_X =</span> 1L, <span class="at">SCALE_Y =</span> <span class="fu">c</span>(1L, 2L))</span>
|
||
<span id="cb34-17"><a href="#cb34-17" tabindex="-1"></a> },</span>
|
||
<span id="cb34-18"><a href="#cb34-18" tabindex="-1"></a> <span class="co"># Same as before</span></span>
|
||
<span id="cb34-19"><a href="#cb34-19" tabindex="-1"></a> <span class="at">map_data =</span> <span class="cf">function</span>(data, layout, params) {</span>
|
||
<span id="cb34-20"><a href="#cb34-20" tabindex="-1"></a> <span class="cf">if</span> (<span class="fu">is.null</span>(data) <span class="sc">||</span> <span class="fu">nrow</span>(data) <span class="sc">==</span> <span class="dv">0</span>) {</span>
|
||
<span id="cb34-21"><a href="#cb34-21" tabindex="-1"></a> <span class="fu">return</span>(<span class="fu">cbind</span>(data, <span class="at">PANEL =</span> <span class="fu">integer</span>(<span class="dv">0</span>)))</span>
|
||
<span id="cb34-22"><a href="#cb34-22" tabindex="-1"></a> }</span>
|
||
<span id="cb34-23"><a href="#cb34-23" tabindex="-1"></a> <span class="fu">rbind</span>(</span>
|
||
<span id="cb34-24"><a href="#cb34-24" tabindex="-1"></a> <span class="fu">cbind</span>(data, <span class="at">PANEL =</span> 1L),</span>
|
||
<span id="cb34-25"><a href="#cb34-25" tabindex="-1"></a> <span class="fu">cbind</span>(data, <span class="at">PANEL =</span> 2L)</span>
|
||
<span id="cb34-26"><a href="#cb34-26" tabindex="-1"></a> )</span>
|
||
<span id="cb34-27"><a href="#cb34-27" tabindex="-1"></a> },</span>
|
||
<span id="cb34-28"><a href="#cb34-28" tabindex="-1"></a> <span class="co"># This is new. We create a new scale with the defined transformation</span></span>
|
||
<span id="cb34-29"><a href="#cb34-29" tabindex="-1"></a> <span class="at">init_scales =</span> <span class="cf">function</span>(layout, <span class="at">x_scale =</span> <span class="cn">NULL</span>, <span class="at">y_scale =</span> <span class="cn">NULL</span>, params) {</span>
|
||
<span id="cb34-30"><a href="#cb34-30" tabindex="-1"></a> scales <span class="ot"><-</span> <span class="fu">list</span>()</span>
|
||
<span id="cb34-31"><a href="#cb34-31" tabindex="-1"></a> <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(x_scale)) {</span>
|
||
<span id="cb34-32"><a href="#cb34-32" tabindex="-1"></a> scales<span class="sc">$</span>x <span class="ot"><-</span> <span class="fu">lapply</span>(<span class="fu">seq_len</span>(<span class="fu">max</span>(layout<span class="sc">$</span>SCALE_X)), <span class="cf">function</span>(i) x_scale<span class="sc">$</span><span class="fu">clone</span>())</span>
|
||
<span id="cb34-33"><a href="#cb34-33" tabindex="-1"></a> }</span>
|
||
<span id="cb34-34"><a href="#cb34-34" tabindex="-1"></a> <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(y_scale)) {</span>
|
||
<span id="cb34-35"><a href="#cb34-35" tabindex="-1"></a> y_scale_orig <span class="ot"><-</span> y_scale<span class="sc">$</span><span class="fu">clone</span>()</span>
|
||
<span id="cb34-36"><a href="#cb34-36" tabindex="-1"></a> y_scale_new <span class="ot"><-</span> y_scale<span class="sc">$</span><span class="fu">clone</span>()</span>
|
||
<span id="cb34-37"><a href="#cb34-37" tabindex="-1"></a> y_scale_new<span class="sc">$</span>trans <span class="ot"><-</span> params<span class="sc">$</span>trans</span>
|
||
<span id="cb34-38"><a href="#cb34-38" tabindex="-1"></a> <span class="co"># Make sure that oob values are kept</span></span>
|
||
<span id="cb34-39"><a href="#cb34-39" tabindex="-1"></a> y_scale_new<span class="sc">$</span>oob <span class="ot"><-</span> <span class="cf">function</span>(x, ...) x</span>
|
||
<span id="cb34-40"><a href="#cb34-40" tabindex="-1"></a> scales<span class="sc">$</span>y <span class="ot"><-</span> <span class="fu">list</span>(y_scale_orig, y_scale_new)</span>
|
||
<span id="cb34-41"><a href="#cb34-41" tabindex="-1"></a> }</span>
|
||
<span id="cb34-42"><a href="#cb34-42" tabindex="-1"></a> scales</span>
|
||
<span id="cb34-43"><a href="#cb34-43" tabindex="-1"></a> },</span>
|
||
<span id="cb34-44"><a href="#cb34-44" tabindex="-1"></a> <span class="co"># We must make sure that the second scale is trained on transformed data</span></span>
|
||
<span id="cb34-45"><a href="#cb34-45" tabindex="-1"></a> <span class="at">train_scales =</span> <span class="cf">function</span>(x_scales, y_scales, layout, data, params) {</span>
|
||
<span id="cb34-46"><a href="#cb34-46" tabindex="-1"></a> <span class="co"># Transform data for second panel prior to scale training</span></span>
|
||
<span id="cb34-47"><a href="#cb34-47" tabindex="-1"></a> <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(y_scales)) {</span>
|
||
<span id="cb34-48"><a href="#cb34-48" tabindex="-1"></a> data <span class="ot"><-</span> <span class="fu">lapply</span>(data, <span class="cf">function</span>(layer_data) {</span>
|
||
<span id="cb34-49"><a href="#cb34-49" tabindex="-1"></a> match_id <span class="ot"><-</span> <span class="fu">match</span>(layer_data<span class="sc">$</span>PANEL, layout<span class="sc">$</span>PANEL)</span>
|
||
<span id="cb34-50"><a href="#cb34-50" tabindex="-1"></a> y_vars <span class="ot"><-</span> <span class="fu">intersect</span>(y_scales[[<span class="dv">1</span>]]<span class="sc">$</span>aesthetics, <span class="fu">names</span>(layer_data))</span>
|
||
<span id="cb34-51"><a href="#cb34-51" tabindex="-1"></a> trans_scale <span class="ot"><-</span> layer_data<span class="sc">$</span>PANEL <span class="sc">==</span> 2L</span>
|
||
<span id="cb34-52"><a href="#cb34-52" tabindex="-1"></a> <span class="cf">for</span> (i <span class="cf">in</span> y_vars) {</span>
|
||
<span id="cb34-53"><a href="#cb34-53" tabindex="-1"></a> layer_data[trans_scale, i] <span class="ot"><-</span> y_scales[[<span class="dv">2</span>]]<span class="sc">$</span><span class="fu">transform</span>(layer_data[trans_scale, i])</span>
|
||
<span id="cb34-54"><a href="#cb34-54" tabindex="-1"></a> }</span>
|
||
<span id="cb34-55"><a href="#cb34-55" tabindex="-1"></a> layer_data</span>
|
||
<span id="cb34-56"><a href="#cb34-56" tabindex="-1"></a> })</span>
|
||
<span id="cb34-57"><a href="#cb34-57" tabindex="-1"></a> }</span>
|
||
<span id="cb34-58"><a href="#cb34-58" tabindex="-1"></a> Facet<span class="sc">$</span><span class="fu">train_scales</span>(x_scales, y_scales, layout, data, params)</span>
|
||
<span id="cb34-59"><a href="#cb34-59" tabindex="-1"></a> },</span>
|
||
<span id="cb34-60"><a href="#cb34-60" tabindex="-1"></a> <span class="co"># this is where we actually modify the data. It cannot be done in $map_data as that function</span></span>
|
||
<span id="cb34-61"><a href="#cb34-61" tabindex="-1"></a> <span class="co"># doesn't have access to the scales</span></span>
|
||
<span id="cb34-62"><a href="#cb34-62" tabindex="-1"></a> <span class="at">finish_data =</span> <span class="cf">function</span>(data, layout, x_scales, y_scales, params) {</span>
|
||
<span id="cb34-63"><a href="#cb34-63" tabindex="-1"></a> match_id <span class="ot"><-</span> <span class="fu">match</span>(data<span class="sc">$</span>PANEL, layout<span class="sc">$</span>PANEL)</span>
|
||
<span id="cb34-64"><a href="#cb34-64" tabindex="-1"></a> y_vars <span class="ot"><-</span> <span class="fu">intersect</span>(y_scales[[<span class="dv">1</span>]]<span class="sc">$</span>aesthetics, <span class="fu">names</span>(data))</span>
|
||
<span id="cb34-65"><a href="#cb34-65" tabindex="-1"></a> trans_scale <span class="ot"><-</span> data<span class="sc">$</span>PANEL <span class="sc">==</span> 2L</span>
|
||
<span id="cb34-66"><a href="#cb34-66" tabindex="-1"></a> <span class="cf">for</span> (i <span class="cf">in</span> y_vars) {</span>
|
||
<span id="cb34-67"><a href="#cb34-67" tabindex="-1"></a> data[trans_scale, i] <span class="ot"><-</span> y_scales[[<span class="dv">2</span>]]<span class="sc">$</span><span class="fu">transform</span>(data[trans_scale, i])</span>
|
||
<span id="cb34-68"><a href="#cb34-68" tabindex="-1"></a> }</span>
|
||
<span id="cb34-69"><a href="#cb34-69" tabindex="-1"></a> data</span>
|
||
<span id="cb34-70"><a href="#cb34-70" tabindex="-1"></a> },</span>
|
||
<span id="cb34-71"><a href="#cb34-71" tabindex="-1"></a> <span class="co"># A few changes from before to accommodate that axes are now not duplicate of each other</span></span>
|
||
<span id="cb34-72"><a href="#cb34-72" tabindex="-1"></a> <span class="co"># We also add a panel strip to annotate the different panels</span></span>
|
||
<span id="cb34-73"><a href="#cb34-73" tabindex="-1"></a> <span class="at">draw_panels =</span> <span class="cf">function</span>(panels, layout, x_scales, y_scales, ranges, coord,</span>
|
||
<span id="cb34-74"><a href="#cb34-74" tabindex="-1"></a> data, theme, params) {</span>
|
||
<span id="cb34-75"><a href="#cb34-75" tabindex="-1"></a> <span class="co"># Place panels according to settings</span></span>
|
||
<span id="cb34-76"><a href="#cb34-76" tabindex="-1"></a> <span class="cf">if</span> (params<span class="sc">$</span>horizontal) {</span>
|
||
<span id="cb34-77"><a href="#cb34-77" tabindex="-1"></a> <span class="co"># Put panels in matrix and convert to a gtable</span></span>
|
||
<span id="cb34-78"><a href="#cb34-78" tabindex="-1"></a> panels <span class="ot"><-</span> <span class="fu">matrix</span>(panels, <span class="at">ncol =</span> <span class="dv">2</span>)</span>
|
||
<span id="cb34-79"><a href="#cb34-79" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_matrix</span>(<span class="st">"layout"</span>, panels, </span>
|
||
<span id="cb34-80"><a href="#cb34-80" tabindex="-1"></a> <span class="at">widths =</span> <span class="fu">unit</span>(<span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">1</span>), <span class="st">"null"</span>), <span class="at">heights =</span> <span class="fu">unit</span>(<span class="dv">1</span>, <span class="st">"null"</span>), <span class="at">clip =</span> <span class="st">"on"</span>)</span>
|
||
<span id="cb34-81"><a href="#cb34-81" tabindex="-1"></a> <span class="co"># Add spacing according to theme</span></span>
|
||
<span id="cb34-82"><a href="#cb34-82" tabindex="-1"></a> panel_spacing <span class="ot"><-</span> <span class="cf">if</span> (<span class="fu">is.null</span>(theme<span class="sc">$</span>panel.spacing.x)) {</span>
|
||
<span id="cb34-83"><a href="#cb34-83" tabindex="-1"></a> theme<span class="sc">$</span>panel.spacing</span>
|
||
<span id="cb34-84"><a href="#cb34-84" tabindex="-1"></a> } <span class="cf">else</span> {</span>
|
||
<span id="cb34-85"><a href="#cb34-85" tabindex="-1"></a> theme<span class="sc">$</span>panel.spacing.x</span>
|
||
<span id="cb34-86"><a href="#cb34-86" tabindex="-1"></a> }</span>
|
||
<span id="cb34-87"><a href="#cb34-87" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_col_space</span>(panel_table, panel_spacing)</span>
|
||
<span id="cb34-88"><a href="#cb34-88" tabindex="-1"></a> } <span class="cf">else</span> {</span>
|
||
<span id="cb34-89"><a href="#cb34-89" tabindex="-1"></a> panels <span class="ot"><-</span> <span class="fu">matrix</span>(panels, <span class="at">ncol =</span> <span class="dv">1</span>)</span>
|
||
<span id="cb34-90"><a href="#cb34-90" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_matrix</span>(<span class="st">"layout"</span>, panels, </span>
|
||
<span id="cb34-91"><a href="#cb34-91" tabindex="-1"></a> <span class="at">widths =</span> <span class="fu">unit</span>(<span class="dv">1</span>, <span class="st">"null"</span>), <span class="at">heights =</span> <span class="fu">unit</span>(<span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">1</span>), <span class="st">"null"</span>), <span class="at">clip =</span> <span class="st">"on"</span>)</span>
|
||
<span id="cb34-92"><a href="#cb34-92" tabindex="-1"></a> panel_spacing <span class="ot"><-</span> <span class="cf">if</span> (<span class="fu">is.null</span>(theme<span class="sc">$</span>panel.spacing.y)) {</span>
|
||
<span id="cb34-93"><a href="#cb34-93" tabindex="-1"></a> theme<span class="sc">$</span>panel.spacing</span>
|
||
<span id="cb34-94"><a href="#cb34-94" tabindex="-1"></a> } <span class="cf">else</span> {</span>
|
||
<span id="cb34-95"><a href="#cb34-95" tabindex="-1"></a> theme<span class="sc">$</span>panel.spacing.y</span>
|
||
<span id="cb34-96"><a href="#cb34-96" tabindex="-1"></a> }</span>
|
||
<span id="cb34-97"><a href="#cb34-97" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_row_space</span>(panel_table, panel_spacing)</span>
|
||
<span id="cb34-98"><a href="#cb34-98" tabindex="-1"></a> }</span>
|
||
<span id="cb34-99"><a href="#cb34-99" tabindex="-1"></a> <span class="co"># Name panel grobs so they can be found later</span></span>
|
||
<span id="cb34-100"><a href="#cb34-100" tabindex="-1"></a> panel_table<span class="sc">$</span>layout<span class="sc">$</span>name <span class="ot"><-</span> <span class="fu">paste0</span>(<span class="st">"panel-"</span>, <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">2</span>))</span>
|
||
<span id="cb34-101"><a href="#cb34-101" tabindex="-1"></a> </span>
|
||
<span id="cb34-102"><a href="#cb34-102" tabindex="-1"></a> <span class="co"># Construct the axes</span></span>
|
||
<span id="cb34-103"><a href="#cb34-103" tabindex="-1"></a> axes <span class="ot"><-</span> <span class="fu">render_axes</span>(ranges[<span class="dv">1</span>], ranges, coord, theme, </span>
|
||
<span id="cb34-104"><a href="#cb34-104" tabindex="-1"></a> <span class="at">transpose =</span> <span class="cn">TRUE</span>)</span>
|
||
<span id="cb34-105"><a href="#cb34-105" tabindex="-1"></a> </span>
|
||
<span id="cb34-106"><a href="#cb34-106" tabindex="-1"></a> <span class="co"># Add axes around each panel</span></span>
|
||
<span id="cb34-107"><a href="#cb34-107" tabindex="-1"></a> grobWidths <span class="ot"><-</span> <span class="cf">function</span>(x) {</span>
|
||
<span id="cb34-108"><a href="#cb34-108" tabindex="-1"></a> <span class="fu">unit</span>(<span class="fu">vapply</span>(x, <span class="cf">function</span>(x) {</span>
|
||
<span id="cb34-109"><a href="#cb34-109" tabindex="-1"></a> grid<span class="sc">::</span><span class="fu">convertWidth</span>(</span>
|
||
<span id="cb34-110"><a href="#cb34-110" tabindex="-1"></a> grid<span class="sc">::</span><span class="fu">grobWidth</span>(x), <span class="st">"cm"</span>, <span class="cn">TRUE</span>)</span>
|
||
<span id="cb34-111"><a href="#cb34-111" tabindex="-1"></a> }, <span class="fu">numeric</span>(<span class="dv">1</span>)), <span class="st">"cm"</span>)</span>
|
||
<span id="cb34-112"><a href="#cb34-112" tabindex="-1"></a> }</span>
|
||
<span id="cb34-113"><a href="#cb34-113" tabindex="-1"></a> panel_pos_h <span class="ot"><-</span> <span class="fu">panel_cols</span>(panel_table)<span class="sc">$</span>l</span>
|
||
<span id="cb34-114"><a href="#cb34-114" tabindex="-1"></a> panel_pos_v <span class="ot"><-</span> <span class="fu">panel_rows</span>(panel_table)<span class="sc">$</span>t</span>
|
||
<span id="cb34-115"><a href="#cb34-115" tabindex="-1"></a> axis_width_l <span class="ot"><-</span> <span class="fu">grobWidths</span>(axes<span class="sc">$</span>y<span class="sc">$</span>left)</span>
|
||
<span id="cb34-116"><a href="#cb34-116" tabindex="-1"></a> axis_width_r <span class="ot"><-</span> <span class="fu">grobWidths</span>(axes<span class="sc">$</span>y<span class="sc">$</span>right)</span>
|
||
<span id="cb34-117"><a href="#cb34-117" tabindex="-1"></a> <span class="do">## We do it reverse so we don't change the position of panels when we add axes</span></span>
|
||
<span id="cb34-118"><a href="#cb34-118" tabindex="-1"></a> <span class="cf">if</span> (params<span class="sc">$</span>horizontal) {</span>
|
||
<span id="cb34-119"><a href="#cb34-119" tabindex="-1"></a> <span class="cf">for</span> (i <span class="cf">in</span> <span class="fu">rev</span>(<span class="fu">seq_along</span>(panel_pos_h))) {</span>
|
||
<span id="cb34-120"><a href="#cb34-120" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_cols</span>(panel_table, axis_width_r[i], panel_pos_h[i])</span>
|
||
<span id="cb34-121"><a href="#cb34-121" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_grob</span>(panel_table,</span>
|
||
<span id="cb34-122"><a href="#cb34-122" tabindex="-1"></a> axes<span class="sc">$</span>y<span class="sc">$</span>right[i], <span class="at">t =</span> panel_pos_v, <span class="at">l =</span> panel_pos_h[i] <span class="sc">+</span> <span class="dv">1</span>,</span>
|
||
<span id="cb34-123"><a href="#cb34-123" tabindex="-1"></a> <span class="at">clip =</span> <span class="st">"off"</span>)</span>
|
||
<span id="cb34-124"><a href="#cb34-124" tabindex="-1"></a></span>
|
||
<span id="cb34-125"><a href="#cb34-125" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_cols</span>(panel_table, axis_width_l[i], panel_pos_h[i] <span class="sc">-</span> <span class="dv">1</span>)</span>
|
||
<span id="cb34-126"><a href="#cb34-126" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_grob</span>(panel_table,</span>
|
||
<span id="cb34-127"><a href="#cb34-127" tabindex="-1"></a> axes<span class="sc">$</span>y<span class="sc">$</span>left[i], <span class="at">t =</span> panel_pos_v, <span class="at">l =</span> panel_pos_h[i],</span>
|
||
<span id="cb34-128"><a href="#cb34-128" tabindex="-1"></a> <span class="at">clip =</span> <span class="st">"off"</span>)</span>
|
||
<span id="cb34-129"><a href="#cb34-129" tabindex="-1"></a> }</span>
|
||
<span id="cb34-130"><a href="#cb34-130" tabindex="-1"></a> } <span class="cf">else</span> {</span>
|
||
<span id="cb34-131"><a href="#cb34-131" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_cols</span>(panel_table, axis_width_r[<span class="dv">1</span>], panel_pos_h)</span>
|
||
<span id="cb34-132"><a href="#cb34-132" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_grob</span>(panel_table,</span>
|
||
<span id="cb34-133"><a href="#cb34-133" tabindex="-1"></a> axes<span class="sc">$</span>y<span class="sc">$</span>right, <span class="at">t =</span> panel_pos_v, <span class="at">l =</span> panel_pos_h <span class="sc">+</span> <span class="dv">1</span>,</span>
|
||
<span id="cb34-134"><a href="#cb34-134" tabindex="-1"></a> <span class="at">clip =</span> <span class="st">"off"</span>)</span>
|
||
<span id="cb34-135"><a href="#cb34-135" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_cols</span>(panel_table, axis_width_l[<span class="dv">1</span>], panel_pos_h <span class="sc">-</span> <span class="dv">1</span>)</span>
|
||
<span id="cb34-136"><a href="#cb34-136" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_grob</span>(panel_table,</span>
|
||
<span id="cb34-137"><a href="#cb34-137" tabindex="-1"></a> axes<span class="sc">$</span>y<span class="sc">$</span>left, <span class="at">t =</span> panel_pos_v, <span class="at">l =</span> panel_pos_h,</span>
|
||
<span id="cb34-138"><a href="#cb34-138" tabindex="-1"></a> <span class="at">clip =</span> <span class="st">"off"</span>)</span>
|
||
<span id="cb34-139"><a href="#cb34-139" tabindex="-1"></a> }</span>
|
||
<span id="cb34-140"><a href="#cb34-140" tabindex="-1"></a></span>
|
||
<span id="cb34-141"><a href="#cb34-141" tabindex="-1"></a> <span class="do">## Recalculate as gtable has changed</span></span>
|
||
<span id="cb34-142"><a href="#cb34-142" tabindex="-1"></a> panel_pos_h <span class="ot"><-</span> <span class="fu">panel_cols</span>(panel_table)<span class="sc">$</span>l</span>
|
||
<span id="cb34-143"><a href="#cb34-143" tabindex="-1"></a> panel_pos_v <span class="ot"><-</span> <span class="fu">panel_rows</span>(panel_table)<span class="sc">$</span>t</span>
|
||
<span id="cb34-144"><a href="#cb34-144" tabindex="-1"></a> axis_height_t <span class="ot"><-</span> <span class="fu">unit</span>(grid<span class="sc">::</span><span class="fu">convertHeight</span>(</span>
|
||
<span id="cb34-145"><a href="#cb34-145" tabindex="-1"></a> grid<span class="sc">::</span><span class="fu">grobHeight</span>(axes<span class="sc">$</span>x<span class="sc">$</span>top[[<span class="dv">1</span>]]), <span class="st">"cm"</span>, <span class="cn">TRUE</span>), <span class="st">"cm"</span>)</span>
|
||
<span id="cb34-146"><a href="#cb34-146" tabindex="-1"></a> axis_height_b <span class="ot"><-</span> <span class="fu">unit</span>(grid<span class="sc">::</span><span class="fu">convertHeight</span>(</span>
|
||
<span id="cb34-147"><a href="#cb34-147" tabindex="-1"></a> grid<span class="sc">::</span><span class="fu">grobHeight</span>(axes<span class="sc">$</span>x<span class="sc">$</span>bottom[[<span class="dv">1</span>]]), <span class="st">"cm"</span>, <span class="cn">TRUE</span>), <span class="st">"cm"</span>)</span>
|
||
<span id="cb34-148"><a href="#cb34-148" tabindex="-1"></a> <span class="cf">for</span> (i <span class="cf">in</span> <span class="fu">rev</span>(panel_pos_v)) {</span>
|
||
<span id="cb34-149"><a href="#cb34-149" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_rows</span>(panel_table, axis_height_b, i)</span>
|
||
<span id="cb34-150"><a href="#cb34-150" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_grob</span>(panel_table, </span>
|
||
<span id="cb34-151"><a href="#cb34-151" tabindex="-1"></a> <span class="fu">rep</span>(axes<span class="sc">$</span>x<span class="sc">$</span>bottom, <span class="fu">length</span>(panel_pos_h)), <span class="at">t =</span> i <span class="sc">+</span> <span class="dv">1</span>, <span class="at">l =</span> panel_pos_h, </span>
|
||
<span id="cb34-152"><a href="#cb34-152" tabindex="-1"></a> <span class="at">clip =</span> <span class="st">"off"</span>)</span>
|
||
<span id="cb34-153"><a href="#cb34-153" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_rows</span>(panel_table, axis_height_t, i <span class="sc">-</span> <span class="dv">1</span>)</span>
|
||
<span id="cb34-154"><a href="#cb34-154" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_grob</span>(panel_table, </span>
|
||
<span id="cb34-155"><a href="#cb34-155" tabindex="-1"></a> <span class="fu">rep</span>(axes<span class="sc">$</span>x<span class="sc">$</span>top, <span class="fu">length</span>(panel_pos_h)), <span class="at">t =</span> i, <span class="at">l =</span> panel_pos_h, </span>
|
||
<span id="cb34-156"><a href="#cb34-156" tabindex="-1"></a> <span class="at">clip =</span> <span class="st">"off"</span>)</span>
|
||
<span id="cb34-157"><a href="#cb34-157" tabindex="-1"></a> }</span>
|
||
<span id="cb34-158"><a href="#cb34-158" tabindex="-1"></a> </span>
|
||
<span id="cb34-159"><a href="#cb34-159" tabindex="-1"></a> <span class="co"># Add strips</span></span>
|
||
<span id="cb34-160"><a href="#cb34-160" tabindex="-1"></a> strips <span class="ot"><-</span> <span class="fu">render_strips</span>(</span>
|
||
<span id="cb34-161"><a href="#cb34-161" tabindex="-1"></a> <span class="at">x =</span> <span class="fu">data.frame</span>(<span class="at">name =</span> <span class="fu">c</span>(<span class="st">"Original"</span>, <span class="fu">paste0</span>(<span class="st">"Transformed ("</span>, params<span class="sc">$</span>trans<span class="sc">$</span>name, <span class="st">")"</span>))),</span>
|
||
<span id="cb34-162"><a href="#cb34-162" tabindex="-1"></a> <span class="at">labeller =</span> label_value, <span class="at">theme =</span> theme)</span>
|
||
<span id="cb34-163"><a href="#cb34-163" tabindex="-1"></a> </span>
|
||
<span id="cb34-164"><a href="#cb34-164" tabindex="-1"></a> panel_pos_h <span class="ot"><-</span> <span class="fu">panel_cols</span>(panel_table)<span class="sc">$</span>l</span>
|
||
<span id="cb34-165"><a href="#cb34-165" tabindex="-1"></a> panel_pos_v <span class="ot"><-</span> <span class="fu">panel_rows</span>(panel_table)<span class="sc">$</span>t</span>
|
||
<span id="cb34-166"><a href="#cb34-166" tabindex="-1"></a> strip_height <span class="ot"><-</span> <span class="fu">unit</span>(grid<span class="sc">::</span><span class="fu">convertHeight</span>(</span>
|
||
<span id="cb34-167"><a href="#cb34-167" tabindex="-1"></a> grid<span class="sc">::</span><span class="fu">grobHeight</span>(strips<span class="sc">$</span>x<span class="sc">$</span>top[[<span class="dv">1</span>]]), <span class="st">"cm"</span>, <span class="cn">TRUE</span>), <span class="st">"cm"</span>)</span>
|
||
<span id="cb34-168"><a href="#cb34-168" tabindex="-1"></a> <span class="cf">for</span> (i <span class="cf">in</span> <span class="fu">rev</span>(<span class="fu">seq_along</span>(panel_pos_v))) {</span>
|
||
<span id="cb34-169"><a href="#cb34-169" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_rows</span>(panel_table, strip_height, panel_pos_v[i] <span class="sc">-</span> <span class="dv">1</span>)</span>
|
||
<span id="cb34-170"><a href="#cb34-170" tabindex="-1"></a> <span class="cf">if</span> (params<span class="sc">$</span>horizontal) {</span>
|
||
<span id="cb34-171"><a href="#cb34-171" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_grob</span>(panel_table, strips<span class="sc">$</span>x<span class="sc">$</span>top, </span>
|
||
<span id="cb34-172"><a href="#cb34-172" tabindex="-1"></a> <span class="at">t =</span> panel_pos_v[i], <span class="at">l =</span> panel_pos_h, <span class="at">clip =</span> <span class="st">"off"</span>)</span>
|
||
<span id="cb34-173"><a href="#cb34-173" tabindex="-1"></a> } <span class="cf">else</span> {</span>
|
||
<span id="cb34-174"><a href="#cb34-174" tabindex="-1"></a> panel_table <span class="ot"><-</span> gtable<span class="sc">::</span><span class="fu">gtable_add_grob</span>(panel_table, strips<span class="sc">$</span>x<span class="sc">$</span>top[i], </span>
|
||
<span id="cb34-175"><a href="#cb34-175" tabindex="-1"></a> <span class="at">t =</span> panel_pos_v[i], <span class="at">l =</span> panel_pos_h, <span class="at">clip =</span> <span class="st">"off"</span>)</span>
|
||
<span id="cb34-176"><a href="#cb34-176" tabindex="-1"></a> }</span>
|
||
<span id="cb34-177"><a href="#cb34-177" tabindex="-1"></a> }</span>
|
||
<span id="cb34-178"><a href="#cb34-178" tabindex="-1"></a> </span>
|
||
<span id="cb34-179"><a href="#cb34-179" tabindex="-1"></a> </span>
|
||
<span id="cb34-180"><a href="#cb34-180" tabindex="-1"></a> panel_table</span>
|
||
<span id="cb34-181"><a href="#cb34-181" tabindex="-1"></a> }</span>
|
||
<span id="cb34-182"><a href="#cb34-182" tabindex="-1"></a>)</span></code></pre></div>
|
||
<p>As is very apparent, the <code>draw_panel</code> method can become
|
||
very unwieldy once it begins to take multiple possibilities into
|
||
account. The fact that we want to support both horizontal and vertical
|
||
layout leads to a lot of if/else blocks in the above code. In general,
|
||
this is the big challenge when writing facet extensions so be prepared
|
||
to be very meticulous when writing these methods.</p>
|
||
<p>Enough talk - lets see if our new and powerful faceting extension
|
||
works:</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><span class="fu">ggplot</span>(mtcars, <span class="fu">aes</span>(<span class="at">x =</span> hp, <span class="at">y =</span> mpg)) <span class="sc">+</span> <span class="fu">geom_point</span>() <span class="sc">+</span> <span class="fu">facet_trans</span>(<span class="st">'sqrt'</span>)</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" alt="Scatterplot with two panels showing horsepower against miles per gallon for 32 cars. Both panels show the same datapoints. The left panel is titled 'original' and the right panel is titled 'transformed (sqrt)'. On the right panel, the miles per gallon are displayed on a square root transformed scale." style="display: block; margin: auto;" /></p>
|
||
</div>
|
||
</div>
|
||
<div id="extending-existing-facet-function" class="section level2">
|
||
<h2>Extending existing facet function</h2>
|
||
<p>As the rendering part of a facet class is often the difficult
|
||
development step, it is possible to piggyback on the existing faceting
|
||
classes to achieve a range of new facetings. Below we will subclass
|
||
<code>facet_wrap()</code> to make a <code>facet_bootstrap()</code> class
|
||
that splits the input data into a number of panels at random.</p>
|
||
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" tabindex="-1"></a>facet_bootstrap <span class="ot"><-</span> <span class="cf">function</span>(<span class="at">n =</span> <span class="dv">9</span>, <span class="at">prop =</span> <span class="fl">0.2</span>, <span class="at">nrow =</span> <span class="cn">NULL</span>, <span class="at">ncol =</span> <span class="cn">NULL</span>, </span>
|
||
<span id="cb36-2"><a href="#cb36-2" tabindex="-1"></a> <span class="at">scales =</span> <span class="st">"fixed"</span>, <span class="at">shrink =</span> <span class="cn">TRUE</span>, <span class="at">strip.position =</span> <span class="st">"top"</span>) {</span>
|
||
<span id="cb36-3"><a href="#cb36-3" tabindex="-1"></a> </span>
|
||
<span id="cb36-4"><a href="#cb36-4" tabindex="-1"></a> facet <span class="ot"><-</span> <span class="fu">facet_wrap</span>(<span class="sc">~</span>.bootstrap, <span class="at">nrow =</span> nrow, <span class="at">ncol =</span> ncol, <span class="at">scales =</span> scales, </span>
|
||
<span id="cb36-5"><a href="#cb36-5" tabindex="-1"></a> <span class="at">shrink =</span> shrink, <span class="at">strip.position =</span> strip.position)</span>
|
||
<span id="cb36-6"><a href="#cb36-6" tabindex="-1"></a> facet<span class="sc">$</span>params<span class="sc">$</span>n <span class="ot"><-</span> n</span>
|
||
<span id="cb36-7"><a href="#cb36-7" tabindex="-1"></a> facet<span class="sc">$</span>params<span class="sc">$</span>prop <span class="ot"><-</span> prop</span>
|
||
<span id="cb36-8"><a href="#cb36-8" tabindex="-1"></a> <span class="fu">ggproto</span>(<span class="cn">NULL</span>, FacetBootstrap,</span>
|
||
<span id="cb36-9"><a href="#cb36-9" tabindex="-1"></a> <span class="at">shrink =</span> shrink,</span>
|
||
<span id="cb36-10"><a href="#cb36-10" tabindex="-1"></a> <span class="at">params =</span> facet<span class="sc">$</span>params</span>
|
||
<span id="cb36-11"><a href="#cb36-11" tabindex="-1"></a> )</span>
|
||
<span id="cb36-12"><a href="#cb36-12" tabindex="-1"></a>}</span>
|
||
<span id="cb36-13"><a href="#cb36-13" tabindex="-1"></a></span>
|
||
<span id="cb36-14"><a href="#cb36-14" tabindex="-1"></a>FacetBootstrap <span class="ot"><-</span> <span class="fu">ggproto</span>(<span class="st">"FacetBootstrap"</span>, FacetWrap,</span>
|
||
<span id="cb36-15"><a href="#cb36-15" tabindex="-1"></a> <span class="at">compute_layout =</span> <span class="cf">function</span>(data, params) {</span>
|
||
<span id="cb36-16"><a href="#cb36-16" tabindex="-1"></a> id <span class="ot"><-</span> <span class="fu">seq_len</span>(params<span class="sc">$</span>n)</span>
|
||
<span id="cb36-17"><a href="#cb36-17" tabindex="-1"></a></span>
|
||
<span id="cb36-18"><a href="#cb36-18" tabindex="-1"></a> dims <span class="ot"><-</span> <span class="fu">wrap_dims</span>(params<span class="sc">$</span>n, params<span class="sc">$</span>nrow, params<span class="sc">$</span>ncol)</span>
|
||
<span id="cb36-19"><a href="#cb36-19" tabindex="-1"></a> layout <span class="ot"><-</span> <span class="fu">data.frame</span>(<span class="at">PANEL =</span> <span class="fu">factor</span>(id))</span>
|
||
<span id="cb36-20"><a href="#cb36-20" tabindex="-1"></a></span>
|
||
<span id="cb36-21"><a href="#cb36-21" tabindex="-1"></a> <span class="cf">if</span> (params<span class="sc">$</span>as.table) {</span>
|
||
<span id="cb36-22"><a href="#cb36-22" tabindex="-1"></a> layout<span class="sc">$</span>ROW <span class="ot"><-</span> <span class="fu">as.integer</span>((id <span class="sc">-</span> 1L) <span class="sc">%/%</span> dims[<span class="dv">2</span>] <span class="sc">+</span> 1L)</span>
|
||
<span id="cb36-23"><a href="#cb36-23" tabindex="-1"></a> } <span class="cf">else</span> {</span>
|
||
<span id="cb36-24"><a href="#cb36-24" tabindex="-1"></a> layout<span class="sc">$</span>ROW <span class="ot"><-</span> <span class="fu">as.integer</span>(dims[<span class="dv">1</span>] <span class="sc">-</span> (id <span class="sc">-</span> 1L) <span class="sc">%/%</span> dims[<span class="dv">2</span>])</span>
|
||
<span id="cb36-25"><a href="#cb36-25" tabindex="-1"></a> }</span>
|
||
<span id="cb36-26"><a href="#cb36-26" tabindex="-1"></a> layout<span class="sc">$</span>COL <span class="ot"><-</span> <span class="fu">as.integer</span>((id <span class="sc">-</span> 1L) <span class="sc">%%</span> dims[<span class="dv">2</span>] <span class="sc">+</span> 1L)</span>
|
||
<span id="cb36-27"><a href="#cb36-27" tabindex="-1"></a></span>
|
||
<span id="cb36-28"><a href="#cb36-28" tabindex="-1"></a> layout <span class="ot"><-</span> layout[<span class="fu">order</span>(layout<span class="sc">$</span>PANEL), , drop <span class="ot">=</span> <span class="cn">FALSE</span>]</span>
|
||
<span id="cb36-29"><a href="#cb36-29" tabindex="-1"></a> <span class="fu">rownames</span>(layout) <span class="ot"><-</span> <span class="cn">NULL</span></span>
|
||
<span id="cb36-30"><a href="#cb36-30" tabindex="-1"></a></span>
|
||
<span id="cb36-31"><a href="#cb36-31" tabindex="-1"></a> <span class="co"># Add scale identification</span></span>
|
||
<span id="cb36-32"><a href="#cb36-32" tabindex="-1"></a> layout<span class="sc">$</span>SCALE_X <span class="ot"><-</span> <span class="cf">if</span> (params<span class="sc">$</span>free<span class="sc">$</span>x) id <span class="cf">else</span> 1L</span>
|
||
<span id="cb36-33"><a href="#cb36-33" tabindex="-1"></a> layout<span class="sc">$</span>SCALE_Y <span class="ot"><-</span> <span class="cf">if</span> (params<span class="sc">$</span>free<span class="sc">$</span>y) id <span class="cf">else</span> 1L</span>
|
||
<span id="cb36-34"><a href="#cb36-34" tabindex="-1"></a></span>
|
||
<span id="cb36-35"><a href="#cb36-35" tabindex="-1"></a> <span class="fu">cbind</span>(layout, <span class="at">.bootstrap =</span> id)</span>
|
||
<span id="cb36-36"><a href="#cb36-36" tabindex="-1"></a> },</span>
|
||
<span id="cb36-37"><a href="#cb36-37" tabindex="-1"></a> <span class="at">map_data =</span> <span class="cf">function</span>(data, layout, params) {</span>
|
||
<span id="cb36-38"><a href="#cb36-38" tabindex="-1"></a> <span class="cf">if</span> (<span class="fu">is.null</span>(data) <span class="sc">||</span> <span class="fu">nrow</span>(data) <span class="sc">==</span> <span class="dv">0</span>) {</span>
|
||
<span id="cb36-39"><a href="#cb36-39" tabindex="-1"></a> <span class="fu">return</span>(<span class="fu">cbind</span>(data, <span class="at">PANEL =</span> <span class="fu">integer</span>(<span class="dv">0</span>)))</span>
|
||
<span id="cb36-40"><a href="#cb36-40" tabindex="-1"></a> }</span>
|
||
<span id="cb36-41"><a href="#cb36-41" tabindex="-1"></a> n_samples <span class="ot"><-</span> <span class="fu">round</span>(<span class="fu">nrow</span>(data) <span class="sc">*</span> params<span class="sc">$</span>prop)</span>
|
||
<span id="cb36-42"><a href="#cb36-42" tabindex="-1"></a> new_data <span class="ot"><-</span> <span class="fu">lapply</span>(<span class="fu">seq_len</span>(params<span class="sc">$</span>n), <span class="cf">function</span>(i) {</span>
|
||
<span id="cb36-43"><a href="#cb36-43" tabindex="-1"></a> <span class="fu">cbind</span>(data[<span class="fu">sample</span>(<span class="fu">nrow</span>(data), n_samples), , <span class="at">drop =</span> <span class="cn">FALSE</span>], <span class="at">PANEL =</span> i)</span>
|
||
<span id="cb36-44"><a href="#cb36-44" tabindex="-1"></a> })</span>
|
||
<span id="cb36-45"><a href="#cb36-45" tabindex="-1"></a> <span class="fu">do.call</span>(rbind, new_data)</span>
|
||
<span id="cb36-46"><a href="#cb36-46" tabindex="-1"></a> }</span>
|
||
<span id="cb36-47"><a href="#cb36-47" tabindex="-1"></a>)</span>
|
||
<span id="cb36-48"><a href="#cb36-48" tabindex="-1"></a></span>
|
||
<span id="cb36-49"><a href="#cb36-49" tabindex="-1"></a><span class="fu">ggplot</span>(diamonds, <span class="fu">aes</span>(carat, price)) <span class="sc">+</span> </span>
|
||
<span id="cb36-50"><a href="#cb36-50" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="at">alpha =</span> <span class="fl">0.1</span>) <span class="sc">+</span> </span>
|
||
<span id="cb36-51"><a href="#cb36-51" tabindex="-1"></a> <span class="fu">facet_bootstrap</span>(<span class="at">n =</span> <span class="dv">9</span>, <span class="at">prop =</span> <span class="fl">0.05</span>)</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" alt="Scatterplot with three-by-three panels showing the weight versus the price of about 10.000 diamonds in every panel. The panels are titled 1 to 9 and show different points, but are visually similar." style="display: block; margin: auto;" /></p>
|
||
<p>What we are doing above is to intercept the
|
||
<code>compute_layout</code> and <code>map_data</code> methods and
|
||
instead of dividing the data by a variable we randomly assigns rows to a
|
||
panel based on the sampling parameters (<code>n</code> determines the
|
||
number of panels, <code>prop</code> determines the proportion of data in
|
||
each panel). It is important here that the layout returned by
|
||
<code>compute_layout</code> is a valid layout for <code>FacetWrap</code>
|
||
as we are counting on the <code>draw_panel</code> method from
|
||
<code>FacetWrap</code> to do all the work for us. Thus if you want to
|
||
subclass FacetWrap or FacetGrid, make sure you understand the nature of
|
||
their layout specification.</p>
|
||
<div id="exercises-2" class="section level3">
|
||
<h3>Exercises</h3>
|
||
<ol style="list-style-type: decimal">
|
||
<li>Rewrite FacetTrans to take a vector of transformations and create an
|
||
additional panel for each transformation.</li>
|
||
<li>Based on the FacetWrap implementation rewrite FacetTrans to take the
|
||
strip.placement theme setting into account.</li>
|
||
<li>Think about which caveats there are in FacetBootstrap specifically
|
||
related to adding multiple layers with the same data.</li>
|
||
</ol>
|
||
</div>
|
||
</div>
|
||
<div id="creating-new-guides" class="section level2">
|
||
<h2>Creating new guides</h2>
|
||
<p>Guides are closely related to scales and aesthetics, so an important
|
||
part of guides is taking information from the scale and translating it
|
||
to a graphic. This information is passed around inside guides as a
|
||
<code>key</code> dataframe. For existing guides, you can glance at what
|
||
a key contains by using the <code>get_guide_data()</code> function.
|
||
Typical variables you may see in guides are the aesthetic mapped by the
|
||
scale, such as the hexadecimal colours in the example below, what those
|
||
aesthetic represent in the <code>.value</code> column and how they
|
||
should be labelled in the <code>.label</code> column. Sometimes, the
|
||
aesthetic is used in computations. To avoid interpreting the values and
|
||
labels as aesthetics, it is customary to prefix these with
|
||
<code>.</code>.</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>p <span class="ot"><-</span> <span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, hwy, <span class="at">colour =</span> drv)) <span class="sc">+</span></span>
|
||
<span id="cb37-2"><a href="#cb37-2" tabindex="-1"></a> <span class="fu">geom_point</span>() <span class="sc">+</span></span>
|
||
<span id="cb37-3"><a href="#cb37-3" tabindex="-1"></a> <span class="fu">scale_colour_discrete</span>(</span>
|
||
<span id="cb37-4"><a href="#cb37-4" tabindex="-1"></a> <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">"4-wheel drive"</span>, <span class="st">"front wheel drive"</span>, <span class="st">"rear wheel drive"</span>)</span>
|
||
<span id="cb37-5"><a href="#cb37-5" tabindex="-1"></a> )</span>
|
||
<span id="cb37-6"><a href="#cb37-6" tabindex="-1"></a></span>
|
||
<span id="cb37-7"><a href="#cb37-7" tabindex="-1"></a><span class="fu">get_guide_data</span>(p, <span class="st">"colour"</span>)</span>
|
||
<span id="cb37-8"><a href="#cb37-8" tabindex="-1"></a><span class="co">#> colour .value .label</span></span>
|
||
<span id="cb37-9"><a href="#cb37-9" tabindex="-1"></a><span class="co">#> 1 #F8766D 4 4-wheel drive</span></span>
|
||
<span id="cb37-10"><a href="#cb37-10" tabindex="-1"></a><span class="co">#> 2 #00BA38 f front wheel drive</span></span>
|
||
<span id="cb37-11"><a href="#cb37-11" tabindex="-1"></a><span class="co">#> 3 #619CFF r rear wheel drive</span></span></code></pre></div>
|
||
<div id="overriding-scale-extraction" class="section level3">
|
||
<h3>Overriding scale extraction</h3>
|
||
<p>Let’s now make a first guide extension by adjusting the guide’s key.
|
||
Axes are most straightforward to extend, because they are the least
|
||
complicated. We’ll build an axis that accepts custom values for the
|
||
guide’s <code>key</code>. We can begin by making a custom ggproto class
|
||
that inherits from the axis guide. An important extension point is the
|
||
<code>extract_key()</code> method, which determines how break
|
||
information is transferred from the scale to the guide. In our class, we
|
||
reject the scale’s reality and substitute our own.</p>
|
||
<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" tabindex="-1"></a>GuideKey <span class="ot"><-</span> <span class="fu">ggproto</span>(</span>
|
||
<span id="cb38-2"><a href="#cb38-2" tabindex="-1"></a> <span class="st">"Guide"</span>, GuideAxis,</span>
|
||
<span id="cb38-3"><a href="#cb38-3" tabindex="-1"></a> </span>
|
||
<span id="cb38-4"><a href="#cb38-4" tabindex="-1"></a> <span class="co"># Some parameters are required, so it is easiest to copy the base Guide's</span></span>
|
||
<span id="cb38-5"><a href="#cb38-5" tabindex="-1"></a> <span class="co"># parameters into our new parameters.</span></span>
|
||
<span id="cb38-6"><a href="#cb38-6" tabindex="-1"></a> <span class="co"># We add a new 'key' parameter for our own guide.</span></span>
|
||
<span id="cb38-7"><a href="#cb38-7" tabindex="-1"></a> <span class="at">params =</span> <span class="fu">c</span>(GuideAxis<span class="sc">$</span>params, <span class="fu">list</span>(<span class="at">key =</span> <span class="cn">NULL</span>)),</span>
|
||
<span id="cb38-8"><a href="#cb38-8" tabindex="-1"></a> </span>
|
||
<span id="cb38-9"><a href="#cb38-9" tabindex="-1"></a> <span class="co"># It is important for guides to have a mapped aesthetic with the correct name</span></span>
|
||
<span id="cb38-10"><a href="#cb38-10" tabindex="-1"></a> <span class="at">extract_key =</span> <span class="cf">function</span>(scale, aesthetic, key, ...) {</span>
|
||
<span id="cb38-11"><a href="#cb38-11" tabindex="-1"></a> key<span class="sc">$</span>aesthetic <span class="ot"><-</span> scale<span class="sc">$</span><span class="fu">map</span>(key<span class="sc">$</span>aesthetic)</span>
|
||
<span id="cb38-12"><a href="#cb38-12" tabindex="-1"></a> <span class="fu">names</span>(key)[<span class="fu">names</span>(key) <span class="sc">==</span> <span class="st">"aesthetic"</span>] <span class="ot"><-</span> aesthetic</span>
|
||
<span id="cb38-13"><a href="#cb38-13" tabindex="-1"></a> key</span>
|
||
<span id="cb38-14"><a href="#cb38-14" tabindex="-1"></a> }</span>
|
||
<span id="cb38-15"><a href="#cb38-15" tabindex="-1"></a>)</span></code></pre></div>
|
||
</div>
|
||
<div id="guide-constructors" class="section level3">
|
||
<h3>Guide constructors</h3>
|
||
<p>Now we can make a guide constructor that creates a custom key to pass
|
||
along on. The <code>new_guide()</code> function instantiates a new guide
|
||
with the given parameters. This function automatically rejects any
|
||
parameters that are not in the class’ <code>params</code> field, so it
|
||
is important to declare these.</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>guide_key <span class="ot"><-</span> <span class="cf">function</span>(</span>
|
||
<span id="cb39-2"><a href="#cb39-2" tabindex="-1"></a> aesthetic, <span class="at">value =</span> aesthetic, <span class="at">label =</span> <span class="fu">as.character</span>(aesthetic),</span>
|
||
<span id="cb39-3"><a href="#cb39-3" tabindex="-1"></a> ...,</span>
|
||
<span id="cb39-4"><a href="#cb39-4" tabindex="-1"></a> <span class="co"># Standard guide arguments</span></span>
|
||
<span id="cb39-5"><a href="#cb39-5" tabindex="-1"></a> <span class="at">theme =</span> <span class="cn">NULL</span>, <span class="at">title =</span> <span class="fu">waiver</span>(), <span class="at">order =</span> <span class="dv">0</span>, <span class="at">position =</span> <span class="fu">waiver</span>()</span>
|
||
<span id="cb39-6"><a href="#cb39-6" tabindex="-1"></a>) {</span>
|
||
<span id="cb39-7"><a href="#cb39-7" tabindex="-1"></a> </span>
|
||
<span id="cb39-8"><a href="#cb39-8" tabindex="-1"></a> key <span class="ot"><-</span> <span class="fu">data.frame</span>(aesthetic, <span class="at">.value =</span> value, <span class="at">.label =</span> label, ...)</span>
|
||
<span id="cb39-9"><a href="#cb39-9" tabindex="-1"></a> </span>
|
||
<span id="cb39-10"><a href="#cb39-10" tabindex="-1"></a> <span class="fu">new_guide</span>(</span>
|
||
<span id="cb39-11"><a href="#cb39-11" tabindex="-1"></a> <span class="co"># Arguments passed on to the GuideKey$params field</span></span>
|
||
<span id="cb39-12"><a href="#cb39-12" tabindex="-1"></a> <span class="at">key =</span> key, <span class="at">theme =</span> theme, <span class="at">title =</span> title, <span class="at">order =</span> order, <span class="at">position =</span> position,</span>
|
||
<span id="cb39-13"><a href="#cb39-13" tabindex="-1"></a> <span class="co"># Declare which aesthetics are supported</span></span>
|
||
<span id="cb39-14"><a href="#cb39-14" tabindex="-1"></a> <span class="at">available_aes =</span> <span class="fu">c</span>(<span class="st">"x"</span>, <span class="st">"y"</span>),</span>
|
||
<span id="cb39-15"><a href="#cb39-15" tabindex="-1"></a> <span class="co"># Set the guide class</span></span>
|
||
<span id="cb39-16"><a href="#cb39-16" tabindex="-1"></a> <span class="at">super =</span> GuideKey</span>
|
||
<span id="cb39-17"><a href="#cb39-17" tabindex="-1"></a> )</span>
|
||
<span id="cb39-18"><a href="#cb39-18" tabindex="-1"></a>}</span></code></pre></div>
|
||
<p>Our new guide can now be used inside the <code>guides()</code>
|
||
function or as the <code>guide</code> argument in a position scale.</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">ggplot</span>(mpg, <span class="fu">aes</span>(displ, hwy)) <span class="sc">+</span></span>
|
||
<span id="cb40-2"><a href="#cb40-2" tabindex="-1"></a> <span class="fu">geom_point</span>() <span class="sc">+</span></span>
|
||
<span id="cb40-3"><a href="#cb40-3" tabindex="-1"></a> <span class="fu">scale_x_continuous</span>(</span>
|
||
<span id="cb40-4"><a href="#cb40-4" tabindex="-1"></a> <span class="at">guide =</span> <span class="fu">guide_key</span>(<span class="at">aesthetic =</span> <span class="dv">2</span><span class="sc">:</span><span class="dv">6</span> <span class="sc">+</span> <span class="fl">0.5</span>)</span>
|
||
<span id="cb40-5"><a href="#cb40-5" tabindex="-1"></a> )</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" alt="Scatterplot of engine displacement versus highway miles per gallon. The x-axis axis ticks are at 2.5, 3.5, 4.5, 5.5 and 6.5." style="display: block; margin: auto;" /></p>
|
||
</div>
|
||
<div id="custom-drawings" class="section level3">
|
||
<h3>Custom drawings</h3>
|
||
<p>If we are feeling more adventurous, we can also alter they way guides
|
||
are drawn. The majority of drawing code is in the
|
||
<code>Guide$build_*()</code> methods, which is all orchestrated by the
|
||
<code>Guide$draw()</code> method. For derived guides, such as the custom
|
||
key guide we’re extending here, overriding a
|
||
<code>Guide$build_*()</code> method should be sufficient. If you are
|
||
writing a completely novel guide that does not resemble the structure of
|
||
any existing guide, overriding the <code>Guide$draw()</code> method
|
||
might be wise.</p>
|
||
<p>In this example, we are changing the way the labels are drawn, so we
|
||
should edit the <code>Guide$build_labels()</code> method. We’ll edit the
|
||
method so that the labels are drawn with a <code>colour</code> set in
|
||
the key. In addition to the <code>key</code> and <code>params</code>
|
||
variable we’ve seen before, we now also have an <code>elements</code>
|
||
variable, which is a list of precomputed theme elements. We can use the
|
||
<code>elements$text</code> element to draw a graphical object (grob) in
|
||
the style of axis text. Perhaps the most finicky thing about drawing
|
||
guides is that a lot of settings depend on the guide’s
|
||
<code>position</code> parameter.</p>
|
||
<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" tabindex="-1"></a><span class="co"># Same as before</span></span>
|
||
<span id="cb41-2"><a href="#cb41-2" tabindex="-1"></a>GuideKey <span class="ot"><-</span> <span class="fu">ggproto</span>(</span>
|
||
<span id="cb41-3"><a href="#cb41-3" tabindex="-1"></a> <span class="st">"Guide"</span>, GuideAxis,</span>
|
||
<span id="cb41-4"><a href="#cb41-4" tabindex="-1"></a> <span class="at">params =</span> <span class="fu">c</span>(GuideAxis<span class="sc">$</span>params, <span class="fu">list</span>(<span class="at">key =</span> <span class="cn">NULL</span>)),</span>
|
||
<span id="cb41-5"><a href="#cb41-5" tabindex="-1"></a> <span class="at">extract_key =</span> <span class="cf">function</span>(scale, aesthetic, key, ...) {</span>
|
||
<span id="cb41-6"><a href="#cb41-6" tabindex="-1"></a> key<span class="sc">$</span>aesthetic <span class="ot"><-</span> scale<span class="sc">$</span><span class="fu">map</span>(key<span class="sc">$</span>aesthetic)</span>
|
||
<span id="cb41-7"><a href="#cb41-7" tabindex="-1"></a> <span class="fu">names</span>(key)[<span class="fu">names</span>(key) <span class="sc">==</span> <span class="st">"aesthetic"</span>] <span class="ot"><-</span> aesthetic</span>
|
||
<span id="cb41-8"><a href="#cb41-8" tabindex="-1"></a> key</span>
|
||
<span id="cb41-9"><a href="#cb41-9" tabindex="-1"></a> },</span>
|
||
<span id="cb41-10"><a href="#cb41-10" tabindex="-1"></a> </span>
|
||
<span id="cb41-11"><a href="#cb41-11" tabindex="-1"></a> <span class="co"># New method to draw labels</span></span>
|
||
<span id="cb41-12"><a href="#cb41-12" tabindex="-1"></a> <span class="at">build_labels =</span> <span class="cf">function</span>(key, elements, params) {</span>
|
||
<span id="cb41-13"><a href="#cb41-13" tabindex="-1"></a> position <span class="ot"><-</span> params<span class="sc">$</span>position</span>
|
||
<span id="cb41-14"><a href="#cb41-14" tabindex="-1"></a> <span class="co"># Downstream code expects a list of labels</span></span>
|
||
<span id="cb41-15"><a href="#cb41-15" tabindex="-1"></a> <span class="fu">list</span>(<span class="fu">element_grob</span>(</span>
|
||
<span id="cb41-16"><a href="#cb41-16" tabindex="-1"></a> elements<span class="sc">$</span>text,</span>
|
||
<span id="cb41-17"><a href="#cb41-17" tabindex="-1"></a> <span class="at">label =</span> key<span class="sc">$</span>.label,</span>
|
||
<span id="cb41-18"><a href="#cb41-18" tabindex="-1"></a> <span class="at">x =</span> <span class="cf">switch</span>(position, <span class="at">left =</span> <span class="dv">1</span>, <span class="at">right =</span> <span class="dv">0</span>, key<span class="sc">$</span>x),</span>
|
||
<span id="cb41-19"><a href="#cb41-19" tabindex="-1"></a> <span class="at">y =</span> <span class="cf">switch</span>(position, <span class="at">top =</span> <span class="dv">0</span>, <span class="at">bottom =</span> <span class="dv">1</span>, key<span class="sc">$</span>y),</span>
|
||
<span id="cb41-20"><a href="#cb41-20" tabindex="-1"></a> <span class="at">margin_x =</span> position <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"left"</span>, <span class="st">"right"</span>),</span>
|
||
<span id="cb41-21"><a href="#cb41-21" tabindex="-1"></a> <span class="at">margin_y =</span> position <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"top"</span>, <span class="st">"bottom"</span>),</span>
|
||
<span id="cb41-22"><a href="#cb41-22" tabindex="-1"></a> <span class="at">colour =</span> key<span class="sc">$</span>colour</span>
|
||
<span id="cb41-23"><a href="#cb41-23" tabindex="-1"></a> ))</span>
|
||
<span id="cb41-24"><a href="#cb41-24" tabindex="-1"></a> }</span>
|
||
<span id="cb41-25"><a href="#cb41-25" tabindex="-1"></a>)</span></code></pre></div>
|
||
<p>Because we are incorporating the <code>...</code> argument to
|
||
<code>guide_key()</code> in the key, adding a <code>colour</code> column
|
||
to the key is straightforward. We can check that are guide looks correct
|
||
in the different positions around the panel.</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><span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ, hwy)) <span class="sc">+</span></span>
|
||
<span id="cb42-2"><a href="#cb42-2" tabindex="-1"></a> <span class="fu">geom_point</span>() <span class="sc">+</span></span>
|
||
<span id="cb42-3"><a href="#cb42-3" tabindex="-1"></a> <span class="fu">guides</span>(</span>
|
||
<span id="cb42-4"><a href="#cb42-4" tabindex="-1"></a> <span class="at">x =</span> <span class="fu">guide_key</span>(</span>
|
||
<span id="cb42-5"><a href="#cb42-5" tabindex="-1"></a> <span class="at">aesthetic =</span> <span class="dv">2</span><span class="sc">:</span><span class="dv">6</span> <span class="sc">+</span> <span class="fl">0.5</span>,</span>
|
||
<span id="cb42-6"><a href="#cb42-6" tabindex="-1"></a> <span class="at">colour =</span> <span class="fu">c</span>(<span class="st">"red"</span>, <span class="st">"grey"</span>, <span class="st">"red"</span>, <span class="st">"grey"</span>, <span class="st">"red"</span>)</span>
|
||
<span id="cb42-7"><a href="#cb42-7" tabindex="-1"></a> ),</span>
|
||
<span id="cb42-8"><a href="#cb42-8" tabindex="-1"></a> <span class="at">x.sec =</span> <span class="fu">guide_key</span>(</span>
|
||
<span id="cb42-9"><a href="#cb42-9" tabindex="-1"></a> <span class="at">aesthetic =</span> <span class="fu">c</span>(<span class="dv">2</span>, <span class="dv">4</span>, <span class="dv">6</span>), </span>
|
||
<span id="cb42-10"><a href="#cb42-10" tabindex="-1"></a> <span class="at">colour =</span> <span class="fu">c</span>(<span class="st">"tomato"</span>, <span class="st">"limegreen"</span>, <span class="st">"dodgerblue"</span>)</span>
|
||
<span id="cb42-11"><a href="#cb42-11" tabindex="-1"></a> )</span>
|
||
<span id="cb42-12"><a href="#cb42-12" tabindex="-1"></a> )</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAKgCAYAAABEPM/FAAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+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/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAKgoAMABAAAAAEAAAKgAAAAAMw48TsAAEAASURBVHgB7N0JvFXj/vjx76nTOQ2nUYNKA4pUmgiJiChkKOFXIUL4X2Qu3Iu43LoqKdOla8hU5iTda4oiGdKgJKUilNN4qlOdzvTvu+5rbXs87WGttdda+/O8XnX2fvZaz/B+1l77u9ewn6zyfUlICCCAAAIIIIAAAgg4JFDJoXqoBgEEEEAAAQQQQAABQ4AAlA0BAQQQQAABBBBAwFEBAlBHuakMAQQQQAABBBBAgACUbQABBBBAAAEEEEDAUQECUEe5qQwBBBBAAAEEEECAAJRtAAEEEEAAAQQQQMBRAQJQR7mpDAEEEEAAAQQQQIAAlG0AAQQQQAABBBBAwFEBAlBHuakMAQQQQAABBBBAIBsCbwuUFxeLLP5GpGo1kXYdJSsry9sdovUIpElgeeEyo+YjarRLUwuoFgFvCuze9zE0d5VIpX0fPz1aieQQWXhzIB1uNUdAHQa3srry/PVSdtUAKf/6cyn/73Qpu+b/pLxoj5VVUBYCGSGwtXiLXPfDMJm79ZOM6C+dRMAqgcIikXOeEHl/+f/+9ZwgsmdfQEpCYH8CfE/Zn5CLXy+f/qpknX62VBp0pdHKstF/lfJP39+Xd46LW03TEHCfwH2r/yb1q9R3X8NoEQIuF3jpa5EOTUXG9v9fQx/+SGTJbyLHtHR5w2le2gUIQNM+BMk3IOuya/et/Ocp9/Lt2yRr9+7kC2RNBDJQ4M38V+WAKgdI86otMrD3dBmB1AQ++VHkkmNFZu/7W1omMrznvlPxnFtNDTVD1mYz8fBAZ1XJkawqVYwelM39UGT9r5LV6ywP94imI+CswC97fpZpG16SW1qMdLZiakPAJwIbtos8OVdk2gKRl/cdDT3pYZFde33SObphqwABqK28zhRe9p/pUv7c41LpvgmSVSPPmUqpBQGPC5SUl8jfVo2Quw4eJdUqV/d4b2g+AukRKNl31LNd431B6ECRZy4RObyRyMyl6WkLtXpLgFPw3hqviNaWvfq8lH88Syr943HJanhgxOtkIIBAdIHVu1bJ0p1L5JrlQ40F9pbvNS5oyd+bL389ZFT0lchFAIEQgca1Rbo0/zOrfRORRb+KXNDlzzweIRBNgAA0mopH8so+nCnl+069Vxr7lGTl1fJIq2kmAu4QOKxGG1lw3PeBxjz88z+lRuUaMuygvwTyeIAAAhULnNlOZPpikf4dRYr3HQ3977631DUnVrwOryKgAgSgHt4Oyl+aLLJxg5QN7BPoRVbfAVLp6psDz3mAAAIIIICAXQIDjxZZuO5/134WlYj0aiNy7r5glITA/gSyyvel/S3E6wgggAACCCCAQCwB/T1Q/SH6ajmxliAfgVABAtBQD54hgAACCCCAAAII2CzAXfA2AztR/KOPPioDBgxwoirqQMCXAg8++KAMHfq/m5F82UE6hYDNAiNHjpThw4fbXAvF+0mAa0B9MJo7d+6ULVu2+KAndAGB9Ajs2LFDtm7dmp7KqRUBHwhs375ddu3a5YOe0AWnBDgC6pQ09SCAAAIIIIAAAggYAhwB9cGG0KJFC+nYkdsOfTCUdCFNAocccohkZ7M7TBM/1fpA4LDDDpM9e/b4oCd0wSkBz92EtHHjRikp2fdbDymk6tWrS2lpqRQV7bttzwepZs2aUrVqVVEbv6S6dev65pRopX0TIzdq1Mjoj1920Hl5ecb7p7i42BebXO3ataXKvmltN23a5Iv+aCf89B6qXLmyNGzYUDZv3ix79/pjnsdatWpJYWGh8Vnkh41Ot7esrCzfXA6m+239bC0oKPDD8Bj7t/r16xv7OKf227m5uVKvXr2YfpyCj0nDCwgggAACCCCAAAJ2CBCA2qFKmQgggAACCCCAAAIxBQhAY9LwAgIIIIAAAggggIAdAgSgdqhSJgIIIIAAAggggEBMAQLQmDS8gAACCCCAAAIIIGCHAAGoHaqUiQACCCCAAAIIIBBTgAA0Jg0vIIAAAggggAACCNghQABqhyplIoAAAggggAACCMQU8NzUHzpbif4ocSpJy9AfmfVLUg/9AWD90Ve/JB0fv/RHx0aT/tB5eXm5L4ZItzntj1/eR7yH3L1ZmtuZbnPm+8ndLd5/63Sby8nJkbKysv0v7IEldIz89DmkfdEx8svnkDnTm5P7bfN9G2vz9VwAur8OxepocL5Zhm5cfkjmG98v/dExMd/8fhgf8wNTx8kvY+Snvpjbm5+2ObNPftretE/aH798iTO3N3P/oP3zctJ+mH3ycj/MtvutP26MezwXgOo0bKlOxakbmJ+m4tSdsv7btWuX+d7x/F/91umX/ugbX6fd06lf/TIVp/ZJ++PUlG52b9DmkTW/bHPq5af3kO7fdFpEff/4ZSpOPSK1e/du30zFqdubBm1+eQ/pPk7HyC/90X2cTqGs7yGn9tv7O3rsn/PQdn9CUT4CCCCAAAIIIICAJQIEoJYwUggCCCCAAAIIIIBAvAIEoPFKsRwCCCCAAAIIIICAJQIEoJYwUggCCCCAAAIIIIBAvAIEoPFKsRwCCCCAAAIIIICAJQIEoJYwUggCCCCAAAIIIIBAvAIEoPFKsRwCCCCAAAIIIICAJQIEoJYwUggCCCCAAAIIIIBAvAIEoPFKsRwCCCCAAAIIIICAJQIEoJYwUggCCCCAAAIIIIBAvAIEoPFKsRwCCCCAAAIIIICAJQIEoJYwUggCCCCAAAIIIIBAvAIEoPFKObjc9u3b5aSTTpKDDjpIWrRoIR9//LGDtVMVAggggAACCCBgrwABqL2+SZXepk0bWblypZSVlUlxcbFcfPHF8uqrryZVFishgAACCCCAAAJuEyAAddmIPPDAA1FbNHLkyKj5ZCKAAAIIIIAAAl4TIAB12Yht2rQpaov0SCgJAQQQQAABBBDwgwABqMtGsX///lFbdPDBB0fNJxMBBBBAAAEEEPCaAAGoy0bsxBNPlGuuuSakVXl5eTJnzpyQPJ4ggAACCCCAAAJeFcj2asP93O67775bevXqJe+8847UrVtXRowY4efu0jcEEEAAAQQQyDABAlCXDvjxxx8v+o+EAAIIIIAAAgj4TYBT8H4bUfqDAAIIIIAAAgi4XIAA1OUDRPMQQAABBBBAAAG/CRCA+m1E6Q8CCCCAAAIIIOByAQJQlw8QzUMAAQQQQAABBPwmQADqtxGlPwgggAACCCCAgMsFCEBdPkA0DwEEEEAAAQQQ8JsAAajfRpT+IIAAAggggAACLhcgAHX5ANE8BBBAAAEEEEDAbwIEoH4bUfqDAAIIIIAAAgi4XIAA1OUDRPMQQAABBBBAAAG/CRCA+m1E6Q8CCCCAAAIIIOByAQJQlw8QzUMAAQQQQAABBPwmQADqtxGlPwgggAACCCCAgMsFCEBdPkA0DwEEEEAAAQQQ8JsAAajfRpT+IIAAAggggAACLhcgAHX5ANE8BBBAAAEEEEDAbwIEoH4bUfqDAAIIIIAAAgi4XIAA1OUDRPMQQAABBBBAAAG/CRCA+m1E6Q8CCCCAAAIIIOByAQJQlw8QzUMAAQQQQAABBPwmQADqtxGlPwgggAACCCCAgMsFCEBdPkA0DwEEEEAAAQQQ8JsAAajfRpT+IIAAAggggAACLhcgAHX5ANE8BBBAAAEEEEDAbwIEoH4bUfqDAAIIIIAAAgi4XIAA1OUDRPMQQAABBBBAAAG/CRCA+m1E6Q8CCCCAAAIIIOByAQJQlw8QzUMAAQQQQAABBPwmQADqtxGlPwgggAACCCCAgMsFCEBdPkA0DwEEEEAAAQQQ8JsAAajfRpT+IIAAAggggAACLhcgAHX5ANE8BBBAAAEEEEDAbwIEoH4bUfqDAAIIIIAAAgi4XIAA1OUDRPMQQAABBBBAAAG/CRCA+m1E6Q8CCCCAAAIIIOByAQJQlw8QzUMAAQQQQAABBPwmQADqtxGlPwgggAACCCCAgMsFCEBdPkA0DwEEEEAAAQQQ8JsAAajfRpT+IIAAAggggAACLhcgAHX5ANE8BBBAAAEEEEDAbwIEoH4bUfqDAAIIIIAAAgi4XIAA1OUDRPMQQAABBBBAAAG/CRCA+m1E6Q8CCCCAAAIIIOByAQJQlw7Qtm3b5PPPP5dly5a5tIU0CwEEEEAAAQQQSE4gO7nVWMtOgcWLF8vgwYOlvLxctm7dKt27d5fXXnvNziopGwEEEEAAAQQQcEyAI6COUcdXUX5+vpxxxhmyZcsWI/jUtb744guZMGFCfAWwFAIIIIAAAggg4HIBAlCXDdCnn34qNWvWDGlVWVkZR0BDRHiCAAIIIIAAAl4WIAB12ehVrVpVKlWKHJacnByXtZTmIIAAAggggAACyQlERjrJlcNaFgn06dNH6tSpI9nZf16eq4/HjRtnUQ0UgwACCCCAAAIIpFeAADS9/hG1V6lSRT766CNp0aKF5ObmSqNGjeSpp56SLl26RCxLBgIIIIAAAggg4EWBPw+zebH1Pm1z9erVZe7cuT7tHd1CAAEEEEAAgUwX4Ahopm8B9B8BBBBAAAEEEHBYgADUYXCqQwABBBBAAAEEMl2AADTTtwD6jwACCCCAAAIIOCxAAOowONUhgAACCCCAAAKZLkAAmulbAP1HAAEEEEAAAQQcFiAAdRic6hBAAAEEEEAAgUwXIADN9C2A/iOAAAIIIIAAAg4LEIA6DE51CCCAAAIIIIBApgsQgGb6FkD/EUAAAQQQQAABhwUcD0Bnz54tO3bsCOnmihUr5P3335dNmzaF5PMEAQQQQAABBBBAwH8Cjgagc+bMkbvvvlu2bNkSkHz44YfloYcekoULF8oVV1whv/zyS+A1HiCAAAIIIIAAAgj4T8CxueD16OYzzzwjderUCSiuXbvWmPP89ddfl0qVKsnUqVPlpZdekjvuuCOwDA8QQAABBBBAAAEE/CXgSABaXl4uo0ePluuuu07GjBkTEFy9erV06NDBCD41s0uXLjJz5szA6/pAT82/++67gbzhw4dLkyZNAs+TeZCdnS3apmrVqiWzuuvW0f5oAB8c3LuukQk2qEqVKr7qj3a/Ro0aUrVq1QQl3Lm4bnM5OTlSVlbmzgYm2Crd3ngPJYjm4OJZWVlGbXl5eb7a5ipXrmx8FjlIaVtV+h7S5JfPId3mdD/nl/7o/k2Tvoc0/nEilZSUVFiNIwGoHuFs1qyZHH300SGNWb9+vdSuXTuQV6tWLdm8eXPguT7YvXt3yCl7/cAzIUMWTOCJuTNLtZwEqrR1Ue2P/vNLfxTLb/3xW5/8Nj7aH028hwwG1/1njo+ftjuzL04FA04MqtknJ+qyuw7ti9/6o2a6j3Nqm1O/ipLtAeiaNWtk1qxZ8sQTT0S0Q7/9lZaWBvI1Wg4/KnnuueeK/jPTxo0bQwJSMz+Rv9WrVzfqLSoqSmQ11y5bs2ZN48ha8LW1rm1snA2rW7eubN26Nc6l3b2YvuEbNWokO3fulD179ri7sXG2Tr9F6/unuLg4zjXcvZh+EdYjOLyH3DlO+lnRsGFD4wbWvXv3urORCbZKD7gUFhaGfAYmWISrFtd9tgYcfnkP6X5bP1sLCgpc5ZxsY3T/Vr9+fdm+fbtj++3c3NwKm2v7TUgfffSR/Pzzz3L22WfL6aefLhs2bJArr7xSvvrqK2nQoEHIxqobbuPGjStsMC8igAACCCCAAAIIeFvA9iOgGmzqPzNdcMEFMnbsWGnRooURiT/yyCOybt06I/CcMWOGHHPMMeai/EUAAQQQQAABBBDwoYDtAWhFZnoKYtiwYUaAWq9ePSMoHTRoUEWr8BoCCCCAAAIIIICAxwUcD0Bfe+21ELK+fftK7969jevJ9LoyEgIIIIAAAggggIC/BRwPQKNx6sWx5k84RHudPAQQQAABBBBAAAH/CNh+E5J/qOgJAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUCBKBWKFIGAggggAACCCCAQNwCBKBxU7EgAggggAACCCCAgBUC2VYUQhnxCRQVFckrr7wiBQUF0qNHD+ncuXN8K7IUAggggAACCCDgIwECUIcGs7i4WLp3724En3v37pUxY8bI/fffL1dccYVDLaAaBBBAAAEEEEDAHQKcgndoHIYNGyYbN26UwsJC0WBU0wMPPCA//PCDQy2gGgQQQAABBBBAwB0CBKAOjcPy5csDgadZZZUqVWTp0qXmU/4igAACCCCAAAIZIUAA6tAwN2zYMKKmHTt2yAEHHBCRTwYCCCCAAAIIIOBnAQJQh0Z33LhxRk1ZWVnGXz362bFjR+nZs6dDLaAaBBBAAAEEEEDAHQIEoA6NQ+vWreW7776TXr16yVFHHSW33367zJo1y6HaqQYBBBBAAAEEEHCPAHfBOzgWerr9+eefd7BGqkIAAQQQQAABBNwnwBFQ940JLUIAAQQQQAABBHwtQADq6+GlcwgggAACCCCAgPsECEDdNya0CAEEEEAAAQQQ8LUAAaivh5fOIYAAAggggAAC7hMgAHXfmNAiBBBAAAEEEEDA1wIEoL4eXjqHAAIIIIAAAgi4T4AA1H1jQosQQAABBBBAAAFfCxCA+np46RwCCCCAAAIIIOA+AQJQ940JLUIAAQQQQAABBHwtwExIDg/v1KlT5Y8//pCzzjpLWrVq5XDtVIcAAggggAACCKRfgADUwTHo1KmT5OfnGzWOGTNGRo8eLZdeeqmDLaAqBBBAAAEEEEAg/QKcgndoDE455ZRA8GlWOXLkSFm6dKn5lL8IIIAAAggggEBGCBCAOjTMK1eujFrTlClTouaTiQACCCCAAAII+FWAANShkc3Ojn61Q61atRxqAdUggAACCCCAAALuECAAdWgcbr311qg13XnnnVHzyUQAAQQQQAABBPwqQADq0Mj+5S9/kXvuuSdQW6NGjWTJkiVSqRJDEEDhAQIIIIAAAghkhED088IZ0XXnO3n11VeL/iMhgAACCCCAAAKZLMDht0weffqOAAIIIIAAAgikQYAANA3oVIkAAggggAACCGSyAAFoJo8+fUcAAQQQQAABBNIgQACaBnSqRAABBBBAAAEEMlmAADSTR5++I4AAAggggAACaRAgAE0DOlUigAACCCCAAAKZLEAAmsmjT98RQAABBBBAAIE0CBCApgGdKhFAAAEEEEAAgUwWIADN5NGn7wgggAACCCCAQBoECEAdRs/Pz5eVK1fK3r17Ha6Z6hBAAAEEEEAAAXcIMBWng+MwaNAg+eSTTwI1fvzxx9KmTZvAcx4ggAACCCCAAAKZIMARUIdG+bbbbgsJPrXaU089VfSIKAkBBBBAAAEEEMgkAc8dAc3Ozhb9l0qqUqWKVKpUSbKyslIpJqF1X3vttajLT5kyRf76179GfS3eTPXQvlStWjXeVVy/XOXKlX3TH3M70+3OL0m3ufLyctFx8kPSfvjtPaT7OL/sE7QvmnJycox9t1+2udzcXCkrK/NDdwKfqX7Z5nR/4KfPIXNfre8h87HdG5752RerntQiuVil2pi/vw7FW7WWY1VZ8dRp7kCDl9UPcN35WNUOq8oJbmM6H/ulP2Y/9K/5OJ2uVtXtt/6oi5/Gx0/9McfFT9uc2Rezb1a9L9NVjtkP82+62mFVvWY/zL9WlZuucsx+6F/zsd1t2V89ngtAi4uLpaSkJCU3RSktLZWioqKUyklk5TPPPFPefPPNiFWGDh0qu3fvjshPJMM8KpxqOYnUafey+i3aL/3RLx+1a9c2bjzbs2eP3XSOlK/foPX9o+9HPyTzyJpftjkdEz+9h3R7q1WrlrHN+eUGTj0jovsD/SzyQ9LtTT9b/fIe0v22jpFf+qN9qVmzpqP7bT3CX1HiGtCKdCx87dFHH5VOnToFStSN4Z133pH69esH8niAAAIIIIAAAghkgoDnjoB6eVDee+89WbdunXHjUbt27XxzfZaXx4S2I4AAAggggIDzAgSgDps3a9ZM9B8JAQQQQAABBBDIVAFOwWfqyNNvBBBAAAEEEEAgTQIEoGmCp1oEEEAAAQQQQCBTBQhAM3Xk6TcCCCCAAAIIIJAmAQLQNMFTLQIIIIAAAgggkKkCBKCZOvL0GwEEEEAAAQQQSJMAAWia4KkWAQQQQAABBBDIVAEC0EwdefqNAAIIIIAAAgikSYAANE3wVIsAAggggAACCGSqAAFopo48/UYAAQQQQAABBNIkQADqIPycOXPk+OOPl7Zt28qFF14oJSUlDtZOVQgggAACCCCAgDsECEAdGoelS5fK4MGDZe3atbJt2zb58ssvpU+fPlJWVuZQC6gGAQQQQAABBBBwhwABqEPjcO2110ppaWmgtuLiYvn111/lv//9byCPBwgggAACCCCAQCYIEIA6NMpFRUVRayosLIyaTyYCCCCAAAIIIOBXAQJQh0b27LPPluzs7JDatm/fLt26dQvJ4wkCCCCAAAIIIOB3AQJQh0b4zjvvlBYtWkilSpWkdu3akpeXJ2+88YY0bdrUoRZQDQIIIIAAAggg4A6B0ENy7miTL1tRuXJlmTt3rnzxxReyY8cO6dSpkzRs2NCXfaVTCCCAAAIIIIBARQIEoBXp2PAap9xtQKVIBBBAAAEEEPCUAKfgPTVcNBYBBBBAAAEEEPC+AAGo98eQHiCAAAIIIIAAAp4SIAD11HDRWAQQQAABBBBAwPsCBKDeH0N6gAACCCCAAAIIeEqAANRTw0VjEUAAAQQQQAAB7wsQgHp/DOkBAggggAACCCDgKQECUE8NF41FAAEEEEAAAQS8L0AA6v0xpAcIIIAAAggggICnBAhAPTVcNBYBBBBAAAEEEPC+AAFojDHcvHmz3HTTTXLRRRfJhAkTYiyVWPa6devkmmuukQsuuECeeeaZxFZmaQQQQAABBBBAwCcCTMUZZSB3794tRx55pGRnZ0tJSYkxh/srr7wiX375ZZSl48vatGmTdO/eXcrKyox/n3/+ucyePVteeOGF+ApgKQQQQAABBBBAwCcCHAGNMpC33XabVK5c2Qg+zZe3bNkir7/+uvk04b8XX3yxlJaWGsGnufJXX31lBLfmc/4igAACCCCAAAKZIEAAGmWUf/31VyNYDH6pqKhI1q9fH5yV0ONt27ZJeXl5xDr5+fkReWQggAACCCCAAAJ+FiAAjTK6Xbt2lZycnJBX9FR8+/btQ/ISedK5c2fjlH7wOjt27JA2bdoEZ/EYAQQQQAABBBDwvQABaJQh1lPwWVlZUqVKFeNvbm6uDBgwQHr27Bll6fiyxowZY5zS17IqVapkBKNaT7t27eIrgKUQQAABBBBAAAGfCHATUpSB1KOfq1evlilTpojePNSxY0fp1atXlCXjz6pVq5b88ssvxt3vBQUFcuKJJ8pxxx0XfwEsiQACCCCAAAII+ESAADTGQOoR0CFDhsR4Nblsvat+2LBhya3MWggggAACCCCAgE8EOAXvk4GkGwgggAACCCCAgFcECEC9MlK0EwEEEEAAAQQQ8IkAAahPBpJuIIAAAggggAACXhEgAPXKSNFOBBBAAAEEEEDAJwIEoD4ZSLqBAAIIIIAAAgh4RYAA1CsjRTsRQAABBBBAAAGfCBCA+mQg6QYCCCCAAAIIIOAVAQJQr4wU7UQAAQQQQAABBHwiQADqk4GkGwgggAACCCCAgFcEmAkpxkjt3btXzj33XNmyZYscffTR8thjj8VYkmwrBcrLy+Wll16SHTt2SKtWreS0006zsnjKQgABBBBAAAEXCBCARhmE0tJSadmyZeCVdevWyQcffCA//vhjII8H9ggceeSRUlRUJLt375aysjLp168fwb891JSKAAIIIIBA2gQ4BR+FXo94hqedO3fK8OHDw7N5bqHAP//5T9m2bZsUFhYawacWPWvWLPnwww8trIWiEEAAAQQQQCDdAgSgUUZg48aNUXJFFixYEDWfTGsEvv7660DgaZaol0IsXbrUfMpfBBBAAAEEEPCBAAFolEHMzc2NkivSsGHDqPlkWiPQrFkzyc4OvSqkUqVKuFvDSykIIIAAAgi4RoAANMpQTJ06NUquyJtvvhk1n0xrBP72t79JSUmJVK5c2SgwJydH8vLyZNCgQdZUQCkIIIAAAggg4AoBAtAow9C1a1f56quvpEaNGkYwdNBBB8lPP/0UZUmyrBSoW7eurF69Wi644AI56aST5Prrr5clS5ZYWQVlIYAAAggggIALBELPd7qgQW5pggadK1eudEtzMqYdVatWlfHjx4sGo1u3bs2YftNRBBBAAAEEMkmAI6CZNNr0FQEEEEAAAQQQcIEAAagLBoEmIIAAAggggAACmSRAAJpJo01fEUAAAQQQQAABFwgQgLpgEGgCAggggAACCCCQSQIEoJk02vQVAQQQQAABBBBwgQABqAsGgSYggAACCCCAAAKZJEAAmkmjTV8RQAABBBBAAAEXCBCAumAQaAICCCCAAAIIIJBJAgSgmTTa9BUBBBBAAAEEEHCBADMhuWAQUmlCeXm5zJkzR4qLi6VNmzZSq1atVIqzbd0ffvhBbrnlFsnOzpYpU6ZI7dq1Y9al028WFhaKzkbVrFmzmMvxAgIIIIAAAgh4U4AA1JvjZrRag85+/foZ86dnZWUZU1fOnTtXDj30UFf16pVXXjGCT7NRRxxxhDz33HNy+umnm1mBvxqkzpgxQ3JycmTLli3y0EMPyeDBgwOv8wABBBBAAAEEvC/AKXgPj+G5554r3333nWzbti0wb3r//v1l586drunVpk2bQoJPs2GXXXaZ+TDw98UXXxQNVrX9Gnxquv3220WPiJIQQAABBBBAwD8CBKAeHks9ra1HQYNTSUmJLFq0KDgrrY8fe+yxmPVr+4PT22+/HfzUeFy5cmX5+OOPI/LJQAABBBBAAAHvChCAenfsJC8vL6L127dvl+rVq0fkpyujoms49XrQ4BTt+lUNQKtVqxa8GI8RQAABBBBAwOMCBKAeHsB77rnHuFbS7IIGay1atJAuXbqYWWn/O3ToUNHrU8OTXuPZqlWrkOw77rjDuEnJzKxUqZIUFRXJZVFO15vL8BcBBBBAAAEEvCdAAOq9MQu0+Pzzz5fHH39c6tevb/y75JJL5JNPPgm87pYHv/32m2gwaaaaNWvK2rVrzaeBv61btzbaf+CBBxr9Ofnkk41rXHNzcwPL8AABBBBAAAEEvC8Qeg7U+/3JuB6ceeaZctFFF0nVqlVl48aNru3/r7/+GlfbDjnkEPn222+lbt26gRur4lqRhRBAAAEEEEDAMwJ/HpbyTJNpKAIIIIAAAggggICXBQhAvTx6tB0BBBBAAAEEEPCgAAGoBweNJiOAAAIIIIAAAl4WIAD18ujRdgQQQAABBBBAwIMCBKAeHDSajAACCCCAAAIIeFmAANTLo0fbEUAAAQQQQAABDwoQgHpw0GgyAggggAACCCDgZQECUC+PHm1HAAEEEEAAAQQ8KEAA6sFBo8kIIIAAAggggICXBRwLQPPz8+U///mPrFmzJsJrxYoV8v7778umTZsiXvNbRkFBgfz+++9SUlJiWde2bt1qlFleXm5ZmXYUtGrVKtFpOUkIIIAAAgggkNkCjgSg7777rtx2222i0zH+9a9/lZkzZwbUH374YXnooYdk4cKFcsUVV8gvv/wSeM1vD8aNGyedO3eWk046SZo3by4bNmxIuYt33XWXHH744ca/pk2byvbt21Mu0+oCNEBu166d9OjRQ7p27SqHHnqo7Nmzx+pqKA8BBBBAAAEEPCJgewCqR+X06OaoUaPkyiuvlBtvvFGmTZtm8Kxdu1bmzp0rTz31lIwYMUIGDhwoL730kkfoEmvmyy+/LI899pgReBUWFhora0C2c+fOxAoKWlqD+VdeeUV2794dCOjOO+88S4+uBlWX9EMNPjUINZO2t2fPnuZT/iKAAAIIIIBAhglk293frKwsmThxolFNcXGxfP7559KyZUvj+erVq6VDhw5SqdL/4uAuXbqEHB3VhfRo6Ztvvmksr/+NHDnSOHoYyEjigdangXGNGjWSWDu5VZ555plAkGiWUKVKFVm0aJGcc845ZlZCf996662IMvUyhp9//tk40phQYTYt/PXXX0ctWdtYtWpVqV69etTXs7OzpV69elFf82pmXl5ezP56rU+VK1eW3Nxc433ktbZHa69ub7qv8tM256f3kI6Nppo1a/pmm9P3kI6RXxLvIXePpPkeqlWrlmPvIY35KkqObf16anjw4MHG0bqnn37aaNP69euldu3agfYpzObNmwPP9YGiBb9J9blV1zpaVU5Ig2M8Ce6DuUhpaanRl2TbkZOTYxYV+Lt3714pKyuzzChQcJIPdCcbK2m/K+p7Ra/FKtPN+fvrr5vbHqttfhkj7YeV+5ZYXk7n+2V8gt381ic/9Uf74pf+mAGbX/pjvofcNEaOBaAaXL7zzjsyZ84cufrqq2X69OmiwYkGYWbSG3OqVatmPjX+nnnmmaL/zLRx48aQ07lmfiJ/9aib1ltUVJTIaiktO2TIELnllltCytCg/Oijj066P5dccomMHTs2UKa+YbRMvcYy+JR3YIE0PGi572h33bp1I9qjXzzUP9YYRFsnDc23pEo94q5He/XSC79c+6pHc3Xs9vcN1xJABwrR7VHPSLjlfWNFl/30HtLPioYNG8qOHTtEv2T7Ielnou4Tgj8Dvdwv3d70M8gv7yHdb+sRd71x2A9J92961krfQ07tt7W+ipLt14DqzmL+/PlGG3Tj1Btw6tSpI8uWLZMGDRrIli1bAu3Tx40bNw4899MDvb715ptvNrqkAfBhhx0mCxYsiAi4E+mzlnf++ecHytQbnL7//vvAJQ2JlGXnsl988UVIm5o1ayaLFy+2s0rKRgABBBBAAAEXC9h+BFSj7ieffNIIQI455hjRn1zS0+wtWrQwovFHHnlE1q1bZwSeM2bMEF3Gr+nWW2+Va6+9Vnbt2mVca1bR6el4DSZNmmRcY6tl6al3Nyb9pq+/gKA/waTbgx7JICGAAAIIIIBA5grYHoDqUU+9812DUL3bXQ/J6h3xevRT07Bhw4y74/Xifw1KBw0a5OvR0BufrL75SQM8PcWrlye4OenPRJEQQAABBBBAAAHbA1Al7tSpkxGA6k8O6bVjwalv377Su3dv43qy8NeCl+MxAggggAACCCCAgD8EHAlATapYAaaeltV/JAQQQAABBBBAAAH/C9h+E5L/CekhAggggAACCCCAQCICBKCJaLEsAggggAACCCCAQMoCBKApE1IAAggggAACCCCAQCICBKCJaLEsAggggAACCCCAQMoCBKApE1IAAggggAACCCCAQCICBKCJaLEsAggggAACCCCAQMoCBKApE1IAAggggAACCCCAQCICBKAxtL799ls58cQTpU2bNsb89Tt27IixZPzZF1xwgTRp0iTw748//oh/5RhLank1a9Y0fkdVH//yyy8xlow/+7LLLpODDjpIdOaiHj16pDzFZ3l5uYwePVo6duwo7du3l0cffTRmY8aNG2f4VKtWzfirU7eS3CNQUlIid955pzFr2aGHHirTpk1zT+NoCQIIIICAZwQc/SF6r6jovOU6Q5OZCgsL5fjjj5f58+cnPY3mTTfdJJ9//rlZpPG3c+fO8uOPP0bMDhWyUAVPNOAMT8cdd5z8/vvv4dlxP9d+a/BtplWrVhlB6GeffWZmJfz30ksvlY8++iiw3vjx46W4uFjUJDjpMhqABqeePXvKp59+Kq1btw7O5nGaBHQ81qxZE/hSMnLkSGMsL7744jS1iGoRQAABBLwowBHQKKN2xx13hOSWlpbKnj175NVXXw3JT+RJrCNFF110USLFxLXskUceGddy4QsVFBSEBJ/m66tXr5aFCxeaTxP6q0d5g4NPXVktJ02aZPwNLuyKK64Ifhp4PGzYsMBjHqRPYOnSpaLbQllZWaARRUVFxtHtQAYPEEAAAQQQiEOAADQKUrTT7XrEbteuXVGWTi0rWl2plSiye/fupIoIDizCC9i2bVt4VlzP1axWrVoRy6pneH0a6EdLyfYnWlnkJS+gY1m7du2IAuzYhiMqIQMBBBBAwFcCBKBRhrN///5SqVIozd69e+WUU06JsnR8WdWrV4+64I033hg1P5XMUaNGJbV63bp1owaLWli3bt2SKrN58+bSsGHDkHXVNicnR8JN9JKEaGnQoEHRsslzWECv361atWpIrVWqVJFWrVqF5PEEAQQQQACB/QmERln7WzpDXtdrFs844wyjtxqU6U0+kydPliOOOCJpAT19GZ70piQNdpNN0S4J6NSpkwwePDjZIkNOl2dlZRmBuF6DGR54xFtB5cqV5fXXXzcW16Nn6nnYYYfJkiVLIop45513RG8+Ck56zeENN9wQnMXjNAnoF4a3337bqL1OnTqi//Ryj1mzZqWpRVSLAAIIIOBVgax9dyiXe6nxGzduFL0TN5WkH6R6ulevX6soLVu2TPLz8+Xwww837siuaNl4X9NgasOGDXLNNdekdEQ1uD4NjPUI7V133SVDhw4Nfimpx+r7wgsvGEYaJEc77Zpowdq+BQsWiAakXbp0kezs2Pe/Pfnkk8aNLhp89unTJ9GqXLe8HvFt1KiRbN26NeK6V9c1No4G6al4fW/oF5Sjjz46jjXcv4hu43o0d9OmTe5vbJwt1C97us35Iel+Q8+kbN682djX+aFPemmS3uAa69Ijr/VRtzfdJ2zZssVrTY/aXt1v68EnvTfCD0n3b/Xr1zf2cXoJnBMpNzdX6tWrF7OquAPQ7du3xzw9G7N0G15wMgC1ofm2FKlvEj1CqTZ+SX768PRbAKrbWF5envEFzqkdmd3bNQGo3cKplU8AmpqfE2sTgDqhnHwdbgxA4z4Fr6dNL7zwQnnvvfd8840t+aFkTQQQQAABBBBAAIFkBeIOQF977TXjCOjAgQONHym/7bbbjNNwyVbMeggggAACCCCAAAKZKRB3AKqzAumNOHr94sMPPyzLly+Xo446Srp27SqPPfaYb66TyMzNgF4jgAACCCCAAALOCcQdgJpN0ruU/+///k/uvvtuufLKK2XRokVy++23G9M23nrrrcasKOay/EUAAQQQQAABBBBAIFwgoQBUp2W89957jWkRdWrKtWvXytSpU407LfWnevQnWvR1EgIIIIAAAggggAACsQRi/xZO2Br6I+yzZ882fgvzqquukksuuUQaN24cWEpPx/fr10+i/d5lYCEeIIAAAggggAACCGS8QNwBqP524z/+8Q859thjY6Lpb1yGz24Tc2FeQAABBBBAAAEEEMhIgbgDUL0JqW3bthUiNWvWrMLXeREBBBBAAAEEEEAAgbivAdWfXdJf0T/zzDPlqaeeMu6Ghw8BBBBAAAEEEEAAgUQF4g5AV6xYIfPmzTNOwT/99NPGb4HqjUj//Oc/ZfXq1YnWy/IWCUycONGY6kpnQjrnnHMsKjV9xehUe9ddd5306tXLuOTDYzPFpg8uRs0fffSRXHzxxTJkyBBZvHhxjKUSy3711VflrLPOEv1NYL0R0cmk+yG9/vy8886T559/3smqqQsBBBBAwEKBuKfiDK/z66+/lltuuUXmzp0rOl+4fig5kZiK809lDT5Hjx79Z8a+Rzodms7T7cWkc4y3atXKmCde56M35+LV/uhjr6Z0TcWpv9urP5cWnB599FHp379/cFZCj0eOHClTpkwJWUcnqejevXtInh1PVq5cKXozZPDc2RdddJHxu8Sp1sdUnKkK2rs+U3Ha62tF6UzFaYWifWV4eipODfw0yLzmmmtEp+XUm5F0fngNQm+++Wb71Cg5pkB48KkLbt26VZ599tmY67j5BQ1u9INGg09NZWVlxuOXX37Zzc12ZdsKCgoigk9tqBrv3r07qTbrmY7w4FMLGj58eFLlJbrSgAEDQoJPXX/mzJmWHdlNtD0sjwACCCCQvEDcNyFp0KkBpx49ue+++4wjEQ0bNky+Zta0TeCHH36wrWw7C/7tt98iAgwNlvTLDykxAQ1A9aie/g1OeknDnj17RCeUSDTl5+cbR9j1S05wCn8e/JqVj80vJsFlZmdns30Eg/AYAQQQ8IhA3Oc177rrLunRo4fMmDHDOO2r137OmjVLCgsLPdJV/zVTjxZGS2effXa0bNfndevWTXJyckLaqUdBO3bsGJLHk/0LNGjQQPS64PCk79e8vLzw7Liet27dWsKvyc3Kykq6vLgqDVqoU6dOxhHyoCzZtm2btGnTJjiLxwgggAACHhCIOwDVaTb1h+j1JpEHHnjAOFKlp/Pq1asnI0aM8EBX/dfEadOmRXTq9NNPlxNOOCEi3wsZN954o9SsWdO4BlSPbGkApdO+6nV/pMQE9AineZOOOurzGjVqGNds67VAyaQDDjhAHn/8cWPV3Nxc4zd/a9WqJR9//HEyxSW8jl6/qtd/an+0Dxr8TpgwwbghMuHCWAEBBBBAIK0CcZ+CN1upp8GKiopEbxjRU/L6QaBBA8l5Af0VggULFsjll19uXNendzvrLFVeTRp0fvfdd8b0rjt37pRDDz1Uevbs6dXupL3dHTp0kEWLFsm7775rBPV9+vSRVC+bOfnkk0Wn3f3kk0+M975ekqNBqBNJb3LQu+5feOEF48zLqaeeKu3atXOiaupAAAEEELBYIO674HUWJP0g+/LLL40PMT3Ne+655xpHp6Kd6rO4nYHiuAs+QBF4oF8AdAz8dK2kBhtOXVsYgLTpQbrugrepO0axehpfv4gWFxfbWY1jZXMXvGPUSVXEXfBJsTm6EnfBO8qdcGVuvAs+7iOg7733nnE0Sk95HX300cbpr4QFWAEBBBBAAAEEEEAg4wXiDkD19z41/f777xJ+l7WegmvatGnGYwKAAAIIIIAAAgggsH+BuANQ/b29wYMHR/ysi1bh5A/R779LLIEAAggggAACCCDgZoG4A9Crr75aevfuLddff33EXad6dy0JAQQQQAABBBBAAIF4BOIKQPVud/2R8PHjx3OqPR5VlkEAAQQQQAABBBCIKRDX74DqNZ4tW7aUX375JWZBvIAAAggggAACCCCAQDwCFR4B/eqrrwIzHen1n3qtp86IpL/PGPxj1vrbgvweXzzcLIMAAggggAACCCBQYQA6dOhQWbZsWYjS//t//y/kuT7hJqQIEjIQQAABBBBAAAEEYghUGIDqLDvhcz9HKyfWnOTRliUPAQQQQAABBBBAILMFKgxAdb5nkrsF1qxZI3/729+MWWkuvPBC42h0qi1euHChjBs3zpjlRn/1wIq55V9++WXRubz10o2xY8dK165dozZz9+7dcs4558i2bdukffv28uyzz0Zdzo+ZGzZskNdff1327t0r5513nhxyyCEpd1OnrjSn4jz//POlQYMGKZep06V+9NFHovsHnf7Vyal4CwoKjDnudTvRqTh1UgwSAggggID3BOKeitMtXWMqzj9HYsWKFRFzpV9xxRVy//33/7lQgo8++OADGTJkSMha9957rwwbNiwkL5EnI0eOlClTpoSsMmrUqIh560tLS6VZs2Yhy2mQo0G2l1M8U3GuXr1aTjvtNFGDsrIyKSkpkWnTpsmJJ56YdNe//vprY7rc7OxsY+YynTZTxzeV67Xffvtt0ctwtE9argbLeq34QQcdlHQ7411Rv5Qcd9xxRp1arzrpF6WBAwfGW0TM5ZiKMyaNK15gKk5XDEOFjWAqzgp50v6iG6fiJABN+2aRfAP0Q18/hMPTrFmzpGPHjuHZ+32+a9cuadWqVcRyWVlZokdF9WazRJP+coIGDdHS8uXLRT/4zdSlSxfRo4DhqW/fvvLUU0+FZ3vm+f4CUA02W7RoEXG5S7169WTevHmiv0KRaIo1lrrNfPbZZ5KTk5NokZKfny+dOnWKWE/zdKpeu1PPnj1l5cqVIdu8zkmvQbX6pZIIQFPRs39dAlD7jVOtgQA0VUF713djABrXzzDZy0LpyQpECz41WJw/f35SRRYVFcVcT4PFZJIGULHSjh07Ql7atGlTyHPzSfiNcGa+n/5Gu9xFr7/+448/kuqmnimoU6dOxLp6FFGD02SSns7XoDg8/fjjj+FZtjzX7SN8m9ed6qpVq2ypj0IRQAABBOwTIAC1z9b2kvXDNzxp0NK6devw7Lie61EGDWDDk5bZct/vwCaTKjrdG34Urlq1alGrSObIa9SCXJqpp92jXUe5detW0aMKySRdT4+shqedO3ca126G58fz/MADDzROf4cvG8+NiuHrJPNc6w9PatS4cePwbJ4jgAACCLhcgADU5QNUUfN0ZqrwpAHfKaecEp4d13M91XvrrbdGLKs3BSV7ivPII48UPbUenvSnu8IDS70BJ1p66623omX7Jk+Pfk6aNCmiP3rtbf369SPy48nQsQy/Fli/sDz//PMSK9DfX7nNmzeXa6+9NmQxvQ50+vTpIXl2PTGNzC9JemmD3njXtm1bu6qkXAQQQAABmwS4BtQmWKeKXbx4sXGDkJ4+10DxvvvuS7lqvaZOJxzQI2iXX3656J3wqaZ77rlHXnjhBeMIq97QNGLEiKhF6mlWvfFGTxM3bdrUuNs62YApagVpyNzfNaBmk/RUsn6p0LHUu8v1msdUk/6U2uTJk40bhi677DI56qijUi3SuN7zjTfekKpVq8rw4cPlsMMOS7nMeAvQ7UN/9UGnB+7fv7/onf1WJK4BtULRvjK4BtQ+W6tK5hpQqyTtKceN14ASgNoz1o6WqqdvNRjQ6/78knRnpqdX/ZDiDUC91Fe9+UcDZb2z3g+JANTdo0gA6u7x0dYRgLp7jNwYgHIK3t3bDK1DAAEEEEAAAQR8J0AA6rshpUMIIIAAAggggIC7BQhA3T0+tA4BBBBAAAEEEPCdAAGo74aUDiGAAAIIIIAAAu4WIAB19/jQOgQQQAABBBBAwHcCBKC+G1I6hAACCCCAAAIIuFuAANTd40PrEEAAAQQQQAAB3wkQgPpuSOkQAggggAACCCDgbgECUHePD61DAAEEEEAAAQR8J0AA6vCQzpkzR2bOnCkbNmywpOY9e/bIWWedJd26dZPPPvvMkjJ37txpzE0+btw42bx5syVlFhYWyscffyyffPLJfmfPGTRokOic9jp9p9PpzTfflD59+oi2war0wAMPyCGHHCLnnXdehUXq9KOPPPKIjB07VnTKSbembdu2ybvvvmtMk1peXl5hM5988knROe2/+eabCpeL98WysjKZOHGiMc/9ypUr412twuV0Niedflbfl7rtkxDwosCvv/5qvC8///xzLzY/os36XtT35ezZs6W0tDTidTK8L8BUnA6O4YABA+S7774TnVZOP8RffvllOfnkk5NuwR9//CGdO3cOWf+6666TO++8MyQvkSe//fabEczqG94MLmbMmJHSHOIabJ9yyinGPPBars7jvWzZMmPqtvC2NWnSJCRLpxldsWJFSJ5dTwYPHmzs7ILL//7776VOnTrBWQk97tChQ0Qw+fvvv0eUoUbHHHOMsaM13d966y059thjI5ZNZ4YGffqFR6d106k4NWjWvBo1akQ0S79EBE+neu211xrzuEcsGGfG3r175YgjjpDdu3cb25I6TZo0KaX54PWL0RlnnGF80dLy9H35xRdfSIsWLeJslXsX89N0tkzFWfF2NmvWLNF9f05OjhQUFEj79u3lv//9r/E+qXhN6161cirOdevWyWmnnSY6jXFJSYnxxXD58uWiU+Y6lbRu/fxRTz8kpuL0wygm2YdbbrlFFixYIDt27DA+5LSYyy+/XPRba7IpPPjUch599FGjnmTL7Nq1q/GGN4MgLadfv35GoJFMmXp0qUuXLkafNRjR4DMrK0suvfTSiOLatGkTkadeGhjanV588cWI4FPrjGYcb1uuuuqqiOBT123dunVEEWqkO9pgd/3Col5uSRpsnnTSScaHgY6lPtedtH7whScN6oKDT339iSeekE8//TR80bifd+zY0Qg+dQXT6frrr5fVq1fHXUb4gscdd5ysWbPGaKsGn5rUXc8skBDwgsCqVavkiiuuMN4bZrD0ww8/yPjx473Q/Ig26hdb/eKt+z59T+qRUN3PDB06NGJZMrwtwCl4h8ZPT73rGys46bdVPdpidZowYUJSRcY63a6B0fz585MqU7/J5uXlhayrwYMeCdYAJjjFCrbmzZsXvJgtj59++umo5YaPWdSFYmTqJQfRkh51C06xTvvq0WI3nU5bsmRJxNFgPSUeLajUI8fR0muvvRYtO64888M1fGG9bCLZpB9w4af3dHysOr2fbLtYD4F4BXQfUb169ZDFdZ/9zjvvhOR55clPP/0ktWrVCmmu7mf0AA7JXwIEoA6NZ7TTuHpKMTw4s6I54aex4y1Tv2XGSvXr14/1UoX5empWd4bhSfuuR0LjSdnZ2fEsltIy0cYnpQL3raynPOJJunONlZJ1j1VeKvl6+ivaWOpR7vAUq+96SivZpKdho6VUxq5atWoRRWpQmko7IwokAwEbBfQzJNp7w8nT1VZ2T9970fYz+plB8pdA7IjDX/1Me2/uv/9+CQ6kzMd6qjLZpNfDRUtjxoyJlr3fPL2G59RTT41YTgNavZYxmdSoUSMZOHCgBH/QV61aVW6//faQPC376quvjlqFXoRud3rjjTeiVnHllVdGzY8n8+2334662EMPPRSSr9/29can8NSwYUPRSyLcknR7O/nkkyU3NzfQJD2KrzcFhacHH3wwPMt4ru+DZNMNN9wQsap+8KYyRnq9dHB/9EtRz549pWXLlhF1kYGAGwXOPfdc0S+qwV/69GCCXo7lxdSsWTPRPunnhJn0PTpq1CjzKX99IlB53x2q93qpL3ratqIjRvH0Rd+oeho4/NRbPOsmu8xBBx1kBHd6V58GY2eeeaZogGIGosmUO2TIEOPUuJ7m1qRvWD31qUFBsql///7y888/Gzf+6E6sR48e8v777ydbnLGeBrV6KlvL1aDqtttuk2HDhkWUqcGNBgDBp9yfe+65lK7DjKgkRob2VQMpo8KJAABAAElEQVTg4J326aefbtyVHmOV/Wbrh4LesPP8888HltUd6x133BF4bj7Q/PXr1xvjpwbHH3+88asB5utu+du3b1/55ZdfJD8/X/TLxd///veod/frDUh6Ta/eWa5JP1T0VxpSOeLfvXt348ikXs6iScv/8ssvU3oPderUyQg2Fy1aZNzgoMGs/gpBvEfnjYa49D/dz/jlWlZ9f+rZFL0Bzcn9tp1Dq0GVnj0wr2dOti610Wvqv/rqK+OyJv2seeaZZ4wb9pItM5n1dHvT942OUaqpd+/exnXZeo+E7mfuuuuuqPcNpFpPRetrX3SMUrkMq6LynX5Nv6zrpRpWxFDxtl3jm+CDT+HrcRd8uIgHn+spCw0+N27c6MHWR2+yn+7g1Q8I3YnqTTl+CQg0kNQdc7TT79FH1N25erpSv5i6+eevEhX003tIPzz1y6tep+6XU7F65kOvN/ZLQG3lXfCJbut2LK/7bf1sjXXtuR112lmm7t/0oIju45zab2sAX69evZjd4hR8TBpeQAABBBBAAAEEELBDgADUDlXKRAABBBBAAAEEEIgpQAAak4YXEEAAAQQQQAABBOwQIAC1Q5UyEUAAAQQQQAABBGIKEIDGpOEFBBBAAAEEEEAAATsECEDtUKVMBBBAAAEEEEAAgZgCBKAxaXgBAQQQQAABBBBAwA4BAlA7VCkTAQQQQAABBBBAIKYAAWhMGl5AAAEEEEAAAQQQsEOAANQO1QrK1Jk81q5d69hMBBU0xbUv6ZRykyZNkjVr1jjeRp2mTKeMXLFihWV16+xHP/30kzET0v4KnT9/vuh0rW5OOnWgOR1nOtqpM35p/X6ZQSYdhtSZmMBvv/3mq1myEut9+pbW6Yn9NMNf+iTdWXO2O5vlz1b94x//kMmTJxtzV+/YscOYu1fn7SX9KdC1a1fRnb2ZLrroInn44YfNp7b+1XnFBwwYEAhsjjjiCPnoo49SqvO7774z5jDW6QN1Ks577rnHmHM+vNCdO3dKhw4dQqbqnDdvnjFPefiy6Xy+bds2ufDCC40AcPv27XLiiSfKK6+8IjptnRNJ54SeOnWqUZ9OY6i+BxxwgBNVU0cGCug2PmjQIFm1apXoPrtz584yffp00alBSfYJ6P5Q3fVAgL7P27ZtK++9957x2WlfrZTstIAznxpO98qF9T333HNG8Ll7925jR6ZNPOmkk0R3cKT/CVx55ZUhwafmTps2TZ599lnbiTZs2CD9+vULBJ9a4fLly2XYsGFJ163f3Hv37i1//PFH4OjnqFGjoh7hbNOmTUjwqZWecMIJSddtx4plZWXGB8HSpUsD2+3cuXPlvvvus6O6iDLvv/9+I9jV95B+KGk6/fTTI9wiViQDgSQF9H25cOFCY3vXI//6eOTIkUmWxmrxChx22GGyYMEC47NS9zvLli2T22+/Pd7VWc4jAln73lTlHmmr0Uw9ApNqysnJEd2oS0pKUi0q7vWPPfZY+fHHH0OWr1OnjkycOFHOPvvskPxEn1StWlWqVKkSCGwTXd8ty9erV0+ibY6NGjWSH374wdZmamA4YcKEiDp0W9EAMpn09NNPy9133x0RIJ166qny+uuvB4rUb/vNmjULPA9+oGXoUVk3pMWLF8s555wTCD7NNtWtW1dWr15tPrXtr34ohZ+O021Gv9zpkdhUUrVq1YyjWjoWfknVq1cXvaTED0mPsNeqVUt0fJzab+v+ulevXhH71Zo1axpnAFJ11W2uqKjI+CxKtSw3rF+jRg2jGeaXw2TbpJde6fs5vBwt/9dff0222ITXy8rKEv1s1S+8fkh61F63XT2S79TlS2pYu3btmHyeOwUfLUCJ2bsKXtByrCqrgmoCL2kgE540CNaNwqp2WFVOeDudeq4ba7Q+6IdPtHwr26XjEKv+ZOvWdkc7NR0+5tGWMfsWvqyZn46/2pbs7Mhdhl5ekKxRIv3QD+zwpMGIlduHE/0I74Odz/3SH7Mf+td8bKeblq3bVbTtXa/ptqINZl+sKMtui3jK137E2ofGs37wMnpAJTxpsJ4Oq3TUGd53K56b/dC/5mMryk2ljMhPk1RKc2Bd/cBJ9Ruw7lj0G4Bu0E6lq666yjiFEFynnn7v1q1bxBGyRNukb1bdUeqO0cupT58+xnU+4X0YP3687X0bMmSIca1p+BtTr0NK1vW0006Te++9N7w7Mnz48JAydaetR8OjHd1Xk2Trj6g4xYyDDz7YOFJbUFAQ+AatRwgGDhzoSBuHDh0qo0ePDtSlbvoe6tixYyAv2S7m5uYaAYdbrJPtR/B6GrD7pT/65UeTftnRf06kpk2bSuvWreXbb78NfObodtK3b19LXPWghH4eOHU0ym4z3d70PZnqNnfggQeKXn//9ddfh7jr/jTVshMx0DhBx8jJOhNpX6LLmkG9vn+Ki4sTXT2p5fX9UlHiGtCKdCx87YILLpBbbrnFKFE/tNu1ayeLFi0yDvFbWI2ni9IbtPRGnOB00003GdfKBufZ8bh+/fryySefhBStp98efPDBkLxEnjRo0ED++9//Gqvo6SM9/aGXXOiNDOFJtwVzB6Gv6WPNc1PSHfIbb7xh7JT1C49ux5dddplj14Dq9bj6RUGT1t2lSxf5/vvvuSHETRuJz9qiN9jpKUQNgHWb0y+k+h4m2Svw0ksvie6TTXe9GfVf//qXvZVSuuMCnrsGVK8BS/UIqF4b5fQRUHNk9Zos/fah1zPpt0UrkgY2unMMvz7OirLTUYZeA6TfOjWA052Qk0kvi9C78HUbseruat3WNADVa4n2N+b5+fnGER63/zqC9kO/SQcHzU6Nk76H1FS3e6uSBhnal02bNllVZNrL0Wtz9ZcX/JA0EGnYsKHoz9g5dQQ02E23Of0CpvtZq5J+Buh1jn45Aqrbm+4XtmzZYhWRcQ2zjv3+jqRZVmFQQTreuo/RMz5+SLp/089T3cc5eQRUr9OPlTx3Cj5WR7ySr4ENqWIBPdWbrg9P3enEuiGo4lbHflXf+NofTfs7naMfsl5IGlDrUVCndmTBJryHgjV47IQA25wTypF14B5p4qccTsH7aTTpCwIIIIAAAggg4AEBAlAPDBJNRAABBBBAAAEE/CRAAOqn0aQvCCCAAAIIIICABwQIQD0wSDQRAQQQQAABBBDwkwABqJ9Gk74ggAACCCCAAAIeECAA9cAg0UQEEEAAAQQQQMBPAgSgfhpN+oIAAggggAACCHhAgADUA4NEExFAAAEEEEAAAT8JEID6aTTpCwIIIIAAAggg4AEBAlAPDJLXm6jTW953333Svn17Oeyww+Shhx6K2aVvvvlGunXrZkzDecwxx1gyrdu1114rTZo0Cfz79ddfo9a/du1aadmyZWC5G264IepyiWTq9Kj9+/c3pl7VGZaWLFmSyOopLavuffv2laZNmxp90sekSIGnnnrKGHedreryyy8XdSMhgAACfhFYuXKlnHLKKcaMfPo5HOsz0On+Mhe80+I21Of2ueAvuOAC+fzzzwM91/mUNSi87bbbAnn6QN8UGnSaSecV1jm6v/jiC+OvmZ/I37Fjx8r48eMjVlm+fHlImTq/tAaf4WnAgAEyceLE8Oy4nuu0m4cccogxP3J5ebmxjk5f+e6770qHDh3iKiOVhY488khj7uzgMjp37iwzZ84MzkrqcV5enhQVFaVlKs6kGhxjJd0+Hn/88cAUqTrn9GmnnSYalHo9pWs6Wzvc0j0XvB19Yi54O1StK9Mvc8Fv2bLFOPgTLKP7uXnz5knjxo2Dsy1/rPVUNBc8R0AtJ6fAYIENGzaEBJ/6mgZmTzzxROBD31z+7rvvNh8afzVo08Bw2rRpIfmJPIkWfOr6f/nLX0KK0YA4Wnr99dejZceVN3XqVKlWrZqYwaeuVFJSImPGjIlr/VQW+uGHHyKCTy1v4cKFsmnTplSK9tW6un3o9mgmDapnz54tP/30k5nFXwQQQMCzAuPGjRP9AhecSktL5V//+ldwVloeE4CmhT1zKtUPdz1CG56Ki4tDAjN9fefOneGLGQHo7t27I/JTzSgsLAwpIlrdIQsk8UT7Hu10bnjdSRS931UKCgpiLqP2pP8J6Df08KRHPoKD0vDXeY4AAgh4RUA/bzTgDE56IMSOz9XgOuJ5TAAajxLLJC2g1z3q9ZfBSb+N6Wl4PToYnPR0d3jSN4qeEk02NW/ePOqq4dd3Xn/99VGXq+j0QdQVgjJPPfVU4zR1UJbx8MILLwzPsvz5UUcdZRhHK9ju0y7R6nRrXo8ePUQDzuC0Y8cO41rl4DweI4AAAl4UOPfcc6M2+5xzzoma72Rm6J7XyZqpKyMENNg0T2PrNU8a0LVt21YWL14c0X8NzAYPHmzkH3DAAcY1ms8884yxfMTCcWbotafhpx+GDx8uPXv2DCnhhBNOEM0PThokL126NDgrocetW7eWyZMnG+tof/S6yaFDh8qgQYMSKieZhfVa0w8//DCwql5Pq/1ZtGhRII8HYlwKotuHfhnSbVO/MKlRlSpV4EEAAQQ8L6CfdSNGjDD6Ub9+feNzdfTo0dK9e/e0942bkNI+BKk3wO03IWkP9bSvBp36wa43x4QfdQpW0Dv29PRAo0aNjH/BryX7WG8k+u2334zgr2PHjjGL+e677+TFF180Ls6+8cYbYy6XyAubN2+W/Px8IwA8+OCDE1k15WX1VPLbb79tBOH6TTgnJyflMrUAv9yEZGKsWrXKOE2lN6JFOy1vLuelv9yE5O7R4iYkd4+PX25CMpX1fozt27cbv8hy4IEHmtm2/t3fTUgEoLbyO1O4FwLQRCX89OGpOzINprdu3eqbawv9FoDqry3olyM/3aDlp/eQHqVu2LChcWOd3pjoh0QA6u5R9FsAqvs3PQKq+zin7gPYXwDKKXh3vwdoHQIIIIAAAggg4DsBAlDfDSkdQgABBBBAAAEE3C1AAOru8aF1CCCAAAIIIICA7wQIQH03pHQIAQQQQAABBBBwtwABqLvHh9YhgAACCCCAAAK+EyAA9d2Q0iEEEEAAAQQQQMDdAgSg7h4fWocAAggggAACCPhOgADUd0NKhxBAAAEEEEAAAXcLEIC6e3xoHQIIIIAAAggg4DsBAlDfDam3O6RTVl511VWic7OPGjVKysvLHeuQ1qVTdvbu3duYstNPs+I4hmhzRTpV6sCBA+WMM86QyZMnW1Lb/PnzpW/fvqJzJuu0pVak999/32intvXLL7+0okhbynjiiSekT58+RlvXr19vSx0Uar9AaWmpPPTQQ8a+65JLLpFt27bZXyk1IJCiQHaK67M6ApYJFBYWSqdOnYx5y3WHunDhQpkyZYr8+OOPRp5lFcUo6LTTThOdh96cpqxDhw7y8ccfS5s2bWKsQbaTAsuXL5czzzzTmLNd6128eLEsWLBANIhKNn300UcydOjQwJh//vnnsmLFChkxYkSyRRqB8d133x1Y/9NPP5UxY8aIBgZuSv369TP8SkpKjGYdddRR8p///Ed0uyd5S+C4444T/fKu+66srCxp27atzJs3T1q2bOmtjtDajBLgCGhGDbe7O6sf+jrnswafmsrKykTn433ppZdsb/js2bPl+++/DwQiZoW33HKL+ZC/aRa46KKLAtuG2RQNIBctWmQ+TfivBoXmFw5z5aefflp+++0382lCf3fs2CHBwae5sh7N37Vrl/k07X/1qK8emTWDT7NBN910k/mQvx4RePfdd2XDhg2B7VjP5GgQOnLkSI/0gGZmqgABaKaOvAv7racAzeDTbN6ePXtk8+bN5lPb/modtWrViihfjyqQ3CEQvm1oq6pUqSIbN25MuoE5OTkR6+bm5srWrVsj8uPJ2L59u9SsWTNiUf0i5aYAVLf32rVrR7QzFcuIwshwRGDLli2i22xw0iD0jz/+CM7iMQKuEyAAdd2QZG6DTjzxRAkPCDTo6Ny5s+0oHTt2lL1794bUo8FN+/btQ/J4kj4BPUWsR8iDkwaK7dq1C85K6PHhhx8esbyWmeypy/r160cNQHfu3Bk14Iuo3KEM3a7Dj/xmZ2dL69atHWoB1VgloJcthX8504D02GOPtaoKykHAFgECUFtYKTQZgeuvv14aNGhgBBm6A61ataoMGTJETj755GSKS2gd/eC95557jHW07ry8PGncuLHo6ViSOwT0BjH9oK1WrZqxbehRRb3+s0mTJkk38NlnnzXWrV69uug/TdOnTzfG33iS4H+67bz88svGWtrOGjVqGI8/++wz42htgsXZtniLFi2M61K1AnN71/eeE5e72NapDC1Yr9m9+eabjd6bY9msWTP5xz/+kaEidNsrAln7DtU7d5uxBSp6iij8uqVEi9UPGv0gKyoqSnRVVy6vp/w0WPPL6bO33npL9IjRIYccIt27d3fUfMmSJcbF+2p64YUXWhI0aKDUqFEj47SuXlLgh6QBur5/wo+i2d03rW/q1KmiN6zpXevRjmAm2gY9Nf7ee+8Zv7igN3Poh3eqSU9xz5o1yyhG79g/4IADUi0y4fXr1q2730sJli1bJnPnzjUC7gsuuCDiVG7Cldq0gh75btiwoXE5TviZCpuqtL1YveRHt+Pwo5fJVqw35H3zzTdSp04dGTBgQMTZgmTLjXc93d702lO9JMAPSffb+jlQUFDgh+4Yn2V6hkZ/3cWp/bZ+IapXr15MPwLQmDTeecFvAajKx/Ph6ZURIgB1/0jp9ZB6yYWffnrLT+8hAlD3v4cIQN09Rrp/c1sAyil4d28ztA4BBBBAAAEEEPCdAAGo74aUDiGAAAIIIIAAAu4WIAB19/jQOgQQQAABBBBAwHcCBKC+G1I6hAACCCCAAAIIuFuAANTd40PrEEAAAQQQQAAB3wkQgPpuSOkQAggggAACCCDgbgECUHePD61DAAEEEEAAAQR8J0AA6rshpUMIIIAAAggggIC7BQhA3T0+tA4BBBBAAAEEEPCdQLbvekSHEAgT0OnuJk+ebEwR16NHDzn11FPDlrD36TvvvCPff/+9MbvTFVdcITozklPp448/lscee8yYlu+uu+6Sjh07OlW1LfVs3bpV/v3vfxtTtfbu3Vu6detmSz2ZUqhuG9OnTzem4nz66afTMmVopljTTwQQCBVgKs5QD08+YyrO2MOm85V36dJFdu/eLeY87FdffbXcc889sVey8JXLLrtM5syZY9StU6HpHLw//fSTVKtWzcJaohf1r3/9S0aNGhXy4qRJk+T8888PyUvmSTrmgtc5po899lgpKSkx5qHXdj/44IOixqmmTJyKU+cLnzdvXgidfmFp06ZNSJ4bnjAVpxtGoeI2MBVnxT7pfpWpONM9AtSfcQLXXHON7NixIxB8KsALL7wg3377re0WH3zwgbz//vuBujX4zMnJkdGjR9te9+bNmyOCT630xhtvlO3bt9tevx0V9OvXT3bt2hUIPrUODUDXrFljR3W+LlO3y/DgUztsxZcTX8PROQQQsEzAuXOBljWZghCIX0CDEz1iFpz0FLgehbQ7ad3Z2aFXuezdu1eWLFlid9WyatUqycrKiqintLTUCMgjXvBAhp5+Ly8vD2mpfqtfu3ZtSB5P9i+wcOHCqNtHQUHB/ldmCQQQQMACAQJQCxApwr0CTZs2jbjmcufOndK4cWPbG92oUSPJzc0NqUdPJbZs2TIkz44nzZs3jwjWzHr09LkXU5MmTSKarUGpOpMSE2jVqlXU7UO3TxICCCDghAABqBPK1JE2gbFjx0pZWZlxE442Qo+YnXzyyXLCCSfY3qZzzz1XWrRoEahH69YjkE6cgtcAe+jQoYG6zQd33HGH6PWOXkyPPvqo0WzzJi79O2TIEGnbtq0Xu5PWNuup9sMPPzyiDTNnzozIIwMBBBCwQyD0/KAdNVAmAmkU0EBs5cqVcu+994peF9mrVy8ZNGiQYy368MMPjbu29ZrT+vXry8033xxxVNSuxvz973+XDh06yIQJE4wAXK//9PI1fnrU7rvvvhPtl54qPu+88+Scc86xi8/35c6ePVv0C8msWbOkRo0aMnHiRGnfvr3v+00HEUDAHQLcBe+OcUipFdwFnxKf7SvrkTo9Tayni8078W2v1OYK0nEXvJ1dysS74O30tLps7oK3WtT68rgL3npTK0vkLngrNSkLAQQQQAABBBBAwJMCXAPqyWGj0QgggAACCCCAgHcFCEC9O3a0HAEEEEAAAQQQ8KQAAagnh41GI4AAAggggAAC3hUgAPXu2NFyBBBAAAEEEEDAkwIEoJ4cNhqNAAIIIIAAAgh4V4AA1LtjR8sRQAABBBBAAAFPChCAenLYaDQCCCCAAAIIIOBdAQJQ744dLUcAAQQQQAABBDwpwFScnhw2Go1AZgqUlZWJTm9aWFgoxx9/vDHDVGZKWNPr9evXy/z580VnU9NpakkIIICAUwIEoE5JUw8CCKQkUFJSImeccYasW7fOKGf79u0yY8YMOeqoo1IqN1NX/uyzz+TSSy+VnJwcKSoqkuzsbFm6dKnk5uZmKgn9RgABBwU4Be8gNlUhgEDyAhdeeKH8+OOPooGn/tM0aNAg2bx5c/KFZuia+fn5op579uwxLDUA1X933nlnhorQbQQQcFqAANRpcepDAIGkBL7//nspLi4OWbdy5cqycOHCkDye7F/gm2++kdq1a4csqEeYP/jgg5A8niCAAAJ2CRCA2iVLuQggYKlAeMCkheuR0Bo1alhaTyYUlpeXJ+Xl5RFd1dPwJAQQQMAJAQJQJ5SpAwEEUhYYPXq0cZ2iWZAe/TzkkEOkW7duZhZ/4xTo0aOHtG3bNuR6Tw0+//3vf8dZAoshgAACqQkQgKbmx9oIIOCQQM+ePeWtt96SFi1aSJMmTeTKK6+U2bNnO1S7/6p544035Oyzz5YDDzxQ2rRpI6+88op07tzZfx2lRwgg4EoBzre4clhoFAIIRBPQO96/+OKLaC+Rl6BAVlaWTJw4McG1WBwBBBCwRoAjoNY4UgoCCCCAAAIIIIBAnAIEoHFCsRgCCCCAAAIIIICANQIEoNY4UgoCCCCAAAIIIIBAnAIEoHFCsRgCCCCAAAIIIICANQIEoNY4UgoCCCCAAAIIIIBAnAIEoHFCsRgCCCCAAAIIIICANQIEoNY4UgoCCCCAAAIIIIBAnAIEoHFCsRgCCCCAAAIIIICANQIEoNY4UgoCCCCAAAIIIIBAnAKOBaBbt26VDz/8UNavXx/RtBUrVsj7778vmzZtiniNDASsEHjzzTfloYceknXr1llRHGX4TOD5559nViCfjalV3SkrK5PPPvtMFixYsN8i8/PzZeXKlbJ79+79LssCCGS6gCNTcU6fPl1ef/116dGjh/FX5x2+8cYbDfuHH35Yli1bJq1bt5bHHntMJk2aJM2bN8/0caH/FgoceuihgQ8E3d5GjBghw4cPt7AGivKqwLZt26R9+/aiQYamW2+9VWbMmCE65ScJgZ9//ll69uwpe/bsMTByc3Nl+fLlUrVq1Qic8ePHy+OPPy5VqlSRgoIC46CKblskBBCILmD7EdDS0lJ54YUX5L777pOrrrpK9E2qR0L1iOjatWtl7ty58tRTTxlBwcCBA+Wll16K3lJyEUhC4Oijjw4En+bqY8aMkXnz5plP+ZvBAm3btg0EnybD2WefLSUlJeZT/maogH4p6datWyD4VIaioiI59dRTI0RmzZolY8eOlV27dhnBpy5w+umny4YNGyKWJQMBBP4nYPsR0MqVK4ue3qpRo4ZRo36T3Llzp2hgunr1aunQoYNUqvS/OLhLly4yc+bMkLF56623ZNq0aYG8+++/X1q0aBF4nswDra+8vFzy8vKSWd1166ix9umAAw5wXduSbVB2drYl/fn999+jNmHUqFHyzTffRH3NrsyaNWsG3gd21eFUubrN6VEgfR/5MU2ePFnuuusuT3fNqveQmxBq1arl2DYX/llkOqxZsyZi3/Tvf//bfDnwVz/z9IuuHniJlvQ9pEdL/ZJ0e9Pkp88hHSO/9CcrK8sYn9q1azv2Htq7d69RZ6z/bA9AtWIz+NRvlI888oj06dNH6tevb1wPqhhm0p3L5s2bzafGXw0SmzRpEsjTDUKD11STfnCap91SLSvd6+uGpf+scEl3X8z6rRpns7zwv3aXH16fPtftzS9jpNub9scv76Hw8dIPU6+PVTq28XBHq56bH55ObnP6pV7rjfYlK3zbyMnJidrVivbLWr72J1r5UQtzeaZub5rCbVze7Aqbp2Pkl/5oXzRpf5za5vZXjyMBqHZaT13oaXhtkHlkIXwHqae9qlWrposH0mmnnSb6z0wbN24UvW4rlVS9enVjELRNfkh6ZE2PRqXq4iaLunXrWtIfvQZr6dKlEV3T642d8tI3vm7XhYWFIafzIhrloQz9Yqjvn+LiYg+1OrKpOi7Rbhi58sorHds+IltlTY5V7yFrWpNaKfpZofs4PXu2v6MqqdX059rHHHOM6L51+/btf2bue9SqVauIbWPYsGHG0c7gtun7XT+7Yu1n9ICLLuOXAEe3Nw24Y/U3BNEDT3S/reOv1/P6IenRdr2GWd9DTu23tb6Kku3XgGrlel2MXtyvg6mn0M1viw0aNJAtW7YE2qePGzduHHjOAwRSFdBfVzjooINCitFrjg8++OCQPJ5kpoD+Aod55EYF9AP0yy+/zEwMeh0h8NVXX4VsH3rJ2Jw5cyKWO/nkk2XChAlGvn7o6o22uq5fLvOK6DAZCFgg4MgR0HvuuUcOP/xwue6660Ka3LVrV+OUvP40jgaeevepfuskIWClgH4Q6DamR+xatmwp5rVKVtZBWd4U0G1Btw09CqpHCNg2vDmOdrVaj1Lq9qFHjfR0uT6Plc477zzj8jJdTo/Wmqc8Yy1PPgKZLmB7AKo/WTF//nzj36uvvhrwfvTRR40bkPTUhZ7uqlevnnFz0aBBgwLL8AABqwSaNWtmVVGU40OBAw880AhA+S1iHw6uBV2K90imBp4kBBCIT8D2APSII44wfmopVnP69u0rvXv3No5Oxfsmj1UW+QgggAACCCCAAALuF7A9AI2HQE99+ennKOLpM8sggAACCCCAAAKZKuDITUiZiku/EUAAAQQQQAABBCIFCEAjTchBAAEEEEAAAQQQsFGAANRGXIpGAAEEEEAAAQQQiBQgAI00IQcBBBBAAAEEEEDARgECUBtxKRoBBBBAAAEEEEAgUoAANNKEHAQQQAABBBBAAAEbBQhAbcSlaAQQQAABBBBAAIFIAQLQSBNyEMg4gSlTpojOFtWkSRNp2rSpfP/9944ZrF+/Xo488kijbq3/7rvvdqxurah79+5So0YNycnJMQx0Ws5MSPn5+cZsdGqu/0aMGBGz2zrNpLmc/s2UGaOKi4vl1ltvlcMOO0wOPvhgGTduXEwjXkAAgcQECEAT82JpBHwn8PXXX8vIkSOltLTU6Ft5ebn06tVLVq9ebXtfS0pK5KijjpLNmzcH6po8ebLcf//9ged2PujcubOsWbMmUIUa6Oxtfk86X3mnTp1CAskXXnghavA/ZMgQ+eqrr0JIOnbsKHv37g3J8+OTnj17ytSpU4254IuKiuSxxx6TSZMm+bGr9AkBxwUIQB0np0IE3CVw9dVXR22QE0ciH3744ah1axDqRPrjjz8iqtHASoMOP6eJEydG7d5zzz0Xkf/BBx9E5OmXlIqOmEas4MGMlStXytq1a0WDdTPt2bOHANTE4C8CKQoQgKYIyOoIeF1ATzNGS/pha3eKdbrbPBprd/2xyi8oKIj1ki/y1T0rKyuiL4m479q1K2J9P2XoF5FatWpFdCnWNhuxIBkIIFChAAFohTy8iID/Bfr16xe1k5dffnnUfCszL7nkkqjFtWnTJmq+1Zm5ublRixw8eHDUfL9kXnzxxaJHMcOTXucYnho0aBCeZTy/+eabo+b7JVOv+6xXr15Id6pUqWJcCxqSyRMEEEhKgAA0KTZWQsA/Avfdd5/otZCa9KiY/rvhhhvkrLPOsr2TGvA888wzIfXoTVAffvhhSJ5dT+bNmxdR9PTp0yUvLy8i308ZesPZiy++GNKlxo0by5w5c0Ly9IleIxx+tHT8+PFy+OGHRyzrpwwNNt966y2jS3oktHbt2nL00Uc7tm36yZK+IBBNIDtaJnkIIJBZAjNnzpT58+fLjz/+KMcee6yjwUWfPn3k22+/lffee080CDrjjDMcw9f6fv/9d3n66aeNm2oGDhwYcdTLscY4XNEpp5wiS5YskRkzZsiBBx4Y011/HeC3336TJ598UvSaWT0y3rx5c4dbm57q9Ojvzz//LMuWLRMNSNu1a5eehlArAj4UyNp3GibyPIyLO7px40bRO2dTSdWrVzfu+NW7Gv2QatasKVWrVhW18UuqW7eubN261RfdqVSpkjRq1MjojxPXVTqBpkcI9f0T6/pRJ9pgZR16dEsDDD/9vJCf3kOVK1eWhg0bGr+W4Je77/WoamFhYeDXJ6zcntNRlm5veqR8y5Yt6aje8jp1v62frX65Hlz3b/Xr1zf2cU7tt/USp/DLWIIHilPwwRo8RgABBBBAAAEEELBdgADUdmIqQAABBBBAAAEEEAgWIAAN1uAxAggggAACCCCAgO0CBKC2E1MBAggggAACCCCAQLAAAWiwBo8RQAABBBBAAAEEbBcgALWdmAoQQAABBBBAAAEEggUIQIM1eIwAAggggAACCCBguwABqO3EVIAAAggggAACCCAQLEAAGqzBYwQQQAABBBBAAAHbBQhAbSemAgQQqEhAJ2P7/+3dB5QURf7A8d/sLknCIiwoSckgoIKgAnKIkkE9UUTljPjQdwr6R955ApLeyemJGAD1REVBxXhGkogIyCmIShYERQQJknPcnfnPr7wZJ+7OzkxP6m+9p0zXVFdXfbp75jfV1dvjxo0TfTTktddeK9u3by+seNzf0+efd+7cWdq2bSvvvPNOXOr/6KOP5JprrjH9+eKLL+JS5yuvvCJdunSRXr16ydq1a+NSJ5XET+DLL780j+qsU6eOdO3aNS4Vr1q1yhxHHTt2NOdIuEoPHz4szZs3l+rVq5vHpC5btixcUfIRSBkBngWfMruChiBgT4FOnTrJhg0bvI/YbdmypcyePVvOO+88y0FmzZolf/3rX81z4HVjq1evNsHdiBEjot72M888I2PGjPGur8+5Hz58uPTr18+bV9wX9913n19wrAHOpEmTpFu3bsWtivIWCOjz4nv37u2tWQNHDUT1uM7Jie5r9scff5QePXp4H9X5888/ix5Lr7/+unc7nhcNGzb0vDTnUc+ePeWxxx6Tm266yZvPCwRSTYAR0FTbI7QHARsJLFiwQNatW+cNPj1dHzx4sOelpf/ecccd3uBTN3T8+HGZOnWq/Prrr1Ft9+DBg37Bp1Zy4sQJk3f06NGo6ty4caNf8KmV5OfnywMPPBBVfawUfwEN+AKT7vehQ4cGZke8rMFjQUGBt7wem998840sWrTIm6cvwgWZQ4YM8SvHAgKpJkAAmmp7hPYgYCOBffv2Sfny5YN6vHv37qA8KzJKliwZVK3m7d27Nyg/kgwNQEP1JysrS44cORJJFUFl1Oj0008Pyt+/f39QHhnJEQj342LXrl1RN0gDzsCkx1HgsRnuXPENXgPrYRmBVBAgAE2FvUAbELCpgM5bO3XqlF/vS5QoIc2aNfPLs2rhnHPOCapaA7u6desG5UeSUbVqValYsWJQUQ0+QwWRQQVDZNSvX18cDoffO7ocajt+hVhImECTJk1Cbkvn7EabLrnkkqDL9wcOHDBzPX3r1Mv0oRLHRygV8lJJgAA0lfYGbUHAZgK1a9c2c9W022XKlDGjhzVr1pTJkycnREJv7NFUrlw585++1nmhuhxN0tHTadOmmVXLli3rrWfJkiVBwUSk9efm5sqUKVNM8dNOO80YValSRXT6Aik1BN59992gHwnt27eXG2+8MeoGjh071ky1KF26tDk3dC7pxIkTzU1GvpXee++90qhRI98s05bvv//eL48FBFJNILrZ0anWC9qDAAJpK6B3vuuIp85tq1ChgrnLO9obN4qLcMYZZ4je7PHpp5+K3o1/8cUXS7Vq1YpbjV/5evXqiX75e+rUUbBYR6P0xiyd//fZZ5+JjhBfeeWVogEuKTUENEjcunWrPPTQQ7Jz507RG+v69OkTU+P0x4bORdbgVu9y1xHRwEDTs4HPP/9cnnzySZk5c6YZaX/ttdc8b/EvAikr4HB/6LpStnUhGqZzanQCfixJT2ydH6OTxDMh6Zwz/QCMZb5Rqjno5Uqd+5YJSedtaaCj/Qk1rysd+6gjhHr+BF4+T8e+aJt1lFEDu3Dz6dKxX5l0DmVnZ4tOb9izZ4/fTWPpuF88bdYfWzo1I1PmaurxplNDAueoevqbbv/q57Z+t+q0h0xI+vmWl5dnPuMS9bldqlQpqVSpUlg+LsGHpeENBBBAAAEEEEAAASsECECtUKVOBBBAAAEEEEAAgbACBKBhaXgDAQQQQAABBBBAwAoBAlArVKkTAQQQQAABBBBAIKwAAWhYGt5AAAEEEEAAAQQQsEKAANQKVepEAAEEEEAAAQQQCCtAABqWhjcQQAABBBBAAAEErBAgALVClToRQAABBBBAAAEEwgoQgIal4Q0EEEAAAQQQQAABKwQIQK1QpU4EUkTg+eefl+bNm8sFF1wgM2bMCNsqfdTfTTfdJJdffrkMHjw4bLl0euOGG26Qc845xzxeU/sXLn388ceij8+sUaOGjB8/PlyxYuXrYzivuuoq88hMfQ58qqann35aOnToIFdccUWhT4F68803jU/16tWlTZs2Ce/OfffdJ+eff7785S9/EafTGfP29Wl6kyZNkuHDh8vrr78ec31agT42Ux/lqsdc375941JncSoZNWqUXHrppXLNNdeYR3cWZ91Yy+rjbEeOHGkeRbpx48ZYq0v6+vv375dHHnlE7r33Xpk7d27S25OpDeBRnBmwZ3kUZ2rvxGQ9ilO/tN955x0/nAEDBsjQoUP98vTL+KyzzvLL0+eMb9iwwS/PdyHVH8V59tlnBz0m9Msvv5TatWv7dkOmTJkiQ4YM8cvT53hPnTrVL684Cxr0jx492m8V/TK79dZb/fKsXijqUZwaIOvz5X3T7Nmz5bzzzvPNEg0+77//fr88fTTmli1b/PKsWqhTp07QY5M14NFHKkeT9OnTDRo0EP332LFjpgpdXrBgQTTVmXX0B07Dhg391tfHEP78889+eb4L8XwU50UXXWQCYN/69YdPrVq1fLMsef3tt99K7969veeb/kDQY6Z9+/aWbM/qSnVftmzZ0hxzJ0+eNJu7++67TXBt9batrJ9HcVqpS90IIOAV2Lx5c1DwqW9OnDgxaKSrV69e3vU8L/QZ1To6lI7pnnvu8X4Z+ra/W7duvovmdWDwqZk64hEYmAWtGCZDn1UeGHxq0WHDhqXUM6VnzZoVso833nhjUM8Cg08toM8vv/POO4PKxjvjb3/7W1Dwqdu4+eabo96UjvqeOHHCG3xqRTp6+dZbb0VdpwYsgUm3oSNoVicdydX2B6brrrsuMCvuy8ePHzej/NpXDTw9o9N6Dh49ejTu20tEhbfccotpuyf41G3qD9WVK1cmYvO22gaX4G21u+msXQRWrFgRtqsaJPmmTZs2+S56X//www/e1+n0Yv369SGbW9hl+MAVog1ANRBwOByB1Zkv5l27dgXlJytDv0xDtfPAgQMRN+m7776LuGy0BcMdg4WNLBa1rXXr1pkA2recjoTqqGq0SX+whUrh2h+qbLR5a9asCbnqzp07Q+bHM1MDUB3JDUwaiO7duzcwOy2Wd+zYEXR86Ij/L7/8khbtT6dGEoCm096irQhEKNCkSZOwJfXSrG+qVq2a76L3tV7GTsekl2xDpTJlyoTKDpmn82ajSTqPNFyqXLlyuLcSnt+oUSNzCTpwwzk5OYFZYZebNWsW9r14vRHuGCzMuaht161bV/TyuG/SACNwGorv+0W9Ll26dMgigVM+QhaKMVP7Eyrl5uaGyo5rnjqGOmb27dsnlSpViuu2ElWZTlvQaVO+6eDBg2YOtG8er2MX8FeOvT5qQACBFBDQm2q6dOkS1BKdh1i1alW//GnTpvkt64J+qYwdOzYoPx0y9JKkBhSBKdTNQDpPNjDpzS6tW7cOzI5oOS8vT3SebWDSS/2BgX9gmUQuX3311RIqgNQ5oIHptttuC8wyy6+88krI/Hhm6uXywGBA6//Pf/4T9Wb0srheMvYETho86n6L5bL++++/H9QePQZ1PrDVSY/hKlWqBG1m/vz5QXnxztAfdc8++2xQtS+++GLUc3SDKktwxhNPPGGuWHiOD/1X57jqjZyk+ApwE1J8PZNSGzchJYU94o0m6yYkbeAzzzwjEyZMMAGZTqTXuVmhkl5K69Onj2zfvt3cbaxBXKgvfs+6qX4TkrazZ8+esnbtWtG26s1YOuoXKul8SJ3nqJcN9YtmzJgxoYoVK08DuX/961/mMrcGPBrwJToVdROStmeU+85p/esIaqTHSaigVMvpXwq466679KUJ1hI9H65fv36il7PPPPNMefnll0Ne9jWNi/B/uq810NDLqk2bNjXzWQs73iOpdvfu3eYvCujcR70CMX369EJXi+dNSLqhQYMGycKFC80PnZdeeknCjR4X2qgo39TpC2+88Ya5dK1zyvVHXDonna6jNw4eOnRILrvsMgk1Tz7d+peKNyERgKbbURSivQSgIVBSKCuZAahVDOkQgBan73q5Uj+gNYjIlBRJAJoufdXRRB251/nLvjeHpEv7Q7Uz3gFoqG0kMk+PN51XnK5zPwOt9HNbv1uLMy86sI5UWk7FAJRL8Kl0hNAWBBBAAAEEEEDABgIEoDbYyXQRAQQQQAABBBBIJQEC0FTaG7QFAQQQQAABBBCwgQABqA12Ml1EAAEEEEAAAQRSSYAANJX2Bm1BAAEEEEAAAQRsIEAAaoOdTBcRQAABBBBAAIFUEiAATaW9QVsQQAABBBBAAAEbCBCA2mAn00UEEEAAAQQQQCCVBAhAU2lv0BYEEEAAAQQQQMAGAjk26CNdRAABBBAIIbB06VKZNm2aVKpUSYYPHx6ixO9Z+fn55tGE+lSY66+/Xi688MKwZdPlDX3c7E8//SQtW7Y0j6GNR7vnzJljngTUvHlzady4cTyqpA4EMlaAADRjdy0dQwABBMILPPnkkzJ27Fhvgeeee07WrFljniXuzXS/8Dzb3PMITA1YH3roIbn77rt9i6XVaw06t2/fbtr86quvygsvvCCffvppTH3o2rWrbNq0ydShzxB/+OGHRZ9hT0IAgdACXIIP7UIuAgggkLECy5Yt8ws+PR3t0aOH56X331atWgU9f12Dq5UrV3rLpNOLQYMGeYNPT7s18NaAPNo0btw4WbVqlWjgqf9pGjVqVNoaRevAeggUR4AAtDhalEUAAQQyQGD69Okhe7F58+agfL3sHpgcDod89NFHgdlpsTxv3ryQ7Zw5c2bI/Egy9dJ7YMrJyZHFixcHZrOMAAL/EyAA5VBAAAEEbCZQuXLlkD3WwDIwaSAVmFwul1SpUiUwOy2Wc3NzQ7azYsWKIfMjyQzlqUYVKlSIZHXKIGBLAQJQW+52Oo0AAnYW0PmboQKxCRMmBLEMHTo0KK9EiRJy1113BeWnQ8a///3vkM18+eWXQ+ZHkjl69GjJyvrj61R9Tpw4YW7YimR9yiBgR4E/zhg79p4+I4AAAjYVWLt2rTRo0ECys7OldOnS5i73Xr16BWlooKk36ZQsWdKUbdGihWzYsCGoXLpkNGnSRJYsWSLlypUTDRSrVasmX331lVmOtg/qqH9RoGnTpnLWWWfJtddea+6wDzWiHO02WA+BTBMIvraSaT2kPwgggAACIQUWLFgQMj8ws2fPnqL/ZUqqVauWrF+/Pq7d0UA21jvp49ogKkMgxQUYAU3xHUTzEEAAAQQQQACBTBMgAM20PUp/EEAAAQQQQACBFBcgAE3xHUTzEEAAAQQQQACBTBMgAM20PUp/EEAAAQQQQACBFBcgAE3xHUTzEEAAAQQQQACBTBMgAM20PUp/EEAAAQQQQACBFBcgAE3xHUTzEEAAAQQQQACBTBMgAM20PUp/EEAAAQQQQACBFBcgAE3xHUTzEEAAAQQQQACBTBMgAM20PUp/EECg2AI//fSTrFu3TlwuV7HXTecV9Hnla9askV9++aXIbmiZ77//3jzjvMjCaVBAHyc6e/bsIvteUFBgHj26adOmInu1detWWbVqlRw5cqTIsvEucPToUVm9erX8+uuvcat6y5YtMmvWLHNuxK1SKkLgfwI8ipNDAQEEbCtw6tQpuf322+Wbb74xweehQ4dMkFWxYsWMN9m8ebN5Zvnhw4flwIED0qlTJ5kyZYqEen75oEGD5OOPP5acnBw5ePCgfP3111KzZs20Nbrnnnvk/fffN33VHx0DBgyQoUOHBvVn37590rt3b9m2bZuokz7CUx9fqs+QD0yPPvqovPDCC1KqVCnZv3+/zJw5U5o3bx5YzJJl/WHQt29f8+NA9+Utt9wi2p5Y0oQJE7x1qFHHjh3l1VdfjaVK1kXAT4ARUD8OFhBAwE4CV155pSxatMgEVRp8atKAQ0e9MjnpaFnr1q1FR+w0YNE0d+5ceeKJJ4K6PXz4cPnwww9F19HgU1O3bt2SMsoX1LgoMl566SUTfOqqnhHviRMnykcffeRXm9PplKZNm8ratWuNkR4TGrQPHjzYr5wufPDBBzJ+/Hg5duyYCT41r0ePHiZw1ddWpt27d5sfDzt37vTuy6lTp8YULH7++efyyCOPGB+P0WeffSbjxo2zsivUbTMBh/vgSqtrTp4Py1j2U8mSJUU/XPLz82OpJmXWLV26tPlF7vkCTZmGxdCQ0047zXzhxVBFyqyqI0oVKlQw/dERt0xIOsqj50+6B2pVqlSRwH2io596abZx48ZpvasKO4c++eQTufPOO70Bi6ejOqqpl3F9U4MGDWTXrl2+WXL66afLpEmTpHPnzn75Vi1kZWVJ+fLlTdAb6+f2ueeeK3ppOTBddNFFMmfOHG+2Bp5dunSRwM9VddURUd/UtWtXWbJkiW+WlC1bVv7xj39Iv379/PI9C/q5ffLkSfNd5MmL5l8dlRwyZIgZofVd//zzzzejtb55kb6+6qqrZOHChUHFzzjjDPnhhx+C8jVDXfSzLhnTD0I2KMZM7YvuI/1RkQkpOztbypUrZ46TRH5u5+bmhuVLu0vwChdrzKyXkbSeWD/Iwqom+A0NptUkU/qjfJnUH88lzUw65vQSZCb0R3+MBgagOtKn+yzdz6fCfmRrQKf/BaZQ551+aQUGoGqWSCNPW+NxzIW6fK4Omu+7z9XCs11fp+PHj/uV0/f0OAqVCjPy7B/9N5akgUWodur3nG9/irMNXTdUKqxO9dIU7TZDbS+ZebrvPPsome2I97Z1/+h5lIikx2ZhKfRRVtgaSX4vHh9AngBUf31mQtLRKD35M6U/uk/0xM+U/ni+HPTEz5Q+eQK3wOAt3c4nHZ3SeXsaVGjSfVW5cmUz1y/d91Vhnwk62lepUiVzSd3zZaSfI3//+9+DjlGdLzls2DDvzUdqpPMh9RJ+oow8X2R6vMW6TR0t7N+/f9Ch+s9//tOv7tq1a8t5550nixcv9v5I0RGxG2+80a+cVjRw4EC/cpqnI4E6xSNce7Uu7Y/HX9eJJuko9IgRI7zzWbUOPT9HjhwZdttFbefBBx+UefPmBRV7/PHHw9apn9katIXrb1BlKZ6hx3kmfQ/5/kBI1Oe2fqYUloJ/AhdWmvcQQACBDBLQYOS6664zPSpTpowJGL788ssM6mHoruhon04z0NFNDe708qnO9dT5r4FJb27RgESTrtemTRtzV7Tnh1Vg+VRf7tmzp4wdO9bbTO3T5MmTRacaBKbXXntN6tat6zXSaQtjxowJLCbt2rWT559/3uRrfc2aNTM3tqmr1Ukv9eucTQ1odXBFpypooNiqVauoN63tf/vtt73r677WIPfSSy/15vECgVgF0m4OqF4KinWIXz8U9Fen/gmSTEj6gaMfPoGXydK5bzrHTO9AzYSkH946d0r74xlpS/d+aeCi50+ifklb7aXzlDRw0Bs6MiVl0jmkQXLVqlVlz549GTPCpvPCdZQ01hHQVDle9XjTEdC9e/emSpNiaod+but3azzuO4mpIXFaWT/f8vLyzGdcoj63dQRUr7SES4yAhpMhHwEEEEAAAQQQQMASAQJQS1ipFAEEEEAAAQQQQCCcAAFoOBnyEUAAAQQQQAABBCwRIAC1hJVKEUAAAQQQQAABBMIJEICGkyEfAQQQQAABBBBAwBIBAlBLWKkUAQQQQAABBBBAIJwAAWg4GfIRQAABBBBAAAEELBEgALWElUoRQAABBBBAAAEEwgkQgIaTIR8BBBBAAAEEEEDAEgECUEtYqRQBBBBAAAEEEEAgnAABaDgZ8hFAAAEEbC1w8uRJGTBggNSrV09q1aolDz/8sK09Yu38ihUrpHXr1lK/fn2pW7eubNy4MdYqWT+NBXLSuO00HQEEEEAAAcsE2rVrJ9u2bROn02m2MXnyZClbtqwMGjTIsm1masXbt2+X7t27+3WvU6dO8sUXX0iNGjX88lmwhwAjoPbYz/QSAQQQQKAYAj/++KPs2LHDG3zqqsePH5fnnnuuGLVQ1CPw1FNPicPh8Cyaf/Pz82Xq1Kl+eSzYR4AA1D77mp4igAACCEQocOrUKSlXrlxQaQ1CScUXOHbsmLhcLr8VCwoKRPNJ9hQgALXnfqfXCCCAAAKFCDRo0EAqV67sV6JEiRLSqFEjvzwWIhPo3bt3UEENSHv16hWUT4Y9BAhA7bGf6SUCCCCAQDEEcnJy5IMPPjBrlC9fXnJzc6VNmzYyc+bMYtRCUY9A+/btZfTo0WaxUqVKUqFCBXn22WelRYsWniL8azMBbkKy2Q6nuwgggAACkQnoCOiWLVtk3bp1UrJkSdFRUVL0Av3795c///nPZm5t9erVJS8vL/rKWDPtBQhA034X0gEEEEAAAasEsrOzpWnTplZVb7t6q1atKvofCQEuwXMMIIAAAggggAACCCRUgAA0odxsDAEEEEAAAQQQQIAAlGMAAQQQQAABBBBAIKECBKAJ5WZjCCCAAAIIIIAAAgSgHAMIIIAAAggggAACCRUgAE0oNxtDAAEEEEAAAQQQIADlGEAAAQQQQAABBBBIqAABaEK52RgCCCCAAAIIIIAAASjHAAIIIIAAAggggEBCBQhAE8rNxhBAAAEEEEAAAQQIQDkGEEAAAQQQQAABBBIqQACaUG42hgACCCCAAAIIIEAAyjGAAAIIIIAAAgggkFABAtCEcrMxBBBAAAEEEEAAAQJQjgEEEEAAAQQQQACBhAoQgCaUm40hgAACCCCAAAIIEIByDCCAAAIIIIAAAggkVIAANKHcbAwBBBBAAAEEEECAAJRjAAEEEEAAAQQQQCChAgSgCeVmYwgggAACCCCAAAIEoBwDCCCAAAIIIIAAAgkVIABNKDcbQwABBBBAAAEEECAA5RhAAAEEEEAAAQQQSKgAAWhCudkYAggggAACCCCAAAEoxwACCCCAAAIIIIBAQgUIQBPKzcYQQAABBBBAAAEECEA5BhBAAAEEChV44403pGbNmlKjRg3p0KFDoWV5EwEEEIhEgAA0EiXKIIAAAjYVeO+992Tw4MHidDrF5XLJ+vXrpXbt2jbVoNsIIBAvAQLQeElSDwIIIJCBAgMGDAjq1cmTJ01QGvQGGQgggECEAgSgEUJRDAEEEEDgD4GlS5f+scArBBBAoJgCBKDFXJbU8QAADlhJREFUBKM4AggggIDIhRdeCAMCCCAQtQABaNR0rIgAAghkvsCQIUOCOpmdnS3jxo0LyicDAQQQiFSAADRSKcohgAACNhQYOHCgvPvuu6JBp8PhkIYNG8qWLVtsKEGXEUAgngI58ayMuhBAAAEEMk+gbdu2BJ2Zt1vpEQJJFWAENKn8bBwBBBBAAAEEELCfAAGo/fY5PUYAAQQQQAABBJIqQACaVH42jgACCCCAAAII2E+AANR++5weI4AAAggggAACSRUgAE0qPxtHAAEEEEAAAQTsJ0AAar99To8RQAABBBBAAIGkChCAJpWfjSOAAAIIIIAAAvYTIAC13z6nxwgggAACCCCAQFIFCECTys/GEUAAAQQQQAAB+wkQgNpvn9NjBBBAAAEEEEAgqQIEoEnlZ+MIIIAAAggggID9BAhA7bfP6TECCCCAAAIIIJBUAQLQpPKzcQQQQAABBBBAwH4CBKD22+f0GAEEEEAAAQQQSKoAAWhS+dk4AggggAACCCBgPwECUPvtc3qMAAIIIIAAAggkVYAANKn8bBwBBBBAAAEEELCfAAGo/fY5PUYAAQQQQAABBJIqQACaVH42jgACCCCAAAII2E+AANR++5weI4AAAggggAACSRUgAE0qPxtHAAEEEEAAAQTsJ0AAar99To8RQAABBBBAAIGkChCAJpWfjSOAAAIIIIAAAvYTcLjcyX7dpsepLDB37lwZOHCgzJ8/X84444xUbqpt29a9e3dp166dDBs2zLYGqdzx9957z+yb5cuXS6lSpVK5qbZtW9u2baVv374yYMAA2xqkcscnT54s48ePFz2HSNYIMAJqjSu1xiCgv4mcTmcMNbCq1QK6f9hHVitHX79n/zC+EL2h1WvqPmL/WK0cff26bwoKCqKvgDWLFCAALZKIAggggAACCCCAAALxFMiJZ2XUhUA8BPSye7du3bh0GA9Mi+po3769NG7c2KLaqTZWgZo1a5pzKCuLMYZYLa1av2PHjlK/fn2rqqfeGAXq1KkjXbt2jbEWVi9MgDmghenwHgIIIIAAAggggEDcBfh5HHdSKkQAAQQQQAABBBAoTIBL8IXp8J7lAq6Vq8S1foM4uncVR9myIbfn+mG9uH7+2fue48wzxdH8fO8yL6wTyM/Pl507d0pOTo7k5eVJqEu6p06dkr179/o1gr9e4Mdh+YJr/35xzZ0nWb2vCbkt14KF4jp2zPueo0VzcfAXJrweVr2I9NyItJxV7bR7va7FS8T140/i6NJJHFWrhuTgHArJElMml+Bj4mPlWAQKul4hkpMtcm4zcb37vmQ9+7RkdekcVGVBn77i2rxFHFXyzHuOP7WTrAcGB5UjI74Cx9wBy8KFC6VGjRpy+PBhc8dumzZtgjaydetWWb16teTm5nrfa926tfc1L6wXKOh7i7hWr5Gcld8Gbczl/oFQULGqOC7v4H0va8gD4mgbvC+9BXgRF4FIz41Iy8WlUVTiJ1Dw4DBxzV8ojk6Xi2vq65L1yguSdfllfmU4h/w44rbACGjcKKmoOAKurxaLyx245KxeZlZztmoprnFPi4QIQF3LV0j29A/E0bBBcTZB2RgF9Euxbt260qBBAxN8zpo1Sw4dOiTly5f3q/nAgQNSu3ZtadSokV8+C4kRcL7+hrjW/RB+Y9+vFWlQX7I/fj98Gd6xRCDScyPScpY00saV6pU111vvSvbyr8Xh/gHt7OgOPJcsFQkIQIVzyJKjhDmglrBSaZECF18k2UsW/VFs925xHTnyx/L/XrncI2/y205x/fqrOB8d675M8mNQGTKsEdA7dDX41LRr1y4ThJYuXTpoY/rlWaJECdmwYYO5XM/fNgwisizDtXmzOMc+IVljHwm7Df0B56hfT5yvvi7O518Qc06FLc0b8RSI9NyItFw820ZdIq7PPhfHJe4rAb9uNeeHTk3JevBvQTScQ0EkcckgAI0LI5UUV8Dh/vMwnjmfJrj852OS9dCDwdW454jK0aPimjNX3H8VWAo69RDn5FeCy5FjmcDSpUvlq6++koYNG5pAM3BDBw8elN9++02ys7Nl/fr1snjx4sAiLFsg4NKHAdzWX7LHPyGOChXCbsG1bLm4vnVfadi7z/xbUKeRuLZvD1ueN+InEOm5EWm5+LWMmozA1m3ico9uFtxxl7i+WiIF57YU5+xPgnA4h4JI4pLBHNC4MFJJtALm5L+il2QN+7tk3XF7UDUu900w7jtcvBPDnR/PEOeof0jOtwQ5QVgWZhxxj05rEHrOOeeYOaG+mzp+/Lj5m60Oh8M8OWT27Nly6aWXSrly5XyL8TrOAs5/PS6uPXsk+7FHxLX0G/MlGnIOqF5FcD/VxfG/qRMFN98ujqZNQo70xLmJtq8u0nMj0nK2B40zQMGI0eJ68WXJ3uK+Acn9A9r5xlvimvZm0HQVc9WAcyjO+iKMgMadlAojFTBfmp17SNbjj4YMPk097juwZecub5WOxu55hu4bknT0h2StgN7Zrl+Mmsq6/0JBtWrVzKV4363qo+r0BiUNPjXpKOhpp50megMTyVoB5ytTxfXcJMmvkCcFl3URWfO95Fc7O3ij7rt73TvEm6/nkGvTL95lXlgjEOm5EWk5a1pp71odNaqLo9UFJvhUCb0E7/r6m2AUzqFgkzjkEIDGAZEqii/g2rZNCtwjn9nTpkjWNVf7VaBzQb03VezZKwXuINXkuX+BOl+cLA53eb2ET7JWQOd9rl3rvoHFnfTPMellds+d7nozkubpn2VauXKleU/L7du3zwStlStX1kWShQI5a1dKzqE9knNwt2R/PkfEPaqZs/33wNL3HHK+/6E4H3zItMTl3m/Ot94RR+9eFraMqlWgqHODcyj5x4mjZ3cTcOpcak0uPTd0Tqi+dn9HeaaqcA4Zkrj/j0vwcSelwkgECh4YIq6xT7o/pX8fOTPruP8uYc62TeKc+5k4b7pdcnb8/qHgfPgRcb75tsjJkyLuwCb7nWnicD9qkGStgP5twuXLl4tefne6R5x1BFQfv6mjnTNmzBD9U0saaHoCVS2jI58tWrSQM91/q5WUOIHAS/C+55DLPZLt7P9XcW10/y1d980Wjr7XS9aTj/MjLgG7p7Bzg3MoATsggk2Ym/OGjRSpmCvuSe6SPcP9F1fcn18Fd90j7rlFZo4151AEkFEUIQCNAo1VEi9gLrm7/9i2o1KlxG/c5lvUkU69tO65zB6O46T7B4LeDV9UuXDrk2+tgJnH5n6ggCPEXzKwdsvUHum5EWk5ROMr4HJPJRL9finiyg3nUHzdCUDj60ltCCCAAAIIIIAAAkUIMJGuCCDeRgABBBBAAAEEEIivAAFofD2pDQEEEEAAAQQQQKAIAQLQIoB4GwEEEEAAAQQQQCC+AgSg8fWkNgQQQAABBBBAAIEiBAhAiwDibQQQQCAWgYkTJ8rXX39tqnjzzTdl+vTpsVRn1j1x4oSMHj1atmzZEnNdVIAAAggkQ4AANBnqbBMBBGwjMGHCBFm8+PdHx7799tsyc+bMmPuuT6gaNWoUAWjMklSAAALJEshJ1obZLgIIIGA3gffee89uXaa/CCCAQEgBRkBDspCJAAIIRCcwZ84cueOOO+S6666TTz75xK+SZ555Rl577TVv3pQpU+Tqq6+WHj16yJAhQ2Sv+6lFmvQpVP3795dVq1bJgAED5Morr5SnnnpKDh8+7F2XFwgggEA6CxCApvPeo+0IIJBSArNnz5arrrpKXC6XtGrVSvr16ye//PL789m1oRqcLlq0yLR56tSpMmjQIPnTn/4k119/vcybN086depk3itwP5nlxRdflM6dO5vHm2qdOpf01ltvNXWbQvwPAQQQSGMBLsGn8c6j6QggkFoC//d//ydDhw6VESNGmIb16dNHGjZsGLKR//3vf02Qev/995vHl2og+uGHH4reYORJ3bp1k5deeskstmzZUvS/BQsWSIsWLTxF+BcBBBBISwFGQNNyt9FoBBBINYEjR47Ihg0b5PLLL/c2rU6dOmED0BtuuEEWLlwo9evXl4EDB5p19d9SpUp51+/evbv3tQadVapUke+++86bxwsEEEAgXQUIQNN1z9FuBBBIKYFDhw6J0+mU/Px8v3aVKFHCb9mzcNlll8ny5ctFA1G9LK+jnRdffLHs37/fU0TOPvts72uHwyEVK1ZkHqhXhBcIIJDOAgSg6bz3aDsCCKSMwJlnnin6n87z9KRdu3bJypUrPYt+/+oNSnrT0ZgxY2TZsmVmZFPL+t64pPNCPWnz5s1mlPSCCy7wZPEvAgggkLYCBKBpu+toOAIIpJrAbbfdJtOmTZP58+fLvn37ZOTIkWFvGlqxYoXcfPPNJqjUm5Z27NhhRk/r1avn7ZbeqKR/xP63336T4cOHm8v17du3977PCwQQQCBdBbgJKV33HO1GAIGUE9DRzN27d0vPnj1F72Tv0qWLhBuxvPfee83IZ5s2bUT/sHxWVpY8/fTT5sYkXdZ04YUXSocOHcyl/WbNmsmMGTOkQoUKcuDAgZTrOw1CAAEEiiPgcP/ydhVnBcoigAACCBQuoAGk/s3OvLy8wgu639V5o1u3bpWaNWuau+F1BV2/TJky5k8zXXLJJSbg1BuQSAgggECmCDACmil7kn4ggEDKCJQuXVr0v0iSjnzWqlUrbNGSJUuau9/DFuANBBBAIA0FmAOahjuNJiOAQGYL6B3vOuKpwScJAQQQyEQBLsFn4l6lTwgggAACCCCAQAoLMAKawjuHpiGAAAIIIIAAApkoQACaiXuVPiGAAAIIIIAAAiksQACawjuHpiGAAAIIIIAAApkoQACaiXuVPiGAAAIIIIAAAiksQACawjuHpiGAAAIIIIAAApkoQACaiXuVPiGAAAIIIIAAAiks8P/6p9Nr12xfhAAAAABJRU5ErkJggg==" alt="Scatterplot of engine displacement versus highway miles per gallon. There are two x-axes at the bottom and top of the plot. The bottom has labels alternating in red and gray, and the top has red, green and blue labels." style="display: block; margin: auto;" /></p>
|
||
</div>
|
||
<div id="exercises-3" class="section level3">
|
||
<h3>Exercises</h3>
|
||
<ul>
|
||
<li>Extend <code>guide_key()</code> to also pass on <code>family</code>,
|
||
<code>face</code> and <code>size</code> aesthetics from the key to the
|
||
labels.</li>
|
||
<li>Override the <code>GuideKey$build_ticks()</code> method to also pass
|
||
on <code>colour</code> and <code>linewidth</code> settings to the tick
|
||
marks. Looking at <code>Guide$build_ticks()</code> is a good starting
|
||
point.</li>
|
||
<li>Compare <code>GuideKey$extract_key()</code> to
|
||
<code>Guide$extract_key()</code>. What steps have been skimmed over in
|
||
the example?</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
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</body>
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