From 55b3f618790862ba4eeb057c71656bb70288aa7e Mon Sep 17 00:00:00 2001
From: "Documenter.jl" <documenter@juliadocs.github.io>
Date: Tue, 24 Sep 2024 09:25:07 +0000
Subject: [PATCH] build based on a2e749a

---
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diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json
index 1e7b4b0d9..5e8d835bc 100644
--- a/dev/.documenter-siteinfo.json
+++ b/dev/.documenter-siteinfo.json
@@ -1 +1 @@
-{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-17T11:17:59","documenter_version":"1.7.0"}}
\ No newline at end of file
+{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-24T09:24:40","documenter_version":"1.7.0"}}
\ No newline at end of file
diff --git a/dev/API/functions/index.html b/dev/API/functions/index.html
index 78e4e30f2..3ad1291b9 100644
--- a/dev/API/functions/index.html
+++ b/dev/API/functions/index.html
@@ -15,7 +15,7 @@
 - `footnotefont`
 - `footnotecolor`
 - `footnotealign`
-- `footnotelineheight`</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/draw.jl#L36-L74">source</a></section><section><div><pre><code class="language-julia hljs">draw(p::PaginatedLayers; kws...)</code></pre><p>Draw each element of <code>PaginatedLayers</code> <code>p</code> and return a <code>Vector{FigureGrid}</code>. Keywords <code>kws</code> are passed to the underlying <code>draw</code> calls.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/paginate.jl#L23-L28">source</a></section><section><div><pre><code class="language-julia hljs">draw(p::PaginatedLayers, i::Int; kws...)</code></pre><p>Draw the ith element of <code>PaginatedLayers</code> <code>p</code> and return a <code>FigureGrid</code>. Keywords <code>kws</code> are passed to the underlying <code>draw</code> call.</p><p>You can retrieve the number of elements using <code>length(p)</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/paginate.jl#L31-L38">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.draw!" href="#AlgebraOfGraphics.draw!"><code>AlgebraOfGraphics.draw!</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">draw!(fig, d::AbstractDrawable, scales::Scales = scales(); [axis, facet])</code></pre><p>Draw a <a href="../types/#AlgebraOfGraphics.AbstractDrawable"><code>AlgebraOfGraphics.AbstractDrawable</code></a> object <code>d</code> on <code>fig</code>. In practice, <code>d</code> will often be a <a href="../types/#AlgebraOfGraphics.Layer"><code>AlgebraOfGraphics.Layer</code></a> or <a href="../types/#AlgebraOfGraphics.Layers"><code>AlgebraOfGraphics.Layers</code></a>. <code>fig</code> can be a figure, a position in a layout, or an axis if <code>d</code> has no facet specification. The output can be customized by passing named tuples or dictionaries with settings via the <code>axis</code> or <code>facet</code> keywords.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/draw.jl#L200-L208">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.colorbar!" href="#AlgebraOfGraphics.colorbar!"><code>AlgebraOfGraphics.colorbar!</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">colorbar!(figpos, grid; kwargs...)</code></pre><p>Compute colorbar for <code>grid</code> (which should be the output of <a href="#AlgebraOfGraphics.draw!"><code>draw!</code></a>) and draw it in position <code>figpos</code>. Attributes allowed in <code>kwargs</code> are the same as <code>MakieLayout.Colorbar</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/guides/colorbar.jl#L8-L13">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.legend!" href="#AlgebraOfGraphics.legend!"><code>AlgebraOfGraphics.legend!</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">legend!(figpos, grid; order = nothing, kwargs...)</code></pre><p>Compute legend for <code>grid</code> (which should be the output of <a href="#AlgebraOfGraphics.draw!"><code>draw!</code></a>) and draw it in position <code>figpos</code>. All <code>kwargs</code> are forwarded to Makie&#39;s <code>Legend</code> constructor.</p><p>The order of scales represented in the legend can be changed with the <code>order</code> keyword. By default, legend sections are ordered the same as they appear in the plot specification. Assuming three scales <code>Color</code>, <code>MarkerSize</code> and <code>custom</code> exist in a spec, you can pass a vector to reorder them like <code>[:MarkerSize, :custom, :Color]</code>, or merge multiple entries together with a nested vector like <code>[[:MarkerSize, :custom], :Color]</code>, or give merged sections a title with the pair syntax <code>[[:MarkerSize, :custom] =&gt; &quot;Merged group&quot;, :Color]</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/guides/legend.jl#L8-L20">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.paginate" href="#AlgebraOfGraphics.paginate"><code>AlgebraOfGraphics.paginate</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">paginate(l; layout=nothing, row=nothing, col=nothing)</code></pre><p>Paginate <code>l</code>, the <code>Layer</code> or <code>Layers</code> object created by an <code>AlgebraOfGraphics</code> spec, to create a <code>PaginatedLayers</code> object. This contains a vector of layers where each layer operates on a subset of the input data.</p><p>The <code>PaginatedLayers</code> object can be passed to <code>draw</code> which will return a <code>Vector{FigureGrid}</code> rather than a single figure.</p><p>The keywords that limit the number of subplots on each page are the same that are used to specify facets in <code>mapping</code>:</p><ul><li><code>layout</code>: Maximum number of subplots in a wrapped linear layout.</li><li><code>row</code>: Maximum number of rows in a 2D layout.</li><li><code>col</code>: Maximum number of columns in a 2D layout.</li></ul><p><strong>Example</strong></p><pre><code class="nohighlight hljs">d = data((
+- `footnotelineheight`</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/draw.jl#L36-L74">source</a></section><section><div><pre><code class="language-julia hljs">draw(p::PaginatedLayers; kws...)</code></pre><p>Draw each element of <code>PaginatedLayers</code> <code>p</code> and return a <code>Vector{FigureGrid}</code>. Keywords <code>kws</code> are passed to the underlying <code>draw</code> calls.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/paginate.jl#L23-L28">source</a></section><section><div><pre><code class="language-julia hljs">draw(p::PaginatedLayers, i::Int; kws...)</code></pre><p>Draw the ith element of <code>PaginatedLayers</code> <code>p</code> and return a <code>FigureGrid</code>. Keywords <code>kws</code> are passed to the underlying <code>draw</code> call.</p><p>You can retrieve the number of elements using <code>length(p)</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/paginate.jl#L31-L38">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.draw!" href="#AlgebraOfGraphics.draw!"><code>AlgebraOfGraphics.draw!</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">draw!(fig, d::AbstractDrawable, scales::Scales = scales(); [axis, facet])</code></pre><p>Draw a <a href="../types/#AlgebraOfGraphics.AbstractDrawable"><code>AlgebraOfGraphics.AbstractDrawable</code></a> object <code>d</code> on <code>fig</code>. In practice, <code>d</code> will often be a <a href="../types/#AlgebraOfGraphics.Layer"><code>AlgebraOfGraphics.Layer</code></a> or <a href="../types/#AlgebraOfGraphics.Layers"><code>AlgebraOfGraphics.Layers</code></a>. <code>fig</code> can be a figure, a position in a layout, or an axis if <code>d</code> has no facet specification. The output can be customized by passing named tuples or dictionaries with settings via the <code>axis</code> or <code>facet</code> keywords.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/draw.jl#L200-L208">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.colorbar!" href="#AlgebraOfGraphics.colorbar!"><code>AlgebraOfGraphics.colorbar!</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">colorbar!(figpos, grid; kwargs...)</code></pre><p>Compute colorbar for <code>grid</code> (which should be the output of <a href="#AlgebraOfGraphics.draw!"><code>draw!</code></a>) and draw it in position <code>figpos</code>. Attributes allowed in <code>kwargs</code> are the same as <code>MakieLayout.Colorbar</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/guides/colorbar.jl#L8-L13">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.legend!" href="#AlgebraOfGraphics.legend!"><code>AlgebraOfGraphics.legend!</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">legend!(figpos, grid; order = nothing, kwargs...)</code></pre><p>Compute legend for <code>grid</code> (which should be the output of <a href="#AlgebraOfGraphics.draw!"><code>draw!</code></a>) and draw it in position <code>figpos</code>. All <code>kwargs</code> are forwarded to Makie&#39;s <code>Legend</code> constructor.</p><p>The order of scales represented in the legend can be changed with the <code>order</code> keyword. By default, legend sections are ordered the same as they appear in the plot specification. Assuming three scales <code>Color</code>, <code>MarkerSize</code> and <code>custom</code> exist in a spec, you can pass a vector to reorder them like <code>[:MarkerSize, :custom, :Color]</code>, or merge multiple entries together with a nested vector like <code>[[:MarkerSize, :custom], :Color]</code>, or give merged sections a title with the pair syntax <code>[[:MarkerSize, :custom] =&gt; &quot;Merged group&quot;, :Color]</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/guides/legend.jl#L8-L20">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.paginate" href="#AlgebraOfGraphics.paginate"><code>AlgebraOfGraphics.paginate</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">paginate(l; layout=nothing, row=nothing, col=nothing)</code></pre><p>Paginate <code>l</code>, the <code>Layer</code> or <code>Layers</code> object created by an <code>AlgebraOfGraphics</code> spec, to create a <code>PaginatedLayers</code> object. This contains a vector of layers where each layer operates on a subset of the input data.</p><p>The <code>PaginatedLayers</code> object can be passed to <code>draw</code> which will return a <code>Vector{FigureGrid}</code> rather than a single figure.</p><p>The keywords that limit the number of subplots on each page are the same that are used to specify facets in <code>mapping</code>:</p><ul><li><code>layout</code>: Maximum number of subplots in a wrapped linear layout.</li><li><code>row</code>: Maximum number of rows in a 2D layout.</li><li><code>col</code>: Maximum number of columns in a 2D layout.</li></ul><p><strong>Example</strong></p><pre><code class="nohighlight hljs">d = data((
     x = rand(1000),
     y = rand(1000),
     group1 = rand(string.(&#39;a&#39;:&#39;i&#39;), 1000),
@@ -28,7 +28,7 @@
 
 layer_2 = d * mapping(:x, :y, row = :group1, col = :group2) * visual(Scatter)
 paginated_2 = paginate(layer_2, row = 4, col = 3)
-figuregrid = draw(paginated_2, 1) # draw only the first grid</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/paginate.jl#L107-L141">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.scales" href="#AlgebraOfGraphics.scales"><code>AlgebraOfGraphics.scales</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">scales(; kwargs...)</code></pre><p>Create a <code>Scales</code> object containing properties for aesthetic scales that can be passed to <a href="#AlgebraOfGraphics.draw"><code>draw</code></a> and <a href="#AlgebraOfGraphics.draw!"><code>draw!</code></a>. Each keyword should be the name of a scale in the spec that is being drawn. That can either be a default one like <code>Color</code>, <code>Marker</code> or <code>LineStyle</code>, or a custom scale name defined in a <a href="../../layers/mapping/#AlgebraOfGraphics.mapping"><code>mapping</code></a> using the <code>scale</code> function.</p><p>The values attached to the keywords must be dict-like, with <code>Symbol</code>s as keys (such as <code>NamedTuple</code>s).</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/scales.jl#L51-L60">source</a></section></article><h2 id="Mapping-helpers"><a class="docs-heading-anchor" href="#Mapping-helpers">Mapping helpers</a><a id="Mapping-helpers-1"></a><a class="docs-heading-anchor-permalink" href="#Mapping-helpers" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.pregrouped" href="#AlgebraOfGraphics.pregrouped"><code>AlgebraOfGraphics.pregrouped</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">pregrouped(positional...; named...)</code></pre><p>Equivalent to <code>data(Pregrouped()) * mapping(positional...; named...)</code>. Refer to <a href="../../layers/mapping/#AlgebraOfGraphics.mapping"><code>mapping</code></a> for more information.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/helpers.jl#L156-L161">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.direct" href="#AlgebraOfGraphics.direct"><code>AlgebraOfGraphics.direct</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">direct(x)</code></pre><p>Return <code>DirectData(x)</code> which marks <code>x</code> for direct use in a <code>mapping</code> that&#39;s used with a table-like <code>data</code> source. As a result, <code>x</code> will be used directly as data, without lookup in the table. If <code>x</code> is not an <code>AbstractArray</code>, it will be expanded like <code>fill(x, n)</code> where <code>n</code> is the number of rows in the <code>data</code> source.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/helpers.jl#L172-L179">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.renamer" href="#AlgebraOfGraphics.renamer"><code>AlgebraOfGraphics.renamer</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">renamer(arr::Union{AbstractArray, Tuple})</code></pre><p>Utility to rename a categorical variable, as in <code>renamer([value1 =&gt; label1, value2 =&gt; label2])</code>. The keys of all pairs should be all the unique values of the categorical variable and the values should be the corresponding labels. The order of <code>arr</code> is respected in the legend.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl hljs">julia&gt; r = renamer([&quot;class 1&quot; =&gt; &quot;Class One&quot;, &quot;class 2&quot; =&gt; &quot;Class Two&quot;])
+figuregrid = draw(paginated_2, 1) # draw only the first grid</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/paginate.jl#L107-L141">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.scales" href="#AlgebraOfGraphics.scales"><code>AlgebraOfGraphics.scales</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">scales(; kwargs...)</code></pre><p>Create a <code>Scales</code> object containing properties for aesthetic scales that can be passed to <a href="#AlgebraOfGraphics.draw"><code>draw</code></a> and <a href="#AlgebraOfGraphics.draw!"><code>draw!</code></a>. Each keyword should be the name of a scale in the spec that is being drawn. That can either be a default one like <code>Color</code>, <code>Marker</code> or <code>LineStyle</code>, or a custom scale name defined in a <a href="../../layers/mapping/#AlgebraOfGraphics.mapping"><code>mapping</code></a> using the <code>scale</code> function.</p><p>The values attached to the keywords must be dict-like, with <code>Symbol</code>s as keys (such as <code>NamedTuple</code>s).</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/scales.jl#L51-L60">source</a></section></article><h2 id="Mapping-helpers"><a class="docs-heading-anchor" href="#Mapping-helpers">Mapping helpers</a><a id="Mapping-helpers-1"></a><a class="docs-heading-anchor-permalink" href="#Mapping-helpers" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.pregrouped" href="#AlgebraOfGraphics.pregrouped"><code>AlgebraOfGraphics.pregrouped</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">pregrouped(positional...; named...)</code></pre><p>Equivalent to <code>data(Pregrouped()) * mapping(positional...; named...)</code>. Refer to <a href="../../layers/mapping/#AlgebraOfGraphics.mapping"><code>mapping</code></a> for more information.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/helpers.jl#L156-L161">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.direct" href="#AlgebraOfGraphics.direct"><code>AlgebraOfGraphics.direct</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">direct(x)</code></pre><p>Return <code>DirectData(x)</code> which marks <code>x</code> for direct use in a <code>mapping</code> that&#39;s used with a table-like <code>data</code> source. As a result, <code>x</code> will be used directly as data, without lookup in the table. If <code>x</code> is not an <code>AbstractArray</code>, it will be expanded like <code>fill(x, n)</code> where <code>n</code> is the number of rows in the <code>data</code> source.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/helpers.jl#L172-L179">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.renamer" href="#AlgebraOfGraphics.renamer"><code>AlgebraOfGraphics.renamer</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">renamer(arr::Union{AbstractArray, Tuple})</code></pre><p>Utility to rename a categorical variable, as in <code>renamer([value1 =&gt; label1, value2 =&gt; label2])</code>. The keys of all pairs should be all the unique values of the categorical variable and the values should be the corresponding labels. The order of <code>arr</code> is respected in the legend.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl hljs">julia&gt; r = renamer([&quot;class 1&quot; =&gt; &quot;Class One&quot;, &quot;class 2&quot; =&gt; &quot;Class Two&quot;])
 AlgebraOfGraphics.Renamer{Vector{String}, Vector{String}}([&quot;class 1&quot;, &quot;class 2&quot;], [&quot;Class One&quot;, &quot;Class Two&quot;])
 
 julia&gt; println(r(&quot;class 1&quot;))
@@ -40,10 +40,10 @@
 AlgebraOfGraphics.Renamer{Nothing, Vector{String}}(nothing, [&quot;Class One&quot;, &quot;Class Two&quot;])
 
 julia&gt; println(r(2))
-Class Two</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/helpers.jl#L41-L78">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.sorter" href="#AlgebraOfGraphics.sorter"><code>AlgebraOfGraphics.sorter</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">sorter(ks)</code></pre><p>Utility to reorder a categorical variable, as in <code>sorter([&quot;low&quot;, &quot;medium&quot;, &quot;high&quot;])</code>. A vararg method <code>sorter(&quot;low&quot;, &quot;medium&quot;, &quot;high&quot;)</code> is also supported. <code>ks</code> should include all the unique values of the categorical variable. The order of <code>ks</code> is respected in the legend.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/helpers.jl#L90-L97">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.nonnumeric" href="#AlgebraOfGraphics.nonnumeric"><code>AlgebraOfGraphics.nonnumeric</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">nonnumeric(x)</code></pre><p>Transform <code>x</code> into a non numeric type that is printed and sorted in the same way.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/helpers.jl#L107-L111">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.verbatim" href="#AlgebraOfGraphics.verbatim"><code>AlgebraOfGraphics.verbatim</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">verbatim(x)</code></pre><p>Signal that <code>x</code> should not be rescaled, but used in the plot as is.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/helpers.jl#L121-L125">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.scale" href="#AlgebraOfGraphics.scale"><code>AlgebraOfGraphics.scale</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">scale(id::Symbol)</code></pre><p>Create a <code>ScaleID</code> object that can be used in a <a href="../../layers/mapping/#AlgebraOfGraphics.mapping"><code>mapping</code></a> to assign a custom id to the mapped variable. This variable will then not be merged into the default scale for its aesthetic type, but instead be handled separately, leading to a separate legend entry.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/scales.jl#L36-L42">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.presorted" href="#AlgebraOfGraphics.presorted"><code>AlgebraOfGraphics.presorted</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">presorted(x)</code></pre><p>Use within a pair expression in <code>mapping</code> to signal that a categorical column from the data source should be used in the original order and not automatically sorted.</p><p>Example:</p><pre><code class="language-julia hljs"># normally, categories would be sorted a, b, c but with `presorted`
+Class Two</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/helpers.jl#L41-L78">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.sorter" href="#AlgebraOfGraphics.sorter"><code>AlgebraOfGraphics.sorter</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">sorter(ks)</code></pre><p>Utility to reorder a categorical variable, as in <code>sorter([&quot;low&quot;, &quot;medium&quot;, &quot;high&quot;])</code>. A vararg method <code>sorter(&quot;low&quot;, &quot;medium&quot;, &quot;high&quot;)</code> is also supported. <code>ks</code> should include all the unique values of the categorical variable. The order of <code>ks</code> is respected in the legend.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/helpers.jl#L90-L97">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.nonnumeric" href="#AlgebraOfGraphics.nonnumeric"><code>AlgebraOfGraphics.nonnumeric</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">nonnumeric(x)</code></pre><p>Transform <code>x</code> into a non numeric type that is printed and sorted in the same way.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/helpers.jl#L107-L111">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.verbatim" href="#AlgebraOfGraphics.verbatim"><code>AlgebraOfGraphics.verbatim</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">verbatim(x)</code></pre><p>Signal that <code>x</code> should not be rescaled, but used in the plot as is.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/helpers.jl#L121-L125">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.scale" href="#AlgebraOfGraphics.scale"><code>AlgebraOfGraphics.scale</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">scale(id::Symbol)</code></pre><p>Create a <code>ScaleID</code> object that can be used in a <a href="../../layers/mapping/#AlgebraOfGraphics.mapping"><code>mapping</code></a> to assign a custom id to the mapped variable. This variable will then not be merged into the default scale for its aesthetic type, but instead be handled separately, leading to a separate legend entry.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/scales.jl#L36-L42">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.presorted" href="#AlgebraOfGraphics.presorted"><code>AlgebraOfGraphics.presorted</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">presorted(x)</code></pre><p>Use within a pair expression in <code>mapping</code> to signal that a categorical column from the data source should be used in the original order and not automatically sorted.</p><p>Example:</p><pre><code class="language-julia hljs"># normally, categories would be sorted a, b, c but with `presorted`
 # they stay in the order b, c, a
 
-data((; some_column = [&quot;b&quot;, &quot;c&quot;, &quot;a&quot;])) * mapping(:some_column =&gt; presorted)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/helpers.jl#L188-L203">source</a></section></article><h2 id="Theming"><a class="docs-heading-anchor" href="#Theming">Theming</a><a id="Theming-1"></a><a class="docs-heading-anchor-permalink" href="#Theming" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.set_aog_theme!" href="#AlgebraOfGraphics.set_aog_theme!"><code>AlgebraOfGraphics.set_aog_theme!</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">set_aog_theme!(; kwargs...)</code></pre><p>Set the current theme to a predefined and opinionated theme, as defined by the unexported internal function <a href="#AlgebraOfGraphics.aog_theme"><code>AlgebraOfGraphics.aog_theme</code></a>.</p><p>To tweak the predefined theme, use the function <code>Makie.update_theme!</code>. See the example below on how to change, e.g., default fontsize, title, and markersize.</p><p>For more information about setting themes, see the <code>Theming</code> section of the <code>Makie.jl</code> docs.</p><p><strong>Examples</strong></p><pre><code class="language-julia hljs">julia&gt; using CairoMakie, AlgebraOfGraphics
+data((; some_column = [&quot;b&quot;, &quot;c&quot;, &quot;a&quot;])) * mapping(:some_column =&gt; presorted)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/helpers.jl#L188-L203">source</a></section></article><h2 id="Theming"><a class="docs-heading-anchor" href="#Theming">Theming</a><a id="Theming-1"></a><a class="docs-heading-anchor-permalink" href="#Theming" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.set_aog_theme!" href="#AlgebraOfGraphics.set_aog_theme!"><code>AlgebraOfGraphics.set_aog_theme!</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">set_aog_theme!(; kwargs...)</code></pre><p>Set the current theme to a predefined and opinionated theme, as defined by the unexported internal function <a href="#AlgebraOfGraphics.aog_theme"><code>AlgebraOfGraphics.aog_theme</code></a>.</p><p>To tweak the predefined theme, use the function <code>Makie.update_theme!</code>. See the example below on how to change, e.g., default fontsize, title, and markersize.</p><p>For more information about setting themes, see the <code>Theming</code> section of the <code>Makie.jl</code> docs.</p><p><strong>Examples</strong></p><pre><code class="language-julia hljs">julia&gt; using CairoMakie, AlgebraOfGraphics
 
 julia&gt; set_aog_theme!()                # Sets a prefedined theme
 
@@ -51,4 +51,4 @@
            fontsize=30,
            markersize=40,
            Axis=(title=&quot;MyDefaultTitle&quot;,)
-       )</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/theme.jl#L132-L156">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.aog_theme" href="#AlgebraOfGraphics.aog_theme"><code>AlgebraOfGraphics.aog_theme</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">aog_theme(; fonts=[firasans(&quot;Medium&quot;), firasans(&quot;Light&quot;)])</code></pre><p>Return a <code>NamedTuple</code> of theme settings. Intended for internal use. The provided functionality is exposed to the user by the function <a href="#AlgebraOfGraphics.set_aog_theme!"><code>set_aog_theme!</code></a>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/theme.jl#L26-L31">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.from_continuous" href="#AlgebraOfGraphics.from_continuous"><code>AlgebraOfGraphics.from_continuous</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">from_continuous(x)</code></pre><p>Mark a colormap as continuous such that AlgebraOfGraphics will sample a categorical palette from start to end in n steps, and not by using the first n colors.</p><p>You could also use <code>cgrad(colormap, n; categorical = true)</code>, however, this requires you to specify how many levels there are, which <code>from_continuous</code> detects automatically.</p><p>Example:</p><pre><code class="language-julia hljs">draw(scales(Color = (; palette = from_continuous(:viridis))))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/helpers.jl#L221-L237">source</a></section></article><h2 id="Ticks-helpers"><a class="docs-heading-anchor" href="#Ticks-helpers">Ticks helpers</a><a id="Ticks-helpers-1"></a><a class="docs-heading-anchor-permalink" href="#Ticks-helpers" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.datetimeticks" href="#AlgebraOfGraphics.datetimeticks"><code>AlgebraOfGraphics.datetimeticks</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">datetimeticks(datetimes::AbstractVector{&lt;:TimeType}, labels::AbstractVector{&lt;:AbstractString})</code></pre><p>Generate ticks matching <code>datetimes</code> to the corresponding <code>labels</code>. The result can be passed to <code>xticks</code>, <code>yticks</code>, or <code>zticks</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/scales.jl#L318-L323">source</a></section><section><div><pre><code class="language-julia hljs">datetimeticks(f, datetimes::AbstractVector{&lt;:TimeType})</code></pre><p>Compute ticks for the given <code>datetimes</code> using a formatting function <code>f</code>. The result can be passed to <code>xticks</code>, <code>yticks</code>, or <code>zticks</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/scales.jl#L328-L333">source</a></section></article><h2 id="Internal-functions"><a class="docs-heading-anchor" href="#Internal-functions">Internal functions</a><a id="Internal-functions-1"></a><a class="docs-heading-anchor-permalink" href="#Internal-functions" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.scientific_eltype" href="#AlgebraOfGraphics.scientific_eltype"><code>AlgebraOfGraphics.scientific_eltype</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">scientific_eltype(v)</code></pre><p>Determine whether <code>v</code> should be treated as a continuous, geometrical, or categorical array.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/scales.jl#L474-L478">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.scientific_type" href="#AlgebraOfGraphics.scientific_type"><code>AlgebraOfGraphics.scientific_type</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">scientific_type(T::Type)</code></pre><p>Determine whether <code>T</code> represents a continuous, geometrical, or categorical variable.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/scales.jl#L457-L461">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.plottypes_attributes" href="#AlgebraOfGraphics.plottypes_attributes"><code>AlgebraOfGraphics.plottypes_attributes</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">plottypes_attributes(entries)</code></pre><p>Return plottypes and relative attributes, as two vectors of the same length, for the given <code>entries</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/guides/legend.jl#L26-L31">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.compute_attributes" href="#AlgebraOfGraphics.compute_attributes"><code>AlgebraOfGraphics.compute_attributes</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">compute_attributes(pl::ProcessedLayer, categoricalscales, continuousscales_grid, continuousscales)</code></pre><p>Process attributes of a <code>ProcessedLayer</code>. In particular,</p><ul><li>remove AlgebraOfGraphics-specific layout attributes,</li><li>opt out of Makie cycling mechanism,</li><li>customize behavior of <code>color</code> (implementing <code>alpha</code> transparency),</li><li>customize behavior of bar <code>width</code> (default to one unit when not specified),</li><li>set correct <code>colorrange</code>.</li></ul><p>Return computed attributes.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/algebra/layer.jl#L474-L484">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../types/">« Types</a><a class="docs-footer-nextpage" href="../recipes/">Recipes »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+       )</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/theme.jl#L132-L156">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.aog_theme" href="#AlgebraOfGraphics.aog_theme"><code>AlgebraOfGraphics.aog_theme</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">aog_theme(; fonts=[firasans(&quot;Medium&quot;), firasans(&quot;Light&quot;)])</code></pre><p>Return a <code>NamedTuple</code> of theme settings. Intended for internal use. The provided functionality is exposed to the user by the function <a href="#AlgebraOfGraphics.set_aog_theme!"><code>set_aog_theme!</code></a>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/theme.jl#L26-L31">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.from_continuous" href="#AlgebraOfGraphics.from_continuous"><code>AlgebraOfGraphics.from_continuous</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">from_continuous(x)</code></pre><p>Mark a colormap as continuous such that AlgebraOfGraphics will sample a categorical palette from start to end in n steps, and not by using the first n colors.</p><p>You could also use <code>cgrad(colormap, n; categorical = true)</code>, however, this requires you to specify how many levels there are, which <code>from_continuous</code> detects automatically.</p><p>Example:</p><pre><code class="language-julia hljs">draw(scales(Color = (; palette = from_continuous(:viridis))))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/helpers.jl#L221-L237">source</a></section></article><h2 id="Ticks-helpers"><a class="docs-heading-anchor" href="#Ticks-helpers">Ticks helpers</a><a id="Ticks-helpers-1"></a><a class="docs-heading-anchor-permalink" href="#Ticks-helpers" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.datetimeticks" href="#AlgebraOfGraphics.datetimeticks"><code>AlgebraOfGraphics.datetimeticks</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">datetimeticks(datetimes::AbstractVector{&lt;:TimeType}, labels::AbstractVector{&lt;:AbstractString})</code></pre><p>Generate ticks matching <code>datetimes</code> to the corresponding <code>labels</code>. The result can be passed to <code>xticks</code>, <code>yticks</code>, or <code>zticks</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/scales.jl#L318-L323">source</a></section><section><div><pre><code class="language-julia hljs">datetimeticks(f, datetimes::AbstractVector{&lt;:TimeType})</code></pre><p>Compute ticks for the given <code>datetimes</code> using a formatting function <code>f</code>. The result can be passed to <code>xticks</code>, <code>yticks</code>, or <code>zticks</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/scales.jl#L328-L333">source</a></section></article><h2 id="Internal-functions"><a class="docs-heading-anchor" href="#Internal-functions">Internal functions</a><a id="Internal-functions-1"></a><a class="docs-heading-anchor-permalink" href="#Internal-functions" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.scientific_eltype" href="#AlgebraOfGraphics.scientific_eltype"><code>AlgebraOfGraphics.scientific_eltype</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">scientific_eltype(v)</code></pre><p>Determine whether <code>v</code> should be treated as a continuous, geometrical, or categorical array.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/scales.jl#L474-L478">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.scientific_type" href="#AlgebraOfGraphics.scientific_type"><code>AlgebraOfGraphics.scientific_type</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">scientific_type(T::Type)</code></pre><p>Determine whether <code>T</code> represents a continuous, geometrical, or categorical variable.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/scales.jl#L457-L461">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.plottypes_attributes" href="#AlgebraOfGraphics.plottypes_attributes"><code>AlgebraOfGraphics.plottypes_attributes</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">plottypes_attributes(entries)</code></pre><p>Return plottypes and relative attributes, as two vectors of the same length, for the given <code>entries</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/guides/legend.jl#L26-L31">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.compute_attributes" href="#AlgebraOfGraphics.compute_attributes"><code>AlgebraOfGraphics.compute_attributes</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">compute_attributes(pl::ProcessedLayer, categoricalscales, continuousscales_grid, continuousscales)</code></pre><p>Process attributes of a <code>ProcessedLayer</code>. In particular,</p><ul><li>remove AlgebraOfGraphics-specific layout attributes,</li><li>opt out of Makie cycling mechanism,</li><li>customize behavior of <code>color</code> (implementing <code>alpha</code> transparency),</li><li>customize behavior of bar <code>width</code> (default to one unit when not specified),</li><li>set correct <code>colorrange</code>.</li></ul><p>Return computed attributes.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/algebra/layer.jl#L474-L484">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../types/">« Types</a><a class="docs-footer-nextpage" href="../recipes/">Recipes »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/API/recipes/index.html b/dev/API/recipes/index.html
index 02c8327b7..4e757f212 100644
--- a/dev/API/recipes/index.html
+++ b/dev/API/recipes/index.html
@@ -27,11 +27,11 @@
   strokecolormap      :batlow
   strokewidth         0
   transparency        false
-  visible             true</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/recipes/choropleth.jl#L53-L67">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.linesfill" href="#AlgebraOfGraphics.linesfill"><code>AlgebraOfGraphics.linesfill</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">linesfill(xs, ys; lower, upper, attributes...)</code></pre><p>Line plot with a shaded area between <code>lower</code> and <code>upper</code>. If <code>lower</code> and <code>upper</code> are not given, shaded area is between <code>0</code> and <code>ys</code>.</p><p><strong>Attributes</strong></p><p>Available attributes and their defaults for <code>Plot{AlgebraOfGraphics.linesfill}</code> are: </p><pre><code class="nohighlight hljs">  color       :gray25
+  visible             true</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/recipes/choropleth.jl#L53-L67">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.linesfill" href="#AlgebraOfGraphics.linesfill"><code>AlgebraOfGraphics.linesfill</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">linesfill(xs, ys; lower, upper, attributes...)</code></pre><p>Line plot with a shaded area between <code>lower</code> and <code>upper</code>. If <code>lower</code> and <code>upper</code> are not given, shaded area is between <code>0</code> and <code>ys</code>.</p><p><strong>Attributes</strong></p><p>Available attributes and their defaults for <code>Plot{AlgebraOfGraphics.linesfill}</code> are: </p><pre><code class="nohighlight hljs">  color       :gray25
   colormap    :batlow
   colorrange  MakieCore.Automatic()
   fillalpha   0.15
   linestyle   &quot;nothing&quot;
   linewidth   1.5
   lower       MakieCore.Automatic()
-  upper       MakieCore.Automatic()</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/recipes/linesfill.jl#L1-L8">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../functions/">« Functions</a><a class="docs-footer-nextpage" href="../../FAQs/">Frequently Asked Questions »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+  upper       MakieCore.Automatic()</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/recipes/linesfill.jl#L1-L8">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../functions/">« Functions</a><a class="docs-footer-nextpage" href="../../FAQs/">Frequently Asked Questions »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/API/types/index.html b/dev/API/types/index.html
index 1ff62bd60..96aac4b13 100644
--- a/dev/API/types/index.html
+++ b/dev/API/types/index.html
@@ -1,2 +1,2 @@
 <!DOCTYPE html>
-<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>Types · Algebra of Graphics</title><meta name="title" content="Types · Algebra of Graphics"/><meta property="og:title" content="Types · Algebra of Graphics"/><meta property="twitter:title" content="Types · Algebra of Graphics"/><meta name="description" content="Documentation for Algebra of Graphics."/><meta property="og:description" content="Documentation for Algebra of Graphics."/><meta property="twitter:description" content="Documentation for Algebra of Graphics."/><script data-outdated-warner src="../../assets/warner.js"></script><link href="https://cdnjs.cloudflare.com/ajax/libs/lato-font/3.0.0/css/lato-font.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/juliamono/0.050/juliamono.min.css" rel="stylesheet" type="text/css"/><link 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href="../../assets/themes/catppuccin-macchiato.css" data-theme-name="catppuccin-macchiato"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-frappe.css" data-theme-name="catppuccin-frappe"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-latte.css" data-theme-name="catppuccin-latte"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/documenter-dark.css" data-theme-name="documenter-dark" data-theme-primary-dark/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/documenter-light.css" data-theme-name="documenter-light" data-theme-primary/><script src="../../assets/themeswap.js"></script><link href="../../assets/favicon.ico" rel="icon" type="image/x-icon"/><link href="../../democards/bulmagridtheme.css" rel="stylesheet" type="text/css"/></head><body><div id="documenter"><nav class="docs-sidebar"><a class="docs-logo" href="../../"><img class="docs-light-only" src="../../assets/logo.svg" alt="Algebra of Graphics logo"/><img class="docs-dark-only" src="../../assets/logo-dark.svg" alt="Algebra of Graphics logo"/></a><div class="docs-package-name"><span class="docs-autofit"><a href="../../">Algebra of Graphics</a></span></div><button class="docs-search-query input is-rounded is-small is-clickable my-2 mx-auto py-1 px-2" id="documenter-search-query">Search docs (Ctrl + /)</button><ul class="docs-menu"><li><a class="tocitem" href="../../">Home</a></li><li><span class="tocitem">Getting Started</span><ul><li><a class="tocitem" href="../../generated/penguins/">Tutorial 🐧</a></li><li><a class="tocitem" href="../../gallery/">Gallery</a></li></ul></li><li><span class="tocitem">Algebra of Layers</span><ul><li><a class="tocitem" href="../../layers/introduction/">Introduction</a></li><li><a class="tocitem" href="../../layers/data/">Data</a></li><li><a class="tocitem" 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docs-navbar-link fa-solid fa-gear" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="Types"><a class="docs-heading-anchor" href="#Types">Types</a><a id="Types-1"></a><a class="docs-heading-anchor-permalink" href="#Types" title="Permalink"></a></h1><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.AbstractDrawable" href="#AlgebraOfGraphics.AbstractDrawable"><code>AlgebraOfGraphics.AbstractDrawable</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">AbstractDrawable</code></pre><p>Abstract type encoding objects that can be drawn via <a href="../functions/#AlgebraOfGraphics.draw"><code>AlgebraOfGraphics.draw</code></a>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/algebra/layer.jl#L1-L5">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.AbstractAlgebraic" href="#AlgebraOfGraphics.AbstractAlgebraic"><code>AlgebraOfGraphics.AbstractAlgebraic</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">AbstractAlgebraic  &lt;: AbstractDrawable</code></pre><p>Abstract type encoding objects that can be combined together using <code>+</code> and <code>*</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/algebra/layer.jl#L8-L12">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.Layer" href="#AlgebraOfGraphics.Layer"><code>AlgebraOfGraphics.Layer</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">Layer(transformation, data, positional::AbstractVector, named::AbstractDictionary)</code></pre><p>Algebraic object encoding a single layer of a visualization. It is composed of a dataset, positional and named arguments, as well as a transformation to be applied to those. <code>Layer</code> objects can be multiplied, yielding a novel <code>Layer</code> object, or added, yielding a <a href="#AlgebraOfGraphics.Layers"><code>AlgebraOfGraphics.Layers</code></a> object.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/algebra/layer.jl#L15-L22">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.Layers" href="#AlgebraOfGraphics.Layers"><code>AlgebraOfGraphics.Layers</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">Layers(layers::Vector{Layer})</code></pre><p>Algebraic object encoding a list of <a href="#AlgebraOfGraphics.Layer"><code>AlgebraOfGraphics.Layer</code></a> objects. <code>Layers</code> objects can be added or multiplied, yielding a novel <code>Layers</code> object.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/algebra/layers.jl#L1-L6">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.ProcessedLayer" href="#AlgebraOfGraphics.ProcessedLayer"><code>AlgebraOfGraphics.ProcessedLayer</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">ProcessedLayer(l::Layer)</code></pre><p>Process a <code>Layer</code> and return the resulting <code>ProcessedLayer</code>.</p><p>Note that this method should not be used anymore as processing a <code>Layer</code> can now potentially return multiple <code>ProcessedLayer</code> objects. Therefore, you should use the plural form <code>ProcessedLayers(layer)</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/algebra/layer.jl#L152-L160">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.ProcessedLayers" href="#AlgebraOfGraphics.ProcessedLayers"><code>AlgebraOfGraphics.ProcessedLayers</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">ProcessedLayers(layers::Vector{ProcessedLayer})</code></pre><p>Object encoding a list of <a href="#AlgebraOfGraphics.ProcessedLayer"><code>AlgebraOfGraphics.ProcessedLayer</code></a> objects. <code>ProcessedLayers</code> objects are the output of the processing pipeline and can be drawn without further processing.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/algebra/layers.jl#L28-L34">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.Entry" href="#AlgebraOfGraphics.Entry"><code>AlgebraOfGraphics.Entry</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">Entry(plottype::PlotType, positional::Arguments, named::NamedArguments)</code></pre><p>Define plottype as well as positional and named arguments for a single plot.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/entries.jl#L1-L5">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.AxisEntries" href="#AlgebraOfGraphics.AxisEntries"><code>AlgebraOfGraphics.AxisEntries</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">AxisEntries(axis::Union{Axis, Nothing}, entries::Vector{Entry}, categoricalscales, continuousscales)</code></pre><p>Define all ingredients to make plots on an axis. Each categorical scale should be a <code>CategoricalScale</code>, and each continuous scale should be a <code>ContinuousScale</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/entries.jl#L54-L60">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../layers/draw/">« Drawing Layers</a><a class="docs-footer-nextpage" href="../functions/">Functions »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. 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docs-navbar-link fa-solid fa-gear" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="Types"><a class="docs-heading-anchor" href="#Types">Types</a><a id="Types-1"></a><a class="docs-heading-anchor-permalink" href="#Types" title="Permalink"></a></h1><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.AbstractDrawable" href="#AlgebraOfGraphics.AbstractDrawable"><code>AlgebraOfGraphics.AbstractDrawable</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">AbstractDrawable</code></pre><p>Abstract type encoding objects that can be drawn via <a href="../functions/#AlgebraOfGraphics.draw"><code>AlgebraOfGraphics.draw</code></a>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/algebra/layer.jl#L1-L5">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.AbstractAlgebraic" href="#AlgebraOfGraphics.AbstractAlgebraic"><code>AlgebraOfGraphics.AbstractAlgebraic</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">AbstractAlgebraic  &lt;: AbstractDrawable</code></pre><p>Abstract type encoding objects that can be combined together using <code>+</code> and <code>*</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/algebra/layer.jl#L8-L12">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.Layer" href="#AlgebraOfGraphics.Layer"><code>AlgebraOfGraphics.Layer</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">Layer(transformation, data, positional::AbstractVector, named::AbstractDictionary)</code></pre><p>Algebraic object encoding a single layer of a visualization. It is composed of a dataset, positional and named arguments, as well as a transformation to be applied to those. <code>Layer</code> objects can be multiplied, yielding a novel <code>Layer</code> object, or added, yielding a <a href="#AlgebraOfGraphics.Layers"><code>AlgebraOfGraphics.Layers</code></a> object.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/algebra/layer.jl#L15-L22">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.Layers" href="#AlgebraOfGraphics.Layers"><code>AlgebraOfGraphics.Layers</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">Layers(layers::Vector{Layer})</code></pre><p>Algebraic object encoding a list of <a href="#AlgebraOfGraphics.Layer"><code>AlgebraOfGraphics.Layer</code></a> objects. <code>Layers</code> objects can be added or multiplied, yielding a novel <code>Layers</code> object.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/algebra/layers.jl#L1-L6">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.ProcessedLayer" href="#AlgebraOfGraphics.ProcessedLayer"><code>AlgebraOfGraphics.ProcessedLayer</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">ProcessedLayer(l::Layer)</code></pre><p>Process a <code>Layer</code> and return the resulting <code>ProcessedLayer</code>.</p><p>Note that this method should not be used anymore as processing a <code>Layer</code> can now potentially return multiple <code>ProcessedLayer</code> objects. Therefore, you should use the plural form <code>ProcessedLayers(layer)</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/algebra/layer.jl#L152-L160">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.ProcessedLayers" href="#AlgebraOfGraphics.ProcessedLayers"><code>AlgebraOfGraphics.ProcessedLayers</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">ProcessedLayers(layers::Vector{ProcessedLayer})</code></pre><p>Object encoding a list of <a href="#AlgebraOfGraphics.ProcessedLayer"><code>AlgebraOfGraphics.ProcessedLayer</code></a> objects. <code>ProcessedLayers</code> objects are the output of the processing pipeline and can be drawn without further processing.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/algebra/layers.jl#L28-L34">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.Entry" href="#AlgebraOfGraphics.Entry"><code>AlgebraOfGraphics.Entry</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">Entry(plottype::PlotType, positional::Arguments, named::NamedArguments)</code></pre><p>Define plottype as well as positional and named arguments for a single plot.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/entries.jl#L1-L5">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.AxisEntries" href="#AlgebraOfGraphics.AxisEntries"><code>AlgebraOfGraphics.AxisEntries</code></a> — <span class="docstring-category">Type</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">AxisEntries(axis::Union{Axis, Nothing}, entries::Vector{Entry}, categoricalscales, continuousscales)</code></pre><p>Define all ingredients to make plots on an axis. Each categorical scale should be a <code>CategoricalScale</code>, and each continuous scale should be a <code>ContinuousScale</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/entries.jl#L54-L60">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../layers/draw/">« Drawing Layers</a><a class="docs-footer-nextpage" href="../functions/">Functions »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. 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diff --git a/dev/FAQs/index.html b/dev/FAQs/index.html
index 99aebfbde..28b5c5602 100644
--- a/dev/FAQs/index.html
+++ b/dev/FAQs/index.html
@@ -3,19 +3,19 @@
 using AlgebraOfGraphics: density
 df = (x = exp.(randn(1000)),)
 kde1 = data(df) * mapping(:x) * density()
-draw(kde1, axis=(width=225, height=225, xscale=log,))</code></pre><img src="32ccc378.svg" alt="Example block output"/><pre><code class="language-julia hljs">df = (x = exp.(randn(1000)),)
+draw(kde1, axis=(width=225, height=225, xscale=log,))</code></pre><img src="902ac019.svg" alt="Example block output"/><pre><code class="language-julia hljs">df = (x = exp.(randn(1000)),)
 kde2 = data(df) * mapping(:x =&gt; log =&gt; &quot;log(x)&quot;) * density()
-draw(kde2, axis=(width=225, height=225))</code></pre><img src="bc243a84.svg" alt="Example block output"/><p>The two plots look different. The first represents the pdf of <code>x</code> in a log scale, while the second represents the pdf of <code>log(x)</code> in a linear scale. The two curves differ by a factor <code>1 / x</code>, the derivative of <code>log(x)</code>. See e.g. <a href="https://math.stackexchange.com/questions/613614/scaling-a-probability-distribution-function/613623#613623">this post</a> for some mathematical background on the topic.</p><p>In general, the second approach (plotting the density of <code>log(x)</code>) could be considered more principled, as it preserves the proportionality between area and probability mass. On the contrary, the first approach (plotting the density of <code>x</code> in a log scale) breaks this proportionality relationship.</p><p>A similar reasoning applies to histograms:</p><pre><code class="language-julia hljs">using AlgebraOfGraphics
+draw(kde2, axis=(width=225, height=225))</code></pre><img src="f0ce9d73.svg" alt="Example block output"/><p>The two plots look different. The first represents the pdf of <code>x</code> in a log scale, while the second represents the pdf of <code>log(x)</code> in a linear scale. The two curves differ by a factor <code>1 / x</code>, the derivative of <code>log(x)</code>. See e.g. <a href="https://math.stackexchange.com/questions/613614/scaling-a-probability-distribution-function/613623#613623">this post</a> for some mathematical background on the topic.</p><p>In general, the second approach (plotting the density of <code>log(x)</code>) could be considered more principled, as it preserves the proportionality between area and probability mass. On the contrary, the first approach (plotting the density of <code>x</code> in a log scale) breaks this proportionality relationship.</p><p>A similar reasoning applies to histograms:</p><pre><code class="language-julia hljs">using AlgebraOfGraphics
 df = (x = exp.(rand(1000)),)
 hist1 = data(df) * mapping(:x) * histogram()
-draw(hist1, axis=(width=225, height=225, xscale=log))</code></pre><img src="e0703c47.svg" alt="Example block output"/><pre><code class="language-julia hljs">df = (x = exp.(rand(1000)),)
+draw(hist1, axis=(width=225, height=225, xscale=log))</code></pre><img src="09adf3b4.svg" alt="Example block output"/><pre><code class="language-julia hljs">df = (x = exp.(rand(1000)),)
 hist2 = data(df) * mapping(:x =&gt; log =&gt; &quot;log(x)&quot;) * histogram()
-draw(hist2, axis=(width=225, height=225))</code></pre><img src="0369e009.svg" alt="Example block output"/><p>The data transformation approach is preferable as it produces uniform bins, which are easier to interpret.</p><h2 id="How-to-combine-AlgebraOfGraphics-with-plain-Makie-plots?"><a class="docs-heading-anchor" href="#How-to-combine-AlgebraOfGraphics-with-plain-Makie-plots?">How to combine <code>AlgebraOfGraphics</code> with plain <code>Makie</code> plots?</a><a id="How-to-combine-AlgebraOfGraphics-with-plain-Makie-plots?-1"></a><a class="docs-heading-anchor-permalink" href="#How-to-combine-AlgebraOfGraphics-with-plain-Makie-plots?" title="Permalink"></a></h2><p>Since <code>AlgebraOfGraphics</code> is built upon the <code>Makie</code> ecosystem we can easily combine plots from both packages. Two approaches can be taken. Firstly, by using <code>draw!</code> you can pass a <code>Figure</code> or <code>FigurePosition</code> created by <code>Makie</code> to be used by <code>AlgebraOfGraphics</code>, e.g.</p><pre><code class="language-julia hljs">f, a, p = lines(0..2pi, sin; figure = (size = (600, 400),))
+draw(hist2, axis=(width=225, height=225))</code></pre><img src="191fde66.svg" alt="Example block output"/><p>The data transformation approach is preferable as it produces uniform bins, which are easier to interpret.</p><h2 id="How-to-combine-AlgebraOfGraphics-with-plain-Makie-plots?"><a class="docs-heading-anchor" href="#How-to-combine-AlgebraOfGraphics-with-plain-Makie-plots?">How to combine <code>AlgebraOfGraphics</code> with plain <code>Makie</code> plots?</a><a id="How-to-combine-AlgebraOfGraphics-with-plain-Makie-plots?-1"></a><a class="docs-heading-anchor-permalink" href="#How-to-combine-AlgebraOfGraphics-with-plain-Makie-plots?" title="Permalink"></a></h2><p>Since <code>AlgebraOfGraphics</code> is built upon the <code>Makie</code> ecosystem we can easily combine plots from both packages. Two approaches can be taken. Firstly, by using <code>draw!</code> you can pass a <code>Figure</code> or <code>FigurePosition</code> created by <code>Makie</code> to be used by <code>AlgebraOfGraphics</code>, e.g.</p><pre><code class="language-julia hljs">f, a, p = lines(0..2pi, sin; figure = (size = (600, 400),))
 
 df = (x = exp.(rand(1000)),)
 hist1 = data(df) * mapping(:x =&gt; log =&gt; &quot;log(x)&quot;) * histogram()
-draw!(f[1, 2], hist1)</code></pre><img src="e7dcac49.svg" alt="Example block output"/><p>Alternatively, we can create the <code>AlgebraOfGraphics</code> figure first and then add in additional plain <code>Makie</code> axes alongside the result by accessing the <code>.figure</code> field of <code>fg</code>, e.g.</p><pre><code class="language-julia hljs">df = (x = exp.(rand(1000)),)
+draw!(f[1, 2], hist1)</code></pre><img src="6b8c474e.svg" alt="Example block output"/><p>Alternatively, we can create the <code>AlgebraOfGraphics</code> figure first and then add in additional plain <code>Makie</code> axes alongside the result by accessing the <code>.figure</code> field of <code>fg</code>, e.g.</p><pre><code class="language-julia hljs">df = (x = exp.(rand(1000)),)
 hist2 = data(df) * mapping(:x =&gt; log =&gt; &quot;log(x)&quot;) * histogram()
 fg = draw(hist2; figure = (size = (600, 400),))
 
-lines(fg.figure[1, 2], 0..2pi, cos)</code></pre><img src="39b4b993.svg" alt="Example block output"/><div class="admonition is-info"><header class="admonition-header">Note</header><div class="admonition-body"><p>When setting the <code>width</code> and <code>height</code> dimensions of each axis manually you will need to call <code>resize_to_layout!(fg)</code> before displaying the figure such that each axis is sized correctly.</p></div></div></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../API/recipes/">« Recipes</a><a class="docs-footer-nextpage" href="../philosophy/">Philosophy »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+lines(fg.figure[1, 2], 0..2pi, cos)</code></pre><img src="92a25828.svg" alt="Example block output"/><div class="admonition is-info"><header class="admonition-header">Note</header><div class="admonition-body"><p>When setting the <code>width</code> and <code>height</code> dimensions of each axis manually you will need to call <code>resize_to_layout!(fg)</code> before displaying the figure such that each axis is sized correctly.</p></div></div></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../API/recipes/">« Recipes</a><a class="docs-footer-nextpage" href="../philosophy/">Philosophy »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/changelog/index.html b/dev/changelog/index.html
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2024-09-17</a><a id="v0.8.8-2024-09-17-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.8-2024-09-17" title="Permalink"></a></h2><ul><li>Fixed aesthetics of <code>errorbar</code> so that x and y stay labelled correctly when using <code>direction = :x</code> <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/560">#560</a>.</li><li>Added ability to specify <code>title</code>, <code>subtitle</code> and <code>footnotes</code> plus settings in the <code>draw</code> function <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/556">#556</a>.</li><li>Added <code>dodge_x</code> and <code>dodge_y</code> keywords to <code>mapping</code> that allow to dodge any plot types that have <code>AesX</code> or <code>AesY</code> data <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/558">#558</a>.</li></ul><h2 id="v0.8.7-2024-09-06"><a class="docs-heading-anchor" href="#v0.8.7-2024-09-06">v0.8.7 - 2024-09-06</a><a id="v0.8.7-2024-09-06-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.7-2024-09-06" title="Permalink"></a></h2><ul><li>Added ability to return <code>ProcessedLayers</code> from transformations, thereby enabling multi-layer transformations, such as scatter plus errorbars <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/549">#549</a>.</li><li>Fixed bug where <code>mergesorted</code> applied on string vectors used <code>isless</code> instead of natural sort <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/553">#553</a>.</li></ul><h2 id="v0.8.6-2024-09-02"><a class="docs-heading-anchor" href="#v0.8.6-2024-09-02">v0.8.6 - 2024-09-02</a><a id="v0.8.6-2024-09-02-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.6-2024-09-02" title="Permalink"></a></h2><ul><li>Added <code>bar_labels</code> to <code>BarPlot</code>&#39;s aesthetic mapping <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/544">#544</a>.</li><li>Added ability to hide legend or colorbar by passing, e.g., <code>legend = (; show = false)</code> to <code>draw</code> <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/547">#547</a>.</li></ul><h2 id="v0.8.5-2024-08-27"><a class="docs-heading-anchor" href="#v0.8.5-2024-08-27">v0.8.5 - 2024-08-27</a><a id="v0.8.5-2024-08-27-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.5-2024-08-27" title="Permalink"></a></h2><ul><li>Added <code>presorted</code> helper function to keep categorical data in the order encountered in the source table, instead of sorting it alphabetically <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/529">#529</a>.</li><li>Added <code>from_continuous</code> helper function which allows to sample continuous colormaps evenly to use them as categorical palettes without having to specify how many categories there are <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/541">#541</a>.</li></ul><h2 id="v0.8.4-2024-08-26"><a class="docs-heading-anchor" href="#v0.8.4-2024-08-26">v0.8.4 - 2024-08-26</a><a id="v0.8.4-2024-08-26-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.4-2024-08-26" title="Permalink"></a></h2><ul><li>Added <code>fillto</code> to <code>BarPlot</code> aesthetics <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/535">#535</a>.</li><li>Fixed bug when giving <code>datalimits</code> of <code>density</code> as a (low, high) tuple <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/536">#536</a>.</li><li>Fixed bug where facet-local continuous scale limits were used instead of the globally merged ones, possibly leading to mismatches between data and legend <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/539">#539</a>.</li></ul><h2 id="v0.8.3-2024-08-23"><a class="docs-heading-anchor" href="#v0.8.3-2024-08-23">v0.8.3 - 2024-08-23</a><a id="v0.8.3-2024-08-23-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.3-2024-08-23" title="Permalink"></a></h2><ul><li>Fixed incorrect x/y axis assignment for the <code>violin</code> plot type <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/528">#528</a>.</li></ul><h2 id="v0.8.2-2024-08-21"><a class="docs-heading-anchor" href="#v0.8.2-2024-08-21">v0.8.2 - 2024-08-21</a><a id="v0.8.2-2024-08-21-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.2-2024-08-21" title="Permalink"></a></h2><ul><li>Enable use of <code>LaTeXString</code>s and <code>rich</code> text in <code>renamer</code> <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/525">#525</a>.</li><li>Fixed widths of boxplots with color groupings <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/524">#524</a>.</li></ul><h2 id="v0.8.1-2024-08-20"><a class="docs-heading-anchor" href="#v0.8.1-2024-08-20">v0.8.1 - 2024-08-20</a><a id="v0.8.1-2024-08-20-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.1-2024-08-20" title="Permalink"></a></h2><ul><li>Added back support for <code>Hist</code>, <code>CrossBar</code>, <code>ECDFPlot</code> and <code>Density</code> <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/522">#522</a>.</li></ul><h2 id="v0.8.0-2024-07-26"><a class="docs-heading-anchor" href="#v0.8.0-2024-07-26">v0.8.0 - 2024-07-26</a><a id="v0.8.0-2024-07-26-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.0-2024-07-26" title="Permalink"></a></h2><ul><li><strong>Breaking</strong>: Columns with element types of <code>Union{Missing,T}</code> are not treated as categorical by default anymore, instead <code>T</code> decides if data is seen as categorical, continuous or geometrical. If you relied on numerical vectors with <code>missing</code>s being treated as categorical, you can use <code>:columnname =&gt; nonnumeric</code> in the <code>mapping</code> instead.</li><li><strong>Breaking</strong>: <code>AbstractString</code> categories are now sorted with natural sort order by default. This means that where you got <code>[&quot;1&quot;, &quot;10&quot;, &quot;2&quot;]</code> before, you now get <code>[&quot;1&quot;, &quot;2&quot;, &quot;10&quot;]</code>. You can use <code>sorter</code>, the <code>categories</code> keyword or categorical arrays to sort your data differently if needed.</li></ul><h2 id="v0.7.0-2024-07-16"><a class="docs-heading-anchor" href="#v0.7.0-2024-07-16">v0.7.0 - 2024-07-16</a><a id="v0.7.0-2024-07-16-1"></a><a class="docs-heading-anchor-permalink" href="#v0.7.0-2024-07-16" title="Permalink"></a></h2><ul><li><strong>Breaking</strong>: The <code>palette</code> keyword of <code>draw</code> linking palettes to keyword arguments was removed. Instead, palettes need to be passed to specific scales like <code>draw(..., scales(Color = (; palette = :Set1_3)))</code></li><li><strong>Breaking</strong>: All recipes need to have the new function <code>aesthetic_mapping</code> defined for all sets of positional arguments that should be supported, as can be seen in <code>src/aesthetics.jl</code>. This breaks usage of all custom recipes. Additionally, not all Makie plots have been ported to the new system yet. If you encounter missing plots, or missing attributes of already ported plots, please open an issue.</li><li><strong>Breaking</strong>: All custom recipes that should be displayed in a legend, need to have <code>legend_elements(P, attributes, scale_args)</code> defined as can be seen in <code>src/guides/legend.jl</code>. AlgebraOfGraphics cannot use the same default mechanism as Makie, which can create a legend from an existing plot, because AlgebraOfGraphics needs to create the legend before the plot is instantiated.</li><li><strong>Breaking</strong>: Pregrouped data cannot be passed anymore to the plain <code>mapping(...)</code> without any <code>data(tabular)</code>. Instead, you should use <code>pregrouped(...)</code> which is a shortcut for <code>data(Pregrouped()) * mapping(...)</code>.</li><li><strong>Breaking</strong>: <code>Contour</code> and <code>Contourf</code> generally do not work anymore with <code>visual()</code>. Instead, the <code>contours()</code> and <code>filled_contours()</code> analyses should be used. <code>Contour</code> can still be used with categorical colors, but not with continuous ones.</li><li><strong>Breaking</strong>: All colormap properties for continuous color scales need to be passed via <code>scales</code> now, and not through <code>visual</code>. This is to have central control over the scale as it can be used by multiple <code>visual</code>s simultaneously.</li><li>Horizontal barplots, violins, errorbars, rangebars and other plot types that have two different orientations work correctly now. Axis labels switch accordingly when the orientation is changed.</li><li>Plotting functions whose positional arguments don&#39;t correspond to X, Y, Z work correctly now. For example, <code>HLines</code> (1 =&gt; Y) or <code>rangebars</code> (1 =&gt; X, 2 =&gt; Y, 3 =&gt; Y).</li><li>It is possible to add categories beyond those present in the data with the <code>categories</code> keyword within a scale&#39;s settings. It is also possible to reorder or otherwise transform the existing categories by passing a function to <code>categories</code>.</li><li>The supported attributes are not limited anymore to a specific set of names, for example, <code>strokecolor</code> can work the same as <code>color</code> did before, and the two can share a scale via their shared aesthetic type.</li><li>There can be multiple scales of the same aesthetic now. This allows to have separate legends for different plot types using the same aesthetics. Scale separation works by pairing a variable in <code>mapping</code> with a <code>scale(id_symbol)</code>.</li><li>Legend entries can be reordered using the <code>legend = (; order = ...)</code> option in <code>draw</code>. Specific scales can opt out of the legend by passing <code>legend = false</code> in <code>scales</code>.</li><li>Labels can now be anything that Makie supports, primarily <code>String</code>s, <code>LaTeXString</code>s or <code>rich</code> text.</li><li>Legend elements now usually reflect all attributes set in their corresponding <code>visual</code>.</li><li>Simple column vectors of data can now be passed directly to <code>mapping</code> without using <code>data</code> first. Additionally, scalar values are accepted as a shortcut for columns with the same repeated value.</li><li>Columns from outside a table source in <code>data</code> can now be passed to <code>mapping</code> by wrapping them in the <code>direct</code> function. Scalar values are accepted as a shortcut for columns with the same repeated value. For example, to create a label for columns <code>x</code> and <code>y</code> from a dataframe passed to <code>data</code>, one could now do <code>mapping(:x, :y, color = direct(&quot;label&quot;))</code> without having to create a column full of <code>&quot;label&quot;</code> strings first.</li><li>The numbers at which categorical values are plotted on x and y axis can now be changed via <code>scales(X = (; palette = [1, 2, 4]))</code> or similar.</li><li>Continuous marker size scales can now be shown in the legend. Numerical values are proportional to area and not diameter now, which makes more sense with respect to human perception. The min and max marker size can be set using the <code>sizerange</code> property for the respective scale in <code>scales</code>.</li></ul><h2 id="v0.6.11-2022-08-08"><a class="docs-heading-anchor" href="#v0.6.11-2022-08-08">v0.6.11 - 2022-08-08</a><a id="v0.6.11-2022-08-08-1"></a><a class="docs-heading-anchor-permalink" href="#v0.6.11-2022-08-08" title="Permalink"></a></h2><ul><li>Added <code>paginate</code> for pagination of large facet plots. </li></ul><h2 id="v0.6.8-2022-06-14"><a class="docs-heading-anchor" href="#v0.6.8-2022-06-14">v0.6.8 - 2022-06-14</a><a id="v0.6.8-2022-06-14-1"></a><a class="docs-heading-anchor-permalink" href="#v0.6.8-2022-06-14" title="Permalink"></a></h2><ul><li>Added <code>choropleth</code> recipe to supersede <code>geodata</code> for geographical data.</li></ul><h2 id="v0.6.1-2022-01-28"><a class="docs-heading-anchor" href="#v0.6.1-2022-01-28">v0.6.1 - 2022-01-28</a><a id="v0.6.1-2022-01-28-1"></a><a class="docs-heading-anchor-permalink" href="#v0.6.1-2022-01-28" title="Permalink"></a></h2><ul><li>Support <code>level</code> in <code>linear</code> analysis for confidence interval.</li><li>Replaced tuples and named tuples in <code>Layer</code> and <code>Entry</code> with dictionaries from <a href="https://github.com/andyferris/Dictionaries.jl">Dictionaries.jl</a>.</li><li>Split internal <code>Entry</code> type into <code>ProcessedLayer</code> (to be used for analyses) and <code>Entry</code> (to be used for plotting).</li></ul><h2 id="v0.6.0-2021-10-24"><a class="docs-heading-anchor" href="#v0.6.0-2021-10-24">v0.6.0 - 2021-10-24</a><a id="v0.6.0-2021-10-24-1"></a><a class="docs-heading-anchor-permalink" href="#v0.6.0-2021-10-24" title="Permalink"></a></h2><ul><li><strong>Breaking</strong>: Default axis linking behavior has changed: now only axes corresponding to the same variable are linked. For consistency with <code>row</code>/<code>col</code>, <code>layout</code> will hide decorations of linked axes and span axis labels if appropriate.</li><li>Customizable legend and colorbar position and look.</li><li>Customizable axis linking behavior.</li></ul><h2 id="v0.5-2021-08-05"><a class="docs-heading-anchor" href="#v0.5-2021-08-05">v0.5 - 2021-08-05</a><a id="v0.5-2021-08-05-1"></a><a class="docs-heading-anchor-permalink" href="#v0.5-2021-08-05" title="Permalink"></a></h2><ul><li><strong>Breaking</strong>: <code>Axis(ae)</code> has been replaced by <code>ae.axis</code>.</li><li><strong>Breaking</strong>: <code>Legend(fg)</code> has been replaced by <code>legend!(fg)</code> and <code>colorbar!(fg)</code>.</li><li><code>legend!</code> and <code>colorbar!</code> API allows for custom legend placement.</li></ul><h2 id="v0.4-2021-05-21"><a class="docs-heading-anchor" href="#v0.4-2021-05-21">v0.4 - 2021-05-21</a><a id="v0.4-2021-05-21-1"></a><a class="docs-heading-anchor-permalink" href="#v0.4-2021-05-21" title="Permalink"></a></h2><ul><li><strong>Breaking</strong>: Removed deprecations for <code>style</code> and <code>spec</code> (now only <code>mapping</code> and <code>visual</code> are allowed).</li><li><strong>Breaking</strong>: Analyses now require parentheses (i.e. <code>linear()</code> instead of <code>linear</code>).</li><li><strong>Breaking</strong>: Rename <code>layout_x</code> and <code>layout_y</code> to <code>col</code> and <code>row</code>.</li><li><strong>Breaking</strong>: Rename <code>wts</code> keyword argument to <code>weights</code>.</li><li><strong>Breaking</strong>: <code>categorical</code> has been replaced by <code>nonnumeric</code>.</li></ul></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../philosophy/">« Philosophy</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. 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+<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>Changelog · Algebra of Graphics</title><meta name="title" content="Changelog · Algebra of Graphics"/><meta property="og:title" content="Changelog · Algebra of Graphics"/><meta property="twitter:title" content="Changelog · Algebra of Graphics"/><meta name="description" content="Documentation for Algebra of Graphics."/><meta property="og:description" content="Documentation for Algebra of Graphics."/><meta property="twitter:description" content="Documentation for Algebra of Graphics."/><script data-outdated-warner src="../assets/warner.js"></script><link href="https://cdnjs.cloudflare.com/ajax/libs/lato-font/3.0.0/css/lato-font.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/juliamono/0.050/juliamono.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/fontawesome.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/solid.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/brands.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/katex.min.css" rel="stylesheet" type="text/css"/><script>documenterBaseURL=".."</script><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js" data-main="../assets/documenter.js"></script><script src="../search_index.js"></script><script src="../siteinfo.js"></script><script src="../../versions.js"></script><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../assets/themes/catppuccin-mocha.css" data-theme-name="catppuccin-mocha"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../assets/themes/catppuccin-macchiato.css" data-theme-name="catppuccin-macchiato"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../assets/themes/catppuccin-frappe.css" data-theme-name="catppuccin-frappe"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../assets/themes/catppuccin-latte.css" data-theme-name="catppuccin-latte"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../assets/themes/documenter-dark.css" data-theme-name="documenter-dark" data-theme-primary-dark/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../assets/themes/documenter-light.css" data-theme-name="documenter-light" data-theme-primary/><script src="../assets/themeswap.js"></script><link href="../assets/favicon.ico" rel="icon" type="image/x-icon"/><link href="../democards/bulmagridtheme.css" rel="stylesheet" type="text/css"/></head><body><div id="documenter"><nav class="docs-sidebar"><a class="docs-logo" href="../"><img class="docs-light-only" src="../assets/logo.svg" alt="Algebra of Graphics logo"/><img class="docs-dark-only" src="../assets/logo-dark.svg" alt="Algebra of Graphics logo"/></a><div class="docs-package-name"><span class="docs-autofit"><a href="../">Algebra of Graphics</a></span></div><button class="docs-search-query input is-rounded is-small is-clickable my-2 mx-auto py-1 px-2" id="documenter-search-query">Search docs (Ctrl + /)</button><ul class="docs-menu"><li><a class="tocitem" href="../">Home</a></li><li><span class="tocitem">Getting Started</span><ul><li><a class="tocitem" href="../generated/penguins/">Tutorial 🐧</a></li><li><a class="tocitem" href="../gallery/">Gallery</a></li></ul></li><li><span class="tocitem">Algebra of Layers</span><ul><li><a class="tocitem" href="../layers/introduction/">Introduction</a></li><li><a class="tocitem" href="../layers/data/">Data</a></li><li><a class="tocitem" href="../layers/mapping/">Mapping</a></li><li><a class="tocitem" href="../generated/visual/">Visual</a></li><li><a class="tocitem" href="../generated/analyses/">Analyses</a></li><li><a class="tocitem" href="../layers/operations/">Algebraic Operations</a></li><li><a class="tocitem" href="../layers/draw/">Drawing Layers</a></li></ul></li><li><span class="tocitem">API</span><ul><li><a class="tocitem" href="../API/types/">Types</a></li><li><a class="tocitem" href="../API/functions/">Functions</a></li><li><a class="tocitem" href="../API/recipes/">Recipes</a></li></ul></li><li><a class="tocitem" href="../FAQs/">Frequently Asked Questions</a></li><li><a class="tocitem" href="../philosophy/">Philosophy</a></li><li class="is-active"><a class="tocitem" href>Changelog</a><ul class="internal"><li><a class="tocitem" href="#Unreleased"><span>Unreleased</span></a></li><li><a class="tocitem" href="#v0.8.8-2024-09-17"><span>v0.8.8 - 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2022-01-28</span></a></li><li><a class="tocitem" href="#v0.6.0-2021-10-24"><span>v0.6.0 - 2021-10-24</span></a></li><li><a class="tocitem" href="#v0.5-2021-08-05"><span>v0.5 - 2021-08-05</span></a></li><li><a class="tocitem" href="#v0.4-2021-05-21"><span>v0.4 - 2021-05-21</span></a></li></ul></li></ul><div class="docs-version-selector field has-addons"><div class="control"><span class="docs-label button is-static is-size-7">Version</span></div><div class="docs-selector control is-expanded"><div class="select is-fullwidth is-size-7"><select id="documenter-version-selector"></select></div></div></div></nav><div class="docs-main"><header class="docs-navbar"><a class="docs-sidebar-button docs-navbar-link fa-solid fa-bars is-hidden-desktop" id="documenter-sidebar-button" href="#"></a><nav class="breadcrumb"><ul class="is-hidden-mobile"><li class="is-active"><a href>Changelog</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href>Changelog</a></li></ul></nav><div class="docs-right"><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl" title="View the repository on GitHub"><span class="docs-icon fa-brands"></span><span class="docs-label is-hidden-touch">GitHub</span></a><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/master/docs/src/changelog.md" title="Edit source on GitHub"><span class="docs-icon fa-solid"></span></a><a class="docs-settings-button docs-navbar-link fa-solid fa-gear" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="Changelog"><a class="docs-heading-anchor" href="#Changelog">Changelog</a><a id="Changelog-1"></a><a class="docs-heading-anchor-permalink" href="#Changelog" title="Permalink"></a></h1><h2 id="Unreleased"><a class="docs-heading-anchor" href="#Unreleased">Unreleased</a><a id="Unreleased-1"></a><a class="docs-heading-anchor-permalink" href="#Unreleased" title="Permalink"></a></h2><ul><li>Added ability to include layers in the legend without using scales by adding <code>visual(label = &quot;some label&quot;)</code> <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/565">#565</a>.</li></ul><h2 id="v0.8.8-2024-09-17"><a class="docs-heading-anchor" href="#v0.8.8-2024-09-17">v0.8.8 - 2024-09-17</a><a id="v0.8.8-2024-09-17-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.8-2024-09-17" title="Permalink"></a></h2><ul><li>Fixed aesthetics of <code>errorbar</code> so that x and y stay labelled correctly when using <code>direction = :x</code> <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/560">#560</a>.</li><li>Added ability to specify <code>title</code>, <code>subtitle</code> and <code>footnotes</code> plus settings in the <code>draw</code> function <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/556">#556</a>.</li><li>Added <code>dodge_x</code> and <code>dodge_y</code> keywords to <code>mapping</code> that allow to dodge any plot types that have <code>AesX</code> or <code>AesY</code> data <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/558">#558</a>.</li></ul><h2 id="v0.8.7-2024-09-06"><a class="docs-heading-anchor" href="#v0.8.7-2024-09-06">v0.8.7 - 2024-09-06</a><a id="v0.8.7-2024-09-06-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.7-2024-09-06" title="Permalink"></a></h2><ul><li>Added ability to return <code>ProcessedLayers</code> from transformations, thereby enabling multi-layer transformations, such as scatter plus errorbars <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/549">#549</a>.</li><li>Fixed bug where <code>mergesorted</code> applied on string vectors used <code>isless</code> instead of natural sort <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/553">#553</a>.</li></ul><h2 id="v0.8.6-2024-09-02"><a class="docs-heading-anchor" href="#v0.8.6-2024-09-02">v0.8.6 - 2024-09-02</a><a id="v0.8.6-2024-09-02-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.6-2024-09-02" title="Permalink"></a></h2><ul><li>Added <code>bar_labels</code> to <code>BarPlot</code>&#39;s aesthetic mapping <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/544">#544</a>.</li><li>Added ability to hide legend or colorbar by passing, e.g., <code>legend = (; show = false)</code> to <code>draw</code> <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/547">#547</a>.</li></ul><h2 id="v0.8.5-2024-08-27"><a class="docs-heading-anchor" href="#v0.8.5-2024-08-27">v0.8.5 - 2024-08-27</a><a id="v0.8.5-2024-08-27-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.5-2024-08-27" title="Permalink"></a></h2><ul><li>Added <code>presorted</code> helper function to keep categorical data in the order encountered in the source table, instead of sorting it alphabetically <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/529">#529</a>.</li><li>Added <code>from_continuous</code> helper function which allows to sample continuous colormaps evenly to use them as categorical palettes without having to specify how many categories there are <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/541">#541</a>.</li></ul><h2 id="v0.8.4-2024-08-26"><a class="docs-heading-anchor" href="#v0.8.4-2024-08-26">v0.8.4 - 2024-08-26</a><a id="v0.8.4-2024-08-26-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.4-2024-08-26" title="Permalink"></a></h2><ul><li>Added <code>fillto</code> to <code>BarPlot</code> aesthetics <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/535">#535</a>.</li><li>Fixed bug when giving <code>datalimits</code> of <code>density</code> as a (low, high) tuple <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/536">#536</a>.</li><li>Fixed bug where facet-local continuous scale limits were used instead of the globally merged ones, possibly leading to mismatches between data and legend <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/539">#539</a>.</li></ul><h2 id="v0.8.3-2024-08-23"><a class="docs-heading-anchor" href="#v0.8.3-2024-08-23">v0.8.3 - 2024-08-23</a><a id="v0.8.3-2024-08-23-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.3-2024-08-23" title="Permalink"></a></h2><ul><li>Fixed incorrect x/y axis assignment for the <code>violin</code> plot type <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/528">#528</a>.</li></ul><h2 id="v0.8.2-2024-08-21"><a class="docs-heading-anchor" href="#v0.8.2-2024-08-21">v0.8.2 - 2024-08-21</a><a id="v0.8.2-2024-08-21-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.2-2024-08-21" title="Permalink"></a></h2><ul><li>Enable use of <code>LaTeXString</code>s and <code>rich</code> text in <code>renamer</code> <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/525">#525</a>.</li><li>Fixed widths of boxplots with color groupings <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/524">#524</a>.</li></ul><h2 id="v0.8.1-2024-08-20"><a class="docs-heading-anchor" href="#v0.8.1-2024-08-20">v0.8.1 - 2024-08-20</a><a id="v0.8.1-2024-08-20-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.1-2024-08-20" title="Permalink"></a></h2><ul><li>Added back support for <code>Hist</code>, <code>CrossBar</code>, <code>ECDFPlot</code> and <code>Density</code> <a href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/pull/522">#522</a>.</li></ul><h2 id="v0.8.0-2024-07-26"><a class="docs-heading-anchor" href="#v0.8.0-2024-07-26">v0.8.0 - 2024-07-26</a><a id="v0.8.0-2024-07-26-1"></a><a class="docs-heading-anchor-permalink" href="#v0.8.0-2024-07-26" title="Permalink"></a></h2><ul><li><strong>Breaking</strong>: Columns with element types of <code>Union{Missing,T}</code> are not treated as categorical by default anymore, instead <code>T</code> decides if data is seen as categorical, continuous or geometrical. If you relied on numerical vectors with <code>missing</code>s being treated as categorical, you can use <code>:columnname =&gt; nonnumeric</code> in the <code>mapping</code> instead.</li><li><strong>Breaking</strong>: <code>AbstractString</code> categories are now sorted with natural sort order by default. This means that where you got <code>[&quot;1&quot;, &quot;10&quot;, &quot;2&quot;]</code> before, you now get <code>[&quot;1&quot;, &quot;2&quot;, &quot;10&quot;]</code>. You can use <code>sorter</code>, the <code>categories</code> keyword or categorical arrays to sort your data differently if needed.</li></ul><h2 id="v0.7.0-2024-07-16"><a class="docs-heading-anchor" href="#v0.7.0-2024-07-16">v0.7.0 - 2024-07-16</a><a id="v0.7.0-2024-07-16-1"></a><a class="docs-heading-anchor-permalink" href="#v0.7.0-2024-07-16" title="Permalink"></a></h2><ul><li><strong>Breaking</strong>: The <code>palette</code> keyword of <code>draw</code> linking palettes to keyword arguments was removed. Instead, palettes need to be passed to specific scales like <code>draw(..., scales(Color = (; palette = :Set1_3)))</code></li><li><strong>Breaking</strong>: All recipes need to have the new function <code>aesthetic_mapping</code> defined for all sets of positional arguments that should be supported, as can be seen in <code>src/aesthetics.jl</code>. This breaks usage of all custom recipes. Additionally, not all Makie plots have been ported to the new system yet. If you encounter missing plots, or missing attributes of already ported plots, please open an issue.</li><li><strong>Breaking</strong>: All custom recipes that should be displayed in a legend, need to have <code>legend_elements(P, attributes, scale_args)</code> defined as can be seen in <code>src/guides/legend.jl</code>. AlgebraOfGraphics cannot use the same default mechanism as Makie, which can create a legend from an existing plot, because AlgebraOfGraphics needs to create the legend before the plot is instantiated.</li><li><strong>Breaking</strong>: Pregrouped data cannot be passed anymore to the plain <code>mapping(...)</code> without any <code>data(tabular)</code>. Instead, you should use <code>pregrouped(...)</code> which is a shortcut for <code>data(Pregrouped()) * mapping(...)</code>.</li><li><strong>Breaking</strong>: <code>Contour</code> and <code>Contourf</code> generally do not work anymore with <code>visual()</code>. Instead, the <code>contours()</code> and <code>filled_contours()</code> analyses should be used. <code>Contour</code> can still be used with categorical colors, but not with continuous ones.</li><li><strong>Breaking</strong>: All colormap properties for continuous color scales need to be passed via <code>scales</code> now, and not through <code>visual</code>. This is to have central control over the scale as it can be used by multiple <code>visual</code>s simultaneously.</li><li>Horizontal barplots, violins, errorbars, rangebars and other plot types that have two different orientations work correctly now. Axis labels switch accordingly when the orientation is changed.</li><li>Plotting functions whose positional arguments don&#39;t correspond to X, Y, Z work correctly now. For example, <code>HLines</code> (1 =&gt; Y) or <code>rangebars</code> (1 =&gt; X, 2 =&gt; Y, 3 =&gt; Y).</li><li>It is possible to add categories beyond those present in the data with the <code>categories</code> keyword within a scale&#39;s settings. It is also possible to reorder or otherwise transform the existing categories by passing a function to <code>categories</code>.</li><li>The supported attributes are not limited anymore to a specific set of names, for example, <code>strokecolor</code> can work the same as <code>color</code> did before, and the two can share a scale via their shared aesthetic type.</li><li>There can be multiple scales of the same aesthetic now. This allows to have separate legends for different plot types using the same aesthetics. Scale separation works by pairing a variable in <code>mapping</code> with a <code>scale(id_symbol)</code>.</li><li>Legend entries can be reordered using the <code>legend = (; order = ...)</code> option in <code>draw</code>. Specific scales can opt out of the legend by passing <code>legend = false</code> in <code>scales</code>.</li><li>Labels can now be anything that Makie supports, primarily <code>String</code>s, <code>LaTeXString</code>s or <code>rich</code> text.</li><li>Legend elements now usually reflect all attributes set in their corresponding <code>visual</code>.</li><li>Simple column vectors of data can now be passed directly to <code>mapping</code> without using <code>data</code> first. Additionally, scalar values are accepted as a shortcut for columns with the same repeated value.</li><li>Columns from outside a table source in <code>data</code> can now be passed to <code>mapping</code> by wrapping them in the <code>direct</code> function. Scalar values are accepted as a shortcut for columns with the same repeated value. For example, to create a label for columns <code>x</code> and <code>y</code> from a dataframe passed to <code>data</code>, one could now do <code>mapping(:x, :y, color = direct(&quot;label&quot;))</code> without having to create a column full of <code>&quot;label&quot;</code> strings first.</li><li>The numbers at which categorical values are plotted on x and y axis can now be changed via <code>scales(X = (; palette = [1, 2, 4]))</code> or similar.</li><li>Continuous marker size scales can now be shown in the legend. Numerical values are proportional to area and not diameter now, which makes more sense with respect to human perception. The min and max marker size can be set using the <code>sizerange</code> property for the respective scale in <code>scales</code>.</li></ul><h2 id="v0.6.11-2022-08-08"><a class="docs-heading-anchor" href="#v0.6.11-2022-08-08">v0.6.11 - 2022-08-08</a><a id="v0.6.11-2022-08-08-1"></a><a class="docs-heading-anchor-permalink" href="#v0.6.11-2022-08-08" title="Permalink"></a></h2><ul><li>Added <code>paginate</code> for pagination of large facet plots. </li></ul><h2 id="v0.6.8-2022-06-14"><a class="docs-heading-anchor" href="#v0.6.8-2022-06-14">v0.6.8 - 2022-06-14</a><a id="v0.6.8-2022-06-14-1"></a><a class="docs-heading-anchor-permalink" href="#v0.6.8-2022-06-14" title="Permalink"></a></h2><ul><li>Added <code>choropleth</code> recipe to supersede <code>geodata</code> for geographical data.</li></ul><h2 id="v0.6.1-2022-01-28"><a class="docs-heading-anchor" href="#v0.6.1-2022-01-28">v0.6.1 - 2022-01-28</a><a id="v0.6.1-2022-01-28-1"></a><a class="docs-heading-anchor-permalink" href="#v0.6.1-2022-01-28" title="Permalink"></a></h2><ul><li>Support <code>level</code> in <code>linear</code> analysis for confidence interval.</li><li>Replaced tuples and named tuples in <code>Layer</code> and <code>Entry</code> with dictionaries from <a href="https://github.com/andyferris/Dictionaries.jl">Dictionaries.jl</a>.</li><li>Split internal <code>Entry</code> type into <code>ProcessedLayer</code> (to be used for analyses) and <code>Entry</code> (to be used for plotting).</li></ul><h2 id="v0.6.0-2021-10-24"><a class="docs-heading-anchor" href="#v0.6.0-2021-10-24">v0.6.0 - 2021-10-24</a><a id="v0.6.0-2021-10-24-1"></a><a class="docs-heading-anchor-permalink" href="#v0.6.0-2021-10-24" title="Permalink"></a></h2><ul><li><strong>Breaking</strong>: Default axis linking behavior has changed: now only axes corresponding to the same variable are linked. For consistency with <code>row</code>/<code>col</code>, <code>layout</code> will hide decorations of linked axes and span axis labels if appropriate.</li><li>Customizable legend and colorbar position and look.</li><li>Customizable axis linking behavior.</li></ul><h2 id="v0.5-2021-08-05"><a class="docs-heading-anchor" href="#v0.5-2021-08-05">v0.5 - 2021-08-05</a><a id="v0.5-2021-08-05-1"></a><a class="docs-heading-anchor-permalink" href="#v0.5-2021-08-05" title="Permalink"></a></h2><ul><li><strong>Breaking</strong>: <code>Axis(ae)</code> has been replaced by <code>ae.axis</code>.</li><li><strong>Breaking</strong>: <code>Legend(fg)</code> has been replaced by <code>legend!(fg)</code> and <code>colorbar!(fg)</code>.</li><li><code>legend!</code> and <code>colorbar!</code> API allows for custom legend placement.</li></ul><h2 id="v0.4-2021-05-21"><a class="docs-heading-anchor" href="#v0.4-2021-05-21">v0.4 - 2021-05-21</a><a id="v0.4-2021-05-21-1"></a><a class="docs-heading-anchor-permalink" href="#v0.4-2021-05-21" title="Permalink"></a></h2><ul><li><strong>Breaking</strong>: Removed deprecations for <code>style</code> and <code>spec</code> (now only <code>mapping</code> and <code>visual</code> are allowed).</li><li><strong>Breaking</strong>: Analyses now require parentheses (i.e. <code>linear()</code> instead of <code>linear</code>).</li><li><strong>Breaking</strong>: Rename <code>layout_x</code> and <code>layout_y</code> to <code>col</code> and <code>row</code>.</li><li><strong>Breaking</strong>: Rename <code>wts</code> keyword argument to <code>weights</code>.</li><li><strong>Breaking</strong>: <code>categorical</code> has been replaced by <code>nonnumeric</code>.</li></ul></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../philosophy/">« Philosophy</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. 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diff --git a/dev/gallery/gallery/applications/geographic/index.html b/dev/gallery/gallery/applications/geographic/index.html
index ca5389ace..2049766cb 100644
--- a/dev/gallery/gallery/applications/geographic/index.html
+++ b/dev/gallery/gallery/applications/geographic/index.html
@@ -15,4 +15,4 @@
 
 # Draw map
 plt = data(t) * mapping(:geometry, color = :surface) * visual(Choropleth)
-fg = draw(plt; axis=(aspect=1,))</code></pre><p><img src="../geographic-5.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p><section class="footnotes is-size-7"><ul><li class="footnote" id="footnote-1"><a class="tag is-link" href="#citeref-1">1</a>Gerrish, L., Fretwell, P., &amp; Cooper, P. (2021). Medium resolution vector polygons of the Antarctic coastline (7.4) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research &amp; Innovation. https://doi.org/10.5285/747e63e-9d93-49c2-bafc-cf3d3f8e5afa</li></ul></section></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+fg = draw(plt; axis=(aspect=1,))</code></pre><p><img src="../geographic-5.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p><section class="footnotes is-size-7"><ul><li class="footnote" id="footnote-1"><a class="tag is-link" href="#citeref-1">1</a>Gerrish, L., Fretwell, P., &amp; Cooper, P. (2021). Medium resolution vector polygons of the Antarctic coastline (7.4) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research &amp; Innovation. https://doi.org/10.5285/747e63e-9d93-49c2-bafc-cf3d3f8e5afa</li></ul></section></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/gallery/gallery/applications/geometries/index.html b/dev/gallery/gallery/applications/geometries/index.html
index a7149d98c..43dab3c71 100644
--- a/dev/gallery/gallery/applications/geometries/index.html
+++ b/dev/gallery/gallery/applications/geometries/index.html
@@ -7,4 +7,4 @@
 df = (; geometry, group)
 
 plt = data(df) * visual(Poly) * mapping(:geometry, color = :group)
-fg = draw(plt; axis=(aspect=1,))</code></pre><p><img src="../geometries-3.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+fg = draw(plt; axis=(aspect=1,))</code></pre><p><img src="../geometries-3.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/gallery/gallery/applications/time_series-3.svg b/dev/gallery/gallery/applications/time_series-3.svg
index f3ece3f2e..a082d007f 100644
--- a/dev/gallery/gallery/applications/time_series-3.svg
+++ b/dev/gallery/gallery/applications/time_series-3.svg
@@ -2,425 +2,393 @@
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diff --git a/dev/gallery/gallery/applications/time_series/index.html b/dev/gallery/gallery/applications/time_series/index.html
index 830ea463a..bdedb689d 100644
--- a/dev/gallery/gallery/applications/time_series/index.html
+++ b/dev/gallery/gallery/applications/time_series/index.html
@@ -26,4 +26,4 @@
     return (; date, observation)
 end
 plt = data(df) * mapping(:date, :observation) * visual(BoxPlot)
-draw(plt)</code></pre><p><img src="../time_series-5.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+draw(plt)</code></pre><p><img src="../time_series-5.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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diff --git a/dev/gallery/gallery/basic visualizations/lines_and_markers-9.svg b/dev/gallery/gallery/basic visualizations/lines_and_markers-9.svg
index cf57bbdad..60b7500cd 100644
--- a/dev/gallery/gallery/basic visualizations/lines_and_markers-9.svg	
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diff --git a/dev/gallery/gallery/basic visualizations/lines_and_markers/index.html b/dev/gallery/gallery/basic visualizations/lines_and_markers/index.html
index 7ba000b6a..09465e60d 100644
--- a/dev/gallery/gallery/basic visualizations/lines_and_markers/index.html	
+++ b/dev/gallery/gallery/basic visualizations/lines_and_markers/index.html	
@@ -18,4 +18,4 @@
 df1 = (; x, y)
 df2 = (x=rand(10), y=rand(10))
 layers = data(df1) * visual(Lines) + data(df2) * visual(Scatter)
-fg = draw(layers * mapping(:x, :y))</code></pre><p><img src="../lines_and_markers-9.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+fg = draw(layers * mapping(:x, :y))</code></pre><p><img src="../lines_and_markers-9.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/gallery/gallery/basic visualizations/statistical_visualizations-5.svg b/dev/gallery/gallery/basic visualizations/statistical_visualizations-5.svg
index 0b2a1c098..c1a1e8566 100644
--- a/dev/gallery/gallery/basic visualizations/statistical_visualizations-5.svg	
+++ b/dev/gallery/gallery/basic visualizations/statistical_visualizations-5.svg	
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diff --git a/dev/gallery/gallery/basic visualizations/statistical_visualizations/index.html b/dev/gallery/gallery/basic visualizations/statistical_visualizations/index.html
index 1eb7a4997..42edb05e3 100644
--- a/dev/gallery/gallery/basic visualizations/statistical_visualizations/index.html	
+++ b/dev/gallery/gallery/basic visualizations/statistical_visualizations/index.html	
@@ -9,4 +9,4 @@
 fg = draw(plt, axis=(limits=((0.5, 3.5), nothing),))</code></pre><p><img src="../statistical_visualizations-4.svg" alt/></p><pre><code class="language-julia hljs">data(penguins) * visual(BoxPlot, show_notch=true) *
     mapping(:species, :bill_depth_mm, color=:sex, dodge=:sex) |&gt; draw</code></pre><p><img src="../statistical_visualizations-5.svg" alt/></p><pre><code class="language-julia hljs">data(penguins) *
     mapping(:bill_length_mm, :bill_depth_mm, col=:sex) *
-    visual(QQPlot, qqline=:fit) |&gt; draw</code></pre><p><img src="../statistical_visualizations-6.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+    visual(QQPlot, qqline=:fit) |&gt; draw</code></pre><p><img src="../statistical_visualizations-6.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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diff --git a/dev/gallery/gallery/customization/axis/index.html b/dev/gallery/gallery/customization/axis/index.html
index 1ece788e3..6cf0610e6 100644
--- a/dev/gallery/gallery/customization/axis/index.html
+++ b/dev/gallery/gallery/customization/axis/index.html
@@ -2,4 +2,4 @@
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href="../../../../changelog/">Changelog</a></li></ul><div class="docs-version-selector field has-addons"><div class="control"><span class="docs-label button is-static is-size-7">Version</span></div><div class="docs-selector control is-expanded"><div class="select is-fullwidth is-size-7"><select id="documenter-version-selector"></select></div></div></div></nav><div class="docs-main"><header class="docs-navbar"><a class="docs-sidebar-button docs-navbar-link fa-solid fa-bars is-hidden-desktop" id="documenter-sidebar-button" href="#"></a><nav class="breadcrumb"><ul class="is-hidden-mobile"><li class="is-active"><a href>Axis tweaking</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href>Axis tweaking</a></li></ul></nav><div class="docs-right"><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl" title="View the repository on GitHub"><span class="docs-icon fa-brands"></span><span class="docs-label is-hidden-touch">GitHub</span></a><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/master/docs/gallery/gallery/customization/axis.jl" title="Edit source on GitHub"><span class="docs-icon fa-solid"></span></a><a class="docs-settings-button docs-navbar-link fa-solid fa-gear" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="Axis-tweaking"><a class="docs-heading-anchor" href="#Axis-tweaking">Axis tweaking</a><a id="Axis-tweaking-1"></a><a class="docs-heading-anchor-permalink" href="#Axis-tweaking" title="Permalink"></a></h1><p><a href="../axis.jl"><img src="https://img.shields.io/badge/download-julia-brightgreen.svg" alt="Source code"/></a> <a href="https://github.com/piever"><img src="https://img.shields.io/badge/Author-Pietro%20Vertechi-blue" alt="Author"/></a></p><pre><code class="language-julia hljs">using AlgebraOfGraphics, CairoMakie</code></pre><p>To tweak one or more axes, simply use the <code>axis</code> keyword when plotting. For example</p><pre><code class="language-julia hljs">df = (x=rand(100), y=rand(100), z=rand(100))
 layers = linear() + mapping(color=:z)
 plt = data(df) * layers * mapping(:x, :y)
-draw(plt, axis=(aspect=1,))</code></pre><p><img src="../axis-5.svg" alt/></p><pre><code class="language-julia hljs">fg = draw(plt, axis=(aspect=1, xticks=0:0.1:1, yticks=0:0.1:1, ylabel=&quot;custom label&quot;))</code></pre><p><img src="../axis-6.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+draw(plt, axis=(aspect=1,))</code></pre><p><img src="../axis-5.svg" alt/></p><pre><code class="language-julia hljs">fg = draw(plt, axis=(aspect=1, xticks=0:0.1:1, yticks=0:0.1:1, ylabel=&quot;custom label&quot;))</code></pre><p><img src="../axis-6.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/gallery/gallery/customization/colorbar-11.svg b/dev/gallery/gallery/customization/colorbar-11.svg
index 28c0b2360..960555e40 100644
--- a/dev/gallery/gallery/customization/colorbar-11.svg
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diff --git a/dev/gallery/gallery/customization/legend-5.svg b/dev/gallery/gallery/customization/legend-5.svg
index 5b87d2cb2..e334a93e1 100644
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index 6537943d2..c732c8234 100644
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diff --git a/dev/gallery/gallery/customization/legend/index.html b/dev/gallery/gallery/customization/legend/index.html
index 98f06bf32..ff7fbd016 100644
--- a/dev/gallery/gallery/customization/legend/index.html
+++ b/dev/gallery/gallery/customization/legend/index.html
@@ -12,4 +12,4 @@
 
 legend!(makie_fig[1, 1], grid; tellheight=false, tellwidth=false, halign=:right, valign=:top)
 
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+makie_fig</code></pre><p><img src="../legend-10.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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diff --git a/dev/gallery/gallery/data manipulations/new_columns_on_the_fly/index.html b/dev/gallery/gallery/data manipulations/new_columns_on_the_fly/index.html
index 0c460388f..60742b82d 100644
--- a/dev/gallery/gallery/data manipulations/new_columns_on_the_fly/index.html	
+++ b/dev/gallery/gallery/data manipulations/new_columns_on_the_fly/index.html	
@@ -6,4 +6,4 @@
 
 plt = data(df) * layers * mapping(:x =&gt; (x -&gt; x^2) =&gt; L&quot;x^2&quot;, :y =&gt; L&quot;This variable is called $y$&quot;)
 fg = draw(plt)</code></pre><p><img src="../new_columns_on_the_fly-3.svg" alt/></p><p>Use a <code>Tuple</code> to pass combine several columns into a unique operation.</p><pre><code class="language-julia hljs">plt = data(df) * layers * mapping(:x, (:x, :y, :z) =&gt; (+) =&gt; L&quot;the new variable $x + y + z$&quot;, layout=:c)
-fg = draw(plt)</code></pre><p><img src="../new_columns_on_the_fly-5.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+fg = draw(plt)</code></pre><p><img src="../new_columns_on_the_fly-5.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/gallery/gallery/data manipulations/no_data/index.html b/dev/gallery/gallery/data manipulations/no_data/index.html
index b841d2e46..993a655c7 100644
--- a/dev/gallery/gallery/data manipulations/no_data/index.html	
+++ b/dev/gallery/gallery/data manipulations/no_data/index.html	
@@ -9,4 +9,4 @@
     mapping([20, 28, 51], color = &quot;marker&quot; =&gt; scale(:secondary)) *
     visual(VLines, linestyle = :dash)
 
-fg = draw(plt, scales(secondary = (; palette = [:gray80])))</code></pre><p><img src="../no_data-4.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+fg = draw(plt, scales(secondary = (; palette = [:gray80])))</code></pre><p><img src="../no_data-4.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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index 4d104e90f..010d65761 100644
--- a/dev/gallery/gallery/data manipulations/pre_grouped_data-3.svg	
+++ b/dev/gallery/gallery/data manipulations/pre_grouped_data-3.svg	
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index 848f3bceb..ae6f97a77 100644
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diff --git a/dev/gallery/gallery/data manipulations/pre_grouped_data/index.html b/dev/gallery/gallery/data manipulations/pre_grouped_data/index.html
index b673aae0e..e159d47e6 100644
--- a/dev/gallery/gallery/data manipulations/pre_grouped_data/index.html	
+++ b/dev/gallery/gallery/data manipulations/pre_grouped_data/index.html	
@@ -12,4 +12,4 @@
 draw(m)</code></pre><p><img src="../pre_grouped_data-4.svg" alt/></p><pre><code class="language-julia hljs">m = pregrouped(x, [y z], color=dims(1) =&gt; renamer([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;])) * visual(Scatter)
 draw(m)</code></pre><p><img src="../pre_grouped_data-5.svg" alt/></p><pre><code class="language-julia hljs">m = pregrouped(x, [y z], color=[&quot;1&quot; &quot;2&quot;])
 layers = visual(Scatter) + linear()
-fg = draw(m * layers)</code></pre><p><img src="../pre_grouped_data-6.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+fg = draw(m * layers)</code></pre><p><img src="../pre_grouped_data-6.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/gallery/gallery/data manipulations/presorted_data/index.html b/dev/gallery/gallery/data manipulations/presorted_data/index.html
index 1776a3c3a..aff746c8b 100644
--- a/dev/gallery/gallery/data manipulations/presorted_data/index.html	
+++ b/dev/gallery/gallery/data manipulations/presorted_data/index.html	
@@ -15,4 +15,4 @@
 fg = draw(spec)</code></pre><p><img src="../presorted_data-9.svg" alt/></p><p>We can also mark multiple variables as presorted, note how the order in the color legend shifts when we do the same for <code>group</code>:</p><pre><code class="language-julia hljs">spec = data(df) *
     mapping(:countries =&gt; presorted, :some_value, color = :group =&gt; presorted) *
     visual(BarPlot, direction = :x)
-draw(spec)</code></pre><p><img src="../presorted_data-11.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+draw(spec)</code></pre><p><img src="../presorted_data-11.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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diff --git a/dev/gallery/gallery/data manipulations/wide_data/index.html b/dev/gallery/gallery/data manipulations/wide_data/index.html
index 94d3c7c28..1faccab79 100644
--- a/dev/gallery/gallery/data manipulations/wide_data/index.html	
+++ b/dev/gallery/gallery/data manipulations/wide_data/index.html	
@@ -22,4 +22,4 @@
 yvars = [&quot;petal_length&quot; &quot;petal_width&quot;]
 layers = linear() + visual(Scatter)
 plt = data(df) * layers * mapping(xvars, yvars, col=dims(1), row=dims(2))
-draw(plt)</code></pre><p><img src="../wide_data-7.svg" alt/></p><h2 id="Axes-can-be-fully-linked-or-fully-unlinked"><a class="docs-heading-anchor" href="#Axes-can-be-fully-linked-or-fully-unlinked">Axes can be fully linked or fully unlinked</a><a id="Axes-can-be-fully-linked-or-fully-unlinked-1"></a><a class="docs-heading-anchor-permalink" href="#Axes-can-be-fully-linked-or-fully-unlinked" title="Permalink"></a></h2><pre><code class="language-julia hljs">draw(plt, facet = (; linkxaxes = :all, linkyaxes = :all))</code></pre><p><img src="../wide_data-9.svg" alt/></p><pre><code class="language-julia hljs">fg = draw(plt, facet = (; linkxaxes = :none, linkyaxes = :none))</code></pre><p><img src="../wide_data-10.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+draw(plt)</code></pre><p><img src="../wide_data-7.svg" alt/></p><h2 id="Axes-can-be-fully-linked-or-fully-unlinked"><a class="docs-heading-anchor" href="#Axes-can-be-fully-linked-or-fully-unlinked">Axes can be fully linked or fully unlinked</a><a id="Axes-can-be-fully-linked-or-fully-unlinked-1"></a><a class="docs-heading-anchor-permalink" href="#Axes-can-be-fully-linked-or-fully-unlinked" title="Permalink"></a></h2><pre><code class="language-julia hljs">draw(plt, facet = (; linkxaxes = :all, linkyaxes = :all))</code></pre><p><img src="../wide_data-9.svg" alt/></p><pre><code class="language-julia hljs">fg = draw(plt, facet = (; linkxaxes = :none, linkyaxes = :none))</code></pre><p><img src="../wide_data-10.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/gallery/gallery/layout/assets/faceting.png b/dev/gallery/gallery/layout/assets/faceting.png
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diff --git a/dev/gallery/gallery/layout/faceting-5.svg b/dev/gallery/gallery/layout/faceting-5.svg
index fef2f2742..b57ba3d5e 100644
--- a/dev/gallery/gallery/layout/faceting-5.svg
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diff --git a/dev/gallery/gallery/layout/faceting/index.html b/dev/gallery/gallery/layout/faceting/index.html
index 55075436c..a0c79efed 100644
--- a/dev/gallery/gallery/layout/faceting/index.html
+++ b/dev/gallery/gallery/layout/faceting/index.html
@@ -31,4 +31,4 @@
 plt = data(df) * mapping(:x, :y, layout=:l)
 pag = paginate(plt, layout = 4)</code></pre><pre><code class="nohighlight hljs">PaginatedLayers with 2 entries (layout = 4)</code></pre><p>The object returned from <code>draw</code> will be a <code>Vector{FigureGrid}</code>.</p><pre><code class="language-julia hljs">figuregrids = draw(pag)</code></pre><pre><code class="nohighlight hljs">2-element Vector{AlgebraOfGraphics.FigureGrid}:
  FigureGrid()
- FigureGrid()</code></pre><p>You can either extract single figures from this vector...</p><pre><code class="language-julia hljs">figuregrids[1]</code></pre><p><img src="../faceting-26.svg" alt/></p><p>or use <code>draw</code> with an optional second argument specifying the index of the page to draw.</p><pre><code class="language-julia hljs">draw(pag, 2)</code></pre><p><img src="../faceting-28.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+ FigureGrid()</code></pre><p>You can either extract single figures from this vector...</p><pre><code class="language-julia hljs">figuregrids[1]</code></pre><p><img src="../faceting-26.svg" alt/></p><p>or use <code>draw</code> with an optional second argument specifying the index of the page to draw.</p><pre><code class="language-julia hljs">draw(pag, 2)</code></pre><p><img src="../faceting-28.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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diff --git a/dev/gallery/gallery/layout/nested_layouts/index.html b/dev/gallery/gallery/layout/nested_layouts/index.html
index 6831c31f1..12b271787 100644
--- a/dev/gallery/gallery/layout/nested_layouts/index.html
+++ b/dev/gallery/gallery/layout/nested_layouts/index.html
@@ -42,4 +42,4 @@
 grid = draw!(ax2, plt)
 legend!(f[1, 3], grid)
 
-f</code></pre><p><img src="../nested_layouts-9.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+f</code></pre><p><img src="../nested_layouts-9.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/gallery/gallery/scales/assets/custom_scales.png b/dev/gallery/gallery/scales/assets/custom_scales.png
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diff --git a/dev/gallery/gallery/scales/continuous_scales/index.html b/dev/gallery/gallery/scales/continuous_scales/index.html
index 452adbdee..2e117a0a3 100644
--- a/dev/gallery/gallery/scales/continuous_scales/index.html
+++ b/dev/gallery/gallery/scales/continuous_scales/index.html
@@ -25,4 +25,4 @@
         :x,
         :y =&gt; &quot;y&quot;,
     ) * visual(Lines)
-fg = draw(plt, axis=(yscale=log,))</code></pre><p><img src="../continuous_scales-5.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+fg = draw(plt, axis=(yscale=log,))</code></pre><p><img src="../continuous_scales-5.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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diff --git a/dev/gallery/gallery/scales/custom_scales/index.html b/dev/gallery/gallery/scales/custom_scales/index.html
index 7d1b2db83..f4fde861d 100644
--- a/dev/gallery/gallery/scales/custom_scales/index.html
+++ b/dev/gallery/gallery/scales/custom_scales/index.html
@@ -25,4 +25,4 @@
 z = rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;, &quot;d&quot;, &quot;e&quot;, &quot;f&quot;, &quot;g&quot;, &quot;h&quot;], 200)
 df = (; x, y, z)
 plt = data(df) * mapping(:x, :y, color=:z)
-draw(plt, scales(Color = (; palette = from_continuous(:cividis))))</code></pre><p><img src="../custom_scales-9.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+draw(plt, scales(Color = (; palette = from_continuous(:cividis))))</code></pre><p><img src="../custom_scales-9.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/gallery/gallery/scales/discrete_scales-10.svg b/dev/gallery/gallery/scales/discrete_scales-10.svg
index 7fd19e5d3..baa6c583c 100644
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diff --git a/dev/gallery/gallery/scales/discrete_scales-14.svg b/dev/gallery/gallery/scales/discrete_scales-14.svg
index 9e13ba9f8..3bee4a068 100644
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diff --git a/dev/gallery/gallery/scales/discrete_scales/index.html b/dev/gallery/gallery/scales/discrete_scales/index.html
index 0a4a717d7..3bc1a2e8c 100644
--- a/dev/gallery/gallery/scales/discrete_scales/index.html
+++ b/dev/gallery/gallery/scales/discrete_scales/index.html
@@ -36,4 +36,4 @@
 
 draw(plt, scales(X = (;
     categories = cats -&gt; initialed.(sort(cats; by = name -&gt; split(name)[2]))
-)))</code></pre><p><img src="../discrete_scales-23.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+)))</code></pre><p><img src="../discrete_scales-23.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/gallery/gallery/scales/dodging/index.html b/dev/gallery/gallery/scales/dodging/index.html
index 197a0b254..7c0f73096 100644
--- a/dev/gallery/gallery/scales/dodging/index.html
+++ b/dev/gallery/gallery/scales/dodging/index.html
@@ -22,4 +22,4 @@
 draw!(f[2, 1], plt3, scales(DodgeX = (; width = 0.5)); axis = kw(title = &quot;DodgeX = (; width = 0.5)&quot;))
 draw!(f[3, 1], plt3, scales(DodgeX = (; width = 1.0)); axis = (; title = &quot;DodgeX = (; width = 1.0)&quot;))
 
-f</code></pre><p><img src="../dodging-9.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+f</code></pre><p><img src="../dodging-9.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/gallery/gallery/scales/legend_merging-3.svg b/dev/gallery/gallery/scales/legend_merging-3.svg
index 7f31ab18d..bccb8a023 100644
--- a/dev/gallery/gallery/scales/legend_merging-3.svg
+++ b/dev/gallery/gallery/scales/legend_merging-3.svg
@@ -2,61 +2,67 @@
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diff --git a/dev/gallery/gallery/scales/legend_merging/index.html b/dev/gallery/gallery/scales/legend_merging/index.html
index 0cf3e9c0b..e0789296a 100644
--- a/dev/gallery/gallery/scales/legend_merging/index.html
+++ b/dev/gallery/gallery/scales/legend_merging/index.html
@@ -12,4 +12,4 @@
 layers = visual(Lines) + visual(Scatter) * mapping(marker = :grp)
 plt = data(df) * layers * mapping(:x, :y, color = :grp)
 
-fg = draw(plt)</code></pre><p><img src="../legend_merging-3.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+fg = draw(plt)</code></pre><p><img src="../legend_merging-3.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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diff --git a/dev/gallery/gallery/scales/multiple_color_scales/index.html b/dev/gallery/gallery/scales/multiple_color_scales/index.html
index bc454eac4..7a4cb5e98 100644
--- a/dev/gallery/gallery/scales/multiple_color_scales/index.html
+++ b/dev/gallery/gallery/scales/multiple_color_scales/index.html
@@ -7,4 +7,4 @@
 df = (; x, y, ŷ, z, c)
 layers = mapping(:y, color=:z) * visual(Lines) + mapping(:ŷ =&gt; &quot;y&quot;, color=:c)
 plt = data(df) * mapping(:x) * layers
-fg = draw(plt)</code></pre><p><img src="../multiple_color_scales-5.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+fg = draw(plt)</code></pre><p><img src="../multiple_color_scales-5.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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diff --git a/dev/gallery/gallery/scales/prescaled_data-5.svg b/dev/gallery/gallery/scales/prescaled_data-5.svg
index 6fda296e3..6a21421bc 100644
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diff --git a/dev/gallery/gallery/scales/prescaled_data/index.html b/dev/gallery/gallery/scales/prescaled_data/index.html
index 851986263..3e0d2c1a4 100644
--- a/dev/gallery/gallery/scales/prescaled_data/index.html
+++ b/dev/gallery/gallery/scales/prescaled_data/index.html
@@ -12,4 +12,4 @@
 label = rand([&quot;a&quot;, &quot;b&quot;], 100)
 df = (; x, y, label)
 plt = data(df) * mapping(:x, :y, text=:label =&gt; verbatim) * visual(Makie.Text)
-fg = draw(plt)</code></pre><p><img src="../prescaled_data-5.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+fg = draw(plt)</code></pre><p><img src="../prescaled_data-5.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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diff --git a/dev/gallery/gallery/scales/secondary_scales/index.html b/dev/gallery/gallery/scales/secondary_scales/index.html
index 84fcbf4e8..d42b796ad 100644
--- a/dev/gallery/gallery/scales/secondary_scales/index.html
+++ b/dev/gallery/gallery/scales/secondary_scales/index.html
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+)</code></pre><p><img src="../secondary_scales-11.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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diff --git a/dev/gallery/gallery/statistical analyses/density_plots/index.html b/dev/gallery/gallery/statistical analyses/density_plots/index.html
index 0e3b79687..701e5c0a6 100644
--- a/dev/gallery/gallery/statistical analyses/density_plots/index.html	
+++ b/dev/gallery/gallery/statistical analyses/density_plots/index.html	
@@ -6,4 +6,4 @@
 plt = data(df) * mapping(:x, color=:c) * density(bandwidth=0.5)
 fg = draw(plt)</code></pre><p><img src="../density_plots-3.svg" alt/></p><pre><code class="language-julia hljs">df = (x=randn(1000), c=rand([&quot;a&quot;, &quot;b&quot;], 1000))
 plt = data(df) * mapping(:x, color=:c) * density(bandwidth=0.5) * visual(orientation=:vertical)
-&quot;Not yet supported&quot; # hide</code></pre><pre><code class="nohighlight hljs">&quot;Not yet supported&quot;</code></pre><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+&quot;Not yet supported&quot; # hide</code></pre><pre><code class="nohighlight hljs">&quot;Not yet supported&quot;</code></pre><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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index 291d0d463..a3ee35806 100644
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diff --git a/dev/gallery/gallery/statistical analyses/histograms/index.html b/dev/gallery/gallery/statistical analyses/histograms/index.html
index b395438c3..84d4ddbe9 100644
--- a/dev/gallery/gallery/statistical analyses/histograms/index.html	
+++ b/dev/gallery/gallery/statistical analyses/histograms/index.html	
@@ -8,4 +8,4 @@
 plt = data(df) * mapping(:x, color=:c, stack=:c) * histogram(bins=20)
 fg = draw(plt)</code></pre><p><img src="../histograms-4.svg" alt/></p><pre><code class="language-julia hljs">df = (x=rand(1000), y=randn(1000))
 plt = data(df) * mapping(:x, :y) * histogram(bins=20)
-draw(plt)</code></pre><p><img src="../histograms-5.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+draw(plt)</code></pre><p><img src="../histograms-5.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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index 2b7babe94..02f552987 100644
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diff --git a/dev/gallery/gallery/statistical analyses/regression_plots/index.html b/dev/gallery/gallery/statistical analyses/regression_plots/index.html
index 2fcdf100a..b4bf1e340 100644
--- a/dev/gallery/gallery/statistical analyses/regression_plots/index.html	
+++ b/dev/gallery/gallery/statistical analyses/regression_plots/index.html	
@@ -11,4 +11,4 @@
 df = (; x, y)
 xy = data(df) * mapping(:x, :y)
 layers = smooth() + visual(Scatter)
-fg = draw(layers * xy)</code></pre><p><img src="../regression_plots-4.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+fg = draw(layers * xy)</code></pre><p><img src="../regression_plots-4.svg" alt/></p><hr/><p><em>This page was generated using <a href="https://github.com/JuliaDocs/DemoCards.jl">DemoCards.jl</a> and <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/gallery/index.html b/dev/gallery/index.html
index 12d3c49a9..130dc335b 100644
--- a/dev/gallery/index.html
+++ b/dev/gallery/index.html
@@ -323,4 +323,4 @@ <h3 class="is-size-5 bulma-grid-card-text">
         </div>
         <div class="card-image bulma-grid-card-cover"><p><a href="gallery/applications/time_series/#Time-series"><img src="covers/time_series.png" alt="card image"/></a></p>        </div>
     </div>
-</div></div></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../generated/penguins/">« Tutorial 🐧</a><a class="docs-footer-nextpage" href="../layers/introduction/">Introduction »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+</div></div></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../generated/penguins/">« Tutorial 🐧</a><a class="docs-footer-nextpage" href="../layers/introduction/">Introduction »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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class="is-active"><a href>Analyses</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href>Analyses</a></li></ul></nav><div class="docs-right"><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl" title="View the repository on GitHub"><span class="docs-icon fa-brands"></span><span class="docs-label is-hidden-touch">GitHub</span></a><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/master/docs/src/generated/analyses.jl" title="Edit source on GitHub"><span class="docs-icon fa-solid"></span></a><a class="docs-settings-button docs-navbar-link fa-solid fa-gear" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="Analyses"><a class="docs-heading-anchor" href="#Analyses">Analyses</a><a id="Analyses-1"></a><a class="docs-heading-anchor-permalink" href="#Analyses" title="Permalink"></a></h1><h2 id="Histogram"><a class="docs-heading-anchor" href="#Histogram">Histogram</a><a id="Histogram-1"></a><a class="docs-heading-anchor-permalink" href="#Histogram" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.histogram" href="#AlgebraOfGraphics.histogram"><code>AlgebraOfGraphics.histogram</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">histogram(; bins=automatic, datalimits=automatic, closed=:left, normalization=:none)</code></pre><p>Compute a histogram.</p><p>The attribute <code>bins</code> can be an <code>Integer</code>, an <code>AbstractVector</code> (in particular, a range), or a <code>Tuple</code> of either integers or abstract vectors (useful for 2- or 3-dimensional histograms). When <code>bins</code> is an <code>Integer</code>, it denotes the approximate number of equal-width intervals used to compute the histogram. In that case, the range covered by the intervals is defined by <code>datalimits</code> (it defaults to the extrema of the whole data). The keyword argument <code>datalimits</code> can be a tuple of two values, e.g. <code>datalimits=(0, 10)</code>, or a function to be applied group by group, e.g. <code>datalimits=extrema</code>. When <code>bins</code> is an <code>AbstractVector</code>, it denotes the intervals directly.</p><p><code>closed</code> determines whether the the intervals are closed to the left or to the right.</p><p>The histogram can be normalized by setting <code>normalization</code>. Possible values are:</p><ul><li><code>:pdf</code>: Normalize by sum of weights and bin sizes. Resulting histogram  has norm 1 and represents a PDF.</li><li><code>:density</code>: Normalize by bin sizes only. Resulting histogram represents  count density of input and does not have norm 1.</li><li><code>:probability</code>: Normalize by sum of weights only. Resulting histogram  represents the fraction of probability mass for each bin and does not have  norm 1.</li><li><code>:none</code>: Do not normalize.</li></ul><p>Weighted data is supported via the keyword <code>weights</code> (passed to <code>mapping</code>).</p><div class="admonition is-info"><header class="admonition-header">Note</header><div class="admonition-body"><p>Normalizations are computed withing groups. For example, in the case of <code>normalization=:pdf</code>, sum of weights <em>within each group</em> will be equal to <code>1</code>.</p></div></div></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/transformations/histogram.jl#L68-L100">source</a></section></article><pre><code class="language-julia hljs">using AlgebraOfGraphics, CairoMakie
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class="is-active"><a href>Analyses</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href>Analyses</a></li></ul></nav><div class="docs-right"><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl" title="View the repository on GitHub"><span class="docs-icon fa-brands"></span><span class="docs-label is-hidden-touch">GitHub</span></a><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/master/docs/src/generated/analyses.jl" title="Edit source on GitHub"><span class="docs-icon fa-solid"></span></a><a class="docs-settings-button docs-navbar-link fa-solid fa-gear" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="Analyses"><a class="docs-heading-anchor" href="#Analyses">Analyses</a><a id="Analyses-1"></a><a class="docs-heading-anchor-permalink" href="#Analyses" title="Permalink"></a></h1><h2 id="Histogram"><a class="docs-heading-anchor" href="#Histogram">Histogram</a><a id="Histogram-1"></a><a class="docs-heading-anchor-permalink" href="#Histogram" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.histogram" href="#AlgebraOfGraphics.histogram"><code>AlgebraOfGraphics.histogram</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">histogram(; bins=automatic, datalimits=automatic, closed=:left, normalization=:none)</code></pre><p>Compute a histogram.</p><p>The attribute <code>bins</code> can be an <code>Integer</code>, an <code>AbstractVector</code> (in particular, a range), or a <code>Tuple</code> of either integers or abstract vectors (useful for 2- or 3-dimensional histograms). When <code>bins</code> is an <code>Integer</code>, it denotes the approximate number of equal-width intervals used to compute the histogram. In that case, the range covered by the intervals is defined by <code>datalimits</code> (it defaults to the extrema of the whole data). The keyword argument <code>datalimits</code> can be a tuple of two values, e.g. <code>datalimits=(0, 10)</code>, or a function to be applied group by group, e.g. <code>datalimits=extrema</code>. When <code>bins</code> is an <code>AbstractVector</code>, it denotes the intervals directly.</p><p><code>closed</code> determines whether the the intervals are closed to the left or to the right.</p><p>The histogram can be normalized by setting <code>normalization</code>. Possible values are:</p><ul><li><code>:pdf</code>: Normalize by sum of weights and bin sizes. Resulting histogram  has norm 1 and represents a PDF.</li><li><code>:density</code>: Normalize by bin sizes only. Resulting histogram represents  count density of input and does not have norm 1.</li><li><code>:probability</code>: Normalize by sum of weights only. Resulting histogram  represents the fraction of probability mass for each bin and does not have  norm 1.</li><li><code>:none</code>: Do not normalize.</li></ul><p>Weighted data is supported via the keyword <code>weights</code> (passed to <code>mapping</code>).</p><div class="admonition is-info"><header class="admonition-header">Note</header><div class="admonition-body"><p>Normalizations are computed withing groups. For example, in the case of <code>normalization=:pdf</code>, sum of weights <em>within each group</em> will be equal to <code>1</code>.</p></div></div></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/transformations/histogram.jl#L68-L100">source</a></section></article><pre><code class="language-julia hljs">using AlgebraOfGraphics, CairoMakie
 set_aog_theme!()
 
 df = (x=randn(5000), y=randn(5000), z=rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], 5000))
 specs = data(df) * mapping(:x, layout=:z) * histogram(bins=range(-2, 2, length=15))
-draw(specs)</code></pre><img src="fa5f9d15.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) * mapping(:x, dodge=:z, color=:z) * histogram(bins=range(-2, 2, length=15))
-draw(specs)</code></pre><img src="6c612bb8.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) * mapping(:x, stack=:z, color=:z) * histogram(bins=range(-2, 2, length=15))
-draw(specs)</code></pre><img src="3b295117.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) *
+draw(specs)</code></pre><img src="458bfb8d.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) * mapping(:x, dodge=:z, color=:z) * histogram(bins=range(-2, 2, length=15))
+draw(specs)</code></pre><img src="be714405.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) * mapping(:x, stack=:z, color=:z) * histogram(bins=range(-2, 2, length=15))
+draw(specs)</code></pre><img src="49476469.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) *
     mapping((:x, :z) =&gt; ((x, z) -&gt; x + 5 * (z == &quot;b&quot;)) =&gt; &quot;new x&quot;, col=:z) *
     histogram(datalimits=extrema, bins=20)
-draw(specs, facet=(linkxaxes=:minimal,))</code></pre><img src="0e74e7f5.svg" alt="Example block output"/><pre><code class="language-julia hljs">data(df) * mapping(:x, :y, layout=:z) * histogram(bins=15) |&gt; draw</code></pre><img src="bc4f809b.svg" alt="Example block output"/><h2 id="Density"><a class="docs-heading-anchor" href="#Density">Density</a><a id="Density-1"></a><a class="docs-heading-anchor-permalink" href="#Density" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.density" href="#AlgebraOfGraphics.density"><code>AlgebraOfGraphics.density</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">density(; datalimits=automatic, kernel=automatic, bandwidth=automatic, npoints=200)</code></pre><p>Fit a kernel density estimation of <code>data</code>.</p><p>Here, <code>datalimits</code> specifies the range for which the density should be calculated (it defaults to the extrema of the whole data). The keyword argument <code>datalimits</code> can be a tuple of two values, e.g. <code>datalimits=(0, 10)</code>, or a function to be applied group by group, e.g. <code>datalimits=extrema</code>. The keyword arguments <code>kernel</code> and <code>bandwidth</code> are forwarded to <code>KernelDensity.kde</code>. <code>npoints</code> is the number of points used by Makie to draw the line</p><p>Weighted data is supported via the keyword <code>weights</code> (passed to <code>mapping</code>).</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/transformations/density.jl#L42-L55">source</a></section></article><pre><code class="language-julia hljs">df = (x=randn(5000), y=randn(5000), z=rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;, &quot;d&quot;], 5000))
+draw(specs, facet=(linkxaxes=:minimal,))</code></pre><img src="0ee3738f.svg" alt="Example block output"/><pre><code class="language-julia hljs">data(df) * mapping(:x, :y, layout=:z) * histogram(bins=15) |&gt; draw</code></pre><img src="c09cfd3f.svg" alt="Example block output"/><h2 id="Density"><a class="docs-heading-anchor" href="#Density">Density</a><a id="Density-1"></a><a class="docs-heading-anchor-permalink" href="#Density" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.density" href="#AlgebraOfGraphics.density"><code>AlgebraOfGraphics.density</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">density(; datalimits=automatic, kernel=automatic, bandwidth=automatic, npoints=200)</code></pre><p>Fit a kernel density estimation of <code>data</code>.</p><p>Here, <code>datalimits</code> specifies the range for which the density should be calculated (it defaults to the extrema of the whole data). The keyword argument <code>datalimits</code> can be a tuple of two values, e.g. <code>datalimits=(0, 10)</code>, or a function to be applied group by group, e.g. <code>datalimits=extrema</code>. The keyword arguments <code>kernel</code> and <code>bandwidth</code> are forwarded to <code>KernelDensity.kde</code>. <code>npoints</code> is the number of points used by Makie to draw the line</p><p>Weighted data is supported via the keyword <code>weights</code> (passed to <code>mapping</code>).</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/transformations/density.jl#L42-L55">source</a></section></article><pre><code class="language-julia hljs">df = (x=randn(5000), y=randn(5000), z=rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;, &quot;d&quot;], 5000))
 specs = data(df) * mapping(:x, layout=:z) * AlgebraOfGraphics.density(datalimits=((-2.5, 2.5),))
 
-draw(specs)</code></pre><img src="30e04b9f.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) *
+draw(specs)</code></pre><img src="1759b9f6.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) *
     mapping((:x, :z) =&gt; ((x, z) -&gt; x + 5 * (z ∈ [&quot;b&quot;, &quot;d&quot;])) =&gt; &quot;new x&quot;, layout=:z) *
     AlgebraOfGraphics.density(datalimits=extrema)
-draw(specs, facet=(linkxaxes=:minimal,))</code></pre><img src="f895c242.svg" alt="Example block output"/><pre><code class="language-julia hljs">data(df) * mapping(:x, :y, layout=:z) * AlgebraOfGraphics.density(npoints=50) |&gt; draw</code></pre><img src="36e86e46.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) * mapping(:x, :y, layout=:z) *
+draw(specs, facet=(linkxaxes=:minimal,))</code></pre><img src="52c72d73.svg" alt="Example block output"/><pre><code class="language-julia hljs">data(df) * mapping(:x, :y, layout=:z) * AlgebraOfGraphics.density(npoints=50) |&gt; draw</code></pre><img src="33ed711c.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) * mapping(:x, :y, layout=:z) *
     AlgebraOfGraphics.density(npoints=50) * visual(Surface)
 
-draw(specs, axis=(type=Axis3, zticks=0:0.1:0.2, limits=(nothing, nothing, (0, 0.2))))</code></pre><img src="f1523d9b.svg" alt="Example block output"/><h2 id="Frequency"><a class="docs-heading-anchor" href="#Frequency">Frequency</a><a id="Frequency-1"></a><a class="docs-heading-anchor-permalink" href="#Frequency" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.frequency" href="#AlgebraOfGraphics.frequency"><code>AlgebraOfGraphics.frequency</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">frequency()</code></pre><p>Compute a frequency table of the arguments.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/transformations/frequency.jl#L19-L23">source</a></section></article><pre><code class="language-julia hljs">df = (x=rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], 100), y=rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], 100), z=rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], 100))
+draw(specs, axis=(type=Axis3, zticks=0:0.1:0.2, limits=(nothing, nothing, (0, 0.2))))</code></pre><img src="fc45be91.svg" alt="Example block output"/><h2 id="Frequency"><a class="docs-heading-anchor" href="#Frequency">Frequency</a><a id="Frequency-1"></a><a class="docs-heading-anchor-permalink" href="#Frequency" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.frequency" href="#AlgebraOfGraphics.frequency"><code>AlgebraOfGraphics.frequency</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">frequency()</code></pre><p>Compute a frequency table of the arguments.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/transformations/frequency.jl#L19-L23">source</a></section></article><pre><code class="language-julia hljs">df = (x=rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], 100), y=rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], 100), z=rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], 100))
 specs = data(df) * mapping(:x, layout=:z) * frequency()
-draw(specs)</code></pre><img src="cf132ba8.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) * mapping(:x, layout=:z, color=:y, stack=:y) * frequency()
-draw(specs)</code></pre><img src="4a46cb85.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) * mapping(:x, :y, layout=:z) * frequency()
-draw(specs)</code></pre><img src="b49aa99d.svg" alt="Example block output"/><h2 id="Expectation"><a class="docs-heading-anchor" href="#Expectation">Expectation</a><a id="Expectation-1"></a><a class="docs-heading-anchor-permalink" href="#Expectation" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.expectation" href="#AlgebraOfGraphics.expectation"><code>AlgebraOfGraphics.expectation</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">expectation()</code></pre><p>Compute the expected value of the last argument conditioned on the preceding ones.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/transformations/expectation.jl#L12-L16">source</a></section></article><pre><code class="language-julia hljs">df = (x=rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], 100), y=rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], 100), z=rand(100), c=rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], 100))
+draw(specs)</code></pre><img src="9d23f5f7.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) * mapping(:x, layout=:z, color=:y, stack=:y) * frequency()
+draw(specs)</code></pre><img src="33b63072.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) * mapping(:x, :y, layout=:z) * frequency()
+draw(specs)</code></pre><img src="0b73f537.svg" alt="Example block output"/><h2 id="Expectation"><a class="docs-heading-anchor" href="#Expectation">Expectation</a><a id="Expectation-1"></a><a class="docs-heading-anchor-permalink" href="#Expectation" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.expectation" href="#AlgebraOfGraphics.expectation"><code>AlgebraOfGraphics.expectation</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">expectation()</code></pre><p>Compute the expected value of the last argument conditioned on the preceding ones.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/transformations/expectation.jl#L12-L16">source</a></section></article><pre><code class="language-julia hljs">df = (x=rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], 100), y=rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], 100), z=rand(100), c=rand([&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], 100))
 specs = data(df) * mapping(:x, :z, layout=:c) * expectation()
-draw(specs)</code></pre><img src="8084f1f8.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) * mapping(:x, :z, layout=:c, color=:y, dodge=:y) * expectation()
-draw(specs)</code></pre><img src="d62beba8.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) * mapping(:x, :y, :z, layout=:c) * expectation()
-draw(specs)</code></pre><img src="d40b6425.svg" alt="Example block output"/><h2 id="Linear"><a class="docs-heading-anchor" href="#Linear">Linear</a><a id="Linear-1"></a><a class="docs-heading-anchor-permalink" href="#Linear" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.linear" href="#AlgebraOfGraphics.linear"><code>AlgebraOfGraphics.linear</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">linear(; interval=automatic, level=0.95, dropcollinear=false, npoints=200)</code></pre><p>Compute a linear fit of <code>y ~ 1 + x</code>. An optional named mapping <code>weights</code> determines the weights. Use <code>interval</code> to specify what type of interval the shaded band should represent, for a given coverage <code>level</code> (the default <code>0.95</code> equates <code>alpha = 0.05</code>). Valid values of <code>interval</code> are <code>:confidence</code>, to delimit the uncertainty of the predicted relationship, and <code>:prediction</code>, to delimit estimated bounds for new data points. Use <code>interval = nothing</code> to only compute the line fit, without any uncertainty estimate.  By default, this analysis errors on singular (collinear) data. To avoid that, it is possible to set <code>dropcollinear=true</code>. <code>npoints</code> is the number of points used by Makie to draw the shaded band.</p><p>Weighted data is supported via the keyword <code>weights</code> (passed to <code>mapping</code>).</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/transformations/linear.jl#L39-L53">source</a></section></article><pre><code class="language-julia hljs">x = 1:0.05:10
+draw(specs)</code></pre><img src="eb3d97c5.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) * mapping(:x, :z, layout=:c, color=:y, dodge=:y) * expectation()
+draw(specs)</code></pre><img src="f3a3cb9a.svg" alt="Example block output"/><pre><code class="language-julia hljs">specs = data(df) * mapping(:x, :y, :z, layout=:c) * expectation()
+draw(specs)</code></pre><img src="be1cde74.svg" alt="Example block output"/><h2 id="Linear"><a class="docs-heading-anchor" href="#Linear">Linear</a><a id="Linear-1"></a><a class="docs-heading-anchor-permalink" href="#Linear" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.linear" href="#AlgebraOfGraphics.linear"><code>AlgebraOfGraphics.linear</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">linear(; interval=automatic, level=0.95, dropcollinear=false, npoints=200)</code></pre><p>Compute a linear fit of <code>y ~ 1 + x</code>. An optional named mapping <code>weights</code> determines the weights. Use <code>interval</code> to specify what type of interval the shaded band should represent, for a given coverage <code>level</code> (the default <code>0.95</code> equates <code>alpha = 0.05</code>). Valid values of <code>interval</code> are <code>:confidence</code>, to delimit the uncertainty of the predicted relationship, and <code>:prediction</code>, to delimit estimated bounds for new data points. Use <code>interval = nothing</code> to only compute the line fit, without any uncertainty estimate.  By default, this analysis errors on singular (collinear) data. To avoid that, it is possible to set <code>dropcollinear=true</code>. <code>npoints</code> is the number of points used by Makie to draw the shaded band.</p><p>Weighted data is supported via the keyword <code>weights</code> (passed to <code>mapping</code>).</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/transformations/linear.jl#L39-L53">source</a></section></article><pre><code class="language-julia hljs">x = 1:0.05:10
 a = rand(1:7, length(x))
 y = 1.2 .* x .+ a .+ 0.5 .* randn.()
 df = (; x, y, a)
 specs = data(df) * mapping(:x, :y, color=:a =&gt; nonnumeric) * (linear() + visual(Scatter))
-draw(specs)</code></pre><img src="286b83e4.svg" alt="Example block output"/><h2 id="Smoothing"><a class="docs-heading-anchor" href="#Smoothing">Smoothing</a><a id="Smoothing-1"></a><a class="docs-heading-anchor-permalink" href="#Smoothing" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.smooth" href="#AlgebraOfGraphics.smooth"><code>AlgebraOfGraphics.smooth</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">smooth(; span=0.75, degree=2, npoints=200)</code></pre><p>Fit a loess model. <code>span</code> is the degree of smoothing, typically in <code>[0,1]</code>. Smaller values result in smaller local context in fitting. <code>degree</code> is the polynomial degree used in the loess model. <code>npoints</code> is the number of points used by Makie to draw the line</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/transformations/smooth.jl#L19-L26">source</a></section></article><pre><code class="language-julia hljs">x = 1:0.05:10
+draw(specs)</code></pre><img src="a754be35.svg" alt="Example block output"/><h2 id="Smoothing"><a class="docs-heading-anchor" href="#Smoothing">Smoothing</a><a id="Smoothing-1"></a><a class="docs-heading-anchor-permalink" href="#Smoothing" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.smooth" href="#AlgebraOfGraphics.smooth"><code>AlgebraOfGraphics.smooth</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">smooth(; span=0.75, degree=2, npoints=200)</code></pre><p>Fit a loess model. <code>span</code> is the degree of smoothing, typically in <code>[0,1]</code>. Smaller values result in smaller local context in fitting. <code>degree</code> is the polynomial degree used in the loess model. <code>npoints</code> is the number of points used by Makie to draw the line</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/transformations/smooth.jl#L19-L26">source</a></section></article><pre><code class="language-julia hljs">x = 1:0.05:10
 a = rand(1:7, length(x))
 y = sin.(x) .+ a .+ 0.1 .* randn.()
 df = (; x, y, a)
 specs = data(df) * mapping(:x, :y, color=:a =&gt; nonnumeric) * (smooth() + visual(Scatter))
-draw(specs)</code></pre><img src="917d91f4.svg" alt="Example block output"/><h2 id="Contours"><a class="docs-heading-anchor" href="#Contours">Contours</a><a id="Contours-1"></a><a class="docs-heading-anchor-permalink" href="#Contours" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.contours" href="#AlgebraOfGraphics.contours"><code>AlgebraOfGraphics.contours</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">contours(; levels=5, kwargs...)</code></pre><p>Create contour lines over the grid spanned over x and y by args 1 and 2 in the <code>mapping</code>, with height values z passed via arg 3. </p><p>You can pass the number of levels as an integer or a vector of levels. The levels are calculated across the whole z data if they are specified as an integer.</p><p>Note that <code>visual(Contour)</code> only works in a limited way with AlgebraOfGraphics since version 0.7, because the internal calculations it does are not compatible with the scale system. With <code>visual(Contour)</code>, you can only have categorically-colored contours (for example to visualize contours of multiple categories). Alternatively, if you set the <code>colormap</code> attribute, you can get continuously-colored contours but the levels will not be known to AlgebraOfGraphics, so they won&#39;t be synchronized across facets and there will not be a colorbar.</p><p>All other keyword arguments are forwarded as attributes to the underlying <code>Contour</code> plot.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/transformations/contours.jl#L15-L32">source</a></section></article><pre><code class="language-julia hljs">x = repeat(1:10, 10)
+draw(specs)</code></pre><img src="3769cea3.svg" alt="Example block output"/><h2 id="Contours"><a class="docs-heading-anchor" href="#Contours">Contours</a><a id="Contours-1"></a><a class="docs-heading-anchor-permalink" href="#Contours" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.contours" href="#AlgebraOfGraphics.contours"><code>AlgebraOfGraphics.contours</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">contours(; levels=5, kwargs...)</code></pre><p>Create contour lines over the grid spanned over x and y by args 1 and 2 in the <code>mapping</code>, with height values z passed via arg 3. </p><p>You can pass the number of levels as an integer or a vector of levels. The levels are calculated across the whole z data if they are specified as an integer.</p><p>Note that <code>visual(Contour)</code> only works in a limited way with AlgebraOfGraphics since version 0.7, because the internal calculations it does are not compatible with the scale system. With <code>visual(Contour)</code>, you can only have categorically-colored contours (for example to visualize contours of multiple categories). Alternatively, if you set the <code>colormap</code> attribute, you can get continuously-colored contours but the levels will not be known to AlgebraOfGraphics, so they won&#39;t be synchronized across facets and there will not be a colorbar.</p><p>All other keyword arguments are forwarded as attributes to the underlying <code>Contour</code> plot.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/transformations/contours.jl#L15-L32">source</a></section></article><pre><code class="language-julia hljs">x = repeat(1:10, 10)
 y = repeat(11:20, inner = 10)
 z = sqrt.(x .* y)
 df = (; x, y, z)
 specs = data(df) * mapping(:x, :y, :z) * contours(levels = 8)
-draw(specs)</code></pre><img src="34ff133d.svg" alt="Example block output"/><pre><code class="language-julia hljs">x = repeat(1:10, 10)
+draw(specs)</code></pre><img src="a4e6b58e.svg" alt="Example block output"/><pre><code class="language-julia hljs">x = repeat(1:10, 10)
 y = repeat(11:20, inner = 10)
 z = sqrt.(x .* y)
 df = (; x, y, z)
 specs = data(df) * mapping(:x, :y, :z) * contours(levels = 8, labels = true)
-draw(specs)</code></pre><img src="f8b4196f.svg" alt="Example block output"/><h2 id="Filled-Contours"><a class="docs-heading-anchor" href="#Filled-Contours">Filled Contours</a><a id="Filled-Contours-1"></a><a class="docs-heading-anchor-permalink" href="#Filled-Contours" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.filled_contours" href="#AlgebraOfGraphics.filled_contours"><code>AlgebraOfGraphics.filled_contours</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">filled_contours(; bands=automatic, levels=automatic)</code></pre><p>Create filled contours over the grid spanned over x and y by args 1 and 2 in the <code>mapping</code>, with height values z passed via arg 3. </p><p>You can pass either the number of bands to <code>bands</code> or pass a vector of levels (the boundaries of the bands) to <code>levels</code>, but not both. The number of bands when <code>levels</code> is passed is <code>length(levels) - 1</code>. The levels are calculated across the whole z data if the number of <code>bands</code> is specified. If neither levels nor bands are specified, the default is <code>bands = 10</code>.</p><p>Note that <code>visual(Contourf)</code> does not work with AlgebraOfGraphics since version 0.7, because the internal binning it does is not compatible with the scale system.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/transformations/filled_contours.jl#L57-L71">source</a></section></article><pre><code class="language-julia hljs">x = repeat(1:10, 10)
+draw(specs)</code></pre><img src="8213157a.svg" alt="Example block output"/><h2 id="Filled-Contours"><a class="docs-heading-anchor" href="#Filled-Contours">Filled Contours</a><a id="Filled-Contours-1"></a><a class="docs-heading-anchor-permalink" href="#Filled-Contours" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.filled_contours" href="#AlgebraOfGraphics.filled_contours"><code>AlgebraOfGraphics.filled_contours</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">filled_contours(; bands=automatic, levels=automatic)</code></pre><p>Create filled contours over the grid spanned over x and y by args 1 and 2 in the <code>mapping</code>, with height values z passed via arg 3. </p><p>You can pass either the number of bands to <code>bands</code> or pass a vector of levels (the boundaries of the bands) to <code>levels</code>, but not both. The number of bands when <code>levels</code> is passed is <code>length(levels) - 1</code>. The levels are calculated across the whole z data if the number of <code>bands</code> is specified. If neither levels nor bands are specified, the default is <code>bands = 10</code>.</p><p>Note that <code>visual(Contourf)</code> does not work with AlgebraOfGraphics since version 0.7, because the internal binning it does is not compatible with the scale system.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/transformations/filled_contours.jl#L57-L71">source</a></section></article><pre><code class="language-julia hljs">x = repeat(1:10, 10)
 y = repeat(11:20, inner = 10)
 z = sqrt.(x .* y)
 df = (; x, y, z)
 specs = data(df) * mapping(:x, :y, :z) * filled_contours(levels = 3:2:15)
-draw(specs)</code></pre><img src="eb698414.svg" alt="Example block output"/><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../visual/">« Visual</a><a class="docs-footer-nextpage" href="../../layers/operations/">Algebraic Operations »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+draw(specs)</code></pre><img src="eb698414.svg" alt="Example block output"/><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../visual/">« Visual</a><a class="docs-footer-nextpage" href="../../layers/operations/">Algebraic Operations »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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"/>
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diff --git a/dev/generated/penguins/7c21969c.svg b/dev/generated/penguins/219d4ae3.svg
similarity index 96%
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q89JsSJhi1OhfbACAPzfLly1Uqla+vb1PTub+eiKkAAABddOP3H3blXcoLsTkcO0ty7h6W3HJZsryx/pot77+WvrOLK7RVTQ0fZx8uazRdMmBot5Y3M2rK5q5/0+mMfbSy0KDRXRzh0s+NFY31Sza+22CzdH7rBVQ1NZisTYsGDnelDQBSUU+YMGHJkiXDhg1zdycAAAB9zMrc1O8KTrlYJLOm7I30Xee/rLdalmz81zdnT3SriENxvJa+8+btH19o96N2PbRvVY3F3K2JWvnz4e/O1FW6UuHxg+vb3RW5W946uTu1qsjFIgDkoY6PjzcYemSvNgAAAM/27LEtQuo8n7rV5rArimJ3OK7ctGJ3yRnn6nyUfejRA+u6ePPmwszVeWnOTXReg83y+y7P2FZ1k3lFRoqLPSiKYrXbX07d7nodAJLggGkAAABnFNTX7HI2T7Zy1lS9rzRPUZT3MlNcXJ597vjW/WV5XbnzlTQxue7TnCNF3dniuKWVucddedy3pS/OHOvWSjIAmRFTAQAAnLG9OLsrR6R20fdFWXWWxscOrnexjt3heChldae3mW3Wb/NPujhXM6vdvu5sunNjtxVnC+lBUZSKxvpjlYWiqgFwL2IqAACAM1zZOamt9OqSD7IOFNbXuF5qW1H2vtLcju/ZX5bnxAk6F7K9+LRzA1MrRf4eiv0TAeBG52KqSvXDTuIOnpcAAADoTIGISHneWVPVp6ePiKr2aU4npU5Ul4iaS1GUE1VOVitsEPl7mF9fLbAaADfSursBAACAPqne2XNc2lVnbTxaIeyZ1a2FWR3fcKGzWJ1TYq5zbmC9VcyLqc2cPlkHgGx46BcAAMAZXhqRH/c7HIrAp3DTqoo73k9I4FyKC4ndS60R2IZezQIM4CGIqQAAAM4I0RsFVtMLDb0NNkvHh5EahE5n1Ho5N1Ds72GIwUdgNQBuREwFAABwxlD/MIHVIrz9BFZTOnsCVux04QZf5wYODRD5ezjUP1RgNQBuREwFAABwxtSwWIHVRgVGCqymdLY8OywgXOBcwwKdrCbw99BLrZkYGiOqGgD3IqYCAAA4Y3RwZJxvsJBSPlqv2ZFDhJRqplapAr28O7hhYmiMwOd+Z0QMdm7g4pgRonqYG5Xo4+yzxwBkQ0wFAACeqc7S2KP1VYrqzqSpQkrdMGTcRcEDhJRqFuMT2HEKNWi0C6OThMylVasXDRzu3NikgLA5UQlC2hD1ZwFABuf+/uLcVAAA0Ndl15avzkvbVJBxqCK/1GxqtFm1anWYwXd4QPjcqIRFA4ePC4kWO+P9I2e8dXL3mbpKV4r46fR/GrcwWG9M9A/NqCkT0ti08LhO7/nViItX5qa6Pte1cWMiXXjT9flJSyaufqnjfYk7NT087srYka5UACAVVlMBAECfd7A8f/mW9xO/eOb+vStX56WdNVU32qyKoljt9sL6ms2FmY8dXD9+1YtzvvnHhvxTAuf11uienbTYxSJ/GHtJlNFfEfoE7KKBwzq9Z15U4hKXZ/TW6J6asMiVCuNCom9PnOxKBS+15tWpy1SKqvNbAfQRxFQAANCHORTHy2nbp6555fOco52uyG0tylq44e2fbftI4PPAy+PGPD72EqeH3xg//qFRs5p/ffvQyWqVgKwVavBZFju6K3c+P/kKf53Blbn+MPaSWN8gVyooivJa8lWXDBjq3FiVonp7+nLh6+QA3IuYCgAA+iqL3bZk47v3711psdu6PuqDrAPJa18t7/BY0W7587iFD4yc6cTA5XFjVlx83fllwJGBkbe5tq7Y7C/jLu3iZkKJ/qEr59/q9F5KN8VPeOSiOc6NbclLrflszs+md+FB5Va0avVryctuTpjoeg8ApEJMBQAAfZJDcdyx87O1eelOjD1eWbTou3cErqm+MPmKr+bdEqI3dvF+g0b70pSln8z5qU6taXn9yfGXBnW4Q2+nxgYPuCNpStfvnx0Z/+mcnwV4dXtN9bbEye+2yNguCvAybL3s3j+OXaBRdfWn01jfoC2X3nPvsGlCGgAgFc0TTzyhKEpVVVV+fn7zJW9v7yFDRG6JDgAAINxr6TufObrZ6eH59dUVjQ0CXwcdFhB+W+LkEINPalVxBwHYV6e/e1jyR7N/uiA6qW3G89XpZ0bGf3T6kMVud6KHAUb/DQvvDO5yWm6WFBB23eCxKWV5eaaqrtwf5OW9YsZP/m/MPCGPKJ+nVqlmR8XfMGScSqUcqSiwOi74OxDnG/zEuAXvXnxdvH+IwAYAyEPVvK9vTk7Ovn37mi8FBwfPnz/frV0BAAB0pLKpIfHzp118cFetUu1b8usJIQNFddXMbLPuKsnZXJh5uCK/1GwqM5sCvQwR3n4jAiPmRCbMihziq9N3XGFVbur13/+n3mrp1rxRRv8NC+4cFRTpXNsOxfFt/sm/H9v6fVHWhd7yHegTcN/wGXcNm+riG62dKm+s31KYuaUwM6OmrLihtt5mCTP4RBj8JoXFzI1KmBQa0/VFVwB9ETEVAAD0Pb/bv/Zvx7a4XufymOFr5t/uep1WKpsa/HV6V6LUieqSG77/76Hy/C7evyB66L9nXO/KwTDnlZlNW4uyUsryihpqy8wmP50+3OCb4B86NyphZFAEG+oC6AXnYuqZM2f27t3bfCkoKOiSS5zfsA4AAKBH2Rz26E/+UtxQ63opjUqdd+1jzefBuOJEdcmq3NTNhZmHKwrKzCabw65SVKEGn+GB4XOjEhbHjHBizbbJbvsg88Bzx7eeqC7p4LaZkUN+O2rOophhBEgAHoOYCgAA5NJkt3n9eGOhVg6Un5246iVR070/8/qb4ic4PXxP6Zm/Htm0Ni/doXR0HM7MyCG/v2jewuik7ta3Oxz7ynI3F2buLjmTZ6oqNddpVepIo3+MT+CsyCHzoxKHB0Y43TwAyMnJ/ccBAABEyawpW5OXvrkw40hFQanZ1GCzeGt0oQaf0UFRc6MSLo8ZPiwgvOX9+8vyBM6eUprnXEy12u1PHtn45JGNtgtv9nPetqLsbUXZN8VPeCP5qk7fTW1JrVJNDYudGhbrRIcA0EcRUwEAgNuklOU9fXTTytzUVnv2NNgseaaqPFPVurPpv0lZc3nM8Ecvmjvtf+dqZtWWC+whs7bMiVFmm3Xxd//aVJjRrVEfZB04XFGw9bJ7ursZLwD0K2ySBgAA3MBqtz9xaEPymle/OnP8QvvKNnMojjV5aRevff3Xe79ustsURalqbBDYSVVTt6vZHPabtn3Y3Yza7Fhl4WXfvWOyNjkxFgD6CWIqAADobWabdeGGt/90eENXHpdt5lAcr6TtmPvNP0zWJnuHb4F2l7X7J5S+cHzb5zlHnZ5xX2nuI/vXOj0cADweMRUAAPQqm8P+020fbi7MdGLszpKcpRtXeGt0Avvx686booqilJjr/np0o4uTvnli97HKQheLAICnIqYCAIBe9czRzV+4sBS5qTCj4wNauivON7jd601221lT9aHy/N0lZzJrys4/pvvUkU3VTWYXJ7U57I8fXO9iEQDwVOe2UFKpfjhoy9Hh+yEAADjDbrE3Vam0PiotO8f0a8UNtX87tsXFIt8XZQtpptnooKiWXxY31K7ISPmu4NSukhyzzXr+ukpRjQ6KnBOV8F5mipB5155NLzHXhRt8hVQDAE/CTr8AgJ5irT5pzv26qXCTpfyAvbGi+aJKa9T6J3kNmKePvlQfNVdRVB0XgYd54vCGWkuji0Usdptapep446WumzcgofkXJea6xw+ufz9zf8t0ep5DcRytLDwq7kldq92+qSDj+iHjRBUEAI9BTAUAiGcpP1B35Elz3iqlzQY5Dmu9peKQpeKQ6fhzuqCLfEY/4j3kesJqP2G2WT/MOiSklKiMOioosnk1dVNhxs+2fVRQXyOkbBellOURUwGgLd5NBQCI5TClvVy2Ntmc+3XbjNqKpfJo1bYbKzZcam8o6p3m4F67SnJqLK6+1SnW42MuURTlw+xDC759u5czqiL6AFgA8BjEVACAOHZr5eZlNfvuV+yWrg9qLNhQtmayre5Mz/UFSaSU5bm7hR+ZFh63fPBF6/NP3LL9Y1HLs93ixJGtANAfEFMBAKI4qnffZc5d6cRImymvYsN8e0Ox8J4glayaMne38INgvfFfF19ba2m8dfsnFrvNLT04cWQrAPQHvJsKABCjPuPd+ox3nR5urcms3n1P0NwvBbYE2VS5fI6LKN4a3cp5tw4LCL9v71dFDbXuasNfZ3DX1PKrsZi/L8reWpiVXVte1mhqsttC9T4R3r7J4XFzoxLi/ULc3SCAHkRMBQAI4LDU1h583MUi5tyvGgs26AcsENISJGTr7HXl3hHvF/L+jOuz68of3LfKvc8hx/oGuXF2aWXWlL2avuPtk3va3XJ5RUaKoijjQqLvHzHjxvjxGhXPBgIeiHNTAQACmNJesjcIOKij9uDjxFQP5qP1cm8D3hrdz4dOmRU55Prv/5NrqnJvM4qijA6KdHcLcrHYbY8dXP/c8a2dvip8qDz/5u0fv3Fi139n3cjKKuB5+PwJACBAfcYKIXUsZfusVWlCSkFCg3pl8VCtan2+kUalnhI26E/jFmYtf1SjVi3f8oEMGVVRlPkDhrq7BYmUmU0Xr3v978e2dH07q72lueNXvrgmj780AE/DQ78AAFdZazJsdadFVWvM/1YbOEJUNUildxYP7Q7HtwvvtDvs1U1mX60+zOAzLDDcX2dwKI47d37+zqm9vdBDV4wPiU4KCHN3F7Kot1qWblqxrzS3uwNrLOblW97/dsGdMyOH9ERjANyCmAoAcJWlbL/IauUHBFbzbNm15TtLcgrra0rMdV5qTZjBN8YncFbkkDCDr7tba9+syHi1StULR79UNNb/ZPDYVhf/fPg7eTKqoiiPj73E3S1I5GfbP9pVkuPcWLPNunTTimNXPjzQJ0BoUwDchpgKAHCVrS5bYDVrbabAah6p1Fz3evquf2fuz6mraPtdlaIaExx1V1LyLYmTDBq5/qGP9PabE5mwqTCjpydKr2p9uFFmTdlTRzb19LxdNzcq4cpBo9zdhSw2F2Z+kXPUlQpVTQ2PHlj3wczrRbUEwL3aeTeVLZQAAN1ib6oWWM0htJqHsTnsfzq8Ie6zv/7p8IZ2M6qiKA7Fcbii4J7dX8R++uTHpw/3coedemzs/F6YpbihrtWVB/atanLT4ahthRl835p2jbu7kIVDcTyw15nzllv5b9bB/W7dtxmAQGyhBABwmdhTRhyyZAnZFNbXzP7mH08c2lBvtXTl/hJz3fVb/3Prjk/aPdXDXWZHxi+L7fFVRJO1qeWXp2pK1+al9/SkXWTU6lbOuzXBP9TdjchiR3HO0UoB+4Q7FMebJ3e7XgeADIipAABXqXQi34RU6fwEVvMY1U3my757Z0dxt7eqei8j5bqtH1jtUhxY2uzlKVdGG3v2HULvHz/t/En2YYcixcNiSQFhOxb9Mjk81t2NSGR1XqqwUrlpkvxBA3DRuZiqarN1OwAAXaTxGSSymm+cwGqewWq3X/7dO0cqCpwbvio39aGUVWJbckWMT+C3C+8I1ht7bopQg0/LL7cVi3x92ml+Ov0tiZOGB0a4uxG57CrOEVWqxFyXWVMuqhoAN2I1FQDgKl3wGJHVgi4SWM0zvH1qz05nN0Ft9mraTieO+ug5IwMjv7/s3pGBPXU+zbCA8JZfprXZUcktai2Nj+5fN2HVi0crBDzj6jFO1ZQJrHayukRgNQDuQkwFALhKFzxWrQ8RVc1rQG9ssdOHVDU1/PHQty4WcSiOB/ZJtKCqKMqooMiUK3790KhZPlov4cUvjhh8/tcOxVHUUCt8CqelVRUnr32lF7Y77hMciqO80SSwYJlZZDUA7kJMBQC4TKUxDL5OSCWNb5xXWLKQUh7j35n7hfzkvaskZ0/pGdfrCOSt0T03acmZax/7y/hLJ4bGiCo7KihyiN8Pn5vk1lXZHXK9sVhvtSzb9N6B8rPubsT9LHa72HN0pdowDIDTiKkAAAF8R/9OpRXwqtjkAzAAACAASURBVKHvmMcVFf82/cjnrp0n2dJnp4WVEihEb3xszPyUJb++Z9g0IQXv/V+dBpvloX2rh3/5d0VRFMl24ai1NF69+d8Nto42bbba7WfqKveV5m4syDhSUZBf74FnNXmpNRqh/5c3anUCqwFwl3ZO/ebcVABAd2l8YnxGPlh35ElXiuiCxxkTbhHUkYcw26wC3ymVZCehC/nrhMs+yzni4tLx8MCInw+doihKWlXxdVs/OF5ZJKg78c7UVT57bOsfxl7S6nqdpfHfmfu/yT/xfVF2naWx5bcivf3mRCUsix11VexosenOjSK9/QQm8Cijv6hSANzIQ/6CAwC4ne/oR3VhU5wervIKCJjxHkuprWTWlDXZhR0km15VLNfDrz8W5OX91/GXuVJBo1K/NnWZTq3JqCmb880/ZM6ozf5+bEtlU8P5L8026x8OfTvo0yd/ueertXnprTKqoihFDbUfZR+6dssHSV/87b2MlN5ttqeMELr1sdhqANyFnwYAAGKotMbg+Wu1AcOdGav2Cpr9OXv8tlXYUCOwmsnaVNVkFlhQuDuTpj40apbTw1+ccsXcqISihtr5698sMdcJbKyHmKxNn54+3PzrE9UlyWte+cvh71oG1wvJqi2/dccnyza9V95Y38M99rgZLfa7clG8X0hPH8kLoHdwbioAQBi1PiTksm2GmCXdGqXxjQ2+dLOeDX7bU2/t6N1Fpwo2iS0o3LOTFv9y+PTujtKq1c9NWvKr4RcrivKblDW5pqoeaK1HfJt/UlGUY5WFyWtePdzNo3G/zj2evOaVPhHIO3Dt4LGiSl0Tx0ddgIdgNRUAIJLaEBo0b2VA8psan843blVpvH2G/yps6VGv8G7Hkn7CS60RW1CvbmdbCqmoFNWrU5d9PudnwfqubsoV5xu89bJ7m5dh95bm/jfrYE82KFhKWV5Wbfkl375d1YVF1LYyasqWblwh/OOM3pQUEHZp9DDX6xg02ruS2Ccc8BDEVACAcCpj0l3hV2cFznjfMGip2iuw9ffVOq/w6X7jnwxffsZ/yisqHVueXFCowUdgNa1aHeBlEFiw51wdd1H2Nb9/ZuLlHe+Ik+gf+ta0a05c9dvp4XHNV545ulnm92/byjfV3Lnzs2IXTnbdU3rmr0c2Cmyp970weYnO5U9kHho1e7BfsJB+ALidqnlf3/z8/J07dzZf8vPzu+wylzYwAADgBw6btTbL3lBibyxV6fw1hnCN72CVztfdbfUNVU0Nwf/9g6jcNdQ/7OTVjwgp1WtsDvuh8vzNhZlpVcWlZlNVU0Ow3hhu8B0bMmBuVMLIwMiWN9daGkM//IPATaf6Cr1Ge/zKhxP8Q93diPN+f2Dd00c3Oz18qH/YgSvu99XpBbYEwI1kf/IHANDnqTRa/6GK/1B399EnBXp5DwsMT68qFlJtekSckDq9SaNSTwyNmRja+TPkiqLsKsnphxlVUZRGm/XZ41vfmnaNuxtx3l8nXFZQX/PvzP1OjB1g9P924R1kVMCTtPPQL+emAgAgjyUxI0SVunygM/sw9yHHKgvd3YLbfHr6iKUvR3SVovrn9OW3D53c3YHDAsI3LLwzzpfHfQGPwrupAABI7edDp2jVAv69jjYGLBaXeOV01lTt7hbcpqqpYX/ZWXd3odgdjrSq4o0FGR9mH/os58j3RVlZteVdHKtTa96Zfu0ns28KM3TppQCNSn1XUvL+K+5v9ew3AA/AQ78AAEgt0T/0nqRpr6bvcLHOMxMX6TUe/u++SfrjdnrU4Yr85PBYt0xtdzjW5KW9n3Vga2Fm26NcY3wC5w1IvCtp6tSwztu7dvCYxTEj3stMeT19Z9oFHncP8vL+yZBxD42aFe8XIqB7API5988V56YCACCtJ8Yt+OT0YVeOx5weHndj/HiBLclJyLJz35VTV+mWedfkpf12/9oO3qDOM1W9l5HyXkbKzMghL01eOi4kuuOCRq3u3mHT7h02raC+ZmtRVnZteam5rtFmCzP4RHj7JYfHjguOVvOzK+DRPPxTVQAAPECw3rj2ktvnrn+z1tLoxPB4v5Av5t6sUjz/x/ogL293t+BONRZzL8/YaLP+JmXNa+k7u7gZ9bai7OS1rz49YdH9I2d05T/IAUb/G4aMc7lNAH1Pv/7QEQCAvmJiaMxX824J7H4Mi/cL2bDwzghvv57oSjZ9+kQW16l795MIm8N+w/f/fTV9R7cOTGq0WR/ct+rR/et6rjEAHoCYCgBA3zAvKvHIlQ/NiBjc9SHL48bsv+L+If3m/b3JoYPc3YI7BXgZenO6O3d+/uWZY86N/duxLS+nbRfbDwBPQkwFAKDPGOQTuPWye1fNv63jrWhUimpxzIgdl//y0zk3ObEA23eNDIqI9Q1ydxdu05ufR6zJS3s3Y58rFX6bsiajpkxUPwA8TDvvpnJuKgAA0lKrVEtiRiyJGZFeVby5MHNXSc7Z+uoys0mjUkd4+8b6Bs2KjJ8blRBtDHB3p26gUlS3JU7+46Fv3d2Ie0wIGdg7E1nstodTVrtYpMlu+23Kmq/m3SKgIQAehy2UAADok4YHRgwPjPjF8OnubkQuD46c+dbJ3QX1Ne5upLdFePuNCR7QO3N9nnP0ZHWp63W+zj1+vLJoVBCnngJojYd+AQCA5/DV6Z+asMjdXbjBT+PH99oZLR9mHxJV6uPTh0WVAuBJzsVUzk0FAACe4eaEifePnOHuLnqVv87w8KjZvTOXzWHfVpQtqtqmggxRpQB4ElZTAQCAp3l+0hU/jZ/g7i56zx/HXRLZW2cOnamrFHhA67HKwm6dZwOgnyCmAgAAT6NWqT6Yef2/Z/zEqNW5u5cetzhmxH3De2/1ONdUJbCaydpU0dggsCAAz0BMBQAAnulnCROPLH3ozqSpeo3H7hk5I2Lwp3Nu0qp77ye6Okuj2IK14tZmAXgMj/1bGwAAIME/9K1p1/xl/KXfnD2xuTDzUHl+qbmu1GxSq1QWu83d3blEpajuSJry0pSl3poLrhifrC5NKcstNZvKzCajVhfp7Z/oHzolbJAruV2jEhyJtSqN2IIAPADnpgIAAA8XbvC9OWHizQkTz1+58fv/Ctyuti21SmXvyR+oZkYO+ePYBXOjEtr9bk5dxUup2z/POZpfX932u0atbnZkwq9HzFgQPdSJqQO9vJ0Y1WFBg9iCADwAq6kAAKDfuSh4QI/G1IvD47YVnxZbU6tWjw8ZODcq4cpBo6aEDWr3njpL44P7Vq3ITLHa7ReqU2+1rDubvu5s+viQ6H9dfN3Ybp62Gu8f0r2+OxTh7eer0wssCMAzEFMBAEA/cqaucnNh5qnq0p6b4o6hUxbHjBAYU/Va7e7LfzUuOLrj245UFFy75YNTNV39n3awPH/qmlf+PnHxfSMu7noz4QbfWN+gM3WVXR/SgQvlbQD93LmYyrmpAAC0z25VVCqF1+f6uHqr5d2Mfa+l7zjZkwFVrVI9NGrW0xMW1VstRq2u3moRUrbRan36yOZP59zUwT0ZNWULvn27xFzXvco266/3ft1kt3br2NVLo4e9dXJ3tya6kIXRSULqAPAwrKYCAPAjDltDY97axsJNTSU77PUF9sYKRVHUhjCNMdorcpZX1DxD9KWK2vOPOfEkX+QcvWf3l6XdjHDdlegf+kby1fMHJCqK4qfT/2TwuHcz9okq/lnOkb2lsy609lhirpu3/s3uZtTzfpuyNsYn8LrBY7t4/62Jk4TEVKNW1/VJAfQrxFQAQH/hsDep1F4d3dBUbUp/xZT+mt1c0upbdnOp3VxqqThsSntZ4xPjM+IB47C7VRrBe8lAOKvd/ss9X4la+muXQaOdFh53z7BpV8WOVrd4PO3P4xd+cvqwydokaqJ/ndp7oZj66P51eS4cZ+pQHL/a8/XC6KSubI+Ua6r66syxAJ2h2uWDZH47ek6I3uhiEQAeiZgKAPBYlvKDjfnfNBZutlWftDeWOWyNKq2P2jtCFzJeHzVPP3CxxmfgDzeX7av8/gZbbVanZW2mvJqUB+tP/TNw1ke64DE9+b8ALnEojrt3f/6vU8KWNFsZYPR/c9rV8wcMbfdImGhjwKMXzX3s4HpR0609m97u9UPl+e9lprhYvNRc95fDG5+fvKSDe8rMpodSVn+UfUjIWT6DfAJ/M2qO63UAeKTeOwwaAIBe03h2Xfk3M8tWT6g9+FhT4WZbfb7D1qgoisNqstVmm3M+r959T8kXQ6q232ytTlcUpSHrg7J107uSUc+zVqeXr01uzBcWQiDck4c39lxGjTYG7F5835KYkR0cW/roRfP8xG1jW1Bf0+4BMy+kbhNy+M1bJ3fXWhov9N3NhZljVj7/fuZ+IRnVT6f/ct4tRi0PzwNoH+emAgA8isNSW5PycP2ptzu/1W5pyHrfnPOJYfD1Ddn/UezWbs9la6jccnXwgg1e4dPbKd9UaSk7YDcX281lKp2vWh+q9YvXBo1UFLYt7A3ZteVPHd3UQ8VnRAz+z6wbB/kEdnqn2dbt/646kFFTFm0MaHnFYrd9nXtcSHGTtembsyeuHdzOAwIbCzIu/+6dJhEBVVEUo1b35dxbJoQM7PxWAP0VD/0CADyHvaG4fP1sa/WJrg9x2BobMt9zekaHtb5q67Vhy06odH7nrlhq6k++1ZDzqaX8kOJo/WO92jtCP+ASn+H36UInOT0puuKR/WvFRsRmoQaf34ya/dCoWRpV54+k1Vkbhaw9nlfZ2NDqysHy/LoLL4F219airLYxdV9p7pWbVojKqCMDIz+afePooCgh1QB4KmIqAMBDOCw1FRsXdSujCmGrL6g79rTf+KcUh63u2DN1x591NLXzZGYze0NxQ9Z/GrL+4xU1N2DqG9oATuPoEWdN1V+dEbPG2Mxbo5seEbcsdvQtCZO6/qiq1W4X2IOiKE1t1vyPVxYJrJ9WVdzqis1hv33np0I2gor1DXpg5My7k5L1Gn7+BNAJzk0FAHiI6t33WsoPumVqU+qLhthra/bd11S8vYtDmgo3l62e4D/lVWPirT3aW//05ZljNoeYiBig89666O7hARFOhCujtqOdpZ3gq239pmthQ43A+m3fff3Hid2uJ2G1SvV68lW3J07WqTl/GECXsIUSAMATWEr3NGR/6K7ZHTZzxXcLup5Rz42ymqp33mZKe7mHuurPdhSfFlWq2tLgpdY6twBo0GjDDL6iOlEUZZBv67dh68UdeKMoSom5tuWXNof9maObXS9rdzhSSvPIqAC6jpgKAPAENfsfURR3bgFoN5c6N7Am5UFz7ldim0F6deuHV12q1uZR2K4bFRQpqg29RpvoH9bqotjsV2dpKm+sP//ltqLsdvcWdsLnOUfFvqYLwLMRUwEAfZ61JqO7K5kScdird93dweuscEJRQ23nN/VKtdmR8aLaSA6LNbRZ1A3R+4iqryiK3eF4usX2yFuKunFEU8dqLOb9ZWdFVQPg8YipAIA+rzH/W/cupbrIbi6pPfpXd3fhUcTu8VtvtTg99idDxqoEHUH0kyFj215M9A8VUvy89zJSzm/qe7SiQGDlY5WFAqsB8GycmwoA6PMsZSnubsFV9Sde9xvz+PlTbeAivVpTJ66aQXvu5yWr3Z5SlpdZW1ZmNpmsTcF6Y6S336TQmJgLH6A61D/sqrjRX+QcdbGHaGPAT+MntL0+JWyQWqWyi/vhrbyxfndJzqzIeEVR8kxVosoqinKmrlJgNQCejQ3BAQB9nq1W2KOJ7uKw1pvzVnsPucHdjXiIMINvy3csXa+2pTDz1fQdGwsyats7pDREb1w0cPhTEy4b2F5efXbi4rV5aS4u8P5t4uU+7e0bHKw3JofF7izJcaV4K7tKzjTH1DqLyP2Z2v2tA4B28dAvAKDPszeJXPNxl6aiLe5uwXMkBbTeash5DuWvRzbOXf/mV2eOXyholTfWf5B1IObTJ8esfCG1svV+S4P9gp+asMiVFq4YNPKG+HHtfsvucEwMjXGleFunas7tB6ZRi/xBUSu0GgDPdu7vC85NBQD0YQ5P2EHUWpnq7hY8x7TwOGG1VErXDw49WlEw6utnb9r2Yau10wdGznx41Gzn5p8SNujDWTe2+4LrwfL8i75+/uU0wfuHlZtNzb8I9DIILBvo5S2wGgDPxsdaAIA+T6UVudmpu9hMue5uwXMsHTTKjbP/J+vg2JXPZ9eWt7z490mX3zCk/RXRDiT6h66ef3u7j/u+mr4jec0rqVVdjdBddz5jx/uJ3J8pQfRuTwA8GDEVANDnaXwGubsFAexWgZv+eCCzzXrWVJ1aVVRYX9PpCZxJAWHzByT2TmPtOlldOm/9Wy1PsvnHid0fZh/qbp2MmrI7dn5qc9hbXf/HiV337fm6qWdOIm36X0ydGDpQYNnJoh9OBuDB2EIJANDnaYNGK7lfubsLV6nUOne3IB2bw74qN23d2fTNhZktFydVimpUUOTcqIRlsaNmXeBg0pemLB278gWrvXXA6zU5dRWLv/vXnsX3adXqj7IP/WqPk/+JrsxNvW/P168nX3X+yqrc1F86W60rChpqmn9xafQwRVkppGaCfyirqQC6jpgKAOjz9FFz6o782d1duErtFezuFiRidzjeObX378e2ZP340dlmDsVxrLLwWGXhy2nbJ4XG/GHsJYtjRrS6Z2Rg5D1J015N39Er/bbvQPnZN0/uvnbwmLt3feHKmTFvnNi1ZNCIS6OHKYrSYLPct/drgSfQtJVTV9los+o12qSAsClhg/aWCngc/ZaEiV25rcRct7kwc3NBxqmaslJzXYPNEujlHWHwmxg6cG5UwvSIwV5qjevNAJAf56YCAPo8r4gZGp+BNtNZdzfiEo1/grtbkEWJue6W7R9/c/ZEV25OKctbsvHdm+InvJF8la9O3/JbL065Is9U9XXu8Z5ps0v+eOjbvaVnaixmF+s8uG/1vKWJOrXmhePf9/QBpBa7bV9Z3oyIwbWWxupGVztXFCXS2+9XIy7u+J7jlUXPHt/6Ufahdp/oXp9/4skjGyO9/X49Ysbdw5LZjQnweKymAgD6PpXGd/Tvqvf80t19uMQrbKq7WxDMYrftLc1tfl63rNFktlqD9cYIb98pYYPmRiUOMPq3O+p0bcWMda/n11d3a64Psg6kV5dsvvRuvxZJVaNS/3fWjdds+XcXE29PqGis/2/3X0ltK72q+LOco9fGjXktfafr1TqVWlk0NWzQsk3vnagpcb3a3yct9tddcNNgq93+5JGNTx7Z2PYV3FaKGmofPbDuxdRtK2Zct2jgcNcbAyAtYioAwBMYk+42nXzLWnmsl+dVG6Pt9flCSumjLxNSRwYF9TWvpG1/6+SeqqaGtt9tDlozI4f8ZtTsy2OGtzxqpcxsuuy7f3Y3ozbbX5a3dNOKby75uV7zw483Rq1u7SW3v5i67fcHvmn88SExFxJu8Csx13Z+X5eJek7tw+yDA4z+Lbdl6jn59dWvpe/cVJjheqm7kpJvip9woe/WWMyXf/evHcWnu16wxFy3+Lt3/zJ+4f+Nme96ewDkxLmpAACPoNIETn9HpTU6XUCtD1HaO5rywgO0fuP/6j/hb07P2JIueIwutEsv70nOoTheSt0e//lTfzu2pd2Met62ouwlG9+9eO3rOXUV5y/evfuLk9WlTs++pTDz78e2tLqoUlQPjpyVdc2jD4+a3cGanqIoyeGxX827ZWiApDv9fF+UvbUws3fmKm+sf/LIRtfrjAyKeD152YW+22S3Ld/yfrcyajOH4njs4PqXUgUfGAtAHqymAgA8hC50ctDclZUbL3fYm7o7VmOMDlm001p9omb/w9bKzl9l1IVNCZj8si5siuKwm9JftpSlONXyD/wExV33qrdart36/tq89K4P2VWSM3blCx/OunHRwOE7S3K+zHF1Pfzpo5tvTpw0yCew1fVoY8CzkxY/Of7S5ueQs2rLS811dZbGMINvlNF/cmjM3AGJzaMe3LfKxR56SJ2lcX95L72AfayysKKx3vU6eXXVjTabUdv+CYj37125If+U08UfSlk1NmTA7Ats9QygTyOmAgA8h37A/KD5a6u2/8zeUNj1UV7h0wJnfqjxjdX4xoZFL2g8u64+Y0VT0VZ7Y+s9ZtXekfqoecahd3hFzjp3SaX2n/RC+fpZSmev1XXAMOhKffRCp4dLwmK3Ld/y/rqz3ciozaqbzFdv/ve3C+587OA3DsXV52MbbJa/Hd3c8viWlvQa7czIITMjh3RQwWTp9sccvSbPVNU7E52oFvBKqqIoNRbzmry0awePafutIxUFb5/c40pxu8Pxy91fHV76oFbdfgwG0HcRUwEAHkU/YH7Y0iM1KQ82nP5YsXfyLqLaK8hn1MO+o36rqM//g6jSD7xcP/ByxWG3Vp+wmfLs5lJFpVIbIjS+g7T+Q9sW8Yq4OGDKq9V7fuFcw9rAkQEXv+vcWKk8uG+VExm1mdlmXbLx3VpLo5BOPso+9NKUpTpnTy5xemAvaLBaemeiMrNJVKnNhZntxtRH9q/tdM+kTqVWFX2QdeDWxEku1gEgG2IqAMDTqA1hgTM+8Bv3F9OJ1xvPfmOtSm11g0rtpQubYoi9ypj4c5XOt/0qKrU2cIQ2sPVpnO0yDrvXZi6pO/xnpZuLgVr/xOAF36q9gro1SkJHKgr+cWK3KxVcP7LlvMqmhv1lZ5PDY50bHqw3OreHUy/Q9cqyoUpRub6sfd7RyoK2F0/XVrjyuG9Lb5/cQ0wFPA/npgIAPJPGN85/4rPKxGftDUXWmgy7ucTeVK02hGoM4dqg0Sqtj9jp/MY+4RU+rWr7zfaGoi4OMcQtD5j2ttqr9VuUfdHDKatdXxkT6GC58zE10T/0WGU3HhrvTSF6X0UR8zhuB+J8g0632NfKRe2e8rr2bLqoJLy3NLfUXBdmuMDnTQD6Jh7lBwB4OLV3pFfEDEPs1cbE2wwxV+jCpgrPqM30AxaELT3ie9GjnSZPr6i5wQs2BM3+1DMy6onqko0FAk4uESi71vmUNTXMyXzbC0YHRfbCLJPCYgRWa/dZ7r2luaLqOxTHHnHVAEiCmAoAgDBqQ7jf+KfCl+cGzfncOOwXutCJGp+BKo1ebQjVBgzXxyz2n/RC2JXHQxZu0g+4xN3NCvP1mc73Ru5lrjxCfHnMcIGdCBSiN14andTTs4wMjJwUKjKmalTt/LR5qsb5Y4fayhBaDYAMzj30y7mpAACIotL5GWKvNsRe7e5Gesnu0jPubqE1uwtvMI0IjJgeHrezJEdcO2IsiE6aOyDRT6cXtddUW2qV6rXkZdm1rfe4dkWgl3fbi+XitmhSFKWisaMTegH0RaymAgAAl5yo6vG3JbvL30vvyvDnJi9RKdJ9gn9zwkQfrdfNCRN7bornJ10xOzI+3i9EYM12q1nsNoFTmG29tAEygF5DTAUAAC4pNde5u4XWBvu6FLSmhsX+NH68qGaEuDhi8ILooYqi/GncwmC9UXh9laL6y/hL7x85Q1GUscHR7T6p65zJ7b3patR6iaqvKIqP0GoAZEBMBQAALmns7Hza3jcuJNrFCm9Nv2Z6eJyrfQg6PEGtUr00ZWnzAm+w3vjncQvF1P2fcIPvmktue2zM/OYvA7wMTu+T3NbC9t6nHWD0F1VfUZSBPp6wFRmAloipAADAJQaNzt0t/Ii/zjAlbJCLRbw1upXzb7soOMqlKoIeHH5+0hUTQgae//IXw6c3L3u6Lsro/8zEy09d/btFA3+0cZSok0jj/UJmRAxpe31koMgti0cERgisBkAGnJsKAABcEuntV9FY7+4ufrB88EVeao3rdUL0xn2Lf/3gvlVvnNjlejWnLRs0qm0ofX7SFSZL0z9P7u1mElYF6Q2DfYMjvP0mhsbMjUqYFh7X7u/VzQkTX0zddryyq4cAX8jTExep29unc1bkkFfTd7hYvJmP1kvs1sQAZNBOTAWAfsLucGzPrtiYUbotqyKnsr7M1GSzO0J9vKIDDNPjgucmhi5ICvPS8NQJ0IlhAeFpVcXu7uIcL7Xmd6Pniqqm12hfT77qhiHjb93xibtOPVkWN7rtxUa7VavWdCujRnr7fTjrxjlRCV25WaNSPzdpyaUb/tmNCdqYGTnkmriL2v3WpQOHidqy+MrYUUI+lQAgFWIqgP7Ianf8OyXvua1ZJ0pab/2SX23Orzbvy616cVv2AH/Dr2cOvndanK+evy2BC5oRMeTLM8eElPJSa5tce9P14VGzE/xDhTRz3vSIuFNXP7KtOPsPB9fvKM6xOexi63fMT9d61+KzpurLvvtnd5c6rQ6Hl6Ybf5UtjE56fOwlfzn8XbdmOW+gT8AHM2+40IbJPlqve4dN+9uxLc4VP0+tUj0wcqaLRQBI6NwqAeemAug/cisbZr+x6+efHmmbUVspqDE/siZ99HPf78qp6J3e0BNqLOYT1SU7ik/vKD59srq0rsfOnOy3lsWOavfBTicsH3xRjAvb4SSHx/7f//YBEiujpiy9qnh0UNSy2NHjQwaODIoYHhg+PEDEK5GdvWsV6xPU8suqpobLv3vHicdxy8x1iza8c7iioOtD/jxu4T3DpnV3IkVRwg2+GxbcOajDP8r/GzM/yuWNlG5NnNTyrV0AHkPV/CZqeXn5pk2bmi/pdLply5a5tSsA6Cm7cioW/XNftbl7h+zpNOoVPxl743hX9w5FbzpWWfhexv5NhRnHKgvtLbZd0KjUY4MHzBuQeFvi5KSAMDd26Emu2vzeV2eOu1hEo1IfvOIBnVo9Y93r5d1/2XVEYMS2Rb8IEXpYS6PN+kHWgRdSt6W76almL7Wm8sYnjdpzm1Q5FMf89W9tLsx0umC0MSB12W8CvAxdH/J+5v5f7Pmq65/vzI1KeH/m9dHGgE7v/Dzn6HVbP7A7uytKjE9gypJfR3j7OTccgMw0TzzxhKIoDQ0Np0+fPndJ0JoiGgAAIABJREFUoxk+fHhHgwCgb0otql3w1p6qhm4fBG93OFanFU8YGJgY5tMTjUGsoxWFt+745OF9a3aX5hQ31Lb6EdihOAobanaW5LxxYteRioKxwdGhBv5YXTUpNOatk3tcfBr2zqSptw+dHGbwvW7w2JSyvDxTVdfHLosd9fW8W8Vm1G1F2XPX/+M/WQfLzCaBZbvl4oghdyRNOf/lp6ePPHt8qysFay2NVoe9+QjWLhoTPODauDEma1NqVZGtw0g5IjDi2UlLnpu0pIsxeERgRJjBd93Z9K43c16Al+G7hXfF+7t0QC4AaRFTAfQXZqt95mu7CmrMzg23OxyrUotvnhjjb+A9Vam9n7l/2eb30qtKOr3ToSgnqkv+dWqfr04/NUzYKZH9U5DeaLXbvy/KcrrCAKP/J3Nu8tF6KYoS6OV9U8IEH53+WGWRydrU8cCh/mEvTln61IRF3lqR5+I8c3Tzzds/rmpqEFjTCY9eNHfi/7axtdhtSze953pL+8vyfho/IUjv3fUhwXrj0kEjbx86Jd4/xFurq7M0mm2W5sga6OU9Oijqxvjxfxq38LnJi8cGR3frVbJJoTFhBp/NhZnW7nzGkegfumb+7WNDBnR9CIC+hYd+AfQXT2/K+P26Ey4WuWnCwPdvGCekH/SEh/atfiH1eycG/nHsgifGLRDeT7/iUBw/3/H/7N1nYBRV9zDwOzPbd7Npu+m9hwAJvUMIHTV0RAV7wy5YH/VV//KIFRQril1EAUV6D51AgAAhhfReNrvZbLbXmfdDeCKGlN2ZuyXJ/X2SZObsMYRkz9x7z9n2fWkOjXvFbN6JuU+k+XWuOow26+by3H11RSeayjttAw4TemcEx8+PGDwvIgXWydgOnxedefrcDrgxaQgTehcvfKVjx+/OmoL5R3+AEvm11Olrhs9mGKTNbOSz2FC67F5VNjx0euullrper2Th+ANxo9aNzhTd0lkKQZD+BM1NRRBkQFAZLGuP0j/N1eHX3LrV6bGpIUzbfiDO8P61Y/RqVADA21cOBfJF9LrFIO0wgH09fhEAwNFKNVTgvW3qvbfWqAAAHsF6KGH0QwmjSYqSGTRyo05lNvjzBAE8kZQngpP3LXbVFDxz7m8nBXfIqpQpgpuWiHfVFMCKvLumgHmZ6tAB156l+oVczHzucEPJp4WnjjWW6a1dHM0IFXgvihryfMrkKJEfrNdFEMRjoa1rCIIMCFuvNmhMjKZctKMo8ENO7SfzU5iHQuC6qmx47dJ+JhGeO79zWkh8ghg1VaKPjRPfTVw6IzThyey/lPb1QFoYOeSbCUt6PVOKY1iwQMy8MWyvTDbrqpxdVK/td11ie1XecymTOma6ZMurYUW+1tqkthjFbGh1JhQzQhJmhCSYSdt5eU2pWt6oV+usZilPFMATjZSEo4ZnCDKgoDIVQZABYX9R7ycV7XSguBkAVKZ6nBcv7GHYv8dM2l66sOfvaQ/ASmnAWhaddntY8qaSnM+LTpdrWrq8hkuwMsNTXh461dOmiXxSeKq7nF3vbHPVtsq8pdGpAIBGvbq4DdrPMQpQJW3yjlOvHoWDE5MCoycFRrs7EQRB3OlGmYrmpiII0r/l1DjQMrRnxc3aVoPFlw+zWQvC0Imm8sMNJczj7KwpyG2pH+6PJg8xJWJzn0uZ9FzKpHJNy/HG8jKNQmbQGG1WCVcYJPAaI40YHxDFJzzuHxFJUV8WnXF3Fv/yf1cOL41OzWosu+fEZtqDW7qkMLmtfTGCIEiv0GoqgiD9n95sa9TQbPDbpTKFblR4T2PrERf7tTwXVqjN5bmoTIUo1ss/1qvPjAzJbamrcWQKjgsUqJo2FJxefXGXlWS0WeBWRhuEcxAIgiBOgrs7AQRBEKdTGS1we8PRmLyKONXRxlJYoY7AWJVF+qizzdAOf0LkjBoVACAgONBjIgiCwILKVARB+j+rDXI3FLMN/ltGhLY2s7FSo4QVrbBNZiZtsKIhfct1eIc/IXJGjQoACBZ4OSMsgiAIFKhMRRCk/xNyIIz1u5kXF52Y8CDV2laI0awkWedh2z4Rl2k2at2dgouwcSJOLHF3FgiCIN3qokxFc1MRBOln/AQcEdTCMsKHDzEawpDGaoIc0AI5INJXmAbMcc3RknAPbGGFIAjSAa2mIgjS/2EYGBQoghVNzGOFozLVk0BvVY+j7vcDFY8YKBslFkUNdXcKCIIgPblRphoMBvfmgSAI4lRT46Btb0uPlRA4KmM8iDeHBzegmA05INJXBPIHxHFNXw7/3riR7s4CQRCkJ6yioqK6urrWVpgHexAEGTgoCsi0JoXOrDJYfPlsiZAT6MV1d1JdWJYW+n5WGZRQd6aFQImDwBIl8sMARgE4J1Y4OBEm9IYSCulzkrwD3J2CK7w1bJY/V+DuLBAEQXrCunbtmrtzQBCk77HYyK1XG/cUyrJKFc3afx3kC/LiZsRL7hgUuDg1hOUxq45poeI5SQH7rzNt4xnrL1w0NBhKSggsQhYn0VsKq0frMP9QAkMnYgaoiYHR7k7B6UZJwlcmjXN3FgiCIL1Av4kRBHEMSVGfna6MfTdr+ebc3y/Xd6pRAQBNGtNvufV3/Zobvzbr67PVntOUbd28FDbB9IfeJ/NTuCz0k9PjzApNhBVqJrxQSJ+T6hcc6+UPJRThkSecY738d09/kI1Dbn6OIAgCHXqzhSCIA2Qa022bcp7ZkV+r6v1Ae5VSv/LPvBkbsxvURhfk1qukANFbsxKYRFg+Iuz2QYGw8kEgug/SQTscw1bEjoASCumLMIA9lTwRSqhJgTFQ4kA0wj/syOzHBsj5WwRB+jpi+fLlYWFh0dHRTU1N7R/CcXzQoEHuTQtBEA9U3WoY++mp3Po2h+6qVOr/zGtckhoi5rm/heakGP82o/VcNZ3T+Blxkj/uHeE525iRmwULxAWqpkKVjGGcBxNGPxA/CkpKfQtpbDbV7TPVHzLV7THVH7S25tm0lTjbG+f4uDs1VxshCdtadbXFpGcSJNbLf9/Mh38svWCwWWAlxtCssMQjsx/35wrdnQiCIIhdWJmZmRiG3dxCCc1NRRDkVgqdedY35+ra6KyLVrcaZn9z7uRTE3z57h/T93HmIB8+e83hEivpwM+6B0dHbFgwGG339WTrRmfuqyvSW+lXBV5s7jvDZkNMyfNRVr2h7Ed9yTcWZR7oqgcV4RUriLtfkPQEzvVzfXpuwcaJDWMWzD28yUaR9CLgGPb5uAVBfK830mY8d34n3PRoe3nwVA7a64sgSN9BvPXWWwAAo9FYUVHR/iEMw9BqKoIMNCRFXWvSZFe3nqpoOV2pLG/RK3RmfwGnozB7bHvesTIF7fjNWrNSb74jJQhSvvRhGJYe6z8tXnK5Xt2k6Xyw9lbRfoJvlgx9dVo8h/G5VsSpvDm8GC//HTX59B61Ehi+eco9o6URkNPyYIbyn5VH5hqrtpGGblehKXOruemYvvhLgOGcgPHAI89bQhcr9g/ie+2pLaR3+ydj5t8TOxwAMFoScb2tuYDxIj991I2xwgSGrxuTySfc/6AQQRDETu7fg4cgiAsodOZjZYpjZS1VSr1cZzZYbBIhJ0TMGxvp6yNg7y9qPlIiV+jMne4icGxkmM+yYSFpIeLNuXUMc/jufO2jYyNHhnvEHsIJ0X65qyYfLpFvzK4+VqZQ6jsvwQk5xMRov/tGhS/xpH7FSM/uihkmM2hX5exydDgNjmEbxy9eGDnESYl5GspmaDv7qKH8V3uvt2g1l141NRz2nbIF5w2IkS2PJo41kdYXcnabSZv9d7Fx4oORtz8z6MbpVhzDfp50l8psOFRf4pw0e/O/H11jpBG+HL57ckAQBKEFa9/iq1KpDh061P4hHMcXL17s1qwQBIEmr1H90bHy3680WGw0N7ABANgEZrFBOA4wf3DQjgc87uAfSVHXGjU1rQa5zmQlqQARN1jMHRbqjZZP+6hD9SX3ndrSZNDYeX0g3+uHiXfOCUtyalYehLK1Hl9qrP6Lxq0s3yH+c04OnAOrFxW1y0/+Vtwmt+fiBLH0l8l33bogbyXJNVePrLl6hPYuYuY2jl/8aOJYd706giAIDahMRZB+y2Ql3zxY/OGxctJjDpyzCVz29kxPOKGK9G+NevV/847+UJrT81FVLzb3kYSxrw7NkPAGUF+ZtrOP6Uu+oX07N3ia38xDYMCMlrVR5LaqvHX5Jy4oaru7ZqQkfFXK5CVRqSy82y/LOXn1mqtH9tVed3Spn7k4sSR//gtcAm2gQxCkL0FlKoL0T81a0+xvzl92sCuvC+x4YNT8we4/oYoMBHKj9vfKK1kNZaebKxVGXcfHA/lekwKjM4LjlsUMG2g7Ic3y7Ja9E7rslmQ/n4k/8OPuh5RRn9GoVx9rKr/W2igzaDQWk4jNDeSJhvgGZwTHBQvEdgYpVMl21hRkNZbmttQrmTUTtt/u6Q/eHo56jiAI0segMhVB+iGNyTr1y7OX6jyuRgUAvDkz4a1Zie7OwunKFLoimVahM6uMFj8+WyriDgsVB4t57s5r4DLarHKjFgOYlCccwMtKlGLPaIviIsMoOD84YGEJxhZByWnAspC2Rr06evu7Tt3w8vKQqe+NvM158REEQZxkwP6qRpD+7N7fLntmjQoAqFS6aAHBLYqbtZ+eqtxXJKtuNdz62eRA0byUoKcnRYegetXleAQrXDhQTlR2x9x0gnmNCgAgDY2Gyi2ChEeYhxrI2DjRZjHaVaNS/zRDcsj98aPWjpxL504EQRB366JMRXNTEaRPO1qq+Du/yd1ZdEtttLo7Bado0pie/uvaX9eaenjTWSTTFsnK1p+seHhMxHu3JYu46EEh4lKGqm1Q4lgAXl2+gxu80I8r8Blgu6bhUpntm0TteI3KI1jvjbztmUETMXoFLoIgiLuhN0kI0q9QFHh+Z4G7s+hJvxy7eLBYft+WyzI7prACAExW8oszVYdL5L+vGDEs1NvZuSFIB7PsFO17LQA/AOKOUdFnQEQtEIMmALavBQCwcWK0JHxaSPziqKFDfIPhJTsgWB2ZdmMnFo4vjhz6RtqMQT6B0IP3dddaG482lJ2Uldfr1TKDhqKoAL5XqEA8KTAmIzhumH+ouxNEEOQfqExFkH7ldKXyWqPa3Vn0RMzrb21+j5TIM7/LMTs476dErkv/8uzxJ8ajShWxB2VRm+oPmBqzLPLzNoOMNCkwgovzAllesZygdG7oTLb/iN5C2GxtxTRe2grwH6hhG8GIBuB162ctpO1Mc9WZ5qp3rhyZHZb4ZtrMMbdMZEG6I2Rx4AZ8PW36g3Gjo7384Ibt60iK+rsm/4Nrx87Lazp9qkanugjAzpoCAMAw/9CXhkxdEjWUGDCNrBHEk90oU7F+ucCBIAPPnkKZu1PoRYyfwN0pwHShVjXvhwuO1qjt1EbrbZtyzj87MdwHbZtEukUaZLqCdbqSjZT5XwfOKdJis2htmnJTwyFN7n840nHCIS/xIuZ3G8eooEizo69eB8RPUnMvgN5XmShA7a+7fqi+5IXBU94ZPpuNE46+1gAE97w0ByfeTJ3Zw1CcgUlm0Nx/6o8D9dd7vfJyS/1dx39dL434bco9sV7+LsgNQZAeoJ9lCNKvZFcr3Z1CL4aH9Z/FQ5KiVm7P05vpb9trVBtXefYmbcS9jLW75H+naPM/6FSj3sosz27NWtCaNZ80tXR5AWVzuHtZESWdQa2wp0btYKPI968dW3LsZytJ59nNQBMsEEOc2ZvsE4hq1E4uKGqH/P2xPTVqhxx5zchdn5xoKndeVgiC2ANt+kUQyMpbdJfr1TKNqc1oEXIIiZCbHCBKCxXjLtmzUCLX9X6R+/BY+OSY/vOIetP5GuYdlbfnNR4vb0mP7T9flluRhkZTwxFT41Gbppw0yimrDuf44fwgtmQUNziDHTgRwyFvfewftFfe1lx526EZp8aanZaWS/6zjxNesZ0/5+AXuRp4340taqPodKXeWVPwwOk/fp68DPXv6VV6UOz2qjwooUZKwhr1aglPiJay25WpFXcc+V5u1Dp6o8psyDzyw7E5K4ej06oI4j6oTEUQOKpbDZ+crNhxrbHLSSR+AvasxIDnp8SMCnfuSIwWncOb+lxp3uAgMa+f/NihKPDhMTiP2z/IKuuvZaqlJVeX/4Gh+k9A/qvDs01XB1rzTA2HtHn/xfnBwkHPCpNWYmyxu/L0QPrrX2quvEXjRpuuruXQTMnc0zj/Xw2NcK4fAJidRS8FwLPUHBmgv9D3a/mlWaEJy2N7OzHrqdrMxhNN5eWaFrlRq7WYJTxhIF80ShKR5hcC95njsug0WGXqdyU535XkAABCBOIpQbHTQ+KXRKV6sblQgvc5Rpt1zuFNMoOG3u1qizHzyPdFC18asF9ABHE7rH38TFtb28GDB298CMOWLFni1qwQpC/RmKyrdhb8eKHWSvb+/m9avOSbJakx/k45n2kjKdaLe5wRGQoWjl1ZPSUlqIsuLH3RhVrV6E/o9029GYFjzW/P8hP0r+ZSpFWTt0Z79R1A2bX5E+cH+kz8kRs629l59Qnm5rMt+yfZ+aXrEidoqv/srE4fbN4WadN1biHTpR0g+UmK6bzNUIF38aKXoXcJciobRW6vyttQePq8vMbW1dffnytYFDV09eApCWIprFcctnP9tdZGKNE68eXwVyaNf3noVDF7wM1qfjfv6GuX9jMM8srQjLUj0OBZBHGPLs4woLmpCGK/3Lq24etObjpfY0+NCgA4WqoYtu7Elsv1zkiGwDEuy3MPJj09Mbrf1KgAgONlXZ8ApMFGUicroEXzBKS5VbFvvPbK2/YXWqRBpjxymy7/I6cm1kdQ6pznmdSoAABz0zFj9Z+dPsgJGGfXywPwITWeyau3q9e3fVl0lnkclznRVJ745/vLjv96trmqyxoVANBi0n9TfC75rw8eOP2H2mLf1NMeERi+fkwm8zhdLpO3mg3v5h0d+vfHp2WVEF6i72gx6d/L6/yYhob1BSfrdExPdiAIQo/nvqNFEM+X36SZ/nV2mcKx46Bqo/Wezbk/Xqh1RkrBYg99ZD4q3Of/Zie6OwuY8ptgDv4paKK5M80DUTZD69FMi+KC43eS6osv6Uu+dUJSfYmxartFkcM8jvriy51qXW74HfbceJ4KqwJwjif8XH4RShxnowD11uVD0w5sLNfY9cCIpKgfSy8M27k+twXCM8dpwfEPxo9mGqX7ncjV2tapB776q/oa05foO7ZUXNZY7Bpk3TOTzdpXvocRpP9BZSqC0FTXZpy58VyrwULjXooCj27LO1qqgJ7VoEAR9JjMJQWI9j0yRsTtJ6dS29WpICykdKhVdXGkuY9qy37CLDtN926qLXulRX4OZkJ9jb5kE5Q4Nk25WXbi5o/wIhbgvIBebzwJIqEkAADIb21q0Hv0JOd2/7m0/+0rh7pbQe1OhaZl5sGNhSoIY8C+mbB4YeQQ5nG6YyXJu47/erihxHkv4VH21hbBCrUPXigEQRxyo0xFc1MRxFEv7CpoVNMvVCw28tFtV41WyDMbPLCP7qKhwWeenigR9qXzafbQMZhDcyutydr7RX2BpeWSofxnRiEoW9u5pxhuee27KJuhU23JhKnuwM1/xFgCr+Frer3rGgiElQAAILelDmI0Z/j6ejbtDaItJv3sQ9/S6CXbCYHhm6fc81iiXbuy6TGTtuUnfmszw3y+5rHOyathhbqgqEXTlRDELdBqKoLQcaZSufVqA8MgFS36T09WQMmnw9K0ENc8dArz5vd6zZBg8e8rRmy/b2R/aw4EAACARcD8QrOJfvLTWJ2zinmFaWm5ZKjaCiWfPseqvErZIGxWbGe+ZV1aEP8g27+X7rvVAOZwYzu30bpLs1H7yqW9TCLU6lRv5B5kngmPYH09ftGOaffHetn3tNHxRiLNRu1/8444fFtfIzdqVWZo+1PMpK1K6+kDyRGkX+onb4wQxMU+OFYOpdfYuhMVFhvMx7TRfoJ5KUEQA3ZpXJRv1evT9jw8+vHxkQnSziMrkgJET0yI2v/ImKurp9yZFuLsZNwFbu3dPyp5a1uRWXYSSqgBe0LVqi6DGM2muSUaRvhO20WIonq4S4PBnMCh9uzlu9cv7We+wLip5HyeEk6r3vkRg4sXvbx16oo5YUminkeh0HpQtqHwNO0ZLX2F0gT5DIXSpIcbEEEQe/Sro2II4hoak3X/9WYooZq1ppMVymnxEijR2n2cmbL/erMJ9nbiDgSObZg/mMCx25IDb0sOBACYrKRcZ27Vm/0EHKmIw+kvC4M9i/OnP1Kyi2gSmNHcxVTLaFXqZmbZScqiHoCTVElzK8xopi6iEYIQvxkHlIdn27RVEF+r2xxorPq5SotJ/3P5JeZxbBS5oejUpglLmYcCABAYviQqdUlUqoW0XW6pbzRoZAbNz+UXz8iqmAc32ay/V155dtAk5qE8lpmEfIbCRMI84oEgiJ0GxLtJBIEru6oV4hLo8XLIjZRi/AVvzEiAG/Nmn8xLGRn+ry6gXBYe5s0bEiwO9eYNkBoVADAm0hditLFQo7mLWXEeWizSalEMyAabUN9hU1TXPd5Y3onSeVf5Mfd0+VkhZYaYgydP7NxVU2CywfmC/1V1zdEOTL1i48RoacS8iJRHE8dWaaA9vzhU388bKUEf1du3Zv8iSL+BWighiMPyoc4OKWxi2nvjVq9Nj396YjT0sBgG1t6W/JQTIvdF0+MlsGryUG9eakh/WDa0qUshRrOq+/mb6S5hbJjr6jir29bfGFvsM/lX/zmnuOG3d9o/GgZg9uaN9vKDGA2ukzJo3QFazYZrrU2wonXSqFfX66FN77zk8U2tGAoWiHGob2vDhDBPayMIYqeuN/1SFIUKVwTpTn0bzHMvTppEsn5eCoFjn56qgHKGFgAQIOJ+vyy1fZcvAgDwF3IWDAn64wrTTloAgAdGh8N9U+UupAlmsxzSNBDblhDCcKjRInq+gBM40S9wok1XY27MMstOWjVVpEk+RG87Ce88aaqvxx1Qr9O11evbFEZddjO0frAAgEKVLM3PKf+zlVBb+MgMGq3F1MvB176MT7CjRX6wendJeaIAnidOekOQfg+dTUUQh+ktMI+pwJ1r0oHAsfXzUmYmSh/fnlfTyqgS9hdynpwQ9czEaP9+N1SGoXfnJv+d38TwGHCAiPtCeiyslNyLIumMEe42GryGt30Iywfm8EyW72B7LiOEEfy4+/lx97f/8Y764i8OwWlhFSeWeM5q6vGm8p/KLmY1lNboVM6IX6yC07PgVtCnyKj7dZkKAJgSFAurTE0P6ic/nxGkz0FlKoI4jI3DPH7JYTlxGW1OUkDZqxm/X2n44nTVxTqVjexiaTXUmzc9QRrhw79Up6po0TdrTXqzTSriBou5YyJ8M+IlMxKkQg7hvCT7rhh/wXOTY97PYtSade1tSd68/tDmFwCAEQKY0aBuf+0rCGEYSxwHq98vJziDxl3TguODBeJGPYStv3fHDGMehLnjTeUvXdhzQVHr1FfZW1f09vBZzohMwW5DBf0YradZGp36fWkOrFBQ4iAI4ihUpiKIwyBPIuE7d4mSTeArRoStGBGmMlhOlLdUterlWrPebAvw4gZ7ccdG+iYGoO1M9L0zO/Fao3pfEc1VlMfHRz44updtmX0IIQy1aSuhRROEwQrVt/Cil2mvrmEeB8M5vIj5NG5k4fgbqdOfyP6LYQK+HP4z7u4oayXJNVePrLl6xAWFWV5rg9pidEbLKC/YMTuStFHkRUXdeXlNs1GrMOp4BEvCE8aJJelBsUF8L7gv6kozQhKG+YdebqlnGCdeLMmMSIGSEoIgjkJlKoI4LF4Ks66Lv2XuqJP48NnzBjt9pOpAwybwrfeOnPvt+ZMVDm8wu2d46BcLYe7wdDuWT4pZdhpiNFih+hZh8tO6os8oM9OWOYKklTjXn969jyaO/ep69rVWRrNA3xk+258Lc4HdUSRF3XNi89aqq91eQdGcPtolC0kebShbEGnXRmuHRIpgdgJnYfiwXetajHoSkAarxdZNA4NUv5CnkiesiB3BJfree0Ucwz4bu2DS3i8YLkSvG53JwdFmIgRxj4EyOgJBIBob6dP7RXYb1y8mkQxkQg5xdOW4N2cmELi9b3h5LPyT+Sm/3D28f3RO6sAJnAIrFM71Z/v2qxreATiH5Z3ENAbXT5T6Bu3bCQz/efJdTBYGbw8f9FjiONq3M2ewWaYe+KqnGhXArFHbrTj5G/unl4S/vBq5dc3U/V+9dfnQmeYq5lt2w4XeEAt+K0VWapRqi1FrMXdXowIAriobHjmzLWb7u7trC2G9tCtNCIh6OGE0kwjLotNuDx8EKx8EQRyFylQEcVisvzAJ0kZZFo7NSgqAEgpxIxaOvTUr8eJzk5cNC+25WOWziScmRBW+PPXZSTH9q0QFAABe+G0YC86baV7UYoANxN9Q5uYzil1pFjmzCbQY7j3uK9pLqe3S/EJ2Tn+AR2slbbQ04vf05Syox/gdUqBqStnx0ckmaPNm7KSzmq0kqbdaanSq403lb185NHHv58N2rt9cnksy6LqOASwjOB5invZr0KvnHfnhmXN/98XjrF+NX7QwkubTrvSg2B8nLYObD4IgDsEoigIAaLXaffv2dXx0yZIlaCANgvRg/cmKVTsLmMdZMCTor/tHMY/THZnG1KA2yjQmHMMCvbghYp5UhLr1dqu8RZdVqjhdqaxVGeVaE4ZhUiEnwpc/KcZvWrw00pdvT5BaleFQsTyrTFHQpJFrzRqT1ZvHCvTiDgv1zoiXzEyQ9u+GyeoLq3QF65lGwdnSeVdZ3sn2Xk+RltarZtlp0iAjjc0AZ+NcCSEM5wSls8RxTJNxIXPTMeXhOcxbHItHrROmPA8lpYuK2rtPbC5VK+y/ZUXsiC/HLXRjI9mtlVfvPbXFZLOfx1PfAAAgAElEQVS6K4EuTQ2O+2XyXaECmhM4d9YUzD/6A9yUHPJo4tiN4xd391md1XxKVnFGVtU+6QfHMClPFCnynRwYMzYg0o37Zg02y8qzf/5UdtGhu5ZEpW6auMQZx4wRBLFf12Xq4sWLcfc9BEUQz2e2kYM/OF6q0DEJwiHwvBemOKODUU6N6ocLNVmlihJ55wwHBXpNi5c8PDZiaLAY+uv2Xacrle9nle0plPVwzYRov5enxt0+KBA9xOsBaVbJ/0ogjXImQYSDnhWP/sSeK23aal3hekP5r92NbCVEUYLExwSJj+McmHv1ncHSkttyIJ2yaJgEwVhC8ehPBAkPw8oKAKCxmN66fOibknNaSy/182DfoP8bNtsZhzPtl9VYNvfwJk+rUdtJeMIjsx5LpTVblaSosXs2OLtZcc/Wjpj7ytDOvaOvKBs+vHZse1Wemex6uJqIzb0/buSqlCluHE20peLy6gu77WleLeWJ1o6Y+xCz3cIIgkCBylQEoenv/KYFP1xgEuGF9NgP74B87iWnRvXSnsIT5b2088EwMCsx4MM7Bg0O6sO9HKFo1poe+P2K/a165w0O+m5pav9eEWXIUPm76sTdgO55PJY4zv/2HJzTy5ltymbS5L6qK/oc2DGsFWOLxSPWCpKeoJeSK1A2xe4RFmWPByl7Q4ii/GbsZ36utUtKk/6H0gsH64vPNFfqrf/6mkcIfTJC4pdEDZ0TloRBP+7piHp9W/JfH2h6K6fdSMoTnbntqXixhMa9Z5urJjLuCcQEl2Dlz38h7n/JayymJ7P/+qX8kj33snD8pcFT3x42y11bwU026y/llzaVnL+oqLt1AzOOYcP8Qh9MGP1A/Cg+0U8mhCFIX4fKVASh762DxW8fKqF37/QE6d6HR3MIaP/QKApsOF3x0u4is83eE0Q8Fv7e7cnPTOyHhyTtlFOjyvw+R6Zx7E1tmDdv3yNjhqDl6O7pij5Xn3+axo04T+o/9wxL3MsxPJu2qjVroUV52aHgvIh5PpN+wdie+GhGX7yxLftxhkEwgiddUESIomBk1C0zaavXtcmMGr3VIuEKA/miQMcnlxht1rPNVdnNVTKDVmHScQmWhCuME/tPCYpN8qZ/XP/ek1vsrJrcKM0v5GLmcwSto9evXNz7/rVj0FOy35Ko1K1TVwAAClWyeUd/KHNkNzgAYIw0Yue0B2h8w0DUZjaeba6q1alkBg0FQABPFC70GRcQ6efWrtQIgtwKlakIwsiqnQXrTzrcpSM91n/Pw2OEHJjHdZ79O3/DKTojK/8zLf6/c52y/OLhShW6iZ+dadbSWXgJEfNOPz0h2g+9rbnVjREfuqINmosvOXTMkuU72Dd9a69HUkmjvGXfRKuazhMiTtBUvxn7McJtxyZvRZFm0tDcsnecTV/HPJog6QnvsV8wj+M8l1vqP8w/vqP6mrGbfbkRQp8nkic8njjOm+PYycCryoZhO9e7cbHRfhvHL340cSyNGylAPXJm23clOdBTshOOYVVLXsMANmHvZzU6FY0IQ32DT8x9wodj11F/BEEGMlSmIghTO641Pbz1ilLf+85DAACBYy+kx74zO5H973VUrckq15kFbEIi5Ng/16QDk3VdAMCGBYOfnhhN+/a+SG+2Df7weKVSTztCSpDX5VWT2fDWw/soS0uuqf6AuTHLqi4hjXLKZsQ43gQvkC0ZSYjjTdV/WVqv9RoEwzmCpJVeI9ZiRC9vXinS3LJ3nKUll3bC/Lj7fCb+SPt2KGy6OmP1dlPjUUtzdnenaunBuf6By2QA88RJj61mw2Nntm/reULM/4jZvE/GzHsg3oEOc0+f2/F50Rm62blUAE9Us/R1evNIKUBtKDz94oU9lm7OgjrbR6Myvys9X6Tq6SR/z+aGJe+d8RDElBAE6ZdQmYogEDRrTZ+erPzqbFWrodtilUPgdw0PfWlq7KDAG/udCmWa7Vcbs8oUOTUqg+XGGw4MA7H+wvRY/9lJAZkpgfZUQZfr20auP8Vk2gGHwK+9mJ4gFdKO0OcwLOzbrZuX8vzkGCj59EWm2j3a/A/MslM9XIMRXE7wNMpmtMjPU9YuWo4Roihe1BLhoGcJQag9L6or+Fh94QWaGf+P38xD3JAZDIPQY1WXafPWGCu2UKTZSS8hueMS23+4k4LTdlFRuyjrJ0fX35ZFp/04aZmd5Vzs9rUVGpg1v1Ntm3rv4qihtG8/J69+5eK+E03lEFOyU4pvUEFrE8Mgf2Xc795WWwiCeD5UpiIINDqz7Xi5IqtUkVvX1qw1K3RmHz5LKuImBYgy4iTTEyQBohtbDS/Wqt4+VLK3SNZzaRnpy181JfaJCVGsHtdXJ39x5lSFkmHymSlBOx904mgcj9KkMcW+e1RvZroW4cNnV742zYc/4PptUBZN27knDOW/2nk9RvBEw//LkYyyacptBhll1eI8Kc4L5EhGEV4O1PmkUSH/K54009lqeDO2X6rkjlzXz2U1lP/cdu5JyqJ16qv4TPyBH3e/U1/CUYUq2aR9XyhNdDYvZEak/Dn1vl777iiMOumWN2ll5x4PJYzeNGEpwyDn5TU/lV3MaiwtbmPUW9shOIYxeSraLsbL//rCl9juG1SDIIjnu/GEEk1JRRDmhBzituTA25IDe7imvdHRi7uLLHY0OqpuNTz7d/7m3Lrflg+P9e96qfNoqYJ5jQoA2FXQdKVenRY6INoC/XyxlnmNCgBQGSx/XGl4bFwk81B9iE3foDyQblWX2n8LZTNqLqwWxD/kPeFbwKATrKHsR+Y1KgDAorxqlp3gBE1lHsp+6gsv6Ao+dsELWbVVLngV+ylN+lmHvqFXowIAdtUUvHJp70ej7uj5Mocmu3qC8/Ia5kHGSCPGSCMAAHKjtsmgkRt1BIZ9X3rhZwfHhDqEeY0KAKjQtOyvu54ZkcI8FIIg/VXXjycpGD+DEATpxEZSS3+++NzfBfbUqB1yalSj1p/Ka+x64NvmXAhtV6CH8nA9z0d1V6g+gTK3tR65zaEatYO+9Dv1xZeZvLqh+k8mt99MV/gZrFD20Oa965oaFQDAcPIqdG9dPlSna2MS4ZOCU3nKxp6vaTXTP2fuFo72yO2ZlCca4hucERw3JSgW7yMLDztrCtydAoIgHg3t7EUQF6EosPLPvO15vbzZ6lKrwTLj63Olii6O9mWVQnuvcxReKE9mI6kLNRBW5NqdrYKwlN2HqM4+YlFeoX27Lv9DY/Vf9O6lbAZLC7RZI8b6vQ51IWbCLM/W5L7umtcCAGAYncY8TlLcJv+6OJthEBtFrr6wq+drLKQDz/48gdFm7a7XMUPuaq3kqFMyh5vkIwgyoHjQLzME6d+2XK7/9hz9XV7NWtN9Wy6feWrizQ/K24yW6lYDhOQAAAAUNGmsJNXzOdh+oEZlMFqhvaNV6i1yrVkq4sAK6MnMzWeMVdsZBlFfeIEbNhcjHJs1AgCwqksBaVc/bbvYzPrrnwlT6HZjIq2mxiOmxqPmppOkvo40KihA4VwJIQjhBE7ihkznhMzA8PbvCkp9/jngwikpGMfbZa/Vqy+KzkCpmo40lOa3Ng32DeruAgGr7x0RN5NWHq1mvz0TsvrGj6MKjdJM2jjoeCqCIN1AZSqCuILBYvvPviKGQbKrWn+/Un/XsH8aolYpodWoAACzjWxUG8N9+vk4uxYd5A6rLfqBUqaqL7zAvNyyaSv1xRuFg5519EbSwLS5aCfavPeFyc8C3LHyhrIZ9SXf6ArW2245AkoaGklDo6Xlkq7wE4wlIAShLO8kyma0KFw65ZIljnPly/VsR03v44jstLMmv4cyNUzgQcW5PTCAOameDO0jXwobRbYYdcGCAdEQAUEQGlCZiiCu8HV2NZRlz9f3X1+WFtqxoKo1Qd4zpjbC34RmJalalaFZa9IYrb4CToCIE+rNc+PpKRO8pVQnBfRMVlWBRX4OSihD2Q80ylTKCvOhDACANClMTce4ITPtv8XaVqQ6cZdF2fvkT8qqt6pL6R3iZYgt8ZSW3VVaJcNTqTc7Javs4bOxYgmPYDlpG60zhAm9Cef0mu6hmPc0ehu8/REIgvQ7qExFEFf46UItlDgVLfrTlcpJMX7tf4Re7BHwdvxqTdafL9btK2o+WdGi+Xc5LRFy0mP95w0OujMtxJ7BsHAJOJD3mEEP6JlM9QdghbIor9r09T0PSiWNcnPTMXPzWdIgI41ygHOAEwaNmhuz7C9TTfX7W7MWUTbI1TJcLHECIYp2dxY3XG9rhhitSNVTuzIOTowPiMpqLIP4ik7lvGJyUmA0lJkxLiAg+t5WbQRBXAaVqQjidM1aU3d9emk4VNLcUaZ68yH/E/bmQQhospJrj5Z+drpSqe/6SblCZ96e17g9r/E/+66/khG3cnyUK9dWg8UOn4rsAYaBYC8uxIAey6KAOeLCorhARHRdppobs7T5H5rqD7rgPKelNd/eKxU5rceXeniNCgDwqImpMgPMCbF1urakv95vMen9uQIJVzjELzgjOG5acLwfV9B+wZ3RaX2oTM0IdtbebClPlBEcd6TBDSv5DmHhuD+v60FrCIIgAM1NRRAXuFjbBvG59qXafzbRRfsJID41F3KIIC+mJVxxs/auX3Mv19u1069WZXjyr2t/XWv8+e5hIVCrxx4EeXH9hRxYJ1QjfQUi7oB43mfVwCwAbJryWz9IGmSq0/eZ6g9CfKFe0tBV23MZZW5THs2kLDCLLmfA+UHC5KfcncU/DFaYWzpJQBW3yQEACqOuGMjPNFd9fT1bwGI/ED96dcqUaC+/e2KHv3P1MMRtxs6DAWxR5FDnxX95SIbnl6kxIn/UPwlBkB6guakI4nSVSpgD/cpb/hlLw2cTyYEiWJFHhvswfGCV36QZ/9lpO2vUDkdLFRM/O9OoNjJ6bUdM/t9yNHNTYv1hhfJwlBnaFB8AAHlLNHPzGfmuVFfWqMDuEaOavDWkoQ8MyBWPfB9je7k7i384o41tJ3qr5YuiM4N2fPBp4SkBi712xFxnvyIUK+JGRHtB+yl0q+kh8ZkRKc6LD8XkoBh3p4AgiEcbEIsACOJebUaYSwpt/+5yNCtRWtBk11vtXs1KlDK5vbrVMGvjue42+vasUqmf8+35k09OEPe467hZazpW1nK9WavQmdVGi5+AEyDiDA/zmRjtJ3TkgOjioSE7rsFpG7skNRhKHM9HwR3G+O/RMtbWa61H7iDNrTBfwg6YHW1+bfp6fdFnLkiGIUHCI/zYe92dxb8E8KE9ROuZ0WZ97vzO7ObqXybf9Xvllb21TNuqOxWHINYMn+3sV/lk9LxzzdXNRs/dArAgcrC7U0AQxKOhMhVBnA7u7oROW3zvHxW+/mQF85dg4dg9w8OYRHh8e14DgxXRqw3qNw5c/3R+F29crCT1x5WGT09WXKxTdfl/yiHwWUnS1VNi7VzbXDg0OHq/gPkq96BAr1mJAQyD9BUYG+YpspsX/Uhza8uhWa6vUQEAGMen12sM5b9QNpMLkmGCF7nIe9xX7s6is0Rvl/7r+KPyCgcntk5dMevgt6d7bAvcMwxgGACk045Gj5JEhAt7/8ZjKNrL7/Csx6bs/1Jl9sQD1fFiycyQRHdngSCIR3N1j00EGYC8oJ5dFHP/tf4zJFh88yRV2laOj4rwpT8xdW+R7MB1pl09vzxTlX/LyvCpCmXC2qzlm3Mv1HZdowIAzDZyd4Es/cuzGV9l19gx+IfHwj+4YxDDbAEA6+alsOD1RvZwhDDCSdG0l98iDY0Qg9uP5dV7GxuILY6dAmeJ0t70Td8KMI875hfr5R/i2qmYv5Rf+q4kZ9f0B2eGJtAOQgGKjRNPJU+YHZrU0Z8JIpe98RrqF3xw5iNO3V1M27rRmSwcvQVFEKQn6GcEgjhdJIPy71bR/p3fNr13WzLDmSh+AvZbsxg92H77YAmT29tZSWrt0X+1/VhzuHTqV2ftX/Y8VqZI+/jE7oLejxEuHhp8R0ognSz/Z/mIMIbbpPsWtu8QiNFY/4tmVZfqit22DMiWjO7tEgpui2OYMJwXtURy+0WvtLeAcyZwMuf6E5Jv5h60kSSLWdFuIq1HG8p2T3+w5e7/M933/hNJ42GlBwBoMcHsVtCz0dKIK/NWPZQwmu1JzYrmRaTcHg7hQSGCIP2bh/5iQ5D+JC3UG2K01JDOqxPhPvwty4fTXtZjE/hvy0f4CejPrytu1l6ohdNfZ8e1Jr35xhnIdw6XvHHguo10bOtdq8Gy6KeLB4vlvV75x4oRE6NprjNMjZNsWppK794+ihM0FVYonCdl+95oc6ov3tjpnKorccPm9HyBTd9IWXU9X+NiOC+AGzZXPOqjgIUlvulb2X4e/X24MnE84doSutVsePTstn11TI+nFrXJvis9DwDg4AQP6nhPk83a+0XwiNm8TROWli565fmUybFe7m/5NkoS/uvku92dBYIgfQAqUxHE6SJ9+XESaOf6psdLbv1gZkrQ5wuH4I436mXh2M93pTFcFTxaqmBy+80MFtvZKiUAYHNu/f87UEwviMVGLvnpYpGsl94hfDax88HRc5IcPj63cEjwzgdHcVkD6+cnNzgD5zNaf+7Ai1rasfpnqt0JJSYNHOk4lndSz9fA7W8MhWjIS37T9wpTVhNese7OpXdD/YIfiB/l4hfdU1sIJc6blw9SgAIACFgwy1QR2w2TliNFvutGZ5YtfrVi8X8CeHDaQftyHd4oNCkwet+Mh93yFUAQpM+58UYBzU1FEKdaPoJRd6IOgV7c6Qldl5SPjYs8/PhYh6aPRvjyjz0xfhnjo615jWqGEW52tUGtMVlf2FXAJIjGZH3qr2u9XuYnYO99eMz6eSk9dxi++fqNS4b+ef9IuOeN+wacLRr8EvMwGMEVDnqu/b9JY7NVDXMcq0O8Rr7X6zUU5dKFL3voCj8BFNSuy062ZvjsAJ6LWv62s5AklDgyg/acvAYAAPeEbZgQ5v4aR0V7+a0d2csmAnvwCfbZ2555OGEMBux6A8nBibeGzTw2Z6WEB7MZG4Ig/Riam4ogrvDMpGiJkMM8zuvT43vY3JsRJ7myesoL6bG9FlF+AvbrM+LzXphCe9frzaqgDoatajW8c7ikScO0t2pWmcKeQ6oYBp6bHFPzxvT3bktOkHb7/mlQoNe6eSnVr09/dGwkw8T6LmHy0yxvps05hSmrWeIbjYusbTQXzJnjRy/jBE7u9TKc5XFvqW26Os89LtsVK0XOCEnoo0/Dvyo6CwBI8QmCGHOQD5xdCbTdHzdqlCScYZBXhmYkeUu/nbAkf8EL98eP6mFGrg+HvyplSvni/7yZNtPFO8ARBOnTBt6CAIK4gy+f/ebMhKd35DMJkhwoemxcLzWSVMT58I5Br02P357XmFWqOFXR0qgxtR/vZOFYqDdvcoz/tHjJoqHBInjrgVozzLWdVr351yKmTYPbbThdaWefJG8e++WMuJcz4urajCfLWxrVxiaNCcNAkBcv1Js3OcYv2JFl6n4LZ3tP+E55cDplozl5iO03TDTk1Y4/kkY4f9GOYvkOEY/70p4rcUEYwAhPW700y06ypWPcnYVddlTnP3xmq9KFTYPgOiWrBACMlkbwCbbBBucQdXqQm3dr4xj23cSlk/fRn1UzISDqpSE3DqsP8gn8YeKdX41blN1cdVpW2WjQNBnULIwI5IvChD7pQbEj/MNQU18EQWhAZSqCuMjj46MOXJfvLep9fa9LXlzWL3cPZxN2/bL34bMfHhPx8JgIAABFAbnOhAFMKoKwnNsluKskTRqTygDn7eCxMkWrweLLd+BcWZg37+7hEAb89FecgAk+6Vtbjy0EpMO7YQmvGL8Z+zD2P/s/3TKPlCWO9595EOf42nMxRnBZ4jg3rvp2ydp23d0p2OWN3ANrrh5xdxaMNOjbAAA8gpUZkfJH5RXmAf24gmnB8czjMDTEN3j/zIenHfhab3X4h22KT9Cu6Q92Wj7lEaypwXFTg3uf8IQgCGIn9HwLQVyEhWPb7qPZWpZN4NvuGzkijM6JJgwDASKu82pUAIA3D2Z/EZUB2mlAG0mdqVTCioa044Xf4TdtN8VxrO1WCRjJmpqF8/+1eRJjwZzVZA9e1BL/23NwfrD9t0BscQyLTe+eMbMO+bzoTF+vUQEAZtImN2oBAKsHT6HRo+5WTydP5Ha/P9aVxkojT8x5Il7cRU++HsyPGHxi7hPOGCeLIAjSCSpTEcR1+Gzi0GNjn5kU7dBdkb78rJXjPHlEZ8wto1yZMNlgbrDstd8vQsOB1rTxRev/bptso3r/JaK0id9pfCDj2hujvi6raPnX5s9OVatTcQIn+U3f65u+Fef4OHQjP3qZk1KizdNm5NzqbHPVM+f+dncWcNTr1QCAUZLw5bEjGIYKE3q/OCQdQk6QjJSEX8p8/rmUSUJW788xo0R+301cumPa/f6oRkUQxCU84pEe0sFgsZ2pVB4ra6lq1cs0JqOVlAg5wWLu2AjfjHhJuI+rVx4Q6Phs4tP5gyfH+P+/A8WFMk3PFws4xMNjIv5vdiLc5UrohtNa5u2OTA1zI6hM64Ztpf3b8fKWZb9cMlrFT9S88D5n+QOSvVOFufG82k6XmSn2JX3inrYJv7dON5BcAEBFi37mxnOnn54Q5HVjHAVLnAgABgCcpn2EVyxHOtqiuGQzNFIWDUZwcZ6UJU7kBGdwQ2ez/YfTC8sJmsIJnGyWnYSSJBSuX4V2CAWo53N2UZD+Wt1ObzW3/8d7I+YebSit17fRi4Nj+OqUKUqTnk+woSzMQuHF5q4fPe/11BnfFp87UF98Tl7daayrP1eQHhy3KHLIkqhUdMQUQRBXwtqb+hoMht27d3d8dMGCBWy2R78t7n+qlPr1Jys2na/Rd9+NZkSY9zOTYu4ZHkp03+t1AKpS6rPKFGerWpvUJoXOzCYwqYgT7SdIj5VMifW3c9CI61EU2Fsk23qlIatMUd/2r4Y0PBY+LspvVqL0oTERUPoDO1t9mzHinSOkR3YIf3x85FeLhro7i/6jQW0c9P7xNmPn82yB7NZoToOEUIkIQ4tNrLD6FBmjjGQX372TYvxOPDGh4126fOcQayuj1mId+FFLhUNeIkTRONcPkFaAQ/u3b1FeVuweATzmO5wfc4/P5F/dnUW3tlVdXXrsF3dnAc3FzOdG+N+YKFagapq870uGHaEkPGF6UOz8iMFLo1PZOAEjR2j0VkuVVqkw6tQWoz9XGMgXRYn8PKeoRhBkQLnxW7yPdorvH2wk9d8jpe8cLrGSvbwHulTXdt+WyxuzqzffMyzKb6DvuqEosKdQ9v6xsu4OH647UcEh8DvTQl7OiEsJgjPNHCIMA7cPCrx9UCAAoEFtbNaYFToTn01IRdxIXz6X1ZceWod686YnSA4Vy92dSBf4LM96F9jXvbq36NYaFQAgs/jKLHY1JTpVodxyuX5ZCs9Uf8jclEWaWmHlZqjaaqjaCjCc7TuUGzJTkPQ4IXJsg313SJMSw3DKY/r9Mh8L5FTfleS4OwWYJNx/hhKl+AQdnvXYncd/KVMraAdUGHXbq/K2V+W9emnfq0MzHk8aZ+foURcQsNhun5eDIAjS7sZqqtFo3LVrV8dH58+fz+H0gTWcfqDNaMn87sLJihaH7vLmsX9bPnxucoCTsvJ81a2Gezbn2tkdB8ew1ekxa+Ykcexrk4vQcLm+beT6Ux64oPru3KRXp7m/r2b/cLVBPXzdSYZ/y1GcxhfDdi8UH6E91cZeOIsftcRr5EeEIIRJGMqibf4rgTR4UNci/1lHOcEZ7s6ia0ab1Xfz60YbtEZo7sUn2NoV73ZaTtRYTKtydv1YdsFKksxfYnpI/E+T7goRiJmHQhAE6U/Qu3Z3MlhsNGpUAECb0bLoRzo39g+HS+RpH5+wv4MrSVEfHiuf8NkZudbs1MQGsmGh3itGhrk7iy4kBoh6vwixz7fnqpnUqBhGLfc7eDT+mQXCPU6vUQEApNVQsUWxK9VYu7v3i7unzfuvR9WoONefHTDB3Vl061prY7+pUQEA4wIib93y6sXmfjthSemiV54ZNDFUwPRk/pGG0on7Pm/UqxnGQRAE6WdQmepOj23Lo11qGq3kvO8v1LU5/62eh8mpUS388SKNuZoXa1VzN53XmPrP+ydPs2H+4GGhMHspMYdhYHwUnQlASJf2FNKc+gsAYAHb1+EffBD6BR93aVMr0qhoPTrPUPYjzdtNSl3hp1AzYoof/yBGcN2dxS0om0Vx0VCxuaDI7YdmYW6gvT18UHefihL5fTpmft2db1xf+PLmKfe8mTbTi03z76VSo5xzeJPaMuB+oSMIgvQAlaluk13V+mtuHZMIKoPllT2FsPLpE2Qa09xN57V0S82Ltar7tlyGmxLSQcxj7X14dKy/sPdLXWVspG9HU1mEoUa1sbrVQO9eDKPeD/3yDu8zcFOyG6U6+6ip4RCNO41V2ygbzf9rZ8B5UtHQ/7g7i38xy7NVJ5bJtkgVe0apTi6Xlf3hxmTEbN5gX2hHKwUszj2xvTeITvSW3h0z7IKiVmOh/wjmqrLh9UsHaN+OIAjS/3hoE9SB4Lmd+czP8f12uf6ZSTGjIxwbA9h3vbb/eouO0cbdHdeaDlxvnp00cI/1OlWwmHdp1aSV269tuVzPLBIFZUnkifFRzIMg7UoV9Gd1Pui/5y6/wxCTcRhpaTv9oHTBdYzt2CZwU+MRJ2VEj9eI9xyd++o8VnVJ25lHOo3qsWG4uybRcHBie8a9lRrlY2e3Qwn4Wuq0AJ5d3zBHGkr31RUxfLmvis8+mjh2sO+/hgmXa1qyGssuyGtkRq3CqBOxOVKeKM5LkhEcNzYgkuNhjYIRBEEgQmWqe1yoVeXUqJjHoSjwdXbV6Ig05qE837VG9Y8XOs9mpGH1rsJZiQGoubWTtPf3ykwJfC+r7GoD7dNWEP56hiuBZEQAACAASURBVId53z08lHkcpF2LzuGd9u18CM0q6e9wk6HBpq/X5r/vNewdh+6yKq84KR8ahINfFMQ/6O4sbjCU/9x27knKou30cQHlnhYAQXyvX6fcPS043kqSnxWdzm9tYhgwTOi9KmWKnRe/kQthIdRKkmvzjm6ecg8AgKSovXVFa64eyZHXdHnx21cOebG5D8SPenHw1DChZ522QBAEgQJt+nWPXQVMf4PeFErmge1VnWFjdrWtt5k99iiUaU4M1O5TLrNsWOjlVVOOPD7u+ckxqSFit/RYHhth13wUxE5mG82mps8FbvVlaeAmQ4+uYD1lcSgTyqqtclIyNIiGvuruFG4wlP+iOnX/rTUqACAUc/XfNY5hS6JSr85fPS04HgDQYFAniKUMY3IJ1pFZj/MIux7ll6kV5+TVDF+x3Y7qfL3VUqiSDdu5LvPI993VqO00FtOGwtPxf679vOgM5a4lbARBEKdBc1Pd42wltFGBLTpzcbMuObD/tzPdzaB9S+dQBU3psf6woiFdwjAwLV4yLV7S/keVwdJqsOQ1qOf/cME1CXx5tgrHsc8WDHbNy/V7Qg6d7YVszLrUJwt6MvRQVp2xZic/drnd1xsA6UFN10hdHc5x/8MXU/1+1ZkHQTd1URJQYN19znGZESmBPNGeuqIuG+EGC8S3hyU/nzI52ScQAGCyWV+9tO/zojMWktGEWzZOHJr5SKK3vbXu0cZSJi93M4PNsubq4U8KThls9m5eMNqsT5/bcaSh5Pf0FXbW1QiCIH1C1z/RqIGxOudGxfIuHkIzidbvy9S6NmMN3fYttzoD7zEBYicfPtuHz/47H9o+Ant8froyUMR9fQaamwpBsJhH464RgmIfwiOWUtvpi7+yv0wFHvYAl7TC/MVBD2UztGWv7KF6lwB9IlBcBxIoL7cgYvD98aMAAIUqWV5ro9yobTMbvTk8KU801Dd4kM8/3ZIqNcpFx3663MLwVDwQsjjZtz89xDfY/luuKmHOK3r/2jEaO6R21hTcefyXP6fex8LRLjkEQfoJ9ODNPeAO8JRrXTrgwS2KmyEX9hCjIfZrVLt64sKbB4tnJkoHTpsx50kKELFwzOrgxvvhgmIn5UOPufms9tr7oiEv23MxRvAAzgYkzUO50GE4x90pAF3+RzZtLxtc52PX36MmMn8tHsGaH3ljN8Qgn8Cbi9JOmo3a2Ye+LVHLGb7i1KC4XTMeELEcaw9epbV3iLc9aJ/i2VVT8Hru/vdG3gYxGQRBEDdCZaobUBT9U15dMlphRvNMCmYNfjtRGSwWG8l2x4HJAc5gcfX3KklRz+3MP/PURA9bGOszLIqLpsajZtlJm672SnKDjbQpreImi3+OftApbWquPpHssd9VNKfBZanaSXPpVUIQzI+9FwAAAEUaFZRVi3OlXTUBxliiKKsa2pZOhty+45cizbqiDb1edh+4+g0YoQR8hi/3VPJEH07vQUw266yD3zCsUScHxrw9fFZ6UCyNe7UM5tDA9XHBiRVxI1J8gm791Dl59eH6khNNFeWaFrlRayKtUp4okCcaFxCVERw3OzRRRHfoK4IgiJOgMtUNMAxwWbgJXm0pYPf/lvQQv1ztjFZUproBh3BDsZhd1XqwGE0hckyNUlud/6u04QtvY37HB/1wAHAgZakSeTVTvC6/GLi51Bj+lWLBn6p0C9X1bxNvFv0xNk5Dqc4+bmo4alGct6pLAXXjZwtG8NiSkZzgafzIRSzfIe0fZPsP95AyFcM5hCjSvTmYG46QRkWvl3kD42os+zUqg8lrBfBEr6dOt+fKTwpPXVEyehqCAey/I+ZMDIxmEsQTWEnyP5f275z2QMdHKEBtq8z74NqxSy2d57Q36tWNevUVZcNX18/6cQUrk8Y/N2iShOdBg68RBBng0Nt09wjygvnYMkjc/x+C8tkwv1cxbEDU9h7IX+ieXYu/Xur8Fg3pUq3K8OLuwvHv/3Fxy7i4ipU316hdiufVrgvbsCf2xRhu12cC2cCDWhD9w2YwlP9sbSvuqFEBAJTNaJad1l55W74zVXl4jlmeDQDghMxwX5b/wpaOBpibf2qZm07YeeUKcHUSoN/8lsDwjRMWe3N6Pw7dbNS+e/Uo7RdqRwFq9YXdtJvletux5Osye2oLa3Q3xt3Jjdo7jnx/5/Ffbq1RO1Ga9P+9eiRlx4fMp78iCILAgspU9xgU6AUxWnIAzGieKciLTvuW7gSIuASO9oC6QYLUPb2+jpQqUGO4nmlM1se25cW+m3UsZ98m/0fGCXspUG82hF9+MO75qV65t35KT8L8l+sqlKn+QMveCerzz/LCMzGWwN35AAAAN3SOu1MAFlWBnVeyAPkjtnMERnORc/2YzPkRdvXo/q4kR22BcOI9R16T3Uyzro7x8mOeACwkRe2pLQQAFKlkqTvX7a11oOxsNmpvP/z9R/nHnZUcgiCII1CZ6h6TYqD9Vgv34cf4e8S7KKdKDhRBPFuYHNDPGyN7rPFR7jlcJ9OYmjSu7t7Uh+TWtQ1fd/Kbc9VhRN0PkWtojDkV4sZNEWtHCTu/J2609N3JT5SuaIPq1H3CQavcnQnAcA4/doW7swCk3oGykw8sv4Idc4FjW6bFbN6vk+9+OtneDkx/VuU5FL8H2+mGGu4fBisHKM7IKuv1bXMOb+pyik/PKEC9dGHvdyU5zkgMQRDEIWhuqnssGhry2v7rUJZ3Fg5xoG9+3yURcpIDvAplcCZbTEFDU90kRMwbHuadW9fm+peuaNHTG6nS711rVE//OrvVYOFi5s3Rb0lYNP92+Ljpu4h3J5V81Wb75zFQsSkCUpruYarfj3O8CGGETVfjxjSEg54jhOFuTKAdZXXspLE3MG7Cdv0GhnxMjWsEvez6wQA2Nzxpw5j5MV72/nzWWEwMT6Xe7GRTRaeP1OnajjWVVWqUCqPOTNokPGEgXzROGjnMP5TA/nnKPz0kHscw2h16oStuky/O+rlaS3PuGgWox89uTW7elkJocZ4E50nZfsM50jEAZ8PNE0EQpGdobqp7JEiFsxMD9l9vZhiHTeArx7u5qYbLLE4N/r9DcMrUhUMHRG3vmR4fF/noNmgLIPbTmDzykKS71bQaZm4812qwAAAel/4dxWE02FbCalsV8PubjQ93fCRba9fWTU9mqNwqHPyCvugLykZzdDPGElEMRp7i/GBR6mu0b4eJVqFyN7i2BCv8i0raCZJyQKgedA4S4+U/IyThyeTxDk0rBQAUtzXbKGjd9a63NVOAwgCms5q/L8356np2kUrW5ZW+HP6ymGGrUibHiSUAgFCB94yQhIP1njJ7qUqrbDHpmUSwUuClspptYGvHRzC2iBd2u3Dwi2z/4YwTRBAEsQvq9Os2H2emHClVWJhNpnl6YlTigNm/+ujYyI+Ol+vNNoZxMuIkQ4PFUFJCaHhwdMRnp6uuNTq8Gw1xhlW7Cpo0JgCAD6F5Uvon84D3++/9ruWOGvONEZd1loBL+sQRHjY91VGGsp/EEza1nb4PkA4/7BDEPyBIfqblQDplprNMjbEEvhl/YmyP+JGFc2keV2ED251YwZ2gwAKIIiBppoQtgO8TuzQq4Z4YL/8IIc2xxk0GOA8u2+msZrXZtLeu6JlzO3ou81rNhq+un/2m+NzDCWPWjc4UsNjvjph7uKHEQxZUW80QDjicocKPYtHTQGX7HymL1lD5u6Hyd154pvf4b3B+tzNsEQRBYEFnU90mOVC0ekoMkwhRfoI3ZiTAysfzhXrzXpoaxzAIgWPr56dAyQehh8CxT+an4C4/aODNQzvWOjtZ0fJnXmP7fy/wOSnCaa4W3oyN2Zb63Oi8ysKxCdF+7OQXmYd1L9IoJ9WlkrlnCC9H5mribFHam97jN7H90nzTt2Nsh3vdYWyRb8YOjnScozc6CUvM9DcOG9iGAtl0rOJOrGBZ5OD0oFjaNSoAwGCzMMynk0fPbr/nxGY7lyJtFLmxOHvk7k+KVLLh/qErYkfATYY2EtIK8+9gyK0fNNbuku9KNTdmQXkJBEGQHqAy1Z3enZt83yiax40kQs7+R8b48AfWO+8X02MZNkleNSUGLaW6XUac5POFrt4LOhA6jTnq/ayyjv/O8LoEK+yKsIIvFg7Z/8gY5ZrZp5+aMGvqQ1zGY13YPm5+uqQv+4ktGS2546IwZbUdBSfGC79DcnuOV9pbAMMBANyQ6dJ5eQ4VnGz/EZI7LnFDZjLIGjK2dAzUaGMZRuDikHeEba284ugtRSrZ9IMbq7TKDWPnD/MPhZuPe52gIq1dvUskDTLlkdvNslOuTwlBkAEFlanuhGHgmyVDaVSq0X6CQ4+NTRow2307CDjEocfGRvrSHFI3f3DQ2rnJcFNC6Fk5PurduUkuW1MN9eYFQh1W3A+oDJaDxfKOPw7nQ9uXK7UWrhwbPDspwIt7o4oQj9mAc+g3ecY43j7pf3ilvQUnP1ps2kqruhTn+IhHfRSwpEY85jNeeCbO+fdKIM7iBIwXDX1NOj/fd9outl/azZ8kRFH+c054T9jE8k7q+bVY3kneEzZJbstmvnoJFzd0DsDgvG1g+48gBCEMg0h4QijJMNSgV88+9C2B4XumP2R//6ebpfh44h5aLeCUgq63eVM2Q+vReTZNuYtTQhBkQEFnU92MQ+A/LkubmSB95u/8Fp251+sxDCwfHvb5wiFi3gD9uwv15h18dOyiny4WNDl2KunekWHfLElF41I9x6vT4sdG+q747XJ9m9NHxcxIkDr7Jfqc05VKG3njKJ0PoaExhKZblM2mrWB5//NIiOWd5DdjX8vB6Y62igUAYDjHN30byydFlJZi1ZQayjdDy9NBlpZLLHE8AADn+AiTnxImPwUAIE1K0thMkWaCJ8W5UtDz+h7OFsQ/JIh7wNx81tx41NR0gjQ0kgYZAADnB+D8EG7QFE5wBidgAqxqEC5CGMYNm2uq3cM8lCDh4d4v6k2C2FP+XRe3yd/Ly3pn+OzczOcfO7v9D0dWZVfEjtgwdn741jVai8l5GdJTRfkmY4ouP0WaW9U5q3yn7XRxSgiCDBwDtNTxNHcPD81MCfwup+aL01Wliq7fxom4rMVDg19Ij00JYrTrtR9IDBBdeG7SC7sLN2ZXd7zP7oFEyPl0/uC7h/er7Vj9w9Q4yfWXp36XU/Px8YpaFYSDkd1ZMdKzBht6gvybHvT4EA5Xjz0jTcpOH2FLx/rN2Kc6eY9NV2d/HJwf7Dv5V05wRvsffSb+ZKzeQVkZdTGlzabpPLAEAIBz/RxuLIThnMCJnMCJfXE/jHjkB/L6g4BkdCiU5Z0siH+IeTISnjBBLC1Ry3u/1Pk+zj/xSOLYCKHP7+nLMyNS1uYdzW/tpW/2hICoN9JmzApNBADcET5oS8Vll2TqADXW0yYUY+0uU8MRbsh0l+WDIMiAguamegoRl/XspJhnJ8VUKfXHyloqlXq5zqQ326QiboiYNybCZ3SED5vwxOfrbsFnE18sHLJ6Suy6E+VbLtcr9V2/Z0oKEK0cH/XQmAghh3Bxhoid2r/zn5wQnVPTmlXakluvataa5VqTD58tFXIb1MbL9UyHrGbESTLiJFCy7U9uXsRmYbCn9ZBd7A3hBE6WZF5Rn3/WULkF9NrlBcP5UUvEYz7DeTetmGEEN+w2Y9U2qLnai16r3n6G5Z0sTFypK9rAIAYmHvMprCGcmREpH+UfhxKKIYPNsvF69n9HzAEA3B0z7K6YtMP1pXvrCrMay4pU/wzOYePEUN/gUZLw2WGJs0OTuASLAtR1VfMYacTvFVeo/8/eeQZGUXV9/M5sbylb0nshjQQSOoQSem+KooKigo/Y8FWx+9iwF+wFFEQBBRRQOgQIEFoIpEF6I71sNmV7m3k/xCeEsNmd3bm7SeD+PpHZO+ceUnbn3HPO/4B+oRXchZHEgdXHQ3X+FyhMRSAQTgLrHJFqNBr37NnTdXXevHk8noPtfwiEiyFIMru241ylolGlb1YZ2AxcKmCHSvgpEdIAd25fe4egRavWGPnBCSr18L3BwLErz01Aulm38siO7M0Z1Z3/9mc3X4qCkN3qQjo3gyUd0durpo5Sdf4X+up/zOrqW19l8P05gfMEcf9nsTNTX3tEcWwmRFepIxj8gtvwT/pk6/4FYVQcn6evPeLY3W7DPxEMfgGWL8UdzXF7PjER0Kan0iHKXVa4+KVbr5OAzFbU7SjPviSvzpBXdy/u5TKYJAB6M5xzIhGLYzSbdfaPTeqNb7GDi0CBtRU40+e+ln4yMAmBQNxmoKJfxIAHx7CkAPekAHfXbKcxmNMrFOcqFdVtOrnawMCBl5AT5MmbECYZhTLesPHksf5YljTnpwyDoxOGP50Xi2JUi7DwG7+rTUZPE2AwAd2hxF0wBNaqrJluEe6jvwGjvzF1lJjarhKaesKoxFkinOfD9BxsXTeI4z+D4zddX3cUlqvUwdGzeCc4y3PSLkXqHPu1XjFhwqsQY1QAwCA32eqosV8XpEO06TBF7c0NWqUP76bGnJIO+TvZx3ZUZBsJC39fOkgBaifvDZv1Tf5ZiFXQ/sDWgGvCZGg6x/Hvm5MjBAJxe4PCVASCKrn1HZ+cLNuVU6c3WQ6ZRBzmwyMDn5sY7rAWMeJWpg6S/XTvkBW/ZxOk3eVwr06JfHYCrenEtzGe/BtVl0aSWaHzi+RayG06AM6R4DxfKiuZbpGdokR2IRrxiX7fCQAvZUQRhjDExTv2WzCWSDLjhDJ3nSpnHSApnW7gXKn7uJ+5gfOhO/NW4vSdlTmNWngaYDQobG/qHqb+Wpr55IU9rtFGeiJ67NMxyWn1ZbDCVByQg0CLzWWmtgIUpiIQCGeAMj8IhG1UetOKP7KHfnZq6+Wa3mJUAIBSb/rqTEXE+8ffOFxooqDthKDI8mEBqY+P9renhJuBY5MiJIkB7nIaBcO3N5HSm4Z5nFdDm2TL9pkEy5RFWJ4JHuM2A+s9c87Y9+YBM3c6OFM09C3Z/Cxe+DLrjaY4Vyoc+qZsYYEzYlQAgJjDPzr9MQ+2g4eDGNRfpHrNjfTjy5kHHjrzhwtiVAxgz8VN/Hr0IgBAim8ELLNxoMkD2JZhN2vrYe2IQCAQ3UFhKgJhg4JGVdLnp7dcqqaYzDMR5LpjJRO+Pduk6nfTBQYuKRHSrOcmPjM+lKIalpkg00pblmzJ9H7z6KyNF0+WWp6pcCczKvimQab/tCfDsswLvReWqV63CF/mNvwjV0aqnQXJLttuoMD0jPcY/5v3kiqP8b/xIx9me41jug3CuV5Mjzi2T4pg8FrxtINed18XDX0L5zpRxixB7LtnygoJh2/vjXEe3nBVizSmf/X8vi5I/yjvJETLveHPdz8w7dHPRs7rHEO9JCSBhcORDFwAKM1SdmDKFAKBQFABSSghENaoadeN+zq9qtWRWSkJvm6nnhzrwYMjaInopEVt2Hql5niJ/FRZS4fOjrLP6VGyLfcl+oiszVe4oyBJEPpe6vX//W5jGPlP2IvD+JQeTK3AdBskXZiH4WzaDtpGX5faduZBwiXJHEHc/7mN+NwFGyEcpkbdvvz09rSGMiqLMYCtjh7zyci5wl9fgxipbp1w/wPhSVkttcP3feFAn4JdxHv6PhObvDx8GIdxUwPXf879uaHoAk3jHkB3DvuZSjZVEPec24jPaG6HQCAQt4J6UxGIXtGZiBk/XnAsRgUA5NZ3LNuWtX/lSLhe3eFIBOw148MeGRl01y+Zx4rtaME6WtQ85NNTux4aNiFM4jz34NKo1DerDXK1QcRhegnZvm5cJg4tf4hhYNXo4NcPFXZ+SZLYm3Wr9oWvxTBaz9ZuIz93TYwKAOD4TZUtyFFf+1xT9ANhaHPeRhhLKIijJPxj1tQamy8S2gZC34Ix+ThXxhCGsWWjYM1fQVghQOB+ctbq9MaKj/JO7q/O720ZjmGzA2L+O3TaCGkgAMCNzWk32A7GKCLjCgEAay7udVKMKuEInoweG+kunewb6ce3rOn1btLMHRXZNP9TL4N0KjEqAADnoHFfCATCKaC5qQhEr6w/VZbfSEuW40BB496rDQsH+8ByCQEAMJiJu7fYF6N20qTSz/0pI+2JsS7ThXYAgiT35zf+lVt/okRe037TY6KIw5wQJpkV4/Xg8AARB8Ih43MTwzZcuN51EHNFO+hnxdyVkn0OG+SF3c8JmEPfMergXJlo2AfChFd01QcMDScMTecJXSOhk2M4G+dKGcJQts8k3fU/Te20ssTC+FcZfD8rCwhds7rgG13lDosbYUwB2zdFEP0Ux38GHTcQVEj2Dk32Dq1UKU7Wl51tqmjQKpt1aiaGy7iCEKF4ok/4RJ8wcbfyYCGTDTFMjXKXHaopPNNYActgD1r0aimPvyx8mJU1XlzhV6MWrjizw+Es8Xhw/QEsl+JipnuUY7sgEAiEdf4t+jWZTLt37+66OnfuXD7f7h4PBOJ2okmlj3j/hFJPV000XCIoeGkSGlRjL0q96VRZS1Ztu1xtaFEbPHgsqYAd4y2aHCF96UB+18BPB/AWcfJemCQTuijjZxe/Z9W+c7S4sEllfZknj/VUcuirUyO5TLq/V9uu1C7bdqXrSxyQ3wV+Mt/DkfEebJ8U8bRDGKMflFWTBMBufGdMrXkthyY4nG5l+0wSTzvc2/+LJAyqrDfV+V+SZttlFyxxovvYDSzpcMc8QTiDgJ3v1KptjV2hTPU9b7xwad+OimxYBm+FgeFps1Yne4daX/ZR3smXMw84YD8BNP6J7RQCiuJzmPe9dTgPHcUiEAj4oKJfBMIyv1yqph+jAgDKWtSHC5vnxXnTN3WHcLSo+bNTZSdK5BbVkjGMbhtZo1L/3yOF39+VQM8MZDp0pid35229XENlcavW+O6x4j9z635fNmyIH61hng8k+WfXtn+a9m87HwGwp2ueU5jdVkgO2mWHF3a/+5gf+0WMCkD3GBUAwPSMF087JD80ARBGu03hbLdRX/X2/zIry1tP3WuUZ1I0ZlRktRwcJxr2viDuebs9QTgHlRGmGHhBW2NqXTFEg7diJokFxzenz34yxsPax8pL8SluLM7zGfu0Zjt+7WeA0vXYEcoxKmBJR6AYFYFAOAmU4UEgLLP3akM/NHV7UypXj/kqfcaGC0eLmnub6EOSgL7cycYLVVcb+sWUxU7adcZJ352jGKN2UdCoSv7mbGY13Z7Mj+fGrhod1PWlkWS+Wvf4w9dfqzZ4Ubkd5/l6jN/iMWEbxhLS9MR5GOSZjsSoAADC0HZyMaFtvPUVs6au5XAK9Ri1E5IwdFx6QZWzzhFnELAxkwTEil9Agvz2xha9BprBXlDoNY+k77RZ07s6euyl+Wsm+oRTsemNqT8GxzZjf1NsSe2EH/U49cUIBAJhFyibikBYQG8iMqvbYVk7U257QjpiV07doztyoGSwbWImyDkbL3ryWWwGLhOyA9x5kyIkkyOk3n2hA6w3EQs3XcqqdeT3TaU3zf7pYvpTyYNkAturewHDwIYlQ6YNkj22K7dN+28sd6Rj1PGO4fM80h8QHx3OL2RjPWM8AmNwpKP4kQ/zwpf3lyRqLxgaTnVcfMbh200dpW3pK8TTDnW/SBpVisOTzeoqx2wqs95gCAJ4ESsc9goBBZ0Z6hsOBmrU0D44rHOh+frv5dn3hyVaXxbn4ZM2a/XF5qpvC86m1pd0H+vaCQeYhmP1C0HhEnCNjZnt8oElHsqPeNA+vxEIBIIyKExFICxQ1qI2mglY1soVGoOZYKP21N7Zd63x/q1XesugOoOqNm1V241mwg0XruMYNjvG65UpEWNDxC5zAwDw8cnStDLHDzKaVYYVf2SdfSqZphDekiF+Y0LEn6eVbbxYpdKbAAAmwNjTNnFP20Qerk/iF/uxmsWMDhYOIvyDZw4dOigqBWOJaG3pGkii49LzNFPw+trD+ur9nMC5XVdUOW+aOmjJMrVfXMPxn4kKJvsWLgPyU5AZQPvgsMmqs7s+u5omZHGkHEG8p+9k34jRXsFsS0NTR8mCRsmCAAAFbY0VKkWzTq03m0QNh4QVPw0GTVzQM1bXAlYaGZIOAjOBXxMmVJA8JjCLgTYYtI8BNROw68NBLYYx3EZ9BTA4M1oRCATiVpCEEgJhgbSylpTvzkE0WPfmNF83LkSDtxOXa9rHf3NWa7TvIN95zIiSPTwiEMcxqYDtJeREewkZ8MbA9KC2XRf90UkV7Rzy78uSlib6Q3GpXWc8XiI/USLPqu1oVunlaoOIy/QVcaO9hZMjpNMGSQfWb7K2bGvbmeX07TA9YmULrwKAAQDMqsqmvyIASfc3lhtyj+ekHfR9Q9BB9vubcp0alrW3Eme8lXUEljV7kXIFj0aOXBM73reXWTU3Q7alP6wt3dL9Ujvg/kwmbgaJLYBn5c4Y0Px8RMzDyS/haE4EAoFwGiibikBYQAc7ZNL0mxgMCg1K/YkSeX6jslGpb9eZxHyWTMge4uc+KVwiFdinoEuS4Nm9V/tPjAoAOFLUfKToxrQbTx5rUoTk/qSAxfE+9J/J2nVGvYmQCTidlj5NK6MfowIA3jpaDCtMdeeyFsf7Lo73hWKtz9GU/ATFjqkt39B0nu01FgDQceVV+jEqAEBXuVNxrN1t5Hqmewx9awjHiHb3StdBmx8T5S6DZcoB5Dr1R3knvy88/+2YRdaH1gAAAMA8xm4kTWpd5Z+dX58DgU+Ts+qB7SqJAiBbWSrfpv7xtwn3+fP773wvBAIxoEFzUxEIC3BZkAuZ+LAN9gkmgtyVU/f5qfLeZHtwDBsX6vn8xPB5cd4UI7od2bXpFQqobkKmVWvck9ewJ68hQip4b1b0PUOtzc+8FZ2J2JNXn1osP1XWUt2mNZgJAACOYT4izpgQzyNFTVCcLGpSXapuGxHoAcXabQNpVBqazsKypq893Bmm6q/vtrmYss0j8n+GiYZ9IIhdDX8oFwAAIABJREFUA8smwi6SvUPTIY05DRdJhksCoJiiQ4dRt/z072caK74fc5eN92Gc5Tlpl7bs1/bzT/xiinidnGIGdjwNnqwvHfr351snPODO5sh1agaGy7iCYKGnjNt/1dQQCMQAwnI2tbMSGIG4Y/EWwpSEYeCYxM4cYz/kYlXr8m1ZJXJr1XEESZ4pV5wpVwzxc9v2QFKcj+1T+S9OQ8tjOJtSufre3y4fLGz6ZtFgIcd2KYraYP7idPnX6RWNSn2PlwiSrOvQ/ZVbD9G91OJmFKb2wNhyBRDQNHKMzRcAAIb6VNLc8wdKB9Ks7ch4ltArRIlvQzSLoMiioMEf5p6AYyo4PlQkdmdzYaoHO8qGogtMDP92zGKbK3nhD+41Bb92fp8DbbVynXrm0Q09LsZ4eE/1jVwVNSre8zYpykAgEH0C0nRBICwQJuGz4CkehUv4A10/6fNT5eO/OWc9Ru1OTl3HiC/ObM6otr6stl13saqVtncuZcul6mk/XtAYbNR8XmtQjv7yzOuHCm+NUZ1ETl1PDU+ESVkK01pHKQBAU/4HRJtdqHLe0RR+7wzLdxomgqjTdOQq6s81VRa1N7cZtNbXj5QFjZYF09+XjTMeixrNwPAUnwj61qDwXeG5jcUXbS4r7mh++OIhiAJ2BW2NXxekD9n7+ZxjPxe0WRjmhEAgEFRAvakIhAU4THxEoMe5SjjFqOPDJFDsWEdtMGsMZjGfBV3v57uzlc//c83eu7RG86M7s3EMPDQisLc1/bzctzcuXG+9f9uV3SuG91ZQd6iwadHmS3qT6zQ/AQDlLU6f1jjgIPUwD0FIQysAwNyaB9FmdzoynmX7TWG6DXKS/dsbpVH/W9nlwzWFpxrKO4w3JTMj3aSTfSPvDkmY6hdp8d71o+aP3f81zTDt6djkSDcpAGBZeNLeqqv0jEHj5cwDi4PjJRxropjPZ+zTwx3MAwAAgATkwZqCk/WlHwyfvSZ2PHT7CATitgeFqQiEZebFesMKUxfFO2XmBEGShwqbjhY1Hy+Rl7doulSIZEL2qCDPyZHSxfG+wZ7W1Bqp8M+1hqf3OPjIRZLgsV25EVLBuFDLI14KGpU0XOtL/r7a8GtmzQpLEXhGVds9v152cYwKAHDNyNmBBUnC/J6QhBEAYNY5KztEEgZl5ouek/c6yf7tisZkfD/3+LcFZ3tLnJZ0yEs65D8WnU+S+L+dOGNuYGyPBQwM5zFZGlPP4cDUCRJ4vDFkWue/FwXHD5MEXG6pcdgaRBR6zQe5xz8dMa/rSmmH/HJLTZNOJdep3VhchUGzvzrfeQ5ozcZnL/6tNOpfHzLVebsgEIjbEhSmIhAW0BrN+/LhPIxGSAUzorygmOrCTJA/Xaz6NK2s1FIVbrPKsD+/cX9+44v78u8d6vf6tEHRXg4KWuhMxJq91wgazeoGM/Hk7rzL/zfBYo63wVUFsc7g5QMFi+N93bg3vYuqDebFv1yCIt5rL0gI71YwpgCmNZYQAECandh2qKv626jIZomHOm+L24z8tsalaVvzWim1eV9pqZ2Xuml5+LDvx94lYP6rF1DSIZ977Gc6MaqEwz88fZU7+99BTTiGfT5y/sRD39Oc1guLn4ovvpc0q1Gn+uLa6b8qc6vUlgXwnMobVw5780SrBo1y/dYIBGLgMrD75RAIZ2AmyGXbsmClUj+fH8eEWoVb3aad/P35x//MtRijdsdEkNuu1CZ+durLM+WORZrrT5VVKuiWkubUdWy+ZLlJtV/NobGXRqV+U0ZVj4sfniipbe8b9RR3LqtP9u3PMAQwZVcZgiAAAGaPFKoDaMu3OdX+7cS5psoR+76gGKN28VvZ5ZlHN2rNRgCAyqiffmRDk07lsA8cBvPQ9JUxHt5dV4yE+cO8E/0kRgUAtBt081I3Rfz5wfprp/skRu1kzYW9laoB2eWBQCD6ChSmIhA9WX+6fHceHAnW+XE+8+K8ba+jTF59R+Lnp0+Xt1C/RWcint177bFdOfZGqmaC/O5spX339MKXp8stXucyB/acnt+zart/2aI2fJZm+X/qAsIk1trP7kyYHvEQrbE8BwMAMJYbRJu3Yqg77lT7t0Ka1Pqagx2Xnlccmdr8d0LTnyHNf8e3HE7puPi0rmoPYeizwMY619oa5qVuciwLmt5YsTRtq5kkPsg9QTN20ptNZcobFkhArjq761BNIR2b0DlWV2wk+vhMUGs2vpx5sG99QCAQAws0NxXR7yBJoDGaBey+CWCaVYb3UkugmBoe6LHtgUQopjqpbtPO+SmjRW1w4N6fLlZJBewP5sRQv+ViVWsNpMTg1QZlqVwdIe1ZgSkTDuw5PZdr2tt1xq405h/ZdX2YH07yd++rrfstTLcIhiDQrLahOE0Rts9kAADDbZCpoxiKQYsY264CwghwV+TGCW29Ov9LTdGPFmNRQ0OauuAbjMnnRz4iiHuOIQx1gUsUIUhyxZkdCr3jtR7/VF37IPfE+mun6TuzNmPf4uB4Ns4AAGwourClNNP2PSRwcla+P7KzIuftxBlR7rK+dgSBQAwM0NxURL8gvUJxpKjpRElLWYu6WWUgSJLHYniLOCMCPSZHSOcP9vZz47rGk49PlrZpHW9S6kLEYR5cOYrKdE2KkCS459fL1W02hitY4cMTpePDJLNjqDbKwpXhPVOuuDVMjXK0abafYCbIaw3KsSH/CkQdLWruQ2emRaGHPwtwQ5eqr35C3w7G4HEC5wIAWOIEfc1++gZ7hTCaNXUMIYQRKdbRVe5qP/eYzWQpadKoC77RFG8UDn1bOHgtwPpFEdamkoxMOd3Th/dzUrUwFG5rNO2/lmauHDRKadS/lXWU0j13XowKACAB+Xt51luJ0/vaEQQCMTDoF583iDsWkgS7cuqGrT89/puz646VnKtUNCr1nYI9WqO5UqHZlVO3+q/c0HXHH92RY7MVkz4ESf52GY48o1JvaoUR7nax7UrNhet0p2us2XvVYKaqQFvQ6Hi/lgVrTRZEfceGeELcok+43nrj4OByTV+WR6Z8d+6BbVcu17T3oQ/9EEHsGihCSvyYp3C2BwCA7TuZvjXrkEZnK2CT7edXt6bdQ72glzTrlZdfbk1bAgiYb2uOQZDkO9nH6NuBEqN28mLm/laDdv210w3agape7hpS65xYiYBAIG4zUJiK6DPatMalWy/f8+vlK7YerA1mYlNGVfwnaV+ecW7jX26dshGe9uzRoiZYpgiSfONwEX07pXL1ll7UjG4F4reiN2vhEkG8r3M7/ZxN1xgYnYmo6+gb8aROFBrj9iu1w9efnrnhQvfg+Q6HwfcXxr9I0wjO9RIlvNb5b473RJznS9svqzg5Y9mR+aKm6AcHbtRd392W/hAgXT1sqQdpDWXVfScFZJFWvfaZC3splfve2eS21pP9RlwKgUD0c1CYiugbKhWahE9P7cyuo36Lw1JA1IGbDbtSCy2vdbK0hb7ibidbMqmmi3VQJ39qDJabNp8cFwJxF9fTJePcoTP2k26JI0XNQz87BUsG7DZAMHgtSzrc8fsxhvvYHzD2/1p/caYw/iUojvW6IduJbcbast/UVz91/Pby31XXHL8dCsfr4MgHwGVb2ZVypR3idncmSqO+VY8O0RAIBCVQmIroA+Rqw8yNFx1rs9x4oerVgwXQXeqkAlIo2EmZHJq1I/ASsxeut1JsvuUwYb4/8FiWNbFWjgoa0AlVD96/UjdE/4hRO2nTGpdsubwrx45joNsYjMETTz3EdI927Ha3kZ9zgxZ1vyKIfpLpEQfDNQtgDB7Dadla0qhSXn6ZphFVzrtmTV/+amUpam0vcjkoSUiRDmNfVp0gEIgBBApTEX3Ag9uzipoc73v88ETpP9caIPrTRbsOWqsSAKBdB62J63I1tMSsmSCz6zqorPSCKsPbm6gvA8e+XBjHgDpa1pWES/7texT2kTZ1bxAk+eB2aON/Bzo4VyqecZztk2LXXRhL6JH8iyDmmVvMMUUjPsNwp/zEWZIk5xX9qq99Rj/CJI0qVfbbUPxxjEoV3S59RE9cGGIz+ocKFwKB6P+gNwuEqzlY0HSokG5u8Lm/8/VQS1I7gatxDTG9VtYCUz6qjJoYFVwZ3ujeraVESL+/C+Z8S5chYDNivP/9fwk5TImgf83X0ZmIVTtzTf0qz9t3MPh+khmpouEf4WxKwl0cv2nSeVd4EQ/d+pKx+ULHucdI5wyi5Pg7TQeVJDQlP0GxpK3YThphSqzZRYcBpeNg48JzQne2i3T7EQjEQAfNTUW4mlcOQCjZLWtR/3yx6gnYbY0iePNjAABuXGjW4KZ5KRb9jvvfnBUojA+TWHl11ehgtcH8wr5884CKqaZEytiMG4d9Cb5uJ0vlfejPreQ3KjdeuL56bEhfO9I/wHDh4BcFUU9oSn7SVew0tFwCRM+/LAbfj+03XRDzNEuSZNGGsTVXcWwWdY1cOz1kcIMW66r2GOpPGJozCG0DoW/BmAKcK2WKwtm+kzn+M5judkw/7o5RkW1Ww1EyJ40qQ+MpTsAcKNbsBT2wDFy8uEI3FgpTEQgEJdDcVIRLyaxuy62nVHFqk82XqqGHqcGefIjWQsXQrBFQ/yTN1KyNCxV7izhQ9H4HyQRRMmu5Wb2JaFDqB9wbz4oRgd2/nBIp7W9hKgDgk5Nlj48JQU/2XWAsoSD2WUHss6RRqW9IMzamm9WVJGHAmAKmMJwlHcHyGotzLJ/RmNU1iqPTnRWjAsASJ7QcmkAYbipqJU1qQtdkasvXVe8DAHD8pgoGv8Txm2qvcWPzRWiOAmBoOt9XYaobiwsAGrw0IBkuDbS9CIFAIAAAvYWpCISTOFzYDMvU5Zq2RqXeW8SBZRAAkBgAU8tniB80ayIOswNeQtWNw6KyjIljq0YHrTsGQVTzyXGhVsKkRqV+5sYL2bVwzi9cxpgQz4WDfbpfuS/R/43Dhf0t2K5QaC7XtA0P9OhrR/oRpFmrLflFU/KzUZFlYbwKhrPEQ/mRK3mRKzAGr/srysy1hLbRaX5hxpYsm4v0dan6ulRuyBL3sRs6R7lSxNQBc2SlqaPP5HbDRJL8Nuf9FBBOZIZ/VF+7gEAgBgyoNxXhUjLhTXwhSXDZ1sBVexkW4NEl3EqfaYNksEwFefJsL6JMsJiqtZdSIvzc6BZoRXsJV48N7u3VDp1p9k8XB1yMimPYFwsG94i9wyT8ZUkBfeSRNdLK0JyMG2hKNjXtCm6/8ISx5bLlEaAkYWy50n7hiaZdwZqSTV2XDc3ntRU7nOmaHSccuspdLftH2aWHROhh6mmR+j77pUqU+PfV1gg6cBjMpWFD+9oLBAIxYEBhKsKlUBTvoWoNqrAQAICJY0sT/aCYGuwjspJNLWtR/3Sx6uUDBY/syL5v65Wn91x991jx0aLm3oaLQkzM2mVNyGF+Mi+Wzl44hn21aDCL0etbzbLtV67APm5wAZ/Ojx0ZZCGR9cGcGCHUDmco5Dcq+9qFfgFpVLWdWtp+9lFCR6msg9A1t599tDXt3k65IPW1z12qiGoLU0ex4uh0O4JPEpr2OACAJCC0AzjGNL9BsEwh1VlX8lzcBC8uTGU+BAJxe9PvHqcQtzcUxXv6xFonr06J/DWzprdwkTpvzbBQ2qTSm37OqPr6TGVvATabgc+N9X5xcviooJvESCdHSH84d52mS53EeAvtSpDen+RfIle/daTIse3WL4izklU+WNC079rAK957bWrk/00Is/iSvzv3m0WDV+zI7k/hDKhpQ8qogCQMrWl362uP2HujrnKnQtfsOXmPvnq/Mxyjg6ntWvvZRz0n76GyGGPA7L3HmH0WbyR7h4SLJGVKCOncWYHR+6vy6dtB2MSX7/ZKwpS+9gKBQAwk0DkiwqWYoT67O2PSRqAHr7cIhDopEdK7Enx7XNyZXRfy3vFn916zkgQ2mIndefWjv0yfvymjSXUjWTE31tudC6ca+f5Eu6tS35w+6JUpkfZq8DBx7NN5sc+MD+1tAUmCtfsG2AOiG5e57YGkdbOirax5aETghDCYIsn0URlgKkUPUNrTH3YgRu3E0HCyLW0JaYYc7WMsEc7zsb3OKrqqvRT/XzgfZq0sQ9Bn9e0YwN5JmknfjhdXuHncvdHuXvRNIazDY7B2TlouYsHUkkAgELc9KExFuBQhmwHRGtz5MV28OiVyTAilyYoW8RZxNt6T0P2KiSAf25Vz72+XW9QGikb2XWsc8umpM+X/lvPxWIy1KeEOu9SFmM+y0iZqhfdnRx9aNdqHsmBVqJh/6smxz0+y5nNamXxgFaNOHSS79mLK/Um2n/VnR3u7wB/qcHovur5D0Nce0pZvp2Wh7hgsZwAAOM/HfcwPbiPWE9oG+taUl1+msozlGUd/LxKACuCZAfwPEaEHawouyatb9Br6Zu3lvrChyd69HoFR5IPhs6VcwfEZ/8H7kxD27VeHzMYZO1KW0/95IRCIO41/3w3R3FSEa4AsBQTVWhd8NuPQqlGOtYOKOMyDK0eFSwRdV0gSPP5n7sYLVfaaalDq5/x0sUsm6vlJ4SG0J9ysmxUtEbAdu3dGlKzs1SlfLRpsfdBOjLfwp3uGFL6cMtbW2NUBV+7r784NcKdULx3l1b/6r8R8B3/otwmEqSPj+b524iZwrpQf9R9t+TYo1oyKbKM80+YytvcEABz8rCcAdgREPA7mJpBPjCMfWUguva9EMefYzyP3fSnb/mbC3s+ez9hX0uG6aUwYwLaMX+rDEzls4b6wxBURIwAAfgL3RUHx8FyjhQebt2X80tsp6xgqEp+a/cS8QFoaBwgE4s4EzU1FuJR4X7fjJdAeZeJ9YQoLdcedyzr15NjH/8z7I6uW+l2x3qLflycl3OzVutTiny/aHaN2otSbZm+8mP38BF83LpeJb146dOaGC3qTJW1SCsyK9lo12pFUahd8NuPp5NCnk0OvNShPlMqvNSgblXqVwSziMHzduAm+bikR0kEygW1DAAAAzl9vtb2oP3GtgWrud2yIJ4YBeu+jpMMRxa1Q/6Hcluhq9pvaC/rai5sgjWrSqDI0pcMyqK89xJIOBwCYNXWG+uP6+hNmZTmhl5NmHc6R4Fwvtmw023cy2zvZ0HjGXuMHQeSHZHIpsHzwRAIyr7U+r7X+i/zTi4LjPxsxL1joeDUKdcJEkoPTVqYc/r7dYHcx9gz/qF/GL+1Kon44fPb+mny9uY9r43kM1j9THxnvHTrOO+SBU9vPNVXSsebFFTbr1GTfdcl7cYVPxyY/GzteeBtF3QgEwpUgCSWES5kcIf3idDkUU/7u3Ghn5qzcuazflyXNiJKtO1ZiU1LYk8d6Kjn01amRXOZN9VrlLZr3U2lNF2xS6V85WPjL0qEAgEnhkp0PDrvrl0wHmnJHBnnsfHAYE4cT+cT5iOJ8HM9jdFLcrILijMuobtNSXOkl5IwM9LxYRScOh1nhQqeIHTqEvsVQf0Jff9zUXkjo5KRJg7PdcJ4PS5LE9pnM9k7GGHRnIPVAV7kLrkH6YAyuUXEFENBE4AzNF4zyDNXVT3TXd/eYsmNWlgMA9DUHQNYbONe+cnQtYP2XnLQNJNheCgBBkn9V5h6vK9kw7u4lIUPs2sgxEiX+2Queu//UtvNNVBXmMIA9HTvukxHz2PiNDpQIN+kT0WPXXzvtHDcpEeUu2z5xWZLEHwAQIhSfnLX6h8Lz66+drlTZPUYoRCh+Nm78qkGjN5dkPHWBkrwWXJK9Q1dHj10UPJjHgDbgDYFA3IFgXYnTnTt3dl2dOXOmm5uz8lSIOxm9ifB/5xj1Fk0rPD8p/FN6s1IoYibI3Xn1e/IaTpbKG5Q3zWAQcpgTwsSzor0eGhFosVF2yZbMP3PraTqAY1jGs+OHBbh3fnm8RP7g9qy6DjsSCEuG+G1YkgBxJCx9CJJkrt0/sOo2BGyG6oPZFBdvuVS94o9sp/pDEQaOta6b6aRGbrswtReqr36qLd9KmnudZYJzZYKYp/nRT+AcCax9m3YGmDV2lEW4ALZ3Mi9iRfvZlbAM4mwPwtAOd16OETCWk4tOA0dKML4avfDpmGSIzljBQJi/vHZmff7peo2N8cuTfMLfTpwxwceCSF6HUTfx4HfZCjvm0MLCjcV9PHrMf4dOEzDZKqO+e+7RRBAHawpS60rSGsrKlS1qkwEAwMYZXlxhqEjCxhk4hrUbdR0GnZQr8OGJhksDJ/tGJEn8uxpc38w68m52qitzqneFJOyctLxftfsiEIgBCqbT6err62tqaurqbrw7ozAV4Tw+OlH68gG6BXh8NqPgxRS4na5UqG3XNasMcrVeyGH6iDgBHjwr+cmadl3IulQzDDniFSMCNy+9MRW9SaV/cX/B9iu1RrONAuAIqeDdmVFLE2EqfEJBZyJ4Lx3oay/sw5PHUqyjqi9KkOSoL9Mzq9uc6hJFdj00/O5bpKddC6nO/6ojcy3F/CHOlbqP28QNnAdhY0N7w3YLE277Fn7kI0z3mI7MtX3tSK+QADwG5h8gIx27nYHhf05+cGHQYLheWUFvNu2qzDlSW3y8vqR7vMrA8KFiv8l+EXcHJ4yUBVmx0KxTJR/4triD0kxd+giY7GTv0CFiXz6Tc66p4mprQ7NObSTMbJwh5QqGiP0m+0bMDYztLkSsNRsNZrM7275yg2N1xQ+d+cNmDA+FST7hh6av4jL6/lAMgUDcBmA7d+68tRMVhakI52EwE4M/TiuR2yijtc66WdGvTXXk+clMkHn1ypp2rVxtwACQCtkB7rzBPiIGpGrY7nyTXvH0nqtQTHnwWPJ3ZvRwsrpN+8O560eLm7Nq23sEw1IBe1K45N5E/8XxPv3zVJskAXPtfmJApVPDJPyyV+2Y+5deoUj57hydsUkiLlOpg9AvFyrmF78yGVbJt72QJk3riQX6ulQ778OECa+Ikt6jubupLb95LwSFW7i4j/uZ0DUpL7/S1470yk4Q9yxJa+iLmMMvvutlCQfmsFaKqE2GJq1KadTLuAIZV8jEqWrnNulUD5/ZcbDGKZ3M+6c+GijwaNarBEy2D09UoWz9OO/kkdoiK6lODGBzAmNeGzJltIyWrECLXvNtwdlvCs4265zYarE8fNj3Y+8SMO9swTYEAgEPJlJLQrgYNgPfeM+QGTSkgMaEeFqfdHIrJAn25zduyqhKK2tp0/bM53jyWCkR0kdHBc2K9oIY06VX2N1T1BttWmNevXKo/02HR4EevPdmR783O7pdZ6xUaBuUeq3RLBWwfUScMAm/f0anXWAY8BFx7CpddgA+i6ExmmFZi5TaJ0SUHCretHToQ79nOfYue1+if6vWeLiwyZGbb6ZCoTlS1DQnxuljcswEWSJXN6sMzWo9n8XwEnLCPFnE+SX2x6gAAFKV+z7GchPGv0THJdLUB+NSbIGxvcfqa2FOuIGLFrA+JsfRNKLQa97NPvbFqAVQXLILAZMdKnJkdrEXV7hp/JKovz52QJbJJsOlAd48EQDASJjfyzn+bs4xm+d0JCD3V+cfqC64tZ/WLiQc/n+HTlsTN/7drGPr888QpIMfvr0xROy3LmnmXCTni0AgoIKUfhF9wEQaUkCx3qL9j47qoVRkncOFTc//k29lRGer1rg7r353Xn28r9sXC+MmR0jt9aoLvYnIqm1vUOrlasO5SphKtoVNPcPULty5rCF+LFcolkAl1kfk1DA1TML/ZWnitB/PO3wg0oPRwf8KEREkea6y9XiJ/Fyloq5dJ1cbmDgmE3JCxfwJ4eJpg2Sx3v/qSy0fFqDQGF74J9/eX/UlQ/y23Dc05qM0KJ4DAPbnNzovTDUR5K6cup3ZdafKWlpvPgb6r+/mx6UHHbasvPwKS5zA8Z/luHOMfpjbIRVHZgjinu1rNzrBbu1o3U7G1wG6GmkAgO8Lz702ZIqM27/mM92KgTDvrMg+VFN4sqHMScWxvny3zhhVZdRPO7LhQjNVzScAAAnIr/LTcxT1h6avdFiU6ER96cNn/qhSw29DmB0Qs3/aIxhU1TcEAoEAnWEqg8Hw9vbu3puKQDib+XE+fz8y8uE/sptUvYqp3MqsaK9f7hsq5lP9nDaaiVcOFn5+qoziwUtefce0Hy68mBL+7qxouyokjWbij+y63zJrzlYqNAZo6bvuNNrzjRoQTAgTpxY7pQ2MxcBXjAj4dF6cG5c5KVxypAjOLrNjvHUm4peM6s9OlZXeUrVe067Lqm3fnVcPABgfJn4xJWJOjDeGgTXjw8YEi+/fesWmXnQnXCb+4dyYNePDmlR6irdQ4WyFs8b//HKp+u2jxZUKC0nLEHb9o5J99MyTHRefkS64ijEcnGkBUYoJImZ1lTJnXV970QnJCZhlbMkitA1dl3ZjMVA0dwyE+a/KvMejx0Cw5RxMBPFF/un1107XObl1c6pvJADASJjvPvmrXTFqF6cayu45+dueySuo1zB38caVw+/nHndSk4XKpEcxKgKBcAZ4cnLyokWLkpOTsf5dIoi4/Zgd45XzwsT7Ev2p9IX6iDjfLI4/sHKUl5Dq0ypJgpU7cz5LoxqjdkKQ5IcnSlfYU6i59XJNxAcnHtyeday42UkxKgDAeZb7inuG+DnJ8mfzYzcsGeLGZQIAaI6K7SLB103IYYz64szqv3JvjVF7cKZcMe/njFkbL3RKQ48M8rj24qQt9yVGWR2hJOIwnxkfWvLqlDXjwwAAxc3QYlQAQEmzCvpDapvWeM+vlx/+I9tijAoAeN33FxZGt7fW1FGqKd7o8O0Mvh/GhDY2FmNwAeZg4WUPSL0Cw/qF0gzG4HvfWy9beNVjwlbB4Bc6cLdcElri/Xg9rYlcTqVK3ZZy+Pu1l/Y7O0YFANwfnggA+G/WkSO1RQ4b2V+d/2HeCXvvev3K4XU5qc4TAqhRtzvJMgKBuMNh+vk560kRgbCJj4izfVnSulnR356tOFLUfK2hZ10un81IDhV3NzLFAAAgAElEQVTfneD74PBAjj2FvgCA//v72q+ZNY45tu1KbaAH74M5MdaXqfSm1X/lbb3s4C52wWPBeTjuP0R5CWdFex2C0XvZg/dSSx4aHtgZpi6O900OFdPvE5432GfY56d19tQPHylqHr7+9NH/jI71FnGY+IPDA5YN88+tU54olV+uaWtU6tu0Rj6b4SXkxHgLUyKkY0PE3avZocxt6kJnIjQGsxDeWJoOnWnKD+ev1PT6hOrNap3ulgFlL03xBkHMU47ejbFkowz1dj/cW4TtNY4bem/7ucegWCNJCPpY9NHXHQGEkekRx3SPVud9XEwIzfCSY7kKuhO5nER+W+OEg9+26F3RujzGK3iGf1S5suUL2qNZ3885/mDE8CABVfHqn4sz3stxoDPcDjrH5CAQCAR0+sVRLuIOJ0zC/2x+3GcANCj1ZXJ1s9qg1Js6pYDifERsht0FTgCA85WtX6WX0/Hq45Nli+J9Rwb1+jRgMBOLf8k85pyy1VuRUU4jDyA+XxCXWiK3OVbHXhqV+o9Plq6bFQ0AwDDw2fy4sV+n05kMNC5U/OnJUgd6XGvbddN/vHD26eRgTx4AAMewof5uvfUY9wBWS20XWiMB65fIYCYWbr5kJUYFAEwSXmYCOCUAptY8s+o6Q9gtMU6YDM0XDA0nDPJLhE5O6JpxpgDneTHcBnF8J7N9UnDODQUdbsBcWGEqJ3Auf9Aq0tjRkfkSIG+TAgfSqDK25rEkSZqiDcbW3FoQDdF4pQqakhxEqtVtM49udE2MimPYFyMXYAB7K+uozkz3YEJrNr6Xk/rj2LupLG41aF/K3E9zR5ug8TMIBMJJOBIAIBBOwkfEGRcqXjjYZ/mwgFnRXon+7o7FqARJrtl7lWaJE0GSz/7dqxGSBPf9dsVlMSoAINpqvegAJdpL+GKKfaLNFNlw4XpX9DsyyOO7u+IdNjVIJsyp63A4aKxt1931yyUHKu74bMj5cwE8g1+erjhZKre+JolfDGs7AICh+XznP0ijSp3/ZdNf4S2Hxiuz3tRX7zc2XzAry4ytufq6VE3hd60n727a4dt25kFTW37nLbzwZVDqfjGmgBd2PwBAEPe8W+Lb9A32H0wdxYA0q/LeBwCogYMiPRYxEGY97dgMOqvO7qp2gpiQRd4YMm2kLKjDqPuzMheKwd9KrxyoKchva7T5rvJW1hEXhOJ9MnMIgUDcCaAwFXEbcrSo+VI1hEeQ85Wtp8tbLL60I7u2UyzHNYg4zARfCMKb/ZB3Z0Y/PDIQutlmleH89RuiQY+NDv5wTowDDfiDZIJ4X5FKT+s5+3JN+6aManvv8nXj0tm0Bx48Fqy4t0mlfy/VdsNhGLsWynadmDtKAACGhlPNe2M6Mp41q6usLCYJg7bst+a/E5SXXwaEEefKhPEv0/dBmPAKzvXq/LfxfzEwfSC2zjoMqVfoG9LM6hoAAA5FPakbDKx/PWnsq86n0yBqF49FjX4rcToA4FRDudbccxaaY2jNhrnHfo7b84nX72/effLXfdX5Fievqoz6TSWXoOxonUg3mQt2QSAQdyD968MDgYDC71nQno8tmtIazS8fcMr8996YG+vNciix3P/BMPDj3Qn/GQNH6Kg7PfpRX5occew/Y/zsif2WDwvYfG8ilPOIF/fl29XXCgCIkgmoqItRpGtGDn2+OlPRrrP9wC1m9joCygEIvUJ97fOWI1M6QylKkGZV3kctR6eTRpVg8PMMURgdBxiicEHcc11fGprO0bHWHZIwMN0iYVlz0AezvqsuWkTCFBUXsjgOKNM6lTezjrhmo3mBcd+Puavz35lyuw+qbNKi1/xVmTs/ddPgPZ8erOn5kXS4tkhldIU+/FivEBfsgkAg7kAsf3iguamIAU1ameUUqAOcKLFQ2bjtSu31Vi2sLajwxLgQV27nYlgM/Ie7E/5aMZwPVSaqsFHV48qUSGnuCxPfmhElFdiYpTltkOz46jG/3p+4PasGytthq9b4+kH7jjaEHGaSvzuEvQEAAEwKhzaXZdsVSsdAsBpTOzG25nZcesGBdlBDQ5rixAIAcPG0I125UHvBORLx1H0Yg9f5JWnWWk/n2gdhdBvzA9s72c7bYIrzYyyBUZHT+e9gAFO4NVzUvwYC5bXWZ7XAzPNb4YHwRPx/JRylHTaK5OmQ39Y499imNRf3dq+vTm+scN6OXWAAmxtoQ2sQgUAgHAM1viNuN9p1xip4MWRZi0ZrNPdQ2f0DXraWCkuG+CWHim2vG+AsjvfdGVe3IxvaAOdmS0q5EgH7zemD1k4KP14iP1Eqz6hqa1DqWtRGHguXCTnhEn5KhHRGlKxrcsyBAmhCxJ+dKt+RUxfowRvq5z45Ujo1UurBs9EEePcQXyjl652moNipUGh6Gz/TAy0JU/TLUH8SOFqMaqg/ocxc6zbqK/GUfxTH5xE6+1rKca5MPGUf0/3GszihawEkTIErDGNIZpxU5X2ouvoJabQ9HIXtM8msqe0shIYCgx9gVv87zDMKa+GTRg2kDtURUvgl/XRIrXPdgJywbiF6u1Hn1L1IQH6Vn16uVHQNVi1ob3Tqjp1M9YsM62cnEQgE4rbhRpiKYRhKoiIGBARJZtV2XLje2qTSy9UGDgOXCdkRUsHEcImXkFPdBvNpgCDJmnZdkAfvwvXWk6XyylZtk1J/shRattYm7lzWx3PvlLNqgzXJX9Le9JG894EufDZjXpz3vDgbwyEblXqKIRlFatp0NW2685Wt35+rFHKYj44Mem5iWJAnr7f1j44M+vB4aauWbkvbpHBJIqTEbE4d1QmTjUYx6PV/5gC0Pp7Uhd/xB61iyUbJFl5tO7NCX3uI4o1s38ke439l8P1vukpArqUkzVqAM4VDXhfEPK0u+kFXucuoyLo1Esa5Mo7fNEHMMyzZqLZTS7XwwlSmRyxp/Lf6gAXMY7HqVJJWjXQXM/yjoNiBhctSqWycEefh0/UlHaVx6uyvzl99/q+N45YAABo0MKvuLYJj2AfDZzt7FwQCcceCsqmIgUSJXP1ZWtmunDqFxsKDO4aBoX7us2IcrOvrjTcPF+3Pb1TSE9FxDDYD//OhYSHiO0VH0epsWLtLHLNq2r9OrxgT7BnjLXJM5LZErnbgLoqo9KYvz5RvuHD9gznRa8ZbDgkkAvZ/pw/6v7+v0dmIgWNfLBxMx0J3qJcqFOmDpgE4c1MhQJo7Ml8STzuIc73EU/frqv9RX/3Een8pWzZGEP8SN3AeuFUBiAEz/gbdVJQwtrsw/iVh/EuEXmFsuUxoGwhdM8YU4Dxvpiic6Tm46w+B7T1RW7EDyu4MYShDGNL9T+wecC0VQAhTJRz+3MBY+nYg4rIBOeO8Q/nMGxlpIctGowEsfiq+uDg4flZANP3hNzZ5PGrMMEmAs3dBIBB3LChMRQwMVHrTs39f25xRbUWCnyRBVm17Vi3MxioAVZDJLty5rK0PJE4ddAeJKPqIYJaJmknymT1XAQBsBj462HPBYJ+Vo4LcuHa86bVaOg2Bi9Zofnbvtdw65cZ7EnBLSsRPjgv5Pas2o8rx0t/nJoYN8aM0qZUKKgPVZ9/z6sFPyf6EtS999HVH/h2+iuHcoIXcoIWm9gJ93XFD42lCU9tZCYxzZTjfn+09geM3pXuVbw9wrhTgTEBACwNwnk/PKxwxx2+alVu4wYvbM9YAAsKvKC9kCQAAZ7t3Nf7OAcWJWEMW2dMre3k7cUZ/G6rZ4RJVIQDA8vBh3b8MEni6Zl8AwHMZ/0z1i+Q4+Tuf4hvx+cj5Tt0CgUDc4fSvzw8EwiJ59R13b8ksbnZiaqu/MTZEvPWBxNA7Jo/aiZNmwxrMxOnyltPlLe8cLV4zIfS1qZEU5/EaCZj9h1bYlFHFZeHfLrYw3JXFwA+sHDX+m7OFTT0VoaiwNNH/wzkwi8YtxtIWSVclKMxuYgbVImGnQxL6moP86NVdF5juMUz3GEHMU/ZawnA2Uxhm6oAzGBZjcJnCUHvvwnne/PBlmpLNtHfn8WOeBAAwRGFGRXYz4O8HUekgsJKkWyUe5+Hzn6gxNI3ARW82GQiYyl69EeEmfSA8qfuVeE+6MT91Ctubdl/Pk3GdOOgo2Tt075QVvUXCtZr21LqS43UlpUq5XKfuMOplXIEXVzhCGjjZN3KCT1h/O7xAIBD9E/ROgejvlLWop/94oUHpoiPwPmdCmOTFyeGzo70dGPI50Bkf5lwpjnad8Z2jxQcLmrYvS4qU2n6Gg6s8bJ3vzlaODvZcPsxCBZ1UwD76n9FLtly+WNV666tWeHRU0Hd3xVMPLKngTjkdbSSZP8vnrfXeBnF3mhiaz3UPU61DEgaTIsesbSB0cgxn4VwpQxjKdP+305LtPR5WmMryGgtwRz6LRUkfaCv/oiK5ZAVB/IsMQRAAoEGU+D5Q7iJj9TAeDCRs7q6U5f1kFE2jVvlzScax2uLzzdf1zi+FBQB8OWoBG7/p3SPFNwIDmMUBp87g9/KsKHevE/Wl0C3jGPZUzLhPR8xj4RbeHi+31Hycd/KvyjzzzZ3VjVolAOBEfelHeSe9eaKnY8Y9ETPOkw25eB6BQNxmoDAV0a9p0xpv7xgVA1hKpNRLyPYSskcGeU6OkPjaM9iTOhqDOb1Ccf56a5NK36wysBmYTMgJ9uSlREgTfN36SUgc7SWM93XLq3du/i2zum3sV+npT42LspW89Xd3ys+iN9buy18Q52OxLDnQg5e23P2fkwc1dae8mc0yRhsDI1pM7nUmSaY6Jl2dkK2NJMkbP0UfEeerRYOXDPGD7mSYxI4UzffyRfeJjwWwoKkl08RERXOIMGkrd2jLthoaz5CmnhUcOM+X4zeVH72aG7xYU/IzFK94wYsduxHnebsN/6j9PNXA+1aYnvHCwWsBALuv560s1LSSCQ6b6o4QM+2fvirGw4ZEmQuQ69RvZB3+peSSCxo1u3gmNnl2QM8ShmCh51iv4LNNla7xIa2h7OtRC78vhDbdFwDAwhlLQ4e+lJDSXRqqCxNBrMtJfTfnmJXGnE4atcrXrxz+Kj998/h7b/1GIRAIRBeWw1Qk+YvoJ7xztLi8BabUan8jyJN7/PHRTt3iUnXbJyfL/r7a0JuOrreI8/iY4CfHhcqELhL5sMKT40Ie/zPX2bvI1YZZGy+efybZ22o3bKRMyGbgVvWHYdKo1K8/Xf7m9EE3XyZ11fvUeR8bms6OBwCIbrzgw2qJA+XTRJcAAEW6oB/ki/a0T4r19Vw9NuTB4YFcplMSWcMD3HEMs/kk2omOYL9a+/jm4HUMzMHvIcYUkiZHqp0tQuptaHRry7Yqs143q673toDQ1mvLftOW/cb2HscQhppVdEdT4hwJN+wBh2/nRz1uVlerct934F6GIEA89QDGFLyVdfTt7KMO+9CDIaBxy+DoIbJgWAYdJq2hbNmp7bUayGoF1lkaOnT9yAUWX3orcca0Iz+6xo12g26w2JeNM6AUOfvz3daPWpDiEyHtpZC41aCdcWTDJXk1dZtNOtXcY5veGzbrlYTJ9D1EIBC3Jf2iIAeBsEiJXP3t2cq+9sK5jA52oq6GQmO8e0vmyC/O7MqpsxJrNSr1bx8tDnkv9ev0ij4/oVo5KijBF5rejxUqFJoX9uVbX8Nl4k79Ad3Kzxeruv8ICF2zInVe6/EFhqaz1m+M4latD/jyevI7l1b6PjY62EkxKgBAImAPC7CjazFVOfy52me6Z3qpg7HceKH3OHBjb5DmXqdVkUZl25nlbWeWW4lRu2NoPGvWQBBXEyW+i7M9aFlIWidKWmdv2TBLMkwy8xRDEPh94TlYMWog6FiHnTggSEsYsgaKQTr8U3Vt2pEfXRmjMnH8zaHTt068v7cy+6l+ka5MHnYYdEvDEqGYWj9qwZKQIb3FqFqzcUHqJrti1E5IQL56+eCX+WdoO4hAIG5PbjzKYP2k7A+B+B/fpFe4LJFlJ9CCuZnRkMfndHGpum3Ip2l/5dZTXK8xmJ/Zc3X+pgxVX4ze6YKBY18vHsyiJnFEk21XatIrbEynuGco/LpZK1S3afMa/q15Nipymv9O0NccoH472Zol3zeM+lBQx1gxItCu9btaJ79Q95SWsE/GmSEMkUw/yhDBmd7ZSdfclx6QZp0ida62bKt95oheB/NShCUbxR+0iqYRADBhwmuSmaeYHpRGv2AMrjD+ZcmccwxRWLai7ukLe2ntDcgIoHgYZG3G/j6L/fwIyBIPex9jOUULrTda9JpyZUuDVtnVD3mqoezetN9MrpJAAwDM8I86O/uptxKnM24dX9SNb0Yv8uGJrCyAiNKof3/YLAGTbo3MGK/gu0OsVYM/eX73mUbHywqey/gnncbtCATiNgb1piL6L9RDLJcD50zHncu6K8EXiqke5DcqZ264YHG6rHX25zcu2HTp4KpRHKel42wyIUyy5b6hD2zNcrbcCEmCj0+WJoeOtLLmweEB644Vu7I7+nxla4Kvm1lZrjg2i9A22Hs7aVS1nrhLPP0o2zvZGe4BAFaODvryTLldytu/K6ZlaqI/9/t6mKDQ9mqMwQtf5j7yS4ztbmyjNTO2Bwy+xRmPZNup+wyNpyFuRAWmW4R48t+OiSfdCttrrGzhVV31Pk3BN4bGMxbzxgxROC90qSD2GZz77+nY2kv7ekjd2AsJsAWg6Hns3x5IXvhyXvgyOgYpktFctft63on60ixFbfdwNMpdNt4n7J/r11zQjMrA8ESJ/2TfiPvCEoeKKZ1nhYrER2c8NuKfL/TOFxxm4rg/3/31IVNfuXzQYSMcBvOb0Yux3j/yrrTUbinNdNg+AIAgyWcu7s2c9yxcsTcEAnEbgMJURD+lvEVT295rhd7twX+nDxJx4P8NtmqNU38470CM2smJUvnTe/I2LBkC1yu7uC/R/5OTpVm1Tp9lcqigqVVr9OSxelsg4jDfmx396I4cZ3vSRYlcDQij4vh8QuvgMQ1p1raeXCRbVIhznKKczGbgH8+NXbj5kl13legC55V/PFaQt1K6b7wwW4Bb+OvGeT7c4EWC2OeYbhGdV1gecRA8/h9MDwsll9qybboqWhlFAHAA7Av2WJIkz5TdOA+uyBDGDZzPDZxPmnXG5vMmZQWhrSfNOpwrY/B8WNJRDOFN/aJHaotS6yhoStniOzB8JbjiDnTcoIUe4zbBOsXrjdS6kndzjp1uKLf4alF7c1F7s1Md6OTl+MlvJk53YLBKvKfvZL/IQzUUzmvoseTkb348N1++KFHil9VS54AFHMN+HX9fksTfyprnM/6h2KluhayW2t/Ls3qM8EEgEAgUpiL6KfmNyr52wblEeQmfSg5xhuVn916t76CV/fvpYtWDwwOTQ8WwXHKAULHABWGqiSBPl7UsGGxtpOGKEYFbLtWcLrehvgOLVo1RXfidiV4WkdDJlVlvuo/+BpZXPVgw2OeFSeGfppXZe+M5dfw5dXywO/P0AxwpUUloG0ljB86V4VwZSzLs1rJVliQJYwlJIxwVJbbPpB5XSLNWeeU12oYJljjJ1FFImijoveEsQewaUdJ7GO4sxTKMwWX7pLB9Uqwv+6XEvoOG3tAC1j9Y9OohC0RD3gCYE2c46c2mN7OOfHI1jX5cRJPp/oPeSZphcSILFWRcVxRFq4z6YmNzcYeDQTuHwfx29OJ7Qq2dVxa1N6c12P0mYJGNxRdRmIpAIHqAwlREP6W+43ZOpUoE7D0rRrCd0IF5/nrrb5fpKruQJFi7L//8M86qGqWCR+8ZTrjk1Suth6k4hh1aNUr23yMao9Pr9AAAwKRU5bxD34ym+EdB7BqmWyR9Uxb5eG6sXG345ZLduikyIfvgf8YGeVPr0MNZ3KCFdneNWgJjCTl+M3pc1Jb8YlZX0TduVGRJ52boqvdpijf2lgbH2Z688OWCuOd6ZDX7BBKQqfUQUqmdnPde8eLQNaRRZWg+Z+4oJfRy0qzHOVKc582WjYbSYKw1G2cc2UCnBxIWI6SBf6U85HCMCgAQc/r7vNBYD+/tEx8YYquS+UCNDRU66qQ3VrQZtB5okioCgegGClMR/RStEab6BY4BPzdujdUq4iAPXlWbFuKmveHBYx1YOTLG2ykH6g9suwJloNSF662Z1W3DA2lpkNIhXMp3zUbXW21nwPhsxstTIv972Ol1egCAEfgJQm9D2IkShElbskk07AMIpiyBYWDz0qEpEdLVf+VqDFQD+JQI6W/3J9o1kFYQ+3/a8u2AXgslAIAf9fit0j7aij9omv0fpL7+uCjxbVHi26a2a4aG02Z1FaFrJEkC58oYgkC2bCxLkujUZKNdVCpb5To7uoutc1nR2HJ4kqHpHCAs9BowhCG8sPsEMc/gPGvnQVYwk8SyU9v7Q4y6PHzYd2MWC1n26YH1YJCbDJY/DoABzErb/xCx39rBk+4NHcrEbZ+iZjTbfUrVG2aSuCSvnuY3yPZSBAJxx4DmpiL6KXAlfPhsZtlrU44VNx8vlp+pUNS265pVegCATMgJcOdOCJdMiZROjpDwXj7o7N/90cGe2x5ICpPwAQBmgixoUtW165rVBg4TlwnYwZ68ELHj4dnxEnkFvDGze/Ia+jBMHR7goq2V1JSN16aEb8qoqlQ4fYrvEPwcLFP6moPOC1M7eXB4wIhAj/dSi3dk15kIa3880V7CtSnhK0YE2iuUwpIk8SNWaEo20fET53qJEl7vcZE0a43NF+iY7Y6h4SSIfwkAwPSIY0Jtqb0BaTYqsk3tRYReTpq0OEeMc71Y0hEMvt161GVKmEXstXq9qiGdDSyfVphVlarcD9TX1gtinhEmvetAtfNnV0/tvp5H201aJEn830qcMS+QkpyydUb36VBZEpBcBnNFxAit2disUyuNOjGH78NzGyYNSPEJj3CTUjflcEWxRUo65ChMRSAQ3UHZVEQ/RSqA2bglFbDZDHxOjPecGGuaJTIBp0nlLE3XOB/R2knhy4YFYBj4K7d+6+WatLKWNm3P5EOImD81Urp6bEiSPdMpO7E5CNQuzlS4qBvTIsmhYiGH6YLpOEycUtTEZeIrRwW9fsjpCVU/cy4sU8bWPNKocvZ0kBhv4dYHkt6bHbMnr/5EqTyjqq1ZZejsHhSwGZEyQUq4dEa017RBUoeVPEVJ7+vrjprVNY76iLmN+gpj9/yDMrUXkbSHytyw1urEOMrQmK4u+NpQd4wwtN76KtM9mhuyRBDzVJeEr006jDC7KgiAqQBbDKxVo5Bmnerqx/r6VM9JOxmicOrGm3Sq93OP0/bRQWI8vCf7RiwKHjzFF1r9fKyHtweL2wb1R2AXOrNpT9XVosUvubPtqGu4FYUe5rEdXGsIBOI24EaYiuamIvoVg2SWJxw6RhQ1a9FeQohhKpfF8BKyQ8X8ieGSKZHS8aGSzgD1lQMFJfJey+0qFZqfLlb9dLFq6iDZFwvi4nyoDtkraFRl18KcZV/QCEe3xjH4bMa9Q/1+vgihb9A6VJpgS+XqzZeqPzkJRyzECkKGgW2wewhN75AmZSlLPBSewV4J9uQ9OyHs2QlhAACCJFs1Rh6LwWfDqXHFed7iaUdaDo13rBzabfjHvNB7b71uVkMrWQQAmDX1gDACHHJPtaktv/3cY4ams9bWtBeqct5VX/1UEPO0MPEdjGG7JBW6CpGZmsCvseVKy+FJktlnGYIgipY/zjvZbuibiO7TEfOeHzwRokETQXxbePbzq6f6MEbtpFGrfDfn2Kcj5tExYoQ6Vkfv/BlCCARiYIGyqYh+Sqy3yIPHujXZ6BjjqInWjg8TwxJ0DZcISl+d3P2KxmBes/fqT5TjrtTi5hFfnPlsfuzqsSFU1h8rhjyGoUVjMBMkg1qy0Rm8NSPqj6xaNeW+R8eIkPZ6hKE2mH++WPV1ekVp78cKcJk7iAOgToslobS52gmOYRKo1RAAAKZHrHj6sda0e8xKOw4LMJwtGvahIO7/LL5KmuD+WEnCpMLZnhAtaop/6shYQ0lAGADSrFVd/Vhfd8xj0g6b0llCFuQfkBBQfa82q2sUqXOkcy5iTNsNDgRJ/lZ6mZ5rjsOxf96MFarUbQ+c2pbeDzpsO/k6P/2Z2PFBAsfbKwRMmL9FNDt+EQjE7Qd8oVEEAgoMHJsTQ7WAzSbzYilJdyyK94W14+KEm3Y0mInFv1yiHqN2ojWan/grb90xSoKcWVBTqQAAkgRa12jb9kKAO/eFSXYUBzrGyCDLT2m78+pD1qWu2XvVZTEqAODJMXY3GVqHJJxVxO56WJIk2fwsfuQjFIWIWOIhkjnne4tRAQAYBvmgFsNgplI1hd+3n1tFMUbtwqjIUhyeZFZVWl/mq4RWWw4AkAENj3KYCgAwtV5V5X1EZWWOoq5J12dlHW7wAqdrbQ1Jf3/ef2JUAICBMP9amknHgh/f7s4UK/jz3SBaQyAQtwEoTEX0Xx6nlkW0yehgz6H+lD7/hgW4U8y7WofNwP8z5oZIBkmC5duzjhQ5mO1843AhldrXcnjiSZ1gGOCx+liY9LWpkbPhnVbcikTAHnGLTJSZIJ/cnXfXL5lyNbTGRSrcleA7NjwArk2MCbN4vs/BWCL3cT97LS7iRz+B8yz3mWMMDsdvunjqAen8LJbE2iRGjA1TpgvD2RgL2ndbd313+8WnHLvXrKlTHJtJGNp6XUESsoK3+fYEltaJAXa/uamvfkJlFNDlFocbkiEQbo+ekBVq1O2zj/7U0v96Lw/V0Gq2j/OwpvVgvzUHhaARCMTtCgpTEf2X5FDxXQl005sYBj6ZZ4c249eLBjss9NLFsxPCwiU3Hlh3ZNf+P3vnGRDF9bXxO7O9s7ALS+9dEESxYgF7b4kpmqqJr4npzfTee09MVxNjkn80xt4LVlRQFJCOdJayvc+8HzCIy7Jl5i6g3t8ndnfmzHVh1zn3nPM8G/Lr6QRc+XdhTbsLsxy1CdpNZyd+fHY/dvx2ws2rZm8AACAASURBVGLgG+4YCmXvwCG3Dwlm9XCvfeyf81/mVnnpir0RIOJ8ODu5ziAwkTD76BiCUIjRBggMUbRkxBcBixrkcwt9xvwoGvqOMOVpUdrLkhFf+E3ZE3Bbu+/kHZyQ6cDVtCRTHAN3VS6v6CakRaM69iAdDx6rqkSb/0pvr5oa92GqCyMwaBngWFDt6SmkzaAv/srlYZWafuha74SJ46lSOP01iw/+WqPrfdeAGjCGA/KUl6wE9T+z8YHQul18OXyXNq0IBOJGA6WpiAHNuzOThBxajXl3DA0d40mSkx4s6V4IpUCkL/+5iVcGwwwW26qtdOVhDRbbc97XmLUjwd+7CrFuImAz9q8Y9dLkOPrbB3aIOMxVOfYjfO/uK/v0UF835ok4zK1Lh4dJeU/8W1xmhFZQxVhC94VqrkEwpk8yL+Yu4aCnRBlvC9Ne4iesYAdmYwyem+czhOG9lWQpwJIPhxVKe/ZNwtBAM4iu6HOryvGXhqnmHwDAPFBE8xKd4ICcg5VQONFQ9YfLY1T9JzU0yj9CBKPp98+qswcavaC+BuPr0EzYqnUOtKPdZHpIIpS3CACwMCIV+jc8AoG41nGcpiLfVMQAIcqPv+meYZQ9VEeES7+cn+LpWZ/NGzQvhWL3kUzA3rZsuJh7JbVee6oWitnmutO1lU7jiOjl8z0ZG+UHNyBlmDj28pT4Aw+MmprgD/FO5qXJcQrRVfdYdSrjqzsvQruAe0T7Cfb838ghIZLCRs0fBfVH9dAsN9kBY90c47wxIQzNGLz3hxM8FUoc0qrTFX8BJVDHgVsB6WC83NxyFAAwB5TEAwiKcYuwwmCgpnCiTVPuUmy5H+9GlkRn0A9CAvKF09vpx+mCjuKRQ9o9bEUmSPKk8tI75/bN2/PTpB3fMDEI1Q4mjj+clEU/DgKBuM5A1VTEQCc7Rrbu9iECz50tRkf6bls2nIIlBgPH1t42hEKmGuHL33n/iPiri5C/nq7zNI5DSBL87rRzOELqWjbTI+ZSzdW9xJhI323Lhp97YvxbMxInxckDRGyMRkFh7iBFp3tKd1ZtKfK2sHB3WAx82Yiw04+N7ZyPXZNXS5Lgnw5ot2u8iJthhbr+sGnKW/5Js+lpdeN3gXP8uGGzoYQy1W4hLRoooSxt+R2H7+7ZHmpVXwQAMAHxPHaA5iWEwPwU6cwsxznWDhdWz7CKdZ4SI5bdETOUfpzjLTXFqmb6cQAAQhZ788R7Nk28G0q0Lsxum8qYbNZfyvKS/34vc/Mnz+Rt2VhTeLS5ut3sYhrFHVYkjEqCOuaKQCCuD66kqcg3FTFgWZAaeOrRsenB7ooK4hj2UFbkvv8b6Y4lpkP4bMb/7hr21YJUN1uOMQzcPiQ4/3H7RRqtxNFq6i1VduwpVTp5NT0EpuhiskLUU1toIJCsED2THbPz/hEXn8lJDXLXVNYOCZf5/aI0u8nbZq1pPb0RYveJ9hM8Ni6qbFX2tzcN7qq97yltAQDk6ROO6yAUVBnCcG4kSlMdQxhb2nZNJwzQLGpF6a+632zsHFMj3dSxO4byNdqC1656irCQ5suq4DmgcgmgLvmLAfAutisAoy6FbdO72MWLEHprLt05nwyfw8YhVNp31UHrztBazPESfyETct7uZsDTrXWpGz+489B6WFl3FzFi2cvpU+DGRCAQ1wfINxVxbRDvL8x7NGvLhebXd188UdOrFgWbgS9KC1qVE5sYAGGucvmo8EXpQV/mVn11pKpO5XhEisdiLEgNfHJCdGqgAzHhiy1ak5W6QIUd5xqcddZNjIUjStkJhWbpvoQkwYKf8wrqqbQaAgBURuu4L3OPrBzTvVN68/kmiw3aL8shQ0IkL06KSwuWhEvtUxobQZ5ruFxDe6Fh2faYR3F6GinswW/DSpyuP1S5SzsrilBgSlP4cffBimbtOA8rVCeagte4YfOZ0kGdD0nbVR5Fb2G720nuvyCOQuQXsf1zAa2ZeZfWtWl+/SCr82Di6OkhiVBCnW6F003zX7Ta2WHJOIYR8HqhQwSu9zd/Kcu778ifJpsV1kW7kHOFWybdK2WjbyoEAuEAlKYirhlwDJuVHDArOaC4Wbu3VHmsur1Za2rRmblMhlzIjvbjT4iRjY3y6z4XSh8pj/XcxNjnJsZeaNIcKG8tb9W3aE1mGykXsBVizshw6cgIX27vo7Mu5Xk9okljMlqJ3i6XEigeHCSmnLl1J9FfNDZ6oAymOmTt6drdFyka/HRS2KAJennnVwtTF2dcliw6XOl1TdE4uXDOIMet1HUqo/m/JLnQEPWNcu7/yf6mfKEd6uHDRNPpV8OtqmJT/S5z4wGbtpIwtpCEBefKGLxAlv8oTmA2Wz4K4NfefyLmhr3GS//AioZz5dLxf0B8HwhXBUbPI1rVeU/6TtrW+QhjXpUS4ID8EtsST7Z+BEYQbnfRi4D5DWzPQuCiZdclGMNFKS/DL4SF4xYaarSesiQ649MRc2FFq9TC/FYp17TyGKwokV+Z2llnjfsE8yW+HBfTIpsvXbg3dwNFQWDSmdTTIKliw/glcWI5lcgIBOIG4Nq7w0AgEvyFCf7CFaMj+vKiSQGipACPW0y1Jsjbz1qTlcvs1bDk1anxc344SfMSOIZtumcYzSBeRW+2PbsFgkip1mxb8uuZnSUt3y0azGbgxc1a+jGd48Pr9StXffWfyuuNd/kxVDdL91K4ymlD3AOXHt9tpDVka6rbpj33nrlxn93zhKHR2l5oqt+lzX+FIYwUDnqcF3vPtVW2VZ9+DlYonC31nbSNKYmHFRAAQFio99D2hqluu6X9LEuaCgAAGAPn+hPGK62bTEA8jh0ZDmrfIMcWABcjgjggp2Flz4MD4UBFf2E4x8V2GAtnRAj9StW09qTchIUznk3NeTFtkjtD7y1GrdZilnEFzqdnVWaYSsVqsxEAMF4RDStNnRDowpOpRNVy875fqJvW9PJGchjMFQmj3syYzmWgu1AEAtEr6AsCgfAi0H1HmU4Dzk5WTI6X7yyhdUv3TE5MrFzg+rj+44+C+tpeerApsOZUrcZk/fPOoU0ak+uj6RHj1+sba/d7JUnsyboHdQTvLt+tGOZBg9821chH6x7SE1yqawSkWaU6utxQud7lkTZtperYg7qiz3zG/sryG0L5in2JVVViaTkGJRSGMWUzjzPE9p5GdMPiFCfqnWOs3HA5TQWA6ZNkbrSfMBwDarZhaw+DsL9AUi4ZWguummLAAZkAlGOx6sXgbBSANm/PFLtuNp4ZmvjRee+mqRgGRCwOG2N+cuHQ2vJT/jzhcHlYTmBsTlAsj3H510GQ5Pa64m21xXsbykpULbb/XG05DGaGX0h2YMy88JQhfsFeXScBSADAosi07y4ehxLwlsg05wc8duIfI9Re32iR38KI1IeTsgL5DsZkEAgEojsoTUUgvAhlDSeHMHDMpaTTNwtTR3+WW6+mmMWNi/J7YRKVKbW+5K9zdC0l7dhY2Pjs1mIjvCni3hgeLu3tpZ7N6haS+Vz9/fu16a8Frg5jN7kM3mqVvNO0eG3blN4CuoNVXdq2Y6JNV+PBKaqS1q2jJKN/4EXdRuGKfYy5YTesUCRptZmUDAA7TWV6ZZPI3Li/62d2QFb3h90ZA2rGgBqAgSYgrCeFrYDPAoQ/0AViGh8A2cIUY0uYPkkuD5sQGPPR+YNwL90JDrDOxI8kgdpsAsAEAOgwG8o1rUebqz8+f0jOFT6YOPqBxFH/1Fx499w+h+pBJpv1SHPVkeaq1wt25wTGvpg2aaziioQ4XKViMYsLAMgJismUh51o8eBD6pDBvkFTQ5w1AuxtKNtaC6FvRcETPT94ooInGioLDRf2+h2IQCAQdji+j0G+qQgEFGJkMO84I6R859VUAECEL3/7fcPHfXGk3WDxNH5qoHjTvcOcjNoOEA5XwB8i/eBAeYDIu9YXvnzW8LBex0WDxFwOE+8puLVLnblPnTFTkrvYb8cwfhELs69s2Ei80Bi1oT1nfftEA3H5n4BhIMLXY4Mim76+becUj3LUTkibqePwXTjbhxMy3dNz+xhL6xmI0aytp9nykVBCEYYmXdFnhvJfXFqJUsPSUdj1My/iZnv53x4EAG0A5t02eG7wNHd8fccporgMJpSaHo/Bqrn5eaVJ/0zelk01hYQrlbIWo/alMzveKNjtpmvLnobSvQ1lK5NGvzdsVqdQcIRQeq4d2rZapNAXAIAB7JPhc0b9+zlJT2Xt4+FzGE5dT78uPkonfheNBk2GLGSEPBxKNAQCceOAqqkIhBeJkPJlArZSZ4YSbXi4W5o4KYHi04+NvW3d6aNVHvTmLUwN/PbmwRKuVxoOIdKgNlLIwF1iI0gd7EFiOxZnhLAYvd4UMnAsNVB88pIDFWsrYGxUjd2oGsvDTRn84mCW0o+pYmHWJotvi9XnpD5RbbPfDUnwF3puNUx27Fto01Z6eNZ/EJb2/TfL555nCD25GSUshLkDYwn7bLrVqqkYcNEIq6bgFV3h+6QNcrmyO6RZRVo0AMPNTYes7ecYgjAK+xFw4ce7JY8sZnEXRKSuKz9N/4rzI1IMNsus3d97NNvpvrMoAIAE5KcXDp9rb9w6aSmXwUzzC958ia7QVBedusdWgpCwuAvCU/6spm4mtDxh5HhFtJMDrASxo66Ecnw7tlwqQmkqAoHwlCtpKvJNRSCcYCXIkzUde0qV+fWqZq2pw2AVcZhyITvRX5gTKxsd6ctjOcgKMAxMiZevOw1HunNKvL+bR0b48vevGPX+/vKPD1a0aF0kyYkBwhcnxd2S7t2pKlg0em2CVGX0Ypoq5jJX5bjoDp0UJ3eYpnZhIDiHtYPdudykOI/FMw3la8wttIonpFWnzntKOv5354dZVUXG6o3mxr2W1tOE6XJhHGMKmJIEdmA2N2QaWzGBzjJcLNICQQobYjSbrrZj/80033k3ad8zx9R8GBDwd3kowA2d7fwXbbBZdteX7m0oPd5SU6OFMA3LxhkPJ2VN3bkalv6QE/Y1lC3av+avCXfmBMa8lr8LSsxAnuhoc/XTeVsONlZoLLS+BmeFJn02fJ7zYy50NKkt0PZNjjRXwQqFQCBuHFA1FYFwgcZk/eFEzQf7Ky51OHCX2QTA23vLRBzm3ZmhT06ICZHYS9fcMzwMSpoq4bLmpzj2MnEIm4E/mxP76NiodafrthU17y9Xtumvuj0Nl/KyY2XzBgXOSPLHr51dKoOFloBtf/HS5HiFq6bixRkhb+0thTJyseQ/lx13ISwaGPq3xqo/LMrHWbJMh69alCe0Ba8bL/0LejQrkladpfWUpfWUrvA9lm+aIOUZXuQi+uvxOiSteWbC1Nq2c7JVBWH8zx1MPXSb+wuMwRMNe7+3V9vNhs8vHP68KLfZCLPr+JHksS+d2XGhw/WYNxT+qTn/eVHuQ0ljwoXSahhpttpiWpb7B/04yxNGfpQ5h4m7GO64CFVdudT7WwMIBOL6A6WpCIQzdl9sueO3/AZXikQak/XTQ5Wrj9W8NSPh4ayo7i9lx8imJvhvL3agveERL0yKdamf1BMei7F0eNjS4WEESTZpTC06s8pg9ROwAoQcP0GvxjYDGQ7T015WD8AwzBuT+bekBz8yNtLlYYkBwtuHhKw9VUvzcvNSFENDPfNMNdZutenoXhcAAACpv7ha0jNNJW2agte0Ba8D0vUug6Utv+PALcbK9ZLR37k0LPEUjOWxrZSzaGwJ9ZMJa9vumX2Wo8KFIQjCWFJrx3kqJ2MMn7Frmb0oJO9rKFty8Lc6PQS3m+6MV0SPkIe/e65PE/VX8ncujh7yUtrkew67aDFwB52V7vBItMjvg8zZc8KS3Tm41QTTGAluNAQCcYMw0LVSEIh+5OUdJZO/PeYyR+3CYLE9svH8rWtPW4mrUp33ZyW5lD5yTpxcsDLLdZ7jBBzDAsXc1EBxVpRvUoDoGs1RAQByb66cx4L/lTgtwf/nW9PcrFe/PSPR85nSq2Az8HdmulZPtcNUu4XORbtjvNQjFGFp2zNbm/+KOznqlTg1G5Wbh0JKnq/AEEZAjUb9U6kr+RKWNU4fw5Km+k454DdlF9PHrYSnOxjO9hn9PTd8vsNXP7lwaOKOb6DnqEP8gv/Ouev509vghnVJh9nwXuH+O2OGDpOF9vGlu8NnsiYFxa0fv7hkwdNu5qjAw4lcl5hs12QXDAKB6F9QmopAOOadvWWv7LxIobS2/kzdXb+d6X5iskL04y1plPtqJVzWhjuGsntX37mhCPHh8uklck6wEiREoWMmjr00OW7zvZnu/+6CJdzvbh5MuQcbw8BXC1NiPdeXtrTAsWEEABCGBpu2utsTZMeRZabarRRC2bRVbbumdM2vQoHllw4xGmFo1BW+r7+42lizyaO6KGFu1+a/CnElfQTOEiQ97DfzOFMcg/MCZbPyBIkP9TD97RWmJN5vxlFezJ0OX11XfvqxE/8QsNsZlkRnHJz+wPmOpj5r9+3OmvJTGAZ+zFokZVMSCaPxZvAZrA8zZx+ZsbLtttd2TrlvUWSac11fOwRMmBuCcKMhEIgbBNT0i0A44N8LTau2Um/GW3e6LjVI/NSEmK5nFmeENGpMT/17wdN7MBGHuWVp5uAg5IQOKlr1P+dd2lOqNJi9tTFvthIvTop9dVcpzTgYwBalB63KiUkN9PgXd0t6cIvO/NDfha4P7cEb0xLuyQyjcKJVTfefbBetS+9XX/y1oexn6qE6LqiPrfAZtx7S0gAncCKsUAAA7dk3uz9k8IM4wdMESQ8zpSnOTzSU/kSYWiGuxNvgXDkv6jZB0qPdlZwxBlc8/BNe1G3awneNNRudTOoyxTGC5Cd4MXdiDPvp/U7OtjXcffh3uDnqOEX084MnTgyKBQBsvdQ/zdUNevW5tsZU38Btk5flbP/a48ZdGl04eptlXfnph5OyqG17BfJg/qcTLKDRHo9AIG5UkG8qAmGP2UY8tuk8zQ/Ba7tKF2eEBImv3JM9MT46LUjszqRrF0NDfX5dPIRCcew6o0ype35b8Z9nG2xEz98KSetW7mrYDPzlKQnrCxouNtPTbsHIB0dHUMhRO1k5JjLMh3fvhoJWt62MxFzmlwtSbx9CRa6ZtBlJmwN5MMqQ5suCMYS5XZP/Is1ohsrf+Qkr2AFjaa8LAACYPkksvyGWVgjuJj2x6ev1pd/rS3/ghM6QjPiSIei11dNY/ac3FgAdTvBUbtgclnwkyze1tw8aSz5cOuEvm77O3LDH3Ljfqi4nTErSZsK5MgY3gCUfwQnMZsmGAaelvCdObrbA6DKVsLmp0sDswNhZYUkZfleExE4o+819J6/1Uqpv4HB52PFZD926fx1EG1WXnGqtXVdxekl0BoVzk6UeKPa5JMknAGI0BAJxg4CqqQiEPV/mVpUq6eo9aE3WF7eXfHfzVfYhE+Pk+Y+PfX9/+TdHq9VO7U+i/QSPj49aOjzMidPmDcIfBfX3/XG2o1evVJgaxXIhu7xVV9pM97dPkuDJfy8cWTmGcoQ5gxRDQ31e3lGy5lStyepMTpbFwG9ND3plSnyEL7/nq2YbcbSq/VBFW53a0KQxAQACRJxgMS8ryndEuJTT2eFMwPH17YK0XXbL0J3/kDBCUPjUnH7Ob9oh+nE6EaW/2rZ7JqxojiBNl/5VNh+RjP6eGzbXwcs2g1l50psLgIZVVcKPv9+dTxmDH8yLvoMXfQeFq2yvK95Vf5HCiXZECKVlC1c5bG3tR6XZCs3lrvVkH8XRmStfL9j9ZdERl14vPmxuhxmCH8xr+bsWRw/BPP+eDBP4RIp8KzVwWu6de7QiEAiEQ5BvKgJhz9dHq10f5Aa/nq77cHaymHvVZpC/kPPuzKTnJsZuKmzaV6Y8Wt1epzJqTVYAgEzADpfyxkX7TYyTT46TMyipLhEk2aw1K3XmDoNFymPJhWx/oQsflIHMa7suvrgdmsW8SxL8hY9sPE/SGQj7j6NV7XmXOjxV3O1OsIS7+ubBr06N/yWvdm+Z8nBlm75btzOPxRgVIc2OlS3JCAn1cTD2VqrUfbC/fM2pWn0vPdJ8NmNJRsjj46NjHeW3dMBYQgAAAKSh7CcoAc1Nh63qMqY4xvWhbsAJmcEJnmqq2w4lWm8Qprb2fQuk49ZzI26ye8mqLh0g5qUusWkrLa1nWH5DvHqV1SVwRqOrtO17G8omBcX1fKndBLNfwCNU5iuXFjDZb2VMfyYl+6eykzvqSg41VWqvdkANFfhkB8bMCUtecvA3KFcvVSvzlLXUNJxuihgMRRsZwwBBkvV6dRAfTa8gEAgPQNVUBOIqSpq1JTQbPv/DYLHtLm2ZnxLY8yUJl3XH0JA7hoZ0Hcli4HTUgE1WYv2Zui1FzfvKlMqrO0WDxNycWNnsQYp5gxTUUl9voDZatxc37y1THq9pb9KYlTozh4kHCDnRMv74aL/J8f4ZIZJvjlb3ZY4KABgdIX1zTzmsaBsLG+mkqZ0EirlPZ8c8nR1DkqBFZ1LqzCQJ5EK2TMDubeTMZCWe/rfos8OVzif99GbbN0erVx+reSgr8mmegjQ00lxqFwx+CADA2nEBok6vuX4HrDQVACAZ9U3r1tHQZYTtIYmOQ0t8eQp2QFb3pwl4b3UfYG466NU01UoQu+uhjUZvry1xmKZC6SimRs+PoYTNfTgp6+GkLAthazBoGvVqndUs4wr8ucIAnggAcLCxgr79TBd76kuppakPJI7+7MJhg43ulgpJgoePb3z4+MZ0v+BHkrJujUpn4V70FUMgENcNKE1FIK7iWDUEH/YujlS1O0xT7eCxqP+fbSXITw5WfHCgoreR13q1cc2p2jWnaqP9BKtyYu7JDOvfzokmjenDAxXfHK1WGa+6+7HYCK3JWt6q21nS8uzW4vQQcWGDpo/XJmAzrYSzDluPOFgBUyMHw4C/kOOyNl7Tbpjz44n8OrWbYQmS/PhgxZSEoMEsSLkTzmRK4gEAFmUenIAAAAAsracgRmMIwnwnbW/dNhaujHBPSJtJlXuPbE4hxrjyiyOteq9eFC5WVbFX4xepmlx2wLrP0ZYqh88LWGz66RY1xGzHqlEAABbOCBP4hAnsN7MK2ushLuAs1WnYMIHPEynjX8vfBWslZ1rr7jy0/qUzO3/KWjQOtQEjEAhX3OhjbwiEHRdbYLqQn67tgBitJzXthuyvjjyx+YI7skzlrbqlGwqmrj7WqDG5PNhL/HO+Mfm9/e/uK7PLUXtyplZtsfWpltvICOmZenezO3coaoJTlncfpc48dfUx93PULna0JcJaA8svA2OJAABWTQWsmAAAq7oMYjQAANMnWTbrFNt/FNywPbGqy/TFX3Z/pnvKOvAh9N6V/IE7NVqmdrw3FMLvN6XZCKHU01NqtDD/48hvo570Pp0yIdkHppYSAKBK25az/ZtX8nfCDYtAIK4/UJqKQFyF0m1tVXfYV9Ya/MquhzcWVrXBr5+UKnXDPj50qMKzctDOkpasz3Ob+iNTfWXnxbk/nnRfvbYvwTDw0Zzkcw0w09RWvdmRNLG3IEhyzg8nqeXG/6jGkCScIjsvYlHnD6RFBSXg5WhmmNEAAIAkrB3nmdJUnCOHHLkHuvPvA/JK0ynO8fPq5XCOL8RopBXmzl1P4E6Ntpsdf9MOkrruavESab4e629rrTC/nys1rbbevYKcI2Cyd065L0II8y8KAGAjiZfP7IRYp0UgENclKE1FIK7CbIPW89lJvdr46aHK2Lf23v/H2U6pJCg0qI1TvjnWrKVyN1Om1E1bfdy51DB0vsytenlHyYD1uro3M2x4mLTRba8gdyBJYLD03UTcDycuHami2MJaYQreoRlOfw04V8aLvevyA6p3xg4hSYjvJKkv/aFlY1Lb7pn6kq8JUwu8yI6x6evNTVeUihniOJgK1RiDwQ9i+Q7mhs0VZ34kn3OWHZgDLTgAgOlAoAsiVpi/WWAlCIcj2f2lNCtl87r74rgJDlXA3Giz0hFSDuKLd0+9f4gfFbMr57x4Zse6cq9YQyEQiOsDx2kq8k1F3LDQGRN1gpUgvz1WnfHRQQoNmQ5Z8de5ShoV2jN1que3eXfkrDtHqtpW/l3YZ5fzlAkxss/npwAA9BaYmRWGeevPqSc6s+0Fer/QVxruMZMsmssQpb+Gsy+3OP6n9wuHzkZi+hDGlrbds1S591pVfarOZWq4opiKs32YPtC6rPkxd/nfXCebnS/N/luQ9AhTmoJzYdaHcY4MYrSeCJhsiNH4TJZDabF54YM4jH4Q41gUlcbEPa4HSHofZ6XGhsoCOqdHi/yOzFj5ZMp46O/hYyf+UcHw3UEgENclqJqKQFyFQuTFsbGLLbqJ3xylP7K4r0y5sZCu4M1XR6rg9rj2BkmCRzedd64624/kxMo23j3ssoMoVPgsRp9JK2/Ir6c5clxtVrzXdBudCGzFeH7csq6HDEEYnWh2MIUR9IPYtNXKzRmm2i30Q3mKteN894fc0FmwInNC7T1gOyWsYAE3Wk9CeggI0SG0l2i+HP7SOAj9Ah6BA+zOmKEUTowSQW4L399IV8Ccw2C+O3Rm1U3PPZOaDbEHuNmofePsbljREAjEdcaVOzPkm4pAAACSFXCKNr3RqjNPXU2xWbcLKDYtVoJ8cw9kWRqH/Hm2/kSNd6WkqMFm4O/MTNx5/4gub1sRB2bxM0AEuSTihL8LIejcfNGyYLVyNrVzmdIUafb/AHblDWT6Dqa/pG7xU2lGIIzKtl1TbbpLUNbjKXbX5cUuBTjd2jUAgCEI44RMt3uSLR9JP/KVaP6jIUbrSbJPAAavx9XJDOprQ6b6cSBbBDuHAOTCvb9Uaz1Wjx9KyT/GCVWadigONwqe6K2M6ZU3PVu+cFWcGE7R/tuSY/0lwoxAIAY4qJqKQFxFVpSvt3dsatoNz22l3p9Z2abPpTqCaMfGcw0Qx2V747vjNd6+E713ogAAIABJREFUhKeIuczHx0eXP5fz1ISY7i2Ckb4CiFfJCO07cdHcSjh/Eq803Ptt+y0A8+y/Bk7QJL8pu7vafTth+w3F2NDeAU7QRJoRVMf+z9veKk4grVf1UDDFMYKEFfTDioa+i+H2TbMsvwyGwON5SIfgXH+2fASUUL0h5wr5TAgZeycTep9BlbJ5nwyfCzEldoc6vWraru9aTZ4NaKRKA2VcmN9FJCAb9DB7Z/hMNiyJZpXZuLe+LzZMEQjENQdKUxGIq/AXcsZGeVeHEwDww4lLZ+ooKpfuKVVe3T9LvZnWaCUOQ0pvesNgsR0oh2kfSoFJ8fIJMbLpif53Dgt9eUr8wQdGt7w65f1ZSSES+2pnahDMWvqSIXBSBZc0a01tejjlCAJgL9fehmdtY0oS3DkeZ/uIh73vO3kHzvXv8RqLF3ETlFUxxbEsGZXmyS7MzUeMVX9BWQw1eiaTwrSXcB4tqw+2Yjwv8maHF+PH3U8nchf8ODhVXyf8WXUWSqEPAMDE8QURzqrut0cPeWfoDCjXcp+ijqYnT2726BQcwyYHQ+61hvUmd5LbVEnS+K/HjkNNMM2rEAjEdUM/KAogEAOcx8ZFeTuzIkjy44MVP9+aTuHcfPv8llZx4EydampCjwQDHgX1apO1S5eIhClw6jZfzk+JkblVmsiOka0+Bqf2y2UxZiUH9HyeJEFho7pMqW/SmHRmq0zA9hdxMkIk/kLqQ9HNWsgeP828ESlzzxsv/aMv/tLcdJi09bAMwXCWNIUXtZgff78TcSNhyipD+RrSRtddQzj4RZp/OZpTq+hs6NAHY9ubZ+Jsqd/kna3bxhJmKi3xTHGMdPzvvb0tgkGP60u/s2mrKUS+skJegHDQU3QiuMObZ/fACnV3zDAFz8VO05Mp4/lM1hMnNxttfSd1/nNZ3v8ljBrmSSvvndFDf4WqggtX/ahI1QwxWjHUaAgE4roBpakIhD2zkxWT4+U7S7xrU/H3ucbvbiZYDI87GqraYNoMwo3WkzJld9PFfshRmTgWJnXXUWNWskLEYWpgNELfmm7v33DyUsenhyp3lrT0nEzGMDBIIb5pcOCKURF+Ao+FT42wbW/0FhvAcG7YXG7YXNJmtLQct+mqCWMLaTXgPAWDp2DJR+Bc1wKwDFGUIPFhbeG7dBbDko/gRd9OJ4JNU9HdD6ZfYIpjHTwpTZFm/92+bwFh8qypgSlJ8J203UEF+z8wBk889L32/Y5qrW4jHvoexLZthxR1NJ1prYMV7dFB49w57IHE0VkBUcuP/nm02XUaz8RxK0FXAJwgydfyd/0z8R73TwkX2u9r0MQX6lxuk0EDMVoj1GgIBOK6ATX9IhAO+GhOcpesjpfQmKynKfX9qk0w1SbUXp5N3eHlbN8lgwJFbLf3AgRsxuPjIfgrshn4uzOvOI6UKnU5Xx3N/PjQ2lO1DtWzSBKca1C/uL0k/PXdz20t9tS8lwvb9obfLSDG4LIV43jRdwiSHxcOfp4ft5QTOtOdHLUT4eAXWH4ZlFeCs6U+o7+nucFhqt/Vv6VUAABL7lhmlq0YL5tdwFa4lV91woteIpuVxxCGOz+MG3GTKP1VD5Z4NcLUZ3nRSyif7ib7aCvQdqeoo8nNI1N9A4/MWHlg2oqbIgY7zN9wDEv3C352cA4sifJtdcUeTahGinxZOLTPtQ+b58+FaRAFV/RIb0USSggEwgGOb8SRbyriBicpQLTpnsxp3x4zWmEaadpxoVE7PMzjLXOzFebH03v1TZIE9/9ZsPZUrdeu4BaT4zxraX5qQvSPJ2qq22kVmd+emSj7ryi69lTtir/OuVmh1Zltb+4p3XmxZf2SIdF+7mqo+AthOk8CAOh0INuBsYS+k7a3bhtDwacUY/CkOf8wfZJorsHSCrN5kgoYgxM8FQAACKulNc/UsNfSepowNhOmNpwlxrkyliyTrZhgrttubjnWexCcEzJdlPocq3dNI5u22tSw19JylDA0EkYlYLCZkgQKwlGCpEdEQ1739CwKnGuHoFDdxdm2hvnhKe4fP1YRNVYRRZBkQVt9uaa1yaAx2qwyrkDBEw2Thfpy+GvLT8FKU60Esb+hzPnobHfYOGOIX/DxFjgzCKP8I6DE6YLLgDmxzIOnoYVAIK4nUNMvAuGY8dF+f989bMmvZ5Q6yIN/XdSpqNia16pgtul6r2j8zJYiWHOelMEwsDjDvvnWOTwW4/tFadNXH/e0pNlFdqxs5ZjIzp9/yau9a/0ZT2908y51jPviSO7KMeHutSv7Czl+AnYrpD9UXz4rAKp7MM6V+U071HH4bo8MSxnCCJ+x69j+o+gvwKatpB+EDpyQaThLrLvwie78hzadow/Fpc0AAIwl5EffyfBJtKqKCH0DYWgEDA7O9WcIQtmK8ZzACb13+ZLGS//qiz411dO1oMQ5fpLR33HD5tKM4yY1WphWVTU6j61fwH+F03Q/B18U+W31tBd1hYL2BvfTVADAzNAkWGnqjNBE1wd5ghyqEDHcaAgE4rrhyh0q8k1FIOyYmuBf8MS4+zac3VLkbi+ZR+jMHjfcFjVp69RUktveiPLzipHgxsLGd/f1v8fAorTglECxp2flxMp+vyNj4c95NsLjQsrQUMmmu4cxcQwA8O+Fpnt+z6dWjKlTGaetPnb0oTESrlt1htERvv+cb6RypR5keUHpGufKfSdu1hV9oTv3lk3v4tYfY/L5cctE6a85EWfyCMJMUVUbDhjGC5vb8neCTe9iCJO0aPXlP2MMrijjbcGYH93sdbBpqzsO3mZuPkJzmTjXn5/wf4Kkh/Eeak/eA678rMZCV6zLjirPLU+dUKnxbAL5zpihrxfsNtGWehKzuLdEptEMYkeCBKbwHtxoCATiugHNpiIQzggSc/9dmnnykaxFaUFCDuTCI4fp8QdwzalauEN2Q4LhS6SYbcSTmy9AD+spPBbj7RkUawhzByk235vpae/rrenBu5eP7Pw70ZttK/46RyHR7aKoSfvqzotuHjx3EC1rk+7MgxfqajBB4oPyhZWS0T9wQmdirB7bBziLHTBGNOQN/4XV4syPYeWo/Q9JdhxZ5jJHvXK4zag+8Uj7voUk4TqFM9XtUP6T5mmOinNkbMUEll8GJ2giL+p20ZA3ZTOOBSyqF6W93Jc5KgCAAXV/HOIwZydaqHmv2uLZDmOowOfR5LH0r/v84Ilw9ZMAAKP9IyFGGxMAMxoCgbhuQE2/CIRrhob6rF+SYSXI5Hf3XWzRuT7BPWo9b/rddRGmIpGAzRgd6QslVJPGtPlC095SZd6ljpoOg8mbM73ugGFg9c2D3Wyadci0BP+CJ8Y9tfnC+vx6i+MG4Cv+OjEywStT4m8bcqVv8O29ZZc66LZnf3646r4R4fH+rrVPbk4LenZrUaOG7l21QsRZODiIZhAnYDibH3s3P/ZuQFitmjLC2EIYWzC2hMH1Z4iiMKZXev/wfs94PS+pG6v/pzp0p8/YdQDrdTPL0nK8fd8C0urxNxJhUpJWrd/U/RgLpqwOBXzY1D+hPZGw7Z2QBxS451IAz6bmrCk7Vaen3g4QL5E/lDSG8um9ESKQZPiFnGqFID0gYLInBjrQwUYgEAhUTUUg3IWJY+lQa4/fH69Z+Xeh+3ewNoI816CGuIB5KYE82iKx5a26+/4oCH9997INBb+dqStV6vo9RwUAfDA7+fYhnk2l9kQh4vxyW3rpquxVObEZIRLcvvKD+QnYC1IDN9yRUfz0hO45qsFi++wwhHlIs434+JBbxvcCNuON6Qn0r/jm9EQBG3JJyjE4kylJYAdkccPncwJzmNIUL+WoAACGMMJLkb2KoXK99uwbvb1KGBradk+nkKN2YlGe7Mj1wB/FS0SLYXaYx4jcFaB2E7h5L4VoIhZn2+SlUqrJvIwr2JRzD1zH1C4eSBwNJc4dMUOFLJjD8AgE4roBVVMRCA8YES79PR+mqMbnhytJkvx8vlvqlHUqI9wM8DbaidwvebUr/jqrM0P27aSDhMv6emHKLT1sS7vQm22HKtuKmjQtWnO7wSITsGUCdlqweES41KF1TbiU9+b0hDenJ3QYLFVthkaN0WAh5EJ2gIgT7cfvkbsCAMC24uYOAxyLhQ359V/MT3F4FTvuGhb6w4lLuZWezb91Z0yk753DQiifPmBh+kIezOsztGff4kXf4dB7Rn1qladuq3YYq/4w1e/kBE3u+RJhaDI17DE37rOqSwmjkrQZcI4M58rZ8kx2YA5bNhzgcG4ehslCocTpJFMeBjEaACBKBDOLphYtRRq4MefuuXt+bDd71p0hYfO2TLw3XiKncFF3uDNm6JfFR/KUl+gEEbO4L6VNgrUkBAJxnYHSVATCA2YmBTz2z3m4hk1f5FalBonvG+HCBRF4weNUyqNuA0CS4P/+OvvN0WqI63ECh4mTJHCuvstm4IszQl6ZGh8icVC1IEmw82LLRwfK95W1OowjYDOmJfg/MSG6N5cgHx4rLZgFgGtNpgPlrS6PcZM2vaWgXu1OGR/HsE13D8v6IreoSUvhQrEywV93DXUnH77m4ATl9PcSKELaDJozz/tkrbF73tKWbyi3f5IC6pNPyOcUdNdqsrTl6wrfNVT9AYirvm1smgoAgKl2CzjzEkMYLkh+jB97L/0CeHZgLBtnmAkI+1xSNm847DR1iCP5X8pk+FHcAxqriCqY+/jig78ebHSrt6ITldmwaP/ap1LG3x8/0hufaxzDPsqcPWH7V1aC+ubpmxnTAnj93ZOPQCAGKo6bfpFvKgLhkBiZIDsGcl8ZAOCZf4vccRMZUB/MVVuL+ixHBQDcNSy04rmct2YkToqT86/uSuWzGZPi5G/PSKx8PufLBSkYAAX16lKlTm28cp99vlEz7OODU789tqOkpbdcV2e2/Xm2YcQnh6d8e4zC2HB3Chs0dE6343yju9H8BOzty0akBXssbpwWLN69fCREu9QBBVOSyPLL6O9VUMRY9UfPqqm++CtAQmissLafMzcdvvyAsGryX1ZuzjBU/GaXo9ph01arjz/c8nfSlXOp4sfhzwkbRDNIJ7dHD2HDllCaoIiBJcvEZTDHKqIonx4q8Nk7dfnv45cMlnowOl6lbVtx9H+TdnxDZ7rVCWMCIn8acwtG1X77/xJGweocRiAQ1yWomopAeMb7s5KHfnyQjoJrT9oNltd3l340J9n5YWL3vEncR0RVu3hTYeM7e/vUb+b74zVzBimeyY55JjuGJIFSZ27RmQAAcgFHLmQfr2nfkF8/47vjZ+s1RLdk3l/ImRDjx2cx1ufXGyzuVmx2lrSkfXDg51vTZiQGUFstXG9bj6SYwqS8Yw9lrdpS9OnhSnf+Shk49tCYyLdmJFLQnb6GEKW/0rZ7Zn+vggqkzWSq38mLvKX7k6baf2HFN17azA7IIkxtbbumWpQn3T/Rpqtp3TFBkvkpP+H/6CzgzYxpm2oKaRZURSzOs6nwa+YyrmBGSOLGmkL6oeaFp4joTWAyMHycIuq509u6abe5xd6GsuGbP82d8WC4EL6M8+3RQ1QW46PHN3n6G/y/hFGfj5gHfT0IBOJ64sp9CfJNRSDcIS1YvHQ45NYyAMCPJy65zKOCJVyWo+FJamAYiPCl4lLQL34zVoJctqGgcwgWw4BcyE4KECUFiAob1eO+ODLik8MfHqjIr1MTVxecm7Wm3/Prfzzp+r21o1Vnnvdj3vbiZmqrhTusqzV5Fo3DxD+ck1z89ITlo8Kd6CEJ2Izlo8JLnpnw4Zzk6ztHBQBwQmZwgqf29yooYrnab8amqXDpPetB8KbDpM3QvneORznqZQir6tgDhrKf6SwgRiyjX1J7fvDEQL7HTQTu8EbGNCZO99PBxhkvpTmYAfYItcU4bdd3ZWolheJlnV41Zee3SiM0mfrurEgYlTvjwVixu31Gvhz+HxPu+HLk/OtyxACBQEAEVVMRCI/5dN6g307XwZ0UVRktB8pbpyY4czln4tgghehMHZz2rTi5kJqm6+pjNaVKr9zuOKdOZXxnb9mrU+M7H1oJ8vVdF1/bVUp4pxfaYiNu+uXU0YfGDFJ4PDoFcTcBAMCmlEPGyARfLUj9ZO6g49UdByta69XGBrURABAo5gaJuWOj/IaH+ziUjLpekYz6pnXLKPf9SwcOVlXJVQ/V7rrpuhVcXaI6ch+N9l1SdeQ+pu9gFg2dqreHzihsb9xVT/HfNTss+fFB4yhf3TlJPgFL44Z/XXyUTpCHkrJ6ShldVLfsrS872lLdbNC2GLUcBlPGFUQIpdmBseMUUT2tep7J23qmlfpfb4mq5ZETm9aOvY1yBCcMlYWen/fkbxVn3ivcX9je2NthQXzxw0lZyxNGilkD2joIgUAMEFCaikB4DJuBe2Mb+HBlm/M0FQAwMU4GK02dFEdRAfL74zVQFkCBTw5VPJMdw2czzDZi9vcndpTAdJHtidZkXf7n2UMPjPb0ty3lsargLcOXT73Zm83As6J8s6LguONe0zAEYb6Td7RuzSLM7f29Fs8gTFcpchFGmH/2hKndUL6OTgSSMKuPPeg3/ZBnrajdYOOMPybckbP9awo+nDmBsb+PX8Lo3V2WPh8Mm13QVn+0meIo/nB52MvpV0qpBEluqS16s2DPsRbHAT+9cJiFM26JTHs6dUKyj6LzyQsdTd9ePEZtAV38Wn5mefzIMQGRNOM4hIUz7ogZekfM0DK1cm9DWX5bfaNB3WE2CplsOVeYLA2YoIgZ7BuEKqgIBMJ9UJqKQHgMSQKNGbLoLgCgpNm1QOuSjJD39pVDuRw1W9FmrSm/3itqHO6gNlq3FjfPT1Hc9Vu+t3PUTnIr2/53rmFBaqBHZ8XKBbB2EwAAsTJvGYreaDB9kmWzT3ccuM3cQqs41seQNuPVD01ww9MPYW7ONV7azA2dTTmChM09POPBp/P+/exCLuneknAMezBx9PvDZsFSOeoNPpO1MefujE0f1+o7PD13kFSxddJSAZPd+bBK23bbgXUuM14LYVtTfmpt+emVSaOXxY34ojj359I8Gw1B3U5IQL6Sv3PXlPtpxnFOjFgW43YDMAKBQDjhBur4QiBgQQLSG42mLW6I/aYEim8a7IHSY2/MSAwYEU5FTuNETUf/6g0//e+F57eV/Ham71o3PzrogQlEJ5mhPhAXsKGgwZ0tDIQ7MIQRvtP2i4a8jnNddxMwRVGUK4QQsfN9wZj27aADAX3JNzQjcBnMT4bP3Ttt+aSgOOdH4hg2L3zQ8ZkPfTJ8rrdz1E6+Kj5CQSw3iC/eM3W5L+eyBMDmSxdSN37gflWWBOSnFw6nbHz/6+KjBhscH+a9DWX1ejWUUAgEAuFtUJqKQHgMjmECNvxOBDdlfj6YncSnNFPaBYuBfzA7idq55a39MJXanYpW/Vt7Svvyikeq2jqnOt1nOlWJYIf8eKIm6d39N/9yqkkDt4x2g4LhbGHqc/4LqySjvuGGzcHZ9nsKOC+QF7nIJ2utfP5FpiS+XxbZHQb/qsYHnKfor5U4wdywh7RBELger4jeOeW+s3Mffytj+uTguCC+uNNmhsNghggkM0OTPsycXTz/6f9l3zVUFkr/cu7weVHuy2d2ulnj7U69Xn1/7p82kgAAHGgsv3nfLxpLP3+ECZLc09Cn358IBAJBGce32gPKnhGBGICE+fAuNMH0xgQA8FluJZ+hPrzP5g1auqGA8sf04znJ8f5Caud2GOB3Ow9wSBIcrGhblOZBETsxQDgm0vdwpb3dJWUIkvyjoP5gReua29IpDxUjuoMx+fy4+/hx9wHSZtM3WNWlptrNto4iS3uBzag0VP5uqPxddfQ+jNH/Wi9Mn8TuD1k+FPeYvAppM1laz7D9R0GJliINTJEGPpOa3flQazEJ6bm5UGZ7XfFDxzZSPn1jTeEbBXvuic2ctfsHo21AfHnmt9Yvib5WbYQRCMQNBaqmIhBUGBwE3/xAIXb3hviezLA3pye6Ps4Rz0+KXTE6wtOzNCZrZZu+w2CBaxh7rVDk+ZbEe7OSoGuFNGlMM747sY2qTQ7CITZ9g/bc2+17ZurOf2Ss227TNwDicoMladUTJmh7DZRhK66SscV5gUxxTH8txgk2qBLE3emvHNVC2B45volCHbU7b5/dO2/vj/1eR+2iQtPq+iAEAoEYAFyppiLfVATCfbJjZdDHIxM9qXA+kx0TIxPct6Gg3eDuzBKPxXhrRsLDWVFuHn+ipmNTYePeMmV+ncpovazewbghvyjqPWz6BQCMCJfePiRk7SmPlUudY7ERN/9y6sCKUUNCJHAjXzMQFpuxhTApMZyNc2Q4l5ZYi7Hmb1Xu0oGQi/YGzvFjB9i7rXDDF2jPvdMv63GCnSLxdcC3JcdKVHSl2gw2S54S8vcAHdQWj7/NEAgEol9ASr8IBBUWpAau/N+5ruQNCmOj/Tw6fmFq4JBgyXPbiv8sqLc6LXLiGDY/RfHG9MQ4uVuasbsutry+q/RghYObTtsNORGgM7s1NmzHNwtTK1r1R6ogp0Bak3XphoK8R7O8be1AmNuBzYRzZADv//8prO2FhsrfTA17La15gLjSPImxxBzFOHbQZF704p5Tps7RFrymOfMi7JVChhe3DGPY1xL58ct1Fz6xUwCmAIYzSQJaJypsCeL+5+uSa0kR2k2wAaAKhkAgEO7Q/zcfCMS1iJTHWpkVCcsbBgDgL+SMjvBYejfKj//b4iFvTk/48cSlvWXK49Xt3fNVJo4NDfXJjpHdlRnqpqmJ0Uo8/e+Fzw5X3pDZaK9wmVQ0q/hsxqZ7huV8dfRsA2RpzTN1qp9P1t6dCVlChrSZjDUbzQ27TQ37bLqart5XnBfAlo9gB2bzIm7CeZ5589DH0npac+ZFU+1Wh9YppEVtvLTZeGmz5vSz/LhlwsEvuJms6ku+Hvg5KgBAlPJ0zycZwghB8qPas2/RCo0xMLaUhOfCaqdIfK1To+sobG/s71XAR8zunw5qBAKB8BSUpiIQFHlhUtzaU3WeasD2xgOjI1gMirPikb78V6fGvwrijVaiWWNq0BgBAAFCToCIw3NPlqkTndk28eujx6rbqS3jOkYmYFM+8fgjWd7I/N/eW3rXsFBY9VTSZtBd+FR34RPC0NDzVcLQZKzZZKzZpMl7ihe9RJj6LEMYCefCrtalu/CpJu8pknDt1URaNLrzHxqr/vQZu4YdMNb5wZa2fNWxByEt0otgLBHWS9YtTFllrPrTqqau2ipIftTcuJ+Al6baKRJf6+Q2Vfbn5UlveSFFiTxr20EgEIj+AkkoIRAUEXGYq29OZeIQbiVCJNzHx0fTj8Nl4mFS3vAw6fAwaYQv36Mc1WIjFv6ch3JUh7jZLO0QLhP/ZO6ggw+MnpkUALFL92KLrqAeTpHWqipq3TJSc+oZhzlqd0ibSX/xu5ZNg/UXv3U3Ommz6Wotbfnmhr2W9rMuL9H9zI7D96hPPOJOjtqFTVfTuiPHWONCmlWT9yQgqTRy9zEsaWpvL2Eske/kHZSL29ywueKMt5k+yVSX5gCmFGa0fqdUrezPy3utMzfd97raTUAgENcxKE1FIKgzIzHgp1vTaeYeOIZ9Pj9FQM8KlT5v7inbjiRke2F0pC/NCGMifTffm/nlghQo6+lkXzmE22hz4z7lPxmWtgL3TyEtGtWR+zWnnnF2jM2gv/hd+955Tb/Jm/8IVf6T3rojR7lpcNPvQU2/B3YcvM1Q+TtwOhWpznvaUPaT+6u6AmHtOHCruelgb6+b6naY6ndTidznsBXOysIMYaTflF1McZynYXnRS3zGrQcYw2XZ2X1wXgBTkgAr2kCg1dTPHtHegIHhOUGx/b0KBAKBcAvHaSryTUUg3OT2IcF/3zVMxKXeP//+7KQ5gxQQl0SBOpXxvX1l/buGAUusTJBA1WbWjhatB4VBl5xvpOvca2k/2753PmkzUDhXe+4dXeH7Dl4gLNpzbzf/EaE6ssxYs5Ew29fnCUOjoeK3jgO3NP8vTn/xO4cTp+aGvbrC9yisqhPSZuw4cBtp0Tp8VV/6A+XIduAceZe+EcbgMcVx/LhlnLB5sOJzwxc4P4DpkyybdYoftxRgbm064xw/n7FrfbJ+6Vw2N2xuT30mavAibvJiBbA/MBPXQL3dUyYGxSp4ov5eBQKBQLgFqqYiEHSZM0hR9NQECgU3FgN/f1bSo2PddYjxHm/sLqUmZnsjsHxUBKxQdSqYVhCXOqikl12QNlP73nmEuYNyBHXeUxblie7P2LTVrdsnaE6tIoyuK/M2baXqyLK2nVMIw9VCNaRNdeIRyqu6HFxfpy10ZNlCEuaGPTSDd8EJylEsMSpuVylu1yiW6OXzSySjvhVnvA7l/1a2/yiWX4bLwzCWUDJqtXxeET9uGcbu1aaIKY4TZ37sf1M1L+r2ridxji8v5m76S8VwNj9xJf04Awoeg9XfS4AMjmGvpk/p71UgEAiEuyDfVAQCAsES7uEHR+8vVy5ed8bNVCTeX/jLremZYZ5ZaHgDs4349TRkD9jrhig//gOjI2BFg7sXoDXRiqY7/6FNU0FvCaTq+MOyGUc6y2hWVXHrtizC6Fkrsql+V+u2LL/puTjXv/MZQ+Xv1vZz9BYGAAC6wg8EyY/bCf9aNRUQ7T3NypMAAIwl7nqGJMzq448AQN+qChNnfuT+0UxxnGTUt5KRX1mUeeaWY4ShkTA2AwYP58qYwgi2YgJDGO7wRFH6q8bK9XR2KwAA/MSVFHqPBzhBfLHrg64p7ogZmikP6+9VIBAIhLsgpV8EAhrjo2W1L07adbHl7T1lBypabY68TDEMZIZKHxsXtSA1kAFDfok+x6s7VEZLf69iIIJh4JO5gzhMaF0nVLWcHcNmUP/7IS0a7bm36a/B0nIsP+/XtKG32/R1bTuneJqjdmJVl7XtmuY3dT/GEgEADOW/0F8YAIC0GYxVf/LjlnblAWU/AAAgAElEQVR/0qaBZiIFALBpqwBh7e4rq8pdZqrfRT8yP2E5S5bp8WkYgyUfzpIPd/8MnCsXDftAlXuvx9f6D6Y4VjT4BcqnD1iSffp5FgMuyT6KDzNn9/cqEAgEwgNQmopAQGZSnHxSnFxjsh4ob82vUzdrTa16s5THkgs5SQHC8dEyuZCiu4mXOFVLq5ByHfP2jMSZSQEQAxbW050m7Y6UT70p0Vj9P9ICRyj4dO4XLxTErg55ldTVUA7SaY4qzvyItJnMjb2qH3mKqX6XXZpKWlSwggMAAGkjrbquVltz8xFD+Rr6UTkhMySZn9KP4yb82HsI3SVN/ssUzsV5gb6TdzhpNr52GeUfwcRxK0G/MN7/hAl8tk9eJmXz+nshCAQC4QEoTUUgvIKIw5yZFNCV5JAkOFbdvru0ZfWxmnq1UakzAwDkAnaID29slG9OrHxYqE9/9d1XtOr758IDGAwDz02MfWpCDMSYX+ZWnaqDmSPFyKjb5Jjqd8JaRpYof031dpKxhWYcXfHn/LilJGGlJunkEGvbGbtnSBJy1kGS1q4PrvrEww4VoTwDw0UZb3Sv0PYBwrSXAIOjOfOCc/llO5jSQdLxG/rKRLevkbC5k4Pit9YW9fdC6DIpKO7nrFsCr7seZgQCcd2D0lQEwrtYbMTaU3Xv7y+/0GRfSWvSmAobNduLmwEoHhwkfnJCzC1pQX3fCaw2eXBjeiPgL+T8cMvgGYkBFhuh1JkNFsKXz/Lh0dJTadNbXtheDGuFnYwIl1I+16LMg7UMIW54TvEThECEVXv2LW7ETRBC/YdVWwkA2V2BFmfCEW3uorNRGQBgbj4C510lCUPZGtawwRBCeYIw5RmOYnxH7lJrx3mXB2M4mx+/XDT0bYxxPRfoHh807lpPU+eFD/or+07s+hJhRiAQNwgoTUUgvEhVm/62daePVtl7cvSkoF69eN3pzw5V/rp4SJQfvw/W1gXhaIaWPjiGEb1YWzFwbFZSwPgY2SMbC71xaSdgGHDitxUg4tw3IizSV/C/s40P/q+wqu1KnZnFwDPDfHJiZQtTA1MCPa5LvLyjpE0PcwCYy8SzY2RUzyat2kqIi0nmwYlmrPmbrZgAJdRlCCtp1WPMK2VnXAhTQobBD8Lwyz38xpqNsMIaa/4WD3Nk9uNlWPIR8rnnjJf+1Rd9Zm46SNpMPY9hCEK5ETcLkh5hCEL6foV9THZgzNywQRtr+vprCiLjFTEoR0UgENcojtNU5JuKQNBn18WWBT/laTypVR6vac/46OC/92ZSsLehDB3TVycwcGzTXcNO1HRUt+uVOrOVIOUCdqCYOzrSd1y0n5THIkjyu2PVhbTNP90nMUD40y3pOrN1b5mysEHTojOrjVYfHjNAyEkLloyN8j1eo/rkYHmtI61mi43IrWzLrWx7bdfFKfH+L02Oc7+YqTPbfjp5Ceo/BSwcHES5wEtaDYAYiKJZpFVvVZd49RJMcRzG4JI2OM5ATGlq18+W5lwoMQEANk2FTV/H4AfDCugJGDd0Fjd0FmkzWJqPWtWlhLGZtBlwjgznBbBkmUxxbH+sqt/4MHP24eZKpVHX3wuhSJSo7/4rQSAQCLigaioC4RVOXuqY/1Oe1vN+2g6DZcZ3J/avGJUW3EejROFSr7TtWWyEL5/16tT43g7AMeyTeYNyvjpK80JhPrwaVw6ivnzWA6MjV+XE8FgMAMCEHnXI6nbD7etO51a2ubwcSYLtxc27LrY8MT76tanxLDfUe/eUtni0W+ESLhN/rfc31jWw5zMhQrphuOoBOBNjXtWbgDG4LP9R5oa9UMJzAnO6fraqYDZ1W1XF/ZSmXgZj8NiB2ezA7H5cw0AgUuS7ddLS7O1fay0OassDn3S//vwrQiAQCDpcucFCvqkIBCxURsus709QyFG7Tp/300mDBabHphMGB3lLpdOlOFN2jGxRWhCdSwwOEpc+m71/xagHRkckBtiPHfrwWHMGKb5akFr9/MRXp8Z35qg9ya9Tp31wwJ0ctQsbQb6zt2zBz3lWN1qmj7jR9e0RT06IifCl3hmOMfkAg+qNAw8Cnn4SAIApjAA9Oh55UbfDiY7h3MhFl38mrITJg78flxAGqOk6ggbDZKFbJy0N5nv2PcllMNm44y+cPiPZR+HpshEIBGLggKqpCAR8Xt15sUlDa+u9qk3/3r7yFyfHwVqSE0ZHSLlM3GiFX2FTGV0n6j/dmt6gNh2saKUQP0TC3XxvJpuBj4v2GxftBwDQmqyNGlOr3ixgM+UCtkzAdilJVdGqn7b6WIeBShPs5vNNd6/P/+XWdCe7fEYrsaGgnkLw3oiTC+n+YWA4QxBq01ZDWhFMMIyBMXiwxH6Zvuk9n+RFL9ade9uqLqUZnB9zN0MQ2vkzrC7iLiDKHSPokxUQWTD38RVH//qj8izphpjzcHnYD2MW/VZx5vWC3X2wvN5YHD2kH6+OQCAQNBmgG+oIxLVLTbvh88NV9OO8s6+sRWumH8clQg5zbkqgNyL3JqHUHS4T33TPsElxck+DJwWI9q4YFepzVceykMOMkQmGh0kHKUQBIo7LHJUkwR2/nWmksaew9lTtmlO1vb1qI8jF605XQrX8mRDtx6QtB82SQhOSJeEKtGAsdkAWrGCcoEkOroCzRRlv04yMsUSiIa9decjg0gxoH/+6VtC9FvHj8H8fv6Rw3hP3xGb6cRz3MvAYrBmhibun3n9s5kNJPgHPpGYPodZzC0MeJIAnWpE4CkIgBAKB6CdQNRWBgMxPJy+ZbRAqk3qz7dcztQ9nRdEP5ZIXJsX+WVDvTv+qR4g5zr5hzDbiaFX7oYq2erVRxGamBIqLmjTurAHHsOWjwt+fldRbE6/7rDtd61Gvr0Oe+vfC3EEKsSMlquV/nv3rbAPN+HZYYPya2IHZxkv/0I8DAGi3inyZaiihAAA4W8IJmgTF1hVjcLkRCx2+xA2fz4teYihfQzm2ZORXOK/b5g7OxDm+EPt+GbwAWKEQEEnyCfh+zM2ryZsK2urPtNU1GjQqs1HAZMu5giSfgBHycA7jyveAgMnePnlZ1tYvSlQtnl0Gxs7P2xnTxSzIuycIBALRl6A0FYGAzNYiaENlW4ua+yZNTQoQ3Tcy/MvcKrhhe5ufLFXq3t9XvvZ0rd7s2fytkMO8LT34iQnRsTKB66NdQZDkC9sg6Mo2aUxfHal6OjvG7vk9pcrvjtfQj28Hnw1h4I0XuUid9wQgIAg7XTBEjhEV0I/TCUMUxYu6VVv4rrX9HM1QguTHcXavasw+o78nTK2m2q0UIouHvddzwJUpSTA3H6EQzSEMSQKsUAjo4BiW7hfsjjqRnCvcM3X54gO/7m8sdz++lM17KX3yq/m72kwUGzFui0q/M3YotXMRCARigIDSVAQCJhYbcbpOBSvasWrI0jtOeGNawqGKtnMN8MpiGNbTX9RoJZ7afOGL3Cp3+oExDCT6C1ODxAFCbpCEMzrCNzPMxx1lXTc5WNFW1Q6nHfeXvFq7NNVGkI9uOg8luB0KEYd+EJyn4Mfdry/+gmacarNiozoLYprK8ssAGEOc+VHbjol04uC8AGHKU06PYEkn/KU++YS++Ev3mywxllAy/DNezF09X2L7j4GVpjJE0Qw+LWkxxMAhmC/ZPfX+DwoPvHNun8u0EwPY7LCkz0bMCxX4DJOFTtrxjd7q8dj8lOD4H7NuQXapCATiWgf5piKuHxo1pj2lyv1lyvJWfYvWZLISciHHX8geGSHNjpENCZHg3pezrm43WGB0/HaiNlqbNKYAGGlJTwwW25Gq9tzKtjqVsVlrwjFsSIikvE2nN8FRGM4IkfjyrzL2rG43zPnhREG9u5kwSYILTVoOk/HOjKQwL7jm/JEPTdnoQpOmVmUMkVxpsdt0vhFizt+dBH97QWNqiNJfNVauJ0xUxKu6eLXx7nxdLBGM4TDG6TC2hCUfDgDgBOYIkh/Vnf+IYhwGRzruN4zlwtIJY3AlIz7nBk9Vn15lbS90GZUTOkM87MPejEM54XO1he9SWq893PB5UOIgBggMDH8qZcKKhFE/lJ7cUJl/QnnJQth/zUaKfKcEx69MHJPkc7nfe5R/RN6sR249sK6gzd1vKgxgK5NGvzdsVr+LDCMQCAR9UDUVcT2QX6f+8ED5b2fq7CYbS5U6AMDGwkYAQLSfYGVWxH0jwukPNDqhnZJgrBPa9Bboaer5Rs37+8vXn6nzhrpvF7dnXNURp9SZp357rLhZ62mcM3WqiV8fPbxytL8Q8vuw6UIjxGinaztCJIquh7+droMYvAsMA1lRvlBC4Rxfyahv2vcvAiTFjYnf23O2qUYCAA5p0saJztBfEi/yFgxnd/4sHvYBYWg2VKzzOArG8Mlaw1ZMcPNwTuhMeegM06Uthop1psZ9hKHJ7gCmJJETNJEffz/TJ9lJHLZ8JEs2zKI86fGC7cBZ/Lj76AZBDDyELM5DSWMeShqjtZjy2+pbjLoWo5bPZPlzRfESebjQQYN6ok/A0Zkr3zm77/Oiw62uKrFjFVGvpE8Zr4j2zvIRCASir7mSpiLfVMS1iNFKPP3vhc8OV7rsAChv1T2y8fzXR6p/XTwkPdhbVnIQS6mdQFFj6kJntj2ysfD7EzXebpgIlnCXDg/vemgjyJnfnaCQo3ZSqtTN/ynv4AOjINbDTVaiQUXLNMiO8qvlfPeX06pS9saYSF+I6To3fIFkxBeqo8spnLtbM/TJ2gc7f36z6Y4sUT7NgirG4AlTVnV/wmfsGpZsmCbvKZJwV/Ia5yl8xvzECZ7i6cU5oTM5oTMBIK3qcsLQQJiUAGMyuHKGKArn+rsZRTz8k9Yto2nqtAoSH+qtYIu4PhCyOGMCIt08mMdgvZw++cmU8b9VnNlRV7K/sVxp1HW9imNYqjQwOzD2psjUEfJwJ3EQCATimgNVUxHXME0a0+Rvjp31pLWyuFk76tPD3y1Ku30IJZ8AV/BZkD9TQg4DAFCnMu4pVR6saK3tMDRpTTYCyAXsEB/u6Ejf7BhZjHt6QhdbdHN+oJ4resS7M5ME3ZR+vjtec7yG1pxtbmXbTycv3ZMZRntplzlW3e7OfKz7qLuZxDaojUqdV8yEHoItqcWPvx/nylVHlrmvUkuS2Lr2/2fvvgObOLIGgO+uerVly70XuQO2wVTTTAslgVRSCOm9wYWQ3KVeLuW+S0ghFRIuISGNNCCEUE1vpphm3HDHXZat3ne/P5xzjCyr7UiWzPv9ZaTdN4MBo7cz897sl1ofsGB//hGf16f83D39ZkkxnZkIRjzLENp8zsYFWU+xwyeoT7/otPYvzhTwZfcKc18hOHRWm3GmOBUT21bDchE7bAJfdreu+guPh2cI4oWjXvD4djBcCZjs+9PG3Z82DsOwbpO+Xa/WW8xhXKGUK+Ay4IMcAGB4gp9uIFCpDJZ5nx93K0ftZbCQd31Xymcxrh8R6fxqN8UGo2wAQOB4vUK/bFPZ7+XtdlOqr05exjBsUlLIU5OTbhgR5aBNaIvKMGftsXoFygaeg7ktL+a2vL+eAqiNlpe2Iyio+/y2isW5MQIUdW4xDDvTgqzSFYZhGEb1T3qbegxIg/9pUlLIjV7ocMtNuIEVOrr58HJG6yanK6IX9MlvtC3dp8m3ef3FlgezuPXZvFrP5nBIOzI/5gmRvbdY0rEhs3eYFaX62m9NrcVmxRmM+muLAc4UsCMmc6Jn8lLuIrhSz0ZHKGjCGquuxdi8w4N7CU5oyOztBDsY+az8F2k2yUtMrXssygpS30GaeghuGMGVskLy2FFFLMlIDIfu7rYkbJ6EDW11AQDDH6SpIFAt/a709GUPMw0rSS359vS5FVNTQhH0NelPKmBHiDjtajS7SQVsYuanR51edrhOcbhOMT1V+vXteTFBdvJkk5Wcu/a4b3LUolTpF7fm9t+c+31pc4cGwTekTW388WzL3QVx9ENhGNag0COJ8z94/yaxWhOCRi82+GzGRzeMcGXXs5WkzrSoytvVcq1JY7QG85gRIk5+bJCDv+0MYUL8nF/uWPNjivq7KYKzmbw6m3y11SI9pB75i3LqfnWe3Qgqkr+k/qXNKc/Gs23Pdjp1Ri+7t/75nxXWlLBBr2GF5LFC8jAMoyw60ign9e04g0twpQQnDCP86T8ygiWZ9mP3vluMzdvduo8hiJVM/5kZlOmlefkb0iDXVa/TXnyf1NtpLKyv2YBhGEOYKMhaxk97AGfab20FAABgGPOn/90BcNmeavnmC7Tq3+hM1hVbLv56TwGqKfWZlhL6A6ISsmp3iu7uvSTPXbV/0z0Fk5JsNz2uPljnwbKzu3Ace2BcwrsLsznMKxZANtH7k7IJhSpNVRsRZ5JJoX9+km5TG78vRVZDuBeDwDfcnj8q2knp2uON3e8dqNtR0WG3lFeChHfDiKhlU5IHK5v8j4XX5K7i/YukghiaaJY8nNXNxiwKq7jFFNpqcb5Q2W4JmX3pvf/EfHxd0EFXflO9fuqZ/lzzIzqSqzS4VH4MZ/IZzHiGANn2b+Rwlihk1jZt2Tvq0y9QVpfW1bkJNwZNXEtvu3Ig0dd+ozz6KGV28kPJqqlXlSzTlq0Knvw1O3Kqb+YGAADAT0CaCgIPRWFIOlJuutB2sFaBqm5qn1tyo1Glqe6Sa03zPy/Z9+jE3Ji/8pkuren13dXeHjo/NuiNeZlz0m2XwygKO1yHrPvrwVqv1CVCYlS0uF1tfGl75fqTTUbUJZQ/v2WU4z3qNV3aBzae23tJ7uCahm79uwdqPzxcf09B3NvXZYk4tj//syNFj05KXH2wTmkVKq3CckOiu/NUWQUPNz6zM7jgibCf0rmNji8+qct4t+PWveo/9w9byeHUCA0XZD/NS16iLV+tq1wzaNcfgsmNXyTMWcmSon9e5qcoUnn0IV3V567fYdU2de2YIcp/XTjiWe/NCwAAgL+Bvqkg8BypV6DqSLn2WAPyNPW67MisCNHFdjXasC5SGszX/bekbOW0viTkq1OXe1C3yemTFMIvkklvzY2emWZ/v2ar2uDiKpkrFDozqkayYi7Kh3Q8FqNeob/z29JWFfpTqcF8luM15O9Kmx/68ZyL68NmK7n2WMPeS/Iflo4eWPL6neuyL/cYfjlvZx+m637pmfarcupUQelM8clJgnMyblPfFmILxijTJx3WjNyhHndCe8UGVzGXZS9YACN4EaL810V5r5q7Sk1t+6zaBqu+HbMaCW4YwY9hh49nh0/GWWi64AYKVclyt3LUP1FW9anncAZHkLXMC5MCAADgj2A1FQSerRfdPvw2mG3l7SRFIWxzgmEYk8BXX5/jyplSL2nq0b+559Ib8zJ6f/krvZRjICGHuf7WvNQwfoSQ4zRj7FAjLnjboUGTpiaFoDztFiFiz15zDG3p4D4TE+w0VOzz7enmO78tdXfoarl2+sdHbRbesd7dxXfk3baBormpnqLwfZr83kpLBEaFMFUCQq8iBd0Wu2WSMAzDEkOGaVUYnMGSjmFJxwz1PIaetny1tny1x7erTjzNDM7mRM9COCUAAAB+668jZNA3FQSKow3INpEqdGZvNGiZIZMuHROLPKzr3t1f09SjxzDMbCVLGnvQBtcYLbuqO0ZGiV1JFw0WN47XukJnRhNwdCzKeqpNPQYv5agYhs3NGLRv5+6qzru/P+PZ0EqDecG64y0Dln95LMavdxd8eMMIPqKiyiSGyy1BDaZIBzkqn81wsa8SCFCkoVNd+hKtEBSpOv4kRnprbwgAAAC/AqXeQeCp7tQ6v8hlVUij9fn8llEOsgtvM1jIL080YRhWp9AjPyeJYdhnxxobu12qlMtlokl1+vBZaAKOiQuS8JDtMvXeuUo+m3Frnv0evyYr+ejP581Wz/98m5WGlb9dHPg6jmOPTUqse37Gi7PSpAL2YLfjGLKHm1OTQ9kM+P9oOFOf+SdlotsFyqKs0FWtRTIfAAAAfg4+FoDA06VDuY+0S4t4V2ovFoP48a4xQ7im2rs1GkknmIGsJPVtabMrV4aLBk1yPBMuRLDjF8MwFoO4Pd9++udXVk5PHSxRXLWvplpO9yHLt6XNR+vtb08IF3JevSa97ZXZJcsm/2dB1sMTExblRM7NCF86Jnbl9NSf7x5zdsVUB6163RIQfxbAY5RZo69ZjySUtuITJHEAAAD4OTibCgKPicby0UDeWGzsJWAz1t+WNyc9fOXWi81K55V1EkP4aoO5S4dmS9upy0qDhTQg2iI70M7KzueKUp1eFiXiBnFZqKooSXgsJAdTe/19hmz9ycsa1J1pEIqX8J6ZltL/FZOV3HS+bUdl594aeV0Xgka4FIWt2l/zU+KgJycZBF4QF1wQZ3+P9L1j4z475qSir1MZ4cLFudE0gwB/ZmzbS5nRHK+w9JRZ1bUMUTKSaAAAAPwWrKaCwINq22cvAaIDeIO5PT+m9vkZ/12cW5gUwrK3rZHDJKalhG64I//M01MV6EryWkmqRq7lIv1e9Xf6skv793Acm5joqAKQW6akhKIKhWFYTBB35fQU59cNERGH+evdBX0HRE1WctW+mpTX9yz++tR/SxqR5Ki9tl5s15k8fJzx2tyMINoVet9ZmG33nwYYNsydKEvKmToOIYwGAADAP8FqKgg80WIu/b2OfWKCuKhCDYbNIO4ZG3fP2DityXqsobtZaejUGHEcDxOyY4O44+IlvalIU48ebRWeLp0pUuSt353SYFbozCF8JylKY7dejm5b9aIcR71DPfBcUeqR+u7tFR2e3c5mEGjX9vsIOcxN9xbkx/7ZMKZeobv9m9OD7c6lyWgh913qmpflyVHqcCHn05tG3PGN26WG+zw8MWEIT3ED37AoK5FGq0IYDQAAgH/6K03tX+kX+qYCf5YVKUKYpmZFDlp9FDkBmzFDJh3sXeTbjw1mMimE571USmVwkqZuutB23w9nFIi2MUeIODeNjEISqg+LQfy4dPSMT496UA85RsxtVXvl6O/o2KBvl4xOC/uz8u3Jpp4Znx5VGby4OXn+uuN8NiM7QjRDJp2fFVGY5EYz4VvzYhQ682O/nB/sAhynMjn1o/iXwhg9YoZWT3EUFnGNMeaELnNWZsIH149A8TsAfo00dKKMZpQjjIZhmIUkT8ibilurL/a0dxg03Ua9lCuQcgWjJNFF0al5ITFom5YBAABwBaymgsAzNSWUZlPHPmlhgmix11dTXYSq/0f/gCwGMTo2+GiDAm3kXmqHpzrXHG14+KdzCId7fW6GkIP+R5aQw9z/2KRnt15cfbDO9btmpoUtzIl44pcLaCeTEip4elryfePi+8reXpJrF6wr8WqO2ktnsp5o6jnR1PPv4ksFccHPz5QtdHnt+tFJiREizgMbz3ZfuWs9mdP8sHTTXPGxUKadLeJWnM2NnGJtWcaMnYehKxoM/BBFoqxUR1lcKjPuCpXZ8EX1ibfO72vW2fkr+g12GsOwSJ7oofQJy7OnBLH95T8LAAC4GsBxIBB4bh4Vjaq+6M2j/KhwS5iAjfaEXu9+5ptzEa9A9nFQO+enc60Oltc8MDEx5J6xcQgD9sdlEu8vyvn9/nHj4p0fo5VJBesWj9r54Phj6Pr39np+pqzyuemPTEzsy1FJirptw+l276zZOnCiqWfRFydu+epUt8uHpW8cGXXqb1NuGhnVu+4kJnSr4949kPbokpAddnNUDMMYlMnculuxe4F8c6656zSy2QP/gzNQJng4C02L3Z/qzyVsfH3Z8c12c9Q+bXr1P8/szPz1P7tbqpGMCwAAwBWQpoLAExvERbL5k89mPDwxkX4cVFgMIlXKRxVNwGbEB/MwDFs6OpaJKKu38d+SRrtlcjs1pgc2nkXYSjRVKvjl7jHe3nc3LzP82FOFBx6b9LepybkxYpvhZFLBg+MTNt1TUPHc9HvHxuM4drZFhXYCo6LFNs9fvihpOtnk9m5kVH482zJx9aFOjavrYEkh/B/vGlP53PRVk/X7M5fdFLyXwFz6O2DuPtf1+wTtxfdoTBb4NQYP5cMyBo/uGXUKox4/9uvNe7/qMbm6MNuqU83ZufafZ3bSHBoAAICLYNMvCEhvXZv1G43ypL2enZ4a6/36SW6Zkhxa3o6mbcPk5NDenCdUwJ6dHrat3MMqQQ5oTdY/KjoGrki/uL2iB13J4pFR4i33jUXYh8axyckhk5NDMAyzklSn1tStM4u5zDAhmz1gobuxG9nOw142JXMtJPXyDpSFZzxQ0aGZ9/nx4kcmiFzebp3IqL1D9zBJuLfUTJEmVclyjLQIcla4P03g75jBmSijBdGN9syJrR+VH3b3LpKiXindyWewnxkxjeYEAAAAOAVpKghIccG8V+ekr/jtoscRRkSJV0zzu2YkN4+KXnO0AVGov5YvVi/K+aO82BuF0f61q3phTmT/FK5Zafj8ON1Gmr1wDHtictL/LcjiMt3b92EhqRONPftru+q6dB0ao8lKSgXsKDF3QoJkakpoMM+lBioMAo8UcSIHSY8pysnRXA8kh16xlr67qtOVdrvedrKp55nfLn5600hXLrbqWrp2ziFNHm6HVp1cyRDGcxNv8ex24LfYEVPQBcNpRvus6viqC/s9vv3Zk79nBIdfG5dFZw4AAACcgjQVBKqnp6U0Kw3vHqj14N7YIO7v949FXrKIvumpoXkxQaXNLrUkdSBazF2cG9P3yxSp4K6CuC9PNNEMO9D5VtWMT47ueHB83zfzu9JmVNt9KQybKQtzK0dtURneP1C39ljDYMu5DAKfnxnxzPQUt4rZ2psb4qyfx2IkhVyRpu6sRFkclY7PjjU+MD5h9P+64zigPrmS1LfSGIpSHnuCEz0HZzsfCwQQdnghwYsk9QhK37EjCglehMe3d5v0/zi1jc4EKIxadnzzrOg0LgM+QQEAgBfB2VQQwN6+Luu5olR3jyzmxQTte2xiXDDPS1+Ocr4AACAASURBVLOig8DxD2/IoX8G898LMgVXJuGrrssOdtbj1DOH6hQLvzjR1/NmTzXKXhF/31bu4pUkRf1f8aWk1/b8Z+8lB1uOrSS1paxt8oeHr11X4vqpy4EIHBewUX5IHRFlezD1NO2nFRiGYSjSaZKiXnFh+7FZXqKv/ZbuWIYO9bnXaQYBfodgCjIeQxJJkPUUndvfOLtHbqDbz6xW3eXBnmEAAABu+StNhb6pIOAQOP7m/MydD4232S05GA6TeGZ6ytEnC1NC0RSK9IaJiSH3jo2nE2FWWtiS/FibF0P4rB/vHD3wgCUSu6s639xzqfdrtIWFytrUrtQQUhksc9Yef+73ctc7xG692D5q1f6j9Z5X602UoHzSceNI2wWi2i4disBo6k5tr+hwWnBYc2EVkqxYV7mGsiD5vQM/Ish5miFMpBmEHTmNm3Cjx7cbrJZ1VcdpzqHXp5VHkW+pAAAA0B+spoKAN0MmrXqu6Ps7R09ODhmspG1cMO/ZotS652f8Z0EWx82Djr73yY0j5mWGe3bvyCjxxqWj7a7HzkwL+3zxKNw79XL/r/hSQ7feaCHb1IiPU+5wtvfVZCVvWn9yd5XbW2RbVYa5nx0/0+xhXp0bg2xjKo5hD41PsnnRB71SXWchKcfr5JTVaGz6DclYlFllbNmBJBTwHziDJx63GsM9//GLs4RB4z6gM4dD7XXdLpf2deySSl7eg74uHQAAgD5wsgIMBwwCX5wbvTg3Wm20HKpT1Mi1nVqT0UKGCdgRIs74BEmq1H+XTwdiMYgfl465b+PZ70ub3bpxhkz67ZJ8ByWC7hwde6Kp54ODdbTnaEtvtr62q+rf8zORb8U4ddnJauoDG8/ucj9H7aU0mBesO37+mWkS1+oq9TdDJt1w6vLg71Our2TmRImDeLY/jf1trebU5Z7b82MGe9csP0FZkZU+NrXt58Zfjyoa8BPcuGuDxn+kPPqIJzcTLMm0H5mSHDoTONJRT+d2G4fa67KCPT8lCwAAwDFIU8GwIuIw52Z4uA7pV/hsxndL8uekhz3z20W51vkRSiGH+eIs2YppKU5P6s7LCPdGmoph2PdnWv45Jx152Bq5o/2fR+oVXzvKFZ1rVhpe2VH5/iK3P/7eMCLqsV/OD94VyY1V64cnJAx8Uchm+tWCao3DTciWnjKEY1l6PC/iDfwZP/1hijSpTzxDkW6cDCfYwUGT13NirqE5epUKZVkytNEAAADY8PfdjwBcze4uiGt4YeYH1+eMiBIPln6mhApem5vR+OLMldNdqiY1Nl7ibtEpF2mMlnOtKuTBlQZHLViXbSqjv377yZGGyg6329WKucynpyLoaRQXzLu7IG7g64khLp249hmlw164VloFfgdE07m3jwAEEEHmk6HzjzCDs128nh05XbrwLDfuOvpDdxrQdKXuRb8UEwAAAAdgNRUAv8ZnMx4vTHq8MKldbTxQ23VZaWhXGy0kFSniRIm5hUkhCW4W8gnhs8YnSI7UK7wx29PNythgbmM3ss2fGIY5aG9ztL77hAsFlpwyW8m1xxpWXZetNlo6NEYWQUgFbFf6Ff19RurHR+q7XFjuduCdhdl2xxoVLfbSH5NnrA6fB6AtekRZIAEYzliho8MWnjM0bdGWvWPqOIJRdrYk4AwOJ3qOIGcFO2IyqnFN1sH2PnhCb3X04AYAAABNTAzDjEZja2urRoPyKSMAAK0IEefmUdFIQj0wPt5L+U+NXJcfE4Q2TRVzBz01urkMQRvGXp8caVhztEHbbwdvCJ81LUU6M016W17MYMd939lfSzNHvTYr4qaRUXbfmpkm/eRIPZ3gaIk4jh5r4gwOwrFwBhdhtEBEGrtMrXuMrcWWnnLSKKfMGpwdxOBFMEPyOFEz2JFTcIY/ttRyA05w4xdx4xeRph5T+wFLdxlp6KAsWpwlZvAimSG57IhJyH+PXAbKplwCJhthNAAAADaYxcXFXV1d0IEGgKvH0jGxHx6qO3UZSVvOK/TozXPSwzddQJY9YhiWGDLoR9XDdciSbb3ZdplFoTP/cr71l/Otz24tf3BCwj9myEKubDy77njji39U0Bz35GVlj95sNw2elxkh4bG6HW619SXHCTnBCUU4FtpogcWirNSWva2v+ZqyXtkBSItZus8bW3ZrL7xFcEL5GY8JMh8nuGFDNE1kCHYwN+46DMWeXqei+CKU0XhihNEAAADYIORy+cAcFbJWAIYxAsffvi7bGydULSR1S240h+l8u6zrRkYN+lmwwv0DpR5QGy2r9tXkrtq/v6ar9xUrSX16tOHBH8/S/0HZqjK8urPK7ltcJvHcjFTaIyBzvLFn9eDFt5jiNIRjMZBGCxyU9uL78s0jdVWf2+aoVyKNXZqzr3b8ItPX/eCzyQ0DmUEoC/NmBA+Hcn0AAOC3oIQSAFejaSmhH1xPq7WDXWealf/YVl4Qj6yhKIZhM9PsrxeRFNWlo7Xh1i1NPfoZnx79vrS5vF0z5r0Dj/x0bvAzs+758HB9TZf9o5jLpiTL/KmX0tNbys632m8zywobT6clpg12+ESP76XMauPl31UnV/YcWKLYdY1i5+yeA0tUJ1YYmrZQJvQ7CFChrHrFrrmqkmWul8ClTMqe/beqTq706sSGkymRyQijTY1EUEENAADAYP48a8RisXAcN5l895kPADC0Hp2U2Kk1vbKzEmF7zsYe/ZqjDcjCYVi0mDstxf7+T6OF9PG2DytJLfm2lEngRguJMKzZSv73eNPr8zIGvsVmEF/eljtrzbHB2944ES3mtqgM9Cb4FwtJLd9ctvvhCQPfIjgh7LAJpo7DCIbBGZzYeR7cZ1ac0V54W1+/ESPt7JTWlq3CcAY34UZhzjMs6Rjas0SKsvYcWGJs3uHBrdoLbxEssXDUC8gnNfwUSOPiBcGNWgR113pD0Y8DAABgMERSUtKUKVMWLlwoFAqHejIAAJ96eXbayul+tK10oBdmyRiE/c3JHKaXGus4YiUptDlqr7XHGrZXdNhNuycmhmxcOpo5yDfBsakpodePiKQ9uyvsqZb3bX62wU9/CMkQ3Nj5DH6MW7dQFq3y8P3yLXn62m/s5qj/u85qqN8o31rQc/BOymx/WXhIqEtfMjT8QuP2Fw1NvyGcz3BF4PiTWWjqBj+RWYgkDgAAgMEQBQUFkZGRBAG7fwG4Gj1RmOTzXM9V2ZGiB8YnDPYugeNhApTVZYeQXGua+9nxzP/b+/nxxoENeOZnRhx8fFKSm21U7xwde0d+7MdeqBX81cnLdl/nJd/BkhbQjU4wRaPfcOsOq7pG/tsYXfU612/R12zo3JJn6bno5uS8wqqu1ZatohlEVbLM8XFW0OuJrMI0MYK6U9/WlhqsFvpxAAAADAayUwCuarFB3MnJ/lhVVcRhfnVbnuNVxMyIYbUHpKpT+8DGs4UfHq5T2PYgHZ8gObl8yiMTE3ks5+WpRkaJt9w79pnpKY/+fM4b+6J3VnbYfwMnxAXvYDitAlqCrGXM4GzXrycNnYpd8yxKt0suW9W1ip2zrRqUe9Q9oz79PP0M06qu1VV+gmQ+wxubYHww/noG7XPU25srFu/72kKi31sBAACgF6SpAFztXp7jd1VVWQzip7vG5Mc6KcVUmBTim/n40rGG7oL3Dp4bUKkohM/6+MYR9S/MeH1extSUUA7T9qd3vIR3V0HcHw+MO/P01GuzI5ZvLrOgqvJ0pctKQ7vaflrFjigMLvwvhnm4Qs+JnS/Of9ONGyirYvc8i8p+qWSnrLpmxZ4Frpcs8gbS0Elnu29/usq1SOIMe7Nj0r6YvBj39G9pny2NZa+f241kSgAAAAbC+3rP7N69W6H4swnh2LFjExMTh2xSAADfWvjfE1vKUDY7pSNewttwe/7kZOcp6KnLyjHvHvDBlHwvSsw9/ISjjb4GC9mg0Mm1Jr3ZGibkhAvZTILYXNa2p1p+rKG7Rak3Wb1YYOrIE4UTEiWDvau98Lbq1HMY5V7ZJ070TEnRZpzpxt5mXeWnyqOPuDXKQOKCtwXZT9MM4jF97Tc9B5agihZ+Yw1DhLKY7TD2wcVDT5Vsptl+j8dgld+wMkE46L8FAAAAHmP2fYX3q0YCfVMBuHrUKXRLRsccqutS6AavPeMTPBbjvnHxr81ND+KyBr5rJalTl5WnLys7NEaFzizmMqUCdnakqKxN7fupelurynDr16eOPTl5sDJRXCaRHi5MxzAMwxq79f/aVf3liSa92cOCwO5SGhz9VRHkrGBHFHbvv92qGbTP6hUIpnDk86JRL7q1YZgyq9WlL7l+/WDUZ//FS72b4AzN1ndz53GE0UydR3mQprqmy6ij/1FHbzW/XLrjy8m3IpkSAACA/pjOLwEADEcnmnrWHG3YXdXZ0K0f2pmwGcTY+OBrMsIfHJ8QJmQPvOB8q2rV/trNF9p69EOcSPtSSWPP16cuLx0T6/iyn8+1PrDxbLdvvzNOdxOzwsaHLTynq16nLXvHqm0c7DKcYHOTFgtH/oMZZKcfj2P6+o2kodPduwaiTEp97TeCzCfph/KARVXtt9GGMZKi1lWjeUCwse7s++MWBbG5SKIBAADoA2kqAFedsy2qZZsu7BukrYiPsQiiZNnkUdFiu+/KtaaHfzr3y/nWq3OHx/Pbyu/IjxmsJQ+GYf/YVvHmniHITMRc5/934CyhIOspQcZjJnmJqbXY3HWS1HeQxk6cKSR4kUyxjB1VxI6cRrA9bD5pbNzs2Y32Qm0ZqjSVNKL8Z0ghjTaMnVW0XNYqkYTSW8172y4tis9BEg0AAEAfSFMBuLp8cKhu5W8XDV5o/ukZM0m+tqvqx7vGDHzrYK3i1q9PtagMvp+Vn7isNOyuls9Jt98/44NDdUOSo2IYluh6dxyCyQ6fyA6fiHwOpo4jKENRVpo1ij3koMur+6AnjYuOdw66wu+BYx0NkKYCAAByUOkXgKvIi9srnvz1gv/kqL02XWgbWDm2pLFn3ufHr+YctdeOCvvdX47UK57adMHHk+kVJmTHiId4iyNpkCNch6Ssegc7k73KrZJRLkQTIIw2jF1SyxFGq1ahjAYAAKAXpKkAXC0+Olz/2i46i2/e2ndrIakdlVccMqxT6OZ9flxjtHhpxABy8nLPwBcpClu+uWyoNkLPkIUNVtjJZ0gjglOpVwREcczVAwQ/BmE0hsDJSWbQS2G0bU3sP9EAAAD0gjQVgKtCnUK3YksZvRheTE2O1Cv6//Jvm8u6tEPZzdJ/XJLb+QT88/nWkkY76atv3J6HMrPyDPLdrZRlaAqJsYKzEUZjIo02jJlJlDtKTKSPKmwDAMBVBdJUAK4KfnUedaCqTm3f1/tqujZd8JcmrkPObnHjL0qGZocqhmGjY4PmZ4UP1eh9cAYPccAh2i7LjpyCLBbBYodPQhZtWOMz7bS88piAaac+OQAAAJqgbyoAQ6OmS1tcLT91WdmhMfboLQI2QypgZ4QLZ8ikeTFBDoq7eqCiQ/PTuVaEAZFT6P5aO121r2YIZ+JvrAN6v5is5FBVacZx7L1FOcSQb/nFMIIXgTpgJNqALmKHTyZ4UaQewT9Pbsw1OEtEP87VIIYfhDBanMDDatUAAAAcgEq/APiU2Ur+cKbl7X01Z1tUg10TIeI8OTnpkYmJEh6aR/7flTYjieM9fSu9aqPF5pzqVU40oPVLWZtaZxqaTYb/mCErTAoZkqFtEOxgVNkdhmE4S8wQDNFOZoIpzF6uOrmSfiRB9tP0g1wlciQon0pkSxA/NAEAAIDBpl8AfKm0WTny7f13flvqIEfFMKxdbXx+W0XSa3tQpZfF1f5eiFLA/rMXyNH6brPVfzcn+16ixHZ3a02XK/Va0O+IuXN07L+uyUAe1mPsiMlIQw3ZEjE/6ymmWEYzCDfxFnbkVCTzuRpMiUhGuClgWmQqqlAAAAD6QJoKgI+sO944YfWhig6Ni9crDebbN5y+f+PZgds+3XWhTU0zgrfFBv2ZjF1s9/ep+tiIKLHNK3ZPqw6AMuliEvjLs9O+uDXXD3b7/oWbcIMfhvIATrDF4z/GCM+3ThC8CHHBWwinNOxJuYKiKDS5pUwszQuNRhIKAABAf5CmAuAL35U2P/jjOaP7RYzWHW989OfzdIZWGsyuJTZDKTNC2PtFqwpxBddAN0MmtXmF9G3tgBky6ZEnC1+Zk472vDR93PhFDGEi/TgEL5KXeAv9OHRwomcGF36J4Z78j4yzRCEztzEE8chnNbw9O6IISZxnRkzHh24pHgAAhjFIUwHwupLGnru/O+NxdrH2WMM7+2s9Hl1jDIBmCdNSQnu/0JsDYLY+I2AzFubYHqITsn1SUwDHZsjCTiybvPvhCQVx/lghBmdwxGP+Qz+OePSbOEtIPw5NvOTbgwvXuzsThjAxdPZOVmi+l2Y1jM2Mll0XT7d/T25I9L2yAiTzAQAAYAPSVAC8i6KwJ349b6J33vLlHZUtKoNn9/rbIthAIXzW9NQ/1wzZTPih9JflU5NFHNukNH7AaVVveHNe5u6Hx4/xywS1DzfxZk7MNXQisCOn8lKWopoPTbyUJdJrT7MjXGtRgzN4KUul15WywsZ7eV7D1ntjF4ZxPX9CwWey1k66meHRGjgAAACn4McrAN71/ZnmksYemkE0RstL2ys9uzeIy/SrI4UDPTk5mfO/7DSUj7KfIR1D3nYlJoj77HQ7x+dyIkVuzs29ZXw+m7Hm5pHPFQVGVRjJ9J/ZYRM8u5cZnC2Z/otnW229hCmWhc7dHzrvEDfxJpxleyy5F8GL4mc8EnZ9RfDk9QTbr58j+LkkUci2WfeJWBwP7mXgxIYpdxRI45DPCgAAQC/omwqAd314qB5JnG9PN799bVaw+y1qeCxGtJjbrPRwMdbbYoO4K6al9P0yPRzl9suJiZLTl5UG144Ez8uIeH6WzEpSDAILE3I+Plz33oE6hJNxC4tBrFucKxywlIphWDCPlR8bdLLJ9WcfbuS0c9LD3l2Y03dU2P/hTL5kxqbuvTeb2g+4dSMrbJxk+s8Exy/669hgh09ih0/CSIu566RFWUEaOklTD8GRErxwVmg+MyhzqCc4fIyRxu2c8+Bt+76p1yhcv0vKFXxRuHhBXJb3JgYAAAD6pgLgRZ0a09EGNz79OKA3W3dWdt6S60lJyTFxwc3KNiTTQItB4J/cNLKvGw2GYRMSJAjjPzopaYZM+v6B2jVHG7oHqSPFIPD5mRHPFqVMTLwiY+nUmBDOxC04jq29eeSc9LDBLrg9P8adNNW5lFDBDJn0kYmJuTH2V/D8GcEND72mWHPuDc251ymr8xJcOMEW5KwQ5b5Cp7iuLxBMVth42NPrbePDEkoXLl9esmVDzSkL6eSRFo7h18ZnfTLhxmh+4P1LAQCAwAJpKgBedKRegXBrwqE6hWdp6jXp4Zsv+GOa+u7C7AVZEf1fiRJzx8QFI8nB2AxiTnqYVMB+c37ma3MzTl7u2V/TVafQtamMJisZJuREiTgTEiVTU0KDuHbSFRfXYJHjsojvl4weWDmpvwfGxb+1t6bV0+PKfRZkRa6cnpIg4fnmvKsX4QzhqBf5aQ9oL76vq15HGjrtXkVwQnmpdwuylzP4MT6eIPBnwWzeF4WLXxo1672LBzY3ljVougdeE8ETzYvNWJ49ZYQkyvczBACAqxCkqQB4UWWnq11SvRptcV70it/KtCY/KqLLYzHeW5T94PiEgW89PCHhfhRp6k2joqQCdu/XDAIfFy8ZF+/GUi2bMTRHFm8aGe04R8UwTMhhvj43494fztAZKITP+vLWUaH/+xYNAwQvUjT6TdHoN8zd583tB63aJqu+HcMoghvOEMSxIwpZklF+dRIV+JUkUcj74xa9P25RjbrrTFdzu0GjNBmELHY4V5gjicwKjoDGMwAA4EuQpgLgRWg3jnocTcJjPVuU6nERJuTGxgf/d3FudqTI7rt3F8R9cKjubIuKzhA8FuP1uRl0IkiHKH9zcdy7CmI3l7V5vEjOIPB1i3OHU47aD86SjGRJRg71NECgShGFpohCh3oWAABwtYPnygB4EdqNo3R6iq6YlpIw1Bs7mQx8QVbE7ocnHH9q8mA5KoZhDAJ/b1EOzT46zxWlJobw6URAW8zJdWFCl1JHAse/W5I/JdnDD9PvLsxe5GzNFgAAAABgqECaCoAX8Vgo/4nx+5Uacn8mjC33jfWgUHAvAsfvGhPr8egYhkmF7Lp/zPjtvrEzZFKnF09LCf3ohhEej3VrXswLs2Qe395rYiLKYk6uSw9zNT3msRi/3z/2rgL3WmIEcVnfLcl/ojDJ/akBAAAAAPgIpKkAeFGkiIs0mif9/fqMjBL/fPcYD3JdAsdXX5/z5W15f5ua7NnQQVzWrgcnxAa7sZz70ISE/yzI8mBN9foRketvy6Xf9XRUtNj3hYVwHJuU5EaLFCGH+eWtuT/cOTpVKnB6MYHj14+IPPP0lFvzoIAQAAAAAPzaX2kq9E0FALkMpBtHMyMG3Shrw2ghLysNpc3KYw3dtV26vuJJRanSU8unuNV0JELE2Xr/2McmJWIY9ta1WSumpbibAMZLeDsfGu9Bp5NnpqccenxSkst7d7lM4r1F2T/fVYCk+hGB4w9PsFPhyavGxUs8eBhxS2505XPTf7xrzA0jokL4dhbMsyJET01Ovrhy2i93F9DcCw0AAAAA4AN4X0a6b9++jo6O3q/z8/NTU1OHblYADBM9erP0pR1WEs1zn033FDiuAduqMnxxoml3lfxovaL/sVgCx3NjxEWp0iWjY0dFiw0W8tMj9e8eqG3s1juIFsxjPTIx8empyTaFdnZUdj6w8WxTj6N7e+E4dltezEc3jPB4szGGYUYL+cOZltd2VVXLtYNdI+Qw7x0bt2JaSpw7C7ZO6c3WrP/sq1foEMZ0bP1teUvpba4mKaqqU9uuNnZqTRwGIRWwU6T8cCGtdXgAAAAAAB+DNBUA75q95tiuKvtdHN0i5jJbX5492JbdNrXxxT8qvj512eiwaBOOY3PSw1+fm5EfG2S2kn9UdOyukh+o7apX6JUGM4ZhAjYjJog7KSmkKFW6MCdSxLFfDNxkJb873bz6UF1ps9Lu3gsRh3njyKgV01IclEpyi5WkTjcr916Sn7qsbFcbFTqziMMME7IzwoVFqdLCpBA6B3cd+Plc603rT3oj8kD5sUEnlk2mv10ZAAAAACDQQZoKgHf9Xt6+4PMS+nGWT0l+Z2G23bd2V3Uu/e5Mq8rgYigmgT8/U/bS7LT+GZHJSlqslLvJXofGeKBWUdmh6dSY9GZrmJAdLuQUxAUXxAcz6ZXq9R+v7ap+cXuFt0dhMYj9j06cMER1mwAAAAAA/Ar0TQXAu+ZnRsxJD9tRSWtBNYTPemFWmt23vihpum/jGbeOk1tI6p87q5p6DJ/fMqovUWUzCA/WI8OFnJtGRvWPfLyhe3d150eH6+Rak9JgkQrYYUL26NjgolRpZsTQtHih6YVZMp3Z+u/iau+d2cdxbO3NI+3mqD1685H67ja1oVNjYhK4VMCODeZNSJB4afW4l8lKmiykcJC1dFQoq9HcedTcVUoa5aRBjrNEDF4kMyiDHTkFZ7l9khkAAAAAwwmkqQB43bsLc8a9f1BttNCJYLc0zm9l7Q/+eNaz9Om/JY3hQvab8zM9nlV/cq3pw0N1Hx6u79KaBr77RUkThmGpUsHjhYkPTUjkMpHVGK+Wa/ddktd26To0JoPFGibgRIjY4xMkExNDOOhGeWNeRmFSyD3fn+nQGFHF7MNmEO8tyr77yr4yOpP1yxNNX55oOt2sHHi2mcMkJiRIHpqQcNOoaCSr1laS2lHZubOyc3+tvLZLpzJYMAzjMolIMXdioqQoVbooJ9LmiDId5s5jmgtvG5v/oCz2zv0STHZ4oSB7OTd2AYZDOXoAAADgagSbfgHwhb2X5HM/O+744OhgXp6d9sqc9IGvd+vNsjeL7aaFrtv36MSpKaF0ImAY9kVJ05ObLmhcy8NlUsG3S/LHxAXTGVFlsKw91vDR4frB6hvxWIzrR0SunJ46KhrZupzGaFlX0rhqX62D8lFhQvbkpNDdVXKV0exKzLQwwTd32H431hxteHF7RafG+Z9sUgj/gxty5mdGuDKWXWYrufZY4zv7a2q7HFWKErAZ942LXzk9NSaIVo8lq65ZeeheY8tOVy5mSkYEF37JCs2nMyIAAAAAAhGkqQD4yJaytru+O9Ojdyl76UXg+PMzZa9eYydHxTDs8V/Of3S4nuascmPEJ5dN8aA9aS8LSd3z/ZkNpy67dRebQby/KOfhiR62e/nq5OWnNl1w5TuJ49ji3JhPbqRVatiGlaROXVYWX5Kfa1G1qY0qg1nEYUaKudkRoiJZ6Nh4CZPAFTrzJ0fqVx+sc7D6mhUhemZ6yu35Mf3b5ygN5nu/P/vL+VbX54Pj2BOFSW9fm8Vyvw1PQ7f+9g2nj9QrXLw+iMv69KYRHrddNTb/0XPgTtLY5fotOIMjGvOWIPMJz0YEAAAAQID6K03dv39/e3t779d5eXkymWzoZgXA8NTQrV/6bemBWpc+pidIeF/cmjs9VWr33RaVIeFfuy0oWt38eNeY/udLXac1Wm9cf3JHZYcH9+I49t/FuTY7XZ0yWcn7fjjrblacIOH9dNcYp+u3Jisp15p0JmsQlxUmRLDBtS+hrejQdGqMSoMllM8OF7LzY4OKUqXp/XrqUhR2pkW5o6Lj7f01XVo3HmT0uS0vZsMdeW5VCT7e2D17zbHe/b1uefWa9BcHOSntgKltr2LXXMrqya5p8dh3BVnLPLgRAAAAAAEK0lQAfK34kvztvTW7qjoHSzJHRYuXTUm2WWez8c7+2qe3lCGZz8KcyE33FLh1S0O3/pMj9e8fqDNYrB6Py2IQBx+fOC7e1dq2JEXdtuH0xjMtHowVKmAffGzSwBpOGZcuWAAAIABJREFUFpLafKHtj4qO4mp5Xb/9wwwCHx0bVJQqvXFkFM39yY6ZreR3pS1v7b10oU1NM9TK6an/t8DVk8bVcm3hB4c9Pmr73qLspyYnu369peei/PfxlNnT3yNOSIp+5cZd5+HtAAAAAAg0kKYCMDTURsuB2q5TTcpOralTYxRzmREiTnqYsEgmjRY7P/4377Pjf1R4sow5kJjLVPzrGhf3/VIUtvpQ7crfyk1WT87Z2nCrU+jKrRff2lvj8ViRIs75Z6ZJ/1cHyEpSnx5teHtfzWCnW/tMT5W+PDuN/gnegSo7NLdtOF3arEQSjcDx408VupJUm63kqFX7y9s1Ho/FIPATyybnxQS5eL1i5xwXz6MOOqIgNuz6CpwpoBMEAAAAAIEC0lQAAlL0P3e53ijVqaq/F8mkzhMAncl6w5cnaDbXsbHhjvw78p2fdSxv14x8ex/NTc4cBrEgO+LxwqSUUP6Sb1zdfY397/znW9dmOVjfdtfmC213fHNaa/J8OXqgyckhBx6b5PSy1Qfrntp0geZYk5JCDj42yZUnDIamLd17FtIcDsMwUd4/haNeoh8HAAAAAP4Pav0DEHgMFhJhjophmOMqr73MVvLmr06izVExDPvYtSpQf9tSRv8grtFK/nyudfrHR1JeL3Y9R8V615AP1s3ztFbzQPtqum79+hTaHBXDsIO1imMN3Y6v0Zutr+6qoj/W4TqFi+v52rJ36Q+HYZi2/COM9OTgLgAAAAACDqSpAAQetftlb5wEdKGXzIrfLm4rR7PNuL+jDQqnJySrOrWeFWoajJn0JNvcUy1f/PWpgV1M3dWiMiz67wkDoozXhtOzu5svtNFsYtTni5JGp9eQhk5T234kw5GGDlP7QSShAAAAAODnIE0FIPC4U88VjYvtaheXPd1FUdjReicLgD+fa6EQlDRGYPOFtjVHG2gG+fvv5UqDt1YF99bIHV+AcD18V5XcadJu6jiMYcj+8IyIMl4AAAAA+DlIUwEIPCIOE22mGsRlOr5gxZaLSJrf2FXZ6aSWz8FaVxt7+sCL2yvorEaebVFtONWMcD42yts1jlPHk009qMZSGszVcq3jayw9F1ENh2GYRVmOMBoAAAAA/NZfaSre72Mv5ScrFwAAezhMwpVqwK5LDuU7eLe2S7cd6Z5bG3JnWV9Fh+c1aZFT6MzvH6zz+Pa1xxpIb/6ANVpIx9/PGhfOIbuupstJmkrq2xAOR+pbEUYDAAAAgN+C1VQAAlJ+rKu9QJyS8FiJEkdp6m8X27z65EpvdnJKs03tYXtPL/nqZJPH9/5+sR3hTOxyUJnJZCX1ZpR1m3r0Tk41UxaUWTFldpIVAwAAAGB4gDQVgIA0Ky0MVagZMqnjpqlOz47SJGAzHF9gsCCuiEtTQ7e+0qMF3m3lHQ3deuTzscFhDvqDnX79JxsWp+1zGRyEw+EMlJsIAAAAAOC3IE0FICAtzo12kI24ZemYOMcXVHV6dwkrUuQkk+Gg61aKyulmpVvXGyzkU5suLFh33Evz6YPjWDCPNdi7XCaD6fCRhLtEzk41ExwpwuEILrKnMwAAAADwZ3734Q8A4IpwIefJyUn044xPkCzIinB8TaezhjE0ZUaIHF8QJkS5IodEjdyNvaydGtOE1QdXH6zzwan/aDHXweo0jmNxwTyEwyU43C6OYRgzKB3hcIygNITRAAAAAOC3IE0FIFC9OCstil4hJRzH3l+U47RosMnqzewKxw7Udm2v6HBwojIjXOjFCXhEZXS1o4zOZF34RcmZZpVX59NnYmKI4wtGRotRjcVmEE7/aNgRhaiGwzCMHY4yGgAAAAD8FqSpAAQqEYf57ZJ8Lo2tv2/MyxwbH+z0Mh7Lmz8oKOyN3dVzPzse8sL2a9eV7K/pGnjJxESJFyfgEdfPeN77wxlvH+7tb15muOMLilKR7cIdnyBxeq6YIYhnSUYiGQ5nCjhRRUhCAQAAAMDPQZoKQACblhL61e15jgsgDWbZlOTnilJduZLmmq2LTFZy68X2aR8fmf7xkcYr6wwtyonywQTcIuY4OZPZ60i9YuPZFm9Ppk8In3XjSCffq1tyo1EdT70tL8aVy/gZjyAZjpeyBGc52R8OAAAAgOEB+qYCENhuHhV9+IlJjhuf2uCxGO8tyn53YbaL12dG+HTP7b6artxV+38r+6txS26MeFKSk72sPpbk2jd8+eYyX/40fWVOushZ/hwp4tw/Pp7+WLFB3DvHxLpyJT/tAaZkBM3hcJZIlPsyzSAAAAAACBSwmgpAwBsXLzm5fMqKaSliZ2VXmQR+a15M6d+mPDU52fX4U5JD6U3Qbd16843rT26v6Oh75f1FOYTTQ7Q+lOvCCc/SZmVJY48PJtMrJ1L0yMREV658fW5mCH/QasAuevu6bKc7fv+EM8Rj38VwWv/diPJeJXh+t6gOAAAAAC+BNBWA4UDCY711bVbjizM/umHEDSOibJIQLpMoSpW+Njej6u9F3y3JT3ezItG1WRGomt+4zmwlb/7q1MV2de8vR8cG3TvWSeMcn4kSc3Minaepv1/scHoNKuFCzi/3FLi4mzeEz/rohhF00v7FudG3jIp2/XpO1Iyg8R95PBw/7QFB1jKPbwcAAABAwHHpeBUAICAEcVmPTkp8dFIihmEao6VDYzJZyXAhh+bSWaiAfefo2M+PN6KZpcs0RssTv1zY88iE3l9+fOOIph79jspOH09joNvyol1J8Y43+qhyUjCftfOh8TKpwPVbbs2LUejMj/1y3oPhpqdK19+W526Sy09/mDSr1Kf+gVGDlnS2i5d6V9CET9wbDAAAAAABDlZTARiehBxmcig/I1xIf3snhmGvz8sI4iKI467iS/ItZW29X7MYxI93jZmZFub7afQn4jBXTEtx5cpqudbbk+ml1Jv3XpK7e9ejkxLX35bn9CyrjQfGx2+9b6xnq+vCnJWh1xQzBK6uiuMsUfDk9cGFX2K4a7uLvYA09VAWH/05AgAAAKAPrKYCAJwLF3LemJfh2eIbTasP1l2XHdn7tYjD3PHguLf21ry0vdJkJX0/GQzDnp8pc7H0cZfW5O3J9KIo7G9byoK4rHvc3Be9dEzsxETJsk1l2yranZZ6Sg8XvjEv44YRtA6IsiOmhN1Qbaj/QXPuDYuycrDLcHYQP+Uu4cjnfH8elTR0Ghp+MbbuMXccsRraMdKCYRjO5DMECeyo6ZzomdzYBRgxBI9sfIAyKY3tB6zqWtIopyw6ghNK8KLYYWOZwa6WWwMAAABQwfuK+h48eLC1tbX361GjRqWnpw/drAAA/mjl1otv7a3x8aAEjne+OsdmTbipR//egdrPjjWqjRZfTmaGTLrtgXFshktricHPb1cazN6eUh82gzi3Yqq7B497XWhTf3qkfldVZ1Wn7cphqIA9NTn0jtExi3IiUVaxokhz9zlTa7FZUUoaOkmDHGeJGPwopjidHTWDHTbO96mgVdukOf9vffUXlFXv4DKGIE6QtZyf8SjO4Phsbt5FWvT1G3XlH5rkJXa3ZBO8CF7iYkH2coYw0eeTAwAAcJWCNBUA4CqKwl74o+LfxZdI3/as2nxvQd+Can9GC/nFicZHf77gmx5aI6PE+x+bGMxzNX2K/ueuVpXBq1OyMT8zYuv9Y+lEaFEZ6rp0cq3JYCEjRJwIISc9XOBXNZa9xND4q/Lw/aRR4eL1zOBsydTvmZIcr87KB4wtu5RHHrRq6p1fSjD5svvFBW/jTDdOQQMAAACesd83tb6+XqFw9X9rAMBVAsex1+dl7HxofIKE58txy9s1dl/nMIn5mRESni8OL9w8KtqtHBXDsJggl/YGI/R7efvBWlo/uqPF3ElJIQtzIhfnRk9LCc2MEF4NOarmzD+7i29wPUfFMMzSUyb/fbyp/aD3ZuV1FKk+9Zxi1zUu5agYhpEWXeWn8t/GWLoveHdiAAAAQP80VSqVxsXFicViDMOUSuXu3buLi4tbWlqGbm4AAH80Qyat/nvR+tvyXG2bSVu72mj39S6t6Zq1xxU6DzfWshi4006zGIZlRgi/v3P0xqWj3cpRMQzLjhR5NjE6vjjh64LMgU5X8Yn6zCse3EhZtIrdC8yKM6hn5COqkys05/8Po9w7421RVnTtKLKoqrw0KwAAAKDXX2lqRkZGQUGBwfDXFjW5XH7o0KFt27ZVV1dbre61EAAADGMsBrF0TOyeRyawfdJPVWuy//PnyU0X+hqresBspe4fF7/6+px5meHCAQVvY4O4S8fEbr634MIz0xbnutEjtM/U5FCP5+ax7RVD37AngJgVpcrjT3h8O2VW9exbTJE+qpWFkLZslbbsXc/uJQ2dil3zSFMP2ikBAAAA/V3xyUyhUJCk7YNVjUZTWlpaXl4uk8lSUlLYbLYPpwcA8F/j4iUb7xx94/qTVtK7R0N5LDvLtscaur8rbaYZ+cND9RdWTnuiMMlCUq0qQ5vaqDZawgTscCEnQuR5gRyVwfLZsYZPjjTQnJ4HWlWGy0pDrM/3GwcoVclyd/u42rCoqnTlHwiyn0Y1JR+wai+rS1+iFUFdozn7qrjgHVRTAgAAAGwwXnnllb5fCASC1NRUHo+nVCotlivqZ1oslo6OjkuXLmm1WpFIxOEMlwqHAAAaMsKFyaH8nZWdZqsXM9UFWRFTUmxXJu/94WxNl45mZCtFaUyWhTmRBI4HcVkxQdykEH6EiDNwcdV1a442LFhXsqWsvVvvuzK//S3MjkwM4Q/J0IHF2PyH5twb9OOY5McFmU/gRMA8w1Ude9jcVUoziLnrFC9pMcEZgi0DAAAArga2G/ZYLJZMJps/f/7YsWODgoJs3rVYLLW1tdu3bz906JBc7nY3eQDA8LNkdOzJ5VPGxAV7b4iMCNsmK81Kw55qND+CfjzbarCgacGqMVpuWn/y4Z/O9QxRgtpraEcPILrqL5DEoUxKQ8OvSEL5gFXXrK/biCAQadaVf4ggDgAAAGCP/XNlBEEkJibOmTOnqKgoOtr2UBZFUS0tLcXFxbt27aqvr/dNKwgAgN/KCBeWPDX59/vHTU+VMgj0hWEnJkpsXtlTLUfVFEdjtBypR1DY3Gwlb/7q1M/nWumHosni5T3YwwRFmlp2owpmbNmJKpS3GRp+prnPuY++/icMg79sAAAAvMJJ+ROpVFpYWDh79uyEhASCsL24u7u7pKQEaiwBAHAcm5cZXvzIhBdmytBGHhsfHC22PWlZ2qxEOETpZQTRHvjx3PaKDvpx6BPR2LF89bCoL5GmblTRzPITqEJ5G8ImOqS+1aKEkr8AAAC8wqUqncHBwePGjVuwYEF2dvbAEkparba0tHTLli2lpaV6vd4LkwQABIYOjfG9A3VoYz46MXHgi7W0T6VeEU1BN9q+mq71J5qQTIY+H3e1DVBWdS3qaIGxrmjpuYgymrIcYTQAAACgjxsP3blcbnZ2dnp6el1dXWVlpU53xQc7s9lcXV1dU1MTFxeXkZEx8FwrAGDYW32wTmlAeTAyLybozjGxA19XIR2lS0srGklRyzdfQDUZmgRsRnIo1E9yjjShXJCnSBNl0ePMAPjOkzqU7dDRRgMAAAD6uL03jMlkymSy1NTU1tbW8vLyrq6u/u+SJNnQ0NDQ0BARESGTyQaeawUADGP0O8T0x2IQH96QQ+B2DruiXbf69XzL33/nrSxKlfBYHtxeXC0/06xCOiPPTU0JZTF80cw24FFo6mb1C2hxfo0foKwoNz1RFi3CaAAAAEAfD48w4TgeHR0dHR0tl8urq6svX75sU0ipvb29vb09ODg4LS0tPj5+4LlWAMAw09CtR7kXF8fW3DRyYmKI3TfFXJTHL01W6t/Fl7453bzhjrwpyW432Pj5/NCXTepza17MUE8hMOAs2/LR9MIROFOAMqD3EGzMakQWjQEdegEAAHgF3exRKpVOmDBh7ty5MpmMwWDYvNvT01NSUrJ169aysjKTyURzLACAPzvXgnJFMSGYd8/YuMHeTfJCX9CmHn3RJ0e/Oe32gvDBWgSFgpFIlQpuGQV7WFzCEMQjjRaH4bb/A/ongiNFGg36pgIAAPAKNIucQqEwLy+vt8YSh8OxeddgMJSVlW3durW0tNTmRCsAYNho6kG5mVBvdrQnMzfaK6ffrSR1z/dn/nCnYK+FpKo7/WXf43uLsjlM2LriEmZQOo5uJZAlGYkqlLcxg9KQRktHGA0AAADog/IDDYfDyc7Ovvbaa8eOHSsWi23etVgs1dXVv//++6FDh2xOtAIAhgGtCWVXKrXR0Um/GTKpvSOrCJit5B0bTsu1ru7+kGtNJivqU44euXds/PzMiKGeRcDAGVxW+ERU0dhRM1CF8jZ22ARUoXCWMIDycwAAAIEF/XN3giASExPnzJlTWFgYEWH7mYmiqJaWlj179hQXF7e0QIVAAIYPwvO80U5FJBbDUbh4CW+q+4dIXdStN7+0vdLFi/Vmv2gZvSArYs3NkDC4h5d8O5pABJOXdAuaUN7HibsWWaiYazDCk6pjAAAAgFPe2h7WW2Np6tSps2bNSkhIwAcsfMjl8kOHDm3btq26utpq9YvPeQAAOoI9KpOLYRiG2clInUZ7fV6GlxZUMQxbe6yh3rVmqsg32T49LYXPduOUI45jTxQm/XjXGCaN5wRXJ17KnUyxjH4cftoDBC+KfhzfYIXms8LGIQnFT38YSRwAAABgIK+fYpJIJOPGjZs3b15GRgaLZfu5U6PRlJaWQo0lAIaBVCnKSqcpoU6iTUwMuXGEt3IDK0ltOOVSLaUQPttuyxyP3V0Qd3LZlDnpYa5cnBMp2nrfuNXX53DhSKr7cIItGv1vmkEIdrAo91Uk8/EZ8dh37T4bcgsnbgEncLY6AwAACDi4TSMZrzKbzfX19RUVFXq9nVIrBEHExcVlZWWJRCKfTQmAAEVR2LlW1dkWlVxr6tQYQ/jsMCE7K0I0OjaIMUSramqjJeSF7RYSzY+U54pS35yf6fiadrVx0geHa7q8UsFocnLIgccmuXJl0ut7XFx6dYpB4Oo35vJYDAzDzjSr1hyr310lvyS3/Q2GCznTUkOXjomdlxHhvSXlq0TPwaX6mq89vBknJFO/4yYGzI7fPj0H79TXbPD4dpzJl15XyhSjrMYEAAAA9Iey96BTLBZLJpOlpKQ0NjZWVlYqlcr+75Ik2dDQ0NjYGBUVlZGRIZWiLJoPwLBR1qZ+Z3/tbxfbOjV2NiAE81jXZIT/bWpyQVywjycm4jAnJoYcqEVTIO2ajHCn10SIODseGlf4weE2Nbo+kP9zsklJUpQrK6UTEiSo0tTRsUG9OWqrylDWrg7ls6enhmaEC1kEzmYS8RL+5KSQ9HChTCqA7BSV4EnrSGOX8fI2D+4VF6wKxBwVw7CgiWutmgZT+0FPbsYZwVM2QI4KAADAq3y6mmpDLpdXVFQMVkhJIpHIZDK751oBuDp1akyP/Hzul/OtrvyrnZUW9tktoxIkPO/P6y/rTzTd/f0Z+nFSQgVVf5/u4mbaph79km9KUaXH/TW/PCtaPGjPEpKijtZ376mW/3i25UKbGsmIL89OS5UK3jtQe+qy0u4FBI5PSQ5ZMT0F1lERoqwG9clntOUf2a3mZRfOEgdN+JiXfIdXJ+ZVpFGh2D3f3HnMrbtwBjdo4me8lCVemhUAAADQayjT1F49PT2XLl1qaGiwW0hJIBCkpaUlJSUxmT5d+AXA3+y9JL/jm9JWlcH1W4J5rHWLR93gtQOcA1lJKv+dA+daVTTj/HTXmBtHujFtK0l9cKjujT3VdleYPXZx5fTMCOHA101W8uuTl9/eV1PRoUE4HJuBR4q5jd0utZ8dExf89e15GeF2pgc8Y2zaqjr9d0v3BSfX4QQ37lpxwTsMUbJP5uVNpEV97jXN2dcwyqVChsygzOCp37JCcr09LwAAAGDo09ReBoOhpqamurrabiElFouVmJiYkZHB4/l0aQgAP3G0vnvmmqM69xuTEjj+w9LRN7mT8tG0p1o+a81ROj9XpqWE7n3Uk4aW+2rk0z8+6vnAA5x/ZlpOpO1R+TqF7vYNp481dCMcqBeBY24d7BWwGWtuHnVHfgzymVzFKOPlbfqaDca2vaS+3eY9ZnAWJ3oWP/0hZpCTU9OBxaK6pCv/QFe1lrIO+hSMKU7lZzzOT38IZwy6vwAAAABAyF/S1F4Wi6Wurq6yslKns3PQq7fGUkZGRlBQkO/nBsBQqejQTFh9qEdv9ux2LpMofmTihEQJ2lk58Nqu6he3V3h2b1ww7/ATk+KCPXkgdbZFlbtqv2fj2tXwwsz4K3dN76zsvHH9SY3RgnAUOggc//7O/JtHRQ/1RIYfyqqutepaSWMnjrMIbhhDlEJwh3PFBNLUbWrbZ2rda1HXkoZOyqIhuGEMXhQrbBw7qoglGUG/ODAAAADgOv9KU3tRFNXa2lpeXt7VZf+wmVQqzcjIiI6GT2bgqjBn7bGdlZ10ImRFiM6umOrLvprLN5e9d6DW3bsiRZz9j01KC/OwsY3SYA5+frtn9w7EZhDaf8/r/0070dRT9MlR/8lRe3GYxMHHJ/m+YhYAAAAAgPf4Y6s9HMejo6NnzJhRVFQUFxc3sISSXC4/dOjQzp076+vrSZIckkkC4Btbytpo5qgYhl1sV39+rBHJfFz07sLsjUtHB/NsWyU7MCst7PTfpnico2IYFsRlISwZlR0p6p+jKg3ma9eV+FuOimGY0UI+/NM50v8eOAIAAAAAeIzxyiuvDPUcBsXn8+Pi4hISEjAMUyqVNgu/BoOhubm5trbWYrEEBwczGIwhmiYAXnT/xrONPS7V1HHsYrv6ycnJvqwNmx0pui0vxmghy9rUjpup5saI31mY/X/zs0RcupXSzreqS5vt18h1151jYmelhfX98vltFbur5EgiI9eqMqZKBaOixUM9EQAAAAAANPxx069dRqOxrq6uqqrKYLBT44HJZCYlJaWnp/P5fN/PDQAvaVEZYl/dherf6KnlU/Jjh+Bcd4fG+FtZe/EleUljT6vKoDVZGQQeJmAnhwqKZKGz08InJ4egGqv4knzGJ2iqKJX+bWpuzJ+JX2O3XvZmscnqv3s3CuKCS5ZNHupZAAAAAACgETBdXjgcTkZGRlpaWmNjY0VFhUp1RccLi8VSXV196dKlqKiozMzM0NDQoZonAAgdrFUgfI60r6ZrSNLUcCHnvnHx942L7/2l3mzlMhleWtctSpVOSQ6l30N1YU5kX46KYdj6k03+nKNiGHaiqaepR+9Z6SkAAAAAAH/jj2dTHSAIIjExcc6cOYWFhRERETbvUhTV0tKyZ8+e4uLipqamQFkoBmAwZW1qhNEutqOM5jEey1s5aq/3FmUT9AZgM4j/LMjq/8q28g56k/KFg7UKtAHNVrJVZbjQpm7s1uvNbjdDAgAAAADwWMCspvbXW2MpOjq6u7u7qqqqsbHRJiOVy+VyuVwoFMpksuTkZDi2CgJUi2rQNoYeaFaijOa38mKCXp6d9vKOSo8jvLMwu38lJwtJnb6M5ryrV5V3oHkMcbhO8cv51j3V8nOtqv4/WeMlvKJU6dyM8OtHRLIYAfaIEwAAAACBJSDT1D4SiWTcuHE5OTk1NTU1NTVm8xWNJTUaTWlp6cWLF1NTU1NTUzkczlDNEwDP6Ewol7D8sEqtl7w0O02hM79/0O2OOBiGvTBL9tikxP6vbL3Y5uc7fnu1q400I+yo7Pznzsqj9d12323s1n95ounLE03xEt6KaSmPTkxk+LDFEQAAAACuKoGdpvYSCAQjR47MzMysr6+vrKzU6XT93zUajWVlZeXl5XFxcZmZmWIxFMMEAYPFQJkGcJhX0QrYuwuzk0J5K38rdz3D5DKJfy/IfLIwue8VisJW7a/5x+8V3pkjYnQeahgs5LNbL35wqM6VoxKN3fonf73w7enmb+7ITw6FqnUAAAAAQG84pKm9WCyWTCZLSUlpbm6urKxUKK44pkWSZENDQ2NjY1RUlEwmG3iuFQA/FMpno4wmQBnNz+E49tTk5EmJIc9uLS++5KSRDI5jczPC37o2KytC1P/1534v/8/eS96cJko8loenG3Qm65y1xw7VuXe09VhD9/jVBw8+Nik9XOjZuAAAAAAAgxk+aWovgiDi4uLi4uL+n707D2yjvPPHP5J12ZIP2Yp1jg5LY8l2SAgEAkk4ElpuSlpIv20DbZejy9EudH9tabd8WeiyLaW/HiylsIVeu4WkHAUKbTm2ISThCoFAiGNJY1nWNbJj2fIh2br1/WN2tUaWFdnW7ffrr7Hn0cynxZH0nnnm8wQCAavVyjDM/L1sjyWGYaRSKUVROp2OU851JAGWqHtNMQPA/OctV4mNZNvfbj77sGfy14c8ewcDthOh+Xs5HKJX3vwJas31m8hTlNnzLB44MFRDGZUgiDWS5TzXkEylr3ni/aVmVNZYKPaJR95642tbtVJ0GAYAAIBiqreYmiGTybZu3To5OTk4OOhyuZLJj02HCwaDhw4d6u/vNxqNRqORz+dXqk6APM7SSYt4tLN1RVuetLZsJNs2km0EQZwIRf3TUf90hMvhKFqEqhaRbJE7zEPjs3e8OFDeMlfKsqy7mj95fejZj0aWfVLvVOT6Jz989e/PWvYRAAAAABbirIZVWyKRiMPhoGk6Fost3Mvn8/V6vcViaWzEDQGoLql0Wn/v3zyTcys/lETIG7n7QrEAXa8L8pnfvruS8FYRQ9+9wNC+tCdFT4Si3T94bSoSP/nQvP58w6ZLezpXeBAAAACAjFXRUkUkEvX19V1++eUbN25sbm7O2huPx2ma/vOf//zOO+9MTdXAshOwenA5nBvP0hblULtOUyOjFsh2IlRzGXWtonmpGZUgiP9/n2PlGZUgiDv/WhtdpgAAAKDKzczMHD9+PBKJ1O2k34V4PF5XV5fBYPD7/VZCxSTkAAAgAElEQVSrNRD4WFcVtseSy+WSyWQWi0WlUlWqToD5vnG+8bF33O7gim6oNgt5/3xhd7FKqnuPv++rdAlL9rVzDEt9SSqd/s/D3qKc/Yhv6iP/9MLnewEAAAAKMTU15ff7GYZhM5pAIFhFMZXF4XBUKpVKpQoGg3a73e12Z017DgQCBw8ebGtrM5lMOp2uoQE3oKCSGvkNP7i0Z9fj76/kIP98YbeyRVSskmpdNJF62xV0BecC4Vg0keyUCDslwrP10sxzqvscJ+kMXG36FM3Xnbnku+7HRmZGVrzUasar9gBiKgAAACzJ1NSU1+v1eDzT09Pzf+/1elddTM2QSqWbNm3q6+ujaXpoaCirx9Lk5OThw4ePHTtmNBopihIIVtFKHlBtvnCa+qh/+od7l9l1dtdp6n88z1jckmpRKp1+oX/04TeH9w9NzMWzlxjlcjjrVM27TtN85SzdR/6ZilS4PDwu56HPnMLjLrlp+fveYj7jcMSHJyYAAACgIMFg0Ov1er3emZncX7rGxsZWRQulk4pGo06nk6bpubkcUyt5PJ5WqzWbzQufawUoj3Sa+OqzH/3ijeGlvvDqdconrjmN37AqnkLP43XH+M3PHB0YDZ10ZGsjf2quCI9rlgeHQzy6c/31m5bzAPM/v2z73iv2YlVytl765te2FutoAAAAUH8mJibYe6fhcDjPsMbGRrVavXrvps4nFAotFkt3d7fb7bZarVk3nROJxNDQkNPpVCqVPT09HR0dlaoTVi0Oh3joM6dsN8lufPLDYGEhSsTj3nd5zz9s7VrlawOn0um7X7Z//290MlXQJbkayqj8Bu5PPtW7vIxKEMR0JFHEYop7NAAAAKgb7Mxel8sVCuW7YdDY2KjRaFQqVWdnJ4fDQUz9X1wuV6/X6/X60dFRmqYZhpm/N51OMwzDMIxMJqMoSqPRcFb5138ou6vWKbca2h844Hz4zeHJxdOUWNBw/SbtP55n1EmxxhLxjT8d/+n+oUpXURABjysWNARnC8rJlEz8+DWnnUG2lbqqAmFeDgAAAGSk0+nJyUmGYU6aTpuamtRqNUmSMpls/u8RU3OQy+VyuTxPj6VAICCRSCiK6urqQo8lKCd5s/D7l1ru/AT1+tD4XjrwITM9OhOdisSbhbxOiXCtsnm7SXa+qaNVxK90pVXh/tcGayWj9iman7jmNGOH+FfvuH+2f8g5MbvYyHXKlm9sM37uVNUK53I3C4v5/t8iwqcJAADAapdOp8fHxz0ej8fjiUQieUYulk4z8GzqSUQiEYfDYbfb4/EctziEQqHBYOju7haJ0EYVoLoMjc/23v9aNJGqbBkSAS8UyzchVtMq+vp5xlu26EW8/42d/SMzewcDA6OhkZnITDQpbeTLm4XrVS3bTbKujiWvj5rTrw+5r//Dh0U5FEEQn9+gfuKa04p1NAAAAKghhadTsVisUqnypNMMXP8+CZFI1NfXZzabnU6nzWabnf3YLY5oNGq1Wu12O0mSPT09LS1YjwGgWnzzheMVz6iqFlH/t85/9qORvYOBN5wT/ulIJJEiCEIsaDC0N51r7PgEteay3k7BgvuifYrmPkVpe7adqmot5tHUePcDAABYXQpPp3w+nyRJvV5/0nSagbupS5BKpXw+n81mm5iYWLiXw+EolUqKouRyeflrA4D56EDYfN/eir+93bBJ++hn18//zUw0weNyGvmVf1gglU4r7n5lLBQrytHe/8dzN6iLmXsBAACgOhWeTufj8XgGg8FsNjc1FTQvDHdTl4DL5ZIkSZJkIBCwWq2L9ViSSqUURel0OvRYAqiUJz9gKp5RCYK47szsNrzFfSJ0Jbgczhc2aB44UIRnd/sUzcioAAAA9S2TTt1udzQazTOysbExmUzGYh+7FJ5IJGiaHhwcJEmyu7u7vb09/+lwN3X5QqEQTdNDQ0PJZHLhXrFYbDQajUYjn49mNgDlduG/v/2qfayyNXz6FMUfv3xGZWvIb3Qm2n3f3pWvJfPMlzd+5hRlUUoCAACAqpJMJkdHRz0eD8MwOZv1ZLS0tJAkqdVqm5ub0+m03++3Wq2BQCDn4JMunoKYulLRaHRwcJCm6awLBiw+n6/X6wu/uw0ARaG+51VmutBZKKUg4nGPfet8Y4e4gjUU4t5X6f/7knUlR9hmku29+exi1QMAAADVYHnpdOHexRZPYbGLpxgMBh4ve7oZYmpxJBIJt9tts9lmZmYW7uVyuWq12mw2n/TuNgCsXCqd5n3zxQq+t3E5nD3XnrZzvapiFRQsmkhd8ug7rw3mvtJ5UsoW0Rtf22Jox2U4AACAepBJpz6fL5HIN9+KTac6nU4ikZz0sOFw2OFwOByOnImXvbFnsVgaGxszv0RMLaZC7m5bLBaVqga+vALUrlA00fxPf63U2bkczs8/s/bmzfqZaOJEKDo5l2hr5HVKhNXzVGqW6Uji/F+8ecQ3tdQXtor4+27ZjB6/AAAAta7AdMrhcDo6OlQqlUajKSSdZonH48PDwwsXT2GxbYAsFktrayuBmFoi+e9ut7W1mUwmnU7X0FD5bp8A9SeWTAm/9eeKnFrRLPzK2Xrv5Nzf6DFXcG7+Lp208QJqzRV98k/1yblV1l9tJpr4xp+O//JtV+EvWato3n3t6WtLvGoOAAAAlM6S0ilJkhqNZv4Nz+VJpVIej8dutweDwZwDFApFd3c3YmoJhcNhu93udDpz/lcXiURGo5GiKIFAUP7aAOqb5Dt/Ccdy9DZbntvOMfx54MRgIJxnTIdYsFXffmxk2jGe4wLhfJRM/J0LqC+fQbJZlQ6E9w0GvFORsVAslkzJJUJFi3CroX29qqXMafapD5nvvWI/NpLjyYX51kgEt53T9c1txoXLvQIAAED1i8fjPp/P6/WOjIykUosuMp9JpyRJikSiopdx4sQJm83m9/tznx0xtUTm5ub8fj/DMH6/P8//yTweT6vVms3mnM8cA8DynPaT/cuYxboY53cv0Eob/8se+Bsd2OcIuINzgXAskUqvkQjkEuHWrvYzyLZnjvr/MnCi8GNuo2RnadueeN+XddM1o0MsuPZ0zdfP7dJKV3rZsnDpNPFX64k/fuTfSwecEx/L2+1N/POMHZdYOnedpmkSYCYIAABAjYnFYgzDVDydZlls8RTE1CKbnZ31+Xwej2d8fHzh/7fpdDpnz2UOh6NUKi0Wi0wmK0uZAHXu5meOPvLmEqaw5qFsEfnu+uTCf7iJVJrH5RAE4Z+ObP35G0Mnu4m6PPwG7m3nGP71Ukv5b11OzMZHZ6ITs7EWEW+NRNgpEVTbXGUAAAA4qepMp1mi0ajT6aRpem7uvy/fI6YWRygU8nq9Xq93YmIizzAejyeVSlOp1Pj4eM4BUqmUoiidTrfYCkIAUIgXj49e8atDRTnUDZu0j352/WJ7pyLx8x5680NmuijnWsxpmtY/fvkMXRlvqwIAAEBNKzCdcrncNWvWqFQqrVYrFArLWeFCqVTK7XZbrdbp6WnE1BUJh8MMw3g8nsVa+7IaGhrkcrlGo9FoNOyiQJOTkzabzePx5PyjYVcQ6urqQo8lgOVJpNLG7//Nvch82sJxOMRb/7B1k1a62ICvPPXho2+7V3iWQhg7xG98bYu8ucKfHwAAAFDNotGo3+/3er35HzzMxBO1Ws3n88tZYSH8fj9i6nJMTU2xz53mT6d8Pl+lUqlUKqVSuXDJWoIgIpGIw+GgaToWiy3cKxQKDQYDRVErb6gFsArtPuL7wu/fX+FBPrdBvfua0xbb+yEzffpP9ydTZXoXPYNse+NrW/hoXAQAAAAfNzc35/V6GYY5ceJE7abT+RBTl2Bqasrr9Xo8nunpfBP8BAKBUqkkSVKhUHC5J/9CmUgknE5n/hWEenp6WlqwOCHAEqTTxHm/eOPAUL55+PlJhLxj3zw/z1TbT//m3eeOjSz7+MvwwI61/3COoZxnBAAAgKqVaYuz1Kmd1Q8xtVCTk5OvvPJKngEikUitVms0ms7OzmU8WZpOp/1+/8DAwGKPrcrlcoqiVCrVUo8MsGqNhWJbf37QPpZvIZnF8Bu4z193xiWWzsUGTMzGFXe/Ek8u+rBHKbQ38envXNDeVNWXPwEAAKCk6jidZiCmLsFf/vKXUCiU9UuhUKhQKEiSVCqVRel7FAgErFYrwzA596LHEsCSOMbDn3jk7eGJpbXhFfK4v/4/p37hNHWeMX/8yH/Vbw+vrLrl+NmOvtvO6Sr/eQEAAKCylt0WpxYhpi7B0aNHrVYru93U1KRWq0mS7OjoKEViXGwFIZZIJDIajd3d3VU+pxygGkxHErc8c/Tx930Fjrd0Sp645rQN6tb8w+56yfYvr9pXXN2SndPVvv/WLeU/LwAAAFREgemUbYtT+IOHVQ4xdQkmJibefPNNNp2WZ4HTaDQ6ODi4WI8lHo9nMBjMZnNTU1MZigGoaf9lH7v/Ncer9rE8Y4wd4v/v/K6/O1Mr4p38zf3aJ478/j1v8QoslKCBO/39S4QFVAgAAAC1q8CmrUtti1MrEFNrALuC0MDAwMzMzMK9XC5XrVabzeb29vby1wZQW+hA+L/sY/sc466JuZGZSCyZXiMWqFtFZ+mk2ynZ2TppA7fQyRFX/vrdP/WXtX9SxvFvbeuRSypyagAAACipEjVtrTmIqTWD7bFE0/To6GjOATKZzGKxoMcSQHlUMKb+7eazt5vKMaEDAAAAyoNNp263O+d9qYy6T6cZtfpM7SrE4XDYVViDwaDdbne73VmXGAKBwMGDByUSCUVRXV1dDQ0NlSoVYDVoEVXs/TMczfHIOgAAANScAtNp0Zu2Vj/E1NojlUo3bdq0du1au93udDoTicT8vaFQ6MiRI8ePHzeZTBRFCQSCStUJUN8M7RV7JryRX89XTwEAAOoem05dLtfCZUTmK3XT1mqGmFqrxGLxhg0b1q5dOzw8bLVa5+bm5u+NRqP9/f02m02r1ZrN5ubm5krVCVCvTlW3VOrUayTCSp0aAAAAliedTo+PjzMM4/V6C0yn5WnaWp3wbGo9YHss2Wy2qamphXs5HI5SqbRYLKv5Dx2g6CZm44q7X4knU2U+bwOXM/Wvl4gFmNUPAABQA9h06vF4vF5v1o2lLEin8yGm1pVAIGC1WhmGyblXKpVSFKXT6VbbnAGAEvn0b9597li5uyhtJNvevf2cMp8UAAAAliSTTj0eTyQSyTNSLBaz650inc6HmFqHJicnBwcHh4eHU6kc93nEYnF3d7fBYODxMOUbYEU+ZKZP/+n+ZKqs76L3XGS+68Lucp4RAAAACoR0WiyIqXUrEok4HA6apmOx2MK9fD5fr9dbLJbGxsby1wZQN/7+qaO/fNtVttOJeFz7d7aTbfhnCwAAUEUKT6ctLS0kSapUKqlUWrbyahFiap1LJBJOp9Nms83Ozi7cy+VySZK0WCytra3lrw2gDkxF4uf+/M2j/nwLcBfRdz9B3XuJpTznAgAAgPxSqdTY2BjDMG63OxqN5hnJplOSJFtaKtaCsbYgpq4K6XTa7/cPDAyMj4/nHCCXyymKUqlUZS4MoA74pyNbHnzDOZHjSlBx6aSNx755vkSI6foAAACVlEwmR0dHPR4PwzDxeDzPSDadarVarLuxVIipq0sgEKBp2uv15vzv3tbW1t3drdVquVysygiwBIFw7Lo/fPBC/2jpTtEhFhy4dUuPXFK6UwAAAEAeSKflhJi6GoVCIZqmh4aGksnkwr0ikchoNFIUJRAIyl8bQI1Kp4nfvuv5/t/owUA4/0hBAze2xGVsFM3C5647Y5MWD7EAAACUWyad+ny+RCKRZySbTnU6nUSCy8orhZi6ekWj0cHBwcHBwZwz6Xk8nsFgMJvNTU1N5a8NoEal0unnj42+cHxkLx1wBT+2NppO2ridkl3Rq7jQLPvWiwMPvzlc4LvvZT3yX39ufadEWJKKAQAAIJcC0ymHw+no6GB79orF4nJWWN8QU1e7VCrldrutVuv0dI4eMBwOR6lU9vT0dHR0lL82gJo2E02MzESnI4kWEU/RLGz++DOlH/mnf/SaY88HTHzxO6tbDO13bDNd0ScvfbEAAABAEEtMpyRJajQaLJxRCoipQBD/02OJpunR0dwP18lkMovFgh5LAMU1FYm/7hjfPzQxPDF7IhRNpog1EoG6VbRF377N1KFsEVW6QAAAgFUhFosxDOP1ekdGRlKpRa8gZ9IpSZIiET6mSwgxFT4mGAza7Xa3253zD0MikVAU1dXV1dDQUP7aAAAAAACKCOm0aiGmQg7hcNjhcDgcjpxNzIRCoclkQo8lAAAAAKhFS02nWq1WKESTiLJCTIVFxePx4eFhq9U6Nze3cC+XyyVJsre3F422AQAAAKD6RaNRv99/0nTK5XIVCoVKpVKr1UinlVKSmBoIBF5++eUPPvjA7/dHo9GWlhaTybRly5YtW7YUZbJoqY8P87E9lmw229TU1MK9bI8liqLkcnR5gWqRTKXHwrFAONbI58olQsnHexcBAADAqpJJp36/P0/2aWhokMvlGo1GrVbz+fxyVggLFTmmOp3OO++888knn8zZF0uj0dxxxx0333zzssNkqY8PeQQCAavVyjBMzr1SqZSiKJ1Ox+FwylwYAOtDZvoPHzB76cBh72Qy9b/vbGskgm0m2YXdaz63QS0W4M0BAABgVZidnfX5fB6PZ3x8HOm05hQzpu7Zs+eGG24Ih0+ytP3mzZufffbZzs7Oajs+FGJycnJwcNDlciWTyYV7xWJxd3e3wWDg8XD/CsrnHXfwn1+yvWIfy/9+1iEWfHWL/lvbTE0IqwAAAHUqk04DgUCeYZl0qtFo8MW1ChUtpv72t7+97rrr2KNxOJwdO3bs3Lmzt7e3qalpdHT0wIEDjz76qNPpZAd3d3e/8cYbMpmseo4PSxKJRBwOB03TsVhs4V4+n6/X6y0WC1aRglJLp4l/Ozj0zRcG8qw+msXSKdl9zemnqltKWhgAAACUE9JpnSlOTH3nnXe2bt3KTsRVq9VPPvnk5s2bs8bEYrHvfOc7P/nJT9gft23b9re//a3ACaKlPj4sTyKRYB9bnZmZWbiX7bFksVhaW1vLXxusBslU+vO/f/+pD3NPRM9DxOP+9StnbdS07h+aeNsVHJmJjIVijfyGNRKBvr1pm1G2TtXMxbsHAABA1QuHwwzDnDSd8vl8hUKhVCqRTmtFEWJqIpE45ZRTrFYrQRAymeztt982Go2LDf6nf/qnH/zgB+z2z3/+81tvvbXix4cVSqfTfr9/YGBgfHw85wCZTGaxWFQqVZkLg7p38zNHH3nTtbzX8rncNEEkFunyJ28W3rxZf+sWvUyMVZcAAACqToHpVCAQKJVKkiQVCgWXyy1bebByRYipjzzyyM0338xuP/fcc1deeWWewalU6pxzznnzzTcJglizZs3Q0JBEIqns8aFYAoEATdNerzfnH1VbW5vJZNLr9XiPgKJ4+qh/5+8Ol/QUYkHD/Zf33rJFX9KzAAAAQIGmpqa8Xi/DMMFgMM8wpNM6sNKYmk6nzWYzTdMEQZx//vmvvfbaSV9y6NChTZs2sdsPPfTQLbfcUsHjQ9GFQiGapoeGhnL2WBKJREajkaIogQA3qWD5YslU3/37BgMnaahWFDvWKh7fdRq6LgEAAFQKm049Hs/09HSeYUin9WSlMXXfvn3btm1jt1944YXLL7+8kFedc845Bw8eJAji1FNPPXLkSAWPDyUSjUadTidN03Nzcwv38ng8g8FgNpubmprKXxvUgV+8MXzrHz8q2+ku65E/d90ZPC6eVgUAACgfNp263e6cbVAyhEKhQqFAOq0zK32A+Omnn2Y3ZDLZxRdfXOCrrrnmGjZGfvDBBw6HI8+zpqU+PpSIUCi0WCzd3d1ut9tqtWZd+kokEjRNDw4OKpXKnp6ejo6OStUJNeo/DnvLebo/D4x+/fn+Bz+9tpwnBQAAWJ2Wmk6VSiXaptYfXjQaFQqFy379K6+8wm5cdNFFhXfNuvTSS+cfIfPoafmPDyXF5XL1er1Opztx4gRN0wzzsY6s6XSaYRiGYWQyGUVRGo0GbzFQiMm5+GHvZJlP+os3hr+4UXMG2Vbm8wIAAKwSbDp1uVyhUCjPsKamJrVaTZJkR0cHvjrWMd5KMuro6Cj71ChBEOedd17hLyRJ0mAwsMuc7t+/f7EYWerjQ3lwOBy5XC6Xy4PBoN1ud7vdWVPNA4FAIBCQSCQURXV1dTU04CFAyOdDZjqZKs6Cz4VLpdPffOH4vluyl8ICAACAZUun0+Pj42zP3nA4X8uJTDqVyWRlKw8qaEWTfuc/9nn66acv6bWnnXYaGyPzPDta6uNDmUml0k2bNq1du9bhcDgcjng8Pn9vKBQ6cuTI8ePHTSaTyWRayQUUqG/OidmKnPd1x/hR//Q6ZUtFzg4AAFA32HTq8Xi8Xm/OPiYZSKer1opian9/P7vB4XAsFsuSXms2m9mNwcHBxSYel/r4UBFisXjdunU9PT3Dw8M2m2129mORIxqN9vf3DwwMkCTZ09PT0oJIANmmI4lKnfrZj0YQUwEAAJYnk049Hk8kEskzUiwWq1QqpNPVbEUxlb1dSRCEQqFYasvWTFujZDLp8XhMJlP5jw8VxOfzKYoyGo0+n89ms01MTMzfm0qlXC6X2+1WKpUURcnl8krVCVUoTZR7xm/GQefEyQcBAADAPEinsAwriqle738321QqlUt9rUKhyGwvFiNLfXyoOC6XS5IkSZKBQMBqtS7WY0kqlVIUpdPp8KA8EATRIuRX6tQDo/laDgIAAEBGJp263e5oNJpnZEtLC/uFENPoIGNFMTUQCLAbnZ2dS33t/JdkjlPm40P1kMlkW7dunZqaomna5XIlk8n5e4PB4KFDh/r7+41Go9Fo5PMrllKgGujbGyt16hOhWKVODQAAUBNSqdTIyAjDMD6fD+kUlm1FMXVy8r/XhFjG31Zzc3NmOxgMVuT4UG1aW1s3btzI9liiaToW+1gkCIfDR48eHRgY0Ov1FoulsbFiWQUqa72qlcvhpNIVmPobT6biyRS/AUuHAwAAfEwymRwdHfV4PAzDZLXJzMKmU61WO//rOkCWFcXUTNvopT44ShCEWCxeeJwyHx+qk0gk6uvrM5vNbrfbZrNlrewcj8dpmnY4HCRJWiyW1tbWStUJldLexN+gbnnPO1X+U/O4HGRUAACAjKWmU51OJ5FIylYe1K4VxdTM3yKPt+TjzH/JYn/TpT4+VDMej9fV1WUwGPx+v9VqzZq5zfZYcrlcMpnMYrGoVKpK1QkVce1GTUViqrwZPcMBAAD+N536fL5EIl8HfqRTWJ4VxdRUKsVuNDQ0LPW181+y2B93qY8P1Y/D4ahUKpVKFQwG7Xa72+1Of3yqZyAQOHjwYFtbm8lk0ul0y/hTgVr0lbN0P319yBXMt9JaKVg68RELAACrV4HplMPhdHR0kCSp0WjwlBYsz4piauaO5TJy4PyXLNYRp9THhxoilUo3bdrU19dH0/TQ0FBWj6XJycnDhw8fO3bMaDRSFCUQCCpVJ5RHI7/h+5f27Hr8/TKfd6uhvcxnBAAAqLh4PJ7pioR0CuWxopiaCQPLmFU7/yWLxchSHx9qjkQi2bBhQ29vr9PppGl6bu5jN9MikUh/f7/NZtNqtWazGc/l17fPb1C/ZD3xn+95y3nST5+y5MWxAAAAalQsFmMYxuv1joyMZCY5LpRJpyRJikSiclYIdWxFMTUTA7Ka3BQiFAplthdr5Fvq40ONEgqFFoulu7vb7XZbrdbp6en5exOJxNDQkNPpVCqVPT09HR0dlaoTSorDIX71f9aPz8b+MnCiPGfcamhfr8KbCQAA1DmkU6gGK4qpUqmU3ZiaWnIvk/nRInOcMh8fahqXy9Xr9Xq9fnR0lKZphmHm702n0wzDMAwjk8koitJoNBwOp1KlQonwG7jPX3fmva/a/+VVutTr03A5nJ9c2VfSUwAAAFTQUtOpVqsVCtFZEEplRTFVLpezGyMjI0t97ejo6MLjlPn4UB/kcrlcLp+cnLTZbB6PJ+uNNRAIBAIBiURCUVRXVxd6LNUZHpdz90Xmc40d//evtjeHJ0p3or8/W3cG2Va64wMAAFRENBr1+/0nTadcLlehUKhUKrVajXQKZbCimEqSJLvh8/mW+tr59760Wm1Fjg/1pK2tbdOmTevXr3c4HHa7Pet55lAodOTIkePHjxsMBoqi8Fh/ndlukm3/muygc+IPHzB76cDx0Y89JiAR8s4xtK9VNj/6lnsyspzlqS6gZD/bgVupAABQPzLp1O/3pxefkdTQ0CCXyzUajVqtRrcXKKcVxVSKotiNqampsbGxNWvWFP7awcFBdqOpqWmxRS9LfXyoPyKRqK+vz2w2O51Om802Ozs7f280GrVarXa7nSTJnp4ePLRcZ7Ya2tlOvDPRhH86OhaKigW8NRJBp0TAb+ASBHFln+Lq3x0emYku6bCX9nQ++cWNggZuSYoGAAAoo9nZWZ/P5/F4xsfHkU6hmq0opp5yyimZ7WPHjm3btq3w1x4/fpzd6Ovr43Jzf/8r9fGhXvF4PIqijEajz+ez2WwTEx+bC5pKpVwul9vt7uzspCgKVzHqT7OQ17yG171GnPX7LYb2D79x3t/t+aDArkuN/IYfXGb5h61deK4ZAABqWiadBgKBPMMy6VSj0WQWhgSoiBX9/a1fv14gEMRiMYIg3n333SXFyPfee4/dOPPMMyt1fKhvXC6X7T4XCASsVuvCHkujo6Ojo6NSqZSiKJ1Ohx5Lq0GnRPjnGzYdGJq4/7XBv1pPJFO5LyS3N/FvOlv/D+cY5M14/AYAAGpVOBxmGAbpFGoRJ8/t/kJs2bLlzTffJAjiwgsvfPnllwt81UcffbRu3Tp2+8knn9y5c2eljg+rRygUoml6aGgomcehJeEAACAASURBVEwu3CsWi41Go9FoxMyW1WNyLv66Y/xtV9A/Ex0LRYU8rqJZZGhvOs/YcbqmtYGLyxYAAFCTCkynfD5fpVKpVCqlUol0CtVmpX+Rl19+ORsj9+3bNzk52dZWUCfMP/3pT+yGQCC48MILK3h8WD0kEsmGDRt6e3sHBwdpmmbv0meEw+GjR48ODAzo9Xqz2dzU1FSpOqFs2hr5V65VXLlWUelCAAAAiqDAdCoQCJRKJUmSCoUCT8ZB1Vrp3dTBwcHu7m72IA8++OBXv/rVk74klUpZLBaapgmC+NSnPvX8889X8PiwOiWTSY/HMzAwMDMzs3Avl8tVq9Vms7m9vb38tQEAAAAUbmpqyuv1ejye6enpPMOQTqG2rDSmEgRx4YUXvvrqqwRB6PV6q9V60pWU/vCHP3zuc59jt1966aWLLrqosseHVSudTvv9fqvVuthFR5lMZrFY0GMJAAAAqg3SKdS3fDE1EokEg0GlUpn/EPv37z/vvPPY7bvuuuuee+7JM3hycnLdunUej4cgiDPPPPOdd945aYmlPj5AMBi02+1utzvnP4fW1la2x1JDQ0P5awMAAADIYNOp2+3OOSMsQygUKhQKkiSVSiWaREItWjSm3nvvvd/73vfi8Xhvb+/TTz/d09OT5yg7d+58+umnCYJoaGj4wx/+cNVVV+UcFo1Gr7jiCvbWaENDw4EDB84+++xCqiz18QEIggiHw3a73el0JhKJhXtFIpHRaKQoSiAQlL82AAAAWM3YdOpyuUKhUJ5hXC63s7PTZDIhnUKta7j44osXzgF49dVXr7vuulQqRRDE2NjYoUOHbrzxxjxH2b59+549e6anp9Pp9DPPPJNKpc4666ysjmEfffTRjh07Dhw4wP541113XXvttQVWWerjAxD/MyvGZDI1NjZOTU1lhdVEIjE2NjY4OBgOh5ubm086+RwAAABghaamphwOx+HDhwcGBsbGxrIaQC6UTqdDodDExASHw2lra8MsX6hdHJFIFA6Hs/6I77nnnrvvvvt/B3E40Wg0/0Id/f3927dvP3HiBPtje3v7FVdc0dfX19jYeOLEiQMHDrz++uuZO7fXX3/9o48+uqRrPKU+PsB8qVTK7XZbrdacz3twOBylUmmxWGQyWflrAwAAgDqWTqfHx8c9Ho/X652bm8szsrGxUSAQhEKhnIvtiUQik8lkNBpxbR1qEeeUU045evRo1m+feOKJXbt2ZX7U6XTDw8MnPdbw8PAXvvCFt956K88YoVB4zz333HHHHcuotdTHB1goEAhYrVaGYXLulUql7GOruCYCAAAAK1F4OhWLxSqViiRJ9nJ5NBp1Op00Ted8FZfLJUmyp6enpaWlVKUDlEDDV77yle3bt2f9tre397333mPXdGlsbPzVr35lNptPeqy2tra/+7u/s1gsIyMjPp8v66nX9vb2L37xi48//vgVV1yxvFpLfXyAhZqamrRarVqtTiQSMzMzWX91kUjE5/O53W6CIDC1BgAAAJaKTac2m+3dd9+laXpiYiJnjwyCIMRisV6vP/XUU0899VSlUplZ453H48lkMoqiJBJJKBSKRqNZx2dnDgcCAYFA0NzcXPL/SQDFkK/T74EDB/x+/9lnn02S5FKPOzExcfToUb/fH41GW1paTCZTb29v1tOkK1Hq4wMsFIlEBgcHaZqOx+ML9wqFQoPBQFFUY2Nj+WsDAACAGpK5d+rxeCKRSJ6RWfdOT6qQiWBarRbX1qHKFWHdVIC6V/hnCTu1xmKxtLa2lq08AAAAqAmZbxRutzvrtmeWlpYWkiRJklzeZN3JycnBwUGXy7XYY6tYvwCqHGIqwKIK/yxZSC6XUxSlUqlKVBsAAADUilQqNTIywjCMz+crJJ1qtdqizM6NRCIOh4Om6Zwtgnk8nsFgMJvNmfnDANUDMRUgWzKZHBkZ8Xq9DMPknN+b0dbW1tHREQqFRkdHFxvQ3d2NqTUAAACrUDKZHB0d9Xg8J/1GUdx0miWRSDidTrvdHg6HF+7lcDgkSW7cuBFPz0FVQUwF+G8r+SwJhUI0TQ8NDeWZWtPd3Z1/VScAAACoA5lvFD6fb7F+SCz2G4VOp5NIJKWuKp1O+/1+mqYXXltvbW296KKLSl0AwJIgpsJqV8TPkmg0Ojg4ODg4mHM+D6bWAAAA1LECv1FwOJy2tjaVSlWedLpQMBi02+1utzuTAs4880y9Xl/+SgDyQEyFVap0nyWpVMrtdg8MDMzMzCzcy+Vy1Wq12Wxub29ffvUAAABQHQr/RtHR0UGSpEajqYZFAcLhsMPhcDgcXC738ssvb2hoqHRFAB+DmAqrlNfrffPNNxfbu/LPkjxTa1gymcxisaDHEgAAQC2Kx+M+n8/v9/v9/hpKp1lisdj09HSBS90AlBNiKqxSyWTy+eefz/pcyXyWkCQpEomKcqKFU2vmk0gkFEV1dXXhKiYAAED1i8ViDMN4vd6RkZFUKrXYsFJ8owBYVRBTYfV66623PB4PUZbPknA4bLfbnU5nzguuQqHQZDJh+TIAAIDqhHQKUGaIqbB6eb3e4eFhjUajUqnKkw/j8fjw8LDVap2bm1u4t6GhQaPR9Pb2lqIZPQAAACzVUtOpVqsVCoXlrBCgXiGmApQb22PJZrNNTU0t3MvhcJRKpcViwYMiAAAAFRGNRv1+v9fr9fv9eb4qNzQ0yOVyjUajVqux5hxAcSGmAlRMIBCwWq0Mw+TcK5VKKYrS6XQcDqfMhQEAAKxCc3Nzfr+fYRikU4CKQ0wFqLDJycnBwUGXy5VMJhfuFYvF3d3dBoOBx+OVvzYAAIC6Nzs76/P5PB7P+Ph4IelUo9HgQxmg1BBTAapCJBJxOBw0TcdisYV7+Xy+Xq+3WCxV2MseAACgFmXSaSAQyDMM6RSgIhBTAapIIpFwOp02m212dnbhXi6XS5KkxWJpbW0tf20AAAB1IBwOMwyDdApQ5RBTAapOOp32+/0DAwPj4+M5B8hkMovFolKpylwYAABAjSownfL5fNX/wJLmABWEmApQvQKBAE3TXq8357/Ttra27u5urVbL5XLLXxsAAED1KzCdCgQCpVJJkqRCocCnKkA1QEwFqHahUIim6aGhoZw9lkQikdFopCiqPEu/AgAAVL+pqSmv1+vxeKanp/MMQzoFqFqIqQC1IRqNOp1Ou90eiUQW7uXxeAaDwWw2NzU1lb82AACAasCmU7fbPTMzk2cY0ilA9UNMBaglqVTK7XZbrdacl4c5HI5Sqezp6eno6Ch/bQAAABVRYDoVCoUKhYIkSaVSiTXJAaocYipA7WF7LNE0PTo6mnMAeiwBAEDdY9Opy+UKhUJ5hjU2Nmo0GpVK1dnZiXQKUCsQUwFqWDAYtNvtbrc75z9kiURCUVRXVxd6FQIAQH1Ip9OTk5MMw5w0nTY1NanVapIkZTJZ2coDgGJBTAWoeeFw2OFwOByOeDy+cK9QKDSZTCaTSSgUlr82AACAlUun0+Pj4x6Px+v1zs3N5RmJdApQHxBTAepEPB4fHh62Wq05P7+5XC5Jkr29vc3NzeWvDQAAYBkKT6disVilUiGdAtQNxFSAusL2WLLZbFNTUwv3sj2WKIqSy+Xlrw0AAKAQmXTq8Xhy9rfPQDoFqFeIqQD1KRAIWK1WhmFy7pVKpRRF6XQ6NJMAAIAqUXg6bWlpUalUKpUK6RSgXiGmAtSzycnJwcFBl8uVTCYX7hWLxd3d3QaDgcfjlb82AAAAYl46dbvd0Wg0z8iWlhaSJEmSbGlpKVt5AFARiKkA9S8SiTgcDpqmY7HYwr18Pl+v11sslsbGxvLXBgAAq1MymRwdHfV4PAzD5GwBmMGmU61Wi/YKAKsHYirAapFIJNjHVnOufs72WLJYLK2treWvDQAAVgmk03r17rvvPvPMM8t77Z133imRSIpbD9Q6xNS6NTc3t3//fva/74UXXsjlcpd9qJdeemkllahUqnXr1mX98ujRo4s9NplfU1PTueeeu5J6Vrl0Ou33+wcGBsbHx3MOkMlkFotFpVKVuTAAAKhjmXTq8/kSiUSekWw61el0yC215bHHHrvxxhuX8cKWlpbJyUk0y4AseCCtPv31r3+99dZbnU4n++Pc3JxIJFr20S655JKVFLNr167f//73Wb+8//77H3/88WUcbdu2bXv37l1JPasch8Nh204EAgGapr1eb9a1qkAgcPDgwba2NpPJpNPpGhoaKlUqAADUugLTKYfDaWtrU6lUSKer0Lp165BRYSHE1HrDMMztt9/+1FNPVbqQUlm/fn2lS6gTMplMJpOFQiGapoeGhrJ6LE1OTh4+fPjYsWNGo5GiKIFAUKk6AQCg5hSeTjs6OkiS1Gg06I9QN+Ry+cL7E3koFIrSFQO1CzG1fqRSqYceeujOO++cnp4u7pFvu+22Zbzq4YcfZhv25H/3Wbdu3RVXXFH4YZc0GE5KIpFs2LCht7fX6XTSNJ21eHokEunv77fZbAaDwWw2NzU1VapOAACofvF43Ofz+f1+hmFydphnZdIpSZIrme0F1UkkEn3iE5+odBVQ8xBT68Thw4dvuumm9957j/2Rx+OdddZZBw8eLMrBf/azny31JXv37n3ggQfY7euvvz7PyA0bNtx7773LrAyKRCgUWiyW7u5ut9tttVqzrnQkEgmapgcHB5VKZU9PT0dHR6XqBACAKhSLxRiG8Xq9IyMjqVRqsWFIpwBQOMTUmjc9PX3nnXc+9NBDmQ+GzZs3P/LII/39/cWKqcvw85//nN3Yvn17T09PpcqAJeFyuXq9Xq/Xj46O0jSd1eMqnU4zDMMwjEwmoyhKo9HgSRIAgNUM6RQASgcxtbY9+eSTt99+u9/vZ3+USqX33XffjTfeyOFw+vv7K1WVx+P505/+xG7feuutlSoDlk0ul8vl8mAwaLfb3W73wh5LgUBAIpFQFNXV1YUeSwAAq0qB6ZTL5a5Zs0alUmm1WqFQWM4KAaAOIKbWKpfLddNNN81fKmbXrl0/+clPOjs7K1gV6+GHH2afSNFoNJ/61KcqXQ4sk1Qq3bRp09q1ax0Oh8PhyFrdLhQKHTly5Pjx4waDobu7GxfIAQDqWzQa9fv9Xq/X7/fnWc6woaFBLpdrNBq1Ws3n88tZIQDUE8TUWpVIJPbt28duUxT1i1/8okqeVo9Go4899hi7/fd///c8Hv7GaptYLF63bl1PT8/w8LDNZpudnZ2/NxqNWq1Wu91OkmRPT09LS0ul6gQAgFKYm5vzer0Mw5w4cQLpFADKBhGiVhmNxjvvvPN73/veHXfc8d3vfrd6ptPs2bNnbGyMIAg+n7+8VZ6hCvH5fIqijEajz+ez2WwTExPz96ZSKZfL5Xa7lUolRVFyubxSdQIAQFHMzs76fD6PxxMIBPIMy6RTjUaDC9MAUER4Q6lh3/rWtz772c9SFFXpQj7moYceYjeuuuoqxJU6w+Vy2R4YgUDAarUu1mNJKpVSFKXT6dBjCQCgtiCdAkCVwDtLDWNvcFW6io9555133n33XXYbzZPqmEwm27p168zMzODg4NDQUNbieMFg8NChQ/39/Uaj0Wg0YvYXAECVC4fDDMMgnQJA9cBbDBRTZh2adevWbd26tbLFQKk1Nzdv2LChp6fH4XDQNB2LxebvDYfDR48eHRgY0Ov1FoulsbGxUnUCAEBOBaZTPp+vUqlIklQoFFwut2zlAcBqhpgKRTM2NvbUU0+x27iVunqIRKK+vj6z2ex2u20228zMzPy98XicpmmHw0GSpMViaW1trVSdAADAmpqa8vv9DMPkT6cCgUCpVCKdwgo5nU6PxxMKhdra2jo7O7u6uvDnBIVATIWi+eUvfxmNRgmCaG1t3bVr17KPk0wmY7EYbr7VFh6P19XVZTAY/H6/1WrN+urD9lhyuVwymcxisahUqkrVCQCwak1NTXm9Xo/HMz09nWcY0ikUxSuvvPKf//mff/nLX7LaLra3t1900UU333zzOeecU6naoCYgpkJxJJPJf//3f2e3v/SlL4nF4iW93Ol07tmzZ+/evR988MH4+Hg6nW5qalIqleedd95ll1125ZVXNjQ0lKBqKDIOh6NSqVQqVTAYtNvtbrc7a/WCQCBw8ODBtrY2k8mk0+nwnxUAoNTYdOp2u7Nmu2RBOoVimZycPP30099///2ceycmJnbv3r179+4rr7zysccek8lkZS4PagViKhTHs88+6/F4CILgcDi33HJL4S8cHh6+6qqrnn322aw8Mzs763A4HA7Hr3/9656enh//+MeXXHJJkYuGkpFKpZs2bVq7dq3dbl/YY2lycvLw4cPHjh0zGo0URQkEgkrVCQBQrwpMp0KhUKFQkCSpVCrRnh2KYmpqis2oJElu3rzZZDK1trbOzs4yDLNv3z673c4Oe/75548fP/7SSy91dXVVtF6oUoipUByZ5kkXXHCB2Wwu/IWvv/56ZttkMqlUqubm5mAwODw8nFnvZGBg4LLLLrvvvvu+9a1vFbFmKDWxWLxhw4a1a9cODw9brda5ubn5eyORSH9/v81m02q1ZrO5ubm5UnUCANQNNp26XK5QKJRnWGNjo0ajUalUnZ2dSKdQdDt27Lj99tvPO++8hbvefvvtb3zjG2+88QZBEDRNX3LJJe+///5SZ+HBaoCYCkVw7NixTNpcRvOkU0899aabbtq5c2d7e/v83/f39z/wwAO/+c1vEolEOp2+4447uFzuN77xjeIUDeXCrpxkNBrdbrfVas16JiqRSAwNDTmdTqVS2dPT09HRUak6AQBqVDqdHh8fZxjG6/XmT6dNTU1qtZokScy0hKK76KKLXnjhBYIg9Hr92rVrFxt21lln7du375Zbbnn00UcJgrDb7bfddttjjz1WvkKhRnCyZlpC3dizZ8/nP/95dntubk4kEpXuXDfddBP7YCpJkk6ns5AHDnfv3v3ee+8RBLFly5YdO3bkuZT7xhtv7Nixg23Jw+fzDx48eOaZZxavdii30dFRmqYzt8qzyGQyiqI0Gg2u7gMA5MemU4/H4/V6s6arZEE6hWqTSqUuuOCCffv2EQTB5XJtNpvJZKp0UVBdEFPrVtli6tTUlFqtDofDBEHce++93/3ud4t+infeeeecc86Jx+MEQWzZsuXgwYNFPwWU2eTkpM1m83g8qVRq4V6JREJRVFdXF3osAQBkyaRTj8cTiUTyjBSLxex6p0inUIWOHz/e19fHbn/1q1998MEHK1sPVBvE1LpVtpj6s5/97Otf/zpBEAKBwO12y+XyUpzl29/+9g9/+EN2++233960aVMpzgJlFolEHA6H3W5nr0FkEQqFBoOBoiisTgQAgHQK9Wf79u2vvfYaQRB6vd7pdFa6HKgueDYVViSdTv/iF79gt6+++uoSZVSCIL72ta/9+Mc/TiQSBEHs3r0bMbU+iESivr4+s9nsdDptNtvs7Oz8vdFo1Gq12u12kiR7enpaWloqVScAQKUUnk5bWlpIklSpVFKptGzlAaxEJqayjTOxrDrMh5gKK/LSSy/RNM1uL6N5UuHUavWGDRveffddgiAw6bfO8Hg8tseSz+ez2WxZ64CnUimXy+VyueRyOUVR+AwDgNUglUqNjY0xDON2u6PRaJ6RbDolSRLX8qDmnH766ZntY8eO4SMe5kNMhRXJrENz6qmnbt68uaTn2rRpExtTP/zww2QyiacW6wyXy2W/aQUCAavVurDH0ujo6OjoqFQqpShKp9OhxxIA1J9kMjk6OurxeBiGyfk0RAabTrVaLVbzgtq1Zs2azHbWRWoAxFRYvqGhoZdeeondvuWWW0p9OrVazW4kEolgMIhHbuqVTCbbunVrKBSiaXpoaCiZTM7fGwwGDx061N/fbzQajUYjn8+vVJ0AAMWCdAqrU2tra2YbMRWyIKbC8j300ENsm9a2trZdu3aV+nTzH7aZmJhATK1vEolkw4YNvb29g4ODNE3HYrH5e8Ph8NGjRwcGBvR6vdlsbmpqqlSdAADLlkmnPp+Pbb6wGDad6nQ6iURStvIASm3+Qkq47gxZEFNhmWZnZ3/zm9+w21/+8pfLkBPmr1ySdYcN6pVQKOzr6+vp6XG73QMDAzMzM/P3xuNxmqYdDodarTabze3t7ZWqEwCgcAWmUw6H09HRoVKpNBoN0inUpWAwmNmef2cVgEBMhWV7/PHH2TcXDodz8803l+GM82eDoFHEqsLlcvV6vU6n8/v9Vqs1EAjM35tKpdgemDKZzGKxoAEDAFSnJaVTkiQ1Gg2W44L6ZrfbM9sURVWwEqhCiKmwTA899BC78clPfrK7u7sMZ/R6veyGUChUKBRlOCNUFQ6Ho1KpVCpVMBi02+1utztr2edAIHDw4MHm5maTydTV1YUmWwBQDeLxuM/n83q9IyMj86cFZcmkU5IkS7fUOUBVefvtt9kNkUjU09NT2WKg2iCmwnLs37//ww8/ZLdLug7NfJn3st7eXiSQ1UwqlW7atGnt2rV2u93pdGbdlJiZmTly5MjAwIDRaKQoSiAQVKpOAFjNYrEYwzBIpwCLicViL774Irt9/vnn4+8fsiCmwnJk1qHRarWXXXZZGc7IMMxHH33Ebn/yk58swxmhyonF4g0bNqxdu3Z4eNhqtc5vw0AQRCQS6e/vt9lsWq3WbDajJSYAlMdS06lWqxUKheWsEKBK7N69+8SJE+z2tddeW9lioAohpsKSMQzz7LPPsts33XRTeW5sPvLII5m2SVdffXUZzgg1gc/nUxRlNBrdbrfNZpuampq/N5FIDA0NOZ1OpVJpsVjQHRoASqTAdMrlctesWaNSqZBOoc68/PLLbrf7xhtvLHB8IBD49re/zW5rtdqdO3eWrDSoVYipsGSPPPIIO81SKBTecMMNyzjC/v37e3t7C88Mbrf7gQceYLc3b958xhlnLOOkUMfYHkt6vT4QCFitVoZh5u9Np9MMwzAMI5VKKYrS6XQcDqdSpQJAPYlGo36/3+v1+v3+rKfl52toaJDL5RqNRq1WY9UNqD8vvPDCzp072Ys1d91110k/ZMPh8Oc+97mRkRH2x5/+9Kf4dwELIaYCQRBEOp1+7bXXRkdHt2zZotVq84yMx+OPPvoou71z5841a9Ys9Vwvvvji1VdfbTKZXnzxRb1ef9Lxc3Nzu3btmp6eJgiCw+Hcf//9Sz0jrB4ymWzr1q2Tk5M2m83j8WTd0wgGg4cOHerv7+/u7kaPJQBYttnZWZ/P5/F4xsfHkU4Bfve730WjUYIg7r777tdee+3BBx885ZRTFhtss9m+9KUvvfPOO+yP11133Wc+85kyFQo1hZPn7RVq2p49ez7/+c+z23Nzc3keTE8kEpdeeumrr75KEIRIJNqzZ8+VV1652OAnnnhi165d7PZbb7111llnLamqYDDY1dU1OTlJEERHR8eDDz6YKTKnEydOXHXVVQcPHmR/vP3223/6058u6YywakUiEYfDQdN0LBZbuJfP5+v1eovFgvUeAKBAmXSatSxWlkw61Wg0PB7uB0D9SyQSt9566y9/+Uv2Rw6Hc+65515xxRXbt29XKpVr1qxJpVJjY2OHDh167rnnHn/88UzvwyuuuOLpp59Gs0PICTG1bhUeU//jP/7jS1/6UuZHjUbj8XgWG3z22WezHXc3bNjw/vvvL6OwAwcOfPrTnx4fH2d/3Lhx4xe/+MVPfepTOp1u/jCHw7F79+4f/ehH7H1UgiAuu+yy5557Dh/5sCSJRMLpdNpsttnZ2YV7uVwuSZIWiwWrigPAYpBOAQqxe/fu2267bWxsLOv37BzgrMTB4XC+/vWv//CHP8Q/FlgMYmrdKjym3nPPPXfffXfmRy6XG4lEck5Pev/9908//XR2+7HHHrv++uuXV5vL5br22msPHDgw/5ednZ1yuVwmk4XDYbYRxfy911133SOPPIJJU7A86XTa7/cPDAxkro9kkcvlFEWpVKoyFwYAVYv9MDppOuXz+QqFQqlUIp0CTE1NPfDAAw8//HDmudOcLrjggu9973ubN28uW2FQixBT61bhMXXfvn3btm3L/Lhp06bMCqVZbrjhhl/96lcEQUilUp/Pt5LZkul0+ve///2//Mu/0DSdf2R3d/cPf/jDHTt2LPtcABmBQICmaa/Xm/Otr62trbu7W6vVcrnc8tcGANWgwHTK9uylKEqhUOAdA2C+ZDL5+uuv792798MPP3S5XDMzMw0NDW1tbRRFbdy4cceOHUajsdI1Qg1ATK1bx44de/rpp9ntO++8M/8l3h/96Ed33XVXJBJZu3btk08+2dPTk3PYzMzMCy+88NRTT/X29v7rv/7ryotMpVL79u374x//uH//fpvNlnmGkMPhdHV1bdmy5eqrr7700kvR6gaKKxQK0TQ9NDSUWeVoPpFIZDQau7u7cfceYPWYmpryer0MwwSDwcJfhf7hAAAlgpgK/y0ejweDwc7OzkoVkEqlAoHA7OysUCiUSqV5bv8CFEU0Gh0cHFysxxKPxzMYDGazuampqfy1AUB5sOnU4/Fk+iDkxOfzBQLB7Oxszm9NYrG4u7vbYDBg0i8AQLEgpgLAasR+N3W5XKFQKM8wLperVqu7u7s7OjrKVhsAlBr7DuB2u2dmZvIMEwqFCoWCJEl2Zm8h/cNxbQsAoCgQUwFgtUin0+Pj4+xTZ+FweEmvlclkFosFPZYAatpS06lSqVw4mzeRSLjdbpvNlvMg7LUts9nc3t5ezNIBAFYZxFQAqHPpdHpsbMzr9Xq93kgkkmekRCKRyWSxWMzv9+d8b5RIJBRFdXV14XlpgBpS4OyJpqYmtVpNkmRHR8dJnzVl+4dbrdbFOi3h2hYAwEogpgJAfWLvnXo8Ho/Hkz+disVilUpFkqRMJmN/Ew6H7Xa70+nMLEE+n1AoNJlMFEVhRXKAqpWZPeH1egtMp5l3gCUJBoN27bNg2wAAIABJREFUu93tduf8QtXc3GwymXBtCwBgqRBTAaCurCSdZonH48PDw1ardW5ubuFeLpdLkmRvb29zc3NxSgeAFcu8A3i93pz/cjNWmE6zFNI/HNe2AAAKh5gKAPUg893U7XZHo9E8I1taWkiSJEmypaWlkCOnUin2ObSpqamFezkcjlKptFgsRfmmCwDLU8TrUysRjUYdDsfg4GDOGng8nl6vX7duHRoCAwCcFGIqANSDQCCwd+/ePAOWmk5znsJqtTIMk3Mvlk8EKL8qSadZ8lzbam5uvvjii/EuAQBwUoipAFAnXnzxxdnZ2axfsulUq9UWa2ru5OTk4OCgy+XKObUPyycClMFSZ0+oVCqpVFq28jIWXtvauHFjV1dX+SsBAKg5iKkAUCeOHDlC0zS7zX431el0EomkFOcqZPlEi8XS2NhYirMDrE6pVGpkZIRhGJ/PV9y5/SWVubbF4/Euv/xy9FICACgEYioA1IlAIHD06FGNRqPRaJqamspwxkQi4XQ6bTbbwru4xP/0WLJYLK2trWUoBqBeJZPJ0dFRj8fDMEw8Hs8zsuizJ4pobm5uampKoVBUuhAAgNqAmAoAsCLs8okDAwPj4+M5B2D5RIBlqI90CgAAy4OYCgBQHIFAgKZpr9eb8321ra2tu7tbq9Vyudzy1wZQKzLp1Ofz5Vy4OKPUc/sBAKCCEFMBAIoJyycCLEOB6ZTD4XR0dJAkqdFo8Ow3AEAdQ0wFACi+aDTqdDrtdvtiyycaDAaz2VyeZ2gBqlY8Hs90RUI6BQCADMRUAIBSYZdPtFqt09PTC/dyOBylUtnT09PR0VH+2gAqKBaLMQzj9XpHRkZSqdRiwzLplCRJkUhUzgoBAKCyEFMBAEqL7bFE0/To6GjOATKZjKIojUbD4XDKXBtAOSGdAgBAgRBTAQDKJBgM2u12t9ud841XIpFQFNXV1YVlFaHOLDWdarVaoVBYzgoBAKDaIKYCAJRVOBx2OBwOhyPnGhtCodBkMplMJnxNh1oXjUb9fv9J0ymXy1UoFCqVSq1W488eAABYiKkAABUQj8eHh4dtNtvs7OzCvVwulyTJ3t5erAMJNSeTTv1+f57vGA0NDXK5XKPRqNVqPp9fzgoBAKD6IaYCAFRMKpXy+Xw2m21iYmLhXrbHEkVRcrm8/LUBLMns7KzP5/N4POPj40inAACwQtUSU3/3u9/dd999V1999c6dO9etW1fpcgAAyioQCFitVoZhcu6VSqUURel0OvRYgmqTSaeBQCDPsEw61Wg0PB6vbOUBAECNqpaYun79+qNHj7LbFEVdffXVV1999WmnnVbZqgAAymlycnJwcNDlciWTyYV7xWKx0Wg0Go24BwUVh3QKAAAlVRUx9eWXX7744osX/v68887bt29f2csBAKikSCTicDhomo7FYgv38vl8vV5vsVgaGxvLXxuscuFwmGGYk6ZTPp+vUCiUSiXSKQAALE9VfHj8+Mc/zvn7yy+/vMyVAABUnEgk6uvrM5vNbrfbZrPNzMzM3xuPx2madjgcJElaLJbW1tZK1QmrR4HpVCAQKJVKkiQVCgWXyy1beQAAUH8qfzf16NGj69evX/j75uZmj8eDb2AAsJql02m/3z8wMDA+Pp5zgEwms1gsKpWqzIXBajA1NeX1ej0ez/T0dJ5hSKcAAFB0lb+butit1BtvvBEZFQBWOQ6Ho1KpVCpVMBi02+1utzvr2mIgEDh48GBbW5vJZNLpdA0NDZUqFeoG0ikAAFRche+mMgyj1+sXrnHP4/EcDodWq61IVQAA1SkUCtE0PTQ0lLPHkkgkMhqNFEUJBILy1wa1jk2nbrc7a555FqFQqFAoSJJUKpVoPQ0AACVS4bup//Zv/7YwoxIEsXPnTmRUAIAsEolkw4YNvb29TqeTpum5ubn5eyORSH9/v81mMxgMZrO5qampUnVCDSkwnTY2NiqVSpVKhXQKAABlUMm7qaFQiCTJycnJhbvee+89rEYDAJBHKpVyu91WqzXnzEwOh6NUKnt6ejo6OspfG1Q/Np26XK5QKJRnWFNTk1qtJkmyo6MD6RQAAMqmkndTH3vssZwZddu2bciosModO3bM6/USBNHV1dXd3b3yAx49epRhmGW8sKmp6dxzzy18fCKRePHFF//85z9/8MEHfr8/Go22tLSYTKYtW7Z89rOftVgsy6gBcuJyuXq9Xq/Xj46O0jSd9d83nU4zDMMwjEwmoyhKo9EgY0A6nR4fH/d4PF6vN+tWfJZMOpXJZGUrDwAAIKNid1OTyaTJZBoeHl6468UXX7zsssvKXhFAVZicnPz2t7/9y1/+kv23eccdd9x3330rP+w111zz+OOPL+OF27Zt27t3b4GDn3rqqW9+85sulyvnXg6H85nPfOaBBx5Qq9XLqATyW6zHEksikVAU1dXVhR5Lq1Dh6VQsFqtUKqTTmjYzM9Pc/P/au/OwJpK0AeCdcClgODUQCAFDjBDEQR0ZBbxvQVlB8VzHY1xQDhF3dD1m9/FYdRwPPFG8RlHXa1Rw8GAERUZRcVgdgYSIQALhMIDIIUkg+f7o7+svT9JpwxkC7++vmqqu7nIexH67qt7qp+tRAABAe+lsNvX69eu4Maqbm9uMGTO6fDgAdAsXL16MiYmpqKjQ9UD+H+55UepaWloiIiKOHz+uXGlpaWlqalpVVSWRSBAEUSgUN27cSEtLS0xM9PHx6ZTh9mJWVlbe3t4eHh4FBQUFBQUq2/7r6+uzs7Nzc3NdXFwGDRrUp08fXY0TdBksOhUKhU1NTQRXQnTak3zzzTfW1tZhYWHBwcGQTQ0AoL90FqZqOodm3bp1nbEyTaFQ3Lp169atW9XV1e7u7n/96185HE6HPwWANsvPz1+9evXDhw87+0Genp4BAQHaX6/lxWvWrDlx4gRatre337hx49y5c+3t7REEaW5ufvny5ZEjRy5fvqxQKKqrq6dOnZqeng5r+zuDmZmZp6cnmmOJx+M1NjYqt0okEi6Xm5+fT6fT3dzcKBSKrsYJOg9Ep73ZvXv3cnNzEQTJzMz09fWFbJQAAP2lm0W/6enpY8eOVa+nUqnFxcUmJiYd+ziJRDJnzpzk5GSshkwmb9iw4d///nfHPgiANpBIJLt27dq9ezc65YggiIeHR3l5uVgsRjph0e/SpUvPnTvX/hsqO3fu3LJly9Dy+PHjr1+/bm1trX7Z7du358+fj743Ozs7v3nzBlamdSq5XF5aWsrj8aqrq9Vb0RxLLBaLSqV2/dhAh8OiU4FAgP0ywUWhUOh0Op1Oh+8UPc/UqVMfPHiAIMi8efOuXLmi6+EAAEDb6WY2VdNUanh4eIfHqAiCbN26VTlGRRBELpfv2rXL3t4+IiKiwx8HgPYePnwYFhbG5/PR/zQ1Nd26dWtMTMyQIUPQMLX7q66ujo6ORstfffXVnTt3NJ2DMnv27EuXLs2ZMwdBkKKioq1btx48eLDrBtr7kMlkNBoRi8VcLldTjiUrKysWi8VgMCDHkj6Sy+Xl5eUikai0tFSb6NTJyQk+D/VUOTk5aIyKIMjatWt1OxgAAGgnHYSp+fn5SUlJ6vWmpqZhYWEd/rjPnz8fO3YMt2nHjh1r1qwhk8kd/lAAvqiiomLdunWXLl3CambMmHH06FFnZ2fdDaot9u7di6bsNjAwuHDhAvFZnX/5y1+WLl36888/Iwhy/PjxmJgYOp3eRQPtxWxtbX19fevr6/l8/vv371taWpRba2pqXrx4kZOTw2QymUymkZGRrsYJtNfS0lJRUSEUCkUiEe7x4xiITnuPAwcOoAVvb+9Ro0bpdjAAANBOOghT9+/fj7vS+Ntvv62srIyNjU1PT3/37l1jYyOFQnFzc5s0adLChQvRTW5tkJeX19DQgNtUWVlZWFjIZDLbdmcA2uzkyZMbNmzADmSi0WgHDx6cO3eubkfVBp8/fz558iRaXrx4sYeHxxe77Nix49KlSzKZTCqVxsXF7dy5s5PHCP6Xubm5l5eXm5tbQUEBn8+XSqXKrQ0NDW/evMnLy3N2dmaz2cSfG4CutDY6ZTAY5ubmXTY8oEMfPnzA0rnDVCoAoAfo6jBVLBafP38etykjI0Nl2rOmpqa4uPjevXubNm2KioraunVrGz4G9+3bl6AV5g2ATrx8+RKNUclk8urVq3fu3Kmnm8SSkpKwfY9aLqF3dHQMDAy8du0agiDnz5/fsWMHrDXtSn369OFwOGw2WyAQ8Hi8uro65VaZTMbn8wsKChwcHNhsNu4eY9D1sOi0tLS0ublZ02UkEsnS0pJGo0F02gsdO3YM3fnv6OgYHBys6+EAAEB7dXWYeuTIEU2Htr1580ZTL6lUunfv3gsXLty+fXvkyJGteuLgwYOdnZ1xD79hMpmQBA/oxI8//piYmOjo6HjixIkRI0boejhtd/PmTbTAYrGGDx+uZa/58+ejYWpJScnLly9b+5catJ+hoeHAgQNdXFzKysq4XK7KRmi5XI7mibW1tR08eDCNRtPVOHs57aNTGxsbOp3u6OhI/GUW9FQSiQQ7Dyw8PNzQUGfnOAAAQEfp0l9kTU1NmraJaqO8vHzSpEnJycm+vr7a9yKRSIcOHQoMDJTL5SpN27Zta/NgAGgPKyurx48fs1gsAwMDXY+lXVJTU9HC1KlTte81ceJEAwMDdIfkw4cPIUzVFRKJRKPRaDRaTU1Nfn6+QCBQ2ZEhFoszMjIsLS1dXV0ZDIa+/7jqC5lMVlpaWlZWVlZWBtEp0MbFixfRA7dNTU1XrVql6+EAAEAH6NIw9eeff/7w4YOmVjKZ7OXl5erqKpPJ/vjjD9z5z7q6ujlz5uTn51taWmr/3ICAgMTExDVr1hQXF6M1xsbGO3fuXLhwIe71DQ0NZmZm2t8fgDYYPHiwrofQXu/evausrETLrfp4ZGFh4e7u/ueffyIIkpGR0SmDA61hZWXl7e3t4eGRn5+vnmPp48ePWVlZb9++ZTKZLBbL2NhYV+Ps2aRSqUgkKikpKS8vV/+uisGiUzqd3qdPn64cIei2sKzpS5cutbKy0u1gAACgQ3RdmKpQKLAcdCrMzc2joqJWr16tvLQsOTk5IiLi/fv3Khd/+PBh8+bNR48ebdXTZ86cOXXq1LS0tNzcXAsLi6lTp2rKySSRSNB5g6CgoODgYBcXl1Y9CIDe4/Xr11jZy8urVX29vLzQMJVgtT/oYmZmZl5eXh4eHkVFRVwuV2WDRlNTU05ODo/Hc3JyYrPZkDa2o0B0CtopJSUF/XVKIpGioqJ0PRwAAOgYXRemJiUl8Xg89XoGg5GRkeHo6KhSP2PGjOHDh/v5+WFHSmLi4+N37drV2pQzhoaGkydPnjx5MvFlFy5cKC8vLy8vf/78+ffff+/l5RUcHBwUFMRms1v1OAB6vLy8PLSAbnRsVV8Wi4UWSkpK6urqIObpPoyMjFgsFpPJFAgEXC7306dPyq3Nzc3v378vLCy0t7d3c3OzsbHR1Tj1XWujUycnp844Vxz0APv370cL06dPh3cVAECP0XVh6r59+3Dro6Oj1WNUFJVKPXz48LRp01TqZTLZ3bt3Q0JCOniICKJQKLBf96js7Ozs7OzNmzcnJSX5+/t3+BMB0LmWlhapVNqGvW3YKnpHR8fWZuxQXqcgEAg4HE5rnw46FZlMdnZ2dnZ2rqio4PP5IpFIuVWhUIhEIpFIZGVlxWKxGAwGpGvWkkQiKSsrKykpKSsrwz2bDUUmk+3s7BwdHR0cHCAjPSCQl5d3//59tAzn0AAAepIuClOzsrLS09PV6y0tLVesWEHQccqUKVQqFU0MoOzBgwedEaYmJydjE0TKnJyc1KNlAPRXYWHhf/7zn9TU1P/+979VVVUKhcLU1NTe3n7s2LEzZ86cPXu2NslySktL0YKdnV1rB0ClUrFySUkJhKndFpVKpVKpHz9+5PF4QqFQZd6vpqbmxYsXubm5LBZr4MCBkGNJk8+fP5eVlYlEIuLo1MDAgEqlQnQKtHfw4EH0J4rD4XxxvRgAAOiRLgpTf/rpJ9z60NBQ4rPdSCSSp6dnSkqKSr36SmAVDx8+ZLPZmuZpNdE05RsVFQXp3UHPUFRUFBQUdPPmTZV35cbGxoKCgoKCgjNnzri5ue3bt2/69OnEt6qqqkILtra2rR2G8mJR7D6g27K0tPT29h46dGhBQQGfz5dKpcqt9fX12dnZubm5Li4uLBYLss5iGhsbS0tLhUIh+jFI02VYdNqGhQmgNxOLxRcuXEDLBFOpaE5KAwMDOp3eNQMDAID264p/DouLi69fv65eb2xsHBER8cXuuLtxampqCLpIJJJFixZVV1eHhISsX79+6NCh2owzOzs7LS1NvZ5CoaxcuVKbOwDQ/T1+/Bgru7q60mi0fv361dTUFBUVYQs78/LyZs6cuXv37u+//57gVrW1tWihDTtLlbt8/Pixtd2BTvTp04fD4bDZ7MLCQh6P19jYqNwqkUi4XG5+fj6dTndzc2tt+oCeBItOVQ6kVQHRKWinuLg4NNWZra3t4sWLca+RSqXoJgsbGxviH0gAAOhWuuLfxQMHDqgcb4BasGCBNqfGC4VC9UrifVAJCQnoOuGEhISEhIRJkyatX7/+i+c6apryXbVqVW9+3wI9z1dffRUaGjp37lxra2vl+pycnNjY2LNnzzY3NysUig0bNpDJ5PXr12u6T1NTE1pow+yZchfsPkAvGBoaslgsV1fXsrKyvLw8lclwuVxeXFxcXFxMpVJZLJY2v+R7jIaGBpFIBNEp6BpSqRQ79SA0NFRT/meVlN0AAKAvOv0fyNra2tOnT+M2xcTEfLG7TCbDzQ9McCyYehqk33777bfffhsyZEhiYqKzszNuL6FQePXqVfV6Q0PDyMhIghHevn379OnTwcHBs2bNatVprgB0pZkzZ6I7SH18fAIDA3E/9HA4nJMnTy5dujQwMBB9z960adOYMWNGjhyJe0+ZTIYW2rAjUfntXGUFKdALJBKJRqPRaDSxWMzlclVyLCEIUlFRUVFR0RtyLGkZnRoZGdH+D2ziBe13+fLl8vJyBEGMjIxWr16t6TIIUwEAeqrTw9S4uLj6+nr1+ilTpgwZMuSL3R8/fow706Ip2kQQ5O7du7m5uer1tbW1BFtVDx482NzcrF4fEhJCvJdjz549z549S0pKMjIymjhxYlBQUGBgYBu26gHQqRYsWLBgwQJtrvTx8blz546fn59MJpPJZOvWrcvIyMC9Ettr14YIhEwmq98H6CNbW1tfX9/6+no+n//+/XuVtTNojqWcnBwmk8lkMntSWiAto1NjY2N7e3s6nW5nZ6f8Yw9AO2Fn0c+fP1/TUfAIhKkAAL3VuWGqTCY7fPgwbhPBSkJlSUlJuPUEIW4b0iB9+vTp1KlTuE3EU75Pnz599uwZWpbJZPfu3bt3715oaGhQUNCVK1cIOgLQnXl7e69bt27Pnj0Igvz+++/Pnz/39vZWvwwLOXA/8RDDZmKV7wP0l7m5uZeXl7u7+7t379RzLDU0NLx58wbNscRms01NTXU1zvarra0tKSkRCoUqJ8qqgOgUdKrU1NTXr1+jZeJzaCBMBQDoqc4NUy9fvowdWaHM09NTm7TpUqn08uXLuE2jR4/Grc/Ozk5NTVWvp1Ao3333naYHnTx5EveFY8KECV5eXgQjxN3O2tLSMmjQIIJeAHR/ERER+/btQ+PPy5cv44apWHoziUTS2vsrhzG4adKAPjIxMeFwOG5ubgKBIC8vr66uTrm1ubmZz+cXFBQ4ODiw2WyVrdHdHBqdCgQClT+UCohOQdfANjf5+fkNGzaM4EqVVGcAAKAvOjdMVdkjitFmVyqCINevX//w4YN6vZWVFe5LM6J5KnXVqlWakpE2NzcfOnQIt4l4yregoOD27dvq9SYmJuHh4QQdAej+HBwcvLy8Xr58iSCIpkW/FhYWaIH4xR2XchfsPqBnIJPJzs7ODAajrKyMy+WqrImVy+VCoVAoFNra2g4ePLib51jSMjo1MTGxs7Oj0+n29vY9eBcu6Cby8/OTk5PRcnR0NPHFMJsKANBTnRimpqSkYCtSlNFoNC33yGlaMOzv74+bf6KkpERTGqSoqChNT7ly5QpuMmF3d/dp06YRDG///v0qJ92jFi9eTKVSCToCoBe8vb3RMPX169ctLS3qf+mwTGbV1dWtvbnyITQEGdGA/sJyLNXU1OTn5wsEApVNyGKxOCMjw9zcnMViDRw4sFtlFUKj0+LiYtzcCpi+ffs6OjrSaLQBAwZAdAq6zIEDB9C/TS4uLrNnzya+GMJUAICe6sQwVdP5LhEREdpsRbt//35mZiZuk6bDwWJjY5U3vGFCQkIIkidpmoCNiYkheO2oqqo6d+6cej2JRNJyrhiAbs7BwQEtNDc319TUqCcGw+bB0GyTraLcpZvPp4F2Qte/eHh45OfnFxYWquxkrq+vz87OzsvLYzKZLBbL2NhYV+NUKBQfP34UiURfjE5NTU0dHBzodDpkywNdr7q6+vz582g5MjLyi2vLIUwFAOipzgpT3759++DBA9wmTUd7KVMoFD/88ANuk4uLy6RJk9Tr6+rq4uPjcbsQxI2pqanZ2dnq9XZ2dosWLSIY4fHjx3H3e8yYMcPNzY2gIwD6QnmSs7q6Wv2NnMFgoIWSkhKFQtGq2aSSkhL1+4AezMzMzMvLy8PDo6ioiMvlqrw6NzU15eTk8Hg8JycnNputaY9GZ1AoFFVVVUKhsKSkhPiFHqJT0B3ExcWhrx/9+vVbvnz5F6+HMBUAoKc6K0zVNJWKIMiTJ0+Is9IhCHLy5MkXL17gNoWHh+N+O4yPj6+trVWvJ06DpGkqNTw8nCCti0QiOXLkCG4TTKWCHkN5TbvKKSMoNpuNFpqamgQCQauiTT6fjxZsbGzgpb/3MDIyYrFYTCZTIBDweDyVX9rNzc3v378vLCy0t7cfPHhwp/5gaB+dmpmZ0Wg0iE5BdyCTyY4ePYqWly9fTqFQvtgFwlQAgJ7qlDC1rKxMU4ZeBEGSkpKKi4sJ3miLi4s3btyI2zRgwIC//e1v6vXNzc2xsbG4XQjSIOXm5t69e1e93tTUNCwsTFMvBEESEhIqKirU64cNGzZ+/HiCjgDoEeUdp7gvQ56enlj59evXrQpT37x5gxa0OT8Z9DBojiVnZ2exWMzlckUikXKrQqEQiUQikcjKyorFYjEYjA7c9olFp0KhEPdQbgxEp6Ab+vXXX7G/L6mpqePGjftil7KyMrRQW1urcv3Zs2ddXFw6eIgAANBBOiVMPXTokMqhecpkMll0dPSNGzdw3zzq6uoCAgKU06so27Bhg5mZmXr9tWvXBAKBej1xGqR9+/appPRALVu2jOCkBIVC0c4MxgDoBWxdLprFVP0Cd3d3CwsLdEIsMzNz1qxZWt5ZJpNhi+01HS4FegNbW1tfX9+PHz/yeDyhUKiSlK6mpubFixc5OTmDBg1ycXHRdPC1NrSPTikUCpr5CaJT0A0pvx39+eefrerb3Nz8+PFj5ZqGhoaOGRYAQLO3b996eHjoehR6qVNOdaNQKJaWliqVyitvb968uXr1avWzFktKSsaPH6/pN6+7u3tERARuUxvSIFVUVFy8eFG9nkwmE6d3v3v3bm5urno9nU6fN28eQUcA9AuWw8zd3R03CyuZTPbz80PLKSkp2t/52bNn2NZuWIAALC0tvb29/f39ORyOegqlhoaG7OzspKSk7Ozs1i5fVCgUYrE4Ozs7MTExNTWVz+drilEpFAqHw5k2bdq0adM8PT0hRgUAANB+ly5d8vT0/Pvf/46b5BUQ65Qw9R//+IdQKDxw4ICzszNa07dv32vXro0aNQq7Ji4ubujQoWfOnBGJRE1NTbm5udu2bRsyZMirV69w72lgYHDixAncFMGPHj3C7UWcBunw4cPqcTKCIIGBgUwmk+BPp2nbbVRUVHs+9gPQrYhEIuyD0eTJkzVdhs2gZmVlFRYWannza9euoQUrK6uxY8e2Y5ig5+jTpw+Hw/H39/fy8jI1NVVplclkfD7/119/ff78OW4aAmVyuVwkEj1//vzWrVtodIr72x75v+h0+vTp06ZN43A42uz0A0CHFi9eXNdKBw8eRPva2NioNLm7u+v2jwNAz5aTk7Nq1SqFQvHTTz+NGTMG9/xLQKCzwipzc/O1a9dGRERcu3Zt3759ISEhTCbz22+/ffbsGXYNj8dbsWKFljf817/+5evri9ukKW4kSIPU2NgYFxeH20SwlxVBkOzs7LS0NPV6CoXy3XffEXQEQL/ExcVhaZOCg4M1XRYUFBQZGYnOUJ04cWL37t1fvHNDQ8OlS5fQ8rx587Q5ngr0HoaGhiwWy9XVtaysLC8vr6qqSrlVLpcXFxcXFxdTqVQWi6VylFFLS0tFRYVQKBSJRMTfrSkUCp1Od3Jy6sqUwgC0n6Ghobm5eau6KL8ItbYvAKDN6uvrg4ODsaX1mZmZw4YNu3jx4pQpU3Q7MD3SubN/BgYG8+fPnz9/ProFdMWKFXFxcbgHwBCbO3fupk2bcJu4XG5ycrJ6vampaWhoqKYbnj17VuXtBzVq1CjlKV91mkLiVatWwWd40G2lp6e7u7trv45RIBBgOclGjx799ddfa7rS2tp64cKFZ86cQRDk2LFj0dHRVCqV+OaxsbFociYSiRQeHq7lkECvQiKR0A2iYrGYz+ejJx4pX1BRUVFRUWFpaTlo0CAHB4cPHz4IhcLS0lKVQ1lVoNEpg8GAl3UAAACdasWKFVwuV7lGLBZPnz59+/btmoIaoKKLFqmiG0QNDAxOnTrl5+eHe+KoJtOnT09ISNB0gDVBGiQbGxvcLnK5HFsDo4J4KlUoFF69elW93tDQMDIykqAjAJ2qpKTkyZMn/fv3nzBhgvrflDt37gQHB7u6ut65cwdbh0/g8+fPixY6Mj5WAAAGy0lEQVQt+vTpE4IgJBLpxx9/JL5+y5YtCQkJUqm0rq4uLCzsl19+Ibg4Jydn586daDkkJASSCgBitra2tra2nz59ys/PLyoqUsmx9PHjR01Hl2FIJJKNjQ2dTndwcFBfSwwAAAB0OKlUqvIPFkoul2/evPn169dnz56Ff5K+qFP2phIYNmzY/fv3tZ94XLlyZWJionpSDVRlZWVCQoJ6PZlMJjia9datW+/evVOvZzKZgYGBBIOJjY3F/VQfEhJCp9MJOgLQeZKTk9ls9sKFCydPnjxhwgSVJNs1NTVLliyRSCQ5OTkjRowgOCkKVVlZOWXKlIyMDPQ/o6KifHx8iLu4uLhgJ0jdvHlz7dq1uL+aEQQpKCiYOXMm+pXK0tJy79692vwBAaBQKCNGjAgICOBwOOpbOXA/YpJIJFtbWy8vL39//wkTJrBYLHghAAAA0DWMjY2vXbu2fft23EyuV69eHTNmDHZYFNCkq8NUBEF8fX2fPn36xbwp/fv3v3z5cnx8PEFeoiNHjuCmbQwMDHR1ddXUS9PC3ejoaE1ztgiCfPr0KT4+HrcJzqEBOhQeHo4tT3j8+PGFCxeUW62srBITE9GVBVVVVQsXLvz6668PHz5cXFyscp+CgoIdO3awWCwsRp05c6aWkeSWLVuwhL2xsbGTJk16+fKl8gWNjY3Hjh0bMWIE+lwymXzu3DlHR8dW/2lBL2ZiYsLhcAICAkaOHKn8rbN///5YGYtOAwIC0Oi0b9++uhgsAACA3m7Lli23bt3C/Uj66tUrb29vHo/X9aPSI7rJTMvhcB49enTjxo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q89JsSJhi1OhfbACAPzfLly1Uqla+vb1PTub+eiKkAAABddOP3H3blXcoLsTkcO0ty7h6W3HJZsryx/pot77+WvrOLK7RVTQ0fZx8uazRdMmBot5Y3M2rK5q5/0+mMfbSy0KDRXRzh0s+NFY31Sza+22CzdH7rBVQ1NZisTYsGDnelDQBSUU+YMGHJkiXDhg1zdycAAAB9zMrc1O8KTrlYJLOm7I30Xee/rLdalmz81zdnT3SriENxvJa+8+btH19o96N2PbRvVY3F3K2JWvnz4e/O1FW6UuHxg+vb3RW5W946uTu1qsjFIgDkoY6PjzcYemSvNgAAAM/27LEtQuo8n7rV5rArimJ3OK7ctGJ3yRnn6nyUfejRA+u6ePPmwszVeWnOTXReg83y+y7P2FZ1k3lFRoqLPSiKYrXbX07d7nodAJLggGkAAABnFNTX7HI2T7Zy1lS9rzRPUZT3MlNcXJ597vjW/WV5XbnzlTQxue7TnCNF3dniuKWVucddedy3pS/OHOvWSjIAmRFTAQAAnLG9OLsrR6R20fdFWXWWxscOrnexjt3heChldae3mW3Wb/NPujhXM6vdvu5sunNjtxVnC+lBUZSKxvpjlYWiqgFwL2IqAACAM1zZOamt9OqSD7IOFNbXuF5qW1H2vtLcju/ZX5bnxAk6F7K9+LRzA1MrRf4eiv0TAeBG52KqSvXDTuIOnpcAAADoTIGISHneWVPVp6ePiKr2aU4npU5Ul4iaS1GUE1VOVitsEPl7mF9fLbAaADfSursBAACAPqne2XNc2lVnbTxaIeyZ1a2FWR3fcKGzWJ1TYq5zbmC9VcyLqc2cPlkHgGx46BcAAMAZXhqRH/c7HIrAp3DTqoo73k9I4FyKC4ndS60R2IZezQIM4CGIqQAAAM4I0RsFVtMLDb0NNkvHh5EahE5n1Ho5N1Ds72GIwUdgNQBuREwFAABwxlD/MIHVIrz9BFZTOnsCVux04QZf5wYODRD5ezjUP1RgNQBuREwFAABwxtSwWIHVRgVGCqymdLY8OywgXOBcwwKdrCbw99BLrZkYGiOqGgD3IqYCAAA4Y3RwZJxvsJBSPlqv2ZFDhJRqplapAr28O7hhYmiMwOd+Z0QMdm7g4pgRonqYG5Xo4+yzxwBkQ0wFAACeqc7S2KP1VYrqzqSpQkrdMGTcRcEDhJRqFuMT2HEKNWi0C6OThMylVasXDRzu3NikgLA5UQlC2hD1ZwFABuf+/uLcVAAA0Ndl15avzkvbVJBxqCK/1GxqtFm1anWYwXd4QPjcqIRFA4ePC4kWO+P9I2e8dXL3mbpKV4r46fR/GrcwWG9M9A/NqCkT0ti08LhO7/nViItX5qa6Pte1cWMiXXjT9flJSyaufqnjfYk7NT087srYka5UACAVVlMBAECfd7A8f/mW9xO/eOb+vStX56WdNVU32qyKoljt9sL6ms2FmY8dXD9+1YtzvvnHhvxTAuf11uienbTYxSJ/GHtJlNFfEfoE7KKBwzq9Z15U4hKXZ/TW6J6asMiVCuNCom9PnOxKBS+15tWpy1SKqvNbAfQRxFQAANCHORTHy2nbp6555fOco52uyG0tylq44e2fbftI4PPAy+PGPD72EqeH3xg//qFRs5p/ffvQyWqVgKwVavBZFju6K3c+P/kKf53Blbn+MPaSWN8gVyooivJa8lWXDBjq3FiVonp7+nLh6+QA3IuYCgAA+iqL3bZk47v3711psdu6PuqDrAPJa18t7/BY0W7587iFD4yc6cTA5XFjVlx83fllwJGBkbe5tq7Y7C/jLu3iZkKJ/qEr59/q9F5KN8VPeOSiOc6NbclLrflszs+md+FB5Va0avVryctuTpjoeg8ApEJMBQAAfZJDcdyx87O1eelOjD1eWbTou3cErqm+MPmKr+bdEqI3dvF+g0b70pSln8z5qU6taXn9yfGXBnW4Q2+nxgYPuCNpStfvnx0Z/+mcnwV4dXtN9bbEye+2yNguCvAybL3s3j+OXaBRdfWn01jfoC2X3nPvsGlCGgAgFc0TTzyhKEpVVVV+fn7zJW9v7yFDRG6JDgAAINxr6TufObrZ6eH59dUVjQ0CXwcdFhB+W+LkEINPalVxBwHYV6e/e1jyR7N/uiA6qW3G89XpZ0bGf3T6kMVud6KHAUb/DQvvDO5yWm6WFBB23eCxKWV5eaaqrtwf5OW9YsZP/m/MPCGPKJ+nVqlmR8XfMGScSqUcqSiwOi74OxDnG/zEuAXvXnxdvH+IwAYAyEPVvK9vTk7Ovn37mi8FBwfPnz/frV0BAAB0pLKpIfHzp118cFetUu1b8usJIQNFddXMbLPuKsnZXJh5uCK/1GwqM5sCvQwR3n4jAiPmRCbMihziq9N3XGFVbur13/+n3mrp1rxRRv8NC+4cFRTpXNsOxfFt/sm/H9v6fVHWhd7yHegTcN/wGXcNm+riG62dKm+s31KYuaUwM6OmrLihtt5mCTP4RBj8JoXFzI1KmBQa0/VFVwB9ETEVAAD0Pb/bv/Zvx7a4XufymOFr5t/uep1WKpsa/HV6V6LUieqSG77/76Hy/C7evyB66L9nXO/KwTDnlZlNW4uyUsryihpqy8wmP50+3OCb4B86NyphZFAEG+oC6AXnYuqZM2f27t3bfCkoKOiSS5zfsA4AAKBH2Rz26E/+UtxQ63opjUqdd+1jzefBuOJEdcmq3NTNhZmHKwrKzCabw65SVKEGn+GB4XOjEhbHjHBizbbJbvsg88Bzx7eeqC7p4LaZkUN+O2rOophhBEgAHoOYCgAA5NJkt3n9eGOhVg6Un5246iVR070/8/qb4ic4PXxP6Zm/Htm0Ni/doXR0HM7MyCG/v2jewuik7ta3Oxz7ynI3F2buLjmTZ6oqNddpVepIo3+MT+CsyCHzoxKHB0Y43TwAyMnJ/ccBAABEyawpW5OXvrkw40hFQanZ1GCzeGt0oQaf0UFRc6MSLo8ZPiwgvOX9+8vyBM6eUprnXEy12u1PHtn45JGNtgtv9nPetqLsbUXZN8VPeCP5qk7fTW1JrVJNDYudGhbrRIcA0EcRUwEAgNuklOU9fXTTytzUVnv2NNgseaaqPFPVurPpv0lZc3nM8Ecvmjvtf+dqZtWWC+whs7bMiVFmm3Xxd//aVJjRrVEfZB04XFGw9bJ7ursZLwD0K2ySBgAA3MBqtz9xaEPymle/OnP8QvvKNnMojjV5aRevff3Xe79ustsURalqbBDYSVVTt6vZHPabtn3Y3Yza7Fhl4WXfvWOyNjkxFgD6CWIqAADobWabdeGGt/90eENXHpdt5lAcr6TtmPvNP0zWJnuHb4F2l7X7J5S+cHzb5zlHnZ5xX2nuI/vXOj0cADweMRUAAPQqm8P+020fbi7MdGLszpKcpRtXeGt0Avvx686booqilJjr/np0o4uTvnli97HKQheLAICnIqYCAIBe9czRzV+4sBS5qTCj4wNauivON7jd601221lT9aHy/N0lZzJrys4/pvvUkU3VTWYXJ7U57I8fXO9iEQDwVOe2UFKpfjhoy9Hh+yEAADjDbrE3Vam0PiotO8f0a8UNtX87tsXFIt8XZQtpptnooKiWXxY31K7ISPmu4NSukhyzzXr+ukpRjQ6KnBOV8F5mipB5155NLzHXhRt8hVQDAE/CTr8AgJ5irT5pzv26qXCTpfyAvbGi+aJKa9T6J3kNmKePvlQfNVdRVB0XgYd54vCGWkuji0Usdptapep446WumzcgofkXJea6xw+ufz9zf8t0ep5DcRytLDwq7kldq92+qSDj+iHjRBUEAI9BTAUAiGcpP1B35Elz3iqlzQY5Dmu9peKQpeKQ6fhzuqCLfEY/4j3kesJqP2G2WT/MOiSklKiMOioosnk1dVNhxs+2fVRQXyOkbBellOURUwGgLd5NBQCI5TClvVy2Ntmc+3XbjNqKpfJo1bYbKzZcam8o6p3m4F67SnJqLK6+1SnW42MuURTlw+xDC759u5czqiL6AFgA8BjEVACAOHZr5eZlNfvuV+yWrg9qLNhQtmayre5Mz/UFSaSU5bm7hR+ZFh63fPBF6/NP3LL9Y1HLs93ixJGtANAfEFMBAKI4qnffZc5d6cRImymvYsN8e0Ox8J4glayaMne38INgvfFfF19ba2m8dfsnFrvNLT04cWQrAPQHvJsKABCjPuPd+ox3nR5urcms3n1P0NwvBbYE2VS5fI6LKN4a3cp5tw4LCL9v71dFDbXuasNfZ3DX1PKrsZi/L8reWpiVXVte1mhqsttC9T4R3r7J4XFzoxLi/ULc3SCAHkRMBQAI4LDU1h583MUi5tyvGgs26AcsENISJGTr7HXl3hHvF/L+jOuz68of3LfKvc8hx/oGuXF2aWXWlL2avuPtk3va3XJ5RUaKoijjQqLvHzHjxvjxGhXPBgIeiHNTAQACmNJesjcIOKij9uDjxFQP5qP1cm8D3hrdz4dOmRU55Prv/5NrqnJvM4qijA6KdHcLcrHYbY8dXP/c8a2dvip8qDz/5u0fv3Fi139n3cjKKuB5+PwJACBAfcYKIXUsZfusVWlCSkFCg3pl8VCtan2+kUalnhI26E/jFmYtf1SjVi3f8oEMGVVRlPkDhrq7BYmUmU0Xr3v978e2dH07q72lueNXvrgmj780AE/DQ78AAFdZazJsdadFVWvM/1YbOEJUNUildxYP7Q7HtwvvtDvs1U1mX60+zOAzLDDcX2dwKI47d37+zqm9vdBDV4wPiU4KCHN3F7Kot1qWblqxrzS3uwNrLOblW97/dsGdMyOH9ERjANyCmAoAcJWlbL/IauUHBFbzbNm15TtLcgrra0rMdV5qTZjBN8YncFbkkDCDr7tba9+syHi1StULR79UNNb/ZPDYVhf/fPg7eTKqoiiPj73E3S1I5GfbP9pVkuPcWLPNunTTimNXPjzQJ0BoUwDchpgKAHCVrS5bYDVrbabAah6p1Fz3evquf2fuz6mraPtdlaIaExx1V1LyLYmTDBq5/qGP9PabE5mwqTCjpydKr2p9uFFmTdlTRzb19LxdNzcq4cpBo9zdhSw2F2Z+kXPUlQpVTQ2PHlj3wczrRbUEwL3aeTeVLZQAAN1ib6oWWM0htJqHsTnsfzq8Ie6zv/7p8IZ2M6qiKA7Fcbii4J7dX8R++uTHpw/3coedemzs/F6YpbihrtWVB/atanLT4ahthRl835p2jbu7kIVDcTyw15nzllv5b9bB/W7dtxmAQGyhBABwmdhTRhyyZAnZFNbXzP7mH08c2lBvtXTl/hJz3fVb/3Prjk/aPdXDXWZHxi+L7fFVRJO1qeWXp2pK1+al9/SkXWTU6lbOuzXBP9TdjchiR3HO0UoB+4Q7FMebJ3e7XgeADIipAABXqXQi34RU6fwEVvMY1U3my757Z0dxt7eqei8j5bqtH1jtUhxY2uzlKVdGG3v2HULvHz/t/En2YYcixcNiSQFhOxb9Mjk81t2NSGR1XqqwUrlpkvxBA3DRuZiqarN1OwAAXaTxGSSymm+cwGqewWq3X/7dO0cqCpwbvio39aGUVWJbckWMT+C3C+8I1ht7bopQg0/LL7cVi3x92ml+Ov0tiZOGB0a4uxG57CrOEVWqxFyXWVMuqhoAN2I1FQDgKl3wGJHVgi4SWM0zvH1qz05nN0Ft9mraTieO+ug5IwMjv7/s3pGBPXU+zbCA8JZfprXZUcktai2Nj+5fN2HVi0crBDzj6jFO1ZQJrHayukRgNQDuQkwFALhKFzxWrQ8RVc1rQG9ssdOHVDU1/PHQty4WcSiOB/ZJtKCqKMqooMiUK3790KhZPlov4cUvjhh8/tcOxVHUUCt8CqelVRUnr32lF7Y77hMciqO80SSwYJlZZDUA7kJMBQC4TKUxDL5OSCWNb5xXWLKQUh7j35n7hfzkvaskZ0/pGdfrCOSt0T03acmZax/7y/hLJ4bGiCo7KihyiN8Pn5vk1lXZHXK9sVhvtSzb9N6B8rPubsT9LHa72HN0pdowDIDTiKkAAAF8R/9OpRXwqtjkAzAAACAASURBVKHvmMcVFf82/cjnrp0n2dJnp4WVEihEb3xszPyUJb++Z9g0IQXv/V+dBpvloX2rh3/5d0VRFMl24ai1NF69+d8Nto42bbba7WfqKveV5m4syDhSUZBf74FnNXmpNRqh/5c3anUCqwFwl3ZO/ebcVABAd2l8YnxGPlh35ElXiuiCxxkTbhHUkYcw26wC3ymVZCehC/nrhMs+yzni4tLx8MCInw+doihKWlXxdVs/OF5ZJKg78c7UVT57bOsfxl7S6nqdpfHfmfu/yT/xfVF2naWx5bcivf3mRCUsix11VexosenOjSK9/QQm8Cijv6hSANzIQ/6CAwC4ne/oR3VhU5wervIKCJjxHkuprWTWlDXZhR0km15VLNfDrz8W5OX91/GXuVJBo1K/NnWZTq3JqCmb880/ZM6ozf5+bEtlU8P5L8026x8OfTvo0yd/ueertXnprTKqoihFDbUfZR+6dssHSV/87b2MlN5ttqeMELr1sdhqANyFnwYAAGKotMbg+Wu1AcOdGav2Cpr9OXv8tlXYUCOwmsnaVNVkFlhQuDuTpj40apbTw1+ccsXcqISihtr5698sMdcJbKyHmKxNn54+3PzrE9UlyWte+cvh71oG1wvJqi2/dccnyza9V95Y38M99rgZLfa7clG8X0hPH8kLoHdwbioAQBi1PiTksm2GmCXdGqXxjQ2+dLOeDX7bU2/t6N1Fpwo2iS0o3LOTFv9y+PTujtKq1c9NWvKr4RcrivKblDW5pqoeaK1HfJt/UlGUY5WFyWtePdzNo3G/zj2evOaVPhHIO3Dt4LGiSl0Tx0ddgIdgNRUAIJLaEBo0b2VA8psan843blVpvH2G/yps6VGv8G7Hkn7CS60RW1CvbmdbCqmoFNWrU5d9PudnwfqubsoV5xu89bJ7m5dh95bm/jfrYE82KFhKWV5Wbfkl375d1YVF1LYyasqWblwh/OOM3pQUEHZp9DDX6xg02ruS2Ccc8BDEVACAcCpj0l3hV2cFznjfMGip2iuw9ffVOq/w6X7jnwxffsZ/yisqHVueXFCowUdgNa1aHeBlEFiw51wdd1H2Nb9/ZuLlHe+Ik+gf+ta0a05c9dvp4XHNV545ulnm92/byjfV3Lnzs2IXTnbdU3rmr0c2Cmyp970weYnO5U9kHho1e7BfsJB+ALidqnlf3/z8/J07dzZf8vPzu+wylzYwAADgBw6btTbL3lBibyxV6fw1hnCN72CVztfdbfUNVU0Nwf/9g6jcNdQ/7OTVjwgp1WtsDvuh8vzNhZlpVcWlZlNVU0Ow3hhu8B0bMmBuVMLIwMiWN9daGkM//IPATaf6Cr1Ge/zKhxP8Q93diPN+f2Dd00c3Oz18qH/YgSvu99XpBbYEwI1kf/IHANDnqTRa/6GK/1B399EnBXp5DwsMT68qFlJtekSckDq9SaNSTwyNmRja+TPkiqLsKsnphxlVUZRGm/XZ41vfmnaNuxtx3l8nXFZQX/PvzP1OjB1g9P924R1kVMCTtPPQL+emAgAgjyUxI0SVunygM/sw9yHHKgvd3YLbfHr6iKUvR3SVovrn9OW3D53c3YHDAsI3LLwzzpfHfQGPwrupAABI7edDp2jVAv69jjYGLBaXeOV01lTt7hbcpqqpYX/ZWXd3odgdjrSq4o0FGR9mH/os58j3RVlZteVdHKtTa96Zfu0ns28KM3TppQCNSn1XUvL+K+5v9ew3AA/AQ78AAEgt0T/0nqRpr6bvcLHOMxMX6TUe/u++SfrjdnrU4Yr85PBYt0xtdzjW5KW9n3Vga2Fm26NcY3wC5w1IvCtp6tSwztu7dvCYxTEj3stMeT19Z9oFHncP8vL+yZBxD42aFe8XIqB7API5988V56YCACCtJ8Yt+OT0YVeOx5weHndj/HiBLclJyLJz35VTV+mWedfkpf12/9oO3qDOM1W9l5HyXkbKzMghL01eOi4kuuOCRq3u3mHT7h02raC+ZmtRVnZteam5rtFmCzP4RHj7JYfHjguOVvOzK+DRPPxTVQAAPECw3rj2ktvnrn+z1tLoxPB4v5Av5t6sUjz/x/ogL293t+BONRZzL8/YaLP+JmXNa+k7u7gZ9bai7OS1rz49YdH9I2d05T/IAUb/G4aMc7lNAH1Pv/7QEQCAvmJiaMxX824J7H4Mi/cL2bDwzghvv57oSjZ9+kQW16l795MIm8N+w/f/fTV9R7cOTGq0WR/ct+rR/et6rjEAHoCYCgBA3zAvKvHIlQ/NiBjc9SHL48bsv+L+If3m/b3JoYPc3YI7BXgZenO6O3d+/uWZY86N/duxLS+nbRfbDwBPQkwFAKDPGOQTuPWye1fNv63jrWhUimpxzIgdl//y0zk3ObEA23eNDIqI9Q1ydxdu05ufR6zJS3s3Y58rFX6bsiajpkxUPwA8TDvvpnJuKgAA0lKrVEtiRiyJGZFeVby5MHNXSc7Z+uoys0mjUkd4+8b6Bs2KjJ8blRBtDHB3p26gUlS3JU7+46Fv3d2Ie0wIGdg7E1nstodTVrtYpMlu+23Kmq/m3SKgIQAehy2UAADok4YHRgwPjPjF8OnubkQuD46c+dbJ3QX1Ne5upLdFePuNCR7QO3N9nnP0ZHWp63W+zj1+vLJoVBCnngJojYd+AQCA5/DV6Z+asMjdXbjBT+PH99oZLR9mHxJV6uPTh0WVAuBJzsVUzk0FAACe4eaEifePnOHuLnqVv87w8KjZvTOXzWHfVpQtqtqmggxRpQB4ElZTAQCAp3l+0hU/jZ/g7i56zx/HXRLZW2cOnamrFHhA67HKwm6dZwOgnyCmAgAAT6NWqT6Yef2/Z/zEqNW5u5cetzhmxH3De2/1ONdUJbCaydpU0dggsCAAz0BMBQAAnulnCROPLH3ozqSpeo3H7hk5I2Lwp3Nu0qp77ye6Okuj2IK14tZmAXgMj/1bGwAAIME/9K1p1/xl/KXfnD2xuTDzUHl+qbmu1GxSq1QWu83d3blEpajuSJry0pSl3poLrhifrC5NKcstNZvKzCajVhfp7Z/oHzolbJAruV2jEhyJtSqN2IIAPADnpgIAAA8XbvC9OWHizQkTz1+58fv/Ctyuti21SmXvyR+oZkYO+ePYBXOjEtr9bk5dxUup2z/POZpfX932u0atbnZkwq9HzFgQPdSJqQO9vJ0Y1WFBg9iCADwAq6kAAKDfuSh4QI/G1IvD47YVnxZbU6tWjw8ZODcq4cpBo6aEDWr3njpL44P7Vq3ITLHa7ReqU2+1rDubvu5s+viQ6H9dfN3Ybp62Gu8f0r2+OxTh7eer0wssCMAzEFMBAEA/cqaucnNh5qnq0p6b4o6hUxbHjBAYU/Va7e7LfzUuOLrj245UFFy75YNTNV39n3awPH/qmlf+PnHxfSMu7noz4QbfWN+gM3WVXR/SgQvlbQD93LmYyrmpAAC0z25VVCqF1+f6uHqr5d2Mfa+l7zjZkwFVrVI9NGrW0xMW1VstRq2u3moRUrbRan36yOZP59zUwT0ZNWULvn27xFzXvco266/3ft1kt3br2NVLo4e9dXJ3tya6kIXRSULqAPAwrKYCAPAjDltDY97axsJNTSU77PUF9sYKRVHUhjCNMdorcpZX1DxD9KWK2vOPOfEkX+QcvWf3l6XdjHDdlegf+kby1fMHJCqK4qfT/2TwuHcz9okq/lnOkb2lsy609lhirpu3/s3uZtTzfpuyNsYn8LrBY7t4/62Jk4TEVKNW1/VJAfQrxFQAQH/hsDep1F4d3dBUbUp/xZT+mt1c0upbdnOp3VxqqThsSntZ4xPjM+IB47C7VRrBe8lAOKvd/ss9X4la+muXQaOdFh53z7BpV8WOVrd4PO3P4xd+cvqwydokaqJ/ndp7oZj66P51eS4cZ+pQHL/a8/XC6KSubI+Ua6r66syxAJ2h2uWDZH47ek6I3uhiEQAeiZgKAPBYlvKDjfnfNBZutlWftDeWOWyNKq2P2jtCFzJeHzVPP3CxxmfgDzeX7av8/gZbbVanZW2mvJqUB+tP/TNw1ke64DE9+b8ALnEojrt3f/6vU8KWNFsZYPR/c9rV8wcMbfdImGhjwKMXzX3s4HpR0609m97u9UPl+e9lprhYvNRc95fDG5+fvKSDe8rMpodSVn+UfUjIWT6DfAJ/M2qO63UAeKTeOwwaAIBe03h2Xfk3M8tWT6g9+FhT4WZbfb7D1qgoisNqstVmm3M+r959T8kXQ6q232ytTlcUpSHrg7J107uSUc+zVqeXr01uzBcWQiDck4c39lxGjTYG7F5835KYkR0cW/roRfP8xG1jW1Bf0+4BMy+kbhNy+M1bJ3fXWhov9N3NhZljVj7/fuZ+IRnVT6f/ct4tRi0PzwNoH+emAgA8isNSW5PycP2ptzu/1W5pyHrfnPOJYfD1Ddn/UezWbs9la6jccnXwgg1e4dPbKd9UaSk7YDcX281lKp2vWh+q9YvXBo1UFLYt7A3ZteVPHd3UQ8VnRAz+z6wbB/kEdnqn2dbt/646kFFTFm0MaHnFYrd9nXtcSHGTtembsyeuHdzOAwIbCzIu/+6dJhEBVVEUo1b35dxbJoQM7PxWAP0VD/0CADyHvaG4fP1sa/WJrg9x2BobMt9zekaHtb5q67Vhy06odH7nrlhq6k++1ZDzqaX8kOJo/WO92jtCP+ASn+H36UInOT0puuKR/WvFRsRmoQaf34ya/dCoWRpV54+k1Vkbhaw9nlfZ2NDqysHy/LoLL4F219airLYxdV9p7pWbVojKqCMDIz+afePooCgh1QB4KmIqAMBDOCw1FRsXdSujCmGrL6g79rTf+KcUh63u2DN1x591NLXzZGYze0NxQ9Z/GrL+4xU1N2DqG9oATuPoEWdN1V+dEbPG2Mxbo5seEbcsdvQtCZO6/qiq1W4X2IOiKE1t1vyPVxYJrJ9WVdzqis1hv33np0I2gor1DXpg5My7k5L1Gn7+BNAJzk0FAHiI6t33WsoPumVqU+qLhthra/bd11S8vYtDmgo3l62e4D/lVWPirT3aW//05ZljNoeYiBig89666O7hARFOhCujtqOdpZ3gq239pmthQ43A+m3fff3Hid2uJ2G1SvV68lW3J07WqTl/GECXsIUSAMATWEr3NGR/6K7ZHTZzxXcLup5Rz42ymqp33mZKe7mHuurPdhSfFlWq2tLgpdY6twBo0GjDDL6iOlEUZZBv67dh68UdeKMoSom5tuWXNof9maObXS9rdzhSSvPIqAC6jpgKAPAENfsfURR3bgFoN5c6N7Am5UFz7ldim0F6deuHV12q1uZR2K4bFRQpqg29RpvoH9bqotjsV2dpKm+sP//ltqLsdvcWdsLnOUfFvqYLwLMRUwEAfZ61JqO7K5kScdird93dweuscEJRQ23nN/VKtdmR8aLaSA6LNbRZ1A3R+4iqryiK3eF4usX2yFuKunFEU8dqLOb9ZWdFVQPg8YipAIA+rzH/W/cupbrIbi6pPfpXd3fhUcTu8VtvtTg99idDxqoEHUH0kyFj215M9A8VUvy89zJSzm/qe7SiQGDlY5WFAqsB8GycmwoA6PMsZSnubsFV9Sde9xvz+PlTbeAivVpTJ66aQXvu5yWr3Z5SlpdZW1ZmNpmsTcF6Y6S336TQmJgLH6A61D/sqrjRX+QcdbGHaGPAT+MntL0+JWyQWqWyi/vhrbyxfndJzqzIeEVR8kxVosoqinKmrlJgNQCejQ3BAQB9nq1W2KOJ7uKw1pvzVnsPucHdjXiIMINvy3csXa+2pTDz1fQdGwsyats7pDREb1w0cPhTEy4b2F5efXbi4rV5aS4u8P5t4uU+7e0bHKw3JofF7izJcaV4K7tKzjTH1DqLyP2Z2v2tA4B28dAvAKDPszeJXPNxl6aiLe5uwXMkBbTeash5DuWvRzbOXf/mV2eOXyholTfWf5B1IObTJ8esfCG1svV+S4P9gp+asMiVFq4YNPKG+HHtfsvucEwMjXGleFunas7tB6ZRi/xBUSu0GgDPdu7vC85NBQD0YQ5P2EHUWpnq7hY8x7TwOGG1VErXDw49WlEw6utnb9r2Yau10wdGznx41Gzn5p8SNujDWTe2+4LrwfL8i75+/uU0wfuHlZtNzb8I9DIILBvo5S2wGgDPxsdaAIA+T6UVudmpu9hMue5uwXMsHTTKjbP/J+vg2JXPZ9eWt7z490mX3zCk/RXRDiT6h66ef3u7j/u+mr4jec0rqVVdjdBddz5jx/uJ3J8pQfRuTwA8GDEVANDnaXwGubsFAexWgZv+eCCzzXrWVJ1aVVRYX9PpCZxJAWHzByT2TmPtOlldOm/9Wy1PsvnHid0fZh/qbp2MmrI7dn5qc9hbXf/HiV337fm6qWdOIm36X0ydGDpQYNnJoh9OBuDB2EIJANDnaYNGK7lfubsLV6nUOne3IB2bw74qN23d2fTNhZktFydVimpUUOTcqIRlsaNmXeBg0pemLB278gWrvXXA6zU5dRWLv/vXnsX3adXqj7IP/WqPk/+JrsxNvW/P168nX3X+yqrc1F86W60rChpqmn9xafQwRVkppGaCfyirqQC6jpgKAOjz9FFz6o782d1duErtFezuFiRidzjeObX378e2ZP340dlmDsVxrLLwWGXhy2nbJ4XG/GHsJYtjRrS6Z2Rg5D1J015N39Er/bbvQPnZN0/uvnbwmLt3feHKmTFvnNi1ZNCIS6OHKYrSYLPct/drgSfQtJVTV9los+o12qSAsClhg/aWCngc/ZaEiV25rcRct7kwc3NBxqmaslJzXYPNEujlHWHwmxg6cG5UwvSIwV5qjevNAJAf56YCAPo8r4gZGp+BNtNZdzfiEo1/grtbkEWJue6W7R9/c/ZEV25OKctbsvHdm+InvJF8la9O3/JbL065Is9U9XXu8Z5ps0v+eOjbvaVnaixmF+s8uG/1vKWJOrXmhePf9/QBpBa7bV9Z3oyIwbWWxupGVztXFCXS2+9XIy7u+J7jlUXPHt/6Ufahdp/oXp9/4skjGyO9/X49Ysbdw5LZjQnweKymAgD6PpXGd/Tvqvf80t19uMQrbKq7WxDMYrftLc1tfl63rNFktlqD9cYIb98pYYPmRiUOMPq3O+p0bcWMda/n11d3a64Psg6kV5dsvvRuvxZJVaNS/3fWjdds+XcXE29PqGis/2/3X0ltK72q+LOco9fGjXktfafr1TqVWlk0NWzQsk3vnagpcb3a3yct9tddcNNgq93+5JGNTx7Z2PYV3FaKGmofPbDuxdRtK2Zct2jgcNcbAyAtYioAwBMYk+42nXzLWnmsl+dVG6Pt9flCSumjLxNSRwYF9TWvpG1/6+SeqqaGtt9tDlozI4f8ZtTsy2OGtzxqpcxsuuy7f3Y3ozbbX5a3dNOKby75uV7zw483Rq1u7SW3v5i67fcHvmn88SExFxJu8Csx13Z+X5eJek7tw+yDA4z+Lbdl6jn59dWvpe/cVJjheqm7kpJvip9woe/WWMyXf/evHcWnu16wxFy3+Lt3/zJ+4f+Nme96ewDkxLmpAACPoNIETn9HpTU6XUCtD1HaO5rywgO0fuP/6j/hb07P2JIueIwutEsv70nOoTheSt0e//lTfzu2pd2Met62ouwlG9+9eO3rOXUV5y/evfuLk9WlTs++pTDz78e2tLqoUlQPjpyVdc2jD4+a3cGanqIoyeGxX827ZWiApDv9fF+UvbUws3fmKm+sf/LIRtfrjAyKeD152YW+22S3Ld/yfrcyajOH4njs4PqXUgUfGAtAHqymAgA8hC50ctDclZUbL3fYm7o7VmOMDlm001p9omb/w9bKzl9l1IVNCZj8si5siuKwm9JftpSlONXyD/wExV33qrdart36/tq89K4P2VWSM3blCx/OunHRwOE7S3K+zHF1Pfzpo5tvTpw0yCew1fVoY8CzkxY/Of7S5ueQs2rLS811dZbGMINvlNF/cmjM3AGJzaMe3LfKxR56SJ2lcX95L72AfayysKKx3vU6eXXVjTabUdv+CYj37125If+U08UfSlk1NmTA7Ats9QygTyOmAgA8h37A/KD5a6u2/8zeUNj1UV7h0wJnfqjxjdX4xoZFL2g8u64+Y0VT0VZ7Y+s9ZtXekfqoecahd3hFzjp3SaX2n/RC+fpZSmev1XXAMOhKffRCp4dLwmK3Ld/y/rqz3ciozaqbzFdv/ve3C+587OA3DsXV52MbbJa/Hd3c8viWlvQa7czIITMjh3RQwWTp9sccvSbPVNU7E52oFvBKqqIoNRbzmry0awePafutIxUFb5/c40pxu8Pxy91fHV76oFbdfgwG0HcRUwEAHkU/YH7Y0iM1KQ82nP5YsXfyLqLaK8hn1MO+o36rqM//g6jSD7xcP/ByxWG3Vp+wmfLs5lJFpVIbIjS+g7T+Q9sW8Yq4OGDKq9V7fuFcw9rAkQEXv+vcWKk8uG+VExm1mdlmXbLx3VpLo5BOPso+9NKUpTpnTy5xemAvaLBaemeiMrNJVKnNhZntxtRH9q/tdM+kTqVWFX2QdeDWxEku1gEgG2IqAMDTqA1hgTM+8Bv3F9OJ1xvPfmOtSm11g0rtpQubYoi9ypj4c5XOt/0qKrU2cIQ2sPVpnO0yDrvXZi6pO/xnpZuLgVr/xOAF36q9gro1SkJHKgr+cWK3KxVcP7LlvMqmhv1lZ5PDY50bHqw3OreHUy/Q9cqyoUpRub6sfd7RyoK2F0/XVrjyuG9Lb5/cQ0wFPA/npgIAPJPGN85/4rPKxGftDUXWmgy7ucTeVK02hGoM4dqg0Sqtj9jp/MY+4RU+rWr7zfaGoi4OMcQtD5j2ttqr9VuUfdHDKatdXxkT6GC58zE10T/0WGU3HhrvTSF6X0UR8zhuB+J8g0632NfKRe2e8rr2bLqoJLy3NLfUXBdmuMDnTQD6Jh7lBwB4OLV3pFfEDEPs1cbE2wwxV+jCpgrPqM30AxaELT3ie9GjnSZPr6i5wQs2BM3+1DMy6onqko0FAk4uESi71vmUNTXMyXzbC0YHRfbCLJPCYgRWa/dZ7r2luaLqOxTHHnHVAEiCmAoAgDBqQ7jf+KfCl+cGzfncOOwXutCJGp+BKo1ebQjVBgzXxyz2n/RC2JXHQxZu0g+4xN3NCvP1mc73Ru5lrjxCfHnMcIGdCBSiN14andTTs4wMjJwUKjKmalTt/LR5qsb5Y4fayhBaDYAMzj30y7mpAACIotL5GWKvNsRe7e5Gesnu0jPubqE1uwtvMI0IjJgeHrezJEdcO2IsiE6aOyDRT6cXtddUW2qV6rXkZdm1rfe4dkWgl3fbi+XitmhSFKWisaMTegH0RaymAgAAl5yo6vG3JbvL30vvyvDnJi9RKdJ9gn9zwkQfrdfNCRN7bornJ10xOzI+3i9EYM12q1nsNoFTmG29tAEygF5DTAUAAC4pNde5u4XWBvu6FLSmhsX+NH68qGaEuDhi8ILooYqi/GncwmC9UXh9laL6y/hL7x85Q1GUscHR7T6p65zJ7b3patR6iaqvKIqP0GoAZEBMBQAALmns7Hza3jcuJNrFCm9Nv2Z6eJyrfQg6PEGtUr00ZWnzAm+w3vjncQvF1P2fcIPvmktue2zM/OYvA7wMTu+T3NbC9t6nHWD0F1VfUZSBPp6wFRmAloipAADAJQaNzt0t/Ii/zjAlbJCLRbw1upXzb7soOMqlKoIeHH5+0hUTQgae//IXw6c3L3u6Lsro/8zEy09d/btFA3+0cZSok0jj/UJmRAxpe31koMgti0cERgisBkAGnJsKAABcEuntV9FY7+4ufrB88EVeao3rdUL0xn2Lf/3gvlVvnNjlejWnLRs0qm0ofX7SFSZL0z9P7u1mElYF6Q2DfYMjvP0mhsbMjUqYFh7X7u/VzQkTX0zddryyq4cAX8jTExep29unc1bkkFfTd7hYvJmP1kvs1sQAZNBOTAWAfsLucGzPrtiYUbotqyKnsr7M1GSzO0J9vKIDDNPjgucmhi5ICvPS8NQJ0IlhAeFpVcXu7uIcL7Xmd6Pniqqm12hfT77qhiHjb93xibtOPVkWN7rtxUa7VavWdCujRnr7fTjrxjlRCV25WaNSPzdpyaUb/tmNCdqYGTnkmriL2v3WpQOHidqy+MrYUUI+lQAgFWIqgP7Ianf8OyXvua1ZJ0pab/2SX23Orzbvy616cVv2AH/Dr2cOvndanK+evy2BC5oRMeTLM8eElPJSa5tce9P14VGzE/xDhTRz3vSIuFNXP7KtOPsPB9fvKM6xOexi63fMT9d61+KzpurLvvtnd5c6rQ6Hl6Ybf5UtjE56fOwlfzn8XbdmOW+gT8AHM2+40IbJPlqve4dN+9uxLc4VP0+tUj0wcqaLRQBI6NwqAeemAug/cisbZr+x6+efHmmbUVspqDE/siZ99HPf78qp6J3e0BNqLOYT1SU7ik/vKD59srq0rsfOnOy3lsWOavfBTicsH3xRjAvb4SSHx/7f//YBEiujpiy9qnh0UNSy2NHjQwaODIoYHhg+PEDEK5GdvWsV6xPU8suqpobLv3vHicdxy8x1iza8c7iioOtD/jxu4T3DpnV3IkVRwg2+GxbcOajDP8r/GzM/yuWNlG5NnNTyrV0AHkPV/CZqeXn5pk2bmi/pdLply5a5tSsA6Cm7cioW/XNftbl7h+zpNOoVPxl743hX9w5FbzpWWfhexv5NhRnHKgvtLbZd0KjUY4MHzBuQeFvi5KSAMDd26Emu2vzeV2eOu1hEo1IfvOIBnVo9Y93r5d1/2XVEYMS2Rb8IEXpYS6PN+kHWgRdSt6W76almL7Wm8sYnjdpzm1Q5FMf89W9tLsx0umC0MSB12W8CvAxdH/J+5v5f7Pmq65/vzI1KeH/m9dHGgE7v/Dzn6HVbP7A7uytKjE9gypJfR3j7OTccgMw0TzzxhKIoDQ0Np0+fPndJ0JoiGgAAIABJREFUoxk+fHhHgwCgb0otql3w1p6qhm4fBG93OFanFU8YGJgY5tMTjUGsoxWFt+745OF9a3aX5hQ31Lb6EdihOAobanaW5LxxYteRioKxwdGhBv5YXTUpNOatk3tcfBr2zqSptw+dHGbwvW7w2JSyvDxTVdfHLosd9fW8W8Vm1G1F2XPX/+M/WQfLzCaBZbvl4oghdyRNOf/lp6ePPHt8qysFay2NVoe9+QjWLhoTPODauDEma1NqVZGtw0g5IjDi2UlLnpu0pIsxeERgRJjBd93Z9K43c16Al+G7hXfF+7t0QC4AaRFTAfQXZqt95mu7CmrMzg23OxyrUotvnhjjb+A9Vam9n7l/2eb30qtKOr3ToSgnqkv+dWqfr04/NUzYKZH9U5DeaLXbvy/KcrrCAKP/J3Nu8tF6KYoS6OV9U8IEH53+WGWRydrU8cCh/mEvTln61IRF3lqR5+I8c3Tzzds/rmpqEFjTCY9eNHfi/7axtdhtSze953pL+8vyfho/IUjv3fUhwXrj0kEjbx86Jd4/xFurq7M0mm2W5sga6OU9Oijqxvjxfxq38LnJi8cGR3frVbJJoTFhBp/NhZnW7nzGkegfumb+7WNDBnR9CIC+hYd+AfQXT2/K+P26Ey4WuWnCwPdvGCekH/SEh/atfiH1eycG/nHsgifGLRDeT7/iUBw/3/H/7N1nYBRV9zDwOzPbd7Npu+m9hwAJvUMIHTV0RAV7wy5YH/VV//KIFRQril1EAUV6D51AgAAhhfReNrvZbLbXmfdDeCKGlN2ZuyXJ/X2SZObsMYRkz9x7z9n2fWkOjXvFbN6JuU+k+XWuOow26+by3H11RSeayjttAw4TemcEx8+PGDwvIgXWydgOnxedefrcDrgxaQgTehcvfKVjx+/OmoL5R3+AEvm11Olrhs9mGKTNbOSz2FC67F5VNjx0euullrper2Th+ANxo9aNzhTd0lkKQZD+BM1NRRBkQFAZLGuP0j/N1eHX3LrV6bGpIUzbfiDO8P61Y/RqVADA21cOBfJF9LrFIO0wgH09fhEAwNFKNVTgvW3qvbfWqAAAHsF6KGH0QwmjSYqSGTRyo05lNvjzBAE8kZQngpP3LXbVFDxz7m8nBXfIqpQpgpuWiHfVFMCKvLumgHmZ6tAB156l+oVczHzucEPJp4WnjjWW6a1dHM0IFXgvihryfMrkKJEfrNdFEMRjoa1rCIIMCFuvNmhMjKZctKMo8ENO7SfzU5iHQuC6qmx47dJ+JhGeO79zWkh8ghg1VaKPjRPfTVw6IzThyey/lPb1QFoYOeSbCUt6PVOKY1iwQMy8MWyvTDbrqpxdVK/td11ie1XecymTOma6ZMurYUW+1tqkthjFbGh1JhQzQhJmhCSYSdt5eU2pWt6oV+usZilPFMATjZSEo4ZnCDKgoDIVQZABYX9R7ycV7XSguBkAVKZ6nBcv7GHYv8dM2l66sOfvaQ/ASmnAWhaddntY8qaSnM+LTpdrWrq8hkuwMsNTXh461dOmiXxSeKq7nF3vbHPVtsq8pdGpAIBGvbq4DdrPMQpQJW3yjlOvHoWDE5MCoycFRrs7EQRB3OlGmYrmpiII0r/l1DjQMrRnxc3aVoPFlw+zWQvC0Imm8sMNJczj7KwpyG2pH+6PJg8xJWJzn0uZ9FzKpHJNy/HG8jKNQmbQGG1WCVcYJPAaI40YHxDFJzzuHxFJUV8WnXF3Fv/yf1cOL41OzWosu+fEZtqDW7qkMLmtfTGCIEiv0GoqgiD9n95sa9TQbPDbpTKFblR4T2PrERf7tTwXVqjN5bmoTIUo1ss/1qvPjAzJbamrcWQKjgsUqJo2FJxefXGXlWS0WeBWRhuEcxAIgiBOgrs7AQRBEKdTGS1we8PRmLyKONXRxlJYoY7AWJVF+qizzdAOf0LkjBoVACAgONBjIgiCwILKVARB+j+rDXI3FLMN/ltGhLY2s7FSo4QVrbBNZiZtsKIhfct1eIc/IXJGjQoACBZ4OSMsgiAIFKhMRRCk/xNyIIz1u5kXF52Y8CDV2laI0awkWedh2z4Rl2k2at2dgouwcSJOLHF3FgiCIN3qokxFc1MRBOln/AQcEdTCMsKHDzEawpDGaoIc0AI5INJXmAbMcc3RknAPbGGFIAjSAa2mIgjS/2EYGBQoghVNzGOFozLVk0BvVY+j7vcDFY8YKBslFkUNdXcKCIIgPblRphoMBvfmgSAI4lRT46Btb0uPlRA4KmM8iDeHBzegmA05INJXBPIHxHFNXw7/3riR7s4CQRCkJ6yioqK6urrWVpgHexAEGTgoCsi0JoXOrDJYfPlsiZAT6MV1d1JdWJYW+n5WGZRQd6aFQImDwBIl8sMARgE4J1Y4OBEm9IYSCulzkrwD3J2CK7w1bJY/V+DuLBAEQXrCunbtmrtzQBCk77HYyK1XG/cUyrJKFc3afx3kC/LiZsRL7hgUuDg1hOUxq45poeI5SQH7rzNt4xnrL1w0NBhKSggsQhYn0VsKq0frMP9QAkMnYgaoiYHR7k7B6UZJwlcmjXN3FgiCIL1Av4kRBHEMSVGfna6MfTdr+ebc3y/Xd6pRAQBNGtNvufV3/Zobvzbr67PVntOUbd28FDbB9IfeJ/NTuCz0k9PjzApNhBVqJrxQSJ+T6hcc6+UPJRThkSecY738d09/kI1Dbn6OIAgCHXqzhSCIA2Qa022bcp7ZkV+r6v1Ae5VSv/LPvBkbsxvURhfk1qukANFbsxKYRFg+Iuz2QYGw8kEgug/SQTscw1bEjoASCumLMIA9lTwRSqhJgTFQ4kA0wj/syOzHBsj5WwRB+jpi+fLlYWFh0dHRTU1N7R/CcXzQoEHuTQtBEA9U3WoY++mp3Po2h+6qVOr/zGtckhoi5rm/heakGP82o/VcNZ3T+Blxkj/uHeE525iRmwULxAWqpkKVjGGcBxNGPxA/CkpKfQtpbDbV7TPVHzLV7THVH7S25tm0lTjbG+f4uDs1VxshCdtadbXFpGcSJNbLf9/Mh38svWCwWWAlxtCssMQjsx/35wrdnQiCIIhdWJmZmRiG3dxCCc1NRRDkVgqdedY35+ra6KyLVrcaZn9z7uRTE3z57h/T93HmIB8+e83hEivpwM+6B0dHbFgwGG339WTrRmfuqyvSW+lXBV5s7jvDZkNMyfNRVr2h7Ed9yTcWZR7oqgcV4RUriLtfkPQEzvVzfXpuwcaJDWMWzD28yUaR9CLgGPb5uAVBfK830mY8d34n3PRoe3nwVA7a64sgSN9BvPXWWwAAo9FYUVHR/iEMw9BqKoIMNCRFXWvSZFe3nqpoOV2pLG/RK3RmfwGnozB7bHvesTIF7fjNWrNSb74jJQhSvvRhGJYe6z8tXnK5Xt2k6Xyw9lbRfoJvlgx9dVo8h/G5VsSpvDm8GC//HTX59B61Ehi+eco9o6URkNPyYIbyn5VH5hqrtpGGblehKXOruemYvvhLgOGcgPHAI89bQhcr9g/ie+2pLaR3+ydj5t8TOxwAMFoScb2tuYDxIj991I2xwgSGrxuTySfc/6AQQRDETu7fg4cgiAsodOZjZYpjZS1VSr1cZzZYbBIhJ0TMGxvp6yNg7y9qPlIiV+jMne4icGxkmM+yYSFpIeLNuXUMc/jufO2jYyNHhnvEHsIJ0X65qyYfLpFvzK4+VqZQ6jsvwQk5xMRov/tGhS/xpH7FSM/uihkmM2hX5exydDgNjmEbxy9eGDnESYl5GspmaDv7qKH8V3uvt2g1l141NRz2nbIF5w2IkS2PJo41kdYXcnabSZv9d7Fx4oORtz8z6MbpVhzDfp50l8psOFRf4pw0e/O/H11jpBG+HL57ckAQBKEFa9/iq1KpDh061P4hHMcXL17s1qwQBIEmr1H90bHy3680WGw0N7ABANgEZrFBOA4wf3DQjgc87uAfSVHXGjU1rQa5zmQlqQARN1jMHRbqjZZP+6hD9SX3ndrSZNDYeX0g3+uHiXfOCUtyalYehLK1Hl9qrP6Lxq0s3yH+c04OnAOrFxW1y0/+Vtwmt+fiBLH0l8l33bogbyXJNVePrLl6hPYuYuY2jl/8aOJYd706giAIDahMRZB+y2Ql3zxY/OGxctJjDpyzCVz29kxPOKGK9G+NevV/847+UJrT81FVLzb3kYSxrw7NkPAGUF+ZtrOP6Uu+oX07N3ia38xDYMCMlrVR5LaqvHX5Jy4oaru7ZqQkfFXK5CVRqSy82y/LOXn1mqtH9tVed3Spn7k4sSR//gtcAm2gQxCkL0FlKoL0T81a0+xvzl92sCuvC+x4YNT8we4/oYoMBHKj9vfKK1kNZaebKxVGXcfHA/lekwKjM4LjlsUMG2g7Ic3y7Ja9E7rslmQ/n4k/8OPuh5RRn9GoVx9rKr/W2igzaDQWk4jNDeSJhvgGZwTHBQvEdgYpVMl21hRkNZbmttQrmTUTtt/u6Q/eHo56jiAI0segMhVB+iGNyTr1y7OX6jyuRgUAvDkz4a1Zie7OwunKFLoimVahM6uMFj8+WyriDgsVB4t57s5r4DLarHKjFgOYlCccwMtKlGLPaIviIsMoOD84YGEJxhZByWnAspC2Rr06evu7Tt3w8vKQqe+NvM158REEQZxkwP6qRpD+7N7fLntmjQoAqFS6aAHBLYqbtZ+eqtxXJKtuNdz62eRA0byUoKcnRYegetXleAQrXDhQTlR2x9x0gnmNCgAgDY2Gyi2ChEeYhxrI2DjRZjHaVaNS/zRDcsj98aPWjpxL504EQRB366JMRXNTEaRPO1qq+Du/yd1ZdEtttLo7Bado0pie/uvaX9eaenjTWSTTFsnK1p+seHhMxHu3JYu46EEh4lKGqm1Q4lgAXl2+gxu80I8r8Blgu6bhUpntm0TteI3KI1jvjbztmUETMXoFLoIgiLuhN0kI0q9QFHh+Z4G7s+hJvxy7eLBYft+WyzI7prACAExW8oszVYdL5L+vGDEs1NvZuSFIB7PsFO17LQA/AOKOUdFnQEQtEIMmALavBQCwcWK0JHxaSPziqKFDfIPhJTsgWB2ZdmMnFo4vjhz6RtqMQT6B0IP3dddaG482lJ2Uldfr1TKDhqKoAL5XqEA8KTAmIzhumH+ouxNEEOQfqExFkH7ldKXyWqPa3Vn0RMzrb21+j5TIM7/LMTs476dErkv/8uzxJ8ajShWxB2VRm+oPmBqzLPLzNoOMNCkwgovzAllesZygdG7oTLb/iN5C2GxtxTRe2grwH6hhG8GIBuB162ctpO1Mc9WZ5qp3rhyZHZb4ZtrMMbdMZEG6I2Rx4AZ8PW36g3Gjo7384Ibt60iK+rsm/4Nrx87Lazp9qkanugjAzpoCAMAw/9CXhkxdEjWUGDCNrBHEk90oU7F+ucCBIAPPnkKZu1PoRYyfwN0pwHShVjXvhwuO1qjt1EbrbZtyzj87MdwHbZtEukUaZLqCdbqSjZT5XwfOKdJis2htmnJTwyFN7n840nHCIS/xIuZ3G8eooEizo69eB8RPUnMvgN5XmShA7a+7fqi+5IXBU94ZPpuNE46+1gAE97w0ByfeTJ3Zw1CcgUlm0Nx/6o8D9dd7vfJyS/1dx39dL434bco9sV7+LsgNQZAeoJ9lCNKvZFcr3Z1CL4aH9Z/FQ5KiVm7P05vpb9trVBtXefYmbcS9jLW75H+naPM/6FSj3sosz27NWtCaNZ80tXR5AWVzuHtZESWdQa2wp0btYKPI968dW3LsZytJ59nNQBMsEEOc2ZvsE4hq1E4uKGqH/P2xPTVqhxx5zchdn5xoKndeVgiC2ANt+kUQyMpbdJfr1TKNqc1oEXIIiZCbHCBKCxXjLtmzUCLX9X6R+/BY+OSY/vOIetP5GuYdlbfnNR4vb0mP7T9flluRhkZTwxFT41Gbppw0yimrDuf44fwgtmQUNziDHTgRwyFvfewftFfe1lx526EZp8aanZaWS/6zjxNesZ0/5+AXuRp4340taqPodKXeWVPwwOk/fp68DPXv6VV6UOz2qjwooUZKwhr1aglPiJay25WpFXcc+V5u1Dp6o8psyDzyw7E5K4ej06oI4j6oTEUQOKpbDZ+crNhxrbHLSSR+AvasxIDnp8SMCnfuSIwWncOb+lxp3uAgMa+f/NihKPDhMTiP2z/IKuuvZaqlJVeX/4Gh+k9A/qvDs01XB1rzTA2HtHn/xfnBwkHPCpNWYmyxu/L0QPrrX2quvEXjRpuuruXQTMnc0zj/Xw2NcK4fAJidRS8FwLPUHBmgv9D3a/mlWaEJy2N7OzHrqdrMxhNN5eWaFrlRq7WYJTxhIF80ShKR5hcC95njsug0WGXqdyU535XkAABCBOIpQbHTQ+KXRKV6sblQgvc5Rpt1zuFNMoOG3u1qizHzyPdFC18asF9ABHE7rH38TFtb28GDB298CMOWLFni1qwQpC/RmKyrdhb8eKHWSvb+/m9avOSbJakx/k45n2kjKdaLe5wRGQoWjl1ZPSUlqIsuLH3RhVrV6E/o9029GYFjzW/P8hP0r+ZSpFWTt0Z79R1A2bX5E+cH+kz8kRs629l59Qnm5rMt+yfZ+aXrEidoqv/srE4fbN4WadN1biHTpR0g+UmK6bzNUIF38aKXoXcJciobRW6vyttQePq8vMbW1dffnytYFDV09eApCWIprFcctnP9tdZGKNE68eXwVyaNf3noVDF7wM1qfjfv6GuX9jMM8srQjLUj0OBZBHGPLs4woLmpCGK/3Lq24etObjpfY0+NCgA4WqoYtu7Elsv1zkiGwDEuy3MPJj09Mbrf1KgAgONlXZ8ApMFGUicroEXzBKS5VbFvvPbK2/YXWqRBpjxymy7/I6cm1kdQ6pznmdSoAABz0zFj9Z+dPsgJGGfXywPwITWeyau3q9e3fVl0lnkclznRVJ745/vLjv96trmqyxoVANBi0n9TfC75rw8eOP2H2mLf1NMeERi+fkwm8zhdLpO3mg3v5h0d+vfHp2WVEF6i72gx6d/L6/yYhob1BSfrdExPdiAIQo/nvqNFEM+X36SZ/nV2mcKx46Bqo/Wezbk/Xqh1RkrBYg99ZD4q3Of/Zie6OwuY8ptgDv4paKK5M80DUTZD69FMi+KC43eS6osv6Uu+dUJSfYmxartFkcM8jvriy51qXW74HfbceJ4KqwJwjif8XH4RShxnowD11uVD0w5sLNfY9cCIpKgfSy8M27k+twXCM8dpwfEPxo9mGqX7ncjV2tapB776q/oa05foO7ZUXNZY7Bpk3TOTzdpXvocRpP9BZSqC0FTXZpy58VyrwULjXooCj27LO1qqgJ7VoEAR9JjMJQWI9j0yRsTtJ6dS29WpICykdKhVdXGkuY9qy37CLDtN926qLXulRX4OZkJ9jb5kE5Q4Nk25WXbi5o/wIhbgvIBebzwJIqEkAADIb21q0Hv0JOd2/7m0/+0rh7pbQe1OhaZl5sGNhSoIY8C+mbB4YeQQ5nG6YyXJu47/erihxHkv4VH21hbBCrUPXigEQRxyo0xFc1MRxFEv7CpoVNMvVCw28tFtV41WyDMbPLCP7qKhwWeenigR9qXzafbQMZhDcyutydr7RX2BpeWSofxnRiEoW9u5pxhuee27KJuhU23JhKnuwM1/xFgCr+Frer3rGgiElQAAILelDmI0Z/j6ejbtDaItJv3sQ9/S6CXbCYHhm6fc81iiXbuy6TGTtuUnfmszw3y+5rHOyathhbqgqEXTlRDELdBqKoLQcaZSufVqA8MgFS36T09WQMmnw9K0ENc8dArz5vd6zZBg8e8rRmy/b2R/aw4EAACARcD8QrOJfvLTWJ2zinmFaWm5ZKjaCiWfPseqvErZIGxWbGe+ZV1aEP8g27+X7rvVAOZwYzu30bpLs1H7yqW9TCLU6lRv5B5kngmPYH09ftGOaffHetn3tNHxRiLNRu1/8444fFtfIzdqVWZo+1PMpK1K6+kDyRGkX+onb4wQxMU+OFYOpdfYuhMVFhvMx7TRfoJ5KUEQA3ZpXJRv1evT9jw8+vHxkQnSziMrkgJET0yI2v/ImKurp9yZFuLsZNwFbu3dPyp5a1uRWXYSSqgBe0LVqi6DGM2muSUaRvhO20WIonq4S4PBnMCh9uzlu9cv7We+wLip5HyeEk6r3vkRg4sXvbx16oo5YUminkeh0HpQtqHwNO0ZLX2F0gT5DIXSpIcbEEEQe/Sro2II4hoak3X/9WYooZq1ppMVymnxEijR2n2cmbL/erMJ9nbiDgSObZg/mMCx25IDb0sOBACYrKRcZ27Vm/0EHKmIw+kvC4M9i/OnP1Kyi2gSmNHcxVTLaFXqZmbZScqiHoCTVElzK8xopi6iEYIQvxkHlIdn27RVEF+r2xxorPq5SotJ/3P5JeZxbBS5oejUpglLmYcCABAYviQqdUlUqoW0XW6pbzRoZAbNz+UXz8iqmAc32ay/V155dtAk5qE8lpmEfIbCRMI84oEgiJ0GxLtJBIEru6oV4hLo8XLIjZRi/AVvzEiAG/Nmn8xLGRn+ry6gXBYe5s0bEiwO9eYNkBoVADAm0hditLFQo7mLWXEeWizSalEMyAabUN9hU1TXPd5Y3onSeVf5Mfd0+VkhZYaYgydP7NxVU2CywfmC/1V1zdEOTL1i48RoacS8iJRHE8dWaaA9vzhU388bKUEf1du3Zv8iSL+BWighiMPyoc4OKWxi2nvjVq9Nj396YjT0sBgG1t6W/JQTIvdF0+MlsGryUG9eakh/WDa0qUshRrOq+/mb6S5hbJjr6jir29bfGFvsM/lX/zmnuOG3d9o/GgZg9uaN9vKDGA2ukzJo3QFazYZrrU2wonXSqFfX66FN77zk8U2tGAoWiHGob2vDhDBPayMIYqeuN/1SFIUKVwTpTn0bzHMvTppEsn5eCoFjn56qgHKGFgAQIOJ+vyy1fZcvAgDwF3IWDAn64wrTTloAgAdGh8N9U+UupAlmsxzSNBDblhDCcKjRInq+gBM40S9wok1XY27MMstOWjVVpEk+RG87Ce88aaqvxx1Qr9O11evbFEZddjO0frAAgEKVLM3PKf+zlVBb+MgMGq3F1MvB176MT7CjRX6wendJeaIAnidOekOQfg+dTUUQh+ktMI+pwJ1r0oHAsfXzUmYmSh/fnlfTyqgS9hdynpwQ9czEaP9+N1SGoXfnJv+d38TwGHCAiPtCeiyslNyLIumMEe42GryGt30Iywfm8EyW72B7LiOEEfy4+/lx97f/8Y764i8OwWlhFSeWeM5q6vGm8p/KLmY1lNboVM6IX6yC07PgVtCnyKj7dZkKAJgSFAurTE0P6ic/nxGkz0FlKoI4jI3DPH7JYTlxGW1OUkDZqxm/X2n44nTVxTqVjexiaTXUmzc9QRrhw79Up6po0TdrTXqzTSriBou5YyJ8M+IlMxKkQg7hvCT7rhh/wXOTY97PYtSade1tSd68/tDmFwCAEQKY0aBuf+0rCGEYSxwHq98vJziDxl3TguODBeJGPYStv3fHDGMehLnjTeUvXdhzQVHr1FfZW1f09vBZzohMwW5DBf0YradZGp36fWkOrFBQ4iAI4ihUpiKIwyBPIuE7d4mSTeArRoStGBGmMlhOlLdUterlWrPebAvw4gZ7ccdG+iYGoO1M9L0zO/Fao3pfEc1VlMfHRz44updtmX0IIQy1aSuhRROEwQrVt/Cil2mvrmEeB8M5vIj5NG5k4fgbqdOfyP6LYQK+HP4z7u4oayXJNVePrLl6xAWFWV5rg9pidEbLKC/YMTuStFHkRUXdeXlNs1GrMOp4BEvCE8aJJelBsUF8L7gv6kozQhKG+YdebqlnGCdeLMmMSIGSEoIgjkJlKoI4LF4Ks66Lv2XuqJP48NnzBjt9pOpAwybwrfeOnPvt+ZMVDm8wu2d46BcLYe7wdDuWT4pZdhpiNFih+hZh8tO6os8oM9OWOYKklTjXn969jyaO/ep69rVWRrNA3xk+258Lc4HdUSRF3XNi89aqq91eQdGcPtolC0kebShbEGnXRmuHRIpgdgJnYfiwXetajHoSkAarxdZNA4NUv5CnkiesiB3BJfree0Ucwz4bu2DS3i8YLkSvG53JwdFmIgRxj4EyOgJBIBob6dP7RXYb1y8mkQxkQg5xdOW4N2cmELi9b3h5LPyT+Sm/3D28f3RO6sAJnAIrFM71Z/v2qxreATiH5Z3ENAbXT5T6Bu3bCQz/efJdTBYGbw8f9FjiONq3M2ewWaYe+KqnGhXArFHbrTj5G/unl4S/vBq5dc3U/V+9dfnQmeYq5lt2w4XeEAt+K0VWapRqi1FrMXdXowIAriobHjmzLWb7u7trC2G9tCtNCIh6OGE0kwjLotNuDx8EKx8EQRyFylQEcVisvzAJ0kZZFo7NSgqAEgpxIxaOvTUr8eJzk5cNC+25WOWziScmRBW+PPXZSTH9q0QFAABe+G0YC86baV7UYoANxN9Q5uYzil1pFjmzCbQY7j3uK9pLqe3S/EJ2Tn+AR2slbbQ04vf05Syox/gdUqBqStnx0ckmaPNm7KSzmq0kqbdaanSq403lb185NHHv58N2rt9cnksy6LqOASwjOB5invZr0KvnHfnhmXN/98XjrF+NX7QwkubTrvSg2B8nLYObD4IgDsEoigIAaLXaffv2dXx0yZIlaCANgvRg/cmKVTsLmMdZMCTor/tHMY/THZnG1KA2yjQmHMMCvbghYp5UhLr1dqu8RZdVqjhdqaxVGeVaE4ZhUiEnwpc/KcZvWrw00pdvT5BaleFQsTyrTFHQpJFrzRqT1ZvHCvTiDgv1zoiXzEyQ9u+GyeoLq3QF65lGwdnSeVdZ3sn2Xk+RltarZtlp0iAjjc0AZ+NcCSEM5wSls8RxTJNxIXPTMeXhOcxbHItHrROmPA8lpYuK2rtPbC5VK+y/ZUXsiC/HLXRjI9mtlVfvPbXFZLOfx1PfAAAgAElEQVS6K4EuTQ2O+2XyXaECmhM4d9YUzD/6A9yUHPJo4tiN4xd391md1XxKVnFGVtU+6QfHMClPFCnynRwYMzYg0o37Zg02y8qzf/5UdtGhu5ZEpW6auMQZx4wRBLFf12Xq4sWLcfc9BEUQz2e2kYM/OF6q0DEJwiHwvBemOKODUU6N6ocLNVmlihJ55wwHBXpNi5c8PDZiaLAY+uv2Xacrle9nle0plPVwzYRov5enxt0+KBA9xOsBaVbJ/0ogjXImQYSDnhWP/sSeK23aal3hekP5r92NbCVEUYLExwSJj+McmHv1ncHSkttyIJ2yaJgEwVhC8ehPBAkPw8oKAKCxmN66fOibknNaSy/182DfoP8bNtsZhzPtl9VYNvfwJk+rUdtJeMIjsx5LpTVblaSosXs2OLtZcc/Wjpj7ytDOvaOvKBs+vHZse1Wemex6uJqIzb0/buSqlCluHE20peLy6gu77WleLeWJ1o6Y+xCz3cIIgkCBylQEoenv/KYFP1xgEuGF9NgP74B87iWnRvXSnsIT5b2088EwMCsx4MM7Bg0O6sO9HKFo1poe+P2K/a165w0O+m5pav9eEWXIUPm76sTdgO55PJY4zv/2HJzTy5ltymbS5L6qK/oc2DGsFWOLxSPWCpKeoJeSK1A2xe4RFmWPByl7Q4ii/GbsZ36utUtKk/6H0gsH64vPNFfqrf/6mkcIfTJC4pdEDZ0TloRBP+7piHp9W/JfH2h6K6fdSMoTnbntqXixhMa9Z5urJjLuCcQEl2Dlz38h7n/JayymJ7P/+qX8kj33snD8pcFT3x42y11bwU026y/llzaVnL+oqLt1AzOOYcP8Qh9MGP1A/Cg+0U8mhCFIX4fKVASh762DxW8fKqF37/QE6d6HR3MIaP/QKApsOF3x0u4is83eE0Q8Fv7e7cnPTOyHhyTtlFOjyvw+R6Zx7E1tmDdv3yNjhqDl6O7pij5Xn3+axo04T+o/9wxL3MsxPJu2qjVroUV52aHgvIh5PpN+wdie+GhGX7yxLftxhkEwgiddUESIomBk1C0zaavXtcmMGr3VIuEKA/miQMcnlxht1rPNVdnNVTKDVmHScQmWhCuME/tPCYpN8qZ/XP/ek1vsrJrcKM0v5GLmcwSto9evXNz7/rVj0FOy35Ko1K1TVwAAClWyeUd/KHNkNzgAYIw0Yue0B2h8w0DUZjaeba6q1alkBg0FQABPFC70GRcQ6efWrtQIgtwKlakIwsiqnQXrTzrcpSM91n/Pw2OEHJjHdZ79O3/DKTojK/8zLf6/c52y/OLhShW6iZ+dadbSWXgJEfNOPz0h2g+9rbnVjREfuqINmosvOXTMkuU72Dd9a69HUkmjvGXfRKuazhMiTtBUvxn7McJtxyZvRZFm0tDcsnecTV/HPJog6QnvsV8wj+M8l1vqP8w/vqP6mrGbfbkRQp8nkic8njjOm+PYycCryoZhO9e7cbHRfhvHL340cSyNGylAPXJm23clOdBTshOOYVVLXsMANmHvZzU6FY0IQ32DT8x9wodj11F/BEEGMlSmIghTO641Pbz1ilLf+85DAACBYy+kx74zO5H973VUrckq15kFbEIi5Ng/16QDk3VdAMCGBYOfnhhN+/a+SG+2Df7weKVSTztCSpDX5VWT2fDWw/soS0uuqf6AuTHLqi4hjXLKZsQ43gQvkC0ZSYjjTdV/WVqv9RoEwzmCpJVeI9ZiRC9vXinS3LJ3nKUll3bC/Lj7fCb+SPt2KGy6OmP1dlPjUUtzdnenaunBuf6By2QA88RJj61mw2Nntm/reULM/4jZvE/GzHsg3oEOc0+f2/F50Rm62blUAE9Us/R1evNIKUBtKDz94oU9lm7OgjrbR6Myvys9X6Tq6SR/z+aGJe+d8RDElBAE6ZdQmYogEDRrTZ+erPzqbFWrodtilUPgdw0PfWlq7KDAG/udCmWa7Vcbs8oUOTUqg+XGGw4MA7H+wvRY/9lJAZkpgfZUQZfr20auP8Vk2gGHwK+9mJ4gFdKO0OcwLOzbrZuX8vzkGCj59EWm2j3a/A/MslM9XIMRXE7wNMpmtMjPU9YuWo4Roihe1BLhoGcJQag9L6or+Fh94QWaGf+P38xD3JAZDIPQY1WXafPWGCu2UKTZSS8hueMS23+4k4LTdlFRuyjrJ0fX35ZFp/04aZmd5Vzs9rUVGpg1v1Ntm3rv4qihtG8/J69+5eK+E03lEFOyU4pvUEFrE8Mgf2Xc795WWwiCeD5UpiIINDqz7Xi5IqtUkVvX1qw1K3RmHz5LKuImBYgy4iTTEyQBohtbDS/Wqt4+VLK3SNZzaRnpy181JfaJCVGsHtdXJ39x5lSFkmHymSlBOx904mgcj9KkMcW+e1RvZroW4cNnV742zYc/4PptUBZN27knDOW/2nk9RvBEw//LkYyyacptBhll1eI8Kc4L5EhGEV4O1PmkUSH/K54009lqeDO2X6rkjlzXz2U1lP/cdu5JyqJ16qv4TPyBH3e/U1/CUYUq2aR9XyhNdDYvZEak/Dn1vl777iiMOumWN2ll5x4PJYzeNGEpwyDn5TU/lV3MaiwtbmPUW9shOIYxeSraLsbL//rCl9juG1SDIIjnu/GEEk1JRRDmhBzituTA25IDe7imvdHRi7uLLHY0OqpuNTz7d/7m3Lrflg+P9e96qfNoqYJ5jQoA2FXQdKVenRY6INoC/XyxlnmNCgBQGSx/XGl4bFwk81B9iE3foDyQblWX2n8LZTNqLqwWxD/kPeFbwKATrKHsR+Y1KgDAorxqlp3gBE1lHsp+6gsv6Ao+dsELWbVVLngV+ylN+lmHvqFXowIAdtUUvHJp70ej7uj5Mocmu3qC8/Ia5kHGSCPGSCMAAHKjtsmgkRt1BIZ9X3rhZwfHhDqEeY0KAKjQtOyvu54ZkcI8FIIg/VXXjycpGD+DEATpxEZSS3+++NzfBfbUqB1yalSj1p/Ka+x64NvmXAhtV6CH8nA9z0d1V6g+gTK3tR65zaEatYO+9Dv1xZeZvLqh+k8mt99MV/gZrFD20Oa965oaFQDAcPIqdG9dPlSna2MS4ZOCU3nKxp6vaTXTP2fuFo72yO2ZlCca4hucERw3JSgW7yMLDztrCtydAoIgHg3t7EUQF6EosPLPvO15vbzZ6lKrwTLj63Olii6O9mWVQnuvcxReKE9mI6kLNRBW5NqdrYKwlN2HqM4+YlFeoX27Lv9DY/Vf9O6lbAZLC7RZI8b6vQ51IWbCLM/W5L7umtcCAGAYncY8TlLcJv+6OJthEBtFrr6wq+drLKQDz/48gdFm7a7XMUPuaq3kqFMyh5vkIwgyoHjQLzME6d+2XK7/9hz9XV7NWtN9Wy6feWrizQ/K24yW6lYDhOQAAAAUNGmsJNXzOdh+oEZlMFqhvaNV6i1yrVkq4sAK6MnMzWeMVdsZBlFfeIEbNhcjHJs1AgCwqksBaVc/bbvYzPrrnwlT6HZjIq2mxiOmxqPmppOkvo40KihA4VwJIQjhBE7ihkznhMzA8PbvCkp9/jngwikpGMfbZa/Vqy+KzkCpmo40lOa3Ng32DeruAgGr7x0RN5NWHq1mvz0TsvrGj6MKjdJM2jjoeCqCIN1AZSqCuILBYvvPviKGQbKrWn+/Un/XsH8aolYpodWoAACzjWxUG8N9+vk4uxYd5A6rLfqBUqaqL7zAvNyyaSv1xRuFg5519EbSwLS5aCfavPeFyc8C3LHyhrIZ9SXf6ArW2245AkoaGklDo6Xlkq7wE4wlIAShLO8kyma0KFw65ZIljnPly/VsR03v44jstLMmv4cyNUzgQcW5PTCAOameDO0jXwobRbYYdcGCAdEQAUEQGlCZiiCu8HV2NZRlz9f3X1+WFtqxoKo1Qd4zpjbC34RmJalalaFZa9IYrb4CToCIE+rNc+PpKRO8pVQnBfRMVlWBRX4OSihD2Q80ylTKCvOhDACANClMTce4ITPtv8XaVqQ6cZdF2fvkT8qqt6pL6R3iZYgt8ZSW3VVaJcNTqTc7Javs4bOxYgmPYDlpG60zhAm9Cef0mu6hmPc0ehu8/REIgvQ7qExFEFf46UItlDgVLfrTlcpJMX7tf4Re7BHwdvxqTdafL9btK2o+WdGi+Xc5LRFy0mP95w0OujMtxJ7BsHAJOJD3mEEP6JlM9QdghbIor9r09T0PSiWNcnPTMXPzWdIgI41ygHOAEwaNmhuz7C9TTfX7W7MWUTbI1TJcLHECIYp2dxY3XG9rhhitSNVTuzIOTowPiMpqLIP4ik7lvGJyUmA0lJkxLiAg+t5WbQRBXAaVqQjidM1aU3d9emk4VNLcUaZ68yH/E/bmQQhospJrj5Z+drpSqe/6SblCZ96e17g9r/E/+66/khG3cnyUK9dWg8UOn4rsAYaBYC8uxIAey6KAOeLCorhARHRdppobs7T5H5rqD7rgPKelNd/eKxU5rceXeniNCgDwqImpMgPMCbF1urakv95vMen9uQIJVzjELzgjOG5acLwfV9B+wZ3RaX2oTM0IdtbebClPlBEcd6TBDSv5DmHhuD+v60FrCIIgAM1NRRAXuFjbBvG59qXafzbRRfsJID41F3KIIC+mJVxxs/auX3Mv19u1069WZXjyr2t/XWv8+e5hIVCrxx4EeXH9hRxYJ1QjfQUi7oB43mfVwCwAbJryWz9IGmSq0/eZ6g9CfKFe0tBV23MZZW5THs2kLDCLLmfA+UHC5KfcncU/DFaYWzpJQBW3yQEACqOuGMjPNFd9fT1bwGI/ED96dcqUaC+/e2KHv3P1MMRtxs6DAWxR5FDnxX95SIbnl6kxIn/UPwlBkB6guakI4nSVSpgD/cpb/hlLw2cTyYEiWJFHhvswfGCV36QZ/9lpO2vUDkdLFRM/O9OoNjJ6bUdM/t9yNHNTYv1hhfJwlBnaFB8AAHlLNHPzGfmuVFfWqMDuEaOavDWkoQ8MyBWPfB9je7k7i384o41tJ3qr5YuiM4N2fPBp4SkBi712xFxnvyIUK+JGRHtB+yl0q+kh8ZkRKc6LD8XkoBh3p4AgiEcbEIsACOJebUaYSwpt/+5yNCtRWtBk11vtXs1KlDK5vbrVMGvjue42+vasUqmf8+35k09OEPe467hZazpW1nK9WavQmdVGi5+AEyDiDA/zmRjtJ3TkgOjioSE7rsFpG7skNRhKHM9HwR3G+O/RMtbWa61H7iDNrTBfwg6YHW1+bfp6fdFnLkiGIUHCI/zYe92dxb8E8KE9ROuZ0WZ97vzO7ObqXybf9Xvllb21TNuqOxWHINYMn+3sV/lk9LxzzdXNRs/dArAgcrC7U0AQxKOhMhVBnA7u7oROW3zvHxW+/mQF85dg4dg9w8OYRHh8e14DgxXRqw3qNw5c/3R+F29crCT1x5WGT09WXKxTdfl/yiHwWUnS1VNi7VzbXDg0OHq/gPkq96BAr1mJAQyD9BUYG+YpspsX/Uhza8uhWa6vUQEAGMen12sM5b9QNpMLkmGCF7nIe9xX7s6is0Rvl/7r+KPyCgcntk5dMevgt6d7bAvcMwxgGACk045Gj5JEhAt7/8ZjKNrL7/Csx6bs/1Jl9sQD1fFiycyQRHdngSCIR3N1j00EGYC8oJ5dFHP/tf4zJFh88yRV2laOj4rwpT8xdW+R7MB1pl09vzxTlX/LyvCpCmXC2qzlm3Mv1HZdowIAzDZyd4Es/cuzGV9l19gx+IfHwj+4YxDDbAEA6+alsOD1RvZwhDDCSdG0l98iDY0Qg9uP5dV7GxuILY6dAmeJ0t70Td8KMI875hfr5R/i2qmYv5Rf+q4kZ9f0B2eGJtAOQgGKjRNPJU+YHZrU0Z8JIpe98RrqF3xw5iNO3V1M27rRmSwcvQVFEKQn6GcEgjhdJIPy71bR/p3fNr13WzLDmSh+AvZbsxg92H77YAmT29tZSWrt0X+1/VhzuHTqV2ftX/Y8VqZI+/jE7oLejxEuHhp8R0ognSz/Z/mIMIbbpPsWtu8QiNFY/4tmVZfqit22DMiWjO7tEgpui2OYMJwXtURy+0WvtLeAcyZwMuf6E5Jv5h60kSSLWdFuIq1HG8p2T3+w5e7/M933/hNJ42GlBwBoMcHsVtCz0dKIK/NWPZQwmu1JzYrmRaTcHg7hQSGCIP2bh/5iQ5D+JC3UG2K01JDOqxPhPvwty4fTXtZjE/hvy0f4CejPrytu1l6ohdNfZ8e1Jr35xhnIdw6XvHHguo10bOtdq8Gy6KeLB4vlvV75x4oRE6NprjNMjZNsWppK794+ihM0FVYonCdl+95oc6ov3tjpnKorccPm9HyBTd9IWXU9X+NiOC+AGzZXPOqjgIUlvulb2X4e/X24MnE84doSutVsePTstn11TI+nFrXJvis9DwDg4AQP6nhPk83a+0XwiNm8TROWli565fmUybFe7m/5NkoS/uvku92dBYIgfQAqUxHE6SJ9+XESaOf6psdLbv1gZkrQ5wuH4I436mXh2M93pTFcFTxaqmBy+80MFtvZKiUAYHNu/f87UEwviMVGLvnpYpGsl94hfDax88HRc5IcPj63cEjwzgdHcVkD6+cnNzgD5zNaf+7Ai1rasfpnqt0JJSYNHOk4lndSz9fA7W8MhWjIS37T9wpTVhNese7OpXdD/YIfiB/l4hfdU1sIJc6blw9SgAIACFgwy1QR2w2TliNFvutGZ5YtfrVi8X8CeHDaQftyHd4oNCkwet+Mh93yFUAQpM+58UYBzU1FEKdaPoJRd6IOgV7c6Qldl5SPjYs8/PhYh6aPRvjyjz0xfhnjo615jWqGEW52tUGtMVlf2FXAJIjGZH3qr2u9XuYnYO99eMz6eSk9dxi++fqNS4b+ef9IuOeN+wacLRr8EvMwGMEVDnqu/b9JY7NVDXMcq0O8Rr7X6zUU5dKFL3voCj8BFNSuy062ZvjsAJ6LWv62s5AklDgyg/acvAYAAPeEbZgQ5v4aR0V7+a0d2csmAnvwCfbZ2555OGEMBux6A8nBibeGzTw2Z6WEB7MZG4Ig/Riam4ogrvDMpGiJkMM8zuvT43vY3JsRJ7myesoL6bG9FlF+AvbrM+LzXphCe9frzaqgDoatajW8c7ikScO0t2pWmcKeQ6oYBp6bHFPzxvT3bktOkHb7/mlQoNe6eSnVr09/dGwkw8T6LmHy0yxvps05hSmrWeIbjYusbTQXzJnjRy/jBE7u9TKc5XFvqW26Os89LtsVK0XOCEnoo0/Dvyo6CwBI8QmCGHOQD5xdCbTdHzdqlCScYZBXhmYkeUu/nbAkf8EL98eP6mFGrg+HvyplSvni/7yZNtPFO8ARBOnTBt6CAIK4gy+f/ebMhKd35DMJkhwoemxcLzWSVMT58I5Br02P357XmFWqOFXR0qgxtR/vZOFYqDdvcoz/tHjJoqHBInjrgVozzLWdVr351yKmTYPbbThdaWefJG8e++WMuJcz4urajCfLWxrVxiaNCcNAkBcv1Js3OcYv2JFl6n4LZ3tP+E55cDplozl5iO03TDTk1Y4/kkY4f9GOYvkOEY/70p4rcUEYwAhPW700y06ypWPcnYVddlTnP3xmq9KFTYPgOiWrBACMlkbwCbbBBucQdXqQm3dr4xj23cSlk/fRn1UzISDqpSE3DqsP8gn8YeKdX41blN1cdVpW2WjQNBnULIwI5IvChD7pQbEj/MNQU18EQWhAZSqCuMjj46MOXJfvLep9fa9LXlzWL3cPZxN2/bL34bMfHhPx8JgIAABFAbnOhAFMKoKwnNsluKskTRqTygDn7eCxMkWrweLLd+BcWZg37+7hEAb89FecgAk+6Vtbjy0EpMO7YQmvGL8Z+zD2P/s/3TKPlCWO9595EOf42nMxRnBZ4jg3rvp2ydp23d0p2OWN3ANrrh5xdxaMNOjbAAA8gpUZkfJH5RXmAf24gmnB8czjMDTEN3j/zIenHfhab3X4h22KT9Cu6Q92Wj7lEaypwXFTg3uf8IQgCGIn9HwLQVyEhWPb7qPZWpZN4NvuGzkijM6JJgwDASKu82pUAIA3D2Z/EZUB2mlAG0mdqVTCioa044Xf4TdtN8VxrO1WCRjJmpqF8/+1eRJjwZzVZA9e1BL/23NwfrD9t0BscQyLTe+eMbMO+bzoTF+vUQEAZtImN2oBAKsHT6HRo+5WTydP5Ha/P9aVxkojT8x5Il7cRU++HsyPGHxi7hPOGCeLIAjSCSpTEcR1+Gzi0GNjn5kU7dBdkb78rJXjPHlEZ8wto1yZMNlgbrDstd8vQsOB1rTxRev/bptso3r/JaK0id9pfCDj2hujvi6raPnX5s9OVatTcQIn+U3f65u+Fef4OHQjP3qZk1KizdNm5NzqbHPVM+f+dncWcNTr1QCAUZLw5bEjGIYKE3q/OCQdQk6QjJSEX8p8/rmUSUJW788xo0R+301cumPa/f6oRkUQxCU84pEe0sFgsZ2pVB4ra6lq1cs0JqOVlAg5wWLu2AjfjHhJuI+rVx4Q6Phs4tP5gyfH+P+/A8WFMk3PFws4xMNjIv5vdiLc5UrohtNa5u2OTA1zI6hM64Ztpf3b8fKWZb9cMlrFT9S88D5n+QOSvVOFufG82k6XmSn2JX3inrYJv7dON5BcAEBFi37mxnOnn54Q5HVjHAVLnAgABgCcpn2EVyxHOtqiuGQzNFIWDUZwcZ6UJU7kBGdwQ2ez/YfTC8sJmsIJnGyWnYSSJBSuX4V2CAWo53N2UZD+Wt1ObzW3/8d7I+YebSit17fRi4Nj+OqUKUqTnk+woSzMQuHF5q4fPe/11BnfFp87UF98Tl7daayrP1eQHhy3KHLIkqhUdMQUQRBXwtqb+hoMht27d3d8dMGCBWy2R78t7n+qlPr1Jys2na/Rd9+NZkSY9zOTYu4ZHkp03+t1AKpS6rPKFGerWpvUJoXOzCYwqYgT7SdIj5VMifW3c9CI61EU2Fsk23qlIatMUd/2r4Y0PBY+LspvVqL0oTERUPoDO1t9mzHinSOkR3YIf3x85FeLhro7i/6jQW0c9P7xNmPn82yB7NZoToOEUIkIQ4tNrLD6FBmjjGQX372TYvxOPDGh4126fOcQayuj1mId+FFLhUNeIkTRONcPkFaAQ/u3b1FeVuweATzmO5wfc4/P5F/dnUW3tlVdXXrsF3dnAc3FzOdG+N+YKFagapq870uGHaEkPGF6UOz8iMFLo1PZOAEjR2j0VkuVVqkw6tQWoz9XGMgXRYn8PKeoRhBkQLnxW7yPdorvH2wk9d8jpe8cLrGSvbwHulTXdt+WyxuzqzffMyzKb6DvuqEosKdQ9v6xsu4OH647UcEh8DvTQl7OiEsJgjPNHCIMA7cPCrx9UCAAoEFtbNaYFToTn01IRdxIXz6X1ZceWod686YnSA4Vy92dSBf4LM96F9jXvbq36NYaFQAgs/jKLHY1JTpVodxyuX5ZCs9Uf8jclEWaWmHlZqjaaqjaCjCc7TuUGzJTkPQ4IXJsg313SJMSw3DKY/r9Mh8L5FTfleS4OwWYJNx/hhKl+AQdnvXYncd/KVMraAdUGHXbq/K2V+W9emnfq0MzHk8aZ+foURcQsNhun5eDIAjS7sZqqtFo3LVrV8dH58+fz+H0gTWcfqDNaMn87sLJihaH7vLmsX9bPnxucoCTsvJ81a2Gezbn2tkdB8ew1ekxa+Ykcexrk4vQcLm+beT6Ux64oPru3KRXp7m/r2b/cLVBPXzdSYZ/y1GcxhfDdi8UH6E91cZeOIsftcRr5EeEIIRJGMqibf4rgTR4UNci/1lHOcEZ7s6ia0ab1Xfz60YbtEZo7sUn2NoV73ZaTtRYTKtydv1YdsFKksxfYnpI/E+T7goRiJmHQhAE6U/Qu3Z3MlhsNGpUAECb0bLoRzo39g+HS+RpH5+wv4MrSVEfHiuf8NkZudbs1MQGsmGh3itGhrk7iy4kBoh6vwixz7fnqpnUqBhGLfc7eDT+mQXCPU6vUQEApNVQsUWxK9VYu7v3i7unzfuvR9WoONefHTDB3Vl061prY7+pUQEA4wIib93y6sXmfjthSemiV54ZNDFUwPRk/pGG0on7Pm/UqxnGQRAE6WdQmepOj23Lo11qGq3kvO8v1LU5/62eh8mpUS388SKNuZoXa1VzN53XmPrP+ydPs2H+4GGhMHspMYdhYHwUnQlASJf2FNKc+gsAYAHb1+EffBD6BR93aVMr0qhoPTrPUPYjzdtNSl3hp1AzYoof/yBGcN2dxS0om0Vx0VCxuaDI7YdmYW6gvT18UHefihL5fTpmft2db1xf+PLmKfe8mTbTi03z76VSo5xzeJPaMuB+oSMIgvQAlaluk13V+mtuHZMIKoPllT2FsPLpE2Qa09xN57V0S82Ltar7tlyGmxLSQcxj7X14dKy/sPdLXWVspG9HU1mEoUa1sbrVQO9eDKPeD/3yDu8zcFOyG6U6+6ip4RCNO41V2ygbzf9rZ8B5UtHQ/7g7i38xy7NVJ5bJtkgVe0apTi6Xlf3hxmTEbN5gX2hHKwUszj2xvTeITvSW3h0z7IKiVmOh/wjmqrLh9UsHaN+OIAjS/3hoE9SB4Lmd+czP8f12uf6ZSTGjIxwbA9h3vbb/eouO0cbdHdeaDlxvnp00cI/1OlWwmHdp1aSV269tuVzPLBIFZUnkifFRzIMg7UoV9Gd1Pui/5y6/wxCTcRhpaTv9oHTBdYzt2CZwU+MRJ2VEj9eI9xyd++o8VnVJ25lHOo3qsWG4uybRcHBie8a9lRrlY2e3Qwn4Wuq0AJ5d3zBHGkr31RUxfLmvis8+mjh2sO+/hgmXa1qyGssuyGtkRq3CqBOxOVKeKM5LkhEcNzYgkuNhjYIRBEEgQmWqe1yoVeXUqJjHoSjwdXbV6Ig05qE837VG9Y8XOs9mpGH1rsJZiQGoubWTtPf3ykwJfC+r7GoD7dNWEP56hiuBZEQAACAASURBVId53z08lHkcpF2LzuGd9u18CM0q6e9wk6HBpq/X5r/vNewdh+6yKq84KR8ahINfFMQ/6O4sbjCU/9x27knKou30cQHlnhYAQXyvX6fcPS043kqSnxWdzm9tYhgwTOi9KmWKnRe/kQthIdRKkmvzjm6ecg8AgKSovXVFa64eyZHXdHnx21cOebG5D8SPenHw1DChZ522QBAEgQJt+nWPXQVMf4PeFErmge1VnWFjdrWtt5k99iiUaU4M1O5TLrNsWOjlVVOOPD7u+ckxqSFit/RYHhth13wUxE5mG82mps8FbvVlaeAmQ4+uYD1lcSgTyqqtclIyNIiGvuruFG4wlP+iOnX/rTUqACAUc/XfNY5hS6JSr85fPS04HgDQYFAniKUMY3IJ1pFZj/MIux7ll6kV5+TVDF+x3Y7qfL3VUqiSDdu5LvPI993VqO00FtOGwtPxf679vOgM5a4lbARBEKdBc1Pd42wltFGBLTpzcbMuObD/tzPdzaB9S+dQBU3psf6woiFdwjAwLV4yLV7S/keVwdJqsOQ1qOf/cME1CXx5tgrHsc8WDHbNy/V7Qg6d7YVszLrUJwt6MvRQVp2xZic/drnd1xsA6UFN10hdHc5x/8MXU/1+1ZkHQTd1URJQYN19znGZESmBPNGeuqIuG+EGC8S3hyU/nzI52ScQAGCyWV+9tO/zojMWktGEWzZOHJr5SKK3vbXu0cZSJi93M4PNsubq4U8KThls9m5eMNqsT5/bcaSh5Pf0FXbW1QiCIH1C1z/RqIGxOudGxfIuHkIzidbvy9S6NmMN3fYttzoD7zEBYicfPtuHz/47H9o+Ant8froyUMR9fQaamwpBsJhH464RgmIfwiOWUtvpi7+yv0wFHvYAl7TC/MVBD2UztGWv7KF6lwB9IlBcBxIoL7cgYvD98aMAAIUqWV5ro9yobTMbvTk8KU801Dd4kM8/3ZIqNcpFx3663MLwVDwQsjjZtz89xDfY/luuKmHOK3r/2jEaO6R21hTcefyXP6fex8LRLjkEQfoJ9ODNPeAO8JRrXTrgwS2KmyEX9hCjIfZrVLt64sKbB4tnJkoHTpsx50kKELFwzOrgxvvhgmIn5UOPufms9tr7oiEv23MxRvAAzgYkzUO50GE4x90pAF3+RzZtLxtc52PX36MmMn8tHsGaH3ljN8Qgn8Cbi9JOmo3a2Ye+LVHLGb7i1KC4XTMeELEcaw9epbV3iLc9aJ/i2VVT8Hru/vdG3gYxGQRBEDdCZaobUBT9U15dMlphRvNMCmYNfjtRGSwWG8l2x4HJAc5gcfX3KklRz+3MP/PURA9bGOszLIqLpsajZtlJm672SnKDjbQpreImi3+OftApbWquPpHssd9VNKfBZanaSXPpVUIQzI+9FwAAAEUaFZRVi3OlXTUBxliiKKsa2pZOhty+45cizbqiDb1edh+4+g0YoQR8hi/3VPJEH07vQUw266yD3zCsUScHxrw9fFZ6UCyNe7UM5tDA9XHBiRVxI1J8gm791Dl59eH6khNNFeWaFrlRayKtUp4okCcaFxCVERw3OzRRRHfoK4IgiJOgMtUNMAxwWbgJXm0pYPf/lvQQv1ztjFZUproBh3BDsZhd1XqwGE0hckyNUlud/6u04QtvY37HB/1wAHAgZakSeTVTvC6/GLi51Bj+lWLBn6p0C9X1bxNvFv0xNk5Dqc4+bmo4alGct6pLAXXjZwtG8NiSkZzgafzIRSzfIe0fZPsP95AyFcM5hCjSvTmYG46QRkWvl3kD42os+zUqg8lrBfBEr6dOt+fKTwpPXVEyehqCAey/I+ZMDIxmEsQTWEnyP5f275z2QMdHKEBtq8z74NqxSy2d57Q36tWNevUVZcNX18/6cQUrk8Y/N2iShOdBg68RBBng0Nt09wjygvnYMkjc/x+C8tkwv1cxbEDU9h7IX+ieXYu/Xur8Fg3pUq3K8OLuwvHv/3Fxy7i4ipU316hdiufVrgvbsCf2xRhu12cC2cCDWhD9w2YwlP9sbSvuqFEBAJTNaJad1l55W74zVXl4jlmeDQDghMxwX5b/wpaOBpibf2qZm07YeeUKcHUSoN/8lsDwjRMWe3N6Pw7dbNS+e/Uo7RdqRwFq9YXdtJvletux5Osye2oLa3Q3xt3Jjdo7jnx/5/Ffbq1RO1Ga9P+9eiRlx4fMp78iCILAgspU9xgU6AUxWnIAzGieKciLTvuW7gSIuASO9oC6QYLUPb2+jpQqUGO4nmlM1se25cW+m3UsZ98m/0fGCXspUG82hF9+MO75qV65t35KT8L8l+sqlKn+QMveCerzz/LCMzGWwN35AAAAN3SOu1MAFlWBnVeyAPkjtnMERnORc/2YzPkRdvXo/q4kR22BcOI9R16T3Uyzro7x8mOeACwkRe2pLQQAFKlkqTvX7a11oOxsNmpvP/z9R/nHnZUcgiCII1CZ6h6TYqD9Vgv34cf4e8S7KKdKDhRBPFuYHNDPGyN7rPFR7jlcJ9OYmjSu7t7Uh+TWtQ1fd/Kbc9VhRN0PkWtojDkV4sZNEWtHCTu/J2609N3JT5SuaIPq1H3CQavcnQnAcA4/doW7swCk3oGykw8sv4Idc4FjW6bFbN6vk+9+OtneDkx/VuU5FL8H2+mGGu4fBisHKM7IKuv1bXMOb+pyik/PKEC9dGHvdyU5zkgMQRDEIWhuqnssGhry2v7rUJZ3Fg5xoG9+3yURcpIDvAplcCZbTEFDU90kRMwbHuadW9fm+peuaNHTG6nS711rVE//OrvVYOFi5s3Rb0lYNP92+Ljpu4h3J5V81Wb75zFQsSkCUpruYarfj3O8CGGETVfjxjSEg54jhOFuTKAdZXXspLE3MG7Cdv0GhnxMjWsEvez6wQA2Nzxpw5j5MV72/nzWWEwMT6Xe7GRTRaeP1OnajjWVVWqUCqPOTNokPGEgXzROGjnMP5TA/nnKPz0kHscw2h16oStuky/O+rlaS3PuGgWox89uTW7elkJocZ4E50nZfsM50jEAZ8PNE0EQpGdobqp7JEiFsxMD9l9vZhiHTeArx7u5qYbLLE4N/r9DcMrUhUMHRG3vmR4fF/noNmgLIPbTmDzykKS71bQaZm4812qwAAAel/4dxWE02FbCalsV8PubjQ93fCRba9fWTU9mqNwqHPyCvugLykZzdDPGElEMRp7i/GBR6mu0b4eJVqFyN7i2BCv8i0raCZJyQKgedA4S4+U/IyThyeTxDk0rBQAUtzXbKGjd9a63NVOAwgCms5q/L8356np2kUrW5ZW+HP6ymGGrUibHiSUAgFCB94yQhIP1njJ7qUqrbDHpmUSwUuClspptYGvHRzC2iBd2u3Dwi2z/4YwTRBAEsQvq9Os2H2emHClVWJhNpnl6YlTigNm/+ujYyI+Ol+vNNoZxMuIkQ4PFUFJCaHhwdMRnp6uuNTq8Gw1xhlW7Cpo0JgCAD6F5Uvon84D3++/9ruWOGvONEZd1loBL+sQRHjY91VGGsp/EEza1nb4PkA4/7BDEPyBIfqblQDplprNMjbEEvhl/YmyP+JGFc2keV2ED251YwZ2gwAKIIiBppoQtgO8TuzQq4Z4YL/8IIc2xxk0GOA8u2+msZrXZtLeu6JlzO3ou81rNhq+un/2m+NzDCWPWjc4UsNjvjph7uKHEQxZUW80QDjicocKPYtHTQGX7HymL1lD5u6Hyd154pvf4b3B+tzNsEQRBYEFnU90mOVC0ekoMkwhRfoI3ZiTAysfzhXrzXpoaxzAIgWPr56dAyQehh8CxT+an4C4/aODNQzvWOjtZ0fJnXmP7fy/wOSnCaa4W3oyN2Zb63Oi8ysKxCdF+7OQXmYd1L9IoJ9WlkrlnCC9H5mribFHam97jN7H90nzTt2Nsh3vdYWyRb8YOjnScozc6CUvM9DcOG9iGAtl0rOJOrGBZ5OD0oFjaNSoAwGCzMMynk0fPbr/nxGY7lyJtFLmxOHvk7k+KVLLh/qErYkfATYY2EtIK8+9gyK0fNNbuku9KNTdmQXkJBEGQHqAy1Z3enZt83yiax40kQs7+R8b48AfWO+8X02MZNkleNSUGLaW6XUac5POFrt4LOhA6jTnq/ayyjv/O8LoEK+yKsIIvFg7Z/8gY5ZrZp5+aMGvqQ1zGY13YPm5+uqQv+4ktGS2546IwZbUdBSfGC79DcnuOV9pbAMMBANyQ6dJ5eQ4VnGz/EZI7LnFDZjLIGjK2dAzUaGMZRuDikHeEba284ugtRSrZ9IMbq7TKDWPnD/MPhZuPe52gIq1dvUskDTLlkdvNslOuTwlBkAEFlanuhGHgmyVDaVSq0X6CQ4+NTRow2307CDjEocfGRvrSHFI3f3DQ2rnJcFNC6Fk5PurduUkuW1MN9eYFQh1W3A+oDJaDxfKOPw7nQ9uXK7UWrhwbPDspwIt7o4oQj9mAc+g3ecY43j7pf3ilvQUnP1ps2kqruhTn+IhHfRSwpEY85jNeeCbO+fdKIM7iBIwXDX1NOj/fd9outl/azZ8kRFH+c054T9jE8k7q+bVY3kneEzZJbstmvnoJFzd0DsDgvG1g+48gBCEMg0h4QijJMNSgV88+9C2B4XumP2R//6ebpfh44h5aLeCUgq63eVM2Q+vReTZNuYtTQhBkQEFnU92MQ+A/LkubmSB95u/8Fp251+sxDCwfHvb5wiFi3gD9uwv15h18dOyiny4WNDl2KunekWHfLElF41I9x6vT4sdG+q747XJ9m9NHxcxIkDr7Jfqc05VKG3njKJ0PoaExhKZblM2mrWB5//NIiOWd5DdjX8vB6Y62igUAYDjHN30byydFlJZi1ZQayjdDy9NBlpZLLHE8AADn+AiTnxImPwUAIE1K0thMkWaCJ8W5UtDz+h7OFsQ/JIh7wNx81tx41NR0gjQ0kgYZAADnB+D8EG7QFE5wBidgAqxqEC5CGMYNm2uq3cM8lCDh4d4v6k2C2FP+XRe3yd/Ly3pn+OzczOcfO7v9D0dWZVfEjtgwdn741jVai8l5GdJTRfkmY4ouP0WaW9U5q3yn7XRxSgiCDBwDtNTxNHcPD81MCfwup+aL01Wliq7fxom4rMVDg19Ij00JYrTrtR9IDBBdeG7SC7sLN2ZXd7zP7oFEyPl0/uC7h/er7Vj9w9Q4yfWXp36XU/Px8YpaFYSDkd1ZMdKzBht6gvybHvT4EA5Xjz0jTcpOH2FLx/rN2Kc6eY9NV2d/HJwf7Dv5V05wRvsffSb+ZKzeQVkZdTGlzabpPLAEAIBz/RxuLIThnMCJnMCJfXE/jHjkB/L6g4BkdCiU5Z0siH+IeTISnjBBLC1Ry3u/1Pk+zj/xSOLYCKHP7+nLMyNS1uYdzW/tpW/2hICoN9JmzApNBADcET5oS8Vll2TqADXW0yYUY+0uU8MRbsh0l+WDIMiAguamegoRl/XspJhnJ8VUKfXHyloqlXq5zqQ326QiboiYNybCZ3SED5vwxOfrbsFnE18sHLJ6Suy6E+VbLtcr9V2/Z0oKEK0cH/XQmAghh3Bxhoid2r/zn5wQnVPTmlXakluvataa5VqTD58tFXIb1MbL9UyHrGbESTLiJFCy7U9uXsRmYbCn9ZBd7A3hBE6WZF5Rn3/WULkF9NrlBcP5UUvEYz7DeTetmGEEN+w2Y9U2qLnai16r3n6G5Z0sTFypK9rAIAYmHvMprCGcmREpH+UfhxKKIYPNsvF69n9HzAEA3B0z7K6YtMP1pXvrCrMay4pU/wzOYePEUN/gUZLw2WGJs0OTuASLAtR1VfMYacTvFVeo/8/eeQZGUXV9/M5sbylb0nshjQQSOoQSem+KooKigo/Y8FWx+9iwF+wFFEQBBRRQOgQIEFoIpEF6I71sNmV7m3k/xCeEsNmd3bm7SeD+PpHZO+ceUnbn3HPO/4B+oRXchZHEgdXHQ3X+FyhMRSAQTgLrHJFqNBr37NnTdXXevHk8noPtfwiEiyFIMru241ylolGlb1YZ2AxcKmCHSvgpEdIAd25fe4egRavWGPnBCSr18L3BwLErz01Aulm38siO7M0Z1Z3/9mc3X4qCkN3qQjo3gyUd0durpo5Sdf4X+up/zOrqW19l8P05gfMEcf9nsTNTX3tEcWwmRFepIxj8gtvwT/pk6/4FYVQcn6evPeLY3W7DPxEMfgGWL8UdzXF7PjER0Kan0iHKXVa4+KVbr5OAzFbU7SjPviSvzpBXdy/u5TKYJAB6M5xzIhGLYzSbdfaPTeqNb7GDi0CBtRU40+e+ln4yMAmBQNxmoKJfxIAHx7CkAPekAHfXbKcxmNMrFOcqFdVtOrnawMCBl5AT5MmbECYZhTLesPHksf5YljTnpwyDoxOGP50Xi2JUi7DwG7+rTUZPE2AwAd2hxF0wBNaqrJluEe6jvwGjvzF1lJjarhKaesKoxFkinOfD9BxsXTeI4z+D4zddX3cUlqvUwdGzeCc4y3PSLkXqHPu1XjFhwqsQY1QAwCA32eqosV8XpEO06TBF7c0NWqUP76bGnJIO+TvZx3ZUZBsJC39fOkgBaifvDZv1Tf5ZiFXQ/sDWgGvCZGg6x/Hvm5MjBAJxe4PCVASCKrn1HZ+cLNuVU6c3WQ6ZRBzmwyMDn5sY7rAWMeJWpg6S/XTvkBW/ZxOk3eVwr06JfHYCrenEtzGe/BtVl0aSWaHzi+RayG06AM6R4DxfKiuZbpGdokR2IRrxiX7fCQAvZUQRhjDExTv2WzCWSDLjhDJ3nSpnHSApnW7gXKn7uJ+5gfOhO/NW4vSdlTmNWngaYDQobG/qHqb+Wpr55IU9rtFGeiJ67NMxyWn1ZbDCVByQg0CLzWWmtgIUpiIQCGeAMj8IhG1UetOKP7KHfnZq6+Wa3mJUAIBSb/rqTEXE+8ffOFxooqDthKDI8mEBqY+P9renhJuBY5MiJIkB7nIaBcO3N5HSm4Z5nFdDm2TL9pkEy5RFWJ4JHuM2A+s9c87Y9+YBM3c6OFM09C3Z/Cxe+DLrjaY4Vyoc+qZsYYEzYlQAgJjDPzr9MQ+2g4eDGNRfpHrNjfTjy5kHHjrzhwtiVAxgz8VN/Hr0IgBAim8ELLNxoMkD2JZhN2vrYe2IQCAQ3UFhKgJhg4JGVdLnp7dcqqaYzDMR5LpjJRO+Pduk6nfTBQYuKRHSrOcmPjM+lKIalpkg00pblmzJ9H7z6KyNF0+WWp6pcCczKvimQab/tCfDsswLvReWqV63CF/mNvwjV0aqnQXJLttuoMD0jPcY/5v3kiqP8b/xIx9me41jug3CuV5Mjzi2T4pg8FrxtINed18XDX0L5zpRxixB7LtnygoJh2/vjXEe3nBVizSmf/X8vi5I/yjvJETLveHPdz8w7dHPRs7rHEO9JCSBhcORDFwAKM1SdmDKFAKBQFABSSghENaoadeN+zq9qtWRWSkJvm6nnhzrwYMjaInopEVt2Hql5niJ/FRZS4fOjrLP6VGyLfcl+oiszVe4oyBJEPpe6vX//W5jGPlP2IvD+JQeTK3AdBskXZiH4WzaDtpGX5faduZBwiXJHEHc/7mN+NwFGyEcpkbdvvz09rSGMiqLMYCtjh7zyci5wl9fgxipbp1w/wPhSVkttcP3feFAn4JdxHv6PhObvDx8GIdxUwPXf879uaHoAk3jHkB3DvuZSjZVEPec24jPaG6HQCAQt4J6UxGIXtGZiBk/XnAsRgUA5NZ3LNuWtX/lSLhe3eFIBOw148MeGRl01y+Zx4rtaME6WtQ85NNTux4aNiFM4jz34NKo1DerDXK1QcRhegnZvm5cJg4tf4hhYNXo4NcPFXZ+SZLYm3Wr9oWvxTBaz9ZuIz93TYwKAOD4TZUtyFFf+1xT9ANhaHPeRhhLKIijJPxj1tQamy8S2gZC34Ix+ThXxhCGsWWjYM1fQVghQOB+ctbq9MaKj/JO7q/O720ZjmGzA2L+O3TaCGkgAMCNzWk32A7GKCLjCgEAay7udVKMKuEInoweG+kunewb6ce3rOn1btLMHRXZNP9TL4N0KjEqAADnoHFfCATCKaC5qQhEr6w/VZbfSEuW40BB496rDQsH+8ByCQEAMJiJu7fYF6N20qTSz/0pI+2JsS7ThXYAgiT35zf+lVt/okRe037TY6KIw5wQJpkV4/Xg8AARB8Ih43MTwzZcuN51EHNFO+hnxdyVkn0OG+SF3c8JmEPfMergXJlo2AfChFd01QcMDScMTecJXSOhk2M4G+dKGcJQts8k3fU/Te20ssTC+FcZfD8rCwhds7rgG13lDosbYUwB2zdFEP0Ux38GHTcQVEj2Dk32Dq1UKU7Wl51tqmjQKpt1aiaGy7iCEKF4ok/4RJ8wcbfyYCGTDTFMjXKXHaopPNNYActgD1r0aimPvyx8mJU1XlzhV6MWrjizw+Es8Xhw/QEsl+JipnuUY7sgEAiEdf4t+jWZTLt37+66OnfuXD7f7h4PBOJ2okmlj3j/hFJPV000XCIoeGkSGlRjL0q96VRZS1Ztu1xtaFEbPHgsqYAd4y2aHCF96UB+18BPB/AWcfJemCQTuijjZxe/Z9W+c7S4sEllfZknj/VUcuirUyO5TLq/V9uu1C7bdqXrSxyQ3wV+Mt/DkfEebJ8U8bRDGKMflFWTBMBufGdMrXkthyY4nG5l+0wSTzvc2/+LJAyqrDfV+V+SZttlFyxxovvYDSzpcMc8QTiDgJ3v1KptjV2hTPU9b7xwad+OimxYBm+FgeFps1Yne4daX/ZR3smXMw84YD8BNP6J7RQCiuJzmPe9dTgPHcUiEAj4oKJfBMIyv1yqph+jAgDKWtSHC5vnxXnTN3WHcLSo+bNTZSdK5BbVkjGMbhtZo1L/3yOF39+VQM8MZDp0pid35229XENlcavW+O6x4j9z635fNmyIH61hng8k+WfXtn+a9m87HwGwp2ueU5jdVkgO2mWHF3a/+5gf+0WMCkD3GBUAwPSMF087JD80ARBGu03hbLdRX/X2/zIry1tP3WuUZ1I0ZlRktRwcJxr2viDuebs9QTgHlRGmGHhBW2NqXTFEg7diJokFxzenz34yxsPax8pL8SluLM7zGfu0Zjt+7WeA0vXYEcoxKmBJR6AYFYFAOAmU4UEgLLP3akM/NHV7UypXj/kqfcaGC0eLmnub6EOSgL7cycYLVVcb+sWUxU7adcZJ352jGKN2UdCoSv7mbGY13Z7Mj+fGrhod1PWlkWS+Wvf4w9dfqzZ4Ubkd5/l6jN/iMWEbxhLS9MR5GOSZjsSoAADC0HZyMaFtvPUVs6au5XAK9Ri1E5IwdFx6QZWzzhFnELAxkwTEil9Agvz2xha9BprBXlDoNY+k77RZ07s6euyl+Wsm+oRTsemNqT8GxzZjf1NsSe2EH/U49cUIBAJhFyibikBYQG8iMqvbYVk7U257QjpiV07doztyoGSwbWImyDkbL3ryWWwGLhOyA9x5kyIkkyOk3n2hA6w3EQs3XcqqdeT3TaU3zf7pYvpTyYNkAturewHDwIYlQ6YNkj22K7dN+28sd6Rj1PGO4fM80h8QHx3OL2RjPWM8AmNwpKP4kQ/zwpf3lyRqLxgaTnVcfMbh200dpW3pK8TTDnW/SBpVisOTzeoqx2wqs95gCAJ4ESsc9goBBZ0Z6hsOBmrU0D44rHOh+frv5dn3hyVaXxbn4ZM2a/XF5qpvC86m1pd0H+vaCQeYhmP1C0HhEnCNjZnt8oElHsqPeNA+vxEIBIIyKExFICxQ1qI2mglY1soVGoOZYKP21N7Zd63x/q1XesugOoOqNm1V241mwg0XruMYNjvG65UpEWNDxC5zAwDw8cnStDLHDzKaVYYVf2SdfSqZphDekiF+Y0LEn6eVbbxYpdKbAAAmwNjTNnFP20Qerk/iF/uxmsWMDhYOIvyDZw4dOigqBWOJaG3pGkii49LzNFPw+trD+ur9nMC5XVdUOW+aOmjJMrVfXMPxn4kKJvsWLgPyU5AZQPvgsMmqs7s+u5omZHGkHEG8p+9k34jRXsFsS0NTR8mCRsmCAAAFbY0VKkWzTq03m0QNh4QVPw0GTVzQM1bXAlYaGZIOAjOBXxMmVJA8JjCLgTYYtI8BNROw68NBLYYx3EZ9BTA4M1oRCATiVpCEEgJhgbSylpTvzkE0WPfmNF83LkSDtxOXa9rHf3NWa7TvIN95zIiSPTwiEMcxqYDtJeREewkZ8MbA9KC2XRf90UkV7Rzy78uSlib6Q3GpXWc8XiI/USLPqu1oVunlaoOIy/QVcaO9hZMjpNMGSQfWb7K2bGvbmeX07TA9YmULrwKAAQDMqsqmvyIASfc3lhtyj+ekHfR9Q9BB9vubcp0alrW3Eme8lXUEljV7kXIFj0aOXBM73reXWTU3Q7alP6wt3dL9Ujvg/kwmbgaJLYBn5c4Y0Px8RMzDyS/haE4EAoFwGiibikBYQAc7ZNL0mxgMCg1K/YkSeX6jslGpb9eZxHyWTMge4uc+KVwiFdinoEuS4Nm9V/tPjAoAOFLUfKToxrQbTx5rUoTk/qSAxfE+9J/J2nVGvYmQCTidlj5NK6MfowIA3jpaDCtMdeeyFsf7Lo73hWKtz9GU/ATFjqkt39B0nu01FgDQceVV+jEqAEBXuVNxrN1t5Hqmewx9awjHiHb3StdBmx8T5S6DZcoB5Dr1R3knvy88/+2YRdaH1gAAAMA8xm4kTWpd5Z+dX58DgU+Ts+qB7SqJAiBbWSrfpv7xtwn3+fP773wvBAIxoEFzUxEIC3BZkAuZ+LAN9gkmgtyVU/f5qfLeZHtwDBsX6vn8xPB5cd4UI7od2bXpFQqobkKmVWvck9ewJ68hQip4b1b0PUOtzc+8FZ2J2JNXn1osP1XWUt2mNZgJAACOYT4izpgQzyNFTVCcLGpSXapuGxHoAcXabQNpVBqazsKypq893Bmm6q/vtrmYss0j8n+GiYZ9IIhdDX8oFwAAIABJREFUA8smwi6SvUPTIY05DRdJhksCoJiiQ4dRt/z072caK74fc5eN92Gc5Tlpl7bs1/bzT/xiinidnGIGdjwNnqwvHfr351snPODO5sh1agaGy7iCYKGnjNt/1dQQCMQAwnI2tbMSGIG4Y/EWwpSEYeCYxM4cYz/kYlXr8m1ZJXJr1XEESZ4pV5wpVwzxc9v2QFKcj+1T+S9OQ8tjOJtSufre3y4fLGz6ZtFgIcd2KYraYP7idPnX6RWNSn2PlwiSrOvQ/ZVbD9G91OJmFKb2wNhyBRDQNHKMzRcAAIb6VNLc8wdKB9Ks7ch4ltArRIlvQzSLoMiioMEf5p6AYyo4PlQkdmdzYaoHO8qGogtMDP92zGKbK3nhD+41Bb92fp8DbbVynXrm0Q09LsZ4eE/1jVwVNSre8zYpykAgEH0C0nRBICwQJuGz4CkehUv4A10/6fNT5eO/OWc9Ru1OTl3HiC/ObM6otr6stl13saqVtncuZcul6mk/XtAYbNR8XmtQjv7yzOuHCm+NUZ1ETl1PDU+ESVkK01pHKQBAU/4HRJtdqHLe0RR+7wzLdxomgqjTdOQq6s81VRa1N7cZtNbXj5QFjZYF09+XjTMeixrNwPAUnwj61qDwXeG5jcUXbS4r7mh++OIhiAJ2BW2NXxekD9n7+ZxjPxe0WRjmhEAgEFRAvakIhAU4THxEoMe5SjjFqOPDJFDsWEdtMGsMZjGfBV3v57uzlc//c83eu7RG86M7s3EMPDQisLc1/bzctzcuXG+9f9uV3SuG91ZQd6iwadHmS3qT6zQ/AQDlLU6f1jjgIPUwD0FIQysAwNyaB9FmdzoynmX7TWG6DXKS/dsbpVH/W9nlwzWFpxrKO4w3JTMj3aSTfSPvDkmY6hdp8d71o+aP3f81zTDt6djkSDcpAGBZeNLeqqv0jEHj5cwDi4PjJRxropjPZ+zTwx3MAwAAgATkwZqCk/WlHwyfvSZ2PHT7CATitgeFqQiEZebFesMKUxfFO2XmBEGShwqbjhY1Hy+Rl7doulSIZEL2qCDPyZHSxfG+wZ7W1Bqp8M+1hqf3OPjIRZLgsV25EVLBuFDLI14KGpU0XOtL/r7a8GtmzQpLEXhGVds9v152cYwKAHDNyNmBBUnC/J6QhBEAYNY5KztEEgZl5ouek/c6yf7tisZkfD/3+LcFZ3tLnJZ0yEs65D8WnU+S+L+dOGNuYGyPBQwM5zFZGlPP4cDUCRJ4vDFkWue/FwXHD5MEXG6pcdgaRBR6zQe5xz8dMa/rSmmH/HJLTZNOJdep3VhchUGzvzrfeQ5ozcZnL/6tNOpfHzLVebsgEIjbEhSmIhAW0BrN+/LhPIxGSAUzorygmOrCTJA/Xaz6NK2s1FIVbrPKsD+/cX9+44v78u8d6vf6tEHRXg4KWuhMxJq91wgazeoGM/Hk7rzL/zfBYo63wVUFsc7g5QMFi+N93bg3vYuqDebFv1yCIt5rL0gI71YwpgCmNZYQAECandh2qKv626jIZomHOm+L24z8tsalaVvzWim1eV9pqZ2Xuml5+LDvx94lYP6rF1DSIZ977Gc6MaqEwz88fZU7+99BTTiGfT5y/sRD39Oc1guLn4ovvpc0q1Gn+uLa6b8qc6vUlgXwnMobVw5780SrBo1y/dYIBGLgMrD75RAIZ2AmyGXbsmClUj+fH8eEWoVb3aad/P35x//MtRijdsdEkNuu1CZ+durLM+WORZrrT5VVKuiWkubUdWy+ZLlJtV/NobGXRqV+U0ZVj4sfniipbe8b9RR3LqtP9u3PMAQwZVcZgiAAAGaPFKoDaMu3OdX+7cS5psoR+76gGKN28VvZ5ZlHN2rNRgCAyqiffmRDk07lsA8cBvPQ9JUxHt5dV4yE+cO8E/0kRgUAtBt081I3Rfz5wfprp/skRu1kzYW9laoB2eWBQCD6ChSmIhA9WX+6fHceHAnW+XE+8+K8ba+jTF59R+Lnp0+Xt1C/RWcint177bFdOfZGqmaC/O5spX339MKXp8stXucyB/acnt+zart/2aI2fJZm+X/qAsIk1trP7kyYHvEQrbE8BwMAMJYbRJu3Yqg77lT7t0Ka1Pqagx2Xnlccmdr8d0LTnyHNf8e3HE7puPi0rmoPYeizwMY619oa5qVuciwLmt5YsTRtq5kkPsg9QTN20ptNZcobFkhArjq761BNIR2b0DlWV2wk+vhMUGs2vpx5sG99QCAQAws0NxXR7yBJoDGaBey+CWCaVYb3UkugmBoe6LHtgUQopjqpbtPO+SmjRW1w4N6fLlZJBewP5sRQv+ViVWsNpMTg1QZlqVwdIe1ZgSkTDuw5PZdr2tt1xq405h/ZdX2YH07yd++rrfstTLcIhiDQrLahOE0Rts9kAADDbZCpoxiKQYsY264CwghwV+TGCW29Ov9LTdGPFmNRQ0OauuAbjMnnRz4iiHuOIQx1gUsUIUhyxZkdCr3jtR7/VF37IPfE+mun6TuzNmPf4uB4Ns4AAGwourClNNP2PSRwcla+P7KzIuftxBlR7rK+dgSBQAwM0NxURL8gvUJxpKjpRElLWYu6WWUgSJLHYniLOCMCPSZHSOcP9vZz47rGk49PlrZpHW9S6kLEYR5cOYrKdE2KkCS459fL1W02hitY4cMTpePDJLNjqDbKwpXhPVOuuDVMjXK0abafYCbIaw3KsSH/CkQdLWruQ2emRaGHPwtwQ5eqr35C3w7G4HEC5wIAWOIEfc1++gZ7hTCaNXUMIYQRKdbRVe5qP/eYzWQpadKoC77RFG8UDn1bOHgtwPpFEdamkoxMOd3Th/dzUrUwFG5rNO2/lmauHDRKadS/lXWU0j13XowKACAB+Xt51luJ0/vaEQQCMTDoF583iDsWkgS7cuqGrT89/puz646VnKtUNCr1nYI9WqO5UqHZlVO3+q/c0HXHH92RY7MVkz4ESf52GY48o1JvaoUR7nax7UrNhet0p2us2XvVYKaqQFvQ6Hi/lgVrTRZEfceGeELcok+43nrj4OByTV+WR6Z8d+6BbVcu17T3oQ/9EEHsGihCSvyYp3C2BwCA7TuZvjXrkEZnK2CT7edXt6bdQ72glzTrlZdfbk1bAgiYb2uOQZDkO9nH6NuBEqN28mLm/laDdv210w3agape7hpS65xYiYBAIG4zUJiK6DPatMalWy/f8+vlK7YerA1mYlNGVfwnaV+ecW7jX26dshGe9uzRoiZYpgiSfONwEX07pXL1ll7UjG4F4reiN2vhEkG8r3M7/ZxN1xgYnYmo6+gb8aROFBrj9iu1w9efnrnhQvfg+Q6HwfcXxr9I0wjO9RIlvNb5b473RJznS9svqzg5Y9mR+aKm6AcHbtRd392W/hAgXT1sqQdpDWXVfScFZJFWvfaZC3splfve2eS21pP9RlwKgUD0c1CYiugbKhWahE9P7cyuo36Lw1JA1IGbDbtSCy2vdbK0hb7ibidbMqmmi3VQJ39qDJabNp8cFwJxF9fTJePcoTP2k26JI0XNQz87BUsG7DZAMHgtSzrc8fsxhvvYHzD2/1p/caYw/iUojvW6IduJbcbast/UVz91/Pby31XXHL8dCsfr4MgHwGVb2ZVypR3idncmSqO+VY8O0RAIBCVQmIroA+Rqw8yNFx1rs9x4oerVgwXQXeqkAlIo2EmZHJq1I/ASsxeut1JsvuUwYb4/8FiWNbFWjgoa0AlVD96/UjdE/4hRO2nTGpdsubwrx45joNsYjMETTz3EdI927Ha3kZ9zgxZ1vyKIfpLpEQfDNQtgDB7Dadla0qhSXn6ZphFVzrtmTV/+amUpam0vcjkoSUiRDmNfVp0gEIgBBApTEX3Ag9uzipoc73v88ETpP9caIPrTRbsOWqsSAKBdB62J63I1tMSsmSCz6zqorPSCKsPbm6gvA8e+XBjHgDpa1pWES/7texT2kTZ1bxAk+eB2aON/Bzo4VyqecZztk2LXXRhL6JH8iyDmmVvMMUUjPsNwp/zEWZIk5xX9qq99Rj/CJI0qVfbbUPxxjEoV3S59RE9cGGIz+ocKFwKB6P+gNwuEqzlY0HSokG5u8Lm/8/VQS1I7gatxDTG9VtYCUz6qjJoYFVwZ3ujeraVESL+/C+Z8S5chYDNivP/9fwk5TImgf83X0ZmIVTtzTf0qz9t3MPh+khmpouEf4WxKwl0cv2nSeVd4EQ/d+pKx+ULHucdI5wyi5Pg7TQeVJDQlP0GxpK3YThphSqzZRYcBpeNg48JzQne2i3T7EQjEQAfNTUW4mlcOQCjZLWtR/3yx6gnYbY0iePNjAABuXGjW4KZ5KRb9jvvfnBUojA+TWHl11ehgtcH8wr5884CKqaZEytiMG4d9Cb5uJ0vlfejPreQ3KjdeuL56bEhfO9I/wHDh4BcFUU9oSn7SVew0tFwCRM+/LAbfj+03XRDzNEuSZNGGsTVXcWwWdY1cOz1kcIMW66r2GOpPGJozCG0DoW/BmAKcK2WKwtm+kzn+M5judkw/7o5RkW1Ww1EyJ40qQ+MpTsAcKNbsBT2wDFy8uEI3FgpTEQgEJdDcVIRLyaxuy62nVHFqk82XqqGHqcGefIjWQsXQrBFQ/yTN1KyNCxV7izhQ9H4HyQRRMmu5Wb2JaFDqB9wbz4oRgd2/nBIp7W9hKgDgk5Nlj48JQU/2XWAsoSD2WUHss6RRqW9IMzamm9WVJGHAmAKmMJwlHcHyGotzLJ/RmNU1iqPTnRWjAsASJ7QcmkAYbipqJU1qQtdkasvXVe8DAHD8pgoGv8Txm2qvcWPzRWiOAmBoOt9XYaobiwsAGrw0IBkuDbS9CIFAIAAAvYWpCISTOFzYDMvU5Zq2RqXeW8SBZRAAkBgAU8tniB80ayIOswNeQtWNw6KyjIljq0YHrTsGQVTzyXGhVsKkRqV+5sYL2bVwzi9cxpgQz4WDfbpfuS/R/43Dhf0t2K5QaC7XtA0P9OhrR/oRpFmrLflFU/KzUZFlYbwKhrPEQ/mRK3mRKzAGr/srysy1hLbRaX5hxpYsm4v0dan6ulRuyBL3sRs6R7lSxNQBc2SlqaPP5HbDRJL8Nuf9FBBOZIZ/VF+7gEAgBgyoNxXhUjLhTXwhSXDZ1sBVexkW4NEl3EqfaYNksEwFefJsL6JMsJiqtZdSIvzc6BZoRXsJV48N7u3VDp1p9k8XB1yMimPYFwsG94i9wyT8ZUkBfeSRNdLK0JyMG2hKNjXtCm6/8ISx5bLlEaAkYWy50n7hiaZdwZqSTV2XDc3ntRU7nOmaHSccuspdLftH2aWHROhh6mmR+j77pUqU+PfV1gg6cBjMpWFD+9oLBAIxYEBhKsKlUBTvoWoNqrAQAICJY0sT/aCYGuwjspJNLWtR/3Sx6uUDBY/syL5v65Wn91x991jx0aLm3oaLQkzM2mVNyGF+Mi+Wzl44hn21aDCL0etbzbLtV67APm5wAZ/Ojx0ZZCGR9cGcGCHUDmco5Dcq+9qFfgFpVLWdWtp+9lFCR6msg9A1t599tDXt3k65IPW1z12qiGoLU0ex4uh0O4JPEpr2OACAJCC0AzjGNL9BsEwh1VlX8lzcBC8uTGU+BAJxe9PvHqcQtzcUxXv6xFonr06J/DWzprdwkTpvzbBQ2qTSm37OqPr6TGVvATabgc+N9X5xcviooJvESCdHSH84d52mS53EeAvtSpDen+RfIle/daTIse3WL4izklU+WNC079rAK957bWrk/00Is/iSvzv3m0WDV+zI7k/hDKhpQ8qogCQMrWl362uP2HujrnKnQtfsOXmPvnq/Mxyjg6ntWvvZRz0n76GyGGPA7L3HmH0WbyR7h4SLJGVKCOncWYHR+6vy6dtB2MSX7/ZKwpS+9gKBQAwk0DkiwqWYoT67O2PSRqAHr7cIhDopEdK7Enx7XNyZXRfy3vFn916zkgQ2mIndefWjv0yfvymjSXUjWTE31tudC6ca+f5Eu6tS35w+6JUpkfZq8DBx7NN5sc+MD+1tAUmCtfsG2AOiG5e57YGkdbOirax5aETghDCYIsn0URlgKkUPUNrTH3YgRu3E0HCyLW0JaYYc7WMsEc7zsb3OKrqqvRT/XzgfZq0sQ9Bn9e0YwN5JmknfjhdXuHncvdHuXvRNIazDY7B2TlouYsHUkkAgELc9KExFuBQhmwHRGtz5MV28OiVyTAilyYoW8RZxNt6T0P2KiSAf25Vz72+XW9QGikb2XWsc8umpM+X/lvPxWIy1KeEOu9SFmM+y0iZqhfdnRx9aNdqHsmBVqJh/6smxz0+y5nNamXxgFaNOHSS79mLK/Um2n/VnR3u7wB/qcHovur5D0Nce0pZvp2Wh7hgsZwAAOM/HfcwPbiPWE9oG+taUl1+msozlGUd/LxKACuCZAfwPEaEHawouyatb9Br6Zu3lvrChyd69HoFR5IPhs6VcwfEZ/8H7kxD27VeHzMYZO1KW0/95IRCIO41/3w3R3FSEa4AsBQTVWhd8NuPQqlGOtYOKOMyDK0eFSwRdV0gSPP5n7sYLVfaaalDq5/x0sUsm6vlJ4SG0J9ysmxUtEbAdu3dGlKzs1SlfLRpsfdBOjLfwp3uGFL6cMtbW2NUBV+7r784NcKdULx3l1b/6r8R8B3/otwmEqSPj+b524iZwrpQf9R9t+TYo1oyKbKM80+YytvcEABz8rCcAdgREPA7mJpBPjCMfWUguva9EMefYzyP3fSnb/mbC3s+ez9hX0uG6aUwYwLaMX+rDEzls4b6wxBURIwAAfgL3RUHx8FyjhQebt2X80tsp6xgqEp+a/cS8QFoaBwgE4s4EzU1FuJR4X7fjJdAeZeJ9YQoLdcedyzr15NjH/8z7I6uW+l2x3qLflycl3OzVutTiny/aHaN2otSbZm+8mP38BF83LpeJb146dOaGC3qTJW1SCsyK9lo12pFUahd8NuPp5NCnk0OvNShPlMqvNSgblXqVwSziMHzduAm+bikR0kEygW1DAAAAzl9vtb2oP3GtgWrud2yIJ4YBeu+jpMMRxa1Q/6Hcluhq9pvaC/rai5sgjWrSqDI0pcMyqK89xJIOBwCYNXWG+uP6+hNmZTmhl5NmHc6R4Fwvtmw023cy2zvZ0HjGXuMHQeSHZHIpsHzwRAIyr7U+r7X+i/zTi4LjPxsxL1joeDUKdcJEkoPTVqYc/r7dYHcx9gz/qF/GL+1Kon44fPb+mny9uY9r43kM1j9THxnvHTrOO+SBU9vPNVXSsebFFTbr1GTfdcl7cYVPxyY/GzteeBtF3QgEwpUgCSWES5kcIf3idDkUU/7u3Ghn5qzcuazflyXNiJKtO1ZiU1LYk8d6Kjn01amRXOZN9VrlLZr3U2lNF2xS6V85WPjL0qEAgEnhkp0PDrvrl0wHmnJHBnnsfHAYE4cT+cT5iOJ8HM9jdFLcrILijMuobtNSXOkl5IwM9LxYRScOh1nhQqeIHTqEvsVQf0Jff9zUXkjo5KRJg7PdcJ4PS5LE9pnM9k7GGHRnIPVAV7kLrkH6YAyuUXEFENBE4AzNF4zyDNXVT3TXd/eYsmNWlgMA9DUHQNYbONe+cnQtYP2XnLQNJNheCgBBkn9V5h6vK9kw7u4lIUPs2sgxEiX+2Queu//UtvNNVBXmMIA9HTvukxHz2PiNDpQIN+kT0WPXXzvtHDcpEeUu2z5xWZLEHwAQIhSfnLX6h8Lz66+drlTZPUYoRCh+Nm78qkGjN5dkPHWBkrwWXJK9Q1dHj10UPJjHgDbgDYFA3IFgXYnTnTt3dl2dOXOmm5uz8lSIOxm9ifB/5xj1Fk0rPD8p/FN6s1IoYibI3Xn1e/IaTpbKG5Q3zWAQcpgTwsSzor0eGhFosVF2yZbMP3PraTqAY1jGs+OHBbh3fnm8RP7g9qy6DjsSCEuG+G1YkgBxJCx9CJJkrt0/sOo2BGyG6oPZFBdvuVS94o9sp/pDEQaOta6b6aRGbrswtReqr36qLd9KmnudZYJzZYKYp/nRT+AcCax9m3YGmDV2lEW4ALZ3Mi9iRfvZlbAM4mwPwtAOd16OETCWk4tOA0dKML4avfDpmGSIzljBQJi/vHZmff7peo2N8cuTfMLfTpwxwceCSF6HUTfx4HfZCjvm0MLCjcV9PHrMf4dOEzDZKqO+e+7RRBAHawpS60rSGsrKlS1qkwEAwMYZXlxhqEjCxhk4hrUbdR0GnZQr8OGJhksDJ/tGJEn8uxpc38w68m52qitzqneFJOyctLxftfsiEIgBCqbT6err62tqaurqbrw7ozAV4Tw+OlH68gG6BXh8NqPgxRS4na5UqG3XNasMcrVeyGH6iDgBHjwr+cmadl3IulQzDDniFSMCNy+9MRW9SaV/cX/B9iu1RrONAuAIqeDdmVFLE2EqfEJBZyJ4Lx3oay/sw5PHUqyjqi9KkOSoL9Mzq9uc6hJFdj00/O5bpKddC6nO/6ojcy3F/CHOlbqP28QNnAdhY0N7w3YLE277Fn7kI0z3mI7MtX3tSK+QADwG5h8gIx27nYHhf05+cGHQYLheWUFvNu2qzDlSW3y8vqR7vMrA8KFiv8l+EXcHJ4yUBVmx0KxTJR/4triD0kxd+giY7GTv0CFiXz6Tc66p4mprQ7NObSTMbJwh5QqGiP0m+0bMDYztLkSsNRsNZrM7275yg2N1xQ+d+cNmDA+FST7hh6av4jL6/lAMgUDcBmA7d+68tRMVhakI52EwE4M/TiuR2yijtc66WdGvTXXk+clMkHn1ypp2rVxtwACQCtkB7rzBPiIGpGrY7nyTXvH0nqtQTHnwWPJ3ZvRwsrpN+8O560eLm7Nq23sEw1IBe1K45N5E/8XxPv3zVJskAXPtfmJApVPDJPyyV+2Y+5deoUj57hydsUkiLlOpg9AvFyrmF78yGVbJt72QJk3riQX6ulQ778OECa+Ikt6jubupLb95LwSFW7i4j/uZ0DUpL7/S1470yk4Q9yxJa+iLmMMvvutlCQfmsFaKqE2GJq1KadTLuAIZV8jEqWrnNulUD5/ZcbDGKZ3M+6c+GijwaNarBEy2D09UoWz9OO/kkdoiK6lODGBzAmNeGzJltIyWrECLXvNtwdlvCs4265zYarE8fNj3Y+8SMO9swTYEAgEPJlJLQrgYNgPfeM+QGTSkgMaEeFqfdHIrJAn25zduyqhKK2tp0/bM53jyWCkR0kdHBc2K9oIY06VX2N1T1BttWmNevXKo/02HR4EevPdmR783O7pdZ6xUaBuUeq3RLBWwfUScMAm/f0anXWAY8BFx7CpddgA+i6ExmmFZi5TaJ0SUHCretHToQ79nOfYue1+if6vWeLiwyZGbb6ZCoTlS1DQnxuljcswEWSJXN6sMzWo9n8XwEnLCPFnE+SX2x6gAAFKV+z7GchPGv0THJdLUB+NSbIGxvcfqa2FOuIGLFrA+JsfRNKLQa97NPvbFqAVQXLILAZMdKnJkdrEXV7hp/JKovz52QJbJJsOlAd48EQDASJjfyzn+bs4xm+d0JCD3V+cfqC64tZ/WLiQc/n+HTlsTN/7drGPr888QpIMfvr0xROy3LmnmXCTni0AgoIKUfhF9wEQaUkCx3qL9j47qoVRkncOFTc//k29lRGer1rg7r353Xn28r9sXC+MmR0jt9aoLvYnIqm1vUOrlasO5SphKtoVNPcPULty5rCF+LFcolkAl1kfk1DA1TML/ZWnitB/PO3wg0oPRwf8KEREkea6y9XiJ/Fyloq5dJ1cbmDgmE3JCxfwJ4eJpg2Sx3v/qSy0fFqDQGF74J9/eX/UlQ/y23Dc05qM0KJ4DAPbnNzovTDUR5K6cup3ZdafKWlpvPgb6r+/mx6UHHbasvPwKS5zA8Z/luHOMfpjbIRVHZgjinu1rNzrBbu1o3U7G1wG6GmkAgO8Lz702ZIqM27/mM92KgTDvrMg+VFN4sqHMScWxvny3zhhVZdRPO7LhQjNVzScAAAnIr/LTcxT1h6avdFiU6ER96cNn/qhSw29DmB0Qs3/aIxhU1TcEAoEAnWEqg8Hw9vbu3puKQDib+XE+fz8y8uE/sptUvYqp3MqsaK9f7hsq5lP9nDaaiVcOFn5+qoziwUtefce0Hy68mBL+7qxouyokjWbij+y63zJrzlYqNAZo6bvuNNrzjRoQTAgTpxY7pQ2MxcBXjAj4dF6cG5c5KVxypAjOLrNjvHUm4peM6s9OlZXeUrVe067Lqm3fnVcPABgfJn4xJWJOjDeGgTXjw8YEi+/fesWmXnQnXCb+4dyYNePDmlR6irdQ4WyFs8b//HKp+u2jxZUKC0nLEHb9o5J99MyTHRefkS64ijEcnGkBUYoJImZ1lTJnXV970QnJCZhlbMkitA1dl3ZjMVA0dwyE+a/KvMejx0Cw5RxMBPFF/un1107XObl1c6pvJADASJjvPvmrXTFqF6cayu45+dueySuo1zB38caVw+/nHndSk4XKpEcxKgKBcAZ4cnLyokWLkpOTsf5dIoi4/Zgd45XzwsT7Ev2p9IX6iDjfLI4/sHKUl5Dq0ypJgpU7cz5LoxqjdkKQ5IcnSlfYU6i59XJNxAcnHtyeday42UkxKgDAeZb7inuG+DnJ8mfzYzcsGeLGZQIAaI6K7SLB103IYYz64szqv3JvjVF7cKZcMe/njFkbL3RKQ48M8rj24qQt9yVGWR2hJOIwnxkfWvLqlDXjwwAAxc3QYlQAQEmzCvpDapvWeM+vlx/+I9tijAoAeN33FxZGt7fW1FGqKd7o8O0Mvh/GhDY2FmNwAeZg4WUPSL0Cw/qF0gzG4HvfWy9beNVjwlbB4Bc6cLdcElri/Xg9rYlcTqVK3ZZy+Pu1l/Y7O0YFANwfnggA+G/WkSO1RQ4b2V+d/2HeCXvvev3K4XU5qc4TAqhRtzvJMgKBuMNh+vk560kRgbCJj4izfVnSulnR356tOFLUfK2hZ10un81IDhV3NzLFAAAgAElEQVTfneD74PBAjj2FvgCA//v72q+ZNY45tu1KbaAH74M5MdaXqfSm1X/lbb3s4C52wWPBeTjuP0R5CWdFex2C0XvZg/dSSx4aHtgZpi6O900OFdPvE5432GfY56d19tQPHylqHr7+9NH/jI71FnGY+IPDA5YN88+tU54olV+uaWtU6tu0Rj6b4SXkxHgLUyKkY0PE3avZocxt6kJnIjQGsxDeWJoOnWnKD+ev1PT6hOrNap3ulgFlL03xBkHMU47ejbFkowz1dj/cW4TtNY4bem/7ucegWCNJCPpY9NHXHQGEkekRx3SPVud9XEwIzfCSY7kKuhO5nER+W+OEg9+26F3RujzGK3iGf1S5suUL2qNZ3885/mDE8CABVfHqn4sz3stxoDPcDjrH5CAQCAR0+sVRLuIOJ0zC/2x+3GcANCj1ZXJ1s9qg1Js6pYDifERsht0FTgCA85WtX6WX0/Hq45Nli+J9Rwb1+jRgMBOLf8k85pyy1VuRUU4jDyA+XxCXWiK3OVbHXhqV+o9Plq6bFQ0AwDDw2fy4sV+n05kMNC5U/OnJUgd6XGvbddN/vHD26eRgTx4AAMewof5uvfUY9wBWS20XWiMB65fIYCYWbr5kJUYFAEwSXmYCOCUAptY8s+o6Q9gtMU6YDM0XDA0nDPJLhE5O6JpxpgDneTHcBnF8J7N9UnDODQUdbsBcWGEqJ3Auf9Aq0tjRkfkSIG+TAgfSqDK25rEkSZqiDcbW3FoQDdF4pQqakhxEqtVtM49udE2MimPYFyMXYAB7K+uozkz3YEJrNr6Xk/rj2LupLG41aF/K3E9zR5ug8TMIBMJJOBIAIBBOwkfEGRcqXjjYZ/mwgFnRXon+7o7FqARJrtl7lWaJE0GSz/7dqxGSBPf9dsVlMSoAINpqvegAJdpL+GKKfaLNFNlw4XpX9DsyyOO7u+IdNjVIJsyp63A4aKxt1931yyUHKu74bMj5cwE8g1+erjhZKre+JolfDGs7AICh+XznP0ijSp3/ZdNf4S2Hxiuz3tRX7zc2XzAry4ytufq6VE3hd60n727a4dt25kFTW37nLbzwZVDqfjGmgBd2PwBAEPe8W+Lb9A32H0wdxYA0q/LeBwCogYMiPRYxEGY97dgMOqvO7qp2gpiQRd4YMm2kLKjDqPuzMheKwd9KrxyoKchva7T5rvJW1hEXhOJ9MnMIgUDcCaAwFXEbcrSo+VI1hEeQ85Wtp8tbLL60I7u2UyzHNYg4zARfCMKb/ZB3Z0Y/PDIQutlmleH89RuiQY+NDv5wTowDDfiDZIJ4X5FKT+s5+3JN+6aManvv8nXj0tm0Bx48Fqy4t0mlfy/VdsNhGLsWynadmDtKAACGhlPNe2M6Mp41q6usLCYJg7bst+a/E5SXXwaEEefKhPEv0/dBmPAKzvXq/LfxfzEwfSC2zjoMqVfoG9LM6hoAAA5FPakbDKx/PWnsq86n0yBqF49FjX4rcToA4FRDudbccxaaY2jNhrnHfo7b84nX72/effLXfdX5Fievqoz6TSWXoOxonUg3mQt2QSAQdyD968MDgYDC71nQno8tmtIazS8fcMr8996YG+vNciix3P/BMPDj3Qn/GQNH6Kg7PfpRX5occew/Y/zsif2WDwvYfG8ilPOIF/fl29XXCgCIkgmoqItRpGtGDn2+OlPRrrP9wC1m9joCygEIvUJ97fOWI1M6QylKkGZV3kctR6eTRpVg8PMMURgdBxiicEHcc11fGprO0bHWHZIwMN0iYVlz0AezvqsuWkTCFBUXsjgOKNM6lTezjrhmo3mBcd+Puavz35lyuw+qbNKi1/xVmTs/ddPgPZ8erOn5kXS4tkhldIU+/FivEBfsgkAg7kAsf3iguamIAU1ameUUqAOcKLFQ2bjtSu31Vi2sLajwxLgQV27nYlgM/Ie7E/5aMZwPVSaqsFHV48qUSGnuCxPfmhElFdiYpTltkOz46jG/3p+4PasGytthq9b4+kH7jjaEHGaSvzuEvQEAAEwKhzaXZdsVSsdAsBpTOzG25nZcesGBdlBDQ5rixAIAcPG0I125UHvBORLx1H0Yg9f5JWnWWk/n2gdhdBvzA9s72c7bYIrzYyyBUZHT+e9gAFO4NVzUvwYC5bXWZ7XAzPNb4YHwRPx/JRylHTaK5OmQ39Y499imNRf3dq+vTm+scN6OXWAAmxtoQ2sQgUAgHAM1viNuN9p1xip4MWRZi0ZrNPdQ2f0DXraWCkuG+CWHim2vG+AsjvfdGVe3IxvaAOdmS0q5EgH7zemD1k4KP14iP1Eqz6hqa1DqWtRGHguXCTnhEn5KhHRGlKxrcsyBAmhCxJ+dKt+RUxfowRvq5z45Ujo1UurBs9EEePcQXyjl652moNipUGh6Gz/TAy0JU/TLUH8SOFqMaqg/ocxc6zbqK/GUfxTH5xE6+1rKca5MPGUf0/3GszihawEkTIErDGNIZpxU5X2ouvoJabQ9HIXtM8msqe0shIYCgx9gVv87zDMKa+GTRg2kDtURUvgl/XRIrXPdgJywbiF6u1Hn1L1IQH6Vn16uVHQNVi1ob3Tqjp1M9YsM62cnEQgE4rbhRpiKYRhKoiIGBARJZtV2XLje2qTSy9UGDgOXCdkRUsHEcImXkFPdBvNpgCDJmnZdkAfvwvXWk6XyylZtk1J/shRattYm7lzWx3PvlLNqgzXJX9Le9JG894EufDZjXpz3vDgbwyEblXqKIRlFatp0NW2685Wt35+rFHKYj44Mem5iWJAnr7f1j44M+vB4aauWbkvbpHBJIqTEbE4d1QmTjUYx6PV/5gC0Pp7Uhd/xB61iyUbJFl5tO7NCX3uI4o1s38ke439l8P1vukpArqUkzVqAM4VDXhfEPK0u+kFXucuoyLo1Esa5Mo7fNEHMMyzZqLZTS7XwwlSmRyxp/Lf6gAXMY7HqVJJWjXQXM/yjoNiBhctSqWycEefh0/UlHaVx6uyvzl99/q+N45YAABo0MKvuLYJj2AfDZzt7FwQCcceCsqmIgUSJXP1ZWtmunDqFxsKDO4aBoX7us2IcrOvrjTcPF+3Pb1TSE9FxDDYD//OhYSHiO0VH0epsWLtLHLNq2r9OrxgT7BnjLXJM5LZErnbgLoqo9KYvz5RvuHD9gznRa8ZbDgkkAvZ/pw/6v7+v0dmIgWNfLBxMx0J3qJcqFOmDpgE4c1MhQJo7Ml8STzuIc73EU/frqv9RX/3Een8pWzZGEP8SN3AeuFUBiAEz/gbdVJQwtrsw/iVh/EuEXmFsuUxoGwhdM8YU4Dxvpiic6Tm46w+B7T1RW7EDyu4MYShDGNL9T+wecC0VQAhTJRz+3MBY+nYg4rIBOeO8Q/nMGxlpIctGowEsfiq+uDg4flZANP3hNzZ5PGrMMEmAs3dBIBB3LChMRQwMVHrTs39f25xRbUWCnyRBVm17Vi3MxioAVZDJLty5rK0PJE4ddAeJKPqIYJaJmknymT1XAQBsBj462HPBYJ+Vo4LcuHa86bVaOg2Bi9Zofnbvtdw65cZ7EnBLSsRPjgv5Pas2o8rx0t/nJoYN8aM0qZUKKgPVZ9/z6sFPyf6EtS999HVH/h2+iuHcoIXcoIWm9gJ93XFD42lCU9tZCYxzZTjfn+09geM3pXuVbw9wrhTgTEBACwNwnk/PKxwxx2+alVu4wYvbM9YAAsKvKC9kCQAAZ7t3Nf7OAcWJWEMW2dMre3k7cUZ/G6rZ4RJVIQDA8vBh3b8MEni6Zl8AwHMZ/0z1i+Q4+Tuf4hvx+cj5Tt0CgUDc4fSvzw8EwiJ59R13b8ksbnZiaqu/MTZEvPWBxNA7Jo/aiZNmwxrMxOnyltPlLe8cLV4zIfS1qZEU5/EaCZj9h1bYlFHFZeHfLrYw3JXFwA+sHDX+m7OFTT0VoaiwNNH/wzkwi8YtxtIWSVclKMxuYgbVImGnQxL6moP86NVdF5juMUz3GEHMU/ZawnA2Uxhm6oAzGBZjcJnCUHvvwnne/PBlmpLNtHfn8WOeBAAwRGFGRXYz4O8HUekgsJKkWyUe5+Hzn6gxNI3ARW82GQiYyl69EeEmfSA8qfuVeE+6MT91Ctubdl/Pk3GdOOgo2Tt075QVvUXCtZr21LqS43UlpUq5XKfuMOplXIEXVzhCGjjZN3KCT1h/O7xAIBD9E/ROgejvlLWop/94oUHpoiPwPmdCmOTFyeGzo70dGPI50Bkf5lwpjnad8Z2jxQcLmrYvS4qU2n6Gg6s8bJ3vzlaODvZcPsxCBZ1UwD76n9FLtly+WNV666tWeHRU0Hd3xVMPLKngTjkdbSSZP8vnrfXeBnF3mhiaz3UPU61DEgaTIsesbSB0cgxn4VwpQxjKdP+305LtPR5WmMryGgtwRz6LRUkfaCv/oiK5ZAVB/IsMQRAAoEGU+D5Q7iJj9TAeDCRs7q6U5f1kFE2jVvlzScax2uLzzdf1zi+FBQB8OWoBG7/p3SPFNwIDmMUBp87g9/KsKHevE/Wl0C3jGPZUzLhPR8xj4RbeHi+31Hycd/KvyjzzzZ3VjVolAOBEfelHeSe9eaKnY8Y9ETPOkw25eB6BQNxmoDAV0a9p0xpv7xgVA1hKpNRLyPYSskcGeU6OkPjaM9iTOhqDOb1Ccf56a5NK36wysBmYTMgJ9uSlREgTfN36SUgc7SWM93XLq3du/i2zum3sV+npT42LspW89Xd3ys+iN9buy18Q52OxLDnQg5e23P2fkwc1dae8mc0yRhsDI1pM7nUmSaY6Jl2dkK2NJMkbP0UfEeerRYOXDPGD7mSYxI4UzffyRfeJjwWwoKkl08RERXOIMGkrd2jLthoaz5CmnhUcOM+X4zeVH72aG7xYU/IzFK94wYsduxHnebsN/6j9PNXA+1aYnvHCwWsBALuv560s1LSSCQ6b6o4QM+2fvirGw4ZEmQuQ69RvZB3+peSSCxo1u3gmNnl2QM8ShmCh51iv4LNNla7xIa2h7OtRC78vhDbdFwDAwhlLQ4e+lJDSXRqqCxNBrMtJfTfnmJXGnE4atcrXrxz+Kj998/h7b/1GIRAIRBeWw1Qk+YvoJ7xztLi8BabUan8jyJN7/PHRTt3iUnXbJyfL/r7a0JuOrreI8/iY4CfHhcqELhL5sMKT40Ie/zPX2bvI1YZZGy+efybZ22o3bKRMyGbgVvWHYdKo1K8/Xf7m9EE3XyZ11fvUeR8bms6OBwCIbrzgw2qJA+XTRJcAAEW6oB/ki/a0T4r19Vw9NuTB4YFcplMSWcMD3HEMs/kk2omOYL9a+/jm4HUMzMHvIcYUkiZHqp0tQuptaHRry7Yqs143q673toDQ1mvLftOW/cb2HscQhppVdEdT4hwJN+wBh2/nRz1uVlerct934F6GIEA89QDGFLyVdfTt7KMO+9CDIaBxy+DoIbJgWAYdJq2hbNmp7bUayGoF1lkaOnT9yAUWX3orcca0Iz+6xo12g26w2JeNM6AUOfvz3daPWpDiEyHtpZC41aCdcWTDJXk1dZtNOtXcY5veGzbrlYTJ9D1EIBC3Jf2iIAeBsEiJXP3t2cq+9sK5jA52oq6GQmO8e0vmyC/O7MqpsxJrNSr1bx8tDnkv9ev0ij4/oVo5KijBF5rejxUqFJoX9uVbX8Nl4k79Ad3Kzxeruv8ICF2zInVe6/EFhqaz1m+M4latD/jyevI7l1b6PjY62EkxKgBAImAPC7CjazFVOfy52me6Z3qpg7HceKH3OHBjb5DmXqdVkUZl25nlbWeWW4lRu2NoPGvWQBBXEyW+i7M9aFlIWidKWmdv2TBLMkwy8xRDEPh94TlYMWog6FiHnTggSEsYsgaKQTr8U3Vt2pEfXRmjMnH8zaHTt068v7cy+6l+ka5MHnYYdEvDEqGYWj9qwZKQIb3FqFqzcUHqJrti1E5IQL56+eCX+WdoO4hAIG5PbjzKYP2k7A+B+B/fpFe4LJFlJ9CCuZnRkMfndHGpum3Ip2l/5dZTXK8xmJ/Zc3X+pgxVX4ze6YKBY18vHsyiJnFEk21XatIrbEynuGco/LpZK1S3afMa/q15Nipymv9O0NccoH472Zol3zeM+lBQx1gxItCu9btaJ79Q95SWsE/GmSEMkUw/yhDBmd7ZSdfclx6QZp0ida62bKt95oheB/NShCUbxR+0iqYRADBhwmuSmaeYHpRGv2AMrjD+ZcmccwxRWLai7ukLe2ntDcgIoHgYZG3G/j6L/fwIyBIPex9jOUULrTda9JpyZUuDVtnVD3mqoezetN9MrpJAAwDM8I86O/uptxKnM24dX9SNb0Yv8uGJrCyAiNKof3/YLAGTbo3MGK/gu0OsVYM/eX73mUbHywqey/gnncbtCATiNgb1piL6L9RDLJcD50zHncu6K8EXiqke5DcqZ264YHG6rHX25zcu2HTp4KpRHKel42wyIUyy5b6hD2zNcrbcCEmCj0+WJoeOtLLmweEB644Vu7I7+nxla4Kvm1lZrjg2i9A22Hs7aVS1nrhLPP0o2zvZGe4BAFaODvryTLldytu/K6ZlaqI/9/t6mKDQ9mqMwQtf5j7yS4ztbmyjNTO2Bwy+xRmPZNup+wyNpyFuRAWmW4R48t+OiSfdCttrrGzhVV31Pk3BN4bGMxbzxgxROC90qSD2GZz77+nY2kv7ekjd2AsJsAWg6Hns3x5IXvhyXvgyOgYpktFctft63on60ixFbfdwNMpdNt4n7J/r11zQjMrA8ESJ/2TfiPvCEoeKKZ1nhYrER2c8NuKfL/TOFxxm4rg/3/31IVNfuXzQYSMcBvOb0Yux3j/yrrTUbinNdNg+AIAgyWcu7s2c9yxcsTcEAnEbgMJURD+lvEVT295rhd7twX+nDxJx4P8NtmqNU38470CM2smJUvnTe/I2LBkC1yu7uC/R/5OTpVm1Tp9lcqigqVVr9OSxelsg4jDfmx396I4cZ3vSRYlcDQij4vh8QuvgMQ1p1raeXCRbVIhznKKczGbgH8+NXbj5kl13legC55V/PFaQt1K6b7wwW4Bb+OvGeT7c4EWC2OeYbhGdV1gecRA8/h9MDwsll9qybboqWhlFAHAA7Av2WJIkz5TdOA+uyBDGDZzPDZxPmnXG5vMmZQWhrSfNOpwrY/B8WNJRDOFN/aJHaotS6yhoStniOzB8JbjiDnTcoIUe4zbBOsXrjdS6kndzjp1uKLf4alF7c1F7s1Md6OTl+MlvJk53YLBKvKfvZL/IQzUUzmvoseTkb348N1++KFHil9VS54AFHMN+HX9fksTfyprnM/6h2KluhayW2t/Ls3qM8EEgEAgUpiL6KfmNyr52wblEeQmfSg5xhuVn916t76CV/fvpYtWDwwOTQ8WwXHKAULHABWGqiSBPl7UsGGxtpOGKEYFbLtWcLrehvgOLVo1RXfidiV4WkdDJlVlvuo/+BpZXPVgw2OeFSeGfppXZe+M5dfw5dXywO/P0AxwpUUloG0ljB86V4VwZSzLs1rJVliQJYwlJIxwVJbbPpB5XSLNWeeU12oYJljjJ1FFImijoveEsQewaUdJ7GO4sxTKMwWX7pLB9Uqwv+6XEvoOG3tAC1j9Y9OohC0RD3gCYE2c46c2mN7OOfHI1jX5cRJPp/oPeSZphcSILFWRcVxRFq4z6YmNzcYeDQTuHwfx29OJ7Qq2dVxa1N6c12P0mYJGNxRdRmIpAIHqAwlREP6W+43ZOpUoE7D0rRrCd0IF5/nrrb5fpKruQJFi7L//8M86qGqWCR+8ZTrjk1Suth6k4hh1aNUr23yMao9Pr9AAAwKRU5bxD34ym+EdB7BqmWyR9Uxb5eG6sXG345ZLduikyIfvgf8YGeVPr0MNZ3KCFdneNWgJjCTl+M3pc1Jb8YlZX0TduVGRJ52boqvdpijf2lgbH2Z688OWCuOd6ZDX7BBKQqfUQUqmdnPde8eLQNaRRZWg+Z+4oJfRy0qzHOVKc582WjYbSYKw1G2cc2UCnBxIWI6SBf6U85HCMCgAQc/r7vNBYD+/tEx8YYquS+UCNDRU66qQ3VrQZtB5okioCgegGClMR/RStEab6BY4BPzdujdUq4iAPXlWbFuKmveHBYx1YOTLG2ykH6g9suwJloNSF662Z1W3DA2lpkNIhXMp3zUbXW21nwPhsxstTIv972Ol1egCAEfgJQm9D2IkShElbskk07AMIpiyBYWDz0qEpEdLVf+VqDFQD+JQI6W/3J9o1kFYQ+3/a8u2AXgslAIAf9fit0j7aij9omv0fpL7+uCjxbVHi26a2a4aG02Z1FaFrJEkC58oYgkC2bCxLkujUZKNdVCpb5To7uoutc1nR2HJ4kqHpHCAs9BowhCG8sPsEMc/gPGvnQVYwk8SyU9v7Q4y6PHzYd2MWC1n26YH1YJCbDJY/DoABzErb/xCx39rBk+4NHcrEbZ+iZjTbfUrVG2aSuCSvnuY3yPZSBAJxx4DmpiL6KXAlfPhsZtlrU44VNx8vlp+pUNS265pVegCATMgJcOdOCJdMiZROjpDwXj7o7N/90cGe2x5ICpPwAQBmgixoUtW165rVBg4TlwnYwZ68ELHj4dnxEnkFvDGze/Ia+jBMHR7goq2V1JSN16aEb8qoqlQ4fYrvEPwcLFP6moPOC1M7eXB4wIhAj/dSi3dk15kIa3880V7CtSnhK0YE2iuUwpIk8SNWaEo20fET53qJEl7vcZE0a43NF+iY7Y6h4SSIfwkAwPSIY0Jtqb0BaTYqsk3tRYReTpq0OEeMc71Y0hEMvt161GVKmEXstXq9qiGdDSyfVphVlarcD9TX1gtinhEmvetAtfNnV0/tvp5H201aJEn830qcMS+QkpyydUb36VBZEpBcBnNFxAit2disUyuNOjGH78NzGyYNSPEJj3CTUjflcEWxRUo65ChMRSAQ3UHZVEQ/RSqA2bglFbDZDHxOjPecGGuaJTIBp0nlLE3XOB/R2knhy4YFYBj4K7d+6+WatLKWNm3P5EOImD81Urp6bEiSPdMpO7E5CNQuzlS4qBvTIsmhYiGH6YLpOEycUtTEZeIrRwW9fsjpCVU/cy4sU8bWPNKocvZ0kBhv4dYHkt6bHbMnr/5EqTyjqq1ZZejsHhSwGZEyQUq4dEa017RBUoeVPEVJ7+vrjprVNY76iLmN+gpj9/yDMrUXkbSHytyw1urEOMrQmK4u+NpQd4wwtN76KtM9mhuyRBDzVJeEr006jDC7KgiAqQBbDKxVo5Bmnerqx/r6VM9JOxmicOrGm3Sq93OP0/bRQWI8vCf7RiwKHjzFF1r9fKyHtweL2wb1R2AXOrNpT9XVosUvubPtqGu4FYUe5rEdXGsIBOI24EaYiuamIvoVg2SWJxw6RhQ1a9FeQohhKpfF8BKyQ8X8ieGSKZHS8aGSzgD1lQMFJfJey+0qFZqfLlb9dLFq6iDZFwvi4nyoDtkraFRl18KcZV/QCEe3xjH4bMa9Q/1+vgihb9A6VJpgS+XqzZeqPzkJRyzECkKGgW2wewhN75AmZSlLPBSewV4J9uQ9OyHs2QlhAACCJFs1Rh6LwWfDqXHFed7iaUdaDo13rBzabfjHvNB7b71uVkMrWQQAmDX1gDACHHJPtaktv/3cY4ams9bWtBeqct5VX/1UEPO0MPEdjGG7JBW6CpGZmsCvseVKy+FJktlnGYIgipY/zjvZbuibiO7TEfOeHzwRokETQXxbePbzq6f6MEbtpFGrfDfn2Kcj5tExYoQ6Vkfv/BlCCARiYIGyqYh+Sqy3yIPHujXZ6BjjqInWjg8TwxJ0DZcISl+d3P2KxmBes/fqT5TjrtTi5hFfnPlsfuzqsSFU1h8rhjyGoUVjMBMkg1qy0Rm8NSPqj6xaNeW+R8eIkPZ6hKE2mH++WPV1ekVp78cKcJk7iAOgToslobS52gmOYRKo1RAAAKZHrHj6sda0e8xKOw4LMJwtGvahIO7/LL5KmuD+WEnCpMLZnhAtaop/6shYQ0lAGADSrFVd/Vhfd8xj0g6b0llCFuQfkBBQfa82q2sUqXOkcy5iTNsNDgRJ/lZ6mZ5rjsOxf96MFarUbQ+c2pbeDzpsO/k6P/2Z2PFBAsfbKwRMmL9FNDt+EQjE7Qd8oVEEAgoMHJsTQ7WAzSbzYilJdyyK94W14+KEm3Y0mInFv1yiHqN2ojWan/grb90xSoKcWVBTqQAAkgRa12jb9kKAO/eFSXYUBzrGyCDLT2m78+pD1qWu2XvVZTEqAODJMXY3GVqHJJxVxO56WJIk2fwsfuQjFIWIWOIhkjnne4tRAQAYBvmgFsNgplI1hd+3n1tFMUbtwqjIUhyeZFZVWl/mq4RWWw4AkAENj3KYCgAwtV5V5X1EZWWOoq5J12dlHW7wAqdrbQ1Jf3/ef2JUAICBMP9amknHgh/f7s4UK/jz3SBaQyAQtwEoTEX0Xx6nlkW0yehgz6H+lD7/hgW4U8y7WofNwP8z5oZIBkmC5duzjhQ5mO1843AhldrXcnjiSZ1gGOCx+liY9LWpkbPhnVbcikTAHnGLTJSZIJ/cnXfXL5lyNbTGRSrcleA7NjwArk2MCbN4vs/BWCL3cT97LS7iRz+B8yz3mWMMDsdvunjqAen8LJbE2iRGjA1TpgvD2RgL2ndbd313+8WnHLvXrKlTHJtJGNp6XUESsoK3+fYEltaJAXa/uamvfkJlFNDlFocbkiEQbo+ekBVq1O2zj/7U0v96Lw/V0Gq2j/OwpvVgvzUHhaARCMTtCgpTEf2X5FDxXQl005sYBj6ZZ4c249eLBjss9NLFsxPCwiU3Hlh3ZNf+P3vnGRDF9bXxO7O9s7ALS+9dEESxYgF7b4kpmqqJr4npzfTee09MVxNjkn80xt4LVlRQFJCOdJayvc+8HzCIy7Jl5i6g3t8ndnfmzHVh1zn3nPM8G/Lr6QRc+XdhTbsLsxy1CdpNZyd+fHY/dvx2ws2rZm8AACAASURBVGLgG+4YCmXvwCG3Dwlm9XCvfeyf81/mVnnpir0RIOJ8ODu5ziAwkTD76BiCUIjRBggMUbRkxBcBixrkcwt9xvwoGvqOMOVpUdrLkhFf+E3ZE3Bbu+/kHZyQ6cDVtCRTHAN3VS6v6CakRaM69iAdDx6rqkSb/0pvr5oa92GqCyMwaBngWFDt6SmkzaAv/srlYZWafuha74SJ46lSOP01iw/+WqPrfdeAGjCGA/KUl6wE9T+z8YHQul18OXyXNq0IBOJGA6WpiAHNuzOThBxajXl3DA0d40mSkx4s6V4IpUCkL/+5iVcGwwwW26qtdOVhDRbbc97XmLUjwd+7CrFuImAz9q8Y9dLkOPrbB3aIOMxVOfYjfO/uK/v0UF835ok4zK1Lh4dJeU/8W1xmhFZQxVhC94VqrkEwpk8yL+Yu4aCnRBlvC9Ne4iesYAdmYwyem+czhOG9lWQpwJIPhxVKe/ZNwtBAM4iu6HOryvGXhqnmHwDAPFBE8xKd4ICcg5VQONFQ9YfLY1T9JzU0yj9CBKPp98+qswcavaC+BuPr0EzYqnUOtKPdZHpIIpS3CACwMCIV+jc8AoG41nGcpiLfVMQAIcqPv+meYZQ9VEeES7+cn+LpWZ/NGzQvhWL3kUzA3rZsuJh7JbVee6oWitnmutO1lU7jiOjl8z0ZG+UHNyBlmDj28pT4Aw+MmprgD/FO5qXJcQrRVfdYdSrjqzsvQruAe0T7Cfb838ghIZLCRs0fBfVH9dAsN9kBY90c47wxIQzNGLz3hxM8FUoc0qrTFX8BJVDHgVsB6WC83NxyFAAwB5TEAwiKcYuwwmCgpnCiTVPuUmy5H+9GlkRn0A9CAvKF09vpx+mCjuKRQ9o9bEUmSPKk8tI75/bN2/PTpB3fMDEI1Q4mjj+clEU/DgKBuM5A1VTEQCc7Rrbu9iECz50tRkf6bls2nIIlBgPH1t42hEKmGuHL33n/iPiri5C/nq7zNI5DSBL87rRzOELqWjbTI+ZSzdW9xJhI323Lhp97YvxbMxInxckDRGyMRkFh7iBFp3tKd1ZtKfK2sHB3WAx82Yiw04+N7ZyPXZNXS5Lgnw5ot2u8iJthhbr+sGnKW/5Js+lpdeN3gXP8uGGzoYQy1W4hLRoooSxt+R2H7+7ZHmpVXwQAMAHxPHaA5iWEwPwU6cwsxznWDhdWz7CKdZ4SI5bdETOUfpzjLTXFqmb6cQAAQhZ788R7Nk28G0q0Lsxum8qYbNZfyvKS/34vc/Mnz+Rt2VhTeLS5ut3sYhrFHVYkjEqCOuaKQCCuD66kqcg3FTFgWZAaeOrRsenB7ooK4hj2UFbkvv8b6Y4lpkP4bMb/7hr21YJUN1uOMQzcPiQ4/3H7RRqtxNFq6i1VduwpVTp5NT0EpuhiskLUU1toIJCsED2THbPz/hEXn8lJDXLXVNYOCZf5/aI0u8nbZq1pPb0RYveJ9hM8Ni6qbFX2tzcN7qq97yltAQDk6ROO6yAUVBnCcG4kSlMdQxhb2nZNJwzQLGpF6a+632zsHFMj3dSxO4byNdqC1656irCQ5suq4DmgcgmgLvmLAfAutisAoy6FbdO72MWLEHprLt05nwyfw8YhVNp31UHrztBazPESfyETct7uZsDTrXWpGz+489B6WFl3FzFi2cvpU+DGRCAQ1wfINxVxbRDvL8x7NGvLhebXd188UdOrFgWbgS9KC1qVE5sYAGGucvmo8EXpQV/mVn11pKpO5XhEisdiLEgNfHJCdGqgAzHhiy1ak5W6QIUd5xqcddZNjIUjStkJhWbpvoQkwYKf8wrqqbQaAgBURuu4L3OPrBzTvVN68/kmiw3aL8shQ0IkL06KSwuWhEvtUxobQZ5ruFxDe6Fh2faYR3F6GinswW/DSpyuP1S5SzsrilBgSlP4cffBimbtOA8rVCeagte4YfOZ0kGdD0nbVR5Fb2G720nuvyCOQuQXsf1zAa2ZeZfWtWl+/SCr82Di6OkhiVBCnW6F003zX7Ta2WHJOIYR8HqhQwSu9zd/Kcu778ifJpsV1kW7kHOFWybdK2WjbyoEAuEAlKYirhlwDJuVHDArOaC4Wbu3VHmsur1Za2rRmblMhlzIjvbjT4iRjY3y6z4XSh8pj/XcxNjnJsZeaNIcKG8tb9W3aE1mGykXsBVizshw6cgIX27vo7Mu5Xk9okljMlqJ3i6XEigeHCSmnLl1J9FfNDZ6oAymOmTt6drdFyka/HRS2KAJennnVwtTF2dcliw6XOl1TdE4uXDOIMet1HUqo/m/JLnQEPWNcu7/yf6mfKEd6uHDRNPpV8OtqmJT/S5z4wGbtpIwtpCEBefKGLxAlv8oTmA2Wz4K4NfefyLmhr3GS//AioZz5dLxf0B8HwhXBUbPI1rVeU/6TtrW+QhjXpUS4ID8EtsST7Z+BEYQbnfRi4D5DWzPQuCiZdclGMNFKS/DL4SF4xYaarSesiQ649MRc2FFq9TC/FYp17TyGKwokV+Z2llnjfsE8yW+HBfTIpsvXbg3dwNFQWDSmdTTIKliw/glcWI5lcgIBOIG4Nq7w0AgEvyFCf7CFaMj+vKiSQGipACPW0y1Jsjbz1qTlcvs1bDk1anxc344SfMSOIZtumcYzSBeRW+2PbsFgkip1mxb8uuZnSUt3y0azGbgxc1a+jGd48Pr9StXffWfyuuNd/kxVDdL91K4ymlD3AOXHt9tpDVka6rbpj33nrlxn93zhKHR2l5oqt+lzX+FIYwUDnqcF3vPtVW2VZ9+DlYonC31nbSNKYmHFRAAQFio99D2hqluu6X9LEuaCgAAGAPn+hPGK62bTEA8jh0ZDmrfIMcWABcjgjggp2Flz4MD4UBFf2E4x8V2GAtnRAj9StW09qTchIUznk3NeTFtkjtD7y1GrdZilnEFzqdnVWaYSsVqsxEAMF4RDStNnRDowpOpRNVy875fqJvW9PJGchjMFQmj3syYzmWgu1AEAtEr6AsCgfAi0H1HmU4Dzk5WTI6X7yyhdUv3TE5MrFzg+rj+44+C+tpeerApsOZUrcZk/fPOoU0ak+uj6RHj1+sba/d7JUnsyboHdQTvLt+tGOZBg9821chH6x7SE1yqawSkWaU6utxQud7lkTZtperYg7qiz3zG/sryG0L5in2JVVViaTkGJRSGMWUzjzPE9p5GdMPiFCfqnWOs3HA5TQWA6ZNkbrSfMBwDarZhaw+DsL9AUi4ZWguummLAAZkAlGOx6sXgbBSANm/PFLtuNp4ZmvjRee+mqRgGRCwOG2N+cuHQ2vJT/jzhcHlYTmBsTlAsj3H510GQ5Pa64m21xXsbykpULbb/XG05DGaGX0h2YMy88JQhfsFeXScBSADAosi07y4ehxLwlsg05wc8duIfI9Re32iR38KI1IeTsgL5DsZkEAgEojsoTUUgvAhlDSeHMHDMpaTTNwtTR3+WW6+mmMWNi/J7YRKVKbW+5K9zdC0l7dhY2Pjs1mIjvCni3hgeLu3tpZ7N6haS+Vz9/fu16a8Frg5jN7kM3mqVvNO0eG3blN4CuoNVXdq2Y6JNV+PBKaqS1q2jJKN/4EXdRuGKfYy5YTesUCRptZmUDAA7TWV6ZZPI3Li/62d2QFb3h90ZA2rGgBqAgSYgrCeFrYDPAoQ/0AViGh8A2cIUY0uYPkkuD5sQGPPR+YNwL90JDrDOxI8kgdpsAsAEAOgwG8o1rUebqz8+f0jOFT6YOPqBxFH/1Fx499w+h+pBJpv1SHPVkeaq1wt25wTGvpg2aaziioQ4XKViMYsLAMgJismUh51o8eBD6pDBvkFTQ5w1AuxtKNtaC6FvRcETPT94ooInGioLDRf2+h2IQCAQdji+j0G+qQgEFGJkMO84I6R859VUAECEL3/7fcPHfXGk3WDxNH5qoHjTvcOcjNoOEA5XwB8i/eBAeYDIu9YXvnzW8LBex0WDxFwOE+8puLVLnblPnTFTkrvYb8cwfhELs69s2Ei80Bi1oT1nfftEA3H5n4BhIMLXY4Mim76+becUj3LUTkibqePwXTjbhxMy3dNz+xhL6xmI0aytp9nykVBCEYYmXdFnhvJfXFqJUsPSUdj1My/iZnv53x4EAG0A5t02eG7wNHd8fccporgMJpSaHo/Bqrn5eaVJ/0zelk01hYQrlbIWo/alMzveKNjtpmvLnobSvQ1lK5NGvzdsVqdQcIRQeq4d2rZapNAXAIAB7JPhc0b9+zlJT2Xt4+FzGE5dT78uPkonfheNBk2GLGSEPBxKNAQCceOAqqkIhBeJkPJlArZSZ4YSbXi4W5o4KYHi04+NvW3d6aNVHvTmLUwN/PbmwRKuVxoOIdKgNlLIwF1iI0gd7EFiOxZnhLAYvd4UMnAsNVB88pIDFWsrYGxUjd2oGsvDTRn84mCW0o+pYmHWJotvi9XnpD5RbbPfDUnwF3puNUx27Fto01Z6eNZ/EJb2/TfL555nCD25GSUshLkDYwn7bLrVqqkYcNEIq6bgFV3h+6QNcrmyO6RZRVo0AMPNTYes7ecYgjAK+xFw4ce7JY8sZnEXRKSuKz9N/4rzI1IMNsus3d97NNvpvrMoAIAE5KcXDp9rb9w6aSmXwUzzC958ia7QVBedusdWgpCwuAvCU/6spm4mtDxh5HhFtJMDrASxo66Ecnw7tlwqQmkqAoHwlCtpKvJNRSCcYCXIkzUde0qV+fWqZq2pw2AVcZhyITvRX5gTKxsd6ctjOcgKMAxMiZevOw1HunNKvL+bR0b48vevGPX+/vKPD1a0aF0kyYkBwhcnxd2S7t2pKlg0em2CVGX0Ypoq5jJX5bjoDp0UJ3eYpnZhIDiHtYPdudykOI/FMw3la8wttIonpFWnzntKOv5354dZVUXG6o3mxr2W1tOE6XJhHGMKmJIEdmA2N2QaWzGBzjJcLNICQQobYjSbrrZj/80033k3ad8zx9R8GBDwd3kowA2d7fwXbbBZdteX7m0oPd5SU6OFMA3LxhkPJ2VN3bkalv6QE/Y1lC3av+avCXfmBMa8lr8LSsxAnuhoc/XTeVsONlZoLLS+BmeFJn02fJ7zYy50NKkt0PZNjjRXwQqFQCBuHFA1FYFwgcZk/eFEzQf7Ky51OHCX2QTA23vLRBzm3ZmhT06ICZHYS9fcMzwMSpoq4bLmpzj2MnEIm4E/mxP76NiodafrthU17y9Xtumvuj0Nl/KyY2XzBgXOSPLHr51dKoOFloBtf/HS5HiFq6bixRkhb+0thTJyseQ/lx13ISwaGPq3xqo/LMrHWbJMh69alCe0Ba8bL/0LejQrkladpfWUpfWUrvA9lm+aIOUZXuQi+uvxOiSteWbC1Nq2c7JVBWH8zx1MPXSb+wuMwRMNe7+3V9vNhs8vHP68KLfZCLPr+JHksS+d2XGhw/WYNxT+qTn/eVHuQ0ljwoXSahhpttpiWpb7B/04yxNGfpQ5h4m7GO64CFVdudT7WwMIBOL6A6WpCIQzdl9sueO3/AZXikQak/XTQ5Wrj9W8NSPh4ayo7i9lx8imJvhvL3agveERL0yKdamf1BMei7F0eNjS4WEESTZpTC06s8pg9ROwAoQcP0GvxjYDGQ7T015WD8AwzBuT+bekBz8yNtLlYYkBwtuHhKw9VUvzcvNSFENDPfNMNdZutenoXhcAAACpv7ha0jNNJW2agte0Ba8D0vUug6Utv+PALcbK9ZLR37k0LPEUjOWxrZSzaGwJ9ZMJa9vumX2Wo8KFIQjCWFJrx3kqJ2MMn7Frmb0oJO9rKFty8Lc6PQS3m+6MV0SPkIe/e65PE/VX8ncujh7yUtrkew67aDFwB52V7vBItMjvg8zZc8KS3Tm41QTTGAluNAQCcYMw0LVSEIh+5OUdJZO/PeYyR+3CYLE9svH8rWtPW4mrUp33ZyW5lD5yTpxcsDLLdZ7jBBzDAsXc1EBxVpRvUoDoGs1RAQByb66cx4L/lTgtwf/nW9PcrFe/PSPR85nSq2Az8HdmulZPtcNUu4XORbtjvNQjFGFp2zNbm/+KOznqlTg1G5Wbh0JKnq/AEEZAjUb9U6kr+RKWNU4fw5Km+k454DdlF9PHrYSnOxjO9hn9PTd8vsNXP7lwaOKOb6DnqEP8gv/Ouev509vghnVJh9nwXuH+O2OGDpOF9vGlu8NnsiYFxa0fv7hkwdNu5qjAw4lcl5hs12QXDAKB6F9QmopAOOadvWWv7LxIobS2/kzdXb+d6X5iskL04y1plPtqJVzWhjuGsntX37mhCPHh8uklck6wEiREoWMmjr00OW7zvZnu/+6CJdzvbh5MuQcbw8BXC1NiPdeXtrTAsWEEABCGBpu2utsTZMeRZabarRRC2bRVbbumdM2vQoHllw4xGmFo1BW+r7+42lizyaO6KGFu1+a/CnElfQTOEiQ97DfzOFMcg/MCZbPyBIkP9TD97RWmJN5vxlFezJ0OX11XfvqxE/8QsNsZlkRnHJz+wPmOpj5r9+3OmvJTGAZ+zFokZVMSCaPxZvAZrA8zZx+ZsbLtttd2TrlvUWSac11fOwRMmBuCcKMhEIgbBNT0i0A44N8LTau2Um/GW3e6LjVI/NSEmK5nFmeENGpMT/17wdN7MBGHuWVp5uAg5IQOKlr1P+dd2lOqNJi9tTFvthIvTop9dVcpzTgYwBalB63KiUkN9PgXd0t6cIvO/NDfha4P7cEb0xLuyQyjcKJVTfefbBetS+9XX/y1oexn6qE6LqiPrfAZtx7S0gAncCKsUAAA7dk3uz9k8IM4wdMESQ8zpSnOTzSU/kSYWiGuxNvgXDkv6jZB0qPdlZwxBlc8/BNe1G3awneNNRudTOoyxTGC5Cd4MXdiDPvp/U7OtjXcffh3uDnqOEX084MnTgyKBQBsvdQ/zdUNevW5tsZU38Btk5flbP/a48ZdGl04eptlXfnph5OyqG17BfJg/qcTLKDRHo9AIG5UkG8qAmGP2UY8tuk8zQ/Ba7tKF2eEBImv3JM9MT46LUjszqRrF0NDfX5dPIRCcew6o0ype35b8Z9nG2xEz98KSetW7mrYDPzlKQnrCxouNtPTbsHIB0dHUMhRO1k5JjLMh3fvhoJWt62MxFzmlwtSbx9CRa6ZtBlJmwN5MMqQ5suCMYS5XZP/Is1ohsrf+Qkr2AFjaa8LAACYPkksvyGWVgjuJj2x6ev1pd/rS3/ghM6QjPiSIei11dNY/ac3FgAdTvBUbtgclnwkyze1tw8aSz5cOuEvm77O3LDH3Ljfqi4nTErSZsK5MgY3gCUfwQnMZsmGAaelvCdObrbA6DKVsLmp0sDswNhZYUkZfleExE4o+819J6/1Uqpv4HB52PFZD926fx1EG1WXnGqtXVdxekl0BoVzk6UeKPa5JMknAGI0BAJxg4CqqQiEPV/mVpUq6eo9aE3WF7eXfHfzVfYhE+Pk+Y+PfX9/+TdHq9VO7U+i/QSPj49aOjzMidPmDcIfBfX3/XG2o1evVJgaxXIhu7xVV9pM97dPkuDJfy8cWTmGcoQ5gxRDQ31e3lGy5lStyepMTpbFwG9ND3plSnyEL7/nq2YbcbSq/VBFW53a0KQxAQACRJxgMS8ryndEuJTT2eFMwPH17YK0XXbL0J3/kDBCUPjUnH7Ob9oh+nE6EaW/2rZ7JqxojiBNl/5VNh+RjP6eGzbXwcs2g1l50psLgIZVVcKPv9+dTxmDH8yLvoMXfQeFq2yvK95Vf5HCiXZECKVlC1c5bG3tR6XZCs3lrvVkH8XRmStfL9j9ZdERl14vPmxuhxmCH8xr+bsWRw/BPP+eDBP4RIp8KzVwWu6de7QiEAiEQ5BvKgJhz9dHq10f5Aa/nq77cHaymHvVZpC/kPPuzKTnJsZuKmzaV6Y8Wt1epzJqTVYAgEzADpfyxkX7TYyTT46TMyipLhEk2aw1K3XmDoNFymPJhWx/oQsflIHMa7suvrgdmsW8SxL8hY9sPE/SGQj7j6NV7XmXOjxV3O1OsIS7+ubBr06N/yWvdm+Z8nBlm75btzOPxRgVIc2OlS3JCAn1cTD2VqrUfbC/fM2pWn0vPdJ8NmNJRsjj46NjHeW3dMBYQgAAAKSh7CcoAc1Nh63qMqY4xvWhbsAJmcEJnmqq2w4lWm8Qprb2fQuk49ZzI26ye8mqLh0g5qUusWkrLa1nWH5DvHqV1SVwRqOrtO17G8omBcX1fKndBLNfwCNU5iuXFjDZb2VMfyYl+6eykzvqSg41VWqvdkANFfhkB8bMCUtecvA3KFcvVSvzlLXUNJxuihgMRRsZwwBBkvV6dRAfTa8gEAgPQNVUBOIqSpq1JTQbPv/DYLHtLm2ZnxLY8yUJl3XH0JA7hoZ0Hcli4HTUgE1WYv2Zui1FzfvKlMqrO0WDxNycWNnsQYp5gxTUUl9voDZatxc37y1THq9pb9KYlTozh4kHCDnRMv74aL/J8f4ZIZJvjlb3ZY4KABgdIX1zTzmsaBsLG+mkqZ0EirlPZ8c8nR1DkqBFZ1LqzCQJ5EK2TMDubeTMZCWe/rfos8OVzif99GbbN0erVx+reSgr8mmegjQ00lxqFwx+CADA2nEBok6vuX4HrDQVACAZ9U3r1tHQZYTtIYmOQ0t8eQp2QFb3pwl4b3UfYG466NU01UoQu+uhjUZvry1xmKZC6SimRs+PoYTNfTgp6+GkLAthazBoGvVqndUs4wr8ucIAnggAcLCxgr79TBd76kuppakPJI7+7MJhg43ulgpJgoePb3z4+MZ0v+BHkrJujUpn4V70FUMgENcNKE1FIK7iWDUEH/YujlS1O0xT7eCxqP+fbSXITw5WfHCgoreR13q1cc2p2jWnaqP9BKtyYu7JDOvfzokmjenDAxXfHK1WGa+6+7HYCK3JWt6q21nS8uzW4vQQcWGDpo/XJmAzrYSzDluPOFgBUyMHw4C/kOOyNl7Tbpjz44n8OrWbYQmS/PhgxZSEoMEsSLkTzmRK4gEAFmUenIAAAAAsracgRmMIwnwnbW/dNhaujHBPSJtJlXuPbE4hxrjyiyOteq9eFC5WVbFX4xepmlx2wLrP0ZYqh88LWGz66RY1xGzHqlEAABbOCBP4hAnsN7MK2ushLuAs1WnYMIHPEynjX8vfBWslZ1rr7jy0/qUzO3/KWjQOtQEjEAhX3OhjbwiEHRdbYLqQn67tgBitJzXthuyvjjyx+YI7skzlrbqlGwqmrj7WqDG5PNhL/HO+Mfm9/e/uK7PLUXtyplZtsfWpltvICOmZenezO3coaoJTlncfpc48dfUx93PULna0JcJaA8svA2OJAABWTQWsmAAAq7oMYjQAANMnWTbrFNt/FNywPbGqy/TFX3Z/pnvKOvAh9N6V/IE7NVqmdrw3FMLvN6XZCKHU01NqtDD/48hvo570Pp0yIdkHppYSAKBK25az/ZtX8nfCDYtAIK4/UJqKQFyF0m1tVXfYV9Ya/MquhzcWVrXBr5+UKnXDPj50qMKzctDOkpasz3Ob+iNTfWXnxbk/nnRfvbYvwTDw0Zzkcw0w09RWvdmRNLG3IEhyzg8nqeXG/6jGkCScIjsvYlHnD6RFBSXg5WhmmNEAAIAkrB3nmdJUnCOHHLkHuvPvA/JK0ynO8fPq5XCOL8RopBXmzl1P4E6Ntpsdf9MOkrruavESab4e629rrTC/nys1rbbevYKcI2Cyd065L0II8y8KAGAjiZfP7IRYp0UgENclKE1FIK7CbIPW89lJvdr46aHK2Lf23v/H2U6pJCg0qI1TvjnWrKVyN1Om1E1bfdy51DB0vsytenlHyYD1uro3M2x4mLTRba8gdyBJYLD03UTcDycuHami2MJaYQreoRlOfw04V8aLvevyA6p3xg4hSYjvJKkv/aFlY1Lb7pn6kq8JUwu8yI6x6evNTVeUihniOJgK1RiDwQ9i+Q7mhs0VZ34kn3OWHZgDLTgAgOlAoAsiVpi/WWAlCIcj2f2lNCtl87r74rgJDlXA3Giz0hFSDuKLd0+9f4gfFbMr57x4Zse6cq9YQyEQiOsDx2kq8k1F3LDQGRN1gpUgvz1WnfHRQQoNmQ5Z8de5ShoV2jN1que3eXfkrDtHqtpW/l3YZ5fzlAkxss/npwAA9BaYmRWGeevPqSc6s+0Fer/QVxruMZMsmssQpb+Gsy+3OP6n9wuHzkZi+hDGlrbds1S591pVfarOZWq4opiKs32YPtC6rPkxd/nfXCebnS/N/luQ9AhTmoJzYdaHcY4MYrSeCJhsiNH4TJZDabF54YM4jH4Q41gUlcbEPa4HSHofZ6XGhsoCOqdHi/yOzFj5ZMp46O/hYyf+UcHw3UEgENclqJqKQFyFQuTFsbGLLbqJ3xylP7K4r0y5sZCu4M1XR6rg9rj2BkmCRzedd64624/kxMo23j3ssoMoVPgsRp9JK2/Ir6c5clxtVrzXdBudCGzFeH7csq6HDEEYnWh2MIUR9IPYtNXKzRmm2i30Q3mKteN894fc0FmwInNC7T1gOyWsYAE3Wk9CeggI0SG0l2i+HP7SOAj9Ah6BA+zOmKEUTowSQW4L399IV8Ccw2C+O3Rm1U3PPZOaDbEHuNmofePsbljREAjEdcaVOzPkm4pAAACSFXCKNr3RqjNPXU2xWbcLKDYtVoJ8cw9kWRqH/Hm2/kSNd6WkqMFm4O/MTNx5/4gub1sRB2bxM0AEuSTihL8LIejcfNGyYLVyNrVzmdIUafb/AHblDWT6Dqa/pG7xU2lGIIzKtl1TbbpLUNbjKXbX5cUuBTjd2jUAgCEI44RMt3uSLR9JP/KVaP6jIUbrSbJPAAavx9XJDOprQ6b6cSBbBDuHAOTCvb9Uaz1Wjx9KyT/GCVWadigONwqe6K2M6ZU3PVu+cFWcGE7R/tuSY/0lwoxAIAY4qJqKQFxFVpSvt3dsatoNz22l3p9Z2abPpTqCaMfGcw0Qx2V747vjNd6+E713ogAAIABJREFUhKeIuczHx0eXP5fz1ISY7i2Ckb4CiFfJCO07cdHcSjh/Eq803Ptt+y0A8+y/Bk7QJL8pu7vafTth+w3F2NDeAU7QRJoRVMf+z9veKk4grVf1UDDFMYKEFfTDioa+i+H2TbMsvwyGwON5SIfgXH+2fASUUL0h5wr5TAgZeycTep9BlbJ5nwyfCzEldoc6vWraru9aTZ4NaKRKA2VcmN9FJCAb9DB7Z/hMNiyJZpXZuLe+LzZMEQjENQdKUxGIq/AXcsZGeVeHEwDww4lLZ+ooKpfuKVVe3T9LvZnWaCUOQ0pvesNgsR0oh2kfSoFJ8fIJMbLpif53Dgt9eUr8wQdGt7w65f1ZSSES+2pnahDMWvqSIXBSBZc0a01tejjlCAJgL9fehmdtY0oS3DkeZ/uIh73vO3kHzvXv8RqLF3ETlFUxxbEsGZXmyS7MzUeMVX9BWQw1eiaTwrSXcB4tqw+2Yjwv8maHF+PH3U8nchf8ODhVXyf8WXUWSqEPAMDE8QURzqrut0cPeWfoDCjXcp+ijqYnT2726BQcwyYHQ+61hvUmd5LbVEnS+K/HjkNNMM2rEAjEdUM/KAogEAOcx8ZFeTuzIkjy44MVP9+aTuHcfPv8llZx4EydampCjwQDHgX1apO1S5eIhClw6jZfzk+JkblVmsiOka0+Bqf2y2UxZiUH9HyeJEFho7pMqW/SmHRmq0zA9hdxMkIk/kLqQ9HNWsgeP828ESlzzxsv/aMv/tLcdJi09bAMwXCWNIUXtZgff78TcSNhyipD+RrSRtddQzj4RZp/OZpTq+hs6NAHY9ubZ+Jsqd/kna3bxhJmKi3xTHGMdPzvvb0tgkGP60u/s2mrKUS+skJegHDQU3QiuMObZ/fACnV3zDAFz8VO05Mp4/lM1hMnNxttfSd1/nNZ3v8ljBrmSSvvndFDf4WqggtX/ahI1QwxWjHUaAgE4roBpakIhD2zkxWT4+U7S7xrU/H3ucbvbiZYDI87GqraYNoMwo3WkzJld9PFfshRmTgWJnXXUWNWskLEYWpgNELfmm7v33DyUsenhyp3lrT0nEzGMDBIIb5pcOCKURF+Ao+FT42wbW/0FhvAcG7YXG7YXNJmtLQct+mqCWMLaTXgPAWDp2DJR+Bc1wKwDFGUIPFhbeG7dBbDko/gRd9OJ4JNU9HdD6ZfYIpjHTwpTZFm/92+bwFh8qypgSlJ8J203UEF+z8wBk889L32/Y5qrW4jHvoexLZthxR1NJ1prYMV7dFB49w57IHE0VkBUcuP/nm02XUaz8RxK0FXAJwgydfyd/0z8R73TwkX2u9r0MQX6lxuk0EDMVoj1GgIBOK6ATX9IhAO+GhOcpesjpfQmKynKfX9qk0w1SbUXp5N3eHlbN8lgwJFbLf3AgRsxuPjIfgrshn4uzOvOI6UKnU5Xx3N/PjQ2lO1DtWzSBKca1C/uL0k/PXdz20t9tS8lwvb9obfLSDG4LIV43jRdwiSHxcOfp4ft5QTOtOdHLUT4eAXWH4ZlFeCs6U+o7+nucFhqt/Vv6VUAABL7lhmlq0YL5tdwFa4lV91woteIpuVxxCGOz+MG3GTKP1VD5Z4NcLUZ3nRSyif7ib7aCvQdqeoo8nNI1N9A4/MWHlg2oqbIgY7zN9wDEv3C352cA4sifJtdcUeTahGinxZOLTPtQ+b58+FaRAFV/RIb0USSggEwgGOb8SRbyriBicpQLTpnsxp3x4zWmEaadpxoVE7PMzjLXOzFebH03v1TZIE9/9ZsPZUrdeu4BaT4zxraX5qQvSPJ2qq22kVmd+emSj7ryi69lTtir/OuVmh1Zltb+4p3XmxZf2SIdF+7mqo+AthOk8CAOh0INuBsYS+k7a3bhtDwacUY/CkOf8wfZJorsHSCrN5kgoYgxM8FQAACKulNc/UsNfSepowNhOmNpwlxrkyliyTrZhgrttubjnWexCcEzJdlPocq3dNI5u22tSw19JylDA0EkYlYLCZkgQKwlGCpEdEQ1739CwKnGuHoFDdxdm2hvnhKe4fP1YRNVYRRZBkQVt9uaa1yaAx2qwyrkDBEw2Thfpy+GvLT8FKU60Esb+hzPnobHfYOGOIX/DxFjgzCKP8I6DE6YLLgDmxzIOnoYVAIK4nUNMvAuGY8dF+f989bMmvZ5Q6yIN/XdSpqNia16pgtul6r2j8zJYiWHOelMEwsDjDvvnWOTwW4/tFadNXH/e0pNlFdqxs5ZjIzp9/yau9a/0ZT2908y51jPviSO7KMeHutSv7Czl+AnYrpD9UXz4rAKp7MM6V+U071HH4bo8MSxnCCJ+x69j+o+gvwKatpB+EDpyQaThLrLvwie78hzadow/Fpc0AAIwl5EffyfBJtKqKCH0DYWgEDA7O9WcIQtmK8ZzACb13+ZLGS//qiz411dO1oMQ5fpLR33HD5tKM4yY1WphWVTU6j61fwH+F03Q/B18U+W31tBd1hYL2BvfTVADAzNAkWGnqjNBE1wd5ghyqEDHcaAgE4rrhyh0q8k1FIOyYmuBf8MS4+zac3VLkbi+ZR+jMHjfcFjVp69RUktveiPLzipHgxsLGd/f1v8fAorTglECxp2flxMp+vyNj4c95NsLjQsrQUMmmu4cxcQwA8O+Fpnt+z6dWjKlTGaetPnb0oTESrlt1htERvv+cb6RypR5keUHpGufKfSdu1hV9oTv3lk3v4tYfY/L5cctE6a85EWfyCMJMUVUbDhjGC5vb8neCTe9iCJO0aPXlP2MMrijjbcGYH93sdbBpqzsO3mZuPkJzmTjXn5/wf4Kkh/Eeak/eA678rMZCV6zLjirPLU+dUKnxbAL5zpihrxfsNtGWehKzuLdEptEMYkeCBKbwHtxoCATiugHNpiIQzggSc/9dmnnykaxFaUFCDuTCI4fp8QdwzalauEN2Q4LhS6SYbcSTmy9AD+spPBbj7RkUawhzByk235vpae/rrenBu5eP7Pw70ZttK/46RyHR7aKoSfvqzotuHjx3EC1rk+7MgxfqajBB4oPyhZWS0T9wQmdirB7bBziLHTBGNOQN/4XV4syPYeWo/Q9JdhxZ5jJHvXK4zag+8Uj7voUk4TqFM9XtUP6T5mmOinNkbMUEll8GJ2giL+p20ZA3ZTOOBSyqF6W93Jc5KgCAAXV/HOIwZydaqHmv2uLZDmOowOfR5LH0r/v84Ilw9ZMAAKP9IyFGGxMAMxoCgbhuQE2/CIRrhob6rF+SYSXI5Hf3XWzRuT7BPWo9b/rddRGmIpGAzRgd6QslVJPGtPlC095SZd6ljpoOg8mbM73ugGFg9c2D3Wyadci0BP+CJ8Y9tfnC+vx6i+MG4Cv+OjEywStT4m8bcqVv8O29ZZc66LZnf3646r4R4fH+rrVPbk4LenZrUaOG7l21QsRZODiIZhAnYDibH3s3P/ZuQFitmjLC2EIYWzC2hMH1Z4iiMKZXev/wfs94PS+pG6v/pzp0p8/YdQDrdTPL0nK8fd8C0urxNxJhUpJWrd/U/RgLpqwOBXzY1D+hPZGw7Z2QBxS451IAz6bmrCk7Vaen3g4QL5E/lDSG8um9ESKQZPiFnGqFID0gYLInBjrQwUYgEAhUTUUg3IWJY+lQa4/fH69Z+Xeh+3ewNoI816CGuIB5KYE82iKx5a26+/4oCH9997INBb+dqStV6vo9RwUAfDA7+fYhnk2l9kQh4vxyW3rpquxVObEZIRLcvvKD+QnYC1IDN9yRUfz0hO45qsFi++wwhHlIs434+JBbxvcCNuON6Qn0r/jm9EQBG3JJyjE4kylJYAdkccPncwJzmNIUL+WoAACGMMJLkb2KoXK99uwbvb1KGBradk+nkKN2YlGe7Mj1wB/FS0SLYXaYx4jcFaB2E7h5L4VoIhZn2+SlUqrJvIwr2JRzD1zH1C4eSBwNJc4dMUOFLJjD8AgE4roBVVMRCA8YES79PR+mqMbnhytJkvx8vlvqlHUqI9wM8DbaidwvebUr/jqrM0P27aSDhMv6emHKLT1sS7vQm22HKtuKmjQtWnO7wSITsGUCdlqweES41KF1TbiU9+b0hDenJ3QYLFVthkaN0WAh5EJ2gIgT7cfvkbsCAMC24uYOAxyLhQ359V/MT3F4FTvuGhb6w4lLuZWezb91Z0yk753DQiifPmBh+kIezOsztGff4kXf4dB7Rn1qladuq3YYq/4w1e/kBE3u+RJhaDI17DE37rOqSwmjkrQZcI4M58rZ8kx2YA5bNhzgcG4ehslCocTpJFMeBjEaACBKBDOLphYtRRq4MefuuXt+bDd71p0hYfO2TLw3XiKncFF3uDNm6JfFR/KUl+gEEbO4L6VNgrUkBAJxnYHSVATCA2YmBTz2z3m4hk1f5FalBonvG+HCBRF4weNUyqNuA0CS4P/+OvvN0WqI63ECh4mTJHCuvstm4IszQl6ZGh8icVC1IEmw82LLRwfK95W1OowjYDOmJfg/MSG6N5cgHx4rLZgFgGtNpgPlrS6PcZM2vaWgXu1OGR/HsE13D8v6IreoSUvhQrEywV93DXUnH77m4ATl9PcSKELaDJozz/tkrbF73tKWbyi3f5IC6pNPyOcUdNdqsrTl6wrfNVT9AYirvm1smgoAgKl2CzjzEkMYLkh+jB97L/0CeHZgLBtnmAkI+1xSNm847DR1iCP5X8pk+FHcAxqriCqY+/jig78ebHSrt6ITldmwaP/ap1LG3x8/0hufaxzDPsqcPWH7V1aC+ubpmxnTAnj93ZOPQCAGKo6bfpFvKgLhkBiZIDsGcl8ZAOCZf4vccRMZUB/MVVuL+ixHBQDcNSy04rmct2YkToqT86/uSuWzGZPi5G/PSKx8PufLBSkYAAX16lKlTm28cp99vlEz7OODU789tqOkpbdcV2e2/Xm2YcQnh6d8e4zC2HB3Chs0dE6343yju9H8BOzty0akBXssbpwWLN69fCREu9QBBVOSyPLL6O9VUMRY9UfPqqm++CtAQmissLafMzcdvvyAsGryX1ZuzjBU/GaXo9ph01arjz/c8nfSlXOp4sfhzwkbRDNIJ7dHD2HDllCaoIiBJcvEZTDHKqIonx4q8Nk7dfnv45cMlnowOl6lbVtx9H+TdnxDZ7rVCWMCIn8acwtG1X77/xJGweocRiAQ1yWomopAeMb7s5KHfnyQjoJrT9oNltd3l340J9n5YWL3vEncR0RVu3hTYeM7e/vUb+b74zVzBimeyY55JjuGJIFSZ27RmQAAcgFHLmQfr2nfkF8/47vjZ+s1RLdk3l/ImRDjx2cx1ufXGyzuVmx2lrSkfXDg51vTZiQGUFstXG9bj6SYwqS8Yw9lrdpS9OnhSnf+Shk49tCYyLdmJFLQnb6GEKW/0rZ7Zn+vggqkzWSq38mLvKX7k6baf2HFN17azA7IIkxtbbumWpQn3T/Rpqtp3TFBkvkpP+H/6CzgzYxpm2oKaRZURSzOs6nwa+YyrmBGSOLGmkL6oeaFp4joTWAyMHycIuq509u6abe5xd6GsuGbP82d8WC4EL6M8+3RQ1QW46PHN3n6G/y/hFGfj5gHfT0IBOJ64sp9CfJNRSDcIS1YvHQ45NYyAMCPJy65zKOCJVyWo+FJamAYiPCl4lLQL34zVoJctqGgcwgWw4BcyE4KECUFiAob1eO+ODLik8MfHqjIr1MTVxecm7Wm3/Prfzzp+r21o1Vnnvdj3vbiZmqrhTusqzV5Fo3DxD+ck1z89ITlo8Kd6CEJ2Izlo8JLnpnw4Zzk6ztHBQBwQmZwgqf29yooYrnab8amqXDpPetB8KbDpM3QvneORznqZQir6tgDhrKf6SwgRiyjX1J7fvDEQL7HTQTu8EbGNCZO99PBxhkvpTmYAfYItcU4bdd3ZWolheJlnV41Zee3SiM0mfrurEgYlTvjwVixu31Gvhz+HxPu+HLk/OtyxACBQEAEVVMRCI/5dN6g307XwZ0UVRktB8pbpyY4czln4tgghehMHZz2rTi5kJqm6+pjNaVKr9zuOKdOZXxnb9mrU+M7H1oJ8vVdF1/bVUp4pxfaYiNu+uXU0YfGDFJ4PDoFcTcBAMCmlEPGyARfLUj9ZO6g49UdByta69XGBrURABAo5gaJuWOj/IaH+ziUjLpekYz6pnXLKPf9SwcOVlXJVQ/V7rrpuhVcXaI6ch+N9l1SdeQ+pu9gFg2dqreHzihsb9xVT/HfNTss+fFB4yhf3TlJPgFL44Z/XXyUTpCHkrJ6ShldVLfsrS872lLdbNC2GLUcBlPGFUQIpdmBseMUUT2tep7J23qmlfpfb4mq5ZETm9aOvY1yBCcMlYWen/fkbxVn3ivcX9je2NthQXzxw0lZyxNGilkD2joIgUAMEFCaikB4DJuBe2Mb+HBlm/M0FQAwMU4GK02dFEdRAfL74zVQFkCBTw5VPJMdw2czzDZi9vcndpTAdJHtidZkXf7n2UMPjPb0ty3lsargLcOXT73Zm83As6J8s6LguONe0zAEYb6Td7RuzSLM7f29Fs8gTFcpchFGmH/2hKndUL6OTgSSMKuPPeg3/ZBnrajdYOOMPybckbP9awo+nDmBsb+PX8Lo3V2WPh8Mm13QVn+0meIo/nB52MvpV0qpBEluqS16s2DPsRbHAT+9cJiFM26JTHs6dUKyj6LzyQsdTd9ePEZtAV38Wn5mefzIMQGRNOM4hIUz7ogZekfM0DK1cm9DWX5bfaNB3WE2CplsOVeYLA2YoIgZ7BuEKqgIBMJ9UJqKQHgMSQKNGbLoLgCgpNm1QOuSjJD39pVDuRw1W9FmrSm/3itqHO6gNlq3FjfPT1Hc9Vu+t3PUTnIr2/53rmFBaqBHZ8XKBbB2EwAAsTJvGYreaDB9kmWzT3ccuM3cQqs41seQNuPVD01ww9MPYW7ONV7azA2dTTmChM09POPBp/P+/exCLuneknAMezBx9PvDZsFSOeoNPpO1MefujE0f1+o7PD13kFSxddJSAZPd+bBK23bbgXUuM14LYVtTfmpt+emVSaOXxY34ojj359I8Gw1B3U5IQL6Sv3PXlPtpxnFOjFgW43YDMAKBQDjhBur4QiBgQQLSG42mLW6I/aYEim8a7IHSY2/MSAwYEU5FTuNETUf/6g0//e+F57eV/Ham71o3PzrogQlEJ5mhPhAXsKGgwZ0tDIQ7MIQRvtP2i4a8jnNddxMwRVGUK4QQsfN9wZj27aADAX3JNzQjcBnMT4bP3Ttt+aSgOOdH4hg2L3zQ8ZkPfTJ8rrdz1E6+Kj5CQSw3iC/eM3W5L+eyBMDmSxdSN37gflWWBOSnFw6nbHz/6+KjBhscH+a9DWX1ejWUUAgEAuFtUJqKQHgMjmECNvxOBDdlfj6YncSnNFPaBYuBfzA7idq55a39MJXanYpW/Vt7Svvyikeq2jqnOt1nOlWJYIf8eKIm6d39N/9yqkkDt4x2g4LhbGHqc/4LqySjvuGGzcHZ9nsKOC+QF7nIJ2utfP5FpiS+XxbZHQb/qsYHnKfor5U4wdywh7RBELger4jeOeW+s3Mffytj+uTguCC+uNNmhsNghggkM0OTPsycXTz/6f9l3zVUFkr/cu7weVHuy2d2ulnj7U69Xn1/7p82kgAAHGgsv3nfLxpLP3+ECZLc09Cn358IBAJBGce32gPKnhGBGICE+fAuNMH0xgQA8FluJZ+hPrzP5g1auqGA8sf04znJ8f5Caud2GOB3Ow9wSBIcrGhblOZBETsxQDgm0vdwpb3dJWUIkvyjoP5gReua29IpDxUjuoMx+fy4+/hx9wHSZtM3WNWlptrNto4iS3uBzag0VP5uqPxddfQ+jNH/Wi9Mn8TuD1k+FPeYvAppM1laz7D9R0GJliINTJEGPpOa3flQazEJ6bm5UGZ7XfFDxzZSPn1jTeEbBXvuic2ctfsHo21AfHnmt9Yvib5WbYQRCMQNBaqmIhBUGBwE3/xAIXb3hviezLA3pye6Ps4Rz0+KXTE6wtOzNCZrZZu+w2CBaxh7rVDk+ZbEe7OSoGuFNGlMM747sY2qTQ7CITZ9g/bc2+17ZurOf2Ss227TNwDicoMladUTJmh7DZRhK66SscV5gUxxTH8txgk2qBLE3emvHNVC2B45volCHbU7b5/dO2/vj/1eR+2iQtPq+iAEAoEYAFyppiLfVATCfbJjZdDHIxM9qXA+kx0TIxPct6Gg3eDuzBKPxXhrRsLDWVFuHn+ipmNTYePeMmV+ncpovazewbghvyjqPWz6BQCMCJfePiRk7SmPlUudY7ERN/9y6sCKUUNCJHAjXzMQFpuxhTApMZyNc2Q4l5ZYi7Hmb1Xu0oGQi/YGzvFjB9i7rXDDF2jPvdMv63GCnSLxdcC3JcdKVHSl2gw2S54S8vcAHdQWj7/NEAgEol9ASr8IBBUWpAau/N+5ruQNCmOj/Tw6fmFq4JBgyXPbiv8sqLc6LXLiGDY/RfHG9MQ4uVuasbsutry+q/RghYObTtsNORGgM7s1NmzHNwtTK1r1R6ogp0Bak3XphoK8R7O8be1AmNuBzYRzZADv//8prO2FhsrfTA17La15gLjSPImxxBzFOHbQZF704p5Tps7RFrymOfMi7JVChhe3DGPY1xL58ct1Fz6xUwCmAIYzSQJaJypsCeL+5+uSa0kR2k2wAaAKhkAgEO7Q/zcfCMS1iJTHWpkVCcsbBgDgL+SMjvBYejfKj//b4iFvTk/48cSlvWXK49Xt3fNVJo4NDfXJjpHdlRnqpqmJ0Uo8/e+Fzw5X3pDZaK9wmVQ0q/hsxqZ7huV8dfRsA2RpzTN1qp9P1t6dCVlChrSZjDUbzQ27TQ37bLqart5XnBfAlo9gB2bzIm7CeZ5589DH0npac+ZFU+1Wh9YppEVtvLTZeGmz5vSz/LhlwsEvuJms6ku+Hvg5KgBAlPJ0zycZwghB8qPas2/RCo0xMLaUhOfCaqdIfK1To+sobG/s71XAR8zunw5qBAKB8BSUpiIQFHlhUtzaU3WeasD2xgOjI1gMirPikb78V6fGvwrijVaiWWNq0BgBAAFCToCIw3NPlqkTndk28eujx6rbqS3jOkYmYFM+8fgjWd7I/N/eW3rXsFBY9VTSZtBd+FR34RPC0NDzVcLQZKzZZKzZpMl7ihe9RJj6LEMYCefCrtalu/CpJu8pknDt1URaNLrzHxqr/vQZu4YdMNb5wZa2fNWxByEt0otgLBHWS9YtTFllrPrTqqau2ipIftTcuJ+Al6baKRJf6+Q2Vfbn5UlveSFFiTxr20EgEIj+AkkoIRAUEXGYq29OZeIQbiVCJNzHx0fTj8Nl4mFS3vAw6fAwaYQv36Mc1WIjFv6ch3JUh7jZLO0QLhP/ZO6ggw+MnpkUALFL92KLrqAeTpHWqipq3TJSc+oZhzlqd0ibSX/xu5ZNg/UXv3U3Ommz6Wotbfnmhr2W9rMuL9H9zI7D96hPPOJOjtqFTVfTuiPHWONCmlWT9yQgqTRy9zEsaWpvL2Eske/kHZSL29ywueKMt5k+yVSX5gCmFGa0fqdUrezPy3utMzfd97raTUAgENcxKE1FIKgzIzHgp1vTaeYeOIZ9Pj9FQM8KlT5v7inbjiRke2F0pC/NCGMifTffm/nlghQo6+lkXzmE22hz4z7lPxmWtgL3TyEtGtWR+zWnnnF2jM2gv/hd+955Tb/Jm/8IVf6T3rojR7lpcNPvQU2/B3YcvM1Q+TtwOhWpznvaUPaT+6u6AmHtOHCruelgb6+b6naY6ndTidznsBXOysIMYaTflF1McZynYXnRS3zGrQcYw2XZ2X1wXgBTkgAr2kCg1dTPHtHegIHhOUGx/b0KBAKBcAvHaSryTUUg3OT2IcF/3zVMxKXeP//+7KQ5gxQQl0SBOpXxvX1l/buGAUusTJBA1WbWjhatB4VBl5xvpOvca2k/2753PmkzUDhXe+4dXeH7Dl4gLNpzbzf/EaE6ssxYs5Ew29fnCUOjoeK3jgO3NP8vTn/xO4cTp+aGvbrC9yisqhPSZuw4cBtp0Tp8VV/6A+XIduAceZe+EcbgMcVx/LhlnLB5sOJzwxc4P4DpkyybdYoftxRgbm064xw/n7FrfbJ+6Vw2N2xuT30mavAibvJiBbA/MBPXQL3dUyYGxSp4ov5eBQKBQLgFqqYiEHSZM0hR9NQECgU3FgN/f1bSo2PddYjxHm/sLqUmZnsjsHxUBKxQdSqYVhCXOqikl12QNlP73nmEuYNyBHXeUxblie7P2LTVrdsnaE6tIoyuK/M2baXqyLK2nVMIw9VCNaRNdeIRyqu6HFxfpy10ZNlCEuaGPTSDd8EJylEsMSpuVylu1yiW6OXzSySjvhVnvA7l/1a2/yiWX4bLwzCWUDJqtXxeET9uGcbu1aaIKY4TZ37sf1M1L+r2ridxji8v5m76S8VwNj9xJf04Awoeg9XfS4AMjmGvpk/p71UgEAiEuyDfVAQCAsES7uEHR+8vVy5ed8bNVCTeX/jLremZYZ5ZaHgDs4349TRkD9jrhig//gOjI2BFg7sXoDXRiqY7/6FNU0FvCaTq+MOyGUc6y2hWVXHrtizC6Fkrsql+V+u2LL/puTjXv/MZQ+Xv1vZz9BYGAAC6wg8EyY/bCf9aNRUQ7T3NypMAAIwl7nqGJMzq448AQN+qChNnfuT+0UxxnGTUt5KRX1mUeeaWY4ShkTA2AwYP58qYwgi2YgJDGO7wRFH6q8bK9XR2KwAA/MSVFHqPBzhBfLHrg64p7ogZmikP6+9VIBAIhLsgpV8EAhrjo2W1L07adbHl7T1lBypabY68TDEMZIZKHxsXtSA1kAFDfok+x6s7VEZLf69iIIJh4JO5gzhMaF0nVLWcHcNmUP/7IS0a7bm36a/B0nIsP+/XtKG32/R1bTuneJqjdmJVl7XtmuY3dT/GEgEADOW/0F8YAIC0GYxVf/LjlnblAWU/AAAgAElEQVR/0qaBZiIFALBpqwBh7e4rq8pdZqrfRT8yP2E5S5bp8WkYgyUfzpIPd/8MnCsXDftAlXuvx9f6D6Y4VjT4BcqnD1iSffp5FgMuyT6KDzNn9/cqEAgEwgNQmopAQGZSnHxSnFxjsh4ob82vUzdrTa16s5THkgs5SQHC8dEyuZCiu4mXOFVLq5ByHfP2jMSZSQEQAxbW050m7Y6UT70p0Vj9P9ICRyj4dO4XLxTErg55ldTVUA7SaY4qzvyItJnMjb2qH3mKqX6XXZpKWlSwggMAAGkjrbquVltz8xFD+Rr6UTkhMySZn9KP4yb82HsI3SVN/ssUzsV5gb6TdzhpNr52GeUfwcRxK0G/MN7/hAl8tk9eJmXz+nshCAQC4QEoTUUgvIKIw5yZFNCV5JAkOFbdvru0ZfWxmnq1UakzAwDkAnaID29slG9OrHxYqE9/9d1XtOr758IDGAwDz02MfWpCDMSYX+ZWnaqDmSPFyKjb5Jjqd8JaRpYof031dpKxhWYcXfHn/LilJGGlJunkEGvbGbtnSBJy1kGS1q4PrvrEww4VoTwDw0UZb3Sv0PYBwrSXAIOjOfOCc/llO5jSQdLxG/rKRLevkbC5k4Pit9YW9fdC6DIpKO7nrFsCr7seZgQCcd2D0lQEwrtYbMTaU3Xv7y+/0GRfSWvSmAobNduLmwEoHhwkfnJCzC1pQX3fCaw2eXBjeiPgL+T8cMvgGYkBFhuh1JkNFsKXz/Lh0dJTadNbXtheDGuFnYwIl1I+16LMg7UMIW54TvEThECEVXv2LW7ETRBC/YdVWwkA2V2BFmfCEW3uorNRGQBgbj4C510lCUPZGtawwRBCeYIw5RmOYnxH7lJrx3mXB2M4mx+/XDT0bYxxPRfoHh807lpPU+eFD/or+07s+hJhRiAQNwgoTUUgvEhVm/62daePVtl7cvSkoF69eN3pzw5V/rp4SJQfvw/W1gXhaIaWPjiGEb1YWzFwbFZSwPgY2SMbC71xaSdgGHDitxUg4tw3IizSV/C/s40P/q+wqu1KnZnFwDPDfHJiZQtTA1MCPa5LvLyjpE0PcwCYy8SzY2RUzyat2kqIi0nmwYlmrPmbrZgAJdRlCCtp1WPMK2VnXAhTQobBD8Lwyz38xpqNsMIaa/4WD3Nk9uNlWPIR8rnnjJf+1Rd9Zm46SNpMPY9hCEK5ETcLkh5hCEL6foV9THZgzNywQRtr+vprCiLjFTEoR0UgENcojtNU5JuKQNBn18WWBT/laTypVR6vac/46OC/92ZSsLehDB3TVycwcGzTXcNO1HRUt+uVOrOVIOUCdqCYOzrSd1y0n5THIkjyu2PVhbTNP90nMUD40y3pOrN1b5mysEHTojOrjVYfHjNAyEkLloyN8j1eo/rkYHmtI61mi43IrWzLrWx7bdfFKfH+L02Oc7+YqTPbfjp5Ceo/BSwcHES5wEtaDYAYiKJZpFVvVZd49RJMcRzG4JI2OM5ATGlq18+W5lwoMQEANk2FTV/H4AfDCugJGDd0Fjd0FmkzWJqPWtWlhLGZtBlwjgznBbBkmUxxbH+sqt/4MHP24eZKpVHX3wuhSJSo7/4rQSAQCLigaioC4RVOXuqY/1Oe1vN+2g6DZcZ3J/avGJUW3EejROFSr7TtWWyEL5/16tT43g7AMeyTeYNyvjpK80JhPrwaVw6ivnzWA6MjV+XE8FgMAMCEHnXI6nbD7etO51a2ubwcSYLtxc27LrY8MT76tanxLDfUe/eUtni0W+ESLhN/rfc31jWw5zMhQrphuOoBOBNjXtWbgDG4LP9R5oa9UMJzAnO6fraqYDZ1W1XF/ZSmXgZj8NiB2ezA7H5cw0AgUuS7ddLS7O1fay0OassDn3S//vwrQiAQCDpcucFCvqkIBCxURsus709QyFG7Tp/300mDBabHphMGB3lLpdOlOFN2jGxRWhCdSwwOEpc+m71/xagHRkckBtiPHfrwWHMGKb5akFr9/MRXp8Z35qg9ya9Tp31wwJ0ctQsbQb6zt2zBz3lWN1qmj7jR9e0RT06IifCl3hmOMfkAg+qNAw8Cnn4SAIApjAA9Oh55UbfDiY7h3MhFl38mrITJg78flxAGqOk6ggbDZKFbJy0N5nv2PcllMNm44y+cPiPZR+HpshEIBGLggKqpCAR8Xt15sUlDa+u9qk3/3r7yFyfHwVqSE0ZHSLlM3GiFX2FTGV0n6j/dmt6gNh2saKUQP0TC3XxvJpuBj4v2GxftBwDQmqyNGlOr3ixgM+UCtkzAdilJVdGqn7b6WIeBShPs5vNNd6/P/+XWdCe7fEYrsaGgnkLw3oiTC+n+YWA4QxBq01ZDWhFMMIyBMXiwxH6Zvuk9n+RFL9ade9uqLqUZnB9zN0MQ2vkzrC7iLiDKHSPokxUQWTD38RVH//qj8izphpjzcHnYD2MW/VZx5vWC3X2wvN5YHD2kH6+OQCAQNBmgG+oIxLVLTbvh88NV9OO8s6+sRWumH8clQg5zbkqgNyL3JqHUHS4T33TPsElxck+DJwWI9q4YFepzVceykMOMkQmGh0kHKUQBIo7LHJUkwR2/nWmksaew9lTtmlO1vb1qI8jF605XQrX8mRDtx6QtB82SQhOSJeEKtGAsdkAWrGCcoEkOroCzRRlv04yMsUSiIa9decjg0gxoH/+6VtC9FvHj8H8fv6Rw3hP3xGb6cRz3MvAYrBmhibun3n9s5kNJPgHPpGYPodZzC0MeJIAnWpE4CkIgBAKB6CdQNRWBgMxPJy+ZbRAqk3qz7dcztQ9nRdEP5ZIXJsX+WVDvTv+qR4g5zr5hzDbiaFX7oYq2erVRxGamBIqLmjTurAHHsOWjwt+fldRbE6/7rDtd61Gvr0Oe+vfC3EEKsSMlquV/nv3rbAPN+HZYYPya2IHZxkv/0I8DAGi3inyZaiihAAA4W8IJmgTF1hVjcLkRCx2+xA2fz4teYihfQzm2ZORXOK/b5g7OxDm+EPt+GbwAWKEQEEnyCfh+zM2ryZsK2urPtNU1GjQqs1HAZMu5giSfgBHycA7jyveAgMnePnlZ1tYvSlQtnl0Gxs7P2xnTxSzIuycIBALRl6A0FYGAzNYiaENlW4ua+yZNTQoQ3Tcy/MvcKrhhe5ufLFXq3t9XvvZ0rd7s2fytkMO8LT34iQnRsTKB66NdQZDkC9sg6Mo2aUxfHal6OjvG7vk9pcrvjtfQj28Hnw1h4I0XuUid9wQgIAg7XTBEjhEV0I/TCUMUxYu6VVv4rrX9HM1QguTHcXavasw+o78nTK2m2q0UIouHvddzwJUpSTA3H6EQzSEMSQKsUAjo4BiW7hfsjjqRnCvcM3X54gO/7m8sdz++lM17KX3yq/m72kwUGzFui0q/M3YotXMRCARigIDSVAQCJhYbcbpOBSvasWrI0jtOeGNawqGKtnMN8MpiGNbTX9RoJZ7afOGL3Cp3+oExDCT6C1ODxAFCbpCEMzrCNzPMxx1lXTc5WNFW1Q6nHfeXvFq7NNVGkI9uOg8luB0KEYd+EJyn4Mfdry/+gmacarNiozoLYprK8ssAGEOc+VHbjol04uC8AGHKU06PYEkn/KU++YS++Ev3mywxllAy/DNezF09X2L7j4GVpjJE0Qw+LWkxxMAhmC/ZPfX+DwoPvHNun8u0EwPY7LCkz0bMCxX4DJOFTtrxjd7q8dj8lOD4H7NuQXapCATiWgf5piKuHxo1pj2lyv1lyvJWfYvWZLISciHHX8geGSHNjpENCZHg3pezrm43WGB0/HaiNlqbNKYAGGlJTwwW25Gq9tzKtjqVsVlrwjFsSIikvE2nN8FRGM4IkfjyrzL2rG43zPnhREG9u5kwSYILTVoOk/HOjKQwL7jm/JEPTdnoQpOmVmUMkVxpsdt0vhFizt+dBH97QWNqiNJfNVauJ0xUxKu6eLXx7nxdLBGM4TDG6TC2hCUfDgDgBOYIkh/Vnf+IYhwGRzruN4zlwtIJY3AlIz7nBk9Vn15lbS90GZUTOkM87MPejEM54XO1he9SWq893PB5UOIgBggMDH8qZcKKhFE/lJ7cUJl/QnnJQth/zUaKfKcEx69MHJPkc7nfe5R/RN6sR249sK6gzd1vKgxgK5NGvzdsVr+LDCMQCAR9UDUVcT2QX6f+8ED5b2fq7CYbS5U6AMDGwkYAQLSfYGVWxH0jwukPNDqhnZJgrBPa9Bboaer5Rs37+8vXn6nzhrpvF7dnXNURp9SZp357rLhZ62mcM3WqiV8fPbxytL8Q8vuw6UIjxGinaztCJIquh7+droMYvAsMA1lRvlBC4Rxfyahv2vcvAiTFjYnf23O2qUYCAA5p0saJztBfEi/yFgxnd/4sHvYBYWg2VKzzOArG8Mlaw1ZMcPNwTuhMeegM06Uthop1psZ9hKHJ7gCmJJETNJEffz/TJ9lJHLZ8JEs2zKI86fGC7cBZ/Lj76AZBDDyELM5DSWMeShqjtZjy2+pbjLoWo5bPZPlzRfESebjQQYN6ok/A0Zkr3zm77/Oiw62uKrFjFVGvpE8Zr4j2zvIRCASir7mSpiLfVMS1iNFKPP3vhc8OV7rsAChv1T2y8fzXR6p/XTwkPdhbVnIQS6mdQFFj6kJntj2ysfD7EzXebpgIlnCXDg/vemgjyJnfnaCQo3ZSqtTN/ynv4AOjINbDTVaiQUXLNMiO8qvlfPeX06pS9saYSF+I6To3fIFkxBeqo8spnLtbM/TJ2gc7f36z6Y4sUT7NgirG4AlTVnV/wmfsGpZsmCbvKZJwV/Ia5yl8xvzECZ7i6cU5oTM5oTMBIK3qcsLQQJiUAGMyuHKGKArn+rsZRTz8k9Yto2nqtAoSH+qtYIu4PhCyOGMCIt08mMdgvZw++cmU8b9VnNlRV7K/sVxp1HW9imNYqjQwOzD2psjUEfJwJ3EQCATimgNVUxHXME0a0+Rvjp31pLWyuFk76tPD3y1Ku30IJZ8AV/BZkD9TQg4DAFCnMu4pVR6saK3tMDRpTTYCyAXsEB/u6Ejf7BhZjHt6QhdbdHN+oJ4resS7M5ME3ZR+vjtec7yG1pxtbmXbTycv3ZMZRntplzlW3e7OfKz7qLuZxDaojUqdV8yEHoItqcWPvx/nylVHlrmvUkuS2Lr2/2fvvgObOLIGgO+uerVly70XuQO2wVTTTAslgVRSCOm9wYWQ3KVeLuW+S0ghFRIuISGNNCCEUE1vpphm3HDHXZat3ne/P5xzjCyr7UiWzPv9ZaTdN4MBo7cz897sl1ofsGB//hGf16f83D39ZkkxnZkIRjzLENp8zsYFWU+xwyeoT7/otPYvzhTwZfcKc18hOHRWm3GmOBUT21bDchE7bAJfdreu+guPh2cI4oWjXvD4djBcCZjs+9PG3Z82DsOwbpO+Xa/WW8xhXKGUK+Ay4IMcAGB4gp9uIFCpDJZ5nx93K0ftZbCQd31Xymcxrh8R6fxqN8UGo2wAQOB4vUK/bFPZ7+XtdlOqr05exjBsUlLIU5OTbhgR5aBNaIvKMGftsXoFygaeg7ktL+a2vL+eAqiNlpe2Iyio+/y2isW5MQIUdW4xDDvTgqzSFYZhGEb1T3qbegxIg/9pUlLIjV7ocMtNuIEVOrr58HJG6yanK6IX9MlvtC3dp8m3ef3FlgezuPXZvFrP5nBIOzI/5gmRvbdY0rEhs3eYFaX62m9NrcVmxRmM+muLAc4UsCMmc6Jn8lLuIrhSz0ZHKGjCGquuxdi8w4N7CU5oyOztBDsY+az8F2k2yUtMrXssygpS30GaeghuGMGVskLy2FFFLMlIDIfu7rYkbJ6EDW11AQDDH6SpIFAt/a709GUPMw0rSS359vS5FVNTQhH0NelPKmBHiDjtajS7SQVsYuanR51edrhOcbhOMT1V+vXteTFBdvJkk5Wcu/a4b3LUolTpF7fm9t+c+31pc4cGwTekTW388WzL3QVx9ENhGNag0COJ8z94/yaxWhOCRi82+GzGRzeMcGXXs5WkzrSoytvVcq1JY7QG85gRIk5+bJCDv+0MYUL8nF/uWPNjivq7KYKzmbw6m3y11SI9pB75i3LqfnWe3Qgqkr+k/qXNKc/Gs23Pdjp1Ri+7t/75nxXWlLBBr2GF5LFC8jAMoyw60ign9e04g0twpQQnDCP86T8ygiWZ9mP3vluMzdvduo8hiJVM/5kZlOmlefkb0iDXVa/TXnyf1NtpLKyv2YBhGEOYKMhaxk97AGfab20FAABgGPOn/90BcNmeavnmC7Tq3+hM1hVbLv56TwGqKfWZlhL6A6ISsmp3iu7uvSTPXbV/0z0Fk5JsNz2uPljnwbKzu3Ace2BcwrsLsznMKxZANtH7k7IJhSpNVRsRZ5JJoX9+km5TG78vRVZDuBeDwDfcnj8q2knp2uON3e8dqNtR0WG3lFeChHfDiKhlU5IHK5v8j4XX5K7i/YukghiaaJY8nNXNxiwKq7jFFNpqcb5Q2W4JmX3pvf/EfHxd0EFXflO9fuqZ/lzzIzqSqzS4VH4MZ/IZzHiGANn2b+Rwlihk1jZt2Tvq0y9QVpfW1bkJNwZNXEtvu3Ig0dd+ozz6KGV28kPJqqlXlSzTlq0Knvw1O3Kqb+YGAADAT0CaCgIPRWFIOlJuutB2sFaBqm5qn1tyo1Glqe6Sa03zPy/Z9+jE3Ji/8pkuren13dXeHjo/NuiNeZlz0m2XwygKO1yHrPvrwVqv1CVCYlS0uF1tfGl75fqTTUbUJZQ/v2WU4z3qNV3aBzae23tJ7uCahm79uwdqPzxcf09B3NvXZYk4tj//syNFj05KXH2wTmkVKq3CckOiu/NUWQUPNz6zM7jgibCf0rmNji8+qct4t+PWveo/9w9byeHUCA0XZD/NS16iLV+tq1wzaNcfgsmNXyTMWcmSon9e5qcoUnn0IV3V567fYdU2de2YIcp/XTjiWe/NCwAAgL+Bvqkg8BypV6DqSLn2WAPyNPW67MisCNHFdjXasC5SGszX/bekbOW0viTkq1OXe1C3yemTFMIvkklvzY2emWZ/v2ar2uDiKpkrFDozqkayYi7Kh3Q8FqNeob/z29JWFfpTqcF8luM15O9Kmx/68ZyL68NmK7n2WMPeS/Iflo4eWPL6neuyL/cYfjlvZx+m637pmfarcupUQelM8clJgnMyblPfFmILxijTJx3WjNyhHndCe8UGVzGXZS9YACN4EaL810V5r5q7Sk1t+6zaBqu+HbMaCW4YwY9hh49nh0/GWWi64AYKVclyt3LUP1FW9anncAZHkLXMC5MCAADgj2A1FQSerRfdPvw2mG3l7SRFIWxzgmEYk8BXX5/jyplSL2nq0b+559Ib8zJ6f/krvZRjICGHuf7WvNQwfoSQ4zRj7FAjLnjboUGTpiaFoDztFiFiz15zDG3p4D4TE+w0VOzz7enmO78tdXfoarl2+sdHbRbesd7dxXfk3baBormpnqLwfZr83kpLBEaFMFUCQq8iBd0Wu2WSMAzDEkOGaVUYnMGSjmFJxwz1PIaetny1tny1x7erTjzNDM7mRM9COCUAAAB+668jZNA3FQSKow3INpEqdGZvNGiZIZMuHROLPKzr3t1f09SjxzDMbCVLGnvQBtcYLbuqO0ZGiV1JFw0WN47XukJnRhNwdCzKeqpNPQYv5agYhs3NGLRv5+6qzru/P+PZ0EqDecG64y0Dln95LMavdxd8eMMIPqKiyiSGyy1BDaZIBzkqn81wsa8SCFCkoVNd+hKtEBSpOv4kRnprbwgAAAC/AqXeQeCp7tQ6v8hlVUij9fn8llEOsgtvM1jIL080YRhWp9AjPyeJYdhnxxobu12qlMtlokl1+vBZaAKOiQuS8JDtMvXeuUo+m3Frnv0evyYr+ejP581Wz/98m5WGlb9dHPg6jmOPTUqse37Gi7PSpAL2YLfjGLKHm1OTQ9kM+P9oOFOf+SdlotsFyqKs0FWtRTIfAAAAfg4+FoDA06VDuY+0S4t4V2ovFoP48a4xQ7im2rs1GkknmIGsJPVtabMrV4aLBk1yPBMuRLDjF8MwFoO4Pd9++udXVk5PHSxRXLWvplpO9yHLt6XNR+vtb08IF3JevSa97ZXZJcsm/2dB1sMTExblRM7NCF86Jnbl9NSf7x5zdsVUB6163RIQfxbAY5RZo69ZjySUtuITJHEAAAD4OTibCgKPicby0UDeWGzsJWAz1t+WNyc9fOXWi81K55V1EkP4aoO5S4dmS9upy0qDhTQg2iI70M7KzueKUp1eFiXiBnFZqKooSXgsJAdTe/19hmz9ycsa1J1pEIqX8J6ZltL/FZOV3HS+bUdl594aeV0Xgka4FIWt2l/zU+KgJycZBF4QF1wQZ3+P9L1j4z475qSir1MZ4cLFudE0gwB/ZmzbS5nRHK+w9JRZ1bUMUTKSaAAAAPwWrKaCwINq22cvAaIDeIO5PT+m9vkZ/12cW5gUwrK3rZHDJKalhG64I//M01MV6EryWkmqRq7lIv1e9Xf6skv793Acm5joqAKQW6akhKIKhWFYTBB35fQU59cNERGH+evdBX0HRE1WctW+mpTX9yz++tR/SxqR5Ki9tl5s15k8fJzx2tyMINoVet9ZmG33nwYYNsydKEvKmToOIYwGAADAP8FqKgg80WIu/b2OfWKCuKhCDYbNIO4ZG3fP2DityXqsobtZaejUGHEcDxOyY4O44+IlvalIU48ebRWeLp0pUuSt353SYFbozCF8JylKY7dejm5b9aIcR71DPfBcUeqR+u7tFR2e3c5mEGjX9vsIOcxN9xbkx/7ZMKZeobv9m9OD7c6lyWgh913qmpflyVHqcCHn05tG3PGN26WG+zw8MWEIT3ED37AoK5FGq0IYDQAAgH/6K03tX+kX+qYCf5YVKUKYpmZFDlp9FDkBmzFDJh3sXeTbjw1mMimE571USmVwkqZuutB23w9nFIi2MUeIODeNjEISqg+LQfy4dPSMT496UA85RsxtVXvl6O/o2KBvl4xOC/uz8u3Jpp4Znx5VGby4OXn+uuN8NiM7QjRDJp2fFVGY5EYz4VvzYhQ682O/nB/sAhynMjn1o/iXwhg9YoZWT3EUFnGNMeaELnNWZsIH149A8TsAfo00dKKMZpQjjIZhmIUkT8ibilurL/a0dxg03Ua9lCuQcgWjJNFF0al5ITFom5YBAABwBaymgsAzNSWUZlPHPmlhgmix11dTXYSq/0f/gCwGMTo2+GiDAm3kXmqHpzrXHG14+KdzCId7fW6GkIP+R5aQw9z/2KRnt15cfbDO9btmpoUtzIl44pcLaCeTEip4elryfePi+8reXpJrF6wr8WqO2ktnsp5o6jnR1PPv4ksFccHPz5QtdHnt+tFJiREizgMbz3ZfuWs9mdP8sHTTXPGxUKadLeJWnM2NnGJtWcaMnYehKxoM/BBFoqxUR1lcKjPuCpXZ8EX1ibfO72vW2fkr+g12GsOwSJ7oofQJy7OnBLH95T8LAAC4GsBxIBB4bh4Vjaq+6M2j/KhwS5iAjfaEXu9+5ptzEa9A9nFQO+enc60Oltc8MDEx5J6xcQgD9sdlEu8vyvn9/nHj4p0fo5VJBesWj9r54Phj6Pr39np+pqzyuemPTEzsy1FJirptw+l276zZOnCiqWfRFydu+epUt8uHpW8cGXXqb1NuGhnVu+4kJnSr4949kPbokpAddnNUDMMYlMnculuxe4F8c6656zSy2QP/gzNQJng4C02L3Z/qzyVsfH3Z8c12c9Q+bXr1P8/szPz1P7tbqpGMCwAAwBWQpoLAExvERbL5k89mPDwxkX4cVFgMIlXKRxVNwGbEB/MwDFs6OpaJKKu38d+SRrtlcjs1pgc2nkXYSjRVKvjl7jHe3nc3LzP82FOFBx6b9LepybkxYpvhZFLBg+MTNt1TUPHc9HvHxuM4drZFhXYCo6LFNs9fvihpOtnk9m5kVH482zJx9aFOjavrYEkh/B/vGlP53PRVk/X7M5fdFLyXwFz6O2DuPtf1+wTtxfdoTBb4NQYP5cMyBo/uGXUKox4/9uvNe7/qMbm6MNuqU83ZufafZ3bSHBoAAICLYNMvCEhvXZv1G43ypL2enZ4a6/36SW6Zkhxa3o6mbcPk5NDenCdUwJ6dHrat3MMqQQ5oTdY/KjoGrki/uL2iB13J4pFR4i33jUXYh8axyckhk5NDMAyzklSn1tStM4u5zDAhmz1gobuxG9nOw142JXMtJPXyDpSFZzxQ0aGZ9/nx4kcmiFzebp3IqL1D9zBJuLfUTJEmVclyjLQIcla4P03g75jBmSijBdGN9syJrR+VH3b3LpKiXindyWewnxkxjeYEAAAAOAVpKghIccG8V+ekr/jtoscRRkSJV0zzu2YkN4+KXnO0AVGov5YvVi/K+aO82BuF0f61q3phTmT/FK5Zafj8ON1Gmr1wDHtictL/LcjiMt3b92EhqRONPftru+q6dB0ao8lKSgXsKDF3QoJkakpoMM+lBioMAo8UcSIHSY8pysnRXA8kh16xlr67qtOVdrvedrKp55nfLn5600hXLrbqWrp2ziFNHm6HVp1cyRDGcxNv8ex24LfYEVPQBcNpRvus6viqC/s9vv3Zk79nBIdfG5dFZw4AAACcgjQVBKqnp6U0Kw3vHqj14N7YIO7v949FXrKIvumpoXkxQaXNLrUkdSBazF2cG9P3yxSp4K6CuC9PNNEMO9D5VtWMT47ueHB83zfzu9JmVNt9KQybKQtzK0dtURneP1C39ljDYMu5DAKfnxnxzPQUt4rZ2psb4qyfx2IkhVyRpu6sRFkclY7PjjU+MD5h9P+64zigPrmS1LfSGIpSHnuCEz0HZzsfCwQQdnghwYsk9QhK37EjCglehMe3d5v0/zi1jc4EKIxadnzzrOg0LgM+QQEAgBfB2VQQwN6+Luu5olR3jyzmxQTte2xiXDDPS1+Ocr4AACAASURBVLOig8DxD2/IoX8G898LMgVXJuGrrssOdtbj1DOH6hQLvzjR1/NmTzXKXhF/31bu4pUkRf1f8aWk1/b8Z+8lB1uOrSS1paxt8oeHr11X4vqpy4EIHBewUX5IHRFlezD1NO2nFRiGYSjSaZKiXnFh+7FZXqKv/ZbuWIYO9bnXaQYBfodgCjIeQxJJkPUUndvfOLtHbqDbz6xW3eXBnmEAAABu+StNhb6pIOAQOP7m/MydD4232S05GA6TeGZ6ytEnC1NC0RSK9IaJiSH3jo2nE2FWWtiS/FibF0P4rB/vHD3wgCUSu6s639xzqfdrtIWFytrUrtQQUhksc9Yef+73ctc7xG692D5q1f6j9Z5X602UoHzSceNI2wWi2i4disBo6k5tr+hwWnBYc2EVkqxYV7mGsiD5vQM/Ish5miFMpBmEHTmNm3Cjx7cbrJZ1VcdpzqHXp5VHkW+pAAAA0B+spoKAN0MmrXqu6Ps7R09ODhmspG1cMO/ZotS652f8Z0EWx82Djr73yY0j5mWGe3bvyCjxxqWj7a7HzkwL+3zxKNw79XL/r/hSQ7feaCHb1IiPU+5wtvfVZCVvWn9yd5XbW2RbVYa5nx0/0+xhXp0bg2xjKo5hD41PsnnRB71SXWchKcfr5JTVaGz6DclYlFllbNmBJBTwHziDJx63GsM9//GLs4RB4z6gM4dD7XXdLpf2deySSl7eg74uHQAAgD5wsgIMBwwCX5wbvTg3Wm20HKpT1Mi1nVqT0UKGCdgRIs74BEmq1H+XTwdiMYgfl465b+PZ70ub3bpxhkz67ZJ8ByWC7hwde6Kp54ODdbTnaEtvtr62q+rf8zORb8U4ddnJauoDG8/ucj9H7aU0mBesO37+mWkS1+oq9TdDJt1w6vLg71Our2TmRImDeLY/jf1trebU5Z7b82MGe9csP0FZkZU+NrXt58Zfjyoa8BPcuGuDxn+kPPqIJzcTLMm0H5mSHDoTONJRT+d2G4fa67KCPT8lCwAAwDFIU8GwIuIw52Z4uA7pV/hsxndL8uekhz3z20W51vkRSiGH+eIs2YppKU5P6s7LCPdGmoph2PdnWv45Jx152Bq5o/2fR+oVXzvKFZ1rVhpe2VH5/iK3P/7eMCLqsV/OD94VyY1V64cnJAx8Uchm+tWCao3DTciWnjKEY1l6PC/iDfwZP/1hijSpTzxDkW6cDCfYwUGT13NirqE5epUKZVkytNEAAADY8PfdjwBcze4uiGt4YeYH1+eMiBIPln6mhApem5vR+OLMldNdqiY1Nl7ibtEpF2mMlnOtKuTBlQZHLViXbSqjv377yZGGyg6329WKucynpyLoaRQXzLu7IG7g64khLp249hmlw164VloFfgdE07m3jwAEEEHmk6HzjzCDs128nh05XbrwLDfuOvpDdxrQdKXuRb8UEwAAAAdgNRUAv8ZnMx4vTHq8MKldbTxQ23VZaWhXGy0kFSniRIm5hUkhCW4W8gnhs8YnSI7UK7wx29PNythgbmM3ss2fGIY5aG9ztL77hAsFlpwyW8m1xxpWXZetNlo6NEYWQUgFbFf6Ff19RurHR+q7XFjuduCdhdl2xxoVLfbSH5NnrA6fB6AtekRZIAEYzliho8MWnjM0bdGWvWPqOIJRdrYk4AwOJ3qOIGcFO2IyqnFN1sH2PnhCb3X04AYAAABNTAzDjEZja2urRoPyKSMAAK0IEefmUdFIQj0wPt5L+U+NXJcfE4Q2TRVzBz01urkMQRvGXp8caVhztEHbbwdvCJ81LUU6M016W17MYMd939lfSzNHvTYr4qaRUXbfmpkm/eRIPZ3gaIk4jh5r4gwOwrFwBhdhtEBEGrtMrXuMrcWWnnLSKKfMGpwdxOBFMEPyOFEz2JFTcIY/ttRyA05w4xdx4xeRph5T+wFLdxlp6KAsWpwlZvAimSG57IhJyH+PXAbKplwCJhthNAAAADaYxcXFXV1d0IEGgKvH0jGxHx6qO3UZSVvOK/TozXPSwzddQJY9YhiWGDLoR9XDdciSbb3ZdplFoTP/cr71l/Otz24tf3BCwj9myEKubDy77njji39U0Bz35GVlj95sNw2elxkh4bG6HW619SXHCTnBCUU4FtpogcWirNSWva2v+ZqyXtkBSItZus8bW3ZrL7xFcEL5GY8JMh8nuGFDNE1kCHYwN+46DMWeXqei+CKU0XhihNEAAADYIORy+cAcFbJWAIYxAsffvi7bGydULSR1S240h+l8u6zrRkYN+lmwwv0DpR5QGy2r9tXkrtq/v6ar9xUrSX16tOHBH8/S/0HZqjK8urPK7ltcJvHcjFTaIyBzvLFn9eDFt5jiNIRjMZBGCxyU9uL78s0jdVWf2+aoVyKNXZqzr3b8ItPX/eCzyQ0DmUEoC/NmBA+Hcn0AAOC3oIQSAFejaSmhH1xPq7WDXWealf/YVl4Qj6yhKIZhM9PsrxeRFNWlo7Xh1i1NPfoZnx79vrS5vF0z5r0Dj/x0bvAzs+758HB9TZf9o5jLpiTL/KmX0tNbys632m8zywobT6clpg12+ESP76XMauPl31UnV/YcWKLYdY1i5+yeA0tUJ1YYmrZQJvQ7CFChrHrFrrmqkmWul8ClTMqe/beqTq706sSGkymRyQijTY1EUEENAADAYP48a8RisXAcN5l895kPADC0Hp2U2Kk1vbKzEmF7zsYe/ZqjDcjCYVi0mDstxf7+T6OF9PG2DytJLfm2lEngRguJMKzZSv73eNPr8zIGvsVmEF/eljtrzbHB2944ES3mtqgM9Cb4FwtJLd9ctvvhCQPfIjgh7LAJpo7DCIbBGZzYeR7cZ1ac0V54W1+/ESPt7JTWlq3CcAY34UZhzjMs6Rjas0SKsvYcWGJs3uHBrdoLbxEssXDUC8gnNfwUSOPiBcGNWgR113pD0Y8DAABgMERSUtKUKVMWLlwoFAqHejIAAJ96eXbayul+tK10oBdmyRiE/c3JHKaXGus4YiUptDlqr7XHGrZXdNhNuycmhmxcOpo5yDfBsakpodePiKQ9uyvsqZb3bX62wU9/CMkQ3Nj5DH6MW7dQFq3y8P3yLXn62m/s5qj/u85qqN8o31rQc/BOymx/WXhIqEtfMjT8QuP2Fw1NvyGcz3BF4PiTWWjqBj+RWYgkDgAAgMEQBQUFkZGRBAG7fwG4Gj1RmOTzXM9V2ZGiB8YnDPYugeNhApTVZYeQXGua+9nxzP/b+/nxxoENeOZnRhx8fFKSm21U7xwde0d+7MdeqBX81cnLdl/nJd/BkhbQjU4wRaPfcOsOq7pG/tsYXfU612/R12zo3JJn6bno5uS8wqqu1ZatohlEVbLM8XFW0OuJrMI0MYK6U9/WlhqsFvpxAAAADAayUwCuarFB3MnJ/lhVVcRhfnVbnuNVxMyIYbUHpKpT+8DGs4UfHq5T2PYgHZ8gObl8yiMTE3ks5+WpRkaJt9w79pnpKY/+fM4b+6J3VnbYfwMnxAXvYDitAlqCrGXM4GzXrycNnYpd8yxKt0suW9W1ip2zrRqUe9Q9oz79PP0M06qu1VV+gmQ+wxubYHww/noG7XPU25srFu/72kKi31sBAACgF6SpAFztXp7jd1VVWQzip7vG5Mc6KcVUmBTim/n40rGG7oL3Dp4bUKkohM/6+MYR9S/MeH1extSUUA7T9qd3vIR3V0HcHw+MO/P01GuzI5ZvLrOgqvJ0pctKQ7vaflrFjigMLvwvhnm4Qs+JnS/Of9ONGyirYvc8i8p+qWSnrLpmxZ4Frpcs8gbS0Elnu29/usq1SOIMe7Nj0r6YvBj39G9pny2NZa+f241kSgAAAAbC+3rP7N69W6H4swnh2LFjExMTh2xSAADfWvjfE1vKUDY7pSNewttwe/7kZOcp6KnLyjHvHvDBlHwvSsw9/ISjjb4GC9mg0Mm1Jr3ZGibkhAvZTILYXNa2p1p+rKG7Rak3Wb1YYOrIE4UTEiWDvau98Lbq1HMY5V7ZJ070TEnRZpzpxt5mXeWnyqOPuDXKQOKCtwXZT9MM4jF97Tc9B5agihZ+Yw1DhLKY7TD2wcVDT5Vsptl+j8dgld+wMkE46L8FAAAAHmP2fYX3q0YCfVMBuHrUKXRLRsccqutS6AavPeMTPBbjvnHxr81ND+KyBr5rJalTl5WnLys7NEaFzizmMqUCdnakqKxN7fupelurynDr16eOPTl5sDJRXCaRHi5MxzAMwxq79f/aVf3liSa92cOCwO5SGhz9VRHkrGBHFHbvv92qGbTP6hUIpnDk86JRL7q1YZgyq9WlL7l+/WDUZ//FS72b4AzN1ndz53GE0UydR3mQprqmy6ij/1FHbzW/XLrjy8m3IpkSAACA/pjOLwEADEcnmnrWHG3YXdXZ0K0f2pmwGcTY+OBrMsIfHJ8QJmQPvOB8q2rV/trNF9p69EOcSPtSSWPP16cuLx0T6/iyn8+1PrDxbLdvvzNOdxOzwsaHLTynq16nLXvHqm0c7DKcYHOTFgtH/oMZZKcfj2P6+o2kodPduwaiTEp97TeCzCfph/KARVXtt9GGMZKi1lWjeUCwse7s++MWBbG5SKIBAADoA2kqAFedsy2qZZsu7BukrYiPsQiiZNnkUdFiu+/KtaaHfzr3y/nWq3OHx/Pbyu/IjxmsJQ+GYf/YVvHmniHITMRc5/934CyhIOspQcZjJnmJqbXY3HWS1HeQxk6cKSR4kUyxjB1VxI6cRrA9bD5pbNzs2Y32Qm0ZqjSVNKL8Z0ghjTaMnVW0XNYqkYTSW8172y4tis9BEg0AAEAfSFMBuLp8cKhu5W8XDV5o/ukZM0m+tqvqx7vGDHzrYK3i1q9PtagMvp+Vn7isNOyuls9Jt98/44NDdUOSo2IYluh6dxyCyQ6fyA6fiHwOpo4jKENRVpo1ij3koMur+6AnjYuOdw66wu+BYx0NkKYCAAByUOkXgKvIi9srnvz1gv/kqL02XWgbWDm2pLFn3ufHr+YctdeOCvvdX47UK57adMHHk+kVJmTHiId4iyNpkCNch6Ssegc7k73KrZJRLkQTIIw2jF1SyxFGq1ahjAYAAKAXpKkAXC0+Olz/2i46i2/e2ndrIakdlVccMqxT6OZ9flxjtHhpxABy8nLPwBcpClu+uWyoNkLPkIUNVtjJZ0gjglOpVwREcczVAwQ/BmE0hsDJSWbQS2G0bU3sP9EAAAD0gjQVgKtCnUK3YksZvRheTE2O1Cv6//Jvm8u6tEPZzdJ/XJLb+QT88/nWkkY76atv3J6HMrPyDPLdrZRlaAqJsYKzEUZjIo02jJlJlDtKTKSPKmwDAMBVBdJUAK4KfnUedaCqTm3f1/tqujZd8JcmrkPObnHjL0qGZocqhmGjY4PmZ4UP1eh9cAYPccAh2i7LjpyCLBbBYodPQhZtWOMz7bS88piAaac+OQAAAJqgbyoAQ6OmS1tcLT91WdmhMfboLQI2QypgZ4QLZ8ikeTFBDoq7eqCiQ/PTuVaEAZFT6P5aO121r2YIZ+JvrAN6v5is5FBVacZx7L1FOcSQb/nFMIIXgTpgJNqALmKHTyZ4UaQewT9Pbsw1OEtEP87VIIYfhDBanMDDatUAAAAcgEq/APiU2Ur+cKbl7X01Z1tUg10TIeI8OTnpkYmJEh6aR/7flTYjieM9fSu9aqPF5pzqVU40oPVLWZtaZxqaTYb/mCErTAoZkqFtEOxgVNkdhmE4S8wQDNFOZoIpzF6uOrmSfiRB9tP0g1wlciQon0pkSxA/NAEAAIDBpl8AfKm0WTny7f13flvqIEfFMKxdbXx+W0XSa3tQpZfF1f5eiFLA/rMXyNH6brPVfzcn+16ixHZ3a02XK/Va0O+IuXN07L+uyUAe1mPsiMlIQw3ZEjE/6ymmWEYzCDfxFnbkVCTzuRpMiUhGuClgWmQqqlAAAAD6QJoKgI+sO944YfWhig6Ni9crDebbN5y+f+PZgds+3XWhTU0zgrfFBv2ZjF1s9/ep+tiIKLHNK3ZPqw6AMuliEvjLs9O+uDXXD3b7/oWbcIMfhvIATrDF4z/GCM+3ThC8CHHBWwinNOxJuYKiKDS5pUwszQuNRhIKAABAf5CmAuAL35U2P/jjOaP7RYzWHW989OfzdIZWGsyuJTZDKTNC2PtFqwpxBddAN0MmtXmF9G3tgBky6ZEnC1+Zk472vDR93PhFDGEi/TgEL5KXeAv9OHRwomcGF36J4Z78j4yzRCEztzEE8chnNbw9O6IISZxnRkzHh24pHgAAhjFIUwHwupLGnru/O+NxdrH2WMM7+2s9Hl1jDIBmCdNSQnu/0JsDYLY+I2AzFubYHqITsn1SUwDHZsjCTiybvPvhCQVx/lghBmdwxGP+Qz+OePSbOEtIPw5NvOTbgwvXuzsThjAxdPZOVmi+l2Y1jM2Mll0XT7d/T25I9L2yAiTzAQAAYAPSVAC8i6KwJ349b6J33vLlHZUtKoNn9/rbIthAIXzW9NQ/1wzZTPih9JflU5NFHNukNH7AaVVveHNe5u6Hx4/xywS1DzfxZk7MNXQisCOn8lKWopoPTbyUJdJrT7MjXGtRgzN4KUul15WywsZ7eV7D1ntjF4ZxPX9CwWey1k66meHRGjgAAACn4McrAN71/ZnmksYemkE0RstL2ys9uzeIy/SrI4UDPTk5mfO/7DSUj7KfIR1D3nYlJoj77HQ7x+dyIkVuzs29ZXw+m7Hm5pHPFQVGVRjJ9J/ZYRM8u5cZnC2Z/otnW229hCmWhc7dHzrvEDfxJpxleyy5F8GL4mc8EnZ9RfDk9QTbr58j+LkkUci2WfeJWBwP7mXgxIYpdxRI45DPCgAAQC/omwqAd314qB5JnG9PN799bVaw+y1qeCxGtJjbrPRwMdbbYoO4K6al9P0yPRzl9suJiZLTl5UG144Ez8uIeH6WzEpSDAILE3I+Plz33oE6hJNxC4tBrFucKxywlIphWDCPlR8bdLLJ9WcfbuS0c9LD3l2Y03dU2P/hTL5kxqbuvTeb2g+4dSMrbJxk+s8Exy/669hgh09ih0/CSIu566RFWUEaOklTD8GRErxwVmg+MyhzqCc4fIyRxu2c8+Bt+76p1yhcv0vKFXxRuHhBXJb3JgYAAAD6pgLgRZ0a09EGNz79OKA3W3dWdt6S60lJyTFxwc3KNiTTQItB4J/cNLKvGw2GYRMSJAjjPzopaYZM+v6B2jVHG7oHqSPFIPD5mRHPFqVMTLwiY+nUmBDOxC04jq29eeSc9LDBLrg9P8adNNW5lFDBDJn0kYmJuTH2V/D8GcEND72mWHPuDc251ymr8xJcOMEW5KwQ5b5Cp7iuLxBMVth42NPrbePDEkoXLl9esmVDzSkL6eSRFo7h18ZnfTLhxmh+4P1LAQCAwAJpKgBedKRegXBrwqE6hWdp6jXp4Zsv+GOa+u7C7AVZEf1fiRJzx8QFI8nB2AxiTnqYVMB+c37ma3MzTl7u2V/TVafQtamMJisZJuREiTgTEiVTU0KDuHbSFRfXYJHjsojvl4weWDmpvwfGxb+1t6bV0+PKfRZkRa6cnpIg4fnmvKsX4QzhqBf5aQ9oL76vq15HGjrtXkVwQnmpdwuylzP4MT6eIPBnwWzeF4WLXxo1672LBzY3ljVougdeE8ETzYvNWJ49ZYQkyvczBACAqxCkqQB4UWWnq11SvRptcV70it/KtCY/KqLLYzHeW5T94PiEgW89PCHhfhRp6k2joqQCdu/XDAIfFy8ZF+/GUi2bMTRHFm8aGe04R8UwTMhhvj43494fztAZKITP+vLWUaH/+xYNAwQvUjT6TdHoN8zd583tB63aJqu+HcMoghvOEMSxIwpZklF+dRIV+JUkUcj74xa9P25RjbrrTFdzu0GjNBmELHY4V5gjicwKjoDGMwAA4EuQpgLgRWg3jnocTcJjPVuU6nERJuTGxgf/d3FudqTI7rt3F8R9cKjubIuKzhA8FuP1uRl0IkiHKH9zcdy7CmI3l7V5vEjOIPB1i3OHU47aD86SjGRJRg71NECgShGFpohCh3oWAABwtYPnygB4EdqNo3R6iq6YlpIw1Bs7mQx8QVbE7ocnHH9q8mA5KoZhDAJ/b1EOzT46zxWlJobw6URAW8zJdWFCl1JHAse/W5I/JdnDD9PvLsxe5GzNFgAAAABgqECaCoAX8Vgo/4nx+5Uacn8mjC33jfWgUHAvAsfvGhPr8egYhkmF7Lp/zPjtvrEzZFKnF09LCf3ohhEej3VrXswLs2Qe395rYiLKYk6uSw9zNT3msRi/3z/2rgL3WmIEcVnfLcl/ojDJ/akBAAAAAPgIpKkAeFGkiIs0mif9/fqMjBL/fPcYD3JdAsdXX5/z5W15f5ua7NnQQVzWrgcnxAa7sZz70ISE/yzI8mBN9foRketvy6Xf9XRUtNj3hYVwHJuU5EaLFCGH+eWtuT/cOTpVKnB6MYHj14+IPPP0lFvzoIAQAAAAAPzaX2kq9E0FALkMpBtHMyMG3Shrw2ghLysNpc3KYw3dtV26vuJJRanSU8unuNV0JELE2Xr/2McmJWIY9ta1WSumpbibAMZLeDsfGu9Bp5NnpqccenxSkst7d7lM4r1F2T/fVYCk+hGB4w9PsFPhyavGxUs8eBhxS2505XPTf7xrzA0jokL4dhbMsyJET01Ovrhy2i93F9DcCw0AAAAA4AN4X0a6b9++jo6O3q/z8/NTU1OHblYADBM9erP0pR1WEs1zn033FDiuAduqMnxxoml3lfxovaL/sVgCx3NjxEWp0iWjY0dFiw0W8tMj9e8eqG3s1juIFsxjPTIx8empyTaFdnZUdj6w8WxTj6N7e+E4dltezEc3jPB4szGGYUYL+cOZltd2VVXLtYNdI+Qw7x0bt2JaSpw7C7ZO6c3WrP/sq1foEMZ0bP1teUvpba4mKaqqU9uuNnZqTRwGIRWwU6T8cCGtdXgAAAAAAB+DNBUA75q95tiuKvtdHN0i5jJbX5492JbdNrXxxT8qvj512eiwaBOOY3PSw1+fm5EfG2S2kn9UdOyukh+o7apX6JUGM4ZhAjYjJog7KSmkKFW6MCdSxLFfDNxkJb873bz6UF1ps9Lu3gsRh3njyKgV01IclEpyi5WkTjcr916Sn7qsbFcbFTqziMMME7IzwoVFqdLCpBA6B3cd+Plc603rT3oj8kD5sUEnlk2mv10ZAAAAACDQQZoKgHf9Xt6+4PMS+nGWT0l+Z2G23bd2V3Uu/e5Mq8rgYigmgT8/U/bS7LT+GZHJSlqslLvJXofGeKBWUdmh6dSY9GZrmJAdLuQUxAUXxAcz6ZXq9R+v7ap+cXuFt0dhMYj9j06cMER1mwAAAAAA/Ar0TQXAu+ZnRsxJD9tRSWtBNYTPemFWmt23vihpum/jGbeOk1tI6p87q5p6DJ/fMqovUWUzCA/WI8OFnJtGRvWPfLyhe3d150eH6+Rak9JgkQrYYUL26NjgolRpZsTQtHih6YVZMp3Z+u/iau+d2cdxbO3NI+3mqD1685H67ja1oVNjYhK4VMCODeZNSJB4afW4l8lKmiykcJC1dFQoq9HcedTcVUoa5aRBjrNEDF4kMyiDHTkFZ7l9khkAAAAAwwmkqQB43bsLc8a9f1BttNCJYLc0zm9l7Q/+eNaz9Om/JY3hQvab8zM9nlV/cq3pw0N1Hx6u79KaBr77RUkThmGpUsHjhYkPTUjkMpHVGK+Wa/ddktd26To0JoPFGibgRIjY4xMkExNDOOhGeWNeRmFSyD3fn+nQGFHF7MNmEO8tyr77yr4yOpP1yxNNX55oOt2sHHi2mcMkJiRIHpqQcNOoaCSr1laS2lHZubOyc3+tvLZLpzJYMAzjMolIMXdioqQoVbooJ9LmiDId5s5jmgtvG5v/oCz2zv0STHZ4oSB7OTd2AYZDOXoAAADgagSbfgHwhb2X5HM/O+744OhgXp6d9sqc9IGvd+vNsjeL7aaFrtv36MSpKaF0ImAY9kVJ05ObLmhcy8NlUsG3S/LHxAXTGVFlsKw91vDR4frB6hvxWIzrR0SunJ46KhrZupzGaFlX0rhqX62D8lFhQvbkpNDdVXKV0exKzLQwwTd32H431hxteHF7RafG+Z9sUgj/gxty5mdGuDKWXWYrufZY4zv7a2q7HFWKErAZ942LXzk9NSaIVo8lq65ZeeheY8tOVy5mSkYEF37JCs2nMyIAAAAAAhGkqQD4yJaytru+O9Ojdyl76UXg+PMzZa9eYydHxTDs8V/Of3S4nuascmPEJ5dN8aA9aS8LSd3z/ZkNpy67dRebQby/KOfhiR62e/nq5OWnNl1w5TuJ49ji3JhPbqRVatiGlaROXVYWX5Kfa1G1qY0qg1nEYUaKudkRoiJZ6Nh4CZPAFTrzJ0fqVx+sc7D6mhUhemZ6yu35Mf3b5ygN5nu/P/vL+VbX54Pj2BOFSW9fm8Vyvw1PQ7f+9g2nj9QrXLw+iMv69KYRHrddNTb/0XPgTtLY5fotOIMjGvOWIPMJz0YEAAAAQID6K03dv39/e3t779d5eXkymWzoZgXA8NTQrV/6bemBWpc+pidIeF/cmjs9VWr33RaVIeFfuy0oWt38eNeY/udLXac1Wm9cf3JHZYcH9+I49t/FuTY7XZ0yWcn7fjjrblacIOH9dNcYp+u3Jisp15p0JmsQlxUmRLDBtS+hrejQdGqMSoMllM8OF7LzY4OKUqXp/XrqUhR2pkW5o6Lj7f01XVo3HmT0uS0vZsMdeW5VCT7e2D17zbHe/b1uefWa9BcHOSntgKltr2LXXMrqya5p8dh3BVnLPLgRAAAAAAEK0lQAfK34kvztvTW7qjoHSzJHRYuXTUm2WWez8c7+2qe3lCGZz8KcyE33FLh1S0O3/pMj9e8fqDNYrB6Py2IQBx+fOC7e1dq2JEXdtuH0xjMtHowVKmAffGzSwBpOGZcuWAAAIABJREFUFpLafKHtj4qO4mp5Xb/9wwwCHx0bVJQqvXFkFM39yY6ZreR3pS1v7b10oU1NM9TK6an/t8DVk8bVcm3hB4c9Pmr73qLspyYnu369peei/PfxlNnT3yNOSIp+5cZd5+HtAAAAAAg0kKYCMDTURsuB2q5TTcpOralTYxRzmREiTnqYsEgmjRY7P/4377Pjf1R4sow5kJjLVPzrGhf3/VIUtvpQ7crfyk1WT87Z2nCrU+jKrRff2lvj8ViRIs75Z6ZJ/1cHyEpSnx5teHtfzWCnW/tMT5W+PDuN/gnegSo7NLdtOF3arEQSjcDx408VupJUm63kqFX7y9s1Ho/FIPATyybnxQS5eL1i5xwXz6MOOqIgNuz6CpwpoBMEAAAAAIEC0lQAAlL0P3e53ijVqaq/F8mkzhMAncl6w5cnaDbXsbHhjvw78p2fdSxv14x8ex/NTc4cBrEgO+LxwqSUUP6Sb1zdfY397/znW9dmOVjfdtfmC213fHNaa/J8OXqgyckhBx6b5PSy1Qfrntp0geZYk5JCDj42yZUnDIamLd17FtIcDsMwUd4/haNeoh8HAAAAAP4Pav0DEHgMFhJhjophmOMqr73MVvLmr06izVExDPvYtSpQf9tSRv8grtFK/nyudfrHR1JeL3Y9R8V615AP1s3ztFbzQPtqum79+hTaHBXDsIO1imMN3Y6v0Zutr+6qoj/W4TqFi+v52rJ36Q+HYZi2/COM9OTgLgAAAAACDqSpAAQetftlb5wEdKGXzIrfLm4rR7PNuL+jDQqnJySrOrWeFWoajJn0JNvcUy1f/PWpgV1M3dWiMiz67wkDoozXhtOzu5svtNFsYtTni5JGp9eQhk5T234kw5GGDlP7QSShAAAAAODnIE0FIPC4U88VjYvtaheXPd1FUdjReicLgD+fa6EQlDRGYPOFtjVHG2gG+fvv5UqDt1YF99bIHV+AcD18V5XcadJu6jiMYcj+8IyIMl4AAAAA+DlIUwEIPCIOE22mGsRlOr5gxZaLSJrf2FXZ6aSWz8FaVxt7+sCL2yvorEaebVFtONWMcD42yts1jlPHk009qMZSGszVcq3jayw9F1ENh2GYRVmOMBoAAAAA/NZfaSre72Mv5ScrFwAAezhMwpVqwK5LDuU7eLe2S7cd6Z5bG3JnWV9Fh+c1aZFT6MzvH6zz+Pa1xxpIb/6ANVpIx9/PGhfOIbuupstJmkrq2xAOR+pbEUYDAAAAgN+C1VQAAlJ+rKu9QJyS8FiJEkdp6m8X27z65EpvdnJKs03tYXtPL/nqZJPH9/5+sR3hTOxyUJnJZCX1ZpR1m3r0Tk41UxaUWTFldpIVAwAAAGB4gDQVgIA0Ky0MVagZMqnjpqlOz47SJGAzHF9gsCCuiEtTQ7e+0qMF3m3lHQ3deuTzscFhDvqDnX79JxsWp+1zGRyEw+EMlJsIAAAAAOC3IE0FICAtzo12kI24ZemYOMcXVHV6dwkrUuQkk+Gg61aKyulmpVvXGyzkU5suLFh33Evz6YPjWDCPNdi7XCaD6fCRhLtEzk41ExwpwuEILrKnMwAAAADwZ3734Q8A4IpwIefJyUn044xPkCzIinB8TaezhjE0ZUaIHF8QJkS5IodEjdyNvaydGtOE1QdXH6zzwan/aDHXweo0jmNxwTyEwyU43C6OYRgzKB3hcIygNITRAAAAAOC3IE0FIFC9OCstil4hJRzH3l+U47RosMnqzewKxw7Udm2v6HBwojIjXOjFCXhEZXS1o4zOZF34RcmZZpVX59NnYmKI4wtGRotRjcVmEE7/aNgRhaiGwzCMHY4yGgAAAAD8FqSpAAQqEYf57ZJ8Lo2tv2/MyxwbH+z0Mh7Lmz8oKOyN3dVzPzse8sL2a9eV7K/pGnjJxESJFyfgEdfPeN77wxlvH+7tb15muOMLilKR7cIdnyBxeq6YIYhnSUYiGQ5nCjhRRUhCAQAAAMDPQZoKQACblhL61e15jgsgDWbZlOTnilJduZLmmq2LTFZy68X2aR8fmf7xkcYr6wwtyonywQTcIuY4OZPZ60i9YuPZFm9Ppk8In3XjSCffq1tyo1EdT70tL8aVy/gZjyAZjpeyBGc52R8OAAAAgOEB+qYCENhuHhV9+IlJjhuf2uCxGO8tyn53YbaL12dG+HTP7b6artxV+38r+6txS26MeFKSk72sPpbk2jd8+eYyX/40fWVOushZ/hwp4tw/Pp7+WLFB3DvHxLpyJT/tAaZkBM3hcJZIlPsyzSAAAAAACBSwmgpAwBsXLzm5fMqKaSliZ2VXmQR+a15M6d+mPDU52fX4U5JD6U3Qbd16843rT26v6Oh75f1FOYTTQ7Q+lOvCCc/SZmVJY48PJtMrJ1L0yMREV658fW5mCH/QasAuevu6bKc7fv+EM8Rj38VwWv/diPJeJXh+t6gOAAAAAC+BNBWA4UDCY711bVbjizM/umHEDSOibJIQLpMoSpW+Njej6u9F3y3JT3ezItG1WRGomt+4zmwlb/7q1MV2de8vR8cG3TvWSeMcn4kSc3Minaepv1/scHoNKuFCzi/3FLi4mzeEz/rohhF00v7FudG3jIp2/XpO1Iyg8R95PBw/7QFB1jKPbwcAAABAwHHpeBUAICAEcVmPTkp8dFIihmEao6VDYzJZyXAhh+bSWaiAfefo2M+PN6KZpcs0RssTv1zY88iE3l9+fOOIph79jspOH09joNvyol1J8Y43+qhyUjCftfOh8TKpwPVbbs2LUejMj/1y3oPhpqdK19+W526Sy09/mDSr1Kf+gVGDlnS2i5d6V9CET9wbDAAAAAABDlZTARiehBxmcig/I1xIf3snhmGvz8sI4iKI467iS/ItZW29X7MYxI93jZmZFub7afQn4jBXTEtx5cpqudbbk+ml1Jv3XpK7e9ejkxLX35bn9CyrjQfGx2+9b6xnq+vCnJWh1xQzBK6uiuMsUfDk9cGFX2K4a7uLvYA09VAWH/05AgAAAKAPrKYCAJwLF3LemJfh2eIbTasP1l2XHdn7tYjD3PHguLf21ry0vdJkJX0/GQzDnp8pc7H0cZfW5O3J9KIo7G9byoK4rHvc3Be9dEzsxETJsk1l2yranZZ6Sg8XvjEv44YRtA6IsiOmhN1Qbaj/QXPuDYuycrDLcHYQP+Uu4cjnfH8elTR0Ghp+MbbuMXccsRraMdKCYRjO5DMECeyo6ZzomdzYBRgxBI9sfIAyKY3tB6zqWtIopyw6ghNK8KLYYWOZwa6WWwMAAABQwfuK+h48eLC1tbX361GjRqWnpw/drAAA/mjl1otv7a3x8aAEjne+OsdmTbipR//egdrPjjWqjRZfTmaGTLrtgXFshktricHPb1cazN6eUh82gzi3Yqq7B497XWhTf3qkfldVZ1Wn7cphqIA9NTn0jtExi3IiUVaxokhz9zlTa7FZUUoaOkmDHGeJGPwopjidHTWDHTbO96mgVdukOf9vffUXlFXv4DKGIE6QtZyf8SjO4Phsbt5FWvT1G3XlH5rkJXa3ZBO8CF7iYkH2coYw0eeTAwAAcJWCNBUA4CqKwl74o+LfxZdI3/as2nxvQd+Can9GC/nFicZHf77gmx5aI6PE+x+bGMxzNX2K/ueuVpXBq1OyMT8zYuv9Y+lEaFEZ6rp0cq3JYCEjRJwIISc9XOBXNZa9xND4q/Lw/aRR4eL1zOBsydTvmZIcr87KB4wtu5RHHrRq6p1fSjD5svvFBW/jTDdOQQMAAACesd83tb6+XqFw9X9rAMBVAsex1+dl7HxofIKE58txy9s1dl/nMIn5mRESni8OL9w8KtqtHBXDsJggl/YGI/R7efvBWlo/uqPF3ElJIQtzIhfnRk9LCc2MEF4NOarmzD+7i29wPUfFMMzSUyb/fbyp/aD3ZuV1FKk+9Zxi1zUu5agYhpEWXeWn8t/GWLoveHdiAAAAQP80VSqVxsXFicViDMOUSuXu3buLi4tbWlqGbm4AAH80Qyat/nvR+tvyXG2bSVu72mj39S6t6Zq1xxU6DzfWshi4006zGIZlRgi/v3P0xqWj3cpRMQzLjhR5NjE6vjjh64LMgU5X8Yn6zCse3EhZtIrdC8yKM6hn5COqkys05/8Po9w7421RVnTtKLKoqrw0KwAAAKDXX2lqRkZGQUGBwfDXFjW5XH7o0KFt27ZVV1dbre61EAAADGMsBrF0TOyeRyawfdJPVWuy//PnyU0X+hqresBspe4fF7/6+px5meHCAQVvY4O4S8fEbr634MIz0xbnutEjtM/U5FCP5+ax7RVD37AngJgVpcrjT3h8O2VW9exbTJE+qpWFkLZslbbsXc/uJQ2dil3zSFMP2ikBAAAA/V3xyUyhUJCk7YNVjUZTWlpaXl4uk8lSUlLYbLYPpwcA8F/j4iUb7xx94/qTVtK7R0N5LDvLtscaur8rbaYZ+cND9RdWTnuiMMlCUq0qQ5vaqDZawgTscCEnQuR5gRyVwfLZsYZPjjTQnJ4HWlWGy0pDrM/3GwcoVclyd/u42rCoqnTlHwiyn0Y1JR+wai+rS1+iFUFdozn7qrjgHVRTAgAAAGwwXnnllb5fCASC1NRUHo+nVCotlivqZ1oslo6OjkuXLmm1WpFIxOEMlwqHAAAaMsKFyaH8nZWdZqsXM9UFWRFTUmxXJu/94WxNl45mZCtFaUyWhTmRBI4HcVkxQdykEH6EiDNwcdV1a442LFhXsqWsvVvvuzK//S3MjkwM4Q/J0IHF2PyH5twb9OOY5McFmU/gRMA8w1Ude9jcVUoziLnrFC9pMcEZgi0DAAAArga2G/ZYLJZMJps/f/7YsWODgoJs3rVYLLW1tdu3bz906JBc7nY3eQDA8LNkdOzJ5VPGxAV7b4iMCNsmK81Kw55qND+CfjzbarCgacGqMVpuWn/y4Z/O9QxRgtpraEcPILrqL5DEoUxKQ8OvSEL5gFXXrK/biCAQadaVf4ggDgAAAGCP/XNlBEEkJibOmTOnqKgoOtr2UBZFUS0tLcXFxbt27aqvr/dNKwgAgN/KCBeWPDX59/vHTU+VMgj0hWEnJkpsXtlTLUfVFEdjtBypR1DY3Gwlb/7q1M/nWumHosni5T3YwwRFmlp2owpmbNmJKpS3GRp+prnPuY++/icMg79sAAAAvMJJ+ROpVFpYWDh79uyEhASCsL24u7u7pKQEaiwBAHAcm5cZXvzIhBdmytBGHhsfHC22PWlZ2qxEOETpZQTRHvjx3PaKDvpx6BPR2LF89bCoL5GmblTRzPITqEJ5G8ImOqS+1aKEkr8AAAC8wqUqncHBwePGjVuwYEF2dvbAEkparba0tHTLli2lpaV6vd4LkwQABIYOjfG9A3VoYz46MXHgi7W0T6VeEU1BN9q+mq71J5qQTIY+H3e1DVBWdS3qaIGxrmjpuYgymrIcYTQAAACgjxsP3blcbnZ2dnp6el1dXWVlpU53xQc7s9lcXV1dU1MTFxeXkZEx8FwrAGDYW32wTmlAeTAyLybozjGxA19XIR2lS0srGklRyzdfQDUZmgRsRnIo1E9yjjShXJCnSBNl0ePMAPjOkzqU7dDRRgMAAAD6uL03jMlkymSy1NTU1tbW8vLyrq6u/u+SJNnQ0NDQ0BARESGTyQaeawUADGP0O8T0x2IQH96QQ+B2DruiXbf69XzL33/nrSxKlfBYHtxeXC0/06xCOiPPTU0JZTF80cw24FFo6mb1C2hxfo0foKwoNz1RFi3CaAAAAEAfD48w4TgeHR0dHR0tl8urq6svX75sU0ipvb29vb09ODg4LS0tPj5+4LlWAMAw09CtR7kXF8fW3DRyYmKI3TfFXJTHL01W6t/Fl7453bzhjrwpyW432Pj5/NCXTepza17MUE8hMOAs2/LR9MIROFOAMqD3EGzMakQWjQEdegEAAHgF3exRKpVOmDBh7ty5MpmMwWDYvNvT01NSUrJ169aysjKTyURzLACAPzvXgnJFMSGYd8/YuMHeTfJCX9CmHn3RJ0e/Oe32gvDBWgSFgpFIlQpuGQV7WFzCEMQjjRaH4bb/A/ongiNFGg36pgIAAPAKNIucQqEwLy+vt8YSh8OxeddgMJSVlW3durW0tNTmRCsAYNho6kG5mVBvdrQnMzfaK6ffrSR1z/dn/nCnYK+FpKo7/WXf43uLsjlM2LriEmZQOo5uJZAlGYkqlLcxg9KQRktHGA0AAADog/IDDYfDyc7Ovvbaa8eOHSsWi23etVgs1dXVv//++6FDh2xOtAIAhgGtCWVXKrXR0Um/GTKpvSOrCJit5B0bTsu1ru7+kGtNJivqU44euXds/PzMiKGeRcDAGVxW+ERU0dhRM1CF8jZ22ARUoXCWMIDycwAAAIEF/XN3giASExPnzJlTWFgYEWH7mYmiqJaWlj179hQXF7e0QIVAAIYPwvO80U5FJBbDUbh4CW+q+4dIXdStN7+0vdLFi/Vmv2gZvSArYs3NkDC4h5d8O5pABJOXdAuaUN7HibsWWaiYazDCk6pjAAAAgFPe2h7WW2Np6tSps2bNSkhIwAcsfMjl8kOHDm3btq26utpq9YvPeQAAOoI9KpOLYRiG2clInUZ7fV6GlxZUMQxbe6yh3rVmqsg32T49LYXPduOUI45jTxQm/XjXGCaN5wRXJ17KnUyxjH4cftoDBC+KfhzfYIXms8LGIQnFT38YSRwAAABgIK+fYpJIJOPGjZs3b15GRgaLZfu5U6PRlJaWQo0lAIaBVCnKSqcpoU6iTUwMuXGEt3IDK0ltOOVSLaUQPttuyxyP3V0Qd3LZlDnpYa5cnBMp2nrfuNXX53DhSKr7cIItGv1vmkEIdrAo91Uk8/EZ8dh37T4bcgsnbgEncLY6AwAACDi4TSMZrzKbzfX19RUVFXq9nVIrBEHExcVlZWWJRCKfTQmAAEVR2LlW1dkWlVxr6tQYQ/jsMCE7K0I0OjaIMUSramqjJeSF7RYSzY+U54pS35yf6fiadrVx0geHa7q8UsFocnLIgccmuXJl0ut7XFx6dYpB4Oo35vJYDAzDzjSr1hyr310lvyS3/Q2GCznTUkOXjomdlxHhvSXlq0TPwaX6mq89vBknJFO/4yYGzI7fPj0H79TXbPD4dpzJl15XyhSjrMYEAAAA9Iey96BTLBZLJpOlpKQ0NjZWVlYqlcr+75Ik2dDQ0NjYGBUVlZGRIZWiLJoPwLBR1qZ+Z3/tbxfbOjV2NiAE81jXZIT/bWpyQVywjycm4jAnJoYcqEVTIO2ajHCn10SIODseGlf4weE2Nbo+kP9zsklJUpQrK6UTEiSo0tTRsUG9OWqrylDWrg7ls6enhmaEC1kEzmYS8RL+5KSQ9HChTCqA7BSV4EnrSGOX8fI2D+4VF6wKxBwVw7CgiWutmgZT+0FPbsYZwVM2QI4KAADAq3y6mmpDLpdXVFQMVkhJIpHIZDK751oBuDp1akyP/Hzul/OtrvyrnZUW9tktoxIkPO/P6y/rTzTd/f0Z+nFSQgVVf5/u4mbaph79km9KUaXH/TW/PCtaPGjPEpKijtZ376mW/3i25UKbGsmIL89OS5UK3jtQe+qy0u4FBI5PSQ5ZMT0F1lERoqwG9clntOUf2a3mZRfOEgdN+JiXfIdXJ+ZVpFGh2D3f3HnMrbtwBjdo4me8lCVemhUAAADQayjT1F49PT2XLl1qaGiwW0hJIBCkpaUlJSUxmT5d+AXA3+y9JL/jm9JWlcH1W4J5rHWLR93gtQOcA1lJKv+dA+daVTTj/HTXmBtHujFtK0l9cKjujT3VdleYPXZx5fTMCOHA101W8uuTl9/eV1PRoUE4HJuBR4q5jd0utZ8dExf89e15GeF2pgc8Y2zaqjr9d0v3BSfX4QQ37lpxwTsMUbJP5uVNpEV97jXN2dcwyqVChsygzOCp37JCcr09LwAAAGDo09ReBoOhpqamurrabiElFouVmJiYkZHB4/l0aQgAP3G0vnvmmqM69xuTEjj+w9LRN7mT8tG0p1o+a81ROj9XpqWE7n3Uk4aW+2rk0z8+6vnAA5x/ZlpOpO1R+TqF7vYNp481dCMcqBeBY24d7BWwGWtuHnVHfgzymVzFKOPlbfqaDca2vaS+3eY9ZnAWJ3oWP/0hZpCTU9OBxaK6pCv/QFe1lrIO+hSMKU7lZzzOT38IZwy6vwAAAABAyF/S1F4Wi6Wurq6yslKns3PQq7fGUkZGRlBQkO/nBsBQqejQTFh9qEdv9ux2LpMofmTihEQJ2lk58Nqu6he3V3h2b1ww7/ATk+KCPXkgdbZFlbtqv2fj2tXwwsz4K3dN76zsvHH9SY3RgnAUOggc//7O/JtHRQ/1RIYfyqqutepaSWMnjrMIbhhDlEJwh3PFBNLUbWrbZ2rda1HXkoZOyqIhuGEMXhQrbBw7qoglGUG/ODAAAADgOv9KU3tRFNXa2lpeXt7VZf+wmVQqzcjIiI6GT2bgqjBn7bGdlZ10ImRFiM6umOrLvprLN5e9d6DW3bsiRZz9j01KC/OwsY3SYA5+frtn9w7EZhDaf8/r/0070dRT9MlR/8lRe3GYxMHHJ/m+YhYAAAAAgPf4Y6s9HMejo6NnzJhRVFQUFxc3sISSXC4/dOjQzp076+vrSZIckkkC4Btbytpo5qgYhl1sV39+rBHJfFz07sLsjUtHB/NsWyU7MCst7PTfpnico2IYFsRlISwZlR0p6p+jKg3ma9eV+FuOimGY0UI+/NM50v8eOAIAAAAAeIzxyiuvDPUcBsXn8+Pi4hISEjAMUyqVNgu/BoOhubm5trbWYrEEBwczGIwhmiYAXnT/xrONPS7V1HHsYrv6ycnJvqwNmx0pui0vxmghy9rUjpup5saI31mY/X/zs0RcupXSzreqS5vt18h1151jYmelhfX98vltFbur5EgiI9eqMqZKBaOixUM9EQAAAAAANPxx069dRqOxrq6uqqrKYLBT44HJZCYlJaWnp/P5fN/PDQAvaVEZYl/dherf6KnlU/Jjh+Bcd4fG+FtZe/EleUljT6vKoDVZGQQeJmAnhwqKZKGz08InJ4egGqv4knzGJ2iqKJX+bWpuzJ+JX2O3XvZmscnqv3s3CuKCS5ZNHupZAAAAAACgETBdXjgcTkZGRlpaWmNjY0VFhUp1RccLi8VSXV196dKlqKiozMzM0NDQoZonAAgdrFUgfI60r6ZrSNLUcCHnvnHx942L7/2l3mzlMhleWtctSpVOSQ6l30N1YU5kX46KYdj6k03+nKNiGHaiqaepR+9Z6SkAAAAAAH/jj2dTHSAIIjExcc6cOYWFhRERETbvUhTV0tKyZ8+e4uLipqamQFkoBmAwZW1qhNEutqOM5jEey1s5aq/3FmUT9AZgM4j/LMjq/8q28g56k/KFg7UKtAHNVrJVZbjQpm7s1uvNbjdDAgAAAADwWMCspvbXW2MpOjq6u7u7qqqqsbHRJiOVy+VyuVwoFMpksuTkZDi2CgJUi2rQNoYeaFaijOa38mKCXp6d9vKOSo8jvLMwu38lJwtJnb6M5ryrV5V3oHkMcbhO8cv51j3V8nOtqv4/WeMlvKJU6dyM8OtHRLIYAfaIEwAAAACBJSDT1D4SiWTcuHE5OTk1NTU1NTVm8xWNJTUaTWlp6cWLF1NTU1NTUzkczlDNEwDP6Ewol7D8sEqtl7w0O02hM79/0O2OOBiGvTBL9tikxP6vbL3Y5uc7fnu1q400I+yo7Pznzsqj9d12323s1n95ounLE03xEt6KaSmPTkxk+LDFEQAAAACuKoGdpvYSCAQjR47MzMysr6+vrKzU6XT93zUajWVlZeXl5XFxcZmZmWIxFMMEAYPFQJkGcJhX0QrYuwuzk0J5K38rdz3D5DKJfy/IfLIwue8VisJW7a/5x+8V3pkjYnQeahgs5LNbL35wqM6VoxKN3fonf73w7enmb+7ITw6FqnUAAAAAQG84pKm9WCyWTCZLSUlpbm6urKxUKK44pkWSZENDQ2NjY1RUlEwmG3iuFQA/FMpno4wmQBnNz+E49tTk5EmJIc9uLS++5KSRDI5jczPC37o2KytC1P/1534v/8/eS96cJko8loenG3Qm65y1xw7VuXe09VhD9/jVBw8+Nik9XOjZuAAAAAAAgxk+aWovgiDi4uLi4uL+n707D2yjvPPHP5J12ZIP2Yp1jg5LY8l2SAgEAkk4ElpuSlpIv20DbZejy9EudH9tabd8WeiyLaW/HiylsIVeu4WkHAUKbTm2ISThCoFAiGNJY1nWNbJj2fIh2br1/WN2tUaWFdnW7ffrr7Hn0cynxZH0nnnm8wQCAavVyjDM/L1sjyWGYaRSKUVROp2OU851JAGWqHtNMQPA/OctV4mNZNvfbj77sGfy14c8ewcDthOh+Xs5HKJX3vwJas31m8hTlNnzLB44MFRDGZUgiDWS5TzXkEylr3ni/aVmVNZYKPaJR95642tbtVJ0GAYAAIBiqreYmiGTybZu3To5OTk4OOhyuZLJj02HCwaDhw4d6u/vNxqNRqORz+dXqk6APM7SSYt4tLN1RVuetLZsJNs2km0EQZwIRf3TUf90hMvhKFqEqhaRbJE7zEPjs3e8OFDeMlfKsqy7mj95fejZj0aWfVLvVOT6Jz989e/PWvYRAAAAABbirIZVWyKRiMPhoGk6Fost3Mvn8/V6vcViaWzEDQGoLql0Wn/v3zyTcys/lETIG7n7QrEAXa8L8pnfvruS8FYRQ9+9wNC+tCdFT4Si3T94bSoSP/nQvP58w6ZLezpXeBAAAACAjFXRUkUkEvX19V1++eUbN25sbm7O2huPx2ma/vOf//zOO+9MTdXAshOwenA5nBvP0hblULtOUyOjFsh2IlRzGXWtonmpGZUgiP9/n2PlGZUgiDv/WhtdpgAAAKDKzczMHD9+PBKJ1O2k34V4PF5XV5fBYPD7/VZCxSTkAAAgAElEQVSrNRD4WFcVtseSy+WSyWQWi0WlUlWqToD5vnG+8bF33O7gim6oNgt5/3xhd7FKqnuPv++rdAlL9rVzDEt9SSqd/s/D3qKc/Yhv6iP/9MLnewEAAAAKMTU15ff7GYZhM5pAIFhFMZXF4XBUKpVKpQoGg3a73e12Z017DgQCBw8ebGtrM5lMOp2uoQE3oKCSGvkNP7i0Z9fj76/kIP98YbeyRVSskmpdNJF62xV0BecC4Vg0keyUCDslwrP10sxzqvscJ+kMXG36FM3Xnbnku+7HRmZGVrzUasar9gBiKgAAACzJ1NSU1+v1eDzT09Pzf+/1elddTM2QSqWbNm3q6+ujaXpoaCirx9Lk5OThw4ePHTtmNBopihIIVtFKHlBtvnCa+qh/+od7l9l1dtdp6n88z1jckmpRKp1+oX/04TeH9w9NzMWzlxjlcjjrVM27TtN85SzdR/6ZilS4PDwu56HPnMLjLrlp+fveYj7jcMSHJyYAAACgIMFg0Ov1er3emZncX7rGxsZWRQulk4pGo06nk6bpubkcUyt5PJ5WqzWbzQufawUoj3Sa+OqzH/3ijeGlvvDqdconrjmN37AqnkLP43XH+M3PHB0YDZ10ZGsjf2quCI9rlgeHQzy6c/31m5bzAPM/v2z73iv2YlVytl765te2FutoAAAAUH8mJibYe6fhcDjPsMbGRrVavXrvps4nFAotFkt3d7fb7bZarVk3nROJxNDQkNPpVCqVPT09HR0dlaoTVi0Oh3joM6dsN8lufPLDYGEhSsTj3nd5zz9s7VrlawOn0um7X7Z//290MlXQJbkayqj8Bu5PPtW7vIxKEMR0JFHEYop7NAAAAKgb7Mxel8sVCuW7YdDY2KjRaFQqVWdnJ4fDQUz9X1wuV6/X6/X60dFRmqYZhpm/N51OMwzDMIxMJqMoSqPRcFb5138ou6vWKbca2h844Hz4zeHJxdOUWNBw/SbtP55n1EmxxhLxjT8d/+n+oUpXURABjysWNARnC8rJlEz8+DWnnUG2lbqqAmFeDgAAAGSk0+nJyUmGYU6aTpuamtRqNUmSMpls/u8RU3OQy+VyuTxPj6VAICCRSCiK6urqQo8lKCd5s/D7l1ru/AT1+tD4XjrwITM9OhOdisSbhbxOiXCtsnm7SXa+qaNVxK90pVXh/tcGayWj9iman7jmNGOH+FfvuH+2f8g5MbvYyHXKlm9sM37uVNUK53I3C4v5/t8iwqcJAADAapdOp8fHxz0ej8fjiUQieUYulk4z8GzqSUQiEYfDYbfb4/EctziEQqHBYOju7haJ0EYVoLoMjc/23v9aNJGqbBkSAS8UyzchVtMq+vp5xlu26EW8/42d/SMzewcDA6OhkZnITDQpbeTLm4XrVS3bTbKujiWvj5rTrw+5r//Dh0U5FEEQn9+gfuKa04p1NAAAAKghhadTsVisUqnypNMMXP8+CZFI1NfXZzabnU6nzWabnf3YLY5oNGq1Wu12O0mSPT09LS1YjwGgWnzzheMVz6iqFlH/t85/9qORvYOBN5wT/ulIJJEiCEIsaDC0N51r7PgEteay3k7BgvuifYrmPkVpe7adqmot5tHUePcDAABYXQpPp3w+nyRJvV5/0nSagbupS5BKpXw+n81mm5iYWLiXw+EolUqKouRyeflrA4D56EDYfN/eir+93bBJ++hn18//zUw0weNyGvmVf1gglU4r7n5lLBQrytHe/8dzN6iLmXsBAACgOhWeTufj8XgGg8FsNjc1FTQvDHdTl4DL5ZIkSZJkIBCwWq2L9ViSSqUURel0OvRYAqiUJz9gKp5RCYK47szsNrzFfSJ0Jbgczhc2aB44UIRnd/sUzcioAAAA9S2TTt1udzQazTOysbExmUzGYh+7FJ5IJGiaHhwcJEmyu7u7vb09/+lwN3X5QqEQTdNDQ0PJZHLhXrFYbDQajUYjn49mNgDlduG/v/2qfayyNXz6FMUfv3xGZWvIb3Qm2n3f3pWvJfPMlzd+5hRlUUoCAACAqpJMJkdHRz0eD8MwOZv1ZLS0tJAkqdVqm5ub0+m03++3Wq2BQCDn4JMunoKYulLRaHRwcJCm6awLBiw+n6/X6wu/uw0ARaG+51VmutBZKKUg4nGPfet8Y4e4gjUU4t5X6f/7knUlR9hmku29+exi1QMAAADVYHnpdOHexRZPYbGLpxgMBh4ve7oZYmpxJBIJt9tts9lmZmYW7uVyuWq12mw2n/TuNgCsXCqd5n3zxQq+t3E5nD3XnrZzvapiFRQsmkhd8ug7rw3mvtJ5UsoW0Rtf22Jox2U4AACAepBJpz6fL5HIN9+KTac6nU4ikZz0sOFw2OFwOByOnImXvbFnsVgaGxszv0RMLaZC7m5bLBaVqga+vALUrlA00fxPf63U2bkczs8/s/bmzfqZaOJEKDo5l2hr5HVKhNXzVGqW6Uji/F+8ecQ3tdQXtor4+27ZjB6/AAAAta7AdMrhcDo6OlQqlUajKSSdZonH48PDwwsXT2GxbYAsFktrayuBmFoi+e9ut7W1mUwmnU7X0FD5bp8A9SeWTAm/9eeKnFrRLPzK2Xrv5Nzf6DFXcG7+Lp208QJqzRV98k/1yblV1l9tJpr4xp+O//JtV+EvWato3n3t6WtLvGoOAAAAlM6S0ilJkhqNZv4Nz+VJpVIej8dutweDwZwDFApFd3c3YmoJhcNhu93udDpz/lcXiURGo5GiKIFAUP7aAOqb5Dt/Ccdy9DZbntvOMfx54MRgIJxnTIdYsFXffmxk2jGe4wLhfJRM/J0LqC+fQbJZlQ6E9w0GvFORsVAslkzJJUJFi3CroX29qqXMafapD5nvvWI/NpLjyYX51kgEt53T9c1txoXLvQIAAED1i8fjPp/P6/WOjIykUosuMp9JpyRJikSiopdx4sQJm83m9/tznx0xtUTm5ub8fj/DMH6/P8//yTweT6vVms3mnM8cA8DynPaT/cuYxboY53cv0Eob/8se+Bsd2OcIuINzgXAskUqvkQjkEuHWrvYzyLZnjvr/MnCi8GNuo2RnadueeN+XddM1o0MsuPZ0zdfP7dJKV3rZsnDpNPFX64k/fuTfSwecEx/L2+1N/POMHZdYOnedpmkSYCYIAABAjYnFYgzDVDydZlls8RTE1CKbnZ31+Xwej2d8fHzh/7fpdDpnz2UOh6NUKi0Wi0wmK0uZAHXu5meOPvLmEqaw5qFsEfnu+uTCf7iJVJrH5RAE4Z+ObP35G0Mnu4m6PPwG7m3nGP71Ukv5b11OzMZHZ6ITs7EWEW+NRNgpEVTbXGUAAAA4qepMp1mi0ajT6aRpem7uvy/fI6YWRygU8nq9Xq93YmIizzAejyeVSlOp1Pj4eM4BUqmUoiidTrfYCkIAUIgXj49e8atDRTnUDZu0j352/WJ7pyLx8x5680NmuijnWsxpmtY/fvkMXRlvqwIAAEBNKzCdcrncNWvWqFQqrVYrFArLWeFCqVTK7XZbrdbp6WnE1BUJh8MMw3g8nsVa+7IaGhrkcrlGo9FoNOyiQJOTkzabzePx5PyjYVcQ6urqQo8lgOVJpNLG7//Nvch82sJxOMRb/7B1k1a62ICvPPXho2+7V3iWQhg7xG98bYu8ucKfHwAAAFDNotGo3+/3er35HzzMxBO1Ws3n88tZYSH8fj9i6nJMTU2xz53mT6d8Pl+lUqlUKqVSuXDJWoIgIpGIw+GgaToWiy3cKxQKDQYDRVErb6gFsArtPuL7wu/fX+FBPrdBvfua0xbb+yEzffpP9ydTZXoXPYNse+NrW/hoXAQAAAAfNzc35/V6GYY5ceJE7abT+RBTl2Bqasrr9Xo8nunpfBP8BAKBUqkkSVKhUHC5J/9CmUgknE5n/hWEenp6WlqwOCHAEqTTxHm/eOPAUL55+PlJhLxj3zw/z1TbT//m3eeOjSz7+MvwwI61/3COoZxnBAAAgKqVaYuz1Kmd1Q8xtVCTk5OvvPJKngEikUitVms0ms7OzmU8WZpOp/1+/8DAwGKPrcrlcoqiVCrVUo8MsGqNhWJbf37QPpZvIZnF8Bu4z193xiWWzsUGTMzGFXe/Ek8u+rBHKbQ38envXNDeVNWXPwEAAKCk6jidZiCmLsFf/vKXUCiU9UuhUKhQKEiSVCqVRel7FAgErFYrwzA596LHEsCSOMbDn3jk7eGJpbXhFfK4v/4/p37hNHWeMX/8yH/Vbw+vrLrl+NmOvtvO6Sr/eQEAAKCylt0WpxYhpi7B0aNHrVYru93U1KRWq0mS7OjoKEViXGwFIZZIJDIajd3d3VU+pxygGkxHErc8c/Tx930Fjrd0Sp645rQN6tb8w+56yfYvr9pXXN2SndPVvv/WLeU/LwAAAFREgemUbYtT+IOHVQ4xdQkmJibefPNNNp2WZ4HTaDQ6ODi4WI8lHo9nMBjMZnNTU1MZigGoaf9lH7v/Ncer9rE8Y4wd4v/v/K6/O1Mr4p38zf3aJ478/j1v8QoslKCBO/39S4QFVAgAAAC1q8CmrUtti1MrEFNrALuC0MDAwMzMzMK9XC5XrVabzeb29vby1wZQW+hA+L/sY/sc466JuZGZSCyZXiMWqFtFZ+mk2ynZ2TppA7fQyRFX/vrdP/WXtX9SxvFvbeuRSypyagAAACipEjVtrTmIqTWD7bFE0/To6GjOATKZzGKxoMcSQHlUMKb+7eazt5vKMaEDAAAAyoNNp263O+d9qYy6T6cZtfpM7SrE4XDYVViDwaDdbne73VmXGAKBwMGDByUSCUVRXV1dDQ0NlSoVYDVoEVXs/TMczfHIOgAAANScAtNp0Zu2Vj/E1NojlUo3bdq0du1au93udDoTicT8vaFQ6MiRI8ePHzeZTBRFCQSCStUJUN8M7RV7JryRX89XTwEAAOoem05dLtfCZUTmK3XT1mqGmFqrxGLxhg0b1q5dOzw8bLVa5+bm5u+NRqP9/f02m02r1ZrN5ubm5krVCVCvTlW3VOrUayTCSp0aAAAAliedTo+PjzMM4/V6C0yn5WnaWp3wbGo9YHss2Wy2qamphXs5HI5SqbRYLKv5Dx2g6CZm44q7X4knU2U+bwOXM/Wvl4gFmNUPAABQA9h06vF4vF5v1o2lLEin8yGm1pVAIGC1WhmGyblXKpVSFKXT6VbbnAGAEvn0b9597li5uyhtJNvevf2cMp8UAAAAliSTTj0eTyQSyTNSLBaz650inc6HmFqHJicnBwcHh4eHU6kc93nEYnF3d7fBYODxMOUbYEU+ZKZP/+n+ZKqs76L3XGS+68Lucp4RAAAACoR0WiyIqXUrEok4HA6apmOx2MK9fD5fr9dbLJbGxsby1wZQN/7+qaO/fNtVttOJeFz7d7aTbfhnCwAAUEUKT6ctLS0kSapUKqlUWrbyahFiap1LJBJOp9Nms83Ozi7cy+VySZK0WCytra3lrw2gDkxF4uf+/M2j/nwLcBfRdz9B3XuJpTznAgAAgPxSqdTY2BjDMG63OxqN5hnJplOSJFtaKtaCsbYgpq4K6XTa7/cPDAyMj4/nHCCXyymKUqlUZS4MoA74pyNbHnzDOZHjSlBx6aSNx755vkSI6foAAACVlEwmR0dHPR4PwzDxeDzPSDadarVarLuxVIipq0sgEKBp2uv15vzv3tbW1t3drdVquVysygiwBIFw7Lo/fPBC/2jpTtEhFhy4dUuPXFK6UwAAAEAeSKflhJi6GoVCIZqmh4aGksnkwr0ikchoNFIUJRAIyl8bQI1Kp4nfvuv5/t/owUA4/0hBAze2xGVsFM3C5647Y5MWD7EAAACUWyad+ny+RCKRZySbTnU6nUSCy8orhZi6ekWj0cHBwcHBwZwz6Xk8nsFgMJvNTU1N5a8NoEal0unnj42+cHxkLx1wBT+2NppO2ridkl3Rq7jQLPvWiwMPvzlc4LvvZT3yX39ufadEWJKKAQAAIJcC0ymHw+no6GB79orF4nJWWN8QU1e7VCrldrutVuv0dI4eMBwOR6lU9vT0dHR0lL82gJo2E02MzESnI4kWEU/RLGz++DOlH/mnf/SaY88HTHzxO6tbDO13bDNd0ScvfbEAAABAEEtMpyRJajQaLJxRCoipQBD/02OJpunR0dwP18lkMovFgh5LAMU1FYm/7hjfPzQxPDF7IhRNpog1EoG6VbRF377N1KFsEVW6QAAAgFUhFosxDOP1ekdGRlKpRa8gZ9IpSZIiET6mSwgxFT4mGAza7Xa3253zD0MikVAU1dXV1dDQUP7aAAAAAACKCOm0aiGmQg7hcNjhcDgcjpxNzIRCoclkQo8lAAAAAKhFS02nWq1WKESTiLJCTIVFxePx4eFhq9U6Nze3cC+XyyVJsre3F422AQAAAKD6RaNRv99/0nTK5XIVCoVKpVKr1UinlVKSmBoIBF5++eUPPvjA7/dHo9GWlhaTybRly5YtW7YUZbJoqY8P87E9lmw229TU1MK9bI8liqLkcnR5gWqRTKXHwrFAONbI58olQsnHexcBAADAqpJJp36/P0/2aWhokMvlGo1GrVbz+fxyVggLFTmmOp3OO++888knn8zZF0uj0dxxxx0333zzssNkqY8PeQQCAavVyjBMzr1SqZSiKJ1Ox+FwylwYAOtDZvoPHzB76cBh72Qy9b/vbGskgm0m2YXdaz63QS0W4M0BAABgVZidnfX5fB6PZ3x8HOm05hQzpu7Zs+eGG24Ih0+ytP3mzZufffbZzs7Oajs+FGJycnJwcNDlciWTyYV7xWJxd3e3wWDg8XD/CsrnHXfwn1+yvWIfy/9+1iEWfHWL/lvbTE0IqwAAAHUqk04DgUCeYZl0qtFo8MW1ChUtpv72t7+97rrr2KNxOJwdO3bs3Lmzt7e3qalpdHT0wIEDjz76qNPpZAd3d3e/8cYbMpmseo4PSxKJRBwOB03TsVhs4V4+n6/X6y0WC1aRglJLp4l/Ozj0zRcG8qw+msXSKdl9zemnqltKWhgAAACUE9JpnSlOTH3nnXe2bt3KTsRVq9VPPvnk5s2bs8bEYrHvfOc7P/nJT9gft23b9re//a3ACaKlPj4sTyKRYB9bnZmZWbiX7bFksVhaW1vLXxusBslU+vO/f/+pD3NPRM9DxOP+9StnbdS07h+aeNsVHJmJjIVijfyGNRKBvr1pm1G2TtXMxbsHAABA1QuHwwzDnDSd8vl8hUKhVCqRTmtFEWJqIpE45ZRTrFYrQRAymeztt982Go2LDf6nf/qnH/zgB+z2z3/+81tvvbXix4cVSqfTfr9/YGBgfHw85wCZTGaxWFQqVZkLg7p38zNHH3nTtbzX8rncNEEkFunyJ28W3rxZf+sWvUyMVZcAAACqToHpVCAQKJVKkiQVCgWXyy1bebByRYipjzzyyM0338xuP/fcc1deeWWewalU6pxzznnzzTcJglizZs3Q0JBEIqns8aFYAoEATdNerzfnH1VbW5vJZNLr9XiPgKJ4+qh/5+8Ol/QUYkHD/Zf33rJFX9KzAAAAQIGmpqa8Xi/DMMFgMM8wpNM6sNKYmk6nzWYzTdMEQZx//vmvvfbaSV9y6NChTZs2sdsPPfTQLbfcUsHjQ9GFQiGapoeGhnL2WBKJREajkaIogQA3qWD5YslU3/37BgMnaahWFDvWKh7fdRq6LgEAAFQKm049Hs/09HSeYUin9WSlMXXfvn3btm1jt1944YXLL7+8kFedc845Bw8eJAji1FNPPXLkSAWPDyUSjUadTidN03Nzcwv38ng8g8FgNpubmprKXxvUgV+8MXzrHz8q2+ku65E/d90ZPC6eVgUAACgfNp263e6cbVAyhEKhQqFAOq0zK32A+Omnn2Y3ZDLZxRdfXOCrrrnmGjZGfvDBBw6HI8+zpqU+PpSIUCi0WCzd3d1ut9tqtWZd+kokEjRNDw4OKpXKnp6ejo6OStUJNeo/DnvLebo/D4x+/fn+Bz+9tpwnBQAAWJ2Wmk6VSiXaptYfXjQaFQqFy379K6+8wm5cdNFFhXfNuvTSS+cfIfPoafmPDyXF5XL1er1Opztx4gRN0wzzsY6s6XSaYRiGYWQyGUVRGo0GbzFQiMm5+GHvZJlP+os3hr+4UXMG2Vbm8wIAAKwSbDp1uVyhUCjPsKamJrVaTZJkR0cHvjrWMd5KMuro6Cj71ChBEOedd17hLyRJ0mAwsMuc7t+/f7EYWerjQ3lwOBy5XC6Xy4PBoN1ud7vdWVPNA4FAIBCQSCQURXV1dTU04CFAyOdDZjqZKs6Cz4VLpdPffOH4vluyl8ICAACAZUun0+Pj42zP3nA4X8uJTDqVyWRlKw8qaEWTfuc/9nn66acv6bWnnXYaGyPzPDta6uNDmUml0k2bNq1du9bhcDgcjng8Pn9vKBQ6cuTI8ePHTSaTyWRayQUUqG/OidmKnPd1x/hR//Q6ZUtFzg4AAFA32HTq8Xi8Xm/OPiYZSKer1opian9/P7vB4XAsFsuSXms2m9mNwcHBxSYel/r4UBFisXjdunU9PT3Dw8M2m2129mORIxqN9vf3DwwMkCTZ09PT0oJIANmmI4lKnfrZj0YQUwEAAJYnk049Hk8kEskzUiwWq1QqpNPVbEUxlb1dSRCEQqFYasvWTFujZDLp8XhMJlP5jw8VxOfzKYoyGo0+n89ms01MTMzfm0qlXC6X2+1WKpUURcnl8krVCVUoTZR7xm/GQefEyQcBAADAPEinsAwriqle738321QqlUt9rUKhyGwvFiNLfXyoOC6XS5IkSZKBQMBqtS7WY0kqlVIUpdPp8KA8EATRIuRX6tQDo/laDgIAAEBGJp263e5oNJpnZEtLC/uFENPoIGNFMTUQCLAbnZ2dS33t/JdkjlPm40P1kMlkW7dunZqaomna5XIlk8n5e4PB4KFDh/r7+41Go9Fo5PMrllKgGujbGyt16hOhWKVODQAAUBNSqdTIyAjDMD6fD+kUlm1FMXVy8r/XhFjG31Zzc3NmOxgMVuT4UG1aW1s3btzI9liiaToW+1gkCIfDR48eHRgY0Ov1FoulsbFiWQUqa72qlcvhpNIVmPobT6biyRS/AUuHAwAAfEwymRwdHfV4PAzDZLXJzMKmU61WO//rOkCWFcXUTNvopT44ShCEWCxeeJwyHx+qk0gk6uvrM5vNbrfbZrNlrewcj8dpmnY4HCRJWiyW1tbWStUJldLexN+gbnnPO1X+U/O4HGRUAACAjKWmU51OJ5FIylYe1K4VxdTM3yKPt+TjzH/JYn/TpT4+VDMej9fV1WUwGPx+v9VqzZq5zfZYcrlcMpnMYrGoVKpK1QkVce1GTUViqrwZPcMBAAD+N536fL5EIl8HfqRTWJ4VxdRUKsVuNDQ0LPW181+y2B93qY8P1Y/D4ahUKpVKFQwG7Xa72+1Of3yqZyAQOHjwYFtbm8lk0ul0y/hTgVr0lbN0P319yBXMt9JaKVg68RELAACrV4HplMPhdHR0kCSp0WjwlBYsz4piauaO5TJy4PyXLNYRp9THhxoilUo3bdrU19dH0/TQ0FBWj6XJycnDhw8fO3bMaDRSFCUQCCpVJ5RHI7/h+5f27Hr8/TKfd6uhvcxnBAAAqLh4PJ7pioR0CuWxopiaCQPLmFU7/yWLxchSHx9qjkQi2bBhQ29vr9PppGl6bu5jN9MikUh/f7/NZtNqtWazGc/l17fPb1C/ZD3xn+95y3nST5+y5MWxAAAAalQsFmMYxuv1joyMZCY5LpRJpyRJikSiclYIdWxFMTUTA7Ka3BQiFAplthdr5Fvq40ONEgqFFoulu7vb7XZbrdbp6en5exOJxNDQkNPpVCqVPT09HR0dlaoTSorDIX71f9aPz8b+MnCiPGfcamhfr8KbCQAA1DmkU6gGK4qpUqmU3ZiaWnIvk/nRInOcMh8fahqXy9Xr9Xq9fnR0lKZphmHm702n0wzDMAwjk8koitJoNBwOp1KlQonwG7jPX3fmva/a/+VVutTr03A5nJ9c2VfSUwAAAFTQUtOpVqsVCtFZEEplRTFVLpezGyMjI0t97ejo6MLjlPn4UB/kcrlcLp+cnLTZbB6PJ+uNNRAIBAIBiURCUVRXVxd6LNUZHpdz90Xmc40d//evtjeHJ0p3or8/W3cG2Va64wMAAFRENBr1+/0nTadcLlehUKhUKrVajXQKZbCimEqSJLvh8/mW+tr59760Wm1Fjg/1pK2tbdOmTevXr3c4HHa7Pet55lAodOTIkePHjxsMBoqi8Fh/ndlukm3/muygc+IPHzB76cDx0Y89JiAR8s4xtK9VNj/6lnsyspzlqS6gZD/bgVupAABQPzLp1O/3pxefkdTQ0CCXyzUajVqtRrcXKKcVxVSKotiNqampsbGxNWvWFP7awcFBdqOpqWmxRS9LfXyoPyKRqK+vz2w2O51Om802Ozs7f280GrVarXa7nSTJnp4ePLRcZ7Ya2tlOvDPRhH86OhaKigW8NRJBp0TAb+ASBHFln+Lq3x0emYku6bCX9nQ++cWNggZuSYoGAAAoo9nZWZ/P5/F4xsfHkU6hmq0opp5yyimZ7WPHjm3btq3w1x4/fpzd6Ovr43Jzf/8r9fGhXvF4PIqijEajz+ez2WwTEx+bC5pKpVwul9vt7uzspCgKVzHqT7OQ17yG171GnPX7LYb2D79x3t/t+aDArkuN/IYfXGb5h61deK4ZAABqWiadBgKBPMMy6VSj0WQWhgSoiBX9/a1fv14gEMRiMYIg3n333SXFyPfee4/dOPPMMyt1fKhvXC6X7T4XCASsVuvCHkujo6Ojo6NSqZSiKJ1Ohx5Lq0GnRPjnGzYdGJq4/7XBv1pPJFO5LyS3N/FvOlv/D+cY5M14/AYAAGpVOBxmGAbpFGoRJ8/t/kJs2bLlzTffJAjiwgsvfPnllwt81UcffbRu3Tp2+8knn9y5c2eljg+rRygUoml6aGgomcehJeEAACAASURBVEwu3CsWi41Go9FoxMyW1WNyLv66Y/xtV9A/Ex0LRYU8rqJZZGhvOs/YcbqmtYGLyxYAAFCTCkynfD5fpVKpVCqlUol0CtVmpX+Rl19+ORsj9+3bNzk52dZWUCfMP/3pT+yGQCC48MILK3h8WD0kEsmGDRt6e3sHBwdpmmbv0meEw+GjR48ODAzo9Xqz2dzU1FSpOqFs2hr5V65VXLlWUelCAAAAiqDAdCoQCJRKJUmSCoUCT8ZB1Vrp3dTBwcHu7m72IA8++OBXv/rVk74klUpZLBaapgmC+NSnPvX8889X8PiwOiWTSY/HMzAwMDMzs3Avl8tVq9Vms7m9vb38tQEAAAAUbmpqyuv1ejye6enpPMOQTqG2rDSmEgRx4YUXvvrqqwRB6PV6q9V60pWU/vCHP3zuc59jt1966aWLLrqosseHVSudTvv9fqvVuthFR5lMZrFY0GMJAAAAqg3SKdS3fDE1EokEg0GlUpn/EPv37z/vvPPY7bvuuuuee+7JM3hycnLdunUej4cgiDPPPPOdd945aYmlPj5AMBi02+1utzvnP4fW1la2x1JDQ0P5awMAAADIYNOp2+3OOSMsQygUKhQKkiSVSiWaREItWjSm3nvvvd/73vfi8Xhvb+/TTz/d09OT5yg7d+58+umnCYJoaGj4wx/+cNVVV+UcFo1Gr7jiCvbWaENDw4EDB84+++xCqiz18QEIggiHw3a73el0JhKJhXtFIpHRaKQoSiAQlL82AAAAWM3YdOpyuUKhUJ5hXC63s7PTZDIhnUKta7j44osXzgF49dVXr7vuulQqRRDE2NjYoUOHbrzxxjxH2b59+549e6anp9Pp9DPPPJNKpc4666ysjmEfffTRjh07Dhw4wP541113XXvttQVWWerjAxD/MyvGZDI1NjZOTU1lhdVEIjE2NjY4OBgOh5ubm086+RwAAABghaamphwOx+HDhwcGBsbGxrIaQC6UTqdDodDExASHw2lra8MsX6hdHJFIFA6Hs/6I77nnnrvvvvt/B3E40Wg0/0Id/f3927dvP3HiBPtje3v7FVdc0dfX19jYeOLEiQMHDrz++uuZO7fXX3/9o48+uqRrPKU+PsB8qVTK7XZbrdacz3twOBylUmmxWGQyWflrAwAAgDqWTqfHx8c9Ho/X652bm8szsrGxUSAQhEKhnIvtiUQik8lkNBpxbR1qEeeUU045evRo1m+feOKJXbt2ZX7U6XTDw8MnPdbw8PAXvvCFt956K88YoVB4zz333HHHHcuotdTHB1goEAhYrVaGYXLulUql7GOruCYCAAAAK1F4OhWLxSqViiRJ9nJ5NBp1Op00Ted8FZfLJUmyp6enpaWlVKUDlEDDV77yle3bt2f9tre397333mPXdGlsbPzVr35lNptPeqy2tra/+7u/s1gsIyMjPp8v66nX9vb2L37xi48//vgVV1yxvFpLfXyAhZqamrRarVqtTiQSMzMzWX91kUjE5/O53W6CIDC1BgAAAJaKTac2m+3dd9+laXpiYiJnjwyCIMRisV6vP/XUU0899VSlUplZ453H48lkMoqiJBJJKBSKRqNZx2dnDgcCAYFA0NzcXPL/SQDFkK/T74EDB/x+/9lnn02S5FKPOzExcfToUb/fH41GW1paTCZTb29v1tOkK1Hq4wMsFIlEBgcHaZqOx+ML9wqFQoPBQFFUY2Nj+WsDAACAGpK5d+rxeCKRSJ6RWfdOT6qQiWBarRbX1qHKFWHdVIC6V/hnCTu1xmKxtLa2lq08AAAAqAmZbxRutzvrtmeWlpYWkiRJklzeZN3JycnBwUGXy7XYY6tYvwCqHGIqwKIK/yxZSC6XUxSlUqlKVBsAAADUilQqNTIywjCMz+crJJ1qtdqizM6NRCIOh4Om6Zwtgnk8nsFgMJvNmfnDANUDMRUgWzKZHBkZ8Xq9DMPknN+b0dbW1tHREQqFRkdHFxvQ3d2NqTUAAACrUDKZHB0d9Xg8J/1GUdx0miWRSDidTrvdHg6HF+7lcDgkSW7cuBFPz0FVQUwF+G8r+SwJhUI0TQ8NDeWZWtPd3Z1/VScAAACoA5lvFD6fb7F+SCz2G4VOp5NIJKWuKp1O+/1+mqYXXltvbW296KKLSl0AwJIgpsJqV8TPkmg0Ojg4ODg4mHM+D6bWAAAA1LECv1FwOJy2tjaVSlWedLpQMBi02+1utzuTAs4880y9Xl/+SgDyQEyFVap0nyWpVMrtdg8MDMzMzCzcy+Vy1Wq12Wxub29ffvUAAABQHQr/RtHR0UGSpEajqYZFAcLhsMPhcDgcXC738ssvb2hoqHRFAB+DmAqrlNfrffPNNxfbu/LPkjxTa1gymcxisaDHEgAAQC2Kx+M+n8/v9/v9/hpKp1lisdj09HSBS90AlBNiKqxSyWTy+eefz/pcyXyWkCQpEomKcqKFU2vmk0gkFEV1dXXhKiYAAED1i8ViDMN4vd6RkZFUKrXYsFJ8owBYVRBTYfV66623PB4PUZbPknA4bLfbnU5nzguuQqHQZDJh+TIAAIDqhHQKUGaIqbB6eb3e4eFhjUajUqnKkw/j8fjw8LDVap2bm1u4t6GhQaPR9Pb2lqIZPQAAACzVUtOpVqsVCoXlrBCgXiGmApQb22PJZrNNTU0t3MvhcJRKpcViwYMiAAAAFRGNRv1+v9fr9fv9eb4qNzQ0yOVyjUajVqux5hxAcSGmAlRMIBCwWq0Mw+TcK5VKKYrS6XQcDqfMhQEAAKxCc3Nzfr+fYRikU4CKQ0wFqLDJycnBwUGXy5VMJhfuFYvF3d3dBoOBx+OVvzYAAIC6Nzs76/P5PB7P+Ph4IelUo9HgQxmg1BBTAapCJBJxOBw0TcdisYV7+Xy+Xq+3WCxV2MseAACgFmXSaSAQyDMM6RSgIhBTAapIIpFwOp02m212dnbhXi6XS5KkxWJpbW0tf20AAAB1IBwOMwyDdApQ5RBTAapOOp32+/0DAwPj4+M5B8hkMovFolKpylwYAABAjSownfL5fNX/wJLmABWEmApQvQKBAE3TXq8357/Ttra27u5urVbL5XLLXxsAAED1KzCdCgQCpVJJkqRCocCnKkA1QEwFqHahUIim6aGhoZw9lkQikdFopCiqPEu/AgAAVL+pqSmv1+vxeKanp/MMQzoFqFqIqQC1IRqNOp1Ou90eiUQW7uXxeAaDwWw2NzU1lb82AACAasCmU7fbPTMzk2cY0ilA9UNMBaglqVTK7XZbrdacl4c5HI5Sqezp6eno6Ch/bQAAABVRYDoVCoUKhYIkSaVSiTXJAaocYipA7WF7LNE0PTo6mnMAeiwBAEDdY9Opy+UKhUJ5hjU2Nmo0GpVK1dnZiXQKUCsQUwFqWDAYtNvtbrc75z9kiURCUVRXVxd6FQIAQH1Ip9OTk5MMw5w0nTY1NanVapIkZTJZ2coDgGJBTAWoeeFw2OFwOByOeDy+cK9QKDSZTCaTSSgUlr82AACAlUun0+Pj4x6Px+v1zs3N5RmJdApQHxBTAepEPB4fHh62Wq05P7+5XC5Jkr29vc3NzeWvDQAAYBkKT6disVilUiGdAtQNxFSAusL2WLLZbFNTUwv3sj2WKIqSy+Xlrw0AAKAQmXTq8Xhy9rfPQDoFqFeIqQD1KRAIWK1WhmFy7pVKpRRF6XQ6NJMAAIAqUXg6bWlpUalUKpUK6RSgXiGmAtSzycnJwcFBl8uVTCYX7hWLxd3d3QaDgcfjlb82AAAAYl46dbvd0Wg0z8iWlhaSJEmSbGlpKVt5AFARiKkA9S8SiTgcDpqmY7HYwr18Pl+v11sslsbGxvLXBgAAq1MymRwdHfV4PAzD5GwBmMGmU61Wi/YKAKsHYirAapFIJNjHVnOufs72WLJYLK2treWvDQAAVgmk03r17rvvPvPMM8t77Z133imRSIpbD9Q6xNS6NTc3t3//fva/74UXXsjlcpd9qJdeemkllahUqnXr1mX98ujRo4s9NplfU1PTueeeu5J6Vrl0Ou33+wcGBsbHx3MOkMlkFotFpVKVuTAAAKhjmXTq8/kSiUSekWw61el0yC215bHHHrvxxhuX8cKWlpbJyUk0y4AseCCtPv31r3+99dZbnU4n++Pc3JxIJFr20S655JKVFLNr167f//73Wb+8//77H3/88WUcbdu2bXv37l1JPasch8Nh204EAgGapr1eb9a1qkAgcPDgwba2NpPJpNPpGhoaKlUqAADUugLTKYfDaWtrU6lUSKer0Lp165BRYSHE1HrDMMztt9/+1FNPVbqQUlm/fn2lS6gTMplMJpOFQiGapoeGhrJ6LE1OTh4+fPjYsWNGo5GiKIFAUKk6AQCg5hSeTjs6OkiS1Gg06I9QN+Ry+cL7E3koFIrSFQO1CzG1fqRSqYceeujOO++cnp4u7pFvu+22Zbzq4YcfZhv25H/3Wbdu3RVXXFH4YZc0GE5KIpFs2LCht7fX6XTSNJ21eHokEunv77fZbAaDwWw2NzU1VapOAACofvF43Ofz+f1+hmFydphnZdIpSZIrme0F1UkkEn3iE5+odBVQ8xBT68Thw4dvuumm9957j/2Rx+OdddZZBw8eLMrBf/azny31JXv37n3ggQfY7euvvz7PyA0bNtx7773LrAyKRCgUWiyW7u5ut9tttVqzrnQkEgmapgcHB5VKZU9PT0dHR6XqBACAKhSLxRiG8Xq9IyMjqVRqsWFIpwBQOMTUmjc9PX3nnXc+9NBDmQ+GzZs3P/LII/39/cWKqcvw85//nN3Yvn17T09PpcqAJeFyuXq9Xq/Xj46O0jSd1eMqnU4zDMMwjEwmoyhKo9HgSRIAgNUM6RQASgcxtbY9+eSTt99+u9/vZ3+USqX33XffjTfeyOFw+vv7K1WVx+P505/+xG7feuutlSoDlk0ul8vl8mAwaLfb3W73wh5LgUBAIpFQFNXV1YUeSwAAq0qB6ZTL5a5Zs0alUmm1WqFQWM4KAaAOIKbWKpfLddNNN81fKmbXrl0/+clPOjs7K1gV6+GHH2afSNFoNJ/61KcqXQ4sk1Qq3bRp09q1ax0Oh8PhyFrdLhQKHTly5Pjx4waDobu7GxfIAQDqWzQa9fv9Xq/X7/fnWc6woaFBLpdrNBq1Ws3n88tZIQDUE8TUWpVIJPbt28duUxT1i1/8okqeVo9Go4899hi7/fd///c8Hv7GaptYLF63bl1PT8/w8LDNZpudnZ2/NxqNWq1Wu91OkmRPT09LS0ul6gQAgFKYm5vzer0Mw5w4cQLpFADKBhGiVhmNxjvvvPN73/veHXfc8d3vfrd6ptPs2bNnbGyMIAg+n7+8VZ6hCvH5fIqijEajz+ez2WwTExPz96ZSKZfL5Xa7lUolRVFyubxSdQIAQFHMzs76fD6PxxMIBPIMy6RTjUaDC9MAUER4Q6lh3/rWtz772c9SFFXpQj7moYceYjeuuuoqxJU6w+Vy2R4YgUDAarUu1mNJKpVSFKXT6dBjCQCgtiCdAkCVwDtLDWNvcFW6io9555133n33XXYbzZPqmEwm27p168zMzODg4NDQUNbieMFg8NChQ/39/Uaj0Wg0YvYXAECVC4fDDMMgnQJA9cBbDBRTZh2adevWbd26tbLFQKk1Nzdv2LChp6fH4XDQNB2LxebvDYfDR48eHRgY0Ov1FoulsbGxUnUCAEBOBaZTPp+vUqlIklQoFFwut2zlAcBqhpgKRTM2NvbUU0+x27iVunqIRKK+vj6z2ex2u20228zMzPy98XicpmmHw0GSpMViaW1trVSdAADAmpqa8vv9DMPkT6cCgUCpVCKdwgo5nU6PxxMKhdra2jo7O7u6uvDnBIVATIWi+eUvfxmNRgmCaG1t3bVr17KPk0wmY7EYbr7VFh6P19XVZTAY/H6/1WrN+urD9lhyuVwymcxisahUqkrVCQCwak1NTXm9Xo/HMz09nWcY0ikUxSuvvPKf//mff/nLX7LaLra3t1900UU333zzOeecU6naoCYgpkJxJJPJf//3f2e3v/SlL4nF4iW93Ol07tmzZ+/evR988MH4+Hg6nW5qalIqleedd95ll1125ZVXNjQ0lKBqKDIOh6NSqVQqVTAYtNvtbrc7a/WCQCBw8ODBtrY2k8mk0+nwnxUAoNTYdOp2u7Nmu2RBOoVimZycPP30099///2ceycmJnbv3r179+4rr7zysccek8lkZS4PagViKhTHs88+6/F4CILgcDi33HJL4S8cHh6+6qqrnn322aw8Mzs763A4HA7Hr3/9656enh//+MeXXHJJkYuGkpFKpZs2bVq7dq3dbl/YY2lycvLw4cPHjh0zGo0URQkEgkrVCQBQrwpMp0KhUKFQkCSpVCrRnh2KYmpqis2oJElu3rzZZDK1trbOzs4yDLNv3z673c4Oe/75548fP/7SSy91dXVVtF6oUoipUByZ5kkXXHCB2Wwu/IWvv/56ZttkMqlUqubm5mAwODw8nFnvZGBg4LLLLrvvvvu+9a1vFbFmKDWxWLxhw4a1a9cODw9brda5ubn5eyORSH9/v81m02q1ZrO5ubm5UnUCANQNNp26XK5QKJRnWGNjo0ajUalUnZ2dSKdQdDt27Lj99tvPO++8hbvefvvtb3zjG2+88QZBEDRNX3LJJe+///5SZ+HBaoCYCkVw7NixTNpcRvOkU0899aabbtq5c2d7e/v83/f39z/wwAO/+c1vEolEOp2+4447uFzuN77xjeIUDeXCrpxkNBrdbrfVas16JiqRSAwNDTmdTqVS2dPT09HRUak6AQBqVDqdHh8fZxjG6/XmT6dNTU1qtZokScy0hKK76KKLXnjhBYIg9Hr92rVrFxt21lln7du375Zbbnn00UcJgrDb7bfddttjjz1WvkKhRnCyZlpC3dizZ8/nP/95dntubk4kEpXuXDfddBP7YCpJkk6ns5AHDnfv3v3ee+8RBLFly5YdO3bkuZT7xhtv7Nixg23Jw+fzDx48eOaZZxavdii30dFRmqYzt8qzyGQyiqI0Gg2u7gMA5MemU4/H4/V6s6arZEE6hWqTSqUuuOCCffv2EQTB5XJtNpvJZKp0UVBdEFPrVtli6tTUlFqtDofDBEHce++93/3ud4t+infeeeecc86Jx+MEQWzZsuXgwYNFPwWU2eTkpM1m83g8qVRq4V6JREJRVFdXF3osAQBkyaRTj8cTiUTyjBSLxex6p0inUIWOHz/e19fHbn/1q1998MEHK1sPVBvE1LpVtpj6s5/97Otf/zpBEAKBwO12y+XyUpzl29/+9g9/+EN2++233960aVMpzgJlFolEHA6H3W5nr0FkEQqFBoOBoiisTgQAgHQK9Wf79u2vvfYaQRB6vd7pdFa6HKgueDYVViSdTv/iF79gt6+++uoSZVSCIL72ta/9+Mc/TiQSBEHs3r0bMbU+iESivr4+s9nsdDptNtvs7Oz8vdFo1Gq12u12kiR7enpaWloqVScAQKUUnk5bWlpIklSpVFKptGzlAaxEJqayjTOxrDrMh5gKK/LSSy/RNM1uL6N5UuHUavWGDRveffddgiAw6bfO8Hg8tseSz+ez2WxZ64CnUimXy+VyueRyOUVR+AwDgNUglUqNjY0xDON2u6PRaJ6RbDolSRLX8qDmnH766ZntY8eO4SMe5kNMhRXJrENz6qmnbt68uaTn2rRpExtTP/zww2QyiacW6wyXy2W/aQUCAavVurDH0ujo6OjoqFQqpShKp9OhxxIA1J9kMjk6OurxeBiGyfk0RAabTrVaLVbzgtq1Zs2azHbWRWoAxFRYvqGhoZdeeondvuWWW0p9OrVazW4kEolgMIhHbuqVTCbbunVrKBSiaXpoaCiZTM7fGwwGDx061N/fbzQajUYjn8+vVJ0AAMWCdAqrU2tra2YbMRWyIKbC8j300ENsm9a2trZdu3aV+nTzH7aZmJhATK1vEolkw4YNvb29g4ODNE3HYrH5e8Ph8NGjRwcGBvR6vdlsbmpqqlSdAADLlkmnPp+Pbb6wGDad6nQ6iURStvIASm3+Qkq47gxZEFNhmWZnZ3/zm9+w21/+8pfLkBPmr1ySdYcN6pVQKOzr6+vp6XG73QMDAzMzM/P3xuNxmqYdDodarTabze3t7ZWqEwCgcAWmUw6H09HRoVKpNBoN0inUpWAwmNmef2cVgEBMhWV7/PHH2TcXDodz8803l+GM82eDoFHEqsLlcvV6vU6n8/v9Vqs1EAjM35tKpdgemDKZzGKxoAEDAFSnJaVTkiQ1Gg2W44L6ZrfbM9sURVWwEqhCiKmwTA899BC78clPfrK7u7sMZ/R6veyGUChUKBRlOCNUFQ6Ho1KpVCpVMBi02+1utztr2edAIHDw4MHm5maTydTV1YUmWwBQDeLxuM/n83q9IyMj86cFZcmkU5IkS7fUOUBVefvtt9kNkUjU09NT2WKg2iCmwnLs37//ww8/ZLdLug7NfJn3st7eXiSQ1UwqlW7atGnt2rV2u93pdGbdlJiZmTly5MjAwIDRaKQoSiAQVKpOAFjNYrEYwzBIpwCLicViL774Irt9/vnn4+8fsiCmwnJk1qHRarWXXXZZGc7IMMxHH33Ebn/yk58swxmhyonF4g0bNqxdu3Z4eNhqtc5vw0AQRCQS6e/vt9lsWq3WbDajJSYAlMdS06lWqxUKheWsEKBK7N69+8SJE+z2tddeW9lioAohpsKSMQzz7LPPsts33XRTeW5sPvLII5m2SVdffXUZzgg1gc/nUxRlNBrdbrfNZpuampq/N5FIDA0NOZ1OpVJpsVjQHRoASqTAdMrlctesWaNSqZBOoc68/PLLbrf7xhtvLHB8IBD49re/zW5rtdqdO3eWrDSoVYipsGSPPPIIO81SKBTecMMNyzjC/v37e3t7C88Mbrf7gQceYLc3b958xhlnLOOkUMfYHkt6vT4QCFitVoZh5u9Np9MMwzAMI5VKKYrS6XQcDqdSpQJAPYlGo36/3+v1+v3+rKfl52toaJDL5RqNRq1WY9UNqD8vvPDCzp072Ys1d91110k/ZMPh8Oc+97mRkRH2x5/+9Kf4dwELIaYCQRBEOp1+7bXXRkdHt2zZotVq84yMx+OPPvoou71z5841a9Ys9Vwvvvji1VdfbTKZXnzxRb1ef9Lxc3Nzu3btmp6eJgiCw+Hcf//9Sz0jrB4ymWzr1q2Tk5M2m83j8WTd0wgGg4cOHerv7+/u7kaPJQBYttnZWZ/P5/F4xsfHkU4Bfve730WjUYIg7r777tdee+3BBx885ZRTFhtss9m+9KUvvfPOO+yP11133Wc+85kyFQo1hZPn7RVq2p49ez7/+c+z23Nzc3keTE8kEpdeeumrr75KEIRIJNqzZ8+VV1652OAnnnhi165d7PZbb7111llnLamqYDDY1dU1OTlJEERHR8eDDz6YKTKnEydOXHXVVQcPHmR/vP3223/6058u6YywakUiEYfDQdN0LBZbuJfP5+v1eovFgvUeAKBAmXSatSxWlkw61Wg0PB7uB0D9SyQSt9566y9/+Uv2Rw6Hc+65515xxRXbt29XKpVr1qxJpVJjY2OHDh167rnnHn/88UzvwyuuuOLpp59Gs0PICTG1bhUeU//jP/7jS1/6UuZHjUbj8XgWG3z22WezHXc3bNjw/vvvL6OwAwcOfPrTnx4fH2d/3Lhx4xe/+MVPfepTOp1u/jCHw7F79+4f/ehH7H1UgiAuu+yy5557Dh/5sCSJRMLpdNpsttnZ2YV7uVwuSZIWiwWrigPAYpBOAQqxe/fu2267bWxsLOv37BzgrMTB4XC+/vWv//CHP8Q/FlgMYmrdKjym3nPPPXfffXfmRy6XG4lEck5Pev/9908//XR2+7HHHrv++uuXV5vL5br22msPHDgw/5ednZ1yuVwmk4XDYbYRxfy911133SOPPIJJU7A86XTa7/cPDAxkro9kkcvlFEWpVKoyFwYAVYv9MDppOuXz+QqFQqlUIp0CTE1NPfDAAw8//HDmudOcLrjggu9973ubN28uW2FQixBT61bhMXXfvn3btm3L/Lhp06bMCqVZbrjhhl/96lcEQUilUp/Pt5LZkul0+ve///2//Mu/0DSdf2R3d/cPf/jDHTt2LPtcABmBQICmaa/Xm/Otr62trbu7W6vVcrnc8tcGANWgwHTK9uylKEqhUOAdA2C+ZDL5+uuv792798MPP3S5XDMzMw0NDW1tbRRFbdy4cceOHUajsdI1Qg1ATK1bx44de/rpp9ntO++8M/8l3h/96Ed33XVXJBJZu3btk08+2dPTk3PYzMzMCy+88NRTT/X29v7rv/7ryotMpVL79u374x//uH//fpvNlnmGkMPhdHV1bdmy5eqrr7700kvR6gaKKxQK0TQ9NDSUWeVoPpFIZDQau7u7cfceYPWYmpryer0MwwSDwcJfhf7hAAAlgpgK/y0ejweDwc7OzkoVkEqlAoHA7OysUCiUSqV5bv8CFEU0Gh0cHFysxxKPxzMYDGazuampqfy1AUB5sOnU4/Fk+iDkxOfzBQLB7Oxszm9NYrG4u7vbYDBg0i8AQLEgpgLAasR+N3W5XKFQKM8wLperVqu7u7s7OjrKVhsAlBr7DuB2u2dmZvIMEwqFCoWCJEl2Zm8h/cNxbQsAoCgQUwFgtUin0+Pj4+xTZ+FweEmvlclkFosFPZYAatpS06lSqVw4mzeRSLjdbpvNlvMg7LUts9nc3t5ezNIBAFYZxFQAqHPpdHpsbMzr9Xq93kgkkmekRCKRyWSxWMzv9+d8b5RIJBRFdXV14XlpgBpS4OyJpqYmtVpNkmRHR8dJnzVl+4dbrdbFOi3h2hYAwEogpgJAfWLvnXo8Ho/Hkz+disVilUpFkqRMJmN/Ew6H7Xa70+nMLEE+n1AoNJlMFEVhRXKAqpWZPeH1egtMp5l3gCUJBoN27bNg2wAAIABJREFUu93tduf8QtXc3GwymXBtCwBgqRBTAaCurCSdZonH48PDw1ardW5ubuFeLpdLkmRvb29zc3NxSgeAFcu8A3i93pz/cjNWmE6zFNI/HNe2AAAKh5gKAPUg893U7XZHo9E8I1taWkiSJEmypaWlkCOnUin2ObSpqamFezkcjlKptFgsRfmmCwDLU8TrUysRjUYdDsfg4GDOGng8nl6vX7duHRoCAwCcFGIqANSDQCCwd+/ePAOWmk5znsJqtTIMk3Mvlk8EKL8qSadZ8lzbam5uvvjii/EuAQBwUoipAFAnXnzxxdnZ2axfsulUq9UWa2ru5OTk4OCgy+XKObUPyycClMFSZ0+oVCqpVFq28jIWXtvauHFjV1dX+SsBAKg5iKkAUCeOHDlC0zS7zX431el0EomkFOcqZPlEi8XS2NhYirMDrE6pVGpkZIRhGJ/PV9y5/SWVubbF4/Euv/xy9FICACgEYioA1IlAIHD06FGNRqPRaJqamspwxkQi4XQ6bTbbwru4xP/0WLJYLK2trWUoBqBeJZPJ0dFRj8fDMEw8Hs8zsuizJ4pobm5uampKoVBUuhAAgNqAmAoAsCLs8okDAwPj4+M5B2D5RIBlqI90CgAAy4OYCgBQHIFAgKZpr9eb8321ra2tu7tbq9Vyudzy1wZQKzLp1Ofz5Vy4OKPUc/sBAKCCEFMBAIoJyycCLEOB6ZTD4XR0dJAkqdFo8Ow3AEAdQ0wFACi+aDTqdDrtdvtiyycaDAaz2VyeZ2gBqlY8Hs90RUI6BQCADMRUAIBSYZdPtFqt09PTC/dyOBylUtnT09PR0VH+2gAqKBaLMQzj9XpHRkZSqdRiwzLplCRJkUhUzgoBAKCyEFMBAEqL7bFE0/To6GjOATKZjKIojUbD4XDKXBtAOSGdAgBAgRBTAQDKJBgM2u12t9ud841XIpFQFNXV1YVlFaHOLDWdarVaoVBYzgoBAKDaIKYCAJRVOBx2OBwOhyPnGhtCodBkMplMJnxNh1oXjUb9fv9J0ymXy1UoFCqVSq1W488eAABYiKkAABUQj8eHh4dtNtvs7OzCvVwulyTJ3t5erAMJNSeTTv1+f57vGA0NDXK5XKPRqNVqPp9fzgoBAKD6IaYCAFRMKpXy+Xw2m21iYmLhXrbHEkVRcrm8/LUBLMns7KzP5/N4POPj40inAACwQtUSU3/3u9/dd999V1999c6dO9etW1fpcgAAyioQCFitVoZhcu6VSqUURel0OvRYgmqTSaeBQCDPsEw61Wg0PB6vbOUBAECNqpaYun79+qNHj7LbFEVdffXVV1999WmnnVbZqgAAymlycnJwcNDlciWTyYV7xWKx0Wg0Go24BwUVh3QKAAAlVRUx9eWXX7744osX/v68887bt29f2csBAKikSCTicDhomo7FYgv38vl8vV5vsVgaGxvLXxuscuFwmGGYk6ZTPp+vUCiUSiXSKQAALE9VfHj8+Mc/zvn7yy+/vMyVAABUnEgk6uvrM5vNbrfbZrPNzMzM3xuPx2madjgcJElaLJbW1tZK1QmrR4HpVCAQKJVKkiQVCgWXyy1beQAAUH8qfzf16NGj69evX/j75uZmj8eDb2AAsJql02m/3z8wMDA+Pp5zgEwms1gsKpWqzIXBajA1NeX1ej0ez/T0dJ5hSKcAAFB0lb+butit1BtvvBEZFQBWOQ6Ho1KpVCpVMBi02+1utzvr2mIgEDh48GBbW5vJZNLpdA0NDZUqFeoG0ikAAFRche+mMgyj1+sXrnHP4/EcDodWq61IVQAA1SkUCtE0PTQ0lLPHkkgkMhqNFEUJBILy1wa1jk2nbrc7a555FqFQqFAoSJJUKpVoPQ0AACVS4bup//Zv/7YwoxIEsXPnTmRUAIAsEolkw4YNvb29TqeTpum5ubn5eyORSH9/v81mMxgMZrO5qampUnVCDSkwnTY2NiqVSpVKhXQKAABlUMm7qaFQiCTJycnJhbvee+89rEYDAJBHKpVyu91WqzXnzEwOh6NUKnt6ejo6OspfG1Q/Np26XK5QKJRnWFNTk1qtJkmyo6MD6RQAAMqmkndTH3vssZwZddu2bciosModO3bM6/USBNHV1dXd3b3yAx49epRhmGW8sKmp6dxzzy18fCKRePHFF//85z9/8MEHfr8/Go22tLSYTKYtW7Z89rOftVgsy6gBcuJyuXq9Xq/Xj46O0jSd9d83nU4zDMMwjEwmoyhKo9EgY0A6nR4fH/d4PF6vN+tWfJZMOpXJZGUrDwAAIKNid1OTyaTJZBoeHl6468UXX7zsssvKXhFAVZicnPz2t7/9y1/+kv23eccdd9x3330rP+w111zz+OOPL+OF27Zt27t3b4GDn3rqqW9+85sulyvnXg6H85nPfOaBBx5Qq9XLqATyW6zHEksikVAU1dXVhR5Lq1Dh6VQsFqtUKqTTmjYzM9Pc/P/au/OwJpK0AeCdcClgODUQCAFDjBDEQR0ZBbxvQVlB8VzHY1xQDhF3dD1m9/FYdRwPPFG8RlHXa1Rw8GAERUZRcVgdgYSIQALhMIDIIUkg+f7o7+svT9JpwxkC7++vmqqu7nIexH67qt7qp+tRAABAe+lsNvX69eu4Maqbm9uMGTO6fDgAdAsXL16MiYmpqKjQ9UD+H+55UepaWloiIiKOHz+uXGlpaWlqalpVVSWRSBAEUSgUN27cSEtLS0xM9PHx6ZTh9mJWVlbe3t4eHh4FBQUFBQUq2/7r6+uzs7Nzc3NdXFwGDRrUp08fXY0TdBksOhUKhU1NTQRXQnTak3zzzTfW1tZhYWHBwcGQTQ0AoL90FqZqOodm3bp1nbEyTaFQ3Lp169atW9XV1e7u7n/96185HE6HPwWANsvPz1+9evXDhw87+0Genp4BAQHaX6/lxWvWrDlx4gRatre337hx49y5c+3t7REEaW5ufvny5ZEjRy5fvqxQKKqrq6dOnZqeng5r+zuDmZmZp6cnmmOJx+M1NjYqt0okEi6Xm5+fT6fT3dzcKBSKrsYJOg9Ep73ZvXv3cnNzEQTJzMz09fWFbJQAAP2lm0W/6enpY8eOVa+nUqnFxcUmJiYd+ziJRDJnzpzk5GSshkwmb9iw4d///nfHPgiANpBIJLt27dq9ezc65YggiIeHR3l5uVgsRjph0e/SpUvPnTvX/hsqO3fu3LJly9Dy+PHjr1+/bm1trX7Z7du358+fj743Ozs7v3nzBlamdSq5XF5aWsrj8aqrq9Vb0RxLLBaLSqV2/dhAh8OiU4FAgP0ywUWhUOh0Op1Oh+8UPc/UqVMfPHiAIMi8efOuXLmi6+EAAEDb6WY2VdNUanh4eIfHqAiCbN26VTlGRRBELpfv2rXL3t4+IiKiwx8HgPYePnwYFhbG5/PR/zQ1Nd26dWtMTMyQIUPQMLX7q66ujo6ORstfffXVnTt3NJ2DMnv27EuXLs2ZMwdBkKKioq1btx48eLDrBtr7kMlkNBoRi8VcLldTjiUrKysWi8VgMCDHkj6Sy+Xl5eUikai0tFSb6NTJyQk+D/VUOTk5aIyKIMjatWt1OxgAAGgnHYSp+fn5SUlJ6vWmpqZhYWEd/rjPnz8fO3YMt2nHjh1r1qwhk8kd/lAAvqiiomLdunWXLl3CambMmHH06FFnZ2fdDaot9u7di6bsNjAwuHDhAvFZnX/5y1+WLl36888/Iwhy/PjxmJgYOp3eRQPtxWxtbX19fevr6/l8/vv371taWpRba2pqXrx4kZOTw2QymUymkZGRrsYJtNfS0lJRUSEUCkUiEe7x4xiITnuPAwcOoAVvb+9Ro0bpdjAAANBOOghT9+/fj7vS+Ntvv62srIyNjU1PT3/37l1jYyOFQnFzc5s0adLChQvRTW5tkJeX19DQgNtUWVlZWFjIZDLbdmcA2uzkyZMbNmzADmSi0WgHDx6cO3eubkfVBp8/fz558iRaXrx4sYeHxxe77Nix49KlSzKZTCqVxsXF7dy5s5PHCP6Xubm5l5eXm5tbQUEBn8+XSqXKrQ0NDW/evMnLy3N2dmaz2cSfG4CutDY6ZTAY5ubmXTY8oEMfPnzA0rnDVCoAoAfo6jBVLBafP38etykjI0Nl2rOmpqa4uPjevXubNm2KioraunVrGz4G9+3bl6AV5g2ATrx8+RKNUclk8urVq3fu3Kmnm8SSkpKwfY9aLqF3dHQMDAy8du0agiDnz5/fsWMHrDXtSn369OFwOGw2WyAQ8Hi8uro65VaZTMbn8wsKChwcHNhsNu4eY9D1sOi0tLS0ublZ02UkEsnS0pJGo0F02gsdO3YM3fnv6OgYHBys6+EAAEB7dXWYeuTIEU2Htr1580ZTL6lUunfv3gsXLty+fXvkyJGteuLgwYOdnZ1xD79hMpmQBA/oxI8//piYmOjo6HjixIkRI0boejhtd/PmTbTAYrGGDx+uZa/58+ejYWpJScnLly9b+5catJ+hoeHAgQNdXFzKysq4XK7KRmi5XI7mibW1tR08eDCNRtPVOHs57aNTGxsbOp3u6OhI/GUW9FQSiQQ7Dyw8PNzQUGfnOAAAQEfp0l9kTU1NmraJaqO8vHzSpEnJycm+vr7a9yKRSIcOHQoMDJTL5SpN27Zta/NgAGgPKyurx48fs1gsAwMDXY+lXVJTU9HC1KlTte81ceJEAwMDdIfkw4cPIUzVFRKJRKPRaDRaTU1Nfn6+QCBQ2ZEhFoszMjIsLS1dXV0ZDIa+/7jqC5lMVlpaWlZWVlZWBtEp0MbFixfRA7dNTU1XrVql6+EAAEAH6NIw9eeff/7w4YOmVjKZ7OXl5erqKpPJ/vjjD9z5z7q6ujlz5uTn51taWmr/3ICAgMTExDVr1hQXF6M1xsbGO3fuXLhwIe71DQ0NZmZm2t8fgDYYPHiwrofQXu/evausrETLrfp4ZGFh4e7u/ueffyIIkpGR0SmDA61hZWXl7e3t4eGRn5+vnmPp48ePWVlZb9++ZTKZLBbL2NhYV+Ps2aRSqUgkKikpKS8vV/+uisGiUzqd3qdPn64cIei2sKzpS5cutbKy0u1gAACgQ3RdmKpQKLAcdCrMzc2joqJWr16tvLQsOTk5IiLi/fv3Khd/+PBh8+bNR48ebdXTZ86cOXXq1LS0tNzcXAsLi6lTp2rKySSRSNB5g6CgoODgYBcXl1Y9CIDe4/Xr11jZy8urVX29vLzQMJVgtT/oYmZmZl5eXh4eHkVFRVwuV2WDRlNTU05ODo/Hc3JyYrPZkDa2o0B0CtopJSUF/XVKIpGioqJ0PRwAAOgYXRemJiUl8Xg89XoGg5GRkeHo6KhSP2PGjOHDh/v5+WFHSmLi4+N37drV2pQzhoaGkydPnjx5MvFlFy5cKC8vLy8vf/78+ffff+/l5RUcHBwUFMRms1v1OAB6vLy8PLSAbnRsVV8Wi4UWSkpK6urqIObpPoyMjFgsFpPJFAgEXC7306dPyq3Nzc3v378vLCy0t7d3c3OzsbHR1Tj1XWujUycnp844Vxz0APv370cL06dPh3cVAECP0XVh6r59+3Dro6Oj1WNUFJVKPXz48LRp01TqZTLZ3bt3Q0JCOniICKJQKLBf96js7Ozs7OzNmzcnJSX5+/t3+BMB0LmWlhapVNqGvW3YKnpHR8fWZuxQXqcgEAg4HE5rnw46FZlMdnZ2dnZ2rqio4PP5IpFIuVWhUIhEIpFIZGVlxWKxGAwGpGvWkkQiKSsrKykpKSsrwz2bDUUmk+3s7BwdHR0cHCAjPSCQl5d3//59tAzn0AAAepIuClOzsrLS09PV6y0tLVesWEHQccqUKVQqFU0MoOzBgwedEaYmJydjE0TKnJyc1KNlAPRXYWHhf/7zn9TU1P/+979VVVUKhcLU1NTe3n7s2LEzZ86cPXu2NslySktL0YKdnV1rB0ClUrFySUkJhKndFpVKpVKpHz9+5PF4QqFQZd6vpqbmxYsXubm5LBZr4MCBkGNJk8+fP5eVlYlEIuLo1MDAgEqlQnQKtHfw4EH0J4rD4XxxvRgAAOiRLgpTf/rpJ9z60NBQ4rPdSCSSp6dnSkqKSr36SmAVDx8+ZLPZmuZpNdE05RsVFQXp3UHPUFRUFBQUdPPmTZV35cbGxoKCgoKCgjNnzri5ue3bt2/69OnEt6qqqkILtra2rR2G8mJR7D6g27K0tPT29h46dGhBQQGfz5dKpcqt9fX12dnZubm5Li4uLBYLss5iGhsbS0tLhUIh+jFI02VYdNqGhQmgNxOLxRcuXEDLBFOpaE5KAwMDOp3eNQMDAID264p/DouLi69fv65eb2xsHBER8cXuuLtxampqCLpIJJJFixZVV1eHhISsX79+6NCh2owzOzs7LS1NvZ5CoaxcuVKbOwDQ/T1+/Bgru7q60mi0fv361dTUFBUVYQs78/LyZs6cuXv37u+//57gVrW1tWihDTtLlbt8/Pixtd2BTvTp04fD4bDZ7MLCQh6P19jYqNwqkUi4XG5+fj6dTndzc2tt+oCeBItOVQ6kVQHRKWinuLg4NNWZra3t4sWLca+RSqXoJgsbGxviH0gAAOhWuuLfxQMHDqgcb4BasGCBNqfGC4VC9UrifVAJCQnoOuGEhISEhIRJkyatX7/+i+c6apryXbVqVW9+3wI9z1dffRUaGjp37lxra2vl+pycnNjY2LNnzzY3NysUig0bNpDJ5PXr12u6T1NTE1pow+yZchfsPkAvGBoaslgsV1fXsrKyvLw8lclwuVxeXFxcXFxMpVJZLJ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diff --git a/dev/generated/penguins/5b8c4ea4.svg b/dev/generated/penguins/42285119.svg
similarity index 95%
rename from dev/generated/penguins/5b8c4ea4.svg
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index 65005039c..c3da97a4e 100644
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"/>
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index 3d80f7a71..d6e3c0c3f 100644
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similarity index 99%
rename from dev/generated/penguins/93f0d45e.svg
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index 5d912d6ce..de140602c 100644
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diff --git a/dev/generated/penguins/index.html b/dev/generated/penguins/index.html
index b4bf458f5..3b99045c7 100644
--- a/dev/generated/penguins/index.html
+++ b/dev/generated/penguins/index.html
@@ -31,10 +31,10 @@
 plt = penguin_bill * layers * mapping(color = :species, col = :sex)
 draw(plt; axis = axis)</code></pre><img src="0eb9ba7d.svg" alt="Example block output"/><h2 id="Smooth-density-plots"><a class="docs-heading-anchor" href="#Smooth-density-plots">Smooth density plots</a><a id="Smooth-density-plots-1"></a><a class="docs-heading-anchor-permalink" href="#Smooth-density-plots" title="Permalink"></a></h2><p>An alternative approach to understanding how two variables interact is to consider their joint probability density distribution (pdf).</p><pre><code class="language-julia hljs">using AlgebraOfGraphics: density
 plt = penguin_bill * density(npoints=50) * mapping(col = :species)
-draw(plt; axis = axis)</code></pre><img src="9112587d.svg" alt="Example block output"/><p>The default colormap is multi-hue, but it is possible to pass single-hue colormaps as well. The color range is inferred from the data by default, but it can also be passed manually. Both settings are passed via <code>scales</code> to <code>draw</code>, because multiple plots can share the same colormap, so <code>visual</code> is not the appropriate place for this setting.</p><pre><code class="language-julia hljs">draw(plt, scales(Color = (; colormap = :grayC, colorrange = (0, 6))); axis = axis)</code></pre><img src="93f0d45e.svg" alt="Example block output"/><p>A <code>Heatmap</code> (the default visualization for a 2D density) is a bit unfortunate if we want to mark species by color. In that case, one can use <code>visual</code> to change the default visualization and, optionally, fine tune some arguments. In this case, a <code>Wireframe</code> with thin lines looks quite nice. (Note that, for the time being, we must specify explicitly that we require a 3D axis.)</p><pre><code class="language-julia hljs">axis = (type = Axis3, width = 300, height = 300)
+draw(plt; axis = axis)</code></pre><img src="b3cf8f6c.svg" alt="Example block output"/><p>The default colormap is multi-hue, but it is possible to pass single-hue colormaps as well. The color range is inferred from the data by default, but it can also be passed manually. Both settings are passed via <code>scales</code> to <code>draw</code>, because multiple plots can share the same colormap, so <code>visual</code> is not the appropriate place for this setting.</p><pre><code class="language-julia hljs">draw(plt, scales(Color = (; colormap = :grayC, colorrange = (0, 6))); axis = axis)</code></pre><img src="b7b6e421.svg" alt="Example block output"/><p>A <code>Heatmap</code> (the default visualization for a 2D density) is a bit unfortunate if we want to mark species by color. In that case, one can use <code>visual</code> to change the default visualization and, optionally, fine tune some arguments. In this case, a <code>Wireframe</code> with thin lines looks quite nice. (Note that, for the time being, we must specify explicitly that we require a 3D axis.)</p><pre><code class="language-julia hljs">axis = (type = Axis3, width = 300, height = 300)
 layer = density() * visual(Wireframe, linewidth=0.05)
 plt = penguin_bill * layer * mapping(color = :species)
-draw(plt; axis = axis)</code></pre><img src="6065f0d7.svg" alt="Example block output"/><p>Of course, a more traditional approach would be to use a <code>Contour</code> plot instead:</p><pre><code class="language-julia hljs">axis = (width = 225, height = 225)
+draw(plt; axis = axis)</code></pre><img src="0cfaad33.svg" alt="Example block output"/><p>Of course, a more traditional approach would be to use a <code>Contour</code> plot instead:</p><pre><code class="language-julia hljs">axis = (width = 225, height = 225)
 layer = density() * visual(Contour)
 plt = penguin_bill * layer * mapping(color = :species)
 draw(plt; axis = axis)</code></pre><img src="67d5b18a.svg" alt="Example block output"/><p>The data and the linear fit can also be added back to the plot:</p><pre><code class="language-julia hljs">layers = density() * visual(Contour) + linear() + mapping()
@@ -44,10 +44,10 @@
 draw(plt; axis = axis)</code></pre><img src="ce53c236.svg" alt="Example block output"/><h2 id="Correlating-three-variables"><a class="docs-heading-anchor" href="#Correlating-three-variables">Correlating three variables</a><a id="Correlating-three-variables-1"></a><a class="docs-heading-anchor-permalink" href="#Correlating-three-variables" title="Permalink"></a></h2><p>We are now mostly up to speed with <code>bill</code> size, but we have not considered how it relates to other penguin features, such as their weight. For that, a possible approach is to use a continuous color on a gradient to denote weight and different marker shapes to denote species. Here we use <code>group</code> to split the data for the linear regression without adding any additional style.</p><pre><code class="language-julia hljs">body_mass = :body_mass_g =&gt; (t -&gt; t / 1000) =&gt; &quot;body mass (kg)&quot;
 layers = linear() * mapping(group = :species) + mapping(color = body_mass, marker = :species)
 plt = penguin_bill * layers
-draw(plt; axis = axis)</code></pre><img src="09777475.svg" alt="Example block output"/><p>Naturally, within each species, heavier penguins have bigger bills, but perhaps counter-intuitively the species with the shallowest bills features the heaviest penguins. We could also try and see the interplay of these three variables in a 3D plot.</p><pre><code class="language-julia hljs">axis = (type = Axis3, width = 300, height = 300)
+draw(plt; axis = axis)</code></pre><img src="a3c347c0.svg" alt="Example block output"/><p>Naturally, within each species, heavier penguins have bigger bills, but perhaps counter-intuitively the species with the shallowest bills features the heaviest penguins. We could also try and see the interplay of these three variables in a 3D plot.</p><pre><code class="language-julia hljs">axis = (type = Axis3, width = 300, height = 300)
 plt = penguin_bill * mapping(body_mass, color = :species)
-draw(plt; axis = axis)</code></pre><img src="7c21969c.svg" alt="Example block output"/><pre><code class="language-julia hljs">plt = penguin_bill * mapping(body_mass, color = :species, layout = :sex)
-draw(plt; axis = axis)</code></pre><img src="5b8c4ea4.svg" alt="Example block output"/><p>Note that static 3D plot can be misleading, as they only show one projection of 3D data. They are mostly useful when shown interactively.</p><h2 id="Machine-Learning"><a class="docs-heading-anchor" href="#Machine-Learning">Machine Learning</a><a id="Machine-Learning-1"></a><a class="docs-heading-anchor-permalink" href="#Machine-Learning" title="Permalink"></a></h2><p>Finally, let us use Machine Learning techniques to build an automated penguin classifier!</p><p>We would like to investigate whether it is possible to predict the species of a penguin based on its bill size. To do so, we will use a standard classifier technique called <a href="https://en.wikipedia.org/wiki/Support-vector_machine">Support-Vector Machine</a>.</p><p>The strategy is quite simple. We split the data into training and testing subdatasets. We then train our classifier on the training dataset and use it to make predictions on the whole data. We then add the new columns obtained this way to the dataset and visually inspect how well the classifier performed in both training and testing.</p><pre><code class="language-julia hljs">using LIBSVM, Random
+draw(plt; axis = axis)</code></pre><img src="219d4ae3.svg" alt="Example block output"/><pre><code class="language-julia hljs">plt = penguin_bill * mapping(body_mass, color = :species, layout = :sex)
+draw(plt; axis = axis)</code></pre><img src="42285119.svg" alt="Example block output"/><p>Note that static 3D plot can be misleading, as they only show one projection of 3D data. They are mostly useful when shown interactively.</p><h2 id="Machine-Learning"><a class="docs-heading-anchor" href="#Machine-Learning">Machine Learning</a><a id="Machine-Learning-1"></a><a class="docs-heading-anchor-permalink" href="#Machine-Learning" title="Permalink"></a></h2><p>Finally, let us use Machine Learning techniques to build an automated penguin classifier!</p><p>We would like to investigate whether it is possible to predict the species of a penguin based on its bill size. To do so, we will use a standard classifier technique called <a href="https://en.wikipedia.org/wiki/Support-vector_machine">Support-Vector Machine</a>.</p><p>The strategy is quite simple. We split the data into training and testing subdatasets. We then train our classifier on the training dataset and use it to make predictions on the whole data. We then add the new columns obtained this way to the dataset and visually inspect how well the classifier performed in both training and testing.</p><pre><code class="language-julia hljs">using LIBSVM, Random
 
 # use approximately 80% of penguins for training
 Random.seed!(1234) # for reproducibility
@@ -84,4 +84,4 @@
 draw(plt; axis = axis)</code></pre><img src="8123000b.svg" alt="Example block output"/><p>Um, some of the penguins are indeed being misclassified... Let us try to understand why by adding an extra layer, which describes the density of the distributions of the three species.</p><pre><code class="language-julia hljs">pdflayer = density() * visual(Contour, colormap=Reverse(:grays)) * mapping(group = :species)
 layers = pdflayer + datalayer
 plt = penguin_bill * layers
-draw(plt; axis = axis)</code></pre><img src="fc9023ea.svg" alt="Example block output"/><p>We can conclude that the classifier is doing a reasonable job: it is mostly making mistakes on outlier penguins.</p><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p><section class="footnotes is-size-7"><ul><li class="footnote" id="footnote-1"><a class="tag is-link" href="#citeref-1">1</a>Gorman KB, Williams TD, Fraser WR (2014) Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis). PLoS ONE 9(3): e90081. <a href="https://doi.org/10.1371/journal.pone.0090081">DOI</a></li></ul></section></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../">« Home</a><a class="docs-footer-nextpage" href="../../gallery/">Gallery »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+draw(plt; axis = axis)</code></pre><img src="fc9023ea.svg" alt="Example block output"/><p>We can conclude that the classifier is doing a reasonable job: it is mostly making mistakes on outlier penguins.</p><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p><section class="footnotes is-size-7"><ul><li class="footnote" id="footnote-1"><a class="tag is-link" href="#citeref-1">1</a>Gorman KB, Williams TD, Fraser WR (2014) Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis). PLoS ONE 9(3): e90081. <a href="https://doi.org/10.1371/journal.pone.0090081">DOI</a></li></ul></section></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../">« Home</a><a class="docs-footer-nextpage" href="../../gallery/">Gallery »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/generated/visual.jl b/dev/generated/visual.jl
index 140412436..6a1de1d1c 100644
--- a/dev/generated/visual.jl
+++ b/dev/generated/visual.jl
@@ -4,7 +4,7 @@
 # visual
 # ```
 
-## Examples
+# ## Examples
 
 using AlgebraOfGraphics, CairoMakie
 set_aog_theme!()
@@ -28,3 +28,30 @@ draw(plt * visual(Contour)) # plot as contour
 #
 
 draw(plt * visual(Contour, linewidth=2)) # plot as contour with thicker lines
+
+# ## Manual legend entries via `label`
+
+# The legend normally contains entries for all appropriate scales used in the plot.
+# Sometimes, however, you just want to label certain plots such that they appear in the legend without using any scale.
+# You can achieve this by adding the `label` keyword to all `visual`s that you want to label.
+# Layers with the same `label` will be combined within a legend entry.
+
+x = range(0, 4pi, length = 40)
+layer1 = data((; x = x, y = cos.(x))) * mapping(:x, :y) * visual(Lines, linestyle = :dash, label = "A cosine line")
+layer2 = data((; x = x, y = sin.(x) .+ 2)) * mapping(:x, :y) *
+    (visual(Lines, color = (:tomato, 0.4)) + visual(Scatter, color = :tomato)) * visual(label = "A sine line + scatter")
+draw(layer1 + layer2)
+
+# If the figure contains other scales, the legend will list the labelled group last by default. If you want to reorder, use the symbol `:Label` to specify the labelled group.
+
+df = (; x = repeat(1:10, 3), y = cos.(1:30), group = repeat(["A", "B", "C"], inner = 10))
+spec1 = data(df) * mapping(:x, :y, color = :group) * visual(Lines)
+
+spec2 = data((; x = 1:10, y = cos.(1:10) .+ 2)) * mapping(:x, :y) * visual(Scatter, color = :purple, label = "Scatter")
+
+f = Figure()
+fg = draw!(f[1, 1], spec1 + spec2)
+legend!(f[1, 2], fg)
+legend!(f[1, 3], fg, order = [:Label, :Color])
+
+f
diff --git a/dev/generated/visual/ce93eeb4.svg b/dev/generated/visual/55911e08.svg
similarity index 99%
rename from dev/generated/visual/ce93eeb4.svg
rename to dev/generated/visual/55911e08.svg
index 129393bdb..d2fca403d 100644
--- a/dev/generated/visual/ce93eeb4.svg
+++ b/dev/generated/visual/55911e08.svg
@@ -2,106 +2,106 @@
 <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="600" height="450" viewBox="0 0 600 450">
 <defs>
 <g>
-<g id="glyph-0-0-6e706c8d">
+<g id="glyph-0-0-b1dbdaa1">
 <path d="M 4.5625 -3.96875 L 7.046875 0 L 4.96875 0 L 3.4375 -2.9375 L 1.9375 0 L -0.046875 0 L 2.421875 -3.90625 L 0.203125 -7.40625 L 2.25 -7.40625 L 3.515625 -4.890625 L 4.765625 -7.40625 L 6.703125 -7.40625 Z M 4.5625 -3.96875 "/>
 </g>
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diff --git a/dev/generated/visual/d969c7d7.svg b/dev/generated/visual/d69ebb15.svg
similarity index 99%
rename from dev/generated/visual/d969c7d7.svg
rename to dev/generated/visual/d69ebb15.svg
index 32fbb7272..46cc05284 100644
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diff --git a/dev/generated/visual/index.html b/dev/generated/visual/index.html
index 08db035f3..938818ffc 100644
--- a/dev/generated/visual/index.html
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 <!DOCTYPE html>
-<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>Visual · Algebra of Graphics</title><meta name="title" content="Visual · Algebra of Graphics"/><meta property="og:title" content="Visual · Algebra of Graphics"/><meta property="twitter:title" content="Visual · Algebra of Graphics"/><meta name="description" content="Documentation for Algebra of Graphics."/><meta property="og:description" content="Documentation for Algebra of Graphics."/><meta property="twitter:description" content="Documentation for Algebra of Graphics."/><script data-outdated-warner src="../../assets/warner.js"></script><link href="https://cdnjs.cloudflare.com/ajax/libs/lato-font/3.0.0/css/lato-font.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/juliamono/0.050/juliamono.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/fontawesome.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/solid.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/brands.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/katex.min.css" rel="stylesheet" type="text/css"/><script>documenterBaseURL="../.."</script><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js" data-main="../../assets/documenter.js"></script><script src="../../search_index.js"></script><script src="../../siteinfo.js"></script><script src="../../../versions.js"></script><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-mocha.css" data-theme-name="catppuccin-mocha"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-macchiato.css" data-theme-name="catppuccin-macchiato"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-frappe.css" data-theme-name="catppuccin-frappe"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-latte.css" data-theme-name="catppuccin-latte"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/documenter-dark.css" data-theme-name="documenter-dark" data-theme-primary-dark/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/documenter-light.css" data-theme-name="documenter-light" data-theme-primary/><script src="../../assets/themeswap.js"></script><link href="../../assets/favicon.ico" rel="icon" type="image/x-icon"/><link href="../../democards/bulmagridtheme.css" rel="stylesheet" type="text/css"/></head><body><div id="documenter"><nav class="docs-sidebar"><a class="docs-logo" href="../../"><img class="docs-light-only" src="../../assets/logo.svg" alt="Algebra of Graphics logo"/><img class="docs-dark-only" src="../../assets/logo-dark.svg" alt="Algebra of Graphics logo"/></a><div class="docs-package-name"><span class="docs-autofit"><a href="../../">Algebra of Graphics</a></span></div><button class="docs-search-query input is-rounded is-small is-clickable my-2 mx-auto py-1 px-2" id="documenter-search-query">Search docs (Ctrl + /)</button><ul class="docs-menu"><li><a class="tocitem" href="../../">Home</a></li><li><span class="tocitem">Getting Started</span><ul><li><a class="tocitem" href="../penguins/">Tutorial 🐧</a></li><li><a class="tocitem" href="../../gallery/">Gallery</a></li></ul></li><li><span class="tocitem">Algebra of Layers</span><ul><li><a class="tocitem" href="../../layers/introduction/">Introduction</a></li><li><a class="tocitem" href="../../layers/data/">Data</a></li><li><a class="tocitem" href="../../layers/mapping/">Mapping</a></li><li class="is-active"><a class="tocitem" href>Visual</a></li><li><a class="tocitem" href="../analyses/">Analyses</a></li><li><a class="tocitem" href="../../layers/operations/">Algebraic Operations</a></li><li><a class="tocitem" href="../../layers/draw/">Drawing Layers</a></li></ul></li><li><span class="tocitem">API</span><ul><li><a class="tocitem" href="../../API/types/">Types</a></li><li><a class="tocitem" href="../../API/functions/">Functions</a></li><li><a class="tocitem" href="../../API/recipes/">Recipes</a></li></ul></li><li><a class="tocitem" href="../../FAQs/">Frequently Asked Questions</a></li><li><a class="tocitem" href="../../philosophy/">Philosophy</a></li><li><a class="tocitem" href="../../changelog/">Changelog</a></li></ul><div class="docs-version-selector field has-addons"><div class="control"><span class="docs-label button is-static is-size-7">Version</span></div><div class="docs-selector control is-expanded"><div class="select is-fullwidth is-size-7"><select id="documenter-version-selector"></select></div></div></div></nav><div class="docs-main"><header class="docs-navbar"><a class="docs-sidebar-button docs-navbar-link fa-solid fa-bars is-hidden-desktop" id="documenter-sidebar-button" href="#"></a><nav class="breadcrumb"><ul class="is-hidden-mobile"><li><a class="is-disabled">Algebra of Layers</a></li><li class="is-active"><a href>Visual</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href>Visual</a></li></ul></nav><div class="docs-right"><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl" title="View the repository on GitHub"><span class="docs-icon fa-brands"></span><span class="docs-label is-hidden-touch">GitHub</span></a><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/master/docs/src/generated/visual.jl" title="Edit source on GitHub"><span class="docs-icon fa-solid"></span></a><a class="docs-settings-button docs-navbar-link fa-solid fa-gear" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="Visual"><a class="docs-heading-anchor" href="#Visual">Visual</a><a id="Visual-1"></a><a class="docs-heading-anchor-permalink" href="#Visual" title="Permalink"></a></h1><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.visual" href="#AlgebraOfGraphics.visual"><code>AlgebraOfGraphics.visual</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">visual(plottype; attributes...)</code></pre><p>Create a <a href="../../API/types/#AlgebraOfGraphics.Layer"><code>Layer</code></a> that will cause a plot spec multiplied with it to be visualized with plot type <code>plottype</code>, together with optional <code>attributes</code>.</p><p>The available plotting functions are documented <a href="https://docs.makie.org/stable/reference/plots/overview">here</a>. Refer to plotting functions using upper CamelCase for <code>visual</code>&#39;s first argument (e.g. <code>visual(Scatter), visual(BarPlot)</code>). See the documentation of each plotting function to discover the available attributes. These attributes can be passed as additional keyword arguments to <code>visual</code>, or as part of the <a href="../../layers/mapping/#AlgebraOfGraphics.mapping"><code>mapping</code></a> you define.</p><p>The <code>visual</code> function can in principle be used for any plotting function that is defined using the <code>@recipe</code> macro from Makie. AlgebraOfGraphics just needs method definitions for <code>aesthetic_mapping</code>, which define what arguments of the plotting function map to which visual aesthetics. And for legend support, <code>legend_elements</code> must be overloaded for custom recipes as well, as Makie&#39;s default legend mechanism relies on instantiated plot objects, while AlgebraOfGraphics must go by the type and attributes alone.</p><p>Depending on its <code>aesthetic_mapping</code>, a plot type and its attributes may change certain semantics of a given <code>data(...) * mapping(...)</code> spec. For example, <code>visual(BarPlot)</code> will show mapping 1 on the x axis and 2 on the y axis, while <code>visual(BarPlot, direction = :x)</code> shows mapping 1 on y and 2 on x.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/transformations/visual.jl#L14-L38">source</a></section></article><pre><code class="language-julia hljs"># Examples
-
-using AlgebraOfGraphics, CairoMakie
+<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>Visual · Algebra of Graphics</title><meta name="title" content="Visual · Algebra of Graphics"/><meta property="og:title" content="Visual · Algebra of Graphics"/><meta property="twitter:title" content="Visual · Algebra of Graphics"/><meta name="description" content="Documentation for Algebra of Graphics."/><meta property="og:description" content="Documentation for Algebra of Graphics."/><meta property="twitter:description" content="Documentation for Algebra of Graphics."/><script data-outdated-warner src="../../assets/warner.js"></script><link href="https://cdnjs.cloudflare.com/ajax/libs/lato-font/3.0.0/css/lato-font.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/juliamono/0.050/juliamono.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/fontawesome.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/solid.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/brands.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/katex.min.css" rel="stylesheet" type="text/css"/><script>documenterBaseURL="../.."</script><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js" data-main="../../assets/documenter.js"></script><script src="../../search_index.js"></script><script src="../../siteinfo.js"></script><script src="../../../versions.js"></script><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-mocha.css" data-theme-name="catppuccin-mocha"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-macchiato.css" data-theme-name="catppuccin-macchiato"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-frappe.css" data-theme-name="catppuccin-frappe"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-latte.css" data-theme-name="catppuccin-latte"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/documenter-dark.css" data-theme-name="documenter-dark" data-theme-primary-dark/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/documenter-light.css" data-theme-name="documenter-light" data-theme-primary/><script src="../../assets/themeswap.js"></script><link href="../../assets/favicon.ico" rel="icon" type="image/x-icon"/><link href="../../democards/bulmagridtheme.css" rel="stylesheet" type="text/css"/></head><body><div id="documenter"><nav class="docs-sidebar"><a class="docs-logo" href="../../"><img class="docs-light-only" src="../../assets/logo.svg" alt="Algebra of Graphics logo"/><img class="docs-dark-only" src="../../assets/logo-dark.svg" alt="Algebra of Graphics logo"/></a><div class="docs-package-name"><span class="docs-autofit"><a href="../../">Algebra of Graphics</a></span></div><button class="docs-search-query input is-rounded is-small is-clickable my-2 mx-auto py-1 px-2" id="documenter-search-query">Search docs (Ctrl + /)</button><ul class="docs-menu"><li><a class="tocitem" href="../../">Home</a></li><li><span class="tocitem">Getting Started</span><ul><li><a class="tocitem" href="../penguins/">Tutorial 🐧</a></li><li><a class="tocitem" href="../../gallery/">Gallery</a></li></ul></li><li><span class="tocitem">Algebra of Layers</span><ul><li><a class="tocitem" href="../../layers/introduction/">Introduction</a></li><li><a class="tocitem" href="../../layers/data/">Data</a></li><li><a class="tocitem" href="../../layers/mapping/">Mapping</a></li><li class="is-active"><a class="tocitem" href>Visual</a><ul class="internal"><li><a class="tocitem" href="#Examples"><span>Examples</span></a></li><li><a class="tocitem" href="#Manual-legend-entries-via-label"><span>Manual legend entries via <code>label</code></span></a></li></ul></li><li><a class="tocitem" href="../analyses/">Analyses</a></li><li><a class="tocitem" href="../../layers/operations/">Algebraic Operations</a></li><li><a class="tocitem" href="../../layers/draw/">Drawing Layers</a></li></ul></li><li><span class="tocitem">API</span><ul><li><a class="tocitem" href="../../API/types/">Types</a></li><li><a class="tocitem" href="../../API/functions/">Functions</a></li><li><a class="tocitem" href="../../API/recipes/">Recipes</a></li></ul></li><li><a class="tocitem" href="../../FAQs/">Frequently Asked Questions</a></li><li><a class="tocitem" href="../../philosophy/">Philosophy</a></li><li><a class="tocitem" href="../../changelog/">Changelog</a></li></ul><div class="docs-version-selector field has-addons"><div class="control"><span class="docs-label button is-static is-size-7">Version</span></div><div class="docs-selector control is-expanded"><div class="select is-fullwidth is-size-7"><select id="documenter-version-selector"></select></div></div></div></nav><div class="docs-main"><header class="docs-navbar"><a class="docs-sidebar-button docs-navbar-link fa-solid fa-bars is-hidden-desktop" id="documenter-sidebar-button" href="#"></a><nav class="breadcrumb"><ul class="is-hidden-mobile"><li><a class="is-disabled">Algebra of Layers</a></li><li class="is-active"><a href>Visual</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href>Visual</a></li></ul></nav><div class="docs-right"><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl" title="View the repository on GitHub"><span class="docs-icon fa-brands"></span><span class="docs-label is-hidden-touch">GitHub</span></a><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/master/docs/src/generated/visual.jl" title="Edit source on GitHub"><span class="docs-icon fa-solid"></span></a><a class="docs-settings-button docs-navbar-link fa-solid fa-gear" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="Visual"><a class="docs-heading-anchor" href="#Visual">Visual</a><a id="Visual-1"></a><a class="docs-heading-anchor-permalink" href="#Visual" title="Permalink"></a></h1><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.visual" href="#AlgebraOfGraphics.visual"><code>AlgebraOfGraphics.visual</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">visual(plottype; attributes...)</code></pre><p>Create a <a href="../../API/types/#AlgebraOfGraphics.Layer"><code>Layer</code></a> that will cause a plot spec multiplied with it to be visualized with plot type <code>plottype</code>, together with optional <code>attributes</code>.</p><p>The available plotting functions are documented <a href="https://docs.makie.org/stable/reference/plots/overview">here</a>. Refer to plotting functions using upper CamelCase for <code>visual</code>&#39;s first argument (e.g. <code>visual(Scatter), visual(BarPlot)</code>). See the documentation of each plotting function to discover the available attributes. These attributes can be passed as additional keyword arguments to <code>visual</code>, or as part of the <a href="../../layers/mapping/#AlgebraOfGraphics.mapping"><code>mapping</code></a> you define.</p><p>The <code>visual</code> function can in principle be used for any plotting function that is defined using the <code>@recipe</code> macro from Makie. AlgebraOfGraphics just needs method definitions for <code>aesthetic_mapping</code>, which define what arguments of the plotting function map to which visual aesthetics. And for legend support, <code>legend_elements</code> must be overloaded for custom recipes as well, as Makie&#39;s default legend mechanism relies on instantiated plot objects, while AlgebraOfGraphics must go by the type and attributes alone.</p><p>Depending on its <code>aesthetic_mapping</code>, a plot type and its attributes may change certain semantics of a given <code>data(...) * mapping(...)</code> spec. For example, <code>visual(BarPlot)</code> will show mapping 1 on the x axis and 2 on the y axis, while <code>visual(BarPlot, direction = :x)</code> shows mapping 1 on y and 2 on x.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/transformations/visual.jl#L14-L38">source</a></section></article><h2 id="Examples"><a class="docs-heading-anchor" href="#Examples">Examples</a><a id="Examples-1"></a><a class="docs-heading-anchor-permalink" href="#Examples" title="Permalink"></a></h2><pre><code class="language-julia hljs">using AlgebraOfGraphics, CairoMakie
 set_aog_theme!()
 
 df = (x=randn(1000), y=randn(1000))
 plt = data(df) * mapping(:x, :y) * AlgebraOfGraphics.density(npoints=50)
-draw(plt * visual(Heatmap)) # plot as heatmap (the default)</code></pre><img src="ce93eeb4.svg" alt="Example block output"/><p>From AlgebraOfGraphics version 0.7 on, some attributes of the underlying Makie functions will not have an effect if they are controlled by scales instead. For example, continuous colors are completely controlled by color scales, so setting <code>colormap</code> in <code>visual</code> does not have an effect.</p><p>Set the colormap in the <a href="../../layers/draw/#Scale-options">Scale options</a> instead.</p><pre><code class="language-julia hljs">draw(plt, scales(Color = (; colormap = :viridis))) # set a different colormap</code></pre><img src="d969c7d7.svg" alt="Example block output"/><pre><code class="language-julia hljs">draw(plt * visual(Contour)) # plot as contour</code></pre><img src="baf5df50.svg" alt="Example block output"/><pre><code class="language-julia hljs">draw(plt * visual(Contour, linewidth=2)) # plot as contour with thicker lines</code></pre><img src="e0161a91.svg" alt="Example block output"/><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../layers/mapping/">« Mapping</a><a class="docs-footer-nextpage" href="../analyses/">Analyses »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+draw(plt * visual(Heatmap)) # plot as heatmap (the default)</code></pre><img src="55911e08.svg" alt="Example block output"/><p>From AlgebraOfGraphics version 0.7 on, some attributes of the underlying Makie functions will not have an effect if they are controlled by scales instead. For example, continuous colors are completely controlled by color scales, so setting <code>colormap</code> in <code>visual</code> does not have an effect.</p><p>Set the colormap in the <a href="../../layers/draw/#Scale-options">Scale options</a> instead.</p><pre><code class="language-julia hljs">draw(plt, scales(Color = (; colormap = :viridis))) # set a different colormap</code></pre><img src="d69ebb15.svg" alt="Example block output"/><pre><code class="language-julia hljs">draw(plt * visual(Contour)) # plot as contour</code></pre><img src="baf5df50.svg" alt="Example block output"/><pre><code class="language-julia hljs">draw(plt * visual(Contour, linewidth=2)) # plot as contour with thicker lines</code></pre><img src="e0161a91.svg" alt="Example block output"/><h2 id="Manual-legend-entries-via-label"><a class="docs-heading-anchor" href="#Manual-legend-entries-via-label">Manual legend entries via <code>label</code></a><a id="Manual-legend-entries-via-label-1"></a><a class="docs-heading-anchor-permalink" href="#Manual-legend-entries-via-label" title="Permalink"></a></h2><p>The legend normally contains entries for all appropriate scales used in the plot. Sometimes, however, you just want to label certain plots such that they appear in the legend without using any scale. You can achieve this by adding the <code>label</code> keyword to all <code>visual</code>s that you want to label. Layers with the same <code>label</code> will be combined within a legend entry.</p><pre><code class="language-julia hljs">x = range(0, 4pi, length = 40)
+layer1 = data((; x = x, y = cos.(x))) * mapping(:x, :y) * visual(Lines, linestyle = :dash, label = &quot;A cosine line&quot;)
+layer2 = data((; x = x, y = sin.(x) .+ 2)) * mapping(:x, :y) *
+    (visual(Lines, color = (:tomato, 0.4)) + visual(Scatter, color = :tomato)) * visual(label = &quot;A sine line + scatter&quot;)
+draw(layer1 + layer2)</code></pre><img src="b82abf4f.svg" alt="Example block output"/><p>If the figure contains other scales, the legend will list the labelled group last by default. If you want to reorder, use the symbol <code>:Label</code> to specify the labelled group.</p><pre><code class="language-julia hljs">df = (; x = repeat(1:10, 3), y = cos.(1:30), group = repeat([&quot;A&quot;, &quot;B&quot;, &quot;C&quot;], inner = 10))
+spec1 = data(df) * mapping(:x, :y, color = :group) * visual(Lines)
+
+spec2 = data((; x = 1:10, y = cos.(1:10) .+ 2)) * mapping(:x, :y) * visual(Scatter, color = :purple, label = &quot;Scatter&quot;)
+
+f = Figure()
+fg = draw!(f[1, 1], spec1 + spec2)
+legend!(f[1, 2], fg)
+legend!(f[1, 3], fg, order = [:Label, :Color])
+
+f</code></pre><img src="9ef5c958.svg" alt="Example block output"/><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../layers/mapping/">« Mapping</a><a class="docs-footer-nextpage" href="../analyses/">Analyses »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/index.html b/dev/index.html
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--- a/dev/index.html
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@@ -1,2 +1,2 @@
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class="docs-heading-anchor" href="#Introduction">Introduction</a><a id="Introduction-1"></a><a class="docs-heading-anchor-permalink" href="#Introduction" title="Permalink"></a></h1><p>AlgebraOfGraphics defines a language for data visualization. It is based on a few simple building blocks that can be combined using <code>+</code> and <code>*</code>.</p><p>This package can be installed typing</p><pre><code class="language-julia hljs">julia&gt; import Pkg; Pkg.add(&quot;AlgebraOfGraphics&quot;)</code></pre><p>in the Julia REPL.</p><p>See the <a href="generated/penguins/#Tutorial">Tutorial</a> to get started.</p></article><nav class="docs-footer"><a class="docs-footer-nextpage" href="generated/penguins/">Tutorial 🐧 »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. 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Graphics logo"/></a><div class="docs-package-name"><span class="docs-autofit"><a href>Algebra of Graphics</a></span></div><button class="docs-search-query input is-rounded is-small is-clickable my-2 mx-auto py-1 px-2" id="documenter-search-query">Search docs (Ctrl + /)</button><ul class="docs-menu"><li class="is-active"><a class="tocitem" href>Home</a></li><li><span class="tocitem">Getting Started</span><ul><li><a class="tocitem" href="generated/penguins/">Tutorial 🐧</a></li><li><a class="tocitem" href="gallery/">Gallery</a></li></ul></li><li><span class="tocitem">Algebra of Layers</span><ul><li><a class="tocitem" href="layers/introduction/">Introduction</a></li><li><a class="tocitem" href="layers/data/">Data</a></li><li><a class="tocitem" href="layers/mapping/">Mapping</a></li><li><a class="tocitem" href="generated/visual/">Visual</a></li><li><a class="tocitem" href="generated/analyses/">Analyses</a></li><li><a class="tocitem" href="layers/operations/">Algebraic 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href="#"></a><nav class="breadcrumb"><ul class="is-hidden-mobile"><li class="is-active"><a href>Home</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href>Home</a></li></ul></nav><div class="docs-right"><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl" title="View the repository on GitHub"><span class="docs-icon fa-brands"></span><span class="docs-label is-hidden-touch">GitHub</span></a><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/master/docs/src/index.md" title="Edit source on GitHub"><span class="docs-icon fa-solid"></span></a><a class="docs-settings-button docs-navbar-link fa-solid fa-gear" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="Introduction"><a class="docs-heading-anchor" href="#Introduction">Introduction</a><a id="Introduction-1"></a><a class="docs-heading-anchor-permalink" href="#Introduction" title="Permalink"></a></h1><p>AlgebraOfGraphics defines a language for data visualization. It is based on a few simple building blocks that can be combined using <code>+</code> and <code>*</code>.</p><p>This package can be installed typing</p><pre><code class="language-julia hljs">julia&gt; import Pkg; Pkg.add(&quot;AlgebraOfGraphics&quot;)</code></pre><p>in the Julia REPL.</p><p>See the <a href="generated/penguins/#Tutorial">Tutorial</a> to get started.</p></article><nav class="docs-footer"><a class="docs-footer-nextpage" href="generated/penguins/">Tutorial 🐧 »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/layers/data/index.html b/dev/layers/data/index.html
index c68ac2310..0bf77697c 100644
--- a/dev/layers/data/index.html
+++ b/dev/layers/data/index.html
@@ -1,9 +1,9 @@
 <!DOCTYPE html>
-<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>Data · Algebra of Graphics</title><meta name="title" content="Data · Algebra of Graphics"/><meta property="og:title" content="Data · Algebra of Graphics"/><meta property="twitter:title" content="Data · Algebra of Graphics"/><meta name="description" content="Documentation for Algebra of Graphics."/><meta property="og:description" content="Documentation for Algebra of Graphics."/><meta property="twitter:description" content="Documentation for Algebra of Graphics."/><script data-outdated-warner src="../../assets/warner.js"></script><link href="https://cdnjs.cloudflare.com/ajax/libs/lato-font/3.0.0/css/lato-font.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/juliamono/0.050/juliamono.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/fontawesome.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/solid.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/brands.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/katex.min.css" rel="stylesheet" type="text/css"/><script>documenterBaseURL="../.."</script><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js" data-main="../../assets/documenter.js"></script><script src="../../search_index.js"></script><script src="../../siteinfo.js"></script><script src="../../../versions.js"></script><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-mocha.css" data-theme-name="catppuccin-mocha"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-macchiato.css" data-theme-name="catppuccin-macchiato"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-frappe.css" data-theme-name="catppuccin-frappe"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-latte.css" data-theme-name="catppuccin-latte"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/documenter-dark.css" data-theme-name="documenter-dark" data-theme-primary-dark/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/documenter-light.css" data-theme-name="documenter-light" data-theme-primary/><script src="../../assets/themeswap.js"></script><link href="../../assets/favicon.ico" rel="icon" type="image/x-icon"/><link href="../../democards/bulmagridtheme.css" rel="stylesheet" type="text/css"/></head><body><div id="documenter"><nav class="docs-sidebar"><a class="docs-logo" href="../../"><img class="docs-light-only" src="../../assets/logo.svg" alt="Algebra of Graphics logo"/><img class="docs-dark-only" src="../../assets/logo-dark.svg" alt="Algebra of Graphics logo"/></a><div class="docs-package-name"><span class="docs-autofit"><a href="../../">Algebra of Graphics</a></span></div><button class="docs-search-query input is-rounded is-small is-clickable my-2 mx-auto py-1 px-2" id="documenter-search-query">Search docs (Ctrl + /)</button><ul class="docs-menu"><li><a class="tocitem" href="../../">Home</a></li><li><span class="tocitem">Getting Started</span><ul><li><a class="tocitem" href="../../generated/penguins/">Tutorial 🐧</a></li><li><a class="tocitem" href="../../gallery/">Gallery</a></li></ul></li><li><span class="tocitem">Algebra of Layers</span><ul><li><a class="tocitem" href="../introduction/">Introduction</a></li><li class="is-active"><a class="tocitem" href>Data</a></li><li><a class="tocitem" href="../mapping/">Mapping</a></li><li><a class="tocitem" href="../../generated/visual/">Visual</a></li><li><a class="tocitem" href="../../generated/analyses/">Analyses</a></li><li><a class="tocitem" href="../operations/">Algebraic Operations</a></li><li><a class="tocitem" href="../draw/">Drawing Layers</a></li></ul></li><li><span class="tocitem">API</span><ul><li><a class="tocitem" href="../../API/types/">Types</a></li><li><a class="tocitem" href="../../API/functions/">Functions</a></li><li><a class="tocitem" href="../../API/recipes/">Recipes</a></li></ul></li><li><a class="tocitem" href="../../FAQs/">Frequently Asked Questions</a></li><li><a class="tocitem" href="../../philosophy/">Philosophy</a></li><li><a class="tocitem" href="../../changelog/">Changelog</a></li></ul><div class="docs-version-selector field has-addons"><div class="control"><span class="docs-label button is-static is-size-7">Version</span></div><div class="docs-selector control is-expanded"><div class="select is-fullwidth is-size-7"><select id="documenter-version-selector"></select></div></div></div></nav><div class="docs-main"><header class="docs-navbar"><a class="docs-sidebar-button docs-navbar-link fa-solid fa-bars is-hidden-desktop" id="documenter-sidebar-button" href="#"></a><nav class="breadcrumb"><ul class="is-hidden-mobile"><li><a class="is-disabled">Algebra of Layers</a></li><li class="is-active"><a href>Data</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href>Data</a></li></ul></nav><div class="docs-right"><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl" title="View the repository on GitHub"><span class="docs-icon fa-brands"></span><span class="docs-label is-hidden-touch">GitHub</span></a><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/master/docs/src/layers/data.md" title="Edit source on GitHub"><span class="docs-icon fa-solid"></span></a><a class="docs-settings-button docs-navbar-link fa-solid fa-gear" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="Data"><a class="docs-heading-anchor" href="#Data">Data</a><a id="Data-1"></a><a class="docs-heading-anchor-permalink" href="#Data" title="Permalink"></a></h1><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.data" href="#AlgebraOfGraphics.data"><code>AlgebraOfGraphics.data</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">data(table)</code></pre><p>Create a <a href="../../API/types/#AlgebraOfGraphics.Layer"><code>Layer</code></a> with its data field set to a table-like object.</p><p>There are no type restrictions on this object, as long as it respects the Tables interface. In particular, any one of <a href="https://github.com/JuliaData/Tables.jl/blob/main/INTEGRATIONS.md">these formats</a> should work out of the box.</p><p>To create a fully specified layer, the layer created with <code>data</code> needs to be multiplied with the output of <a href="../mapping/#AlgebraOfGraphics.mapping"><code>mapping</code></a>.</p><pre><code class="language-julia hljs">spec = data(...) * mapping(...)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/algebra/layer.jl#L32-L46">source</a></section></article><pre><code class="language-julia hljs">using AlgebraOfGraphics
+<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>Data · Algebra of Graphics</title><meta name="title" content="Data · Algebra of Graphics"/><meta property="og:title" content="Data · Algebra of Graphics"/><meta property="twitter:title" content="Data · Algebra of Graphics"/><meta name="description" content="Documentation for Algebra of Graphics."/><meta property="og:description" content="Documentation for Algebra of Graphics."/><meta property="twitter:description" content="Documentation for Algebra of Graphics."/><script data-outdated-warner src="../../assets/warner.js"></script><link href="https://cdnjs.cloudflare.com/ajax/libs/lato-font/3.0.0/css/lato-font.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/juliamono/0.050/juliamono.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/fontawesome.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/solid.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/brands.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/katex.min.css" rel="stylesheet" type="text/css"/><script>documenterBaseURL="../.."</script><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js" data-main="../../assets/documenter.js"></script><script src="../../search_index.js"></script><script src="../../siteinfo.js"></script><script src="../../../versions.js"></script><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-mocha.css" data-theme-name="catppuccin-mocha"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-macchiato.css" data-theme-name="catppuccin-macchiato"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-frappe.css" data-theme-name="catppuccin-frappe"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-latte.css" data-theme-name="catppuccin-latte"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/documenter-dark.css" data-theme-name="documenter-dark" data-theme-primary-dark/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/documenter-light.css" data-theme-name="documenter-light" data-theme-primary/><script src="../../assets/themeswap.js"></script><link href="../../assets/favicon.ico" rel="icon" type="image/x-icon"/><link href="../../democards/bulmagridtheme.css" rel="stylesheet" type="text/css"/></head><body><div id="documenter"><nav class="docs-sidebar"><a class="docs-logo" href="../../"><img class="docs-light-only" src="../../assets/logo.svg" alt="Algebra of Graphics logo"/><img class="docs-dark-only" src="../../assets/logo-dark.svg" alt="Algebra of Graphics logo"/></a><div class="docs-package-name"><span class="docs-autofit"><a href="../../">Algebra of Graphics</a></span></div><button class="docs-search-query input is-rounded is-small is-clickable my-2 mx-auto py-1 px-2" id="documenter-search-query">Search docs (Ctrl + /)</button><ul class="docs-menu"><li><a class="tocitem" href="../../">Home</a></li><li><span class="tocitem">Getting Started</span><ul><li><a class="tocitem" href="../../generated/penguins/">Tutorial 🐧</a></li><li><a class="tocitem" href="../../gallery/">Gallery</a></li></ul></li><li><span class="tocitem">Algebra of Layers</span><ul><li><a class="tocitem" href="../introduction/">Introduction</a></li><li class="is-active"><a class="tocitem" href>Data</a></li><li><a class="tocitem" href="../mapping/">Mapping</a></li><li><a class="tocitem" href="../../generated/visual/">Visual</a></li><li><a class="tocitem" href="../../generated/analyses/">Analyses</a></li><li><a class="tocitem" href="../operations/">Algebraic Operations</a></li><li><a class="tocitem" href="../draw/">Drawing Layers</a></li></ul></li><li><span class="tocitem">API</span><ul><li><a class="tocitem" href="../../API/types/">Types</a></li><li><a class="tocitem" href="../../API/functions/">Functions</a></li><li><a class="tocitem" href="../../API/recipes/">Recipes</a></li></ul></li><li><a class="tocitem" href="../../FAQs/">Frequently Asked Questions</a></li><li><a class="tocitem" href="../../philosophy/">Philosophy</a></li><li><a class="tocitem" href="../../changelog/">Changelog</a></li></ul><div class="docs-version-selector field has-addons"><div class="control"><span class="docs-label button is-static is-size-7">Version</span></div><div class="docs-selector control is-expanded"><div class="select is-fullwidth is-size-7"><select id="documenter-version-selector"></select></div></div></div></nav><div class="docs-main"><header class="docs-navbar"><a class="docs-sidebar-button docs-navbar-link fa-solid fa-bars is-hidden-desktop" id="documenter-sidebar-button" href="#"></a><nav class="breadcrumb"><ul class="is-hidden-mobile"><li><a class="is-disabled">Algebra of Layers</a></li><li class="is-active"><a href>Data</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href>Data</a></li></ul></nav><div class="docs-right"><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl" title="View the repository on GitHub"><span class="docs-icon fa-brands"></span><span class="docs-label is-hidden-touch">GitHub</span></a><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/master/docs/src/layers/data.md" title="Edit source on GitHub"><span class="docs-icon fa-solid"></span></a><a class="docs-settings-button docs-navbar-link fa-solid fa-gear" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="Data"><a class="docs-heading-anchor" href="#Data">Data</a><a id="Data-1"></a><a class="docs-heading-anchor-permalink" href="#Data" title="Permalink"></a></h1><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="AlgebraOfGraphics.data" href="#AlgebraOfGraphics.data"><code>AlgebraOfGraphics.data</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">data(table)</code></pre><p>Create a <a href="../../API/types/#AlgebraOfGraphics.Layer"><code>Layer</code></a> with its data field set to a table-like object.</p><p>There are no type restrictions on this object, as long as it respects the Tables interface. In particular, any one of <a href="https://github.com/JuliaData/Tables.jl/blob/main/INTEGRATIONS.md">these formats</a> should work out of the box.</p><p>To create a fully specified layer, the layer created with <code>data</code> needs to be multiplied with the output of <a href="../mapping/#AlgebraOfGraphics.mapping"><code>mapping</code></a>.</p><pre><code class="language-julia hljs">spec = data(...) * mapping(...)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/algebra/layer.jl#L32-L46">source</a></section></article><pre><code class="language-julia hljs">using AlgebraOfGraphics
 df = (a = rand(10), b = rand(10))
 data(df)</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi"><span class="sgr1">Layer </span>
   transformation: identity
   data: AlgebraOfGraphics.Columns{@NamedTuple{a::Vector{Float64}, b::Vector{Float64}}}
   positional:
   named:
-</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../introduction/">« Introduction</a><a class="docs-footer-nextpage" href="../mapping/">Mapping »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../introduction/">« Introduction</a><a class="docs-footer-nextpage" href="../mapping/">Mapping »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/layers/draw/2f69a436.svg b/dev/layers/draw/4650dd4c.svg
similarity index 92%
rename from dev/layers/draw/2f69a436.svg
rename to dev/layers/draw/4650dd4c.svg
index 3e3db9874..2ca5480b6 100644
--- a/dev/layers/draw/2f69a436.svg
+++ b/dev/layers/draw/4650dd4c.svg
@@ -2,121 +2,121 @@
 <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="600" height="450" viewBox="0 0 600 450">
 <defs>
 <g>
-<g id="glyph-0-0-03022c7e">
+<g id="glyph-0-0-cda9fbbe">
 <path d="M 4.5625 -3.96875 L 7.046875 0 L 4.96875 0 L 3.4375 -2.9375 L 1.9375 0 L -0.046875 0 L 2.421875 -3.90625 L 0.203125 -7.40625 L 2.25 -7.40625 L 3.515625 -4.890625 L 4.765625 -7.40625 L 6.703125 -7.40625 Z M 4.5625 -3.96875 "/>
 </g>
-<g id="glyph-1-0-03022c7e">
+<g id="glyph-1-0-cda9fbbe">
 <path d="M 3.15625 -9.46875 C 3.9375 -9.46875 4.554688 -9.238281 5.015625 -8.78125 C 5.484375 -8.320312 5.71875 -7.734375 5.71875 -7.015625 C 5.71875 -6.410156 5.585938 -5.84375 5.328125 -5.3125 C 5.078125 -4.78125 4.679688 -4.195312 4.140625 -3.5625 C 3.609375 -2.925781 2.742188 -1.976562 1.546875 -0.71875 L 5.96875 -0.71875 L 5.859375 0 L 0.609375 0 L 0.609375 -0.6875 C 1.878906 -2.050781 2.789062 -3.0625 3.34375 -3.71875 C 3.90625 -4.375 4.300781 -4.941406 4.53125 -5.421875 C 4.757812 -5.898438 4.875 -6.414062 4.875 -6.96875 C 4.875 -7.519531 4.710938 -7.957031 4.390625 -8.28125 C 4.066406 -8.613281 3.640625 -8.78125 3.109375 -8.78125 C 2.679688 -8.78125 2.300781 -8.691406 1.96875 -8.515625 C 1.632812 -8.335938 1.316406 -8.070312 1.015625 -7.71875 L 0.46875 -8.140625 C 0.832031 -8.585938 1.234375 -8.921875 1.671875 -9.140625 C 2.109375 -9.359375 2.601562 -9.46875 3.15625 -9.46875 Z M 3.15625 -9.46875 "/>
 </g>
-<g id="glyph-2-0-03022c7e">
+<g id="glyph-2-0-cda9fbbe">
 <path d="M 6.765625 -3.125 L 6.765625 -2.453125 L 5.5 -2.453125 L 5.5 0 L 4.703125 0 L 4.703125 -2.453125 L 0.625 -2.453125 L 0.625 -3.046875 L 3.46875 -9.46875 L 4.15625 -9.1875 L 1.5 -3.125 L 4.703125 -3.125 L 4.796875 -5.859375 L 5.5 -5.859375 L 5.5 -3.125 Z M 6.765625 -3.125 "/>
 </g>
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similarity index 90%
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 <path fill="none" stroke-width="1" stroke-linecap="butt" stroke-linejoin="miter" stroke="rgb(66.274512%, 66.274512%, 66.274512%)" stroke-opacity="1" stroke-miterlimit="1.154701" d="M 37.5 161 L 32.5 161 "/>
diff --git a/dev/layers/draw/index.html b/dev/layers/draw/index.html
index 940ef54c9..34e664ff4 100644
--- a/dev/layers/draw/index.html
+++ b/dev/layers/draw/index.html
@@ -116,7 +116,7 @@
         highclip = :red,
         colorrange = (2, 9)
     )
-))</code></pre><img src="2f69a436.svg" alt="Example block output"/><h4 id="MarkerSize"><a class="docs-heading-anchor" href="#MarkerSize">MarkerSize</a><a id="MarkerSize-1"></a><a class="docs-heading-anchor-permalink" href="#MarkerSize" title="Permalink"></a></h4><p>The range of marker sizes can be set with the <code>sizerange</code> attribute. Marker sizes are computed such that their area, and not <code>markersize</code> itself, grows linearly with the scale values. </p><pre><code class="language-julia hljs">using AlgebraOfGraphics
+))</code></pre><img src="4650dd4c.svg" alt="Example block output"/><h4 id="MarkerSize"><a class="docs-heading-anchor" href="#MarkerSize">MarkerSize</a><a id="MarkerSize-1"></a><a class="docs-heading-anchor-permalink" href="#MarkerSize" title="Permalink"></a></h4><p>The range of marker sizes can be set with the <code>sizerange</code> attribute. Marker sizes are computed such that their area, and not <code>markersize</code> itself, grows linearly with the scale values. </p><pre><code class="language-julia hljs">using AlgebraOfGraphics
 using CairoMakie
 
 spec = data((; x = 1:10, y = 1:10, z = 10:10:100)) *
@@ -185,4 +185,4 @@
         ],
     ),
     axis = (; width = 100, height = 100)
-)</code></pre><img src="2f2ffd72.svg" alt="Example block output"/></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../operations/">« Algebraic Operations</a><a class="docs-footer-nextpage" href="../../API/types/">Types »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+)</code></pre><img src="89d7c16a.svg" alt="Example block output"/></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../operations/">« Algebraic Operations</a><a class="docs-footer-nextpage" href="../../API/types/">Types »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/layers/introduction/index.html b/dev/layers/introduction/index.html
index 9b28f22c2..6a560ee06 100644
--- a/dev/layers/introduction/index.html
+++ b/dev/layers/introduction/index.html
@@ -1,2 +1,2 @@
 <!DOCTYPE html>
-<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>Introduction · Algebra of Graphics</title><meta name="title" content="Introduction · Algebra of Graphics"/><meta property="og:title" content="Introduction · Algebra of Graphics"/><meta property="twitter:title" content="Introduction · Algebra of Graphics"/><meta name="description" content="Documentation for Algebra of Graphics."/><meta property="og:description" content="Documentation for Algebra of Graphics."/><meta property="twitter:description" content="Documentation for Algebra of Graphics."/><script data-outdated-warner src="../../assets/warner.js"></script><link href="https://cdnjs.cloudflare.com/ajax/libs/lato-font/3.0.0/css/lato-font.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/juliamono/0.050/juliamono.min.css" rel="stylesheet" type="text/css"/><link 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href="../../assets/themes/catppuccin-macchiato.css" data-theme-name="catppuccin-macchiato"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-frappe.css" data-theme-name="catppuccin-frappe"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-latte.css" data-theme-name="catppuccin-latte"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/documenter-dark.css" data-theme-name="documenter-dark" data-theme-primary-dark/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/documenter-light.css" data-theme-name="documenter-light" data-theme-primary/><script src="../../assets/themeswap.js"></script><link href="../../assets/favicon.ico" rel="icon" type="image/x-icon"/><link href="../../democards/bulmagridtheme.css" rel="stylesheet" type="text/css"/></head><body><div id="documenter"><nav class="docs-sidebar"><a class="docs-logo" href="../../"><img class="docs-light-only" src="../../assets/logo.svg" alt="Algebra of Graphics logo"/><img class="docs-dark-only" src="../../assets/logo-dark.svg" alt="Algebra of Graphics logo"/></a><div class="docs-package-name"><span class="docs-autofit"><a href="../../">Algebra of Graphics</a></span></div><button class="docs-search-query input is-rounded is-small is-clickable my-2 mx-auto py-1 px-2" id="documenter-search-query">Search docs (Ctrl + /)</button><ul class="docs-menu"><li><a class="tocitem" href="../../">Home</a></li><li><span class="tocitem">Getting Started</span><ul><li><a class="tocitem" href="../../generated/penguins/">Tutorial 🐧</a></li><li><a class="tocitem" href="../../gallery/">Gallery</a></li></ul></li><li><span class="tocitem">Algebra of Layers</span><ul><li class="is-active"><a class="tocitem" href>Introduction</a></li><li><a class="tocitem" href="../data/">Data</a></li><li><a class="tocitem" href="../mapping/">Mapping</a></li><li><a class="tocitem" href="../../generated/visual/">Visual</a></li><li><a class="tocitem" href="../../generated/analyses/">Analyses</a></li><li><a class="tocitem" href="../operations/">Algebraic Operations</a></li><li><a class="tocitem" href="../draw/">Drawing Layers</a></li></ul></li><li><span class="tocitem">API</span><ul><li><a class="tocitem" href="../../API/types/">Types</a></li><li><a class="tocitem" href="../../API/functions/">Functions</a></li><li><a class="tocitem" href="../../API/recipes/">Recipes</a></li></ul></li><li><a class="tocitem" href="../../FAQs/">Frequently Asked Questions</a></li><li><a class="tocitem" href="../../philosophy/">Philosophy</a></li><li><a class="tocitem" href="../../changelog/">Changelog</a></li></ul><div class="docs-version-selector field has-addons"><div class="control"><span class="docs-label button is-static is-size-7">Version</span></div><div class="docs-selector control is-expanded"><div class="select is-fullwidth is-size-7"><select id="documenter-version-selector"></select></div></div></div></nav><div class="docs-main"><header class="docs-navbar"><a class="docs-sidebar-button docs-navbar-link fa-solid fa-bars is-hidden-desktop" id="documenter-sidebar-button" href="#"></a><nav class="breadcrumb"><ul class="is-hidden-mobile"><li><a class="is-disabled">Algebra of Layers</a></li><li class="is-active"><a href>Introduction</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href>Introduction</a></li></ul></nav><div class="docs-right"><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl" title="View the repository on GitHub"><span class="docs-icon fa-brands"></span><span class="docs-label is-hidden-touch">GitHub</span></a><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/master/docs/src/layers/introduction.md" title="Edit source on GitHub"><span class="docs-icon fa-solid"></span></a><a class="docs-settings-button docs-navbar-link fa-solid fa-gear" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="Introduction"><a class="docs-heading-anchor" href="#Introduction">Introduction</a><a id="Introduction-1"></a><a class="docs-heading-anchor-permalink" href="#Introduction" title="Permalink"></a></h1><p>Layers are the key building blocks of AlgebraOfGraphics. Each layer is the product of the following elementary objects.</p><ul><li><a href="../data/#Data">Data</a> (encoding the dataset).</li><li><a href="../mapping/#Mapping">Mapping</a> (associating variables to plot attributes).</li><li><a href="../../generated/visual/#Visual">Visual</a> (encoding data-independent plot information).</li><li><a href="../../generated/analyses/#Analyses">Analyses</a> (encoding transformations that are applied to the data before plotting).</li></ul><p>Data, mappings, visuals and analyses can be combined together using <a href="../operations/#Algebraic-Operations">Algebraic Operations</a> to form one or more layers.</p><p>The output of these algebraic operations can be visualized, as shown in the section <a href="../draw/#Drawing-Layers">Drawing Layers</a>.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../gallery/">« Gallery</a><a class="docs-footer-nextpage" href="../data/">Data »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>Introduction · Algebra of Graphics</title><meta name="title" content="Introduction · Algebra of Graphics"/><meta property="og:title" content="Introduction · Algebra of Graphics"/><meta property="twitter:title" content="Introduction · Algebra of Graphics"/><meta name="description" content="Documentation for Algebra of Graphics."/><meta property="og:description" content="Documentation for Algebra of Graphics."/><meta property="twitter:description" content="Documentation for Algebra of Graphics."/><script data-outdated-warner src="../../assets/warner.js"></script><link href="https://cdnjs.cloudflare.com/ajax/libs/lato-font/3.0.0/css/lato-font.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/juliamono/0.050/juliamono.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/fontawesome.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/solid.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/brands.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/katex.min.css" rel="stylesheet" type="text/css"/><script>documenterBaseURL="../.."</script><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js" data-main="../../assets/documenter.js"></script><script src="../../search_index.js"></script><script src="../../siteinfo.js"></script><script src="../../../versions.js"></script><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-mocha.css" data-theme-name="catppuccin-mocha"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-macchiato.css" data-theme-name="catppuccin-macchiato"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-frappe.css" data-theme-name="catppuccin-frappe"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/catppuccin-latte.css" data-theme-name="catppuccin-latte"/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/documenter-dark.css" data-theme-name="documenter-dark" data-theme-primary-dark/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../../assets/themes/documenter-light.css" data-theme-name="documenter-light" data-theme-primary/><script src="../../assets/themeswap.js"></script><link href="../../assets/favicon.ico" rel="icon" type="image/x-icon"/><link href="../../democards/bulmagridtheme.css" rel="stylesheet" type="text/css"/></head><body><div id="documenter"><nav class="docs-sidebar"><a class="docs-logo" href="../../"><img class="docs-light-only" src="../../assets/logo.svg" alt="Algebra of Graphics logo"/><img class="docs-dark-only" src="../../assets/logo-dark.svg" alt="Algebra of Graphics logo"/></a><div class="docs-package-name"><span class="docs-autofit"><a href="../../">Algebra of Graphics</a></span></div><button class="docs-search-query input is-rounded is-small is-clickable my-2 mx-auto py-1 px-2" id="documenter-search-query">Search docs (Ctrl + /)</button><ul class="docs-menu"><li><a class="tocitem" href="../../">Home</a></li><li><span class="tocitem">Getting Started</span><ul><li><a class="tocitem" href="../../generated/penguins/">Tutorial 🐧</a></li><li><a class="tocitem" href="../../gallery/">Gallery</a></li></ul></li><li><span class="tocitem">Algebra of Layers</span><ul><li class="is-active"><a class="tocitem" href>Introduction</a></li><li><a class="tocitem" href="../data/">Data</a></li><li><a class="tocitem" href="../mapping/">Mapping</a></li><li><a class="tocitem" href="../../generated/visual/">Visual</a></li><li><a class="tocitem" href="../../generated/analyses/">Analyses</a></li><li><a class="tocitem" href="../operations/">Algebraic Operations</a></li><li><a class="tocitem" href="../draw/">Drawing Layers</a></li></ul></li><li><span class="tocitem">API</span><ul><li><a class="tocitem" href="../../API/types/">Types</a></li><li><a class="tocitem" href="../../API/functions/">Functions</a></li><li><a class="tocitem" href="../../API/recipes/">Recipes</a></li></ul></li><li><a class="tocitem" href="../../FAQs/">Frequently Asked Questions</a></li><li><a class="tocitem" href="../../philosophy/">Philosophy</a></li><li><a class="tocitem" href="../../changelog/">Changelog</a></li></ul><div class="docs-version-selector field has-addons"><div class="control"><span class="docs-label button is-static is-size-7">Version</span></div><div class="docs-selector control is-expanded"><div class="select is-fullwidth is-size-7"><select id="documenter-version-selector"></select></div></div></div></nav><div class="docs-main"><header class="docs-navbar"><a class="docs-sidebar-button docs-navbar-link fa-solid fa-bars is-hidden-desktop" id="documenter-sidebar-button" href="#"></a><nav class="breadcrumb"><ul class="is-hidden-mobile"><li><a class="is-disabled">Algebra of Layers</a></li><li class="is-active"><a href>Introduction</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href>Introduction</a></li></ul></nav><div class="docs-right"><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl" title="View the repository on GitHub"><span class="docs-icon fa-brands"></span><span class="docs-label is-hidden-touch">GitHub</span></a><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/master/docs/src/layers/introduction.md" title="Edit source on GitHub"><span class="docs-icon fa-solid"></span></a><a class="docs-settings-button docs-navbar-link fa-solid fa-gear" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="Introduction"><a class="docs-heading-anchor" href="#Introduction">Introduction</a><a id="Introduction-1"></a><a class="docs-heading-anchor-permalink" href="#Introduction" title="Permalink"></a></h1><p>Layers are the key building blocks of AlgebraOfGraphics. Each layer is the product of the following elementary objects.</p><ul><li><a href="../data/#Data">Data</a> (encoding the dataset).</li><li><a href="../mapping/#Mapping">Mapping</a> (associating variables to plot attributes).</li><li><a href="../../generated/visual/#Visual">Visual</a> (encoding data-independent plot information).</li><li><a href="../../generated/analyses/#Analyses">Analyses</a> (encoding transformations that are applied to the data before plotting).</li></ul><p>Data, mappings, visuals and analyses can be combined together using <a href="../operations/#Algebraic-Operations">Algebraic Operations</a> to form one or more layers.</p><p>The output of these algebraic operations can be visualized, as shown in the section <a href="../draw/#Drawing-Layers">Drawing Layers</a>.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../gallery/">« Gallery</a><a class="docs-footer-nextpage" href="../data/">Data »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/layers/mapping/index.html b/dev/layers/mapping/index.html
index aba2aa986..fde949fdd 100644
--- a/dev/layers/mapping/index.html
+++ b/dev/layers/mapping/index.html
@@ -12,7 +12,7 @@
 )</code></pre><p><strong>Pregrouped</strong></p><p>With <code>data(Pregrouped()) * mapping(...)</code> or the shortcut <code>pregrouped(...)</code>, each element in <code>mapping</code> specifies input data directly, like with <code>nothing</code>. However, in this mode, data should be passed in pregrouped. Categorical variables should come as a vector of categories, while numerical variables should come as a vector of vectors of values, with as many inner vectors as there are groups in the categorical variables.</p><pre><code class="language-julia hljs">pregrouped(
     [[1, 2, 3], [4, 5]], # two grouped vectors, of length 3 and 2
     color = [&quot;A&quot;, &quot;B&quot;]   # a vector with two categorical group values
-)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/49c5a2c45d02e020ecdf90d74a5b77521e9dbd9b/src/algebra/layer.jl#L50-L113">source</a></section></article><h2 id="Aesthetics"><a class="docs-heading-anchor" href="#Aesthetics">Aesthetics</a><a id="Aesthetics-1"></a><a class="docs-heading-anchor-permalink" href="#Aesthetics" title="Permalink"></a></h2><p>The structure of a <code>mapping</code> is always directly tied to the signature of the plotting function (or analysis) that it is being connected with. What visual aspects of the plot the positional or keyword arguments affect depends on the plotting function in use.</p><p>To be used with AlgebraOfGraphics, a plotting function has to add a declaration which aesthetics (like X, Y, Color, MarkerSize, LineStyle) its arguments map to. This mechanism allows AlgebraOfGraphics to correctly convert the raw input data into visual attributes for each plotting function and to correctly create and label axes, colorbars and legends.</p><p>Aesthetics can also change depending on attributes passed to <code>visual</code>.</p><p>For example, for a <code>BarPlot</code>, args 1 and 2 correspond to the X and Y aesthetics by default. But if you change the direction in the <code>visual</code>, then axis labels shift accordingly because the aesthetic mapping has changed to 1 = Y, 2 = X:</p><pre><code class="language-julia hljs">using AlgebraOfGraphics
+)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/a2e749a0c1da4b7fa203305efd2a51f9ea8a7d06/src/algebra/layer.jl#L50-L113">source</a></section></article><h2 id="Aesthetics"><a class="docs-heading-anchor" href="#Aesthetics">Aesthetics</a><a id="Aesthetics-1"></a><a class="docs-heading-anchor-permalink" href="#Aesthetics" title="Permalink"></a></h2><p>The structure of a <code>mapping</code> is always directly tied to the signature of the plotting function (or analysis) that it is being connected with. What visual aspects of the plot the positional or keyword arguments affect depends on the plotting function in use.</p><p>To be used with AlgebraOfGraphics, a plotting function has to add a declaration which aesthetics (like X, Y, Color, MarkerSize, LineStyle) its arguments map to. This mechanism allows AlgebraOfGraphics to correctly convert the raw input data into visual attributes for each plotting function and to correctly create and label axes, colorbars and legends.</p><p>Aesthetics can also change depending on attributes passed to <code>visual</code>.</p><p>For example, for a <code>BarPlot</code>, args 1 and 2 correspond to the X and Y aesthetics by default. But if you change the direction in the <code>visual</code>, then axis labels shift accordingly because the aesthetic mapping has changed to 1 = Y, 2 = X:</p><pre><code class="language-julia hljs">using AlgebraOfGraphics
 using CairoMakie
 
 df = (; name = [&quot;Anna&quot;, &quot;Beatrix&quot;, &quot;Claire&quot;], height_meters = [1.55, 1.76, 1.63])
@@ -101,4 +101,4 @@
       Color = (; palette = [:red, :green, :blue]),
       color2 = (; palette = [:gray30, :gray80]),
    )
-)</code></pre><img src="e27b36e6.svg" alt="Example block output"/></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../data/">« Data</a><a class="docs-footer-nextpage" href="../../generated/visual/">Visual »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+)</code></pre><img src="e27b36e6.svg" alt="Example block output"/></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../data/">« Data</a><a class="docs-footer-nextpage" href="../../generated/visual/">Visual »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/layers/operations/index.html b/dev/layers/operations/index.html
index 7c0462a1a..3d3cc188a 100644
--- a/dev/layers/operations/index.html
+++ b/dev/layers/operations/index.html
@@ -1,2 +1,2 @@
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-<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>Algebraic Operations · Algebra of Graphics</title><meta name="title" content="Algebraic Operations · Algebra of Graphics"/><meta property="og:title" content="Algebraic Operations · Algebra of Graphics"/><meta property="twitter:title" content="Algebraic Operations · Algebra of Graphics"/><meta name="description" content="Documentation for Algebra of Graphics."/><meta property="og:description" content="Documentation for Algebra of Graphics."/><meta property="twitter:description" content="Documentation for Algebra of Graphics."/><script data-outdated-warner src="../../assets/warner.js"></script><link href="https://cdnjs.cloudflare.com/ajax/libs/lato-font/3.0.0/css/lato-font.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/juliamono/0.050/juliamono.min.css" rel="stylesheet" type="text/css"/><link 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repository on GitHub"><span class="docs-icon fa-brands"></span><span class="docs-label is-hidden-touch">GitHub</span></a><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/master/docs/src/layers/operations.md" title="Edit source on GitHub"><span class="docs-icon fa-solid"></span></a><a class="docs-settings-button docs-navbar-link fa-solid fa-gear" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="Algebraic-Operations"><a class="docs-heading-anchor" href="#Algebraic-Operations">Algebraic Operations</a><a id="Algebraic-Operations-1"></a><a class="docs-heading-anchor-permalink" href="#Algebraic-Operations" title="Permalink"></a></h1><p>There are two <em>algebraic types</em> that can be added or multiplied with each other: <a href="../../API/types/#AlgebraOfGraphics.Layer"><code>AlgebraOfGraphics.Layer</code></a> and <a href="../../API/types/#AlgebraOfGraphics.Layers"><code>AlgebraOfGraphics.Layers</code></a>.</p><h2 id="Multiplication-on-individual-layers"><a class="docs-heading-anchor" href="#Multiplication-on-individual-layers">Multiplication on individual layers</a><a id="Multiplication-on-individual-layers-1"></a><a class="docs-heading-anchor-permalink" href="#Multiplication-on-individual-layers" title="Permalink"></a></h2><p>Each layer is composed of data, mappings, and transformations. Datasets can be replaced, mappings can be merged, and transformations can be concatenated. These operations, taken together, define an associative operation on layers, which we call multiplication <code>*</code>.</p><p>Multiplication is primarily useful to combine partially defined layers.</p><h2 id="Addition"><a class="docs-heading-anchor" href="#Addition">Addition</a><a id="Addition-1"></a><a class="docs-heading-anchor-permalink" href="#Addition" title="Permalink"></a></h2><p>The operation <code>+</code> is used to superimpose separate layers. <code>a + b</code> has as many layers as <code>la + lb</code>, where <code>la</code> and <code>lb</code> are the number of layers in <code>a</code> and <code>b</code> respectively.</p><h2 id="Multiplication-on-lists-of-layers"><a class="docs-heading-anchor" href="#Multiplication-on-lists-of-layers">Multiplication on lists of layers</a><a id="Multiplication-on-lists-of-layers-1"></a><a class="docs-heading-anchor-permalink" href="#Multiplication-on-lists-of-layers" title="Permalink"></a></h2><p>Multiplication naturally extends to lists of layers. Given two <code>Layers</code> objects <code>a</code> and <code>b</code>, containing <code>la</code> and <code>lb</code> layers respectively, the product <code>a * b</code> contains <code>la * lb</code> layers—all possible pair-wise products.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../generated/analyses/">« Analyses</a><a class="docs-footer-nextpage" href="../draw/">Drawing Layers »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. 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+<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>Algebraic Operations · Algebra of Graphics</title><meta name="title" content="Algebraic Operations · Algebra of Graphics"/><meta property="og:title" content="Algebraic Operations · Algebra of Graphics"/><meta property="twitter:title" content="Algebraic Operations · Algebra of Graphics"/><meta name="description" content="Documentation for Algebra of Graphics."/><meta property="og:description" content="Documentation for Algebra of Graphics."/><meta property="twitter:description" content="Documentation for Algebra of Graphics."/><script data-outdated-warner src="../../assets/warner.js"></script><link href="https://cdnjs.cloudflare.com/ajax/libs/lato-font/3.0.0/css/lato-font.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/juliamono/0.050/juliamono.min.css" rel="stylesheet" type="text/css"/><link 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class="docs-logo" href="../../"><img class="docs-light-only" src="../../assets/logo.svg" alt="Algebra of Graphics logo"/><img class="docs-dark-only" src="../../assets/logo-dark.svg" alt="Algebra of Graphics logo"/></a><div class="docs-package-name"><span class="docs-autofit"><a href="../../">Algebra of Graphics</a></span></div><button class="docs-search-query input is-rounded is-small is-clickable my-2 mx-auto py-1 px-2" id="documenter-search-query">Search docs (Ctrl + /)</button><ul class="docs-menu"><li><a class="tocitem" href="../../">Home</a></li><li><span class="tocitem">Getting Started</span><ul><li><a class="tocitem" href="../../generated/penguins/">Tutorial 🐧</a></li><li><a class="tocitem" href="../../gallery/">Gallery</a></li></ul></li><li><span class="tocitem">Algebra of Layers</span><ul><li><a class="tocitem" href="../introduction/">Introduction</a></li><li><a class="tocitem" href="../data/">Data</a></li><li><a class="tocitem" href="../mapping/">Mapping</a></li><li><a class="tocitem" href="../../generated/visual/">Visual</a></li><li><a class="tocitem" href="../../generated/analyses/">Analyses</a></li><li class="is-active"><a class="tocitem" href>Algebraic Operations</a><ul class="internal"><li><a class="tocitem" href="#Multiplication-on-individual-layers"><span>Multiplication on individual layers</span></a></li><li><a class="tocitem" href="#Addition"><span>Addition</span></a></li><li><a class="tocitem" href="#Multiplication-on-lists-of-layers"><span>Multiplication on lists of layers</span></a></li></ul></li><li><a class="tocitem" href="../draw/">Drawing Layers</a></li></ul></li><li><span class="tocitem">API</span><ul><li><a class="tocitem" href="../../API/types/">Types</a></li><li><a class="tocitem" href="../../API/functions/">Functions</a></li><li><a class="tocitem" href="../../API/recipes/">Recipes</a></li></ul></li><li><a class="tocitem" href="../../FAQs/">Frequently Asked Questions</a></li><li><a class="tocitem" href="../../philosophy/">Philosophy</a></li><li><a class="tocitem" href="../../changelog/">Changelog</a></li></ul><div class="docs-version-selector field has-addons"><div class="control"><span class="docs-label button is-static is-size-7">Version</span></div><div class="docs-selector control is-expanded"><div class="select is-fullwidth is-size-7"><select id="documenter-version-selector"></select></div></div></div></nav><div class="docs-main"><header class="docs-navbar"><a class="docs-sidebar-button docs-navbar-link fa-solid fa-bars is-hidden-desktop" id="documenter-sidebar-button" href="#"></a><nav class="breadcrumb"><ul class="is-hidden-mobile"><li><a class="is-disabled">Algebra of Layers</a></li><li class="is-active"><a href>Algebraic Operations</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href>Algebraic Operations</a></li></ul></nav><div class="docs-right"><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl" title="View the repository on GitHub"><span class="docs-icon fa-brands"></span><span class="docs-label is-hidden-touch">GitHub</span></a><a class="docs-navbar-link" href="https://github.com/MakieOrg/AlgebraOfGraphics.jl/blob/master/docs/src/layers/operations.md" title="Edit source on GitHub"><span class="docs-icon fa-solid"></span></a><a class="docs-settings-button docs-navbar-link fa-solid fa-gear" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="Algebraic-Operations"><a class="docs-heading-anchor" href="#Algebraic-Operations">Algebraic Operations</a><a id="Algebraic-Operations-1"></a><a class="docs-heading-anchor-permalink" href="#Algebraic-Operations" title="Permalink"></a></h1><p>There are two <em>algebraic types</em> that can be added or multiplied with each other: <a href="../../API/types/#AlgebraOfGraphics.Layer"><code>AlgebraOfGraphics.Layer</code></a> and <a href="../../API/types/#AlgebraOfGraphics.Layers"><code>AlgebraOfGraphics.Layers</code></a>.</p><h2 id="Multiplication-on-individual-layers"><a class="docs-heading-anchor" href="#Multiplication-on-individual-layers">Multiplication on individual layers</a><a id="Multiplication-on-individual-layers-1"></a><a class="docs-heading-anchor-permalink" href="#Multiplication-on-individual-layers" title="Permalink"></a></h2><p>Each layer is composed of data, mappings, and transformations. Datasets can be replaced, mappings can be merged, and transformations can be concatenated. These operations, taken together, define an associative operation on layers, which we call multiplication <code>*</code>.</p><p>Multiplication is primarily useful to combine partially defined layers.</p><h2 id="Addition"><a class="docs-heading-anchor" href="#Addition">Addition</a><a id="Addition-1"></a><a class="docs-heading-anchor-permalink" href="#Addition" title="Permalink"></a></h2><p>The operation <code>+</code> is used to superimpose separate layers. <code>a + b</code> has as many layers as <code>la + lb</code>, where <code>la</code> and <code>lb</code> are the number of layers in <code>a</code> and <code>b</code> respectively.</p><h2 id="Multiplication-on-lists-of-layers"><a class="docs-heading-anchor" href="#Multiplication-on-lists-of-layers">Multiplication on lists of layers</a><a id="Multiplication-on-lists-of-layers-1"></a><a class="docs-heading-anchor-permalink" href="#Multiplication-on-lists-of-layers" title="Permalink"></a></h2><p>Multiplication naturally extends to lists of layers. Given two <code>Layers</code> objects <code>a</code> and <code>b</code>, containing <code>la</code> and <code>lb</code> layers respectively, the product <code>a * b</code> contains <code>la * lb</code> layers—all possible pair-wise products.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../generated/analyses/">« Analyses</a><a class="docs-footer-nextpage" href="../draw/">Drawing Layers »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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diff --git a/dev/philosophy/index.html b/dev/philosophy/index.html
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+++ b/dev/philosophy/index.html
@@ -12,4 +12,4 @@
 draw(plt)</code></pre><img src="171cc707.svg" alt="Example block output"/><p>In this case, thanks to the distributive property, it is clear that the dataset and the positional arguments <code>:y</code>, <code>:z</code> are shared across layers, the transparency and the grouping are specific to the data layer, whereas the <code>density</code> analysis, the <code>Contour</code> visualization, and the choice of color map are specific to the analysis layer.</p><h2 id="User-defined-building-blocks"><a class="docs-heading-anchor" href="#User-defined-building-blocks">User-defined building blocks</a><a id="User-defined-building-blocks-1"></a><a class="docs-heading-anchor-permalink" href="#User-defined-building-blocks" title="Permalink"></a></h2><p>It is common in data analysis tasks to &quot;pipe&quot; a sequence of operations. This became very popular in the data science field with the <code>%&gt;%</code> operator in the R language, and it can allow users to seamlessly compose a sequence of tasks:</p><pre><code class="language-R hljs">df %&gt;%
     filter(Weight &lt; 3) %&gt;%
     group_by(Species) %&gt;%
-    summarise(avg_height = mean(Height))</code></pre><p>Naturally, the alternative would be to create a statement per operation and to assign each intermediate result to its own variable.</p><p>AlgebraOfGraphics is markedly in favor of the latter approach. It is recommended that commonly used <em>building blocks</em> are stored in variables with meaningful names. If we often make a scatter plot with some transparency, we can create a variable <code>transparent_scatter = visual(Scatter, alpha = 0.5)</code> and use it consistently. If some columns of our dataset are always analyzed together, with a similar set of transformations, we can store that information as <code>variables = mapping(variable1 =&gt; f1 =&gt; label1, variable2 =&gt; f2 =&gt; label2)</code>.</p><p>Working over one or more datasets, the user would then create a <em>library</em> of building blocks to be combined with each other with <code>*</code> and <code>+</code>. These two operators allow for a much larger number of possible combinations than just sequential composition, thus fully justifying the extra characters used to name intermediate entities.</p><h2 id="Opinionated-defaults"><a class="docs-heading-anchor" href="#Opinionated-defaults">Opinionated defaults</a><a id="Opinionated-defaults-1"></a><a class="docs-heading-anchor-permalink" href="#Opinionated-defaults" title="Permalink"></a></h2><p>While users should be able to customize every aspect of their plots, it is important to note that this customization can be very time-consuming, and many subtleties can escape the attention of the casual user:</p><ul><li>Is the color palette colorblind-friendly?</li><li>Would the colors be distinguishable in black and white (when printed)?</li><li>Is the color gradient perceptually uniform?</li><li>Are the labels and the ticks legible for readers with low vision?</li><li>Are the spacing and typographic hierarchies respected?</li></ul><p>To remedy this, AlgebraOfGraphics aims to provide solid, opinionated default settings. In particular, it uses a <a href="https://www.nature.com/articles/nmeth.1618?WT.ec_id=NMETH-201106">conservative, colorblind-friendly palette</a> and a <a href="https://www.nature.com/articles/s41467-020-19160-7">perceptually uniform, universally readable color map</a>. It follows <a href="https://www.ibm.com/design/language/typography/type-basics/#titles-and-subtitles">IBM guidelines</a> to differentiate titles and labels from tick labels via font weight, while using the same typeface at a readable size.</p><h2 id="Wide-format-support"><a class="docs-heading-anchor" href="#Wide-format-support">Wide format support</a><a id="Wide-format-support-1"></a><a class="docs-heading-anchor-permalink" href="#Wide-format-support" title="Permalink"></a></h2><p>Finally, AlgebraOfGraphics aims to support many different data formats. Different problems require organizing the data in different formats, and AlgebraOfGraphics should support a wide range of options.</p><p>This is achieved in three different ways. First, the <a href="https://github.com/JuliaData/Tables.jl">Tables interface</a> ensures integration with a large variety of data sources. Second, using the <a href="../gallery/gallery/data manipulations/wide_data/#Wide-data">Wide data</a> syntax, users can compare many different columns in the same visualization, without having to first reshape the dataset to a long format. Finally, tabular datasets are not a requirement: users may also work directly with <a href="../gallery/gallery/data manipulations/pre_grouped_data/#Pre-grouped-data">Pre-grouped data</a>, which are not organized as a table, but rather as a collection of (possibly multi-dimensional) arrays.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../FAQs/">« Frequently Asked Questions</a><a class="docs-footer-nextpage" href="../changelog/">Changelog »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 17 September 2024 11:17">Tuesday 17 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+    summarise(avg_height = mean(Height))</code></pre><p>Naturally, the alternative would be to create a statement per operation and to assign each intermediate result to its own variable.</p><p>AlgebraOfGraphics is markedly in favor of the latter approach. It is recommended that commonly used <em>building blocks</em> are stored in variables with meaningful names. If we often make a scatter plot with some transparency, we can create a variable <code>transparent_scatter = visual(Scatter, alpha = 0.5)</code> and use it consistently. If some columns of our dataset are always analyzed together, with a similar set of transformations, we can store that information as <code>variables = mapping(variable1 =&gt; f1 =&gt; label1, variable2 =&gt; f2 =&gt; label2)</code>.</p><p>Working over one or more datasets, the user would then create a <em>library</em> of building blocks to be combined with each other with <code>*</code> and <code>+</code>. These two operators allow for a much larger number of possible combinations than just sequential composition, thus fully justifying the extra characters used to name intermediate entities.</p><h2 id="Opinionated-defaults"><a class="docs-heading-anchor" href="#Opinionated-defaults">Opinionated defaults</a><a id="Opinionated-defaults-1"></a><a class="docs-heading-anchor-permalink" href="#Opinionated-defaults" title="Permalink"></a></h2><p>While users should be able to customize every aspect of their plots, it is important to note that this customization can be very time-consuming, and many subtleties can escape the attention of the casual user:</p><ul><li>Is the color palette colorblind-friendly?</li><li>Would the colors be distinguishable in black and white (when printed)?</li><li>Is the color gradient perceptually uniform?</li><li>Are the labels and the ticks legible for readers with low vision?</li><li>Are the spacing and typographic hierarchies respected?</li></ul><p>To remedy this, AlgebraOfGraphics aims to provide solid, opinionated default settings. In particular, it uses a <a href="https://www.nature.com/articles/nmeth.1618?WT.ec_id=NMETH-201106">conservative, colorblind-friendly palette</a> and a <a href="https://www.nature.com/articles/s41467-020-19160-7">perceptually uniform, universally readable color map</a>. It follows <a href="https://www.ibm.com/design/language/typography/type-basics/#titles-and-subtitles">IBM guidelines</a> to differentiate titles and labels from tick labels via font weight, while using the same typeface at a readable size.</p><h2 id="Wide-format-support"><a class="docs-heading-anchor" href="#Wide-format-support">Wide format support</a><a id="Wide-format-support-1"></a><a class="docs-heading-anchor-permalink" href="#Wide-format-support" title="Permalink"></a></h2><p>Finally, AlgebraOfGraphics aims to support many different data formats. Different problems require organizing the data in different formats, and AlgebraOfGraphics should support a wide range of options.</p><p>This is achieved in three different ways. First, the <a href="https://github.com/JuliaData/Tables.jl">Tables interface</a> ensures integration with a large variety of data sources. Second, using the <a href="../gallery/gallery/data manipulations/wide_data/#Wide-data">Wide data</a> syntax, users can compare many different columns in the same visualization, without having to first reshape the dataset to a long format. Finally, tabular datasets are not a requirement: users may also work directly with <a href="../gallery/gallery/data manipulations/pre_grouped_data/#Pre-grouped-data">Pre-grouped data</a>, which are not organized as a table, but rather as a collection of (possibly multi-dimensional) arrays.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../FAQs/">« Frequently Asked Questions</a><a class="docs-footer-nextpage" href="../changelog/">Changelog »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Tuesday 24 September 2024 09:24">Tuesday 24 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
diff --git a/dev/search_index.js b/dev/search_index.js
index 7b0a2a9fd..e96174f6b 100644
--- a/dev/search_index.js
+++ b/dev/search_index.js
@@ -1,3 +1,3 @@
 var documenterSearchIndex = {"docs":
-[{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/basic visualizations/statistical_visualizations.jl\"","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/#Statistical-visualizations","page":"Statistical visualizations","title":"Statistical visualizations","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"using AlgebraOfGraphics, CairoMakie, PalmerPenguins, DataFrames\n\npenguins = dropmissing(DataFrame(PalmerPenguins.load()))\n\ndata(penguins) * visual(Violin) *\n    mapping(:species, :bill_depth_mm, color=:sex, dodge=:sex) |> draw","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"plt = data(penguins) * visual(Violin, datalimits=extrema)\nplt *= mapping(:species, :bill_depth_mm, color=:sex, side=:sex, dodge=:island)\nfg = draw(plt, axis=(limits=((0.5, 3.5), nothing),))","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"data(penguins) * visual(BoxPlot, show_notch=true) *\n    mapping(:species, :bill_depth_mm, color=:sex, dodge=:sex) |> draw","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"data(penguins) *\n    mapping(:bill_length_mm, :bill_depth_mm, col=:sex) *\n    visual(QQPlot, qqline=:fit) |> draw","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"FAQs/#Frequently-Asked-Questions","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"","category":"section"},{"location":"FAQs/#What-is-the-algebraic-structure-of-AlgebraOfGraphics?","page":"Frequently Asked Questions","title":"What is the algebraic structure of AlgebraOfGraphics?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"AlgebraOfGraphics is based on two operators, + and *. These two operators induce a semiring structure, with a small caveat. Addition is commutative only up to the drawing order. For example, visual(Lines) + visual(Scatter) is slightly different from visual(Scatter) + visual(Lines), in that the former draws the scatter on top of the lines, and the latter draws the lines on top of the scatter. As a consequence, only right distributivity holds with full generality, whereas left distributivity only holds up to the drawing order.","category":"page"},{"location":"FAQs/#Why-is-the-mapping-pair-syntax-different-from-DataFrames?","page":"Frequently Asked Questions","title":"Why is the mapping pair syntax different from DataFrames?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"The transformations passed within a mapping, e.g. mapping(:x => log => \"log(x)\"), are applied element-wise. Operations that require the whole column are not supported on purpose. An important reason to prefer element-wise operations (other than performance) is that whole-column operations can be error prone in this setting, especially when","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"the data is grouped or\ndifferent datasets are used.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"If you do need column-wise transformations, consider implementing a custom analysis, such as density, which takes the whole data as input, or apply the transformation directly in your data before passing it to AlgebraOfGraphics.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"See also Pair syntax for a detailed description of the pair syntax within a mapping.","category":"page"},{"location":"FAQs/#What-is-the-difference-between-axis-scales-and-data-transformations?","page":"Frequently Asked Questions","title":"What is the difference between axis scales and data transformations?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"There are two overlapping but distinct ways to rescale data.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Keep the data as is and use a nonlinear scale, e.g. axis=(xscale=log,).\nTransform the data directly, e.g. mapping(:x => log => \"log(x)\").","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Note that the resulting plots may \"look different\" in some cases. Consider for instance the following example.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics\nusing AlgebraOfGraphics: density\ndf = (x = exp.(randn(1000)),)\nkde1 = data(df) * mapping(:x) * density()\ndraw(kde1, axis=(width=225, height=225, xscale=log,))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"df = (x = exp.(randn(1000)),)\nkde2 = data(df) * mapping(:x => log => \"log(x)\") * density()\ndraw(kde2, axis=(width=225, height=225))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"The two plots look different. The first represents the pdf of x in a log scale, while the second represents the pdf of log(x) in a linear scale. The two curves differ by a factor 1 / x, the derivative of log(x). See e.g. this post for some mathematical background on the topic.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"In general, the second approach (plotting the density of log(x)) could be considered more principled, as it preserves the proportionality between area and probability mass. On the contrary, the first approach (plotting the density of x in a log scale) breaks this proportionality relationship.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"A similar reasoning applies to histograms:","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics\ndf = (x = exp.(rand(1000)),)\nhist1 = data(df) * mapping(:x) * histogram()\ndraw(hist1, axis=(width=225, height=225, xscale=log))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"df = (x = exp.(rand(1000)),)\nhist2 = data(df) * mapping(:x => log => \"log(x)\") * histogram()\ndraw(hist2, axis=(width=225, height=225))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"The data transformation approach is preferable as it produces uniform bins, which are easier to interpret.","category":"page"},{"location":"FAQs/#How-to-combine-AlgebraOfGraphics-with-plain-Makie-plots?","page":"Frequently Asked Questions","title":"How to combine AlgebraOfGraphics with plain Makie plots?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Since AlgebraOfGraphics is built upon the Makie ecosystem we can easily combine plots from both packages. Two approaches can be taken. Firstly, by using draw! you can pass a Figure or FigurePosition created by Makie to be used by AlgebraOfGraphics, e.g.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics, CairoMakie #hide\n\nf, a, p = lines(0..2pi, sin; figure = (size = (600, 400),))\n\ndf = (x = exp.(rand(1000)),)\nhist1 = data(df) * mapping(:x => log => \"log(x)\") * histogram()\ndraw!(f[1, 2], hist1)\n\nf #hide","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Alternatively, we can create the AlgebraOfGraphics figure first and then add in additional plain Makie axes alongside the result by accessing the .figure field of fg, e.g.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics, CairoMakie #hide\n\ndf = (x = exp.(rand(1000)),)\nhist2 = data(df) * mapping(:x => log => \"log(x)\") * histogram()\nfg = draw(hist2; figure = (size = (600, 400),))\n\nlines(fg.figure[1, 2], 0..2pi, cos)\n\nfg #hide","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"note: Note\nWhen setting the width and height dimensions of each axis manually you will need to call resize_to_layout!(fg) before displaying the figure such that each axis is sized correctly.","category":"page"},{"location":"philosophy/#Philosophy","page":"Philosophy","title":"Philosophy","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"AlgebraOfGraphics aims to be a declarative, question-driven language for data visualizations. This section describes its main guiding principles.","category":"page"},{"location":"philosophy/#From-question-to-plot","page":"Philosophy","title":"From question to plot","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"When analyzing a dataset, we often think in abstract, declarative terms. We have questions concerning our data, which can be answered by appropriate visualizations. For instance, we could ask whether a discrete variable :x affects the distribution of a continuous variable :y. We would then like to generate a visualization that answers this question.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"In imperative programming, this would be implemented via the following steps.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Pick the dataset.\nDivide the dataset into subgroups according to the values of :x.\nCompute the density of :y on each subgroup.\nChoose a plot attribute to distinguish subgroups, for instance color.\nSelect as many distinguishable colors as there are unique values of :x.\nPlot all the density curves on top of each other.\nCreate a legend, describing how unique values of :x are associated to colors.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"While the above procedure is certainly feasible, it can introduce a cognitive overhead, especially when more variables and attributes are involved.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"In a declarative framework, the user needs to express the question, and the library will take care of creating the visualization. Let us solve the above problem in a toy dataset.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\nset_aog_theme!()\nN = 1000\nx = rand([\"Class 1\", \"Class 2\", \"Class 3\", \"Class 4\"], N)\ny = @. (x == \"Class 1\") * randn() + (x == \"Class 2\") - (x == \"Class 3\") + randn()\nz = @. (x == \"Class 2\") * randn() + (x == \"Class 2\") + (x == \"Class 3\") + y + randn()\ndf = (; x, y, z)","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"plt = data(df) # declare the dataset\nplt *= density() # declare the analysis\nplt *= mapping(:y) # declare the arguments of the analysis\nplt *= mapping(color = :x) # declare the grouping and the respective visual attribute\ndraw(plt) # draw the visualization and its legend","category":"page"},{"location":"philosophy/#No-mind-reading","page":"Philosophy","title":"No mind reading","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Plotting packages requires the user to specify a large amount of settings. The temptation is then to engineer a plotting library in such a way that it would guess what the user actually wanted. AlgebraOfGraphics follows a different approach, based on algebraic manipulations of plot descriptors.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"The key intuition is that a large fraction of the \"clutter\" in a plot specification comes from repeating the same information over and over. Different layers of the same plot will share some but not all information, and the user should be able to distinguish settings that are private to a layer from those that are shared across layers.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"We achieve this goal using the distributive properties of addition and multiplication. This is best explained by example. Let us assume that we wish to visually inspect whether a discrete variable :x affects the joint distribution of two continuous variables, :y and :z.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"We would like to have two layers, one with the raw data, the other with an analysis (kernel density estimation).","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Naturally, the axes should represent the same variables (:y and :z) for both layers. Only the density layer should be a contour plot, whereas only the scatter layer should have some transparency and be grouped (according to :x) in different subplots.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"plt = data(df) *\n    (\n        visual(Scatter, alpha = 0.3) * mapping(layout = :x) +\n        density() * visual(Contour, colormap = Reverse(:grays))\n    ) *\n    mapping(:y, :z)\ndraw(plt)","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"In this case, thanks to the distributive property, it is clear that the dataset and the positional arguments :y, :z are shared across layers, the transparency and the grouping are specific to the data layer, whereas the density analysis, the Contour visualization, and the choice of color map are specific to the analysis layer.","category":"page"},{"location":"philosophy/#User-defined-building-blocks","page":"Philosophy","title":"User-defined building blocks","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"It is common in data analysis tasks to \"pipe\" a sequence of operations. This became very popular in the data science field with the %>% operator in the R language, and it can allow users to seamlessly compose a sequence of tasks:","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"df %>%\n    filter(Weight < 3) %>%\n    group_by(Species) %>%\n    summarise(avg_height = mean(Height))","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Naturally, the alternative would be to create a statement per operation and to assign each intermediate result to its own variable.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"AlgebraOfGraphics is markedly in favor of the latter approach. It is recommended that commonly used building blocks are stored in variables with meaningful names. If we often make a scatter plot with some transparency, we can create a variable transparent_scatter = visual(Scatter, alpha = 0.5) and use it consistently. If some columns of our dataset are always analyzed together, with a similar set of transformations, we can store that information as variables = mapping(variable1 => f1 => label1, variable2 => f2 => label2).","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Working over one or more datasets, the user would then create a library of building blocks to be combined with each other with * and +. These two operators allow for a much larger number of possible combinations than just sequential composition, thus fully justifying the extra characters used to name intermediate entities.","category":"page"},{"location":"philosophy/#Opinionated-defaults","page":"Philosophy","title":"Opinionated defaults","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"While users should be able to customize every aspect of their plots, it is important to note that this customization can be very time-consuming, and many subtleties can escape the attention of the casual user:","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Is the color palette colorblind-friendly?\nWould the colors be distinguishable in black and white (when printed)?\nIs the color gradient perceptually uniform?\nAre the labels and the ticks legible for readers with low vision?\nAre the spacing and typographic hierarchies respected?","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"To remedy this, AlgebraOfGraphics aims to provide solid, opinionated default settings. In particular, it uses a conservative, colorblind-friendly palette and a perceptually uniform, universally readable color map. It follows IBM guidelines to differentiate titles and labels from tick labels via font weight, while using the same typeface at a readable size.","category":"page"},{"location":"philosophy/#Wide-format-support","page":"Philosophy","title":"Wide format support","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Finally, AlgebraOfGraphics aims to support many different data formats. Different problems require organizing the data in different formats, and AlgebraOfGraphics should support a wide range of options.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"This is achieved in three different ways. First, the Tables interface ensures integration with a large variety of data sources. Second, using the Wide data syntax, users can compare many different columns in the same visualization, without having to first reshape the dataset to a long format. Finally, tabular datasets are not a requirement: users may also work directly with Pre-grouped data, which are not organized as a table, but rather as a collection of (possibly multi-dimensional) arrays.","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/multiple_color_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/#Multiple-color-scales","page":"Multiple color scales","title":"Multiple color scales","text":"","category":"section"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"Continuous and discrete color scales can coexist in the same plot. This should be used sparingly, as it can make the plot harder to interpret.","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"x = range(-π, π, length=100)\ny = sin.(x)\nŷ = y .+ randn.() .* 0.1\nz = cos.(x)\nc = rand([\"a\", \"b\"], 100)\ndf = (; x, y, ŷ, z, c)\nlayers = mapping(:y, color=:z) * visual(Lines) + mapping(:ŷ => \"y\", color=:c)\nplt = data(df) * mapping(:x) * layers\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"EditURL = \"analyses.jl\"","category":"page"},{"location":"generated/analyses/#Analyses","page":"Analyses","title":"Analyses","text":"","category":"section"},{"location":"generated/analyses/#Histogram","page":"Analyses","title":"Histogram","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"histogram","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.histogram","page":"Analyses","title":"AlgebraOfGraphics.histogram","text":"histogram(; bins=automatic, datalimits=automatic, closed=:left, normalization=:none)\n\nCompute a histogram.\n\nThe attribute bins can be an Integer, an AbstractVector (in particular, a range), or a Tuple of either integers or abstract vectors (useful for 2- or 3-dimensional histograms). When bins is an Integer, it denotes the approximate number of equal-width intervals used to compute the histogram. In that case, the range covered by the intervals is defined by datalimits (it defaults to the extrema of the whole data). The keyword argument datalimits can be a tuple of two values, e.g. datalimits=(0, 10), or a function to be applied group by group, e.g. datalimits=extrema. When bins is an AbstractVector, it denotes the intervals directly.\n\nclosed determines whether the the intervals are closed to the left or to the right.\n\nThe histogram can be normalized by setting normalization. Possible values are:\n\n:pdf: Normalize by sum of weights and bin sizes. Resulting histogram  has norm 1 and represents a PDF.\n:density: Normalize by bin sizes only. Resulting histogram represents  count density of input and does not have norm 1.\n:probability: Normalize by sum of weights only. Resulting histogram  represents the fraction of probability mass for each bin and does not have  norm 1.\n:none: Do not normalize.\n\nWeighted data is supported via the keyword weights (passed to mapping).\n\nnote: Note\nNormalizations are computed withing groups. For example, in the case of normalization=:pdf, sum of weights within each group will be equal to 1.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"using AlgebraOfGraphics, CairoMakie\nset_aog_theme!()\n\ndf = (x=randn(5000), y=randn(5000), z=rand([\"a\", \"b\", \"c\"], 5000))\nspecs = data(df) * mapping(:x, layout=:z) * histogram(bins=range(-2, 2, length=15))\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, dodge=:z, color=:z) * histogram(bins=range(-2, 2, length=15))\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, stack=:z, color=:z) * histogram(bins=range(-2, 2, length=15))\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) *\n    mapping((:x, :z) => ((x, z) -> x + 5 * (z == \"b\")) => \"new x\", col=:z) *\n    histogram(datalimits=extrema, bins=20)\ndraw(specs, facet=(linkxaxes=:minimal,))","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"data(df) * mapping(:x, :y, layout=:z) * histogram(bins=15) |> draw","category":"page"},{"location":"generated/analyses/#Density","page":"Analyses","title":"Density","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"AlgebraOfGraphics.density","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.density","page":"Analyses","title":"AlgebraOfGraphics.density","text":"density(; datalimits=automatic, kernel=automatic, bandwidth=automatic, npoints=200)\n\nFit a kernel density estimation of data.\n\nHere, datalimits specifies the range for which the density should be calculated (it defaults to the extrema of the whole data). The keyword argument datalimits can be a tuple of two values, e.g. datalimits=(0, 10), or a function to be applied group by group, e.g. datalimits=extrema. The keyword arguments kernel and bandwidth are forwarded to KernelDensity.kde. npoints is the number of points used by Makie to draw the line\n\nWeighted data is supported via the keyword weights (passed to mapping).\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"df = (x=randn(5000), y=randn(5000), z=rand([\"a\", \"b\", \"c\", \"d\"], 5000))\nspecs = data(df) * mapping(:x, layout=:z) * AlgebraOfGraphics.density(datalimits=((-2.5, 2.5),))\n\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) *\n    mapping((:x, :z) => ((x, z) -> x + 5 * (z ∈ [\"b\", \"d\"])) => \"new x\", layout=:z) *\n    AlgebraOfGraphics.density(datalimits=extrema)\ndraw(specs, facet=(linkxaxes=:minimal,))","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"data(df) * mapping(:x, :y, layout=:z) * AlgebraOfGraphics.density(npoints=50) |> draw","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :y, layout=:z) *\n    AlgebraOfGraphics.density(npoints=50) * visual(Surface)\n\ndraw(specs, axis=(type=Axis3, zticks=0:0.1:0.2, limits=(nothing, nothing, (0, 0.2))))","category":"page"},{"location":"generated/analyses/#Frequency","page":"Analyses","title":"Frequency","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"frequency","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.frequency","page":"Analyses","title":"AlgebraOfGraphics.frequency","text":"frequency()\n\nCompute a frequency table of the arguments.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"df = (x=rand([\"a\", \"b\", \"c\"], 100), y=rand([\"a\", \"b\", \"c\"], 100), z=rand([\"a\", \"b\", \"c\"], 100))\nspecs = data(df) * mapping(:x, layout=:z) * frequency()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, layout=:z, color=:y, stack=:y) * frequency()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :y, layout=:z) * frequency()\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Expectation","page":"Analyses","title":"Expectation","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"expectation","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.expectation","page":"Analyses","title":"AlgebraOfGraphics.expectation","text":"expectation()\n\nCompute the expected value of the last argument conditioned on the preceding ones.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"df = (x=rand([\"a\", \"b\", \"c\"], 100), y=rand([\"a\", \"b\", \"c\"], 100), z=rand(100), c=rand([\"a\", \"b\", \"c\"], 100))\nspecs = data(df) * mapping(:x, :z, layout=:c) * expectation()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :z, layout=:c, color=:y, dodge=:y) * expectation()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :y, :z, layout=:c) * expectation()\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Linear","page":"Analyses","title":"Linear","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"linear","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.linear","page":"Analyses","title":"AlgebraOfGraphics.linear","text":"linear(; interval=automatic, level=0.95, dropcollinear=false, npoints=200)\n\nCompute a linear fit of y ~ 1 + x. An optional named mapping weights determines the weights. Use interval to specify what type of interval the shaded band should represent, for a given coverage level (the default 0.95 equates alpha = 0.05). Valid values of interval are :confidence, to delimit the uncertainty of the predicted relationship, and :prediction, to delimit estimated bounds for new data points. Use interval = nothing to only compute the line fit, without any uncertainty estimate.  By default, this analysis errors on singular (collinear) data. To avoid that, it is possible to set dropcollinear=true. npoints is the number of points used by Makie to draw the shaded band.\n\nWeighted data is supported via the keyword weights (passed to mapping).\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = 1:0.05:10\na = rand(1:7, length(x))\ny = 1.2 .* x .+ a .+ 0.5 .* randn.()\ndf = (; x, y, a)\nspecs = data(df) * mapping(:x, :y, color=:a => nonnumeric) * (linear() + visual(Scatter))\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Smoothing","page":"Analyses","title":"Smoothing","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"smooth","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.smooth","page":"Analyses","title":"AlgebraOfGraphics.smooth","text":"smooth(; span=0.75, degree=2, npoints=200)\n\nFit a loess model. span is the degree of smoothing, typically in [0,1]. Smaller values result in smaller local context in fitting. degree is the polynomial degree used in the loess model. npoints is the number of points used by Makie to draw the line\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = 1:0.05:10\na = rand(1:7, length(x))\ny = sin.(x) .+ a .+ 0.1 .* randn.()\ndf = (; x, y, a)\nspecs = data(df) * mapping(:x, :y, color=:a => nonnumeric) * (smooth() + visual(Scatter))\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Contours","page":"Analyses","title":"Contours","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"contours","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.contours","page":"Analyses","title":"AlgebraOfGraphics.contours","text":"contours(; levels=5, kwargs...)\n\nCreate contour lines over the grid spanned over x and y by args 1 and 2 in the mapping, with height values z passed via arg 3. \n\nYou can pass the number of levels as an integer or a vector of levels. The levels are calculated across the whole z data if they are specified as an integer.\n\nNote that visual(Contour) only works in a limited way with AlgebraOfGraphics since version 0.7, because the internal calculations it does are not compatible with the scale system. With visual(Contour), you can only have categorically-colored contours (for example to visualize contours of multiple categories). Alternatively, if you set the colormap attribute, you can get continuously-colored contours but the levels will not be known to AlgebraOfGraphics, so they won't be synchronized across facets and there will not be a colorbar.\n\nAll other keyword arguments are forwarded as attributes to the underlying Contour plot.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = repeat(1:10, 10)\ny = repeat(11:20, inner = 10)\nz = sqrt.(x .* y)\ndf = (; x, y, z)\nspecs = data(df) * mapping(:x, :y, :z) * contours(levels = 8)\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = repeat(1:10, 10)\ny = repeat(11:20, inner = 10)\nz = sqrt.(x .* y)\ndf = (; x, y, z)\nspecs = data(df) * mapping(:x, :y, :z) * contours(levels = 8, labels = true)\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Filled-Contours","page":"Analyses","title":"Filled Contours","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"filled_contours","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.filled_contours","page":"Analyses","title":"AlgebraOfGraphics.filled_contours","text":"filled_contours(; bands=automatic, levels=automatic)\n\nCreate filled contours over the grid spanned over x and y by args 1 and 2 in the mapping, with height values z passed via arg 3. \n\nYou can pass either the number of bands to bands or pass a vector of levels (the boundaries of the bands) to levels, but not both. The number of bands when levels is passed is length(levels) - 1. The levels are calculated across the whole z data if the number of bands is specified. If neither levels nor bands are specified, the default is bands = 10.\n\nNote that visual(Contourf) does not work with AlgebraOfGraphics since version 0.7, because the internal binning it does is not compatible with the scale system.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = repeat(1:10, 10)\ny = repeat(11:20, inner = 10)\nz = sqrt.(x .* y)\ndf = (; x, y, z)\nspecs = data(df) * mapping(:x, :y, :z) * filled_contours(levels = 3:2:15)\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"This page was generated using Literate.jl.","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/pre_grouped_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/#Pre-grouped-data","page":"Pre-grouped data","title":"Pre-grouped data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"using AlgebraOfGraphics, CairoMakie\n\n\nx = [rand(10) .+ i for i in 1:3]\ny = [rand(10) .+ i for i in 1:3]\nz = [rand(10) .+ i for i in 1:3]\nc = [\"a\", \"b\", \"c\"]\n\nm = pregrouped(x, y, color=c => (t -> \"Type \" * t ) => \"Category\")\ndraw(m)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"m = pregrouped(x, (y, z) => (+) => \"sum\", color=c => (t -> \"Type \" * t ) => \"Category\")\ndraw(m)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"m = pregrouped(x, [y z], color=dims(1) => renamer([\"a\", \"b\", \"c\"])) * visual(Scatter)\ndraw(m)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"m = pregrouped(x, [y z], color=[\"1\" \"2\"])\nlayers = visual(Scatter) + linear()\nfg = draw(m * layers)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/legend.jl\"","category":"page"},{"location":"gallery/gallery/customization/legend/#Legend-tweaking","page":"Legend tweaking","title":"Legend tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: Source code)","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"To tweak the position and appearance of the legend, simply use the legend keyword when plotting. For example","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"labels = [\"a looooooong label\", \"an even loooooonger label\", \"and one more long label\"]\ndf = (x=rand(100), y=rand(100), group=rand(labels, 100))\nlayers = linear() + mapping(color=:group)\nplt = data(df) * layers * mapping(:x, :y)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"fg = draw(plt, legend=(position=:top, titleposition=:left, framevisible=true, padding=5))","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"To adjust the title and order of labels in a legend you can use the pair syntax.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"layers = linear() +  mapping(color=:group => sorter(labels) => \"Labels\")\nplt = data(df) * layers * mapping(:x, :y)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"Adding a plot to a pre-existing figure with draw! will not draw the legend automatically.  In this case, one must use legend! and specify the axis to which it should be added.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"The tellheight = false, tellwidth = false arguments are useful to avoid changing the dimensions of the axis.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"makie_fig = Figure()\nax_scatter = Axis(makie_fig[1, 1])\n\ngrid = draw!(ax_scatter, plt)\n\nlegend!(makie_fig[1, 1], grid; tellheight=false, tellwidth=false, halign=:right, valign=:top)\n\nmakie_fig","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"EditURL = \"visual.jl\"","category":"page"},{"location":"generated/visual/#Visual","page":"Visual","title":"Visual","text":"","category":"section"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"visual","category":"page"},{"location":"generated/visual/#AlgebraOfGraphics.visual","page":"Visual","title":"AlgebraOfGraphics.visual","text":"visual(plottype; attributes...)\n\nCreate a Layer that will cause a plot spec multiplied with it to be visualized with plot type plottype, together with optional attributes.\n\nThe available plotting functions are documented here. Refer to plotting functions using upper CamelCase for visual's first argument (e.g. visual(Scatter), visual(BarPlot)). See the documentation of each plotting function to discover the available attributes. These attributes can be passed as additional keyword arguments to visual, or as part of the mapping you define.\n\nThe visual function can in principle be used for any plotting function that is defined using the @recipe macro from Makie. AlgebraOfGraphics just needs method definitions for aesthetic_mapping, which define what arguments of the plotting function map to which visual aesthetics. And for legend support, legend_elements must be overloaded for custom recipes as well, as Makie's default legend mechanism relies on instantiated plot objects, while AlgebraOfGraphics must go by the type and attributes alone.\n\nDepending on its aesthetic_mapping, a plot type and its attributes may change certain semantics of a given data(...) * mapping(...) spec. For example, visual(BarPlot) will show mapping 1 on the x axis and 2 on the y axis, while visual(BarPlot, direction = :x) shows mapping 1 on y and 2 on x.\n\n\n\n\n\n","category":"function"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"# Examples\n\nusing AlgebraOfGraphics, CairoMakie\nset_aog_theme!()\n\ndf = (x=randn(1000), y=randn(1000))\nplt = data(df) * mapping(:x, :y) * AlgebraOfGraphics.density(npoints=50)\ndraw(plt * visual(Heatmap)) # plot as heatmap (the default)","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"From AlgebraOfGraphics version 0.7 on, some attributes of the underlying Makie functions will not have an effect if they are controlled by scales instead. For example, continuous colors are completely controlled by color scales, so setting colormap in visual does not have an effect.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"Set the colormap in the Scale options instead.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"draw(plt, scales(Color = (; colormap = :viridis))) # set a different colormap","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"draw(plt * visual(Contour)) # plot as contour","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"draw(plt * visual(Contour, linewidth=2)) # plot as contour with thicker lines","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"This page was generated using Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/axis.jl\"","category":"page"},{"location":"gallery/gallery/customization/axis/#Axis-tweaking","page":"Axis tweaking","title":"Axis tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"To tweak one or more axes, simply use the axis keyword when plotting. For example","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"df = (x=rand(100), y=rand(100), z=rand(100))\nlayers = linear() + mapping(color=:z)\nplt = data(df) * layers * mapping(:x, :y)\ndraw(plt, axis=(aspect=1,))","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"fg = draw(plt, axis=(aspect=1, xticks=0:0.1:1, yticks=0:0.1:1, ylabel=\"custom label\"))","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/secondary_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/#Secondary-scales","page":"Secondary scales","title":"Secondary scales","text":"","category":"section"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"By default, scales with the same aesthetic type have their categories merged. This can be undesirable if there are disjoint sets of categories, for example three different time series with two different event markers:","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"nevents = 500\nngroups = 3\ntime = repeat(1:nevents, ngroups)\ny = reduce(vcat, [cumsum(randn(length(time))) for _ in 1:ngroups])\ngroup = repeat([\"A\", \"B\", \"C\"], inner = nevents)\ndf1 = (; time, y, group)\ndf2 = (; time = [30, 79, 250, 400], event = [\"X\", \"Y\", \"Y\", \"X\"])\n\nspec_a = data(df1) * mapping(:time, :y, color = :group) * visual(Lines)\nspec_b = data(df2) * mapping(:time, color = :event) * visual(VLines)\n\ndraw(spec_a + spec_b)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"By assigning an arbitrary scale identifier to one of the color mappings, we can split the two scales apart and receive a separate legend for both:","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"split_spec = spec_a + spec_b * mapping(color = :event => scale(:secondary))\ndraw(split_spec)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"Each scale can then be modified separately in the scales configuration. For example, it is not desired that both scales use the same color palette:","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"fg = draw(split_spec, scales(secondary = (;\n    palette = [:gray70, :gray30]\n)))","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"If you don't want to have separate legend groups, you can merge them using the order keyword in the legend config.","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"draw(\n    split_spec,\n    scales(\n        secondary = (; palette = [:gray70, :gray30])\n    );\n    legend = (; order = [[:Color, :secondary] => \"Legend\"])\n)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/figure.jl\"","category":"page"},{"location":"gallery/gallery/customization/figure/#Figure-tweaking","page":"Figure tweaking","title":"Figure tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"You can pass attributes to the underlying Makie.Figure using the figure keyword of draw.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"df = (x=rand(100), y=rand(100), z=rand(100), c=rand([\"a\", \"b\"], 100))\nxyc = data(df) * mapping(:x, :y, layout=:c)\nlayers = linear() + mapping(color=:z)\nplt = xyc * layers\ndraw(\n    plt,\n    figure = (;\n        figure_padding = 10,\n        backgroundcolor = :gray80,\n        size = (800, 400)\n    )\n)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"You can also add a figure title, subtitle and footnotes.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"fg = draw(\n    plt,\n    figure = (;\n        figure_padding = 10,\n        backgroundcolor = :gray80,\n        size = (800, 400),\n        title = \"Figure title\",\n        subtitle = \"Some subtitle below the figure title\",\n        footnotes = [\n            rich(superscript(\"1\"), \"First footnote\"),\n            rich(superscript(\"2\"), \"Second footnote\"),\n        ]\n    )\n)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"The appearance of these elements can be modified further, for all options check the draw function.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"draw(\n    plt,\n    figure = (;\n        figure_padding = 10,\n        backgroundcolor = :gray80,\n        size = (800, 400),\n        title = \"Figure title\",\n        subtitle = \"Some subtitle below the figure title\",\n        footnotes = [\n            rich(superscript(\"1\"), \"First footnote\"),\n            rich(superscript(\"2\"), \"Second footnote\"),\n        ],\n        titlecolor = :firebrick,\n        titlealign = :right,\n        footnotefont = :bold,\n    )\n)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/mapping/#Mapping","page":"Mapping","title":"Mapping","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Mappings determine how the data is translated into a plot. For example, this mapping maps columns weight and height to positional arguments 1 and 2, and age to the markersize attribute of the Scatter plotting function:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"mapping(:weight, :height, markersize = :age)","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"mapping","category":"page"},{"location":"layers/mapping/#AlgebraOfGraphics.mapping","page":"Mapping","title":"AlgebraOfGraphics.mapping","text":"mapping(positional...; named...)\n\nCreate a Layer with positional and named selectors. These selectors will be translated into input data for the Makie plotting function or AlgebraOfGraphics analysis that is chosen to visualize the Layer.\n\nA Layer created with mapping does not have a data source by default, you can add one by multiplying with the output of the data function.\n\nThe positional and named selectors of mapping are converted to actual input data for the plotting function that will be selected via visual. The translation from selector to data differs according to the data source.\n\nTabular data\n\nWhen a mapping is combined with a data(tabular) where tabular is some Tables.jl-compatible object, each argument will be interpreted as a column selector. Additionally, it's allowed to specify columns outside of the dataset directly by wrapping the values in direct. The values can either be vectors that have to match the number of rows from the tabular data, or scalars that will be expanded as if they were a column filled with the same value.\n\nmapping(\n    :x,                        # column named \"x\"\n    \"a column\";                # column named \"a column\"\n    color = 1,                 # first column\n    marker = direct(\"abc\"),    # a new column filled with the string \"abc\"\n    linestyle = direct(1:3),   # a new column, length must match the table\n)\n\nnothing\n\nIf no data is set, each entry of mapping should be an AbstractVector that specifies a column of data directly. Scalars like strings for example will be expanded as if they were a column filled with the same value. This is useful when a legend should be shown, but there's only one group.\n\nmapping(\n    1:3,               # a column with values 1 to 3\n    [4, 5, 6],         # a column with values 4 to 6 \n    color = \"group 1\", # a column with repeated value \"group 1\"         \n)\n\nPregrouped\n\nWith data(Pregrouped()) * mapping(...) or the shortcut pregrouped(...), each element in mapping specifies input data directly, like with nothing. However, in this mode, data should be passed in pregrouped. Categorical variables should come as a vector of categories, while numerical variables should come as a vector of vectors of values, with as many inner vectors as there are groups in the categorical variables.\n\npregrouped(\n    [[1, 2, 3], [4, 5]], # two grouped vectors, of length 3 and 2\n    color = [\"A\", \"B\"]   # a vector with two categorical group values\n)\n\n\n\n\n\n","category":"function"},{"location":"layers/mapping/#Aesthetics","page":"Mapping","title":"Aesthetics","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The structure of a mapping is always directly tied to the signature of the plotting function (or analysis) that it is being connected with. What visual aspects of the plot the positional or keyword arguments affect depends on the plotting function in use.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"To be used with AlgebraOfGraphics, a plotting function has to add a declaration which aesthetics (like X, Y, Color, MarkerSize, LineStyle) its arguments map to. This mechanism allows AlgebraOfGraphics to correctly convert the raw input data into visual attributes for each plotting function and to correctly create and label axes, colorbars and legends.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Aesthetics can also change depending on attributes passed to visual.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"For example, for a BarPlot, args 1 and 2 correspond to the X and Y aesthetics by default. But if you change the direction in the visual, then axis labels shift accordingly because the aesthetic mapping has changed to 1 = Y, 2 = X:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (; name = [\"Anna\", \"Beatrix\", \"Claire\"], height_meters = [1.55, 1.76, 1.63])\nm = mapping(:name, :height_meters)\nspec1 = data(df) * m * visual(BarPlot)\nspec2 = data(df) * m * visual(BarPlot, direction = :x)\n\nf = Figure()\ndraw!(f[1, 1], spec1)\ndraw!(f[1, 2], spec2)\nf","category":"page"},{"location":"layers/mapping/#Hardcoded-aesthetics","page":"Mapping","title":"Hardcoded aesthetics","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Most aesthetics are tied to specific attributes of plot types, for example like AesColor to strokecolor of Scatter. There are a few aesthetics, however, which are hardcoded to belong to certain mapping keywords independent of the plot type in use.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"These are layout, row and col for facetting, group for creating a separate plot for each group (like separate lines instead of one long line) and dodge_x and dodge_y for dodging.","category":"page"},{"location":"layers/mapping/#Dodging","page":"Mapping","title":"Dodging","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Dodging refers to the shifting of plots on a (usually categorical) scale depending on the group they belong to. It is used to avoid overlaps. Some plot types, like BarPlot, have their own dodge keyword because their dodging logic additionally needs to transform the visual elements (for example, dodging a bar plot makes thinner bars). For all other plot types, you can use the generic dodge_x and dodge_y keywords.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"They work by shifting each categorical group by some value that depends on the chosen \"dodge width\". The dodge width refers to the width that all dodged elements in a group add up to at a given point. Some plot types have an inherent width, like barplots. Others have no width, like scatters or errorbars. For those plot types that have no width to use for dodging, you have to specify one manually in scales.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Here's an example of a manual width selection:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (\n   x = repeat(1:10, inner = 2),\n   y = cos.(range(0, 2pi, length = 20)),\n   ylow = cos.(range(0, 2pi, length = 20)) .- 0.2,\n   yhigh = cos.(range(0, 2pi, length = 20)) .+ 0.3,\n   dodge = repeat([\"A\", \"B\"], 10)\n)\n\nf = Figure()\nplt = data(df) * (\n   mapping(:x, :y, dodge_x = :dodge, color = :dodge) * visual(Scatter) +\n   mapping(:x, :ylow, :yhigh, dodge_x = :dodge, color = :dodge) * visual(Rangebars)\n)\ndraw!(f[1, 1], plt, scales(DodgeX = (; width = 1)), axis = (; title = \"width = 1\"))\ndraw!(f[1, 2], plt, scales(DodgeX = (; width = 0.75)), axis = (; title = \"width = 0.75\"))\ndraw!(f[2, 1], plt, scales(DodgeX = (; width = 0.5)), axis = (; title = \"width = 0.5\"))\ndraw!(f[2, 2], plt, scales(DodgeX = (; width = 0.25)), axis = (; title = \"width = 0.25\"))\nf","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"A common scenario is plotting errorbars on top of barplots. In this case, AlgebraOfGraphics can detect the inherent dodging width of the barplots and adjust accordingly for the errorbars. Note in this example how choosing a manual dodging width only applies to the errorbars (because the barplot plot type handles this internally) and potentially leads to a misalignment between the different plot elements:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (\n   x = repeat(1:10, inner = 2),\n   y = cos.(range(0, 2pi, length = 20)),\n   ylow = cos.(range(0, 2pi, length = 20)) .- 0.2,\n   yhigh = cos.(range(0, 2pi, length = 20)) .+ 0.3,\n   dodge = repeat([\"A\", \"B\"], 10)\n)\n\nf = Figure()\nplt = data(df) * (\n   mapping(:x, :y, dodge = :dodge, color = :dodge) * visual(BarPlot) +\n   mapping(:x, :ylow, :yhigh, dodge_x = :dodge) * visual(Rangebars)\n)\ndraw!(f[1, 1], plt, axis = (; title = \"No width specified, auto-determined by AlgebraOfGraphics\"))\ndraw!(f[2, 1], plt, scales(DodgeX = (; width = 0.25)), axis = (; title = \"Manually specifying width = 0.25 leads to a mismatch\"))\nf","category":"page"},{"location":"layers/mapping/#Pair-syntax","page":"Mapping","title":"Pair syntax","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The Pair operator => can be used for three different purposes within mapping:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"renaming columns\ntransforming columns by row\nmapping data to a custom scale","category":"page"},{"location":"layers/mapping/#Renaming-columns","page":"Mapping","title":"Renaming columns","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndata((; name = [\"Anna\", \"Beatrix\", \"Claire\"], height_meters = [1.55, 1.76, 1.63])) *\n   mapping(:name => \"Name\", :height_meters => \"Height (m)\") *\n   visual(BarPlot) |> draw ","category":"page"},{"location":"layers/mapping/#Transforming-columns","page":"Mapping","title":"Transforming columns","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"If a Function is paired to the column selector, it is applied by row to the data. Often, you will want to also assign a new name that fits the transformed data, in which case you can use the three-element column => transformation => name syntax:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndata((; name = [\"Anna\", \"Beatrix\", \"Claire\"], height_meters = [1.55, 1.76, 1.63])) *\n   mapping(:name => (n -> n[1] * \".\"), :height_meters => (x -> x * 100) => \"Height (cm)\") *\n   visual(BarPlot) |> draw","category":"page"},{"location":"layers/mapping/#Row-by-row-versus-whole-column-operations","page":"Mapping","title":"Row-by-row versus whole-column operations","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The pair syntax acts row by row, unlike, e.g., DataFrames.transform. This has several advantages.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Simpler for the user in most cases.\nLess error prone especially\nwith grouped data (should a column operation apply to each group or the whole dataset?)\nwhen several datasets are used","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Naturally, this also incurs some downsides, as whole-column operations, such as z-score standardization, are not supported: they should be done by adding a new column to the underlying dataset beforehand.","category":"page"},{"location":"layers/mapping/#Functions-of-several-arguments","page":"Mapping","title":"Functions of several arguments","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"In the case of functions of several arguments, such as isequal, the input variables must be passed as a Tuple.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"accuracy = (:species, :predicted_species) => isequal => \"accuracy\"","category":"page"},{"location":"layers/mapping/#Helper-functions","page":"Mapping","title":"Helper functions","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Some helper functions are provided, which can be used within the pair syntax to either rename and reorder unique values of a categorical column on the fly or to signal whether a numerical column should be treated as categorical.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The complete API of helper functions is available at Mapping helpers, but here are a few examples:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"# column `train` has two unique values, `true` and `false`\n:train => renamer([true => \"training\", false => \"testing\"]) => \"Dataset\"\n# column `price` has three unique values, `\"low\"`, `\"medium\"`, and `\"high\"`\n:price => sorter([\"low\", \"medium\", \"high\"])\n# column `age` is expressed in integers and we want to treat it as categorical\n:age => nonnumeric\n# column `labels` is expressed in strings and we do not want to treat it as categorical\n:labels => verbatim\n# wrap categorical values to signal that the order from the data source should be respected\n:weight => presorted","category":"page"},{"location":"layers/mapping/#Custom-scales","page":"Mapping","title":"Custom scales","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"All columns mapped to the same aesthetic type are represented using the same scale by default. This is evident if you plot two different datasets with two different plot types.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"In the following example, both Scatter and HLines use the Color aesthetic, Scatter for the strokecolor keyword and HLines for color. A single merged legend is rendered for both, which does not have a title because it derives from two differently named columns.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf_a = (; x = 1:9, y = [1, 2, 3, 5, 6, 7, 9, 10, 11], group = repeat([\"A\", \"B\", \"C\"], inner = 3))\n\nspec1 = data(df_a) * mapping(:x, :y, strokecolor = :group) * visual(Scatter, color = :transparent, strokewidth = 3, markersize = 15)\n\ndf_b = (; y = [4, 8], threshold = [\"first\", \"second\"])\n\nspec2 = data(df_b) * mapping(:y, color = :threshold) * visual(HLines)\n\ndraw(spec1 + spec2)","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"If we want to have separate legends for both, we can assign a custom scale identifier to either the strokecolor or the color mapping. The name can be chosen freely, it serves only to disambiguate.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"spec2_custom_scale = data(df_b) * mapping(:y, color = :threshold => scale(:color2)) * visual(HLines)\n\ndraw(spec1 + spec2_custom_scale)","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Each scale can be customized further by passing configuration options via scales as the second argument of the draw function. More information on scale options can be found under Scale options.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"As an example, we can pass separate colormaps using the palette keyword:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"spec2_custom_scale = data(df_b) * mapping(:y, color = :threshold => scale(:color2)) * visual(HLines)\n\ndraw(\n   spec1 + spec2_custom_scale,\n   scales(\n      Color = (; palette = [:red, :green, :blue]),\n      color2 = (; palette = [:gray30, :gray80]),\n   )\n)","category":"page"},{"location":"changelog/#Changelog","page":"Changelog","title":"Changelog","text":"","category":"section"},{"location":"changelog/#Unreleased","page":"Changelog","title":"Unreleased","text":"","category":"section"},{"location":"changelog/#v0.8.8-2024-09-17","page":"Changelog","title":"v0.8.8 - 2024-09-17","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Fixed aesthetics of errorbar so that x and y stay labelled correctly when using direction = :x #560.\nAdded ability to specify title, subtitle and footnotes plus settings in the draw function #556.\nAdded dodge_x and dodge_y keywords to mapping that allow to dodge any plot types that have AesX or AesY data #558.","category":"page"},{"location":"changelog/#v0.8.7-2024-09-06","page":"Changelog","title":"v0.8.7 - 2024-09-06","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added ability to return ProcessedLayers from transformations, thereby enabling multi-layer transformations, such as scatter plus errorbars #549.\nFixed bug where mergesorted applied on string vectors used isless instead of natural sort #553.","category":"page"},{"location":"changelog/#v0.8.6-2024-09-02","page":"Changelog","title":"v0.8.6 - 2024-09-02","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added bar_labels to BarPlot's aesthetic mapping #544.\nAdded ability to hide legend or colorbar by passing, e.g., legend = (; show = false) to draw #547.","category":"page"},{"location":"changelog/#v0.8.5-2024-08-27","page":"Changelog","title":"v0.8.5 - 2024-08-27","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added presorted helper function to keep categorical data in the order encountered in the source table, instead of sorting it alphabetically #529.\nAdded from_continuous helper function which allows to sample continuous colormaps evenly to use them as categorical palettes without having to specify how many categories there are #541.","category":"page"},{"location":"changelog/#v0.8.4-2024-08-26","page":"Changelog","title":"v0.8.4 - 2024-08-26","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added fillto to BarPlot aesthetics #535.\nFixed bug when giving datalimits of density as a (low, high) tuple #536.\nFixed bug where facet-local continuous scale limits were used instead of the globally merged ones, possibly leading to mismatches between data and legend #539.","category":"page"},{"location":"changelog/#v0.8.3-2024-08-23","page":"Changelog","title":"v0.8.3 - 2024-08-23","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Fixed incorrect x/y axis assignment for the violin plot type #528.","category":"page"},{"location":"changelog/#v0.8.2-2024-08-21","page":"Changelog","title":"v0.8.2 - 2024-08-21","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Enable use of LaTeXStrings and rich text in renamer #525.\nFixed widths of boxplots with color groupings #524.","category":"page"},{"location":"changelog/#v0.8.1-2024-08-20","page":"Changelog","title":"v0.8.1 - 2024-08-20","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added back support for Hist, CrossBar, ECDFPlot and Density #522.","category":"page"},{"location":"changelog/#v0.8.0-2024-07-26","page":"Changelog","title":"v0.8.0 - 2024-07-26","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Columns with element types of Union{Missing,T} are not treated as categorical by default anymore, instead T decides if data is seen as categorical, continuous or geometrical. If you relied on numerical vectors with missings being treated as categorical, you can use :columnname => nonnumeric in the mapping instead.\nBreaking: AbstractString categories are now sorted with natural sort order by default. This means that where you got [\"1\", \"10\", \"2\"] before, you now get [\"1\", \"2\", \"10\"]. You can use sorter, the categories keyword or categorical arrays to sort your data differently if needed.","category":"page"},{"location":"changelog/#v0.7.0-2024-07-16","page":"Changelog","title":"v0.7.0 - 2024-07-16","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: The palette keyword of draw linking palettes to keyword arguments was removed. Instead, palettes need to be passed to specific scales like draw(..., scales(Color = (; palette = :Set1_3)))\nBreaking: All recipes need to have the new function aesthetic_mapping defined for all sets of positional arguments that should be supported, as can be seen in src/aesthetics.jl. This breaks usage of all custom recipes. Additionally, not all Makie plots have been ported to the new system yet. If you encounter missing plots, or missing attributes of already ported plots, please open an issue.\nBreaking: All custom recipes that should be displayed in a legend, need to have legend_elements(P, attributes, scale_args) defined as can be seen in src/guides/legend.jl. AlgebraOfGraphics cannot use the same default mechanism as Makie, which can create a legend from an existing plot, because AlgebraOfGraphics needs to create the legend before the plot is instantiated.\nBreaking: Pregrouped data cannot be passed anymore to the plain mapping(...) without any data(tabular). Instead, you should use pregrouped(...) which is a shortcut for data(Pregrouped()) * mapping(...).\nBreaking: Contour and Contourf generally do not work anymore with visual(). Instead, the contours() and filled_contours() analyses should be used. Contour can still be used with categorical colors, but not with continuous ones.\nBreaking: All colormap properties for continuous color scales need to be passed via scales now, and not through visual. This is to have central control over the scale as it can be used by multiple visuals simultaneously.\nHorizontal barplots, violins, errorbars, rangebars and other plot types that have two different orientations work correctly now. Axis labels switch accordingly when the orientation is changed.\nPlotting functions whose positional arguments don't correspond to X, Y, Z work correctly now. For example, HLines (1 => Y) or rangebars (1 => X, 2 => Y, 3 => Y).\nIt is possible to add categories beyond those present in the data with the categories keyword within a scale's settings. It is also possible to reorder or otherwise transform the existing categories by passing a function to categories.\nThe supported attributes are not limited anymore to a specific set of names, for example, strokecolor can work the same as color did before, and the two can share a scale via their shared aesthetic type.\nThere can be multiple scales of the same aesthetic now. This allows to have separate legends for different plot types using the same aesthetics. Scale separation works by pairing a variable in mapping with a scale(id_symbol).\nLegend entries can be reordered using the legend = (; order = ...) option in draw. Specific scales can opt out of the legend by passing legend = false in scales.\nLabels can now be anything that Makie supports, primarily Strings, LaTeXStrings or rich text.\nLegend elements now usually reflect all attributes set in their corresponding visual.\nSimple column vectors of data can now be passed directly to mapping without using data first. Additionally, scalar values are accepted as a shortcut for columns with the same repeated value.\nColumns from outside a table source in data can now be passed to mapping by wrapping them in the direct function. Scalar values are accepted as a shortcut for columns with the same repeated value. For example, to create a label for columns x and y from a dataframe passed to data, one could now do mapping(:x, :y, color = direct(\"label\")) without having to create a column full of \"label\" strings first.\nThe numbers at which categorical values are plotted on x and y axis can now be changed via scales(X = (; palette = [1, 2, 4])) or similar.\nContinuous marker size scales can now be shown in the legend. Numerical values are proportional to area and not diameter now, which makes more sense with respect to human perception. The min and max marker size can be set using the sizerange property for the respective scale in scales.","category":"page"},{"location":"changelog/#v0.6.11-2022-08-08","page":"Changelog","title":"v0.6.11 - 2022-08-08","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added paginate for pagination of large facet plots. ","category":"page"},{"location":"changelog/#v0.6.8-2022-06-14","page":"Changelog","title":"v0.6.8 - 2022-06-14","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added choropleth recipe to supersede geodata for geographical data.","category":"page"},{"location":"changelog/#v0.6.1-2022-01-28","page":"Changelog","title":"v0.6.1 - 2022-01-28","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Support level in linear analysis for confidence interval.\nReplaced tuples and named tuples in Layer and Entry with dictionaries from Dictionaries.jl.\nSplit internal Entry type into ProcessedLayer (to be used for analyses) and Entry (to be used for plotting).","category":"page"},{"location":"changelog/#v0.6.0-2021-10-24","page":"Changelog","title":"v0.6.0 - 2021-10-24","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Default axis linking behavior has changed: now only axes corresponding to the same variable are linked. For consistency with row/col, layout will hide decorations of linked axes and span axis labels if appropriate.\nCustomizable legend and colorbar position and look.\nCustomizable axis linking behavior.","category":"page"},{"location":"changelog/#v0.5-2021-08-05","page":"Changelog","title":"v0.5 - 2021-08-05","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Axis(ae) has been replaced by ae.axis.\nBreaking: Legend(fg) has been replaced by legend!(fg) and colorbar!(fg).\nlegend! and colorbar! API allows for custom legend placement.","category":"page"},{"location":"changelog/#v0.4-2021-05-21","page":"Changelog","title":"v0.4 - 2021-05-21","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Removed deprecations for style and spec (now only mapping and visual are allowed).\nBreaking: Analyses now require parentheses (i.e. linear() instead of linear).\nBreaking: Rename layout_x and layout_y to col and row.\nBreaking: Rename wts keyword argument to weights.\nBreaking: categorical has been replaced by nonnumeric.","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/statistical analyses/density_plots.jl\"","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/#Density-plots","page":"Density plots","title":"Density plots","text":"","category":"section"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\n\ndf = (x=randn(1000), c=rand([\"a\", \"b\"], 1000))\nplt = data(df) * mapping(:x, color=:c) * density(bandwidth=0.5)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"df = (x=randn(1000), c=rand([\"a\", \"b\"], 1000))\nplt = data(df) * mapping(:x, color=:c) * density(bandwidth=0.5) * visual(orientation=:vertical)\n\"Not yet supported\" # hide","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"\"Not yet supported\"","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"API/types/#Types","page":"Types","title":"Types","text":"","category":"section"},{"location":"API/types/","page":"Types","title":"Types","text":"AlgebraOfGraphics.AbstractDrawable\nAlgebraOfGraphics.AbstractAlgebraic\nAlgebraOfGraphics.Layer\nAlgebraOfGraphics.Layers\nAlgebraOfGraphics.ProcessedLayer\nAlgebraOfGraphics.ProcessedLayers\nAlgebraOfGraphics.Entry\nAlgebraOfGraphics.AxisEntries","category":"page"},{"location":"API/types/#AlgebraOfGraphics.AbstractDrawable","page":"Types","title":"AlgebraOfGraphics.AbstractDrawable","text":"AbstractDrawable\n\nAbstract type encoding objects that can be drawn via AlgebraOfGraphics.draw.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.AbstractAlgebraic","page":"Types","title":"AlgebraOfGraphics.AbstractAlgebraic","text":"AbstractAlgebraic  <: AbstractDrawable\n\nAbstract type encoding objects that can be combined together using + and *.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.Layer","page":"Types","title":"AlgebraOfGraphics.Layer","text":"Layer(transformation, data, positional::AbstractVector, named::AbstractDictionary)\n\nAlgebraic object encoding a single layer of a visualization. It is composed of a dataset, positional and named arguments, as well as a transformation to be applied to those. Layer objects can be multiplied, yielding a novel Layer object, or added, yielding a AlgebraOfGraphics.Layers object.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.Layers","page":"Types","title":"AlgebraOfGraphics.Layers","text":"Layers(layers::Vector{Layer})\n\nAlgebraic object encoding a list of AlgebraOfGraphics.Layer objects. Layers objects can be added or multiplied, yielding a novel Layers object.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.ProcessedLayer","page":"Types","title":"AlgebraOfGraphics.ProcessedLayer","text":"ProcessedLayer(l::Layer)\n\nProcess a Layer and return the resulting ProcessedLayer.\n\nNote that this method should not be used anymore as processing a Layer can now potentially return multiple ProcessedLayer objects. Therefore, you should use the plural form ProcessedLayers(layer).\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.ProcessedLayers","page":"Types","title":"AlgebraOfGraphics.ProcessedLayers","text":"ProcessedLayers(layers::Vector{ProcessedLayer})\n\nObject encoding a list of AlgebraOfGraphics.ProcessedLayer objects. ProcessedLayers objects are the output of the processing pipeline and can be drawn without further processing.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.Entry","page":"Types","title":"AlgebraOfGraphics.Entry","text":"Entry(plottype::PlotType, positional::Arguments, named::NamedArguments)\n\nDefine plottype as well as positional and named arguments for a single plot.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.AxisEntries","page":"Types","title":"AlgebraOfGraphics.AxisEntries","text":"AxisEntries(axis::Union{Axis, Nothing}, entries::Vector{Entry}, categoricalscales, continuousscales)\n\nDefine all ingredients to make plots on an axis. Each categorical scale should be a CategoricalScale, and each continuous scale should be a ContinuousScale.\n\n\n\n\n\n","category":"type"},{"location":"API/recipes/#Recipes","page":"Recipes","title":"Recipes","text":"","category":"section"},{"location":"API/recipes/","page":"Recipes","title":"Recipes","text":"choropleth\nlinesfill","category":"page"},{"location":"API/recipes/#AlgebraOfGraphics.choropleth","page":"Recipes","title":"AlgebraOfGraphics.choropleth","text":"choropleth(geometries; transformation, attributes...)\n\nChoropleth map, where regions are defined by geometries. Use transformation to transform coordinates (see Proj.jl for more information).\n\nwarning: Warning\nThe transformation keyword argument is experimental and could be deprecated (even in a non-breaking release) in favor of a different syntax.\n\nAttributes\n\nAvailable attributes and their defaults for Plot{AlgebraOfGraphics.choropleth} are: \n\n  alpha               1.0\n  clip_planes         MakieCore.Automatic()\n  color               :gray25\n  colormap            :batlow\n  colorrange          MakieCore.Automatic()\n  colorscale          identity\n  cycle               [:color => :patchcolor]\n  depth_shift         0.0f0\n  highclip            MakieCore.Automatic()\n  inspectable         true\n  inspector_clear     MakieCore.Automatic()\n  inspector_hover     MakieCore.Automatic()\n  inspector_label     MakieCore.Automatic()\n  joinstyle           :miter\n  linecap             :butt\n  linestyle           \"nothing\"\n  lowclip             MakieCore.Automatic()\n  miter_limit         1.0471975511965976\n  nan_color           :transparent\n  overdraw            false\n  shading             NoShading\n  space               :data\n  ssao                false\n  stroke_depth_shift  -1.0f-5\n  strokecolor         :black\n  strokecolormap      :batlow\n  strokewidth         0\n  transparency        false\n  visible             true\n\n\n\n\n\n","category":"function"},{"location":"API/recipes/#AlgebraOfGraphics.linesfill","page":"Recipes","title":"AlgebraOfGraphics.linesfill","text":"linesfill(xs, ys; lower, upper, attributes...)\n\nLine plot with a shaded area between lower and upper. If lower and upper are not given, shaded area is between 0 and ys.\n\nAttributes\n\nAvailable attributes and their defaults for Plot{AlgebraOfGraphics.linesfill} are: \n\n  color       :gray25\n  colormap    :batlow\n  colorrange  MakieCore.Automatic()\n  fillalpha   0.15\n  linestyle   \"nothing\"\n  linewidth   1.5\n  lower       MakieCore.Automatic()\n  upper       MakieCore.Automatic()\n\n\n\n\n\n","category":"function"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/no_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/#Using-mapping-without-tabular-data","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"Sometimes it's easier to specify columnar data directly within mapping rather than first storing it in some tabular data source and accessing it by column name. Note that you can also use scalar values which will be treated like columns with repeated elements. In the example below, we specify color = \"marker\" instead of the more verbose color = fill(\"marker\", 3).","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"using AlgebraOfGraphics, CairoMakie\n\nx = 1:100\ny = sin.(range(0, 2pi, length = 100))\n\nplt = mapping(x, y, color = repeat([\"high\", \"low\"], inner = 50)) *\n    visual(Lines) +\n    mapping([20, 28, 51], color = \"marker\" => scale(:secondary)) *\n    visual(VLines, linestyle = :dash)\n\nfg = draw(plt, scales(secondary = (; palette = [:gray80])))","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/data/#Data","page":"Data","title":"Data","text":"","category":"section"},{"location":"layers/data/","page":"Data","title":"Data","text":"data","category":"page"},{"location":"layers/data/#AlgebraOfGraphics.data","page":"Data","title":"AlgebraOfGraphics.data","text":"data(table)\n\nCreate a Layer with its data field set to a table-like object.\n\nThere are no type restrictions on this object, as long as it respects the Tables interface. In particular, any one of these formats should work out of the box.\n\nTo create a fully specified layer, the layer created with data needs to be multiplied with the output of mapping.\n\nspec = data(...) * mapping(...)\n\n\n\n\n\n","category":"function"},{"location":"layers/data/","page":"Data","title":"Data","text":"using AlgebraOfGraphics\ndf = (a = rand(10), b = rand(10))\ndata(df)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/applications/time_series.jl\"","category":"page"},{"location":"gallery/gallery/applications/time_series/#Time-series","page":"Time series","title":"Time series","text":"","category":"section"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"using AlgebraOfGraphics, CairoMakie\nusing Dates\n\nx = today() - Year(1) : Day(1) : today()\ny = cumsum(randn(length(x)))\nz = cumsum(randn(length(x)))\ndf = (; x, y, z)\nlabels = [\"series 1\", \"series 2\", \"series 3\", \"series 4\", \"series 5\"]\nplt = data(df) *\n    mapping(:x, [:y, :z] .=> \"value\", color=dims(1) => renamer(labels) => \"series \") *\n    visual(Lines)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"x = now() - Hour(6) : Minute(1) : now()\ny = cumsum(randn(length(x)))\nz = cumsum(randn(length(x)))\ndf = (; x, y, z)\nplt = data(df) *\n    mapping(:x, [:y, :z] .=> \"value\", color=dims(1) => renamer(labels) =>\"series \") *\n    visual(Lines)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"dates = Date(2022, 1, 1):Day(1):Date(2022, 1, 31)\ntrend = cumsum(randn(31))\ndf = map(1:2000) do _\n    idx = rand(1:31)\n    date = dates[idx]\n    observation = trend[idx] + 2 * rand()\n    return (; date, observation)\nend\nplt = data(df) * mapping(:date, :observation) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/draw/#Drawing-Layers","page":"Drawing Layers","title":"Drawing Layers","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A AlgebraOfGraphics.Layer or AlgebraOfGraphics.Layers object can be plotted using the functions draw or draw!.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Whereas draw automatically adds colorbar and legend, draw! does not, as it would be hard to infer a good default placement that works in all scenarios.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Colorbar and legend, should they be necessary, can be added separately with the colorbar! and legend! helper functions. See also Nested layouts for a complex example.","category":"page"},{"location":"layers/draw/#Scale-options","page":"Drawing Layers","title":"Scale options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"All properties that decide how scales are visualized can be modified by passing scale options (using the scales function) as the second argument of draw . The properties that are accepted differ depending on the scale aesthetic type (for example Color, Marker, LineStyle) and whether the scale is categorical or continuous.","category":"page"},{"location":"layers/draw/#Categorical-scale-options","page":"Drawing Layers","title":"Categorical scale options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"All categorical scales take the same properties, independent of aesthetic type.","category":"page"},{"location":"layers/draw/#palette","page":"Drawing Layers","title":"palette","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The palette decides which attribute values are passed to Makie's plotting functions for each categorical value in the scale.","category":"page"},{"location":"layers/draw/#Color","page":"Drawing Layers","title":"Color","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A Symbol is converted to a colormap with Makie.to_colormap. ","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n    mapping(:x, :y, color = :z) *\n    visual(BarPlot)\ndraw(spec, scales(Color = (; palette = :tab10)))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"It's also possible to directly specify a vector of colors, each of which Makie.to_color can handle:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\nusing CairoMakie.Colors: RGB, RGBA, Gray, HSV\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n    mapping(:x, :y, color = :z) *\n    visual(BarPlot)\ndraw(spec, scales(Color = (; palette = [:red, :green, :blue, RGB(1, 0, 1), RGB(1, 1, 0), \"#abcff0\", \"#c88cbccc\", HSV(0.9, 0.3, 0.7), RGBA(0.7, 0.9, 0.6, 0.5), Gray(0.5)])))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"If you want to use a continuous colormap for categorical data, you can use the from_continuous helper function. It automatically takes care that the continuous colormap is sampled evenly from start to end depending on the number of categories. Any colormap that Makie understands can be passed, including named colormaps such as :viridis.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"This example shows the difference in behavior when the two-element colormap [:red, :blue] is used with or without from_continuous:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n    mapping(:x, :y, color = :z) *\n    visual(BarPlot)\n\nf = Figure()\nfg1 = draw!(f[1, 1], spec, scales(Color = (; palette = [:red, :blue])))\nlegend!(f[1, 2], fg1)\nfg2 = draw!(f[1, 3], spec, scales(Color = (; palette = from_continuous([:red, :blue]))))\nlegend!(f[1, 4], fg2)\nf","category":"page"},{"location":"layers/draw/#Marker","page":"Drawing Layers","title":"Marker","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A vector of values that Makie.to_spritemarker can handle.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n    mapping(:x, :y, marker = :z) *\n    visual(Scatter, markersize = 20)\n\ndraw(\n    spec,\n    scales(\n        Marker = (; palette = [:rect, :circle, :utriangle, :dtriangle, :diamond, :hline, :vline, :star5, :star6, :hexagon])\n    )\n)","category":"page"},{"location":"layers/draw/#LineStyle","page":"Drawing Layers","title":"LineStyle","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A vector of values that Makie.to_linestyle can handle.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = repeat('A':'E', inner = 2))) *\n    mapping(:x, :y, linestyle = :z) *\n    visual(Lines, linewidth = 2)\n\ndraw(spec, scales(\n    LineStyle = (; palette = [:solid, :dash, :dot, (:dot, :loose), Linestyle([0, 1, 2, 3, 4, 8])])\n))","category":"page"},{"location":"layers/draw/#X-and-Y","page":"Drawing Layers","title":"X & Y","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The \"palette\" values for X and Y axes are by default simply the numbers from 1 to N, the number of categories. In some circumstances, it might be useful to change these values, for example to visualize that one category is different than others. The palette values are normally assigned category-by-category in the sorted order, or in the order provided manually through the categories keyword. However, if you pass a vector of values, you can always use the category => value pair option to assign a specific category directly to a value, while the others cycle. Here, we do this with \"Unknown\" as it would otherwise be sorted before \"X\".","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (; group = [\"A\", \"B\", \"C\", \"X\", \"Y\", \"Unknown\"], count = [45, 10, 20, 32, 54, 72])\n\nspec = data(df) * mapping(:group, :count) * visual(BarPlot)\n\ndraw(spec, scales(X = (; palette = [1, 2, 3, 5, 6, \"Unknown\" => 8])))","category":"page"},{"location":"layers/draw/#Layout","page":"Drawing Layers","title":"Layout","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Normally, with the Layout aesthetic, rows wrap automatically such that an approximately square distribution of facets is attained. You can overwrite these values, however, to place axes at manually chosen positions:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (;\n    group = repeat([\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"], inner = 20),\n    x = randn(160),\n    y = randn(160)\n)\n\nspec = data(df) * mapping(:x, :y, layout = :group) * visual(Scatter)\n\nclockwise = [(1, 1), (1, 2), (1, 3), (2, 3), (3, 3), (3, 2), (3, 1), (2, 1)]\n\ndraw(spec, scales(Layout = (; palette = clockwise)))","category":"page"},{"location":"layers/draw/#categories","page":"Drawing Layers","title":"categories","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The categories keyword can be used to reorder, label and even add categorical values.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Some reordering and renaming can be done using the sorter and renamer helper functions applied directly to columns in mapping. However, this works less well when several data sources are combined where not all categories appear in each column. Also, no categories can be added this way, which is something that can be useful if the existence of categories should be shown even though there is no data for them.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"New labels can be assigned using the value => label pair syntax.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; group = [\"A\", \"C\", \"D\"], value = [1, 3, 4])) *\n    mapping(:group, :value) * visual(BarPlot)\n\nf = Figure()\n\ndraw!(f[1, 1], spec, scales(\n    X = (; categories = [\"A\", \"B\", \"C\", \"D\"])\n))\ndraw!(f[1, 2], spec, scales(\n    X = (; categories = [\"D\", \"A\", \"C\"])\n))\ndraw!(f[1, 3], spec, scales(\n    X = (; categories = [\"A\" => \"a\", \"C\" => \"c\", \"D\" => \"d\"])\n))\n\nf","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"You can also pass a Function to categories which should take the vector of category values and return a new vector of categories or category/label pairs.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"For example, you could add summary statistics to the facet layout titles this way, by grabbing them from a dictionary computed separately.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nsummary_stats = Dict(\"A\" => 1.32, \"B\" => 4.19, \"C\" => 0.04)\n\ndf = (;\n    x = randn(90),\n    y = randn(90) .+ repeat([0, 5, 10], inner = 30),\n    group = repeat([\"A\", \"B\", \"C\"], inner = 30)\n)\n\nspec = data(df) * mapping(:x, :y, col = :group) * visual(Scatter)\n\ndraw(spec, scales(Col = (;\n    categories = cats -> [\n        cat => rich(\"$cat\\n\", rich(\"λ = $(summary_stats[cat])\", font = :italic))\n            for cat in reverse(cats)\n        ]\n)))","category":"page"},{"location":"layers/draw/#Continuous-scale-options","page":"Drawing Layers","title":"Continuous scale options","text":"","category":"section"},{"location":"layers/draw/#Color-2","page":"Drawing Layers","title":"Color","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Continuous color scales can be modified using the familiar Makie attributes colormap, colorrange, highclip, lowclip and nan_color. By default, colorrange is set to the extrema of the encountered values, so no clipping occurs.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = [1:4; NaN; 6:10])) *\n    mapping(:x, :y, color = :z) *\n    visual(Scatter, markersize = 20)\n\ndraw(spec, scales(\n    Color = (;\n        colormap = :plasma,\n        nan_color = :cyan,\n        lowclip = :lime,\n        highclip = :red,\n        colorrange = (2, 9)\n    )\n))","category":"page"},{"location":"layers/draw/#MarkerSize","page":"Drawing Layers","title":"MarkerSize","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The range of marker sizes can be set with the sizerange attribute. Marker sizes are computed such that their area, and not markersize itself, grows linearly with the scale values. ","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 10:10:100)) *\n    mapping(:x, :y, markersize = :z) *\n    visual(Scatter)\n\nf = Figure()\n\ngrid = draw!(f[1, 1], spec, scales(\n    MarkerSize = (;\n        sizerange = (5, 15)\n    )\n))\nlegend!(f[1, 2], grid)\n\ngrid2 = draw!(f[2, 1], spec, scales(\n    MarkerSize = (;\n        sizerange = (5, 30)\n    )\n))\nlegend!(f[2, 2], grid2)\n\nf","category":"page"},{"location":"layers/draw/#Legend-options","page":"Drawing Layers","title":"Legend options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The legend keyword forwards most attributes to Makie's Legend function. The exceptions are listed here.","category":"page"},{"location":"layers/draw/#order","page":"Drawing Layers","title":"order","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"By default, the legend order depends on the order in which layers and mappings have been defined, as well as whether scales are categorical or continuous.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (;\n    x = 1:12,\n    y = 1:12,\n    z = 1:12,\n    group1 = repeat([\"A\", \"B\", \"C\"], inner = 4),\n    group2 = repeat([\"X\", \"Y\"], 6),\n)\n\nspec = data(df) *\n    mapping(:x, :y, markersize = :z, color = :group1, marker = :group2) *\n    visual(Scatter)\n\ndraw(spec)","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"You can reorder the legend with the order keyword. This expects a vector with either Symbols or Vector{Symbol}s as elements, where each Symbol is the identifier for a scale that's represented in the legend.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Plain Symbols can be used for simple reordering:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"draw(spec; legend = (; order = [:MarkerSize, :Color, :Marker]))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Symbols that are grouped into Vectors indicate that their groups should be merged together. For example, consider this plot that features two color scales, but one for a scatter plot and one for a line plot.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf_a = (; x = 1:9, y = [1, 2, 3, 5, 6, 7, 9, 10, 11], group = repeat([\"A\", \"B\", \"C\"], inner = 3))\n\nspec1 = data(df_a) * mapping(:x, :y, strokecolor = :group) * visual(Scatter, color = :transparent, strokewidth = 3, markersize = 15)\n\ndf_b = (; y = [4, 8], threshold = [\"first\", \"second\"])\n\nspec2_custom_scale = data(df_b) * mapping(:y, color = :threshold => scale(:color2)) * visual(HLines)\n\ndraw(spec1 + spec2_custom_scale)","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"You can group the two scales together using order. The titles are dropped.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"draw(spec1 + spec2_custom_scale; legend = (; order = [[:Color, :color2]]))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"If you want to add a title to a merged group, you can add it with the group => title pair syntax:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"draw(spec1 + spec2_custom_scale; legend = (; order = [[:Color, :color2] => \"Title\"]))","category":"page"},{"location":"layers/draw/#Figure-options","page":"Drawing Layers","title":"Figure options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"AlgebraOfGraphics can add a title, subtitle and footnotes to a figure automatically. Settings for these must be passed to the figure keyword. Check the draw function for a complete list.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = pregrouped(\n    fill(1:5, 6),\n    fill(11:15, 6),\n    [reshape(sin.(1:25), 5, 5) .+ i for i in 1:6],\n    layout = 1:6 => nonnumeric) * visual(Heatmap)\n\ndraw(\n    spec;\n    figure = (;\n        title = \"Numbers in square configuration\",\n        subtitle = \"Arbitrary data exhibits sinusoidal properties\",\n        footnotes = [\n            rich(superscript(\"1\"), \"First footnote\"),\n            rich(superscript(\"2\"), \"Second \", rich(\"footnote\", color = :red)),\n        ],\n    ),\n    axis = (; width = 100, height = 100)\n)","category":"page"},{"location":"","page":"Home","title":"Home","text":"CurrentModule = AlgebraOfGraphics","category":"page"},{"location":"#Introduction","page":"Home","title":"Introduction","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"AlgebraOfGraphics defines a language for data visualization. It is based on a few simple building blocks that can be combined using + and *.","category":"page"},{"location":"","page":"Home","title":"Home","text":"This package can be installed typing","category":"page"},{"location":"","page":"Home","title":"Home","text":"julia> import Pkg; Pkg.add(\"AlgebraOfGraphics\")","category":"page"},{"location":"","page":"Home","title":"Home","text":"in the Julia REPL.","category":"page"},{"location":"","page":"Home","title":"Home","text":"See the Tutorial to get started.","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/legend_merging.jl\"","category":"page"},{"location":"gallery/gallery/scales/legend_merging/#Legend-merging","page":"Legend merging","title":"Legend merging","text":"","category":"section"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"using AlgebraOfGraphics, CairoMakie\n\nN = 40\n\nx = [1:N; 1:N]\ny = [cumsum(randn(N)); cumsum(randn(N))]\ngrp = [fill(\"a\", N); fill(\"b\", N)]\n\ndf = (; x, y, grp)\n\nlayers = visual(Lines) + visual(Scatter) * mapping(marker = :grp)\nplt = data(df) * layers * mapping(:x, :y, color = :grp)\n\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/statistical analyses/regression_plots.jl\"","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/#Regression-plots","page":"Regression plots","title":"Regression plots","text":"","category":"section"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"using AlgebraOfGraphics, CairoMakie\n\nx = rand(100)\ny = @. randn() + x\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayers = linear() + visual(Scatter)\ndraw(layers * xy)","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"x = rand(100)\ny = @. randn() + 5 * x ^ 2\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayers = smooth() + visual(Scatter)\nfg = draw(layers * xy)","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/continuous_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/#Continuous-scales","page":"Continuous scales","title":"Continuous scales","text":"","category":"section"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"using AlgebraOfGraphics, CairoMakie\n\nx = 1:100\ny = @. sqrt(x) + 20x + 100\ndf = (; x, y)\nplt = data(df) *\n    mapping(\n        :x,\n        :y => log => \"√x + 20x + 100 (log scale)\",\n    ) * visual(Lines)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"x = 1:100\ny = @. sqrt(x) + 20x + 100\ndf = (; x, y)\nplt = data(df) *\n    mapping(\n        :x,\n        :y => \"√x + 20x + 100 (log scale)\",\n    ) * visual(Lines)\ndraw(plt, axis=(yscale=log,))","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"x = 0:100\ny = @. 0.01 + x/1000\ndf = (; x, y)\nplt = data(df) *\n    mapping(\n        :x,\n        :y => \"y\",\n    ) * visual(Lines)\nfg = draw(plt, axis=(yscale=log,))","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"API/functions/#Functions","page":"Functions","title":"Functions","text":"","category":"section"},{"location":"API/functions/#Drawing-functions","page":"Functions","title":"Drawing functions","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"draw\ndraw!\ncolorbar!\nlegend!\npaginate\nscales","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.draw","page":"Functions","title":"AlgebraOfGraphics.draw","text":"draw(d, scales::Scales = scales(); [axis, figure, facet, legend, colorbar])\n\nDraw a AlgebraOfGraphics.AbstractDrawable object d. In practice, d will often be a AlgebraOfGraphics.Layer or AlgebraOfGraphics.Layers. Scale options can be passed as an optional second argument. The output can be customized by passing named tuples or dictionaries with settings via the axis, figure, facet, legend or colorbar keywords. Legend and colorbar are drawn automatically unless show = false is passed to the keyword arguments of either legend or colorbar.\n\nFor finer control, use draw!, legend!, and colorbar! independently.\n\nFigure options\n\nAlgebraOfGraphics accepts the following special keywords under the figure keyword, the remaining attributes are forwarded to Makie's Figure constructor. The title, subtitle and footnotes arguments accept objects of any kind that Makie's Label or text function can handle, such as rich text.\n\n- `title`\n- `subtitle`\n- `titlesize::Union{Nothing,Float64}`\n- `subtitlesize::Union{Nothing,Float64}`\n- `titlealign::Union{Nothing,Symbol}`\n- `titlecolor`\n- `subtitlecolor`\n- `titlefont`\n- `subtitlefont`\n- `titlelineheight`\n- `subtitlelineheight`\n- `footnotes::Union{Nothing,Vector{Any}}`\n- `footnotesize::Union{Nothing,Float64}`\n- `footnotefont`\n- `footnotecolor`\n- `footnotealign`\n- `footnotelineheight`\n\n\n\n\n\ndraw(p::PaginatedLayers; kws...)\n\nDraw each element of PaginatedLayers p and return a Vector{FigureGrid}. Keywords kws are passed to the underlying draw calls.\n\n\n\n\n\ndraw(p::PaginatedLayers, i::Int; kws...)\n\nDraw the ith element of PaginatedLayers p and return a FigureGrid. Keywords kws are passed to the underlying draw call.\n\nYou can retrieve the number of elements using length(p).\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.draw!","page":"Functions","title":"AlgebraOfGraphics.draw!","text":"draw!(fig, d::AbstractDrawable, scales::Scales = scales(); [axis, facet])\n\nDraw a AlgebraOfGraphics.AbstractDrawable object d on fig. In practice, d will often be a AlgebraOfGraphics.Layer or AlgebraOfGraphics.Layers. fig can be a figure, a position in a layout, or an axis if d has no facet specification. The output can be customized by passing named tuples or dictionaries with settings via the axis or facet keywords.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.colorbar!","page":"Functions","title":"AlgebraOfGraphics.colorbar!","text":"colorbar!(figpos, grid; kwargs...)\n\nCompute colorbar for grid (which should be the output of draw!) and draw it in position figpos. Attributes allowed in kwargs are the same as MakieLayout.Colorbar.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.legend!","page":"Functions","title":"AlgebraOfGraphics.legend!","text":"legend!(figpos, grid; order = nothing, kwargs...)\n\nCompute legend for grid (which should be the output of draw!) and draw it in position figpos. All kwargs are forwarded to Makie's Legend constructor.\n\nThe order of scales represented in the legend can be changed with the order keyword. By default, legend sections are ordered the same as they appear in the plot specification. Assuming three scales Color, MarkerSize and custom exist in a spec, you can pass a vector to reorder them like [:MarkerSize, :custom, :Color], or merge multiple entries together with a nested vector like [[:MarkerSize, :custom], :Color], or give merged sections a title with the pair syntax [[:MarkerSize, :custom] => \"Merged group\", :Color].\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.paginate","page":"Functions","title":"AlgebraOfGraphics.paginate","text":"paginate(l; layout=nothing, row=nothing, col=nothing)\n\nPaginate l, the Layer or Layers object created by an AlgebraOfGraphics spec, to create a PaginatedLayers object. This contains a vector of layers where each layer operates on a subset of the input data.\n\nThe PaginatedLayers object can be passed to draw which will return a Vector{FigureGrid} rather than a single figure.\n\nThe keywords that limit the number of subplots on each page are the same that are used to specify facets in mapping:\n\nlayout: Maximum number of subplots in a wrapped linear layout.\nrow: Maximum number of rows in a 2D layout.\ncol: Maximum number of columns in a 2D layout.\n\nExample\n\nd = data((\n    x = rand(1000),\n    y = rand(1000),\n    group1 = rand(string.('a':'i'), 1000),\n    group2 = rand(string.('j':'r'), 1000),\n))\n\nlayer_1 = d * mapping(:x, :y, layout = :group1) * visual(Scatter)\npaginated_1 = paginate(layer_1, layout = 9)\nfiguregrids = draw(paginated_1)\n\nlayer_2 = d * mapping(:x, :y, row = :group1, col = :group2) * visual(Scatter)\npaginated_2 = paginate(layer_2, row = 4, col = 3)\nfiguregrid = draw(paginated_2, 1) # draw only the first grid\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.scales","page":"Functions","title":"AlgebraOfGraphics.scales","text":"scales(; kwargs...)\n\nCreate a Scales object containing properties for aesthetic scales that can be passed to draw and draw!. Each keyword should be the name of a scale in the spec that is being drawn. That can either be a default one like Color, Marker or LineStyle, or a custom scale name defined in a mapping using the scale function.\n\nThe values attached to the keywords must be dict-like, with Symbols as keys (such as NamedTuples).\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Mapping-helpers","page":"Functions","title":"Mapping helpers","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"pregrouped\ndirect\nrenamer\nsorter\nnonnumeric\nverbatim\nscale\npresorted","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.pregrouped","page":"Functions","title":"AlgebraOfGraphics.pregrouped","text":"pregrouped(positional...; named...)\n\nEquivalent to data(Pregrouped()) * mapping(positional...; named...). Refer to mapping for more information.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.direct","page":"Functions","title":"AlgebraOfGraphics.direct","text":"direct(x)\n\nReturn DirectData(x) which marks x for direct use in a mapping that's used with a table-like data source. As a result, x will be used directly as data, without lookup in the table. If x is not an AbstractArray, it will be expanded like fill(x, n) where n is the number of rows in the data source.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.renamer","page":"Functions","title":"AlgebraOfGraphics.renamer","text":"renamer(arr::Union{AbstractArray, Tuple})\n\nUtility to rename a categorical variable, as in renamer([value1 => label1, value2 => label2]). The keys of all pairs should be all the unique values of the categorical variable and the values should be the corresponding labels. The order of arr is respected in the legend.\n\nExamples\n\njulia> r = renamer([\"class 1\" => \"Class One\", \"class 2\" => \"Class Two\"])\nAlgebraOfGraphics.Renamer{Vector{String}, Vector{String}}([\"class 1\", \"class 2\"], [\"Class One\", \"Class Two\"])\n\njulia> println(r(\"class 1\"))\nClass One\n\nAlternatively, a sequence of pair arguments may be passed.\n\njulia> r = renamer(\"class 1\" => \"Class One\", \"class 2\" => \"Class Two\")\nAlgebraOfGraphics.Renamer{Tuple{String, String}, Tuple{String, String}}((\"class 1\", \"class 2\"), (\"Class One\", \"Class Two\"))\n\njulia> println(r(\"class 1\"))\nClass One\n\nIf arr does not contain Pairs, elements of arr are assumed to be labels, and the unique values of the categorical variable are taken to be the indices of the array. This is particularly useful for dims mappings.\n\nExamples\n\njulia> r = renamer([\"Class One\", \"Class Two\"])\nAlgebraOfGraphics.Renamer{Nothing, Vector{String}}(nothing, [\"Class One\", \"Class Two\"])\n\njulia> println(r(2))\nClass Two\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.sorter","page":"Functions","title":"AlgebraOfGraphics.sorter","text":"sorter(ks)\n\nUtility to reorder a categorical variable, as in sorter([\"low\", \"medium\", \"high\"]). A vararg method sorter(\"low\", \"medium\", \"high\") is also supported. ks should include all the unique values of the categorical variable. The order of ks is respected in the legend.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.nonnumeric","page":"Functions","title":"AlgebraOfGraphics.nonnumeric","text":"nonnumeric(x)\n\nTransform x into a non numeric type that is printed and sorted in the same way.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.verbatim","page":"Functions","title":"AlgebraOfGraphics.verbatim","text":"verbatim(x)\n\nSignal that x should not be rescaled, but used in the plot as is.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.scale","page":"Functions","title":"AlgebraOfGraphics.scale","text":"scale(id::Symbol)\n\nCreate a ScaleID object that can be used in a mapping to assign a custom id to the mapped variable. This variable will then not be merged into the default scale for its aesthetic type, but instead be handled separately, leading to a separate legend entry.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.presorted","page":"Functions","title":"AlgebraOfGraphics.presorted","text":"presorted(x)\n\nUse within a pair expression in mapping to signal that a categorical column from the data source should be used in the original order and not automatically sorted.\n\nExample:\n\n# normally, categories would be sorted a, b, c but with `presorted`\n# they stay in the order b, c, a\n\ndata((; some_column = [\"b\", \"c\", \"a\"])) * mapping(:some_column => presorted)\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Theming","page":"Functions","title":"Theming","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"set_aog_theme!\nAlgebraOfGraphics.aog_theme\nfrom_continuous","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.set_aog_theme!","page":"Functions","title":"AlgebraOfGraphics.set_aog_theme!","text":"set_aog_theme!(; kwargs...)\n\nSet the current theme to a predefined and opinionated theme, as defined by the unexported internal function AlgebraOfGraphics.aog_theme.\n\nTo tweak the predefined theme, use the function Makie.update_theme!. See the example below on how to change, e.g., default fontsize, title, and markersize.\n\nFor more information about setting themes, see the Theming section of the Makie.jl docs.\n\nExamples\n\njulia> using CairoMakie, AlgebraOfGraphics\n\njulia> set_aog_theme!()                # Sets a prefedined theme\n\njulia> update_theme!(                  # Tweaks the current theme\n           fontsize=30,\n           markersize=40,\n           Axis=(title=\"MyDefaultTitle\",)\n       )\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.aog_theme","page":"Functions","title":"AlgebraOfGraphics.aog_theme","text":"aog_theme(; fonts=[firasans(\"Medium\"), firasans(\"Light\")])\n\nReturn a NamedTuple of theme settings. Intended for internal use. The provided functionality is exposed to the user by the function set_aog_theme!.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.from_continuous","page":"Functions","title":"AlgebraOfGraphics.from_continuous","text":"from_continuous(x)\n\nMark a colormap as continuous such that AlgebraOfGraphics will sample a categorical palette from start to end in n steps, and not by using the first n colors.\n\nYou could also use cgrad(colormap, n; categorical = true), however, this requires you to specify how many levels there are, which from_continuous detects automatically.\n\nExample:\n\ndraw(scales(Color = (; palette = from_continuous(:viridis))))\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Ticks-helpers","page":"Functions","title":"Ticks helpers","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"datetimeticks","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.datetimeticks","page":"Functions","title":"AlgebraOfGraphics.datetimeticks","text":"datetimeticks(datetimes::AbstractVector{<:TimeType}, labels::AbstractVector{<:AbstractString})\n\nGenerate ticks matching datetimes to the corresponding labels. The result can be passed to xticks, yticks, or zticks.\n\n\n\n\n\ndatetimeticks(f, datetimes::AbstractVector{<:TimeType})\n\nCompute ticks for the given datetimes using a formatting function f. The result can be passed to xticks, yticks, or zticks.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Internal-functions","page":"Functions","title":"Internal functions","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"AlgebraOfGraphics.scientific_eltype\nAlgebraOfGraphics.scientific_type\nAlgebraOfGraphics.plottypes_attributes\nAlgebraOfGraphics.compute_attributes","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.scientific_eltype","page":"Functions","title":"AlgebraOfGraphics.scientific_eltype","text":"scientific_eltype(v)\n\nDetermine whether v should be treated as a continuous, geometrical, or categorical array.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.scientific_type","page":"Functions","title":"AlgebraOfGraphics.scientific_type","text":"scientific_type(T::Type)\n\nDetermine whether T represents a continuous, geometrical, or categorical variable.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.plottypes_attributes","page":"Functions","title":"AlgebraOfGraphics.plottypes_attributes","text":"plottypes_attributes(entries)\n\nReturn plottypes and relative attributes, as two vectors of the same length, for the given entries.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.compute_attributes","page":"Functions","title":"AlgebraOfGraphics.compute_attributes","text":"compute_attributes(pl::ProcessedLayer, categoricalscales, continuousscales_grid, continuousscales)\n\nProcess attributes of a ProcessedLayer. In particular,\n\nremove AlgebraOfGraphics-specific layout attributes,\nopt out of Makie cycling mechanism,\ncustomize behavior of color (implementing alpha transparency),\ncustomize behavior of bar width (default to one unit when not specified),\nset correct colorrange.\n\nReturn computed attributes.\n\n\n\n\n\n","category":"function"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/presorted_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/#Pre-sorted-data","page":"Pre-sorted data","title":"Pre-sorted data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"using AlgebraOfGraphics, CairoMakie, DataFrames","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"Sometimes we have datasets that have an inherent order we want to preserve. For example, this dataframe has countries sorted by some_value.","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"countries = [\"Algeria\", \"Bolivia\", \"China\", \"Denmark\", \"Ecuador\", \"France\"]\ngroup = [\"2\", \"3\", \"1\", \"1\", \"3\", \"2\"]\nsome_value = exp.(sin.(1:6))\n\ndf = DataFrame(; countries, group, some_value)\nsort!(df, :some_value)\n\ndf","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"<div><div style = \"float: left;\"><span>6×3 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">countries</th><th style = \"text-align: left;\">group</th><th style = \"text-align: left;\">some_value</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"String\" style = \"text-align: left;\">String</th><th title = \"String\" style = \"text-align: left;\">String</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: left;\">Ecuador</td><td style = \"text-align: left;\">3</td><td style = \"text-align: right;\">0.383305</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: left;\">Denmark</td><td style = \"text-align: left;\">1</td><td style = \"text-align: right;\">0.469164</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: left;\">France</td><td style = \"text-align: left;\">2</td><td style = \"text-align: right;\">0.756226</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: left;\">China</td><td style = \"text-align: left;\">1</td><td style = \"text-align: right;\">1.15156</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: left;\">Algeria</td><td style = \"text-align: left;\">2</td><td style = \"text-align: right;\">2.31978</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: left;\">Bolivia</td><td style = \"text-align: left;\">3</td><td style = \"text-align: right;\">2.48258</td></tr></tbody></table></div>","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"When we plot this, the categorical variable countries is sorted alphabetically by default:","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"spec = data(df) *\n    mapping(:countries, :some_value, color = :group) *\n    visual(BarPlot, direction = :x)\ndraw(spec)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"We don't want this, because we have purposefully sorted the dataframe to visualize which countries have the highest value. To retain the order, we can use the presorted helper.","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"spec = data(df) *\n    mapping(:countries => presorted, :some_value, color = :group) *\n    visual(BarPlot, direction = :x)\nfg = draw(spec)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"We can also mark multiple variables as presorted, note how the order in the color legend shifts when we do the same for group:","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"spec = data(df) *\n    mapping(:countries => presorted, :some_value, color = :group => presorted) *\n    visual(BarPlot, direction = :x)\ndraw(spec)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/basic visualizations/lines_and_markers.jl\"","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#Lines-and-markers","page":"Lines and markers","title":"Lines and markers","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#A-simple-scatter-plot","page":"Lines and markers","title":"A simple scatter plot","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"using AlgebraOfGraphics, CairoMakie\n\ndf = (x=rand(100), y=rand(100))\nxy = data(df) * mapping(:x, :y)\ndraw(xy)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#A-simple-lines-plot","page":"Lines and markers","title":"A simple lines plot","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"x = range(-π, π, length=100)\ny = sin.(x)\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayer = visual(Lines)\ndraw(layer * xy)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#Lines-and-scatter-combined-plot","page":"Lines and markers","title":"Lines and scatter combined plot","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"x = range(-π, π, length=100)\ny = sin.(x)\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayers = visual(Scatter) + visual(Lines)\ndraw(layers * xy)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"x = range(-π, π, length=100)\ny = sin.(x)\ndf1 = (; x, y)\ndf2 = (x=rand(10), y=rand(10))\nlayers = data(df1) * visual(Lines) + data(df2) * visual(Scatter)\nfg = draw(layers * mapping(:x, :y))","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/statistical analyses/histograms.jl\"","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/#Histograms","page":"Histograms","title":"Histograms","text":"","category":"section"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\n\ndf = (x=rand(0:99, 1000),)\nplt = data(df) * mapping(:x) * histogram(bins=20)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"df = (x=randn(1000), c=rand([\"a\", \"b\"], 1000))\nplt = data(df) * mapping(:x, color=:c, stack=:c) * histogram(bins=20)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"df = (x=rand(1000), y=randn(1000))\nplt = data(df) * mapping(:x, :y) * histogram(bins=20)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/wide_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/#Wide-data","page":"Wide data","title":"Wide data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\n\ndf = (a=randn(100), b=randn(100), c=randn(100))\nlabels = [\"Trace 1\", \"Trace 2\", \"Trace 3\"]\nplt = data(df) *\n    density() *\n    mapping([:a, :b, :c] .=> \"some label\") *\n    mapping(color=dims(1) => renamer(labels))\ndraw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"df = (a=rand(100), b=rand(100), c=rand(100), d=rand(100))\nlabels = [\"Trace One\", \"Trace Two\", \"Trace Three\"]\nlayers = linear() + visual(Scatter)\nplt = data(df) * layers * mapping(1, 2:4 .=> \"value\", color=dims(1) => renamer(labels))\ndraw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"The wide format is combined with broadcast semantics.","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/#Axes-are-linked-when-they-correspond-to-the-same-variable","page":"Wide data","title":"Axes are linked when they correspond to the same variable","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"df = (\n    sepal_length = 1 .+ rand(100),\n    sepal_width = 2 .+ rand(100),\n    petal_length = 3 .+ rand(100),\n    petal_width = 4 .+ rand(100)\n)\nxvars = [\"sepal_length\", \"sepal_width\"]\nyvars = [\"petal_length\" \"petal_width\"]\nlayers = linear() + visual(Scatter)\nplt = data(df) * layers * mapping(xvars, yvars, col=dims(1), row=dims(2))\ndraw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/#Axes-can-be-fully-linked-or-fully-unlinked","page":"Wide data","title":"Axes can be fully linked or fully unlinked","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"draw(plt, facet = (; linkxaxes = :all, linkyaxes = :all))","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"fg = draw(plt, facet = (; linkxaxes = :none, linkyaxes = :none))","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/operations/#Algebraic-Operations","page":"Algebraic Operations","title":"Algebraic Operations","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"There are two algebraic types that can be added or multiplied with each other: AlgebraOfGraphics.Layer and AlgebraOfGraphics.Layers.","category":"page"},{"location":"layers/operations/#Multiplication-on-individual-layers","page":"Algebraic Operations","title":"Multiplication on individual layers","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"Each layer is composed of data, mappings, and transformations. Datasets can be replaced, mappings can be merged, and transformations can be concatenated. These operations, taken together, define an associative operation on layers, which we call multiplication *.","category":"page"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"Multiplication is primarily useful to combine partially defined layers.","category":"page"},{"location":"layers/operations/#Addition","page":"Algebraic Operations","title":"Addition","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"The operation + is used to superimpose separate layers. a + b has as many layers as la + lb, where la and lb are the number of layers in a and b respectively.","category":"page"},{"location":"layers/operations/#Multiplication-on-lists-of-layers","page":"Algebraic Operations","title":"Multiplication on lists of layers","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"Multiplication naturally extends to lists of layers. Given two Layers objects a and b, containing la and lb layers respectively, the product a * b contains la * lb layers—all possible pair-wise products.","category":"page"},{"location":"layers/introduction/#Introduction","page":"Introduction","title":"Introduction","text":"","category":"section"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"Layers are the key building blocks of AlgebraOfGraphics. Each layer is the product of the following elementary objects.","category":"page"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"Data (encoding the dataset).\nMapping (associating variables to plot attributes).\nVisual (encoding data-independent plot information).\nAnalyses (encoding transformations that are applied to the data before plotting).","category":"page"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"Data, mappings, visuals and analyses can be combined together using Algebraic Operations to form one or more layers.","category":"page"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"The output of these algebraic operations can be visualized, as shown in the section Drawing Layers.","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/discrete_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/#Discrete-scales","page":"Discrete scales","title":"Discrete scales","text":"","category":"section"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"By default categorical ticks, as well as names from legend entries, are taken from the value of the variable converted to a string. Scales can be equipped with labels to overwrite that. You can either use the renamer function to apply relabeling or reordering to some column on the fly, or you use the categories keyword for the respective scale in the draw call.","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"note: Note\nThe categories keyword (added in v0.7) also allows adding categories not present in the data.","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"df = (x=rand([\"a\", \"b\", \"c\"], 100), y=rand(100))\nplt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt, scales(X = (;\n    categories = [\"a\" => \"label1\", \"b\" => \"label2\", \"c\" => \"label3\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"The same operation can be done with renamer as well which modifies the input data","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) *\n    mapping(\n        :x => renamer(\"a\" => \"label1\", \"b\" => \"label2\", \"c\" => \"label3\"),\n        :y\n    ) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"With categories, you can add further categories that might be missing from your data","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt, scales(X = (;\n    categories = [\"a\", \"missing\", \"b\", \"c\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"The order can also be changed:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt, scales(X = (;\n    categories = [\"b\" => \"label b\", \"a\" => \"label a\", \"c\" => \"label c\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"Or with renamer:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) *\n    mapping(\n        :x => renamer(\"b\" => \"label b\", \"a\" => \"label a\", \"c\" => \"label c\"),\n        :y\n    ) * visual(BoxPlot)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"When categories come from different datasets, you can either apply the same renamer to multiple mappings, or set the ordering at one place in categories:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"df1 = (; x = rand([\"one\", \"two\"], 100), y = randn(100))\ndf2 = (; x = rand([\"three\", \"four\"], 50), y = randn(50))\nplt = (data(df1) + data(df2)) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"draw(plt, scales(X = (;\n    categories = [\"one\", \"two\", \"three\", \"four\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"You can also pass a function to categories, which has to return a vector with (optionally labelled) categories in the desired order. For example, strings are by default ordered alphabetically:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"df = (; name = [\"Anna Coolidge\", \"Berta Bauer\", \"Charlie Archer\"], age = [34, 79, 58])\nplt = data(df) * mapping(:name, :age) * visual(BarPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"Instead of specifying the order manually, we could use a function to order by last name:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"draw(plt, scales(X = (;\n    categories = cats -> sort(cats; by = name -> split(name)[2])\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"Or even combine this with a relabeling function that shortens the first name to the initial:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"function initialed(name)\n    a, b = split(name)\n    return name => \"$(first(a)). $b\"\nend\n\ndraw(plt, scales(X = (;\n    categories = cats -> initialed.(sort(cats; by = name -> split(name)[2]))\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/layout/faceting.jl\"","category":"page"},{"location":"gallery/gallery/layout/faceting/#Faceting","page":"Faceting","title":"Faceting","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-grid","page":"Faceting","title":"Facet grid","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = let\n    N = 100\n    x0 = rand(1:10, N)\n    i = rand([\"α\", \"β\"], N)\n    j = rand([\"a\", \"b\", \"c\"], N)\n\n    x = map(zip(x0, j)) do (xx, jj)\n        shift = jj == \"a\" ? -2.9 : jj == \"c\" ? 2.9 : 0.0\n        xx + shift\n    end\n\n    y = map(zip(x0, i)) do (xx, ii)\n        shift = ii == \"α\" ? -3.9 : 3.9\n        xx + 2 + shift + rand()\n    end\n\n    (; x, y, i, j)\nend\n\nplt = data(df) * mapping(:x, :y, row=:i, col=:j)\n\ndraw(plt)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-grid-with-minimal-axes-linking-needed-to-remove-ticks","page":"Faceting","title":"Facet grid with minimal axes linking needed to remove ticks","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, facet=(; linkxaxes=:minimal, linkyaxes=:minimal))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-grid-with-unlinked-x-axes","page":"Faceting","title":"Facet grid with unlinked x-axes","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, facet=(; linkxaxes=:none))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-wrap","page":"Faceting","title":"Facet wrap","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = (x=rand(100), y=rand(100), l=rand([\"a\", \"b\", \"c\", \"d\", \"e\"], 100))\nplt = data(df) * mapping(:x, :y, layout=:l)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-wrap-with-unlinked-axes","page":"Faceting","title":"Facet wrap with unlinked axes","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, facet=(; linkxaxes=:none, linkyaxes=:none))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-wrap-with-specified-layout-for-rows-and-cols","page":"Faceting","title":"Facet wrap with specified layout for rows and cols","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, scales(Layout = (; palette = [(1, 1), (2, 1), (3, 1), (1, 2), (2, 2)])))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Adding-traces-to-only-some-subplots","page":"Faceting","title":"Adding traces to only some subplots","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df1 = (x=rand(100), y=rand(100), i=rand([\"a\", \"b\", \"c\"], 100), j=rand([\"d\", \"e\", \"f\"], 100))\ndf2 = (x=[0, 1], y=[0.5, 0.5], i=fill(\"a\", 2), j=fill(\"e\", 2))\nlayers = data(df1) * visual(Scatter) + data(df2) * visual(Lines)\nfg = draw(layers * mapping(:x, :y, col=:i, row=:j))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Caveats","page":"Faceting","title":"Caveats","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"The faceting variable must be non-numeric. If the source is numeric, you can convert it with nonnumeric.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = (x=rand(100), y=rand(100), l=rand([1, 2, 3, 4, 5], 100))\nplt = data(df) * mapping(:x, :y, layout=:l => nonnumeric)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Pagination","page":"Faceting","title":"Pagination","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"If you have too many facets for one figure, you can use paginate to split the data into several subsets given a maximum number of plots per layout, row or column.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"Note that pagination is considered an experimental feature. In the current implementation, scales and layouts are not synchronized across pages. This means that, e.g., linked limits on one page are not influenced by limits of other pages. The exact synchronization behavior can be subject to change in non-breaking versions.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = (x=rand(500), y=rand(500), l=rand([\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\"], 500))\nplt = data(df) * mapping(:x, :y, layout=:l)\npag = paginate(plt, layout = 4)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"PaginatedLayers with 2 entries (layout = 4)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"The object returned from draw will be a Vector{FigureGrid}.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"figuregrids = draw(pag)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"2-element Vector{AlgebraOfGraphics.FigureGrid}:\n FigureGrid()\n FigureGrid()","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"You can either extract single figures from this vector...","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"figuregrids[1]","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"or use draw with an optional second argument specifying the index of the page to draw.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(pag, 2)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"EditURL = \"penguins.jl\"","category":"page"},{"location":"generated/penguins/#Tutorial","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This is a gentle and lighthearted tutorial on how to use tools from AlgebraOfGraphics, using as example dataset a collection of measurements on penguins[1]. See the Palmer penguins website for more information.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"[1]: Gorman KB, Williams TD, Fraser WR (2014) Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis). PLoS ONE 9(3): e90081. DOI","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"To follow along this tutorial, you will need to install a few packages. All the required packages can be installed with the following command.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"julia> import Pkg; Pkg.add([\"AlgebraOfGraphics\", \"CairoMakie\", \"DataFrames\", \"LIBSVM\", \"PalmerPenguins\"])","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"After the above command completes, we are ready to go.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using PalmerPenguins, DataFrames\n\npenguins = dropmissing(DataFrame(PalmerPenguins.load()))\nfirst(penguins, 6)","category":"page"},{"location":"generated/penguins/#Frequency-plots","page":"Tutorial 🐧","title":"Frequency plots","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Let us start by getting a rough idea of how the data is distributed.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"note: Note\nDue to julia's compilation model, the first plot may take a while to appear.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using AlgebraOfGraphics, CairoMakie\nset_aog_theme!()\n\naxis = (width = 225, height = 225)\npenguin_frequency = data(penguins) * frequency() * mapping(:species)\n\ndraw(penguin_frequency; axis = axis)","category":"page"},{"location":"generated/penguins/#Small-intermezzo:-saving-the-plot","page":"Tutorial 🐧","title":"Small intermezzo: saving the plot","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"If you are working in an interactive enviroment with inline plotting support, such VSCode or Pluto.jl, the above should have displayed a bar plot. If you are working directly in the console, you can simply save the plot and inspect it in the file explorer.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"fg = draw(penguin_frequency; axis = axis)\nsave(\"figure.png\", fg, px_per_unit = 3) # save high-resolution png","category":"page"},{"location":"generated/penguins/#Styling-by-categorical-variables","page":"Tutorial 🐧","title":"Styling by categorical variables","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Next, let us see whether the distribution is the same across islands.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_frequency * mapping(color = :island)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Oops! The bars are in the same spot and are hiding each other. We need to specify how we want to fix this. Bars can either dodge each other, or be stacked on top of each other.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_frequency * mapping(color = :island, dodge = :island)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This is our first finding. Adelie is the only species of penguins that can be found on all three islands. To be able to see both which species is more numerous and how different species are distributed across islands in a unique plot, we could have used stack.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_frequency * mapping(color = :island, stack = :island)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/#Correlating-two-variables","page":"Tutorial 🐧","title":"Correlating two variables","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Now that we have understood the distribution of these three penguin species, we can start analyzing their features.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"penguin_bill = data(penguins) * mapping(:bill_length_mm, :bill_depth_mm)\ndraw(penguin_bill; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We would actually prefer to visualize these measures in centimeters, and to have cleaner axes labels. As we want this setting to be preserved in all of our bill visualizations, let us save it in the variable penguin_bill, to be reused in subsequent plots.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"penguin_bill = data(penguins) * mapping(\n    :bill_length_mm => (t -> t / 10) => \"bill length (cm)\",\n    :bill_depth_mm => (t -> t / 10) => \"bill depth (cm)\",\n)\ndraw(penguin_bill; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Much better! Note the parentheses around the function t -> t / 10. They are necessary to specify that the function maps t to t / 10, and not to t / 10 => \"bill length (cm)\".","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"There does not seem to be a strong correlation between the two dimensions, which is odd. Maybe dividing the data by species will help.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Ha! Within each species, penguins with a longer bill also have a deeper bill. We can confirm that with a linear regression","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * linear() * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This unfortunately no longer shows our data! We can use + to plot both things on top of each other:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * linear() * mapping(color = :species) + penguin_bill * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Note that the above expression seems a bit redundant, as we wrote the same thing twice. We can \"factor it out\" as follows","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * (linear() + mapping()) * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"where mapping() is a neutral multiplicative element. Of course, the above could be refactored as","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping()\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We could actually take advantage of the spare mapping() and use it to pass some extra info to the scatter, while still using all the species members to compute the linear fit.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping(marker = :sex)\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This plot is getting a little bit crowded. We could instead show female and male penguins in separate subplots.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping(col = :sex)\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"See how both plots show the same fit, because the sex mapping is not applied to linear(). The following on the other hand produces a separate fit for males and females:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping()\nplt = penguin_bill * layers * mapping(color = :species, col = :sex)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/#Smooth-density-plots","page":"Tutorial 🐧","title":"Smooth density plots","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"An alternative approach to understanding how two variables interact is to consider their joint probability density distribution (pdf).","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using AlgebraOfGraphics: density\nplt = penguin_bill * density(npoints=50) * mapping(col = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"The default colormap is multi-hue, but it is possible to pass single-hue colormaps as well. The color range is inferred from the data by default, but it can also be passed manually. Both settings are passed via scales to draw, because multiple plots can share the same colormap, so visual is not the appropriate place for this setting.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"draw(plt, scales(Color = (; colormap = :grayC, colorrange = (0, 6))); axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"A Heatmap (the default visualization for a 2D density) is a bit unfortunate if we want to mark species by color. In that case, one can use visual to change the default visualization and, optionally, fine tune some arguments. In this case, a Wireframe with thin lines looks quite nice. (Note that, for the time being, we must specify explicitly that we require a 3D axis.)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (type = Axis3, width = 300, height = 300)\nlayer = density() * visual(Wireframe, linewidth=0.05)\nplt = penguin_bill * layer * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Of course, a more traditional approach would be to use a Contour plot instead:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (width = 225, height = 225)\nlayer = density() * visual(Contour)\nplt = penguin_bill * layer * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"The data and the linear fit can also be added back to the plot:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = density() * visual(Contour) + linear() + mapping()\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"In the case of many layers (contour, density and scatter) it is important to think about balance. In the above plot, the markers are quite heavy and can obscure the linear fit and the contour lines. We can lighten the markers using alpha transparency.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = density() * visual(Contour) + linear() + visual(alpha = 0.5)\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/#Correlating-three-variables","page":"Tutorial 🐧","title":"Correlating three variables","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We are now mostly up to speed with bill size, but we have not considered how it relates to other penguin features, such as their weight. For that, a possible approach is to use a continuous color on a gradient to denote weight and different marker shapes to denote species. Here we use group to split the data for the linear regression without adding any additional style.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"body_mass = :body_mass_g => (t -> t / 1000) => \"body mass (kg)\"\nlayers = linear() * mapping(group = :species) + mapping(color = body_mass, marker = :species)\nplt = penguin_bill * layers\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Naturally, within each species, heavier penguins have bigger bills, but perhaps counter-intuitively the species with the shallowest bills features the heaviest penguins. We could also try and see the interplay of these three variables in a 3D plot.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (type = Axis3, width = 300, height = 300)\nplt = penguin_bill * mapping(body_mass, color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * mapping(body_mass, color = :species, layout = :sex)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Note that static 3D plot can be misleading, as they only show one projection of 3D data. They are mostly useful when shown interactively.","category":"page"},{"location":"generated/penguins/#Machine-Learning","page":"Tutorial 🐧","title":"Machine Learning","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Finally, let us use Machine Learning techniques to build an automated penguin classifier!","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We would like to investigate whether it is possible to predict the species of a penguin based on its bill size. To do so, we will use a standard classifier technique called Support-Vector Machine.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"The strategy is quite simple. We split the data into training and testing subdatasets. We then train our classifier on the training dataset and use it to make predictions on the whole data. We then add the new columns obtained this way to the dataset and visually inspect how well the classifier performed in both training and testing.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using LIBSVM, Random\n\n# use approximately 80% of penguins for training\nRandom.seed!(1234) # for reproducibility\nN = nrow(penguins)\ntrain = fill(false, N)\nperm = randperm(N)\ntrain_idxs = perm[1:floor(Int, 0.8N)]\ntrain[train_idxs] .= true\nnothing # hide\n\n# fit model on training data and make predictions on the whole dataset\nX = hcat(penguins.bill_length_mm, penguins.bill_depth_mm)\ny = penguins.species\nmodel = SVC() # Support-Vector Machine Classifier\nfit!(model, X[train, :], y[train])\nŷ = predict(model, X)\n\n# incorporate relevant information in the dataset\npenguins.train = train\npenguins.predicted_species = ŷ\nnothing #hide","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Now, we have all the columns we need to evaluate how well our classifier performed.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (width = 225, height = 225)\ndataset =:train => renamer(true => \"training\", false => \"testing\") => \"Dataset\"\naccuracy = (:species, :predicted_species) => isequal => \"accuracy\"\nplt = data(penguins) *\n    expectation() *\n    mapping(:species, accuracy) *\n    mapping(col = dataset)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"That is a bit hard to read, as all values are very close to 1. Let us visualize the error rate instead.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"error_rate = (:species, :predicted_species) => !isequal => \"error rate\"\nplt = data(penguins) *\n    expectation() *\n    mapping(:species, error_rate) *\n    mapping(col = dataset)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"So, mostly our classifier is doing quite well, but there are some mistakes, especially among Chinstrap penguins. Using at the same time the species and predicted_species mappings on different attributes, we can see which penguins are problematic.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"prediction = :predicted_species => \"predicted species\"\ndatalayer = mapping(color = prediction, row = :species, col = dataset)\nplt = penguin_bill * datalayer\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Um, some of the penguins are indeed being misclassified... Let us try to understand why by adding an extra layer, which describes the density of the distributions of the three species.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"pdflayer = density() * visual(Contour, colormap=Reverse(:grays)) * mapping(group = :species)\nlayers = pdflayer + datalayer\nplt = penguin_bill * layers\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We can conclude that the classifier is doing a reasonable job: it is mostly making mistakes on outlier penguins.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This page was generated using Literate.jl.","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/applications/geometries.jl\"","category":"page"},{"location":"gallery/gallery/applications/geometries/#Geometries","page":"Geometries","title":"Geometries","text":"","category":"section"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"using AlgebraOfGraphics, CairoMakie\nusing GeometryBasics\n\ngeometry = [Rect(Vec(i, j), Vec(1, 1)) for i in 0:7 for j in 0:7]\ngroup = [isodd(i + j) ? \"light square\" : \"dark square\" for i in 0:7 for j in 0:7]\ndf = (; geometry, group)\n\nplt = data(df) * visual(Poly) * mapping(:geometry, color = :group)\nfg = draw(plt; axis=(aspect=1,))","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/index.md\"","category":"page"},{"location":"gallery/#Gallery","page":"Gallery","title":"Gallery","text":"","category":"section"},{"location":"gallery/#Basic-visualizations","page":"Gallery","title":"Basic visualizations","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"columns is-multiline bulma-grid-card-section\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Lines and markers\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Basic linear and scatter plots.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Statistical visualizations\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Violin plot, boxplot, qqplot.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"</div>","category":"page"},{"location":"gallery/#Statistical-analyses","page":"Gallery","title":"Statistical analyses","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"columns is-multiline bulma-grid-card-section\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Density plots\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Visualizing kernel density estimation of data.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Histograms\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Computing 1- and 2-dimensional histograms.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Regression plots\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Linear and nonlinear regressions.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"</div>","category":"page"},{"location":"gallery/#Data-manipulations","page":"Gallery","title":"Data manipulations","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"columns is-multiline bulma-grid-card-section\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Renaming and transforming variables\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Transforming data before generating the plot.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Using mapping without tabular data\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Passing columnar data directly to `mapping`.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Pre-grouped data\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Working with arrays of arrays.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Pre-sorted data\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Preserving the original order of categorical data.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Wide data\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Working with data in the wide format.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"</div>","category":"page"},{"location":"gallery/#Scales","page":"Gallery","title":"Scales","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"Some advanced keywords to tweak the plot","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"columns is-multiline bulma-grid-card-section\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Discrete scales\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Sorting and renaming categorical variables.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Continuous scales\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Applying nonlinear transformations.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Custom scales\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Custom palettes and custom attributes.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Secondary scales\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Categorical and continuous scales in the same plot.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Multiple color scales\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Categorical and continuous scales in the same plot.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Pre-scaled data\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Pass data to the plot as is.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Legend merging\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Multiple scales for the same variable.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Dodging\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Dodging groups to avoid overlaps.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"</div>","category":"page"},{"location":"gallery/#Customization","page":"Gallery","title":"Customization","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"Some advanced keywords to tweak the plot","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"columns is-multiline bulma-grid-card-section\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Axis tweaking\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Setting axis attributes.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Figure tweaking\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Setting figure attributes.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Legend tweaking\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Setting legend attributes.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Colorbar tweaking\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Setting colorbar attributes.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"</div>","category":"page"},{"location":"gallery/#Layout","page":"Gallery","title":"Layout","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"columns is-multiline bulma-grid-card-section\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Faceting\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Generating a grid of plots.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Nested layouts\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                AlgebraOfGraphics plots within a Makie figure.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"</div>","category":"page"},{"location":"gallery/#Applications","page":"Gallery","title":"Applications","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"Example applications with different types of data","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"columns is-multiline bulma-grid-card-section\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Geographic data\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Antarctic coastline.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Geometries\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Visualizing geometries.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Time series\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Visualizing time series data.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"</div>","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/new_columns_on_the_fly.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/#Renaming-and-transforming-variables","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"using AlgebraOfGraphics, CairoMakie\n\ndf = (x=rand(100), y=rand(100), z=rand(100), c=rand([\"a\", \"b\"], 100))\nlayers = linear() + mapping(color=:z)\n\nplt = data(df) * layers * mapping(:x => (x -> x^2) => L\"x^2\", :y => L\"This variable is called $y$\")\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"Use a Tuple to pass combine several columns into a unique operation.","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"plt = data(df) * layers * mapping(:x, (:x, :y, :z) => (+) => L\"the new variable $x + y + z$\", layout=:c)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/colorbar.jl\"","category":"page"},{"location":"gallery/gallery/customization/colorbar/#Colorbar-tweaking","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: Source code)","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"To tweak the position and appearance of the colorbar, simply use the colorbar keyword when plotting. For example","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"df = (x=rand(100), y=rand(100), z=rand(100))\nplt = data(df) * mapping(:x, :y, color=:z)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"fg = draw(plt, colorbar=(position=:top, size=25))","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"To change the colormap, you have to modify the corresponding scale. Usually, this will be the Color scale.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"note: Note\nBefore AlgebraOfGraphics v0.7, you would change the colormap by passing it via visual. This was changed so that each color scale, which can be used by multiple plot layers, has a single source of truth for these settings.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"draw(plt, scales(Color = (; colormap = :thermal)))","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"Other continuous color parameters are highclip, lowclip, nan_color and colorrange.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"draw(plt, scales(Color = (;\n    colormap = :thermal,\n    colorrange = (0.25, 0.75),\n    highclip = :cyan,\n    lowclip = :lime,\n)))","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/custom_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/custom_scales/#custom_scales","page":"Custom scales","title":"Custom scales","text":"","category":"section"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"using AlgebraOfGraphics, CairoMakie\nusing Colors","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"A palette maps categorical values to particular attribute specifications (e.g. the first value maps to green, the second maps to red, and so on).","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"x=repeat(1:20, inner=20)\ny=repeat(1:20, outer=20)\nu=cos.(x)\nv=sin.(y)\nc=rand(Bool, length(x))\nd=rand(Bool, length(x))\ndf = (; x, y, u, v, c, d)\ncolors = [colorant\"#E24A33\", colorant\"#348ABD\"]\nheads = ['◮', '◭']\nplt = data(df) *\n    mapping(:x, :y, :u, :v) *\n    mapping(arrowhead = :c => nonnumeric) *\n    mapping(color = :d => nonnumeric) *\n    visual(Arrows, arrowsize=10, lengthscale=0.4, linewidth = 1)\nfg = draw(plt, scales(Marker = (; palette = heads), Color = (; palette = colors)))","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"To associate specific attribute values to specific data values, use pairs. Missing keys will cycle over values that are not pairs.","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"x = rand(100)\ny = rand(100)\nz = rand([\"a\", \"b\", \"c\", \"d\"], 100)\ndf = (; x, y, z)\nplt = data(df) * mapping(:x, :y, color=:z)\ncolors = [\"a\" => :tomato, \"c\" => :lime, colorant\"#988ED5\", colorant\"#777777\"]\ndraw(plt, scales(Color = (; palette = colors)))","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"Categorical color gradients can also be passed to palettes. You can use the from_continuous helper function to wrap continuous colormaps which you want to sample from start to end in n steps, where n is the number of categories you are visualizing.","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"x = rand(200)\ny = rand(200)\nz = rand([\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\"], 200)\ndf = (; x, y, z)\nplt = data(df) * mapping(:x, :y, color=:z)\ndraw(plt, scales(Color = (; palette = from_continuous(:cividis))))","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/dodging.jl\"","category":"page"},{"location":"gallery/gallery/scales/dodging/#dodging","page":"Dodging","title":"Dodging","text":"","category":"section"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"using AlgebraOfGraphics, CairoMakie\nusing Colors","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"Some plot types like barplots natively support a dodge attribute which avoids overlap between groups that share the same coordinates.","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"df = (; x = [\"One\", \"One\", \"Two\", \"Two\"], y = 1:4, err = [0.2, 0.3, 0.4, 0.5], group = [\"A\", \"B\", \"A\", \"B\"])\nplt = data(df) * mapping(:x, :y, dodge = :group, color = :group) * visual(BarPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"Other plot types like errorbars do not have a dodge keyword, however you can dodge them using AlgebraOfGraphic's hardcoded dodge_x or dodge_y mappings. These will only shift the data away from the category centers but will not change other plot attributes (like dodging a barplot makes narrower bars). They are therefore mostly appropriate for \"width-less\" plot types like scatters or errorbars.","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"If you combine errorbars with a barplot, AlgebraOfGraphics will apply the barplot's dodge width to the errorbars automatically so they match:","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"plt2 = data(df) * mapping(:x, :y, :err, dodge_x = :group) * visual(Errorbars)\nfg = draw(plt + plt2)","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"If you only use \"width-less\" plot types, you will get an error if you don't set a dodge width manually. You can do so via the scales function:","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"df = (\n    x = repeat(1:10, inner = 2),\n    y = cos.(range(0, 2pi, length = 20)),\n    ylow = cos.(range(0, 2pi, length = 20)) .- 0.2,\n    yhigh = cos.(range(0, 2pi, length = 20)) .+ 0.3,\n    dodge = repeat([\"A\", \"B\"], 10)\n)\n\nf = Figure()\nplt3 = data(df) * (\n    mapping(:x, :y, dodge_x = :dodge, color = :dodge) * visual(Scatter) +\n    mapping(:x, :ylow, :yhigh, dodge_x = :dodge, color = :dodge) * visual(Rangebars)\n)\nkw(; kwargs...) = (; xticklabelsvisible = false, xticksvisible = false, xlabelvisible = false, kwargs...)\n\ndraw!(f[1, 1], plt3, scales(DodgeX = (; width = 0.25)); axis = kw(title = \"DodgeX = (; width = 0.25)\"))\ndraw!(f[2, 1], plt3, scales(DodgeX = (; width = 0.5)); axis = kw(title = \"DodgeX = (; width = 0.5)\"))\ndraw!(f[3, 1], plt3, scales(DodgeX = (; width = 1.0)); axis = (; title = \"DodgeX = (; width = 1.0)\"))\n\nf","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/prescaled_data.jl\"","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/#Pre-scaled-data","page":"Pre-scaled data","title":"Pre-scaled data","text":"","category":"section"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"using AlgebraOfGraphics, CairoMakie\nusing Colors\n\nx = rand(100)\ny = rand(100)\nz = rand([colorant\"teal\", colorant\"orange\"], 100)\ndf = (; x, y, z)\nplt = data(df) * mapping(:x, :y, color=:z => verbatim)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"Plotting labels instead of markers","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"x = rand(100)\ny = rand(100)\nlabel = rand([\"a\", \"b\"], 100)\ndf = (; x, y, label)\nplt = data(df) * mapping(:x, :y, text=:label => verbatim) * visual(Makie.Text)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/layout/nested_layouts.jl\"","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/#Nested-layouts","page":"Nested layouts","title":"Nested layouts","text":"","category":"section"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"All AlgebraOfGraphics plots can be inserted in any figure position, where the rest of the figure is managed by vanilla Makie. For example","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"fig = Figure(; size=(800, 600))\nax = Axis(fig[1, 1], title=\"Some plot\")\n\ndf = (\n    x=rand(500),\n    y=rand(500),\n    i=rand([\"a\", \"b\", \"c\"], 500),\n    j=rand([\"d\", \"e\", \"f\"], 500),\n    k=rand(Bool, 500),\n    l=rand(Bool, 500)\n)\nplt = data(df) * mapping(:x, :y, col=:i, row=:j, color=:k, marker=:l)\n\nsubfig = fig[1, 2:3]\nag = draw!(subfig, plt)\nfor ae in ag\n    ae.axis.xticklabelrotation[] = π/2\nend\nlegend!(fig[end+1, 2], ag, orientation=:horizontal, tellheight=true)\nfig","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"The above also works in more nested situations.","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"f = Figure(; size=(800, 600))\nax = Axis(f[1, 1], title=\"Some plot\")\nsubfig = f[1, 2]\nax2 = Axis(subfig[1, 1])\n\ndf = (\n    x=rand(500),\n    y=rand(500),\n    c=rand([\"a\", \"b\", \"c\"], 500),\n)\nplt = data(df) * mapping(:x, :y, color=:c)\n\ndraw!(subfig[2, 1], plt)\nf","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"It is also possible to let Makie control the axis and plot directly on top of it.","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"f = Figure(; size=(800, 300))\nax1 = Axis(f[1, 1])\nax2 = Axis(f[1, 2])\n\ndf = (x=rand(100), y=rand(100), c=rand([\"a\", \"b\", \"c\"], 100))\nplt = data(df) * mapping(:x, :y, color=:c)\n\nscatter!(ax1, rand(10), rand(10), color=:black)\ngrid = draw!(ax2, plt)\nlegend!(f[1, 3], grid)\n\nf","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/applications/geographic.jl\"","category":"page"},{"location":"gallery/gallery/applications/geographic/#Geographic-data","page":"Geographic data","title":"Geographic data","text":"","category":"section"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"using AlgebraOfGraphics, CairoMakie\nusing Shapefile, ZipFile\nusing Downloads","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"Antarctic coastline. Data from the SCAR Antarctic Digital Database[1].","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"[1]: Gerrish, L., Fretwell, P., & Cooper, P. (2021). Medium resolution vector polygons of the Antarctic coastline (7.4) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/747e63e-9d93-49c2-bafc-cf3d3f8e5afa","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"# Download, extract, and load shapefile\nt = mktempdir() do dir\n    url = \"https://ramadda.data.bas.ac.uk/repository/entry/get/add_coastline_medium_res_polygon_v7_4.zip?entryid=synth%3Ae747e63e-9d93-49c2-bafc-cf3d3f8e5afa%3AL2FkZF9jb2FzdGxpbmVfbWVkaXVtX3Jlc19wb2x5Z29uX3Y3XzQuemlw\"\n    r = ZipFile.Reader(seekstart(Downloads.download(url, IOBuffer())))\n    for f in r.files\n        open(joinpath(dir, f.name), write = true) do io\n            write(io, read(f, String));\n        end\n    end\n    Shapefile.Table(joinpath(dir, \"add_coastline_medium_res_polygon_v7_4.shp\"))\nend\n\n# Draw map\nplt = data(t) * mapping(:geometry, color = :surface) * visual(Choropleth)\nfg = draw(plt; axis=(aspect=1,))","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"}]
+[{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/basic visualizations/statistical_visualizations.jl\"","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/#Statistical-visualizations","page":"Statistical visualizations","title":"Statistical visualizations","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"using AlgebraOfGraphics, CairoMakie, PalmerPenguins, DataFrames\n\npenguins = dropmissing(DataFrame(PalmerPenguins.load()))\n\ndata(penguins) * visual(Violin) *\n    mapping(:species, :bill_depth_mm, color=:sex, dodge=:sex) |> draw","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"plt = data(penguins) * visual(Violin, datalimits=extrema)\nplt *= mapping(:species, :bill_depth_mm, color=:sex, side=:sex, dodge=:island)\nfg = draw(plt, axis=(limits=((0.5, 3.5), nothing),))","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"data(penguins) * visual(BoxPlot, show_notch=true) *\n    mapping(:species, :bill_depth_mm, color=:sex, dodge=:sex) |> draw","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"data(penguins) *\n    mapping(:bill_length_mm, :bill_depth_mm, col=:sex) *\n    visual(QQPlot, qqline=:fit) |> draw","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"FAQs/#Frequently-Asked-Questions","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"","category":"section"},{"location":"FAQs/#What-is-the-algebraic-structure-of-AlgebraOfGraphics?","page":"Frequently Asked Questions","title":"What is the algebraic structure of AlgebraOfGraphics?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"AlgebraOfGraphics is based on two operators, + and *. These two operators induce a semiring structure, with a small caveat. Addition is commutative only up to the drawing order. For example, visual(Lines) + visual(Scatter) is slightly different from visual(Scatter) + visual(Lines), in that the former draws the scatter on top of the lines, and the latter draws the lines on top of the scatter. As a consequence, only right distributivity holds with full generality, whereas left distributivity only holds up to the drawing order.","category":"page"},{"location":"FAQs/#Why-is-the-mapping-pair-syntax-different-from-DataFrames?","page":"Frequently Asked Questions","title":"Why is the mapping pair syntax different from DataFrames?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"The transformations passed within a mapping, e.g. mapping(:x => log => \"log(x)\"), are applied element-wise. Operations that require the whole column are not supported on purpose. An important reason to prefer element-wise operations (other than performance) is that whole-column operations can be error prone in this setting, especially when","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"the data is grouped or\ndifferent datasets are used.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"If you do need column-wise transformations, consider implementing a custom analysis, such as density, which takes the whole data as input, or apply the transformation directly in your data before passing it to AlgebraOfGraphics.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"See also Pair syntax for a detailed description of the pair syntax within a mapping.","category":"page"},{"location":"FAQs/#What-is-the-difference-between-axis-scales-and-data-transformations?","page":"Frequently Asked Questions","title":"What is the difference between axis scales and data transformations?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"There are two overlapping but distinct ways to rescale data.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Keep the data as is and use a nonlinear scale, e.g. axis=(xscale=log,).\nTransform the data directly, e.g. mapping(:x => log => \"log(x)\").","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Note that the resulting plots may \"look different\" in some cases. Consider for instance the following example.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics\nusing AlgebraOfGraphics: density\ndf = (x = exp.(randn(1000)),)\nkde1 = data(df) * mapping(:x) * density()\ndraw(kde1, axis=(width=225, height=225, xscale=log,))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"df = (x = exp.(randn(1000)),)\nkde2 = data(df) * mapping(:x => log => \"log(x)\") * density()\ndraw(kde2, axis=(width=225, height=225))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"The two plots look different. The first represents the pdf of x in a log scale, while the second represents the pdf of log(x) in a linear scale. The two curves differ by a factor 1 / x, the derivative of log(x). See e.g. this post for some mathematical background on the topic.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"In general, the second approach (plotting the density of log(x)) could be considered more principled, as it preserves the proportionality between area and probability mass. On the contrary, the first approach (plotting the density of x in a log scale) breaks this proportionality relationship.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"A similar reasoning applies to histograms:","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics\ndf = (x = exp.(rand(1000)),)\nhist1 = data(df) * mapping(:x) * histogram()\ndraw(hist1, axis=(width=225, height=225, xscale=log))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"df = (x = exp.(rand(1000)),)\nhist2 = data(df) * mapping(:x => log => \"log(x)\") * histogram()\ndraw(hist2, axis=(width=225, height=225))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"The data transformation approach is preferable as it produces uniform bins, which are easier to interpret.","category":"page"},{"location":"FAQs/#How-to-combine-AlgebraOfGraphics-with-plain-Makie-plots?","page":"Frequently Asked Questions","title":"How to combine AlgebraOfGraphics with plain Makie plots?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Since AlgebraOfGraphics is built upon the Makie ecosystem we can easily combine plots from both packages. Two approaches can be taken. Firstly, by using draw! you can pass a Figure or FigurePosition created by Makie to be used by AlgebraOfGraphics, e.g.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics, CairoMakie #hide\n\nf, a, p = lines(0..2pi, sin; figure = (size = (600, 400),))\n\ndf = (x = exp.(rand(1000)),)\nhist1 = data(df) * mapping(:x => log => \"log(x)\") * histogram()\ndraw!(f[1, 2], hist1)\n\nf #hide","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Alternatively, we can create the AlgebraOfGraphics figure first and then add in additional plain Makie axes alongside the result by accessing the .figure field of fg, e.g.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics, CairoMakie #hide\n\ndf = (x = exp.(rand(1000)),)\nhist2 = data(df) * mapping(:x => log => \"log(x)\") * histogram()\nfg = draw(hist2; figure = (size = (600, 400),))\n\nlines(fg.figure[1, 2], 0..2pi, cos)\n\nfg #hide","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"note: Note\nWhen setting the width and height dimensions of each axis manually you will need to call resize_to_layout!(fg) before displaying the figure such that each axis is sized correctly.","category":"page"},{"location":"philosophy/#Philosophy","page":"Philosophy","title":"Philosophy","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"AlgebraOfGraphics aims to be a declarative, question-driven language for data visualizations. This section describes its main guiding principles.","category":"page"},{"location":"philosophy/#From-question-to-plot","page":"Philosophy","title":"From question to plot","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"When analyzing a dataset, we often think in abstract, declarative terms. We have questions concerning our data, which can be answered by appropriate visualizations. For instance, we could ask whether a discrete variable :x affects the distribution of a continuous variable :y. We would then like to generate a visualization that answers this question.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"In imperative programming, this would be implemented via the following steps.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Pick the dataset.\nDivide the dataset into subgroups according to the values of :x.\nCompute the density of :y on each subgroup.\nChoose a plot attribute to distinguish subgroups, for instance color.\nSelect as many distinguishable colors as there are unique values of :x.\nPlot all the density curves on top of each other.\nCreate a legend, describing how unique values of :x are associated to colors.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"While the above procedure is certainly feasible, it can introduce a cognitive overhead, especially when more variables and attributes are involved.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"In a declarative framework, the user needs to express the question, and the library will take care of creating the visualization. Let us solve the above problem in a toy dataset.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\nset_aog_theme!()\nN = 1000\nx = rand([\"Class 1\", \"Class 2\", \"Class 3\", \"Class 4\"], N)\ny = @. (x == \"Class 1\") * randn() + (x == \"Class 2\") - (x == \"Class 3\") + randn()\nz = @. (x == \"Class 2\") * randn() + (x == \"Class 2\") + (x == \"Class 3\") + y + randn()\ndf = (; x, y, z)","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"plt = data(df) # declare the dataset\nplt *= density() # declare the analysis\nplt *= mapping(:y) # declare the arguments of the analysis\nplt *= mapping(color = :x) # declare the grouping and the respective visual attribute\ndraw(plt) # draw the visualization and its legend","category":"page"},{"location":"philosophy/#No-mind-reading","page":"Philosophy","title":"No mind reading","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Plotting packages requires the user to specify a large amount of settings. The temptation is then to engineer a plotting library in such a way that it would guess what the user actually wanted. AlgebraOfGraphics follows a different approach, based on algebraic manipulations of plot descriptors.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"The key intuition is that a large fraction of the \"clutter\" in a plot specification comes from repeating the same information over and over. Different layers of the same plot will share some but not all information, and the user should be able to distinguish settings that are private to a layer from those that are shared across layers.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"We achieve this goal using the distributive properties of addition and multiplication. This is best explained by example. Let us assume that we wish to visually inspect whether a discrete variable :x affects the joint distribution of two continuous variables, :y and :z.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"We would like to have two layers, one with the raw data, the other with an analysis (kernel density estimation).","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Naturally, the axes should represent the same variables (:y and :z) for both layers. Only the density layer should be a contour plot, whereas only the scatter layer should have some transparency and be grouped (according to :x) in different subplots.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"plt = data(df) *\n    (\n        visual(Scatter, alpha = 0.3) * mapping(layout = :x) +\n        density() * visual(Contour, colormap = Reverse(:grays))\n    ) *\n    mapping(:y, :z)\ndraw(plt)","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"In this case, thanks to the distributive property, it is clear that the dataset and the positional arguments :y, :z are shared across layers, the transparency and the grouping are specific to the data layer, whereas the density analysis, the Contour visualization, and the choice of color map are specific to the analysis layer.","category":"page"},{"location":"philosophy/#User-defined-building-blocks","page":"Philosophy","title":"User-defined building blocks","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"It is common in data analysis tasks to \"pipe\" a sequence of operations. This became very popular in the data science field with the %>% operator in the R language, and it can allow users to seamlessly compose a sequence of tasks:","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"df %>%\n    filter(Weight < 3) %>%\n    group_by(Species) %>%\n    summarise(avg_height = mean(Height))","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Naturally, the alternative would be to create a statement per operation and to assign each intermediate result to its own variable.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"AlgebraOfGraphics is markedly in favor of the latter approach. It is recommended that commonly used building blocks are stored in variables with meaningful names. If we often make a scatter plot with some transparency, we can create a variable transparent_scatter = visual(Scatter, alpha = 0.5) and use it consistently. If some columns of our dataset are always analyzed together, with a similar set of transformations, we can store that information as variables = mapping(variable1 => f1 => label1, variable2 => f2 => label2).","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Working over one or more datasets, the user would then create a library of building blocks to be combined with each other with * and +. These two operators allow for a much larger number of possible combinations than just sequential composition, thus fully justifying the extra characters used to name intermediate entities.","category":"page"},{"location":"philosophy/#Opinionated-defaults","page":"Philosophy","title":"Opinionated defaults","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"While users should be able to customize every aspect of their plots, it is important to note that this customization can be very time-consuming, and many subtleties can escape the attention of the casual user:","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Is the color palette colorblind-friendly?\nWould the colors be distinguishable in black and white (when printed)?\nIs the color gradient perceptually uniform?\nAre the labels and the ticks legible for readers with low vision?\nAre the spacing and typographic hierarchies respected?","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"To remedy this, AlgebraOfGraphics aims to provide solid, opinionated default settings. In particular, it uses a conservative, colorblind-friendly palette and a perceptually uniform, universally readable color map. It follows IBM guidelines to differentiate titles and labels from tick labels via font weight, while using the same typeface at a readable size.","category":"page"},{"location":"philosophy/#Wide-format-support","page":"Philosophy","title":"Wide format support","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Finally, AlgebraOfGraphics aims to support many different data formats. Different problems require organizing the data in different formats, and AlgebraOfGraphics should support a wide range of options.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"This is achieved in three different ways. First, the Tables interface ensures integration with a large variety of data sources. Second, using the Wide data syntax, users can compare many different columns in the same visualization, without having to first reshape the dataset to a long format. Finally, tabular datasets are not a requirement: users may also work directly with Pre-grouped data, which are not organized as a table, but rather as a collection of (possibly multi-dimensional) arrays.","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/multiple_color_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/#Multiple-color-scales","page":"Multiple color scales","title":"Multiple color scales","text":"","category":"section"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"Continuous and discrete color scales can coexist in the same plot. This should be used sparingly, as it can make the plot harder to interpret.","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"x = range(-π, π, length=100)\ny = sin.(x)\nŷ = y .+ randn.() .* 0.1\nz = cos.(x)\nc = rand([\"a\", \"b\"], 100)\ndf = (; x, y, ŷ, z, c)\nlayers = mapping(:y, color=:z) * visual(Lines) + mapping(:ŷ => \"y\", color=:c)\nplt = data(df) * mapping(:x) * layers\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"EditURL = \"analyses.jl\"","category":"page"},{"location":"generated/analyses/#Analyses","page":"Analyses","title":"Analyses","text":"","category":"section"},{"location":"generated/analyses/#Histogram","page":"Analyses","title":"Histogram","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"histogram","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.histogram","page":"Analyses","title":"AlgebraOfGraphics.histogram","text":"histogram(; bins=automatic, datalimits=automatic, closed=:left, normalization=:none)\n\nCompute a histogram.\n\nThe attribute bins can be an Integer, an AbstractVector (in particular, a range), or a Tuple of either integers or abstract vectors (useful for 2- or 3-dimensional histograms). When bins is an Integer, it denotes the approximate number of equal-width intervals used to compute the histogram. In that case, the range covered by the intervals is defined by datalimits (it defaults to the extrema of the whole data). The keyword argument datalimits can be a tuple of two values, e.g. datalimits=(0, 10), or a function to be applied group by group, e.g. datalimits=extrema. When bins is an AbstractVector, it denotes the intervals directly.\n\nclosed determines whether the the intervals are closed to the left or to the right.\n\nThe histogram can be normalized by setting normalization. Possible values are:\n\n:pdf: Normalize by sum of weights and bin sizes. Resulting histogram  has norm 1 and represents a PDF.\n:density: Normalize by bin sizes only. Resulting histogram represents  count density of input and does not have norm 1.\n:probability: Normalize by sum of weights only. Resulting histogram  represents the fraction of probability mass for each bin and does not have  norm 1.\n:none: Do not normalize.\n\nWeighted data is supported via the keyword weights (passed to mapping).\n\nnote: Note\nNormalizations are computed withing groups. For example, in the case of normalization=:pdf, sum of weights within each group will be equal to 1.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"using AlgebraOfGraphics, CairoMakie\nset_aog_theme!()\n\ndf = (x=randn(5000), y=randn(5000), z=rand([\"a\", \"b\", \"c\"], 5000))\nspecs = data(df) * mapping(:x, layout=:z) * histogram(bins=range(-2, 2, length=15))\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, dodge=:z, color=:z) * histogram(bins=range(-2, 2, length=15))\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, stack=:z, color=:z) * histogram(bins=range(-2, 2, length=15))\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) *\n    mapping((:x, :z) => ((x, z) -> x + 5 * (z == \"b\")) => \"new x\", col=:z) *\n    histogram(datalimits=extrema, bins=20)\ndraw(specs, facet=(linkxaxes=:minimal,))","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"data(df) * mapping(:x, :y, layout=:z) * histogram(bins=15) |> draw","category":"page"},{"location":"generated/analyses/#Density","page":"Analyses","title":"Density","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"AlgebraOfGraphics.density","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.density","page":"Analyses","title":"AlgebraOfGraphics.density","text":"density(; datalimits=automatic, kernel=automatic, bandwidth=automatic, npoints=200)\n\nFit a kernel density estimation of data.\n\nHere, datalimits specifies the range for which the density should be calculated (it defaults to the extrema of the whole data). The keyword argument datalimits can be a tuple of two values, e.g. datalimits=(0, 10), or a function to be applied group by group, e.g. datalimits=extrema. The keyword arguments kernel and bandwidth are forwarded to KernelDensity.kde. npoints is the number of points used by Makie to draw the line\n\nWeighted data is supported via the keyword weights (passed to mapping).\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"df = (x=randn(5000), y=randn(5000), z=rand([\"a\", \"b\", \"c\", \"d\"], 5000))\nspecs = data(df) * mapping(:x, layout=:z) * AlgebraOfGraphics.density(datalimits=((-2.5, 2.5),))\n\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) *\n    mapping((:x, :z) => ((x, z) -> x + 5 * (z ∈ [\"b\", \"d\"])) => \"new x\", layout=:z) *\n    AlgebraOfGraphics.density(datalimits=extrema)\ndraw(specs, facet=(linkxaxes=:minimal,))","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"data(df) * mapping(:x, :y, layout=:z) * AlgebraOfGraphics.density(npoints=50) |> draw","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :y, layout=:z) *\n    AlgebraOfGraphics.density(npoints=50) * visual(Surface)\n\ndraw(specs, axis=(type=Axis3, zticks=0:0.1:0.2, limits=(nothing, nothing, (0, 0.2))))","category":"page"},{"location":"generated/analyses/#Frequency","page":"Analyses","title":"Frequency","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"frequency","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.frequency","page":"Analyses","title":"AlgebraOfGraphics.frequency","text":"frequency()\n\nCompute a frequency table of the arguments.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"df = (x=rand([\"a\", \"b\", \"c\"], 100), y=rand([\"a\", \"b\", \"c\"], 100), z=rand([\"a\", \"b\", \"c\"], 100))\nspecs = data(df) * mapping(:x, layout=:z) * frequency()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, layout=:z, color=:y, stack=:y) * frequency()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :y, layout=:z) * frequency()\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Expectation","page":"Analyses","title":"Expectation","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"expectation","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.expectation","page":"Analyses","title":"AlgebraOfGraphics.expectation","text":"expectation()\n\nCompute the expected value of the last argument conditioned on the preceding ones.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"df = (x=rand([\"a\", \"b\", \"c\"], 100), y=rand([\"a\", \"b\", \"c\"], 100), z=rand(100), c=rand([\"a\", \"b\", \"c\"], 100))\nspecs = data(df) * mapping(:x, :z, layout=:c) * expectation()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :z, layout=:c, color=:y, dodge=:y) * expectation()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :y, :z, layout=:c) * expectation()\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Linear","page":"Analyses","title":"Linear","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"linear","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.linear","page":"Analyses","title":"AlgebraOfGraphics.linear","text":"linear(; interval=automatic, level=0.95, dropcollinear=false, npoints=200)\n\nCompute a linear fit of y ~ 1 + x. An optional named mapping weights determines the weights. Use interval to specify what type of interval the shaded band should represent, for a given coverage level (the default 0.95 equates alpha = 0.05). Valid values of interval are :confidence, to delimit the uncertainty of the predicted relationship, and :prediction, to delimit estimated bounds for new data points. Use interval = nothing to only compute the line fit, without any uncertainty estimate.  By default, this analysis errors on singular (collinear) data. To avoid that, it is possible to set dropcollinear=true. npoints is the number of points used by Makie to draw the shaded band.\n\nWeighted data is supported via the keyword weights (passed to mapping).\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = 1:0.05:10\na = rand(1:7, length(x))\ny = 1.2 .* x .+ a .+ 0.5 .* randn.()\ndf = (; x, y, a)\nspecs = data(df) * mapping(:x, :y, color=:a => nonnumeric) * (linear() + visual(Scatter))\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Smoothing","page":"Analyses","title":"Smoothing","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"smooth","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.smooth","page":"Analyses","title":"AlgebraOfGraphics.smooth","text":"smooth(; span=0.75, degree=2, npoints=200)\n\nFit a loess model. span is the degree of smoothing, typically in [0,1]. Smaller values result in smaller local context in fitting. degree is the polynomial degree used in the loess model. npoints is the number of points used by Makie to draw the line\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = 1:0.05:10\na = rand(1:7, length(x))\ny = sin.(x) .+ a .+ 0.1 .* randn.()\ndf = (; x, y, a)\nspecs = data(df) * mapping(:x, :y, color=:a => nonnumeric) * (smooth() + visual(Scatter))\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Contours","page":"Analyses","title":"Contours","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"contours","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.contours","page":"Analyses","title":"AlgebraOfGraphics.contours","text":"contours(; levels=5, kwargs...)\n\nCreate contour lines over the grid spanned over x and y by args 1 and 2 in the mapping, with height values z passed via arg 3. \n\nYou can pass the number of levels as an integer or a vector of levels. The levels are calculated across the whole z data if they are specified as an integer.\n\nNote that visual(Contour) only works in a limited way with AlgebraOfGraphics since version 0.7, because the internal calculations it does are not compatible with the scale system. With visual(Contour), you can only have categorically-colored contours (for example to visualize contours of multiple categories). Alternatively, if you set the colormap attribute, you can get continuously-colored contours but the levels will not be known to AlgebraOfGraphics, so they won't be synchronized across facets and there will not be a colorbar.\n\nAll other keyword arguments are forwarded as attributes to the underlying Contour plot.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = repeat(1:10, 10)\ny = repeat(11:20, inner = 10)\nz = sqrt.(x .* y)\ndf = (; x, y, z)\nspecs = data(df) * mapping(:x, :y, :z) * contours(levels = 8)\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = repeat(1:10, 10)\ny = repeat(11:20, inner = 10)\nz = sqrt.(x .* y)\ndf = (; x, y, z)\nspecs = data(df) * mapping(:x, :y, :z) * contours(levels = 8, labels = true)\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Filled-Contours","page":"Analyses","title":"Filled Contours","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"filled_contours","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.filled_contours","page":"Analyses","title":"AlgebraOfGraphics.filled_contours","text":"filled_contours(; bands=automatic, levels=automatic)\n\nCreate filled contours over the grid spanned over x and y by args 1 and 2 in the mapping, with height values z passed via arg 3. \n\nYou can pass either the number of bands to bands or pass a vector of levels (the boundaries of the bands) to levels, but not both. The number of bands when levels is passed is length(levels) - 1. The levels are calculated across the whole z data if the number of bands is specified. If neither levels nor bands are specified, the default is bands = 10.\n\nNote that visual(Contourf) does not work with AlgebraOfGraphics since version 0.7, because the internal binning it does is not compatible with the scale system.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = repeat(1:10, 10)\ny = repeat(11:20, inner = 10)\nz = sqrt.(x .* y)\ndf = (; x, y, z)\nspecs = data(df) * mapping(:x, :y, :z) * filled_contours(levels = 3:2:15)\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"This page was generated using Literate.jl.","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/pre_grouped_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/#Pre-grouped-data","page":"Pre-grouped data","title":"Pre-grouped data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"using AlgebraOfGraphics, CairoMakie\n\n\nx = [rand(10) .+ i for i in 1:3]\ny = [rand(10) .+ i for i in 1:3]\nz = [rand(10) .+ i for i in 1:3]\nc = [\"a\", \"b\", \"c\"]\n\nm = pregrouped(x, y, color=c => (t -> \"Type \" * t ) => \"Category\")\ndraw(m)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"m = pregrouped(x, (y, z) => (+) => \"sum\", color=c => (t -> \"Type \" * t ) => \"Category\")\ndraw(m)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"m = pregrouped(x, [y z], color=dims(1) => renamer([\"a\", \"b\", \"c\"])) * visual(Scatter)\ndraw(m)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"m = pregrouped(x, [y z], color=[\"1\" \"2\"])\nlayers = visual(Scatter) + linear()\nfg = draw(m * layers)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/legend.jl\"","category":"page"},{"location":"gallery/gallery/customization/legend/#Legend-tweaking","page":"Legend tweaking","title":"Legend tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: Source code)","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"To tweak the position and appearance of the legend, simply use the legend keyword when plotting. For example","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"labels = [\"a looooooong label\", \"an even loooooonger label\", \"and one more long label\"]\ndf = (x=rand(100), y=rand(100), group=rand(labels, 100))\nlayers = linear() + mapping(color=:group)\nplt = data(df) * layers * mapping(:x, :y)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"fg = draw(plt, legend=(position=:top, titleposition=:left, framevisible=true, padding=5))","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"To adjust the title and order of labels in a legend you can use the pair syntax.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"layers = linear() +  mapping(color=:group => sorter(labels) => \"Labels\")\nplt = data(df) * layers * mapping(:x, :y)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"Adding a plot to a pre-existing figure with draw! will not draw the legend automatically.  In this case, one must use legend! and specify the axis to which it should be added.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"The tellheight = false, tellwidth = false arguments are useful to avoid changing the dimensions of the axis.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"makie_fig = Figure()\nax_scatter = Axis(makie_fig[1, 1])\n\ngrid = draw!(ax_scatter, plt)\n\nlegend!(makie_fig[1, 1], grid; tellheight=false, tellwidth=false, halign=:right, valign=:top)\n\nmakie_fig","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"EditURL = \"visual.jl\"","category":"page"},{"location":"generated/visual/#Visual","page":"Visual","title":"Visual","text":"","category":"section"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"visual","category":"page"},{"location":"generated/visual/#AlgebraOfGraphics.visual","page":"Visual","title":"AlgebraOfGraphics.visual","text":"visual(plottype; attributes...)\n\nCreate a Layer that will cause a plot spec multiplied with it to be visualized with plot type plottype, together with optional attributes.\n\nThe available plotting functions are documented here. Refer to plotting functions using upper CamelCase for visual's first argument (e.g. visual(Scatter), visual(BarPlot)). See the documentation of each plotting function to discover the available attributes. These attributes can be passed as additional keyword arguments to visual, or as part of the mapping you define.\n\nThe visual function can in principle be used for any plotting function that is defined using the @recipe macro from Makie. AlgebraOfGraphics just needs method definitions for aesthetic_mapping, which define what arguments of the plotting function map to which visual aesthetics. And for legend support, legend_elements must be overloaded for custom recipes as well, as Makie's default legend mechanism relies on instantiated plot objects, while AlgebraOfGraphics must go by the type and attributes alone.\n\nDepending on its aesthetic_mapping, a plot type and its attributes may change certain semantics of a given data(...) * mapping(...) spec. For example, visual(BarPlot) will show mapping 1 on the x axis and 2 on the y axis, while visual(BarPlot, direction = :x) shows mapping 1 on y and 2 on x.\n\n\n\n\n\n","category":"function"},{"location":"generated/visual/#Examples","page":"Visual","title":"Examples","text":"","category":"section"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"using AlgebraOfGraphics, CairoMakie\nset_aog_theme!()\n\ndf = (x=randn(1000), y=randn(1000))\nplt = data(df) * mapping(:x, :y) * AlgebraOfGraphics.density(npoints=50)\ndraw(plt * visual(Heatmap)) # plot as heatmap (the default)","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"From AlgebraOfGraphics version 0.7 on, some attributes of the underlying Makie functions will not have an effect if they are controlled by scales instead. For example, continuous colors are completely controlled by color scales, so setting colormap in visual does not have an effect.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"Set the colormap in the Scale options instead.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"draw(plt, scales(Color = (; colormap = :viridis))) # set a different colormap","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"draw(plt * visual(Contour)) # plot as contour","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"draw(plt * visual(Contour, linewidth=2)) # plot as contour with thicker lines","category":"page"},{"location":"generated/visual/#Manual-legend-entries-via-label","page":"Visual","title":"Manual legend entries via label","text":"","category":"section"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"The legend normally contains entries for all appropriate scales used in the plot. Sometimes, however, you just want to label certain plots such that they appear in the legend without using any scale. You can achieve this by adding the label keyword to all visuals that you want to label. Layers with the same label will be combined within a legend entry.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"x = range(0, 4pi, length = 40)\nlayer1 = data((; x = x, y = cos.(x))) * mapping(:x, :y) * visual(Lines, linestyle = :dash, label = \"A cosine line\")\nlayer2 = data((; x = x, y = sin.(x) .+ 2)) * mapping(:x, :y) *\n    (visual(Lines, color = (:tomato, 0.4)) + visual(Scatter, color = :tomato)) * visual(label = \"A sine line + scatter\")\ndraw(layer1 + layer2)","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"If the figure contains other scales, the legend will list the labelled group last by default. If you want to reorder, use the symbol :Label to specify the labelled group.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"df = (; x = repeat(1:10, 3), y = cos.(1:30), group = repeat([\"A\", \"B\", \"C\"], inner = 10))\nspec1 = data(df) * mapping(:x, :y, color = :group) * visual(Lines)\n\nspec2 = data((; x = 1:10, y = cos.(1:10) .+ 2)) * mapping(:x, :y) * visual(Scatter, color = :purple, label = \"Scatter\")\n\nf = Figure()\nfg = draw!(f[1, 1], spec1 + spec2)\nlegend!(f[1, 2], fg)\nlegend!(f[1, 3], fg, order = [:Label, :Color])\n\nf","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"This page was generated using Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/axis.jl\"","category":"page"},{"location":"gallery/gallery/customization/axis/#Axis-tweaking","page":"Axis tweaking","title":"Axis tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"To tweak one or more axes, simply use the axis keyword when plotting. For example","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"df = (x=rand(100), y=rand(100), z=rand(100))\nlayers = linear() + mapping(color=:z)\nplt = data(df) * layers * mapping(:x, :y)\ndraw(plt, axis=(aspect=1,))","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"fg = draw(plt, axis=(aspect=1, xticks=0:0.1:1, yticks=0:0.1:1, ylabel=\"custom label\"))","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/secondary_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/#Secondary-scales","page":"Secondary scales","title":"Secondary scales","text":"","category":"section"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"By default, scales with the same aesthetic type have their categories merged. This can be undesirable if there are disjoint sets of categories, for example three different time series with two different event markers:","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"nevents = 500\nngroups = 3\ntime = repeat(1:nevents, ngroups)\ny = reduce(vcat, [cumsum(randn(length(time))) for _ in 1:ngroups])\ngroup = repeat([\"A\", \"B\", \"C\"], inner = nevents)\ndf1 = (; time, y, group)\ndf2 = (; time = [30, 79, 250, 400], event = [\"X\", \"Y\", \"Y\", \"X\"])\n\nspec_a = data(df1) * mapping(:time, :y, color = :group) * visual(Lines)\nspec_b = data(df2) * mapping(:time, color = :event) * visual(VLines)\n\ndraw(spec_a + spec_b)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"By assigning an arbitrary scale identifier to one of the color mappings, we can split the two scales apart and receive a separate legend for both:","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"split_spec = spec_a + spec_b * mapping(color = :event => scale(:secondary))\ndraw(split_spec)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"Each scale can then be modified separately in the scales configuration. For example, it is not desired that both scales use the same color palette:","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"fg = draw(split_spec, scales(secondary = (;\n    palette = [:gray70, :gray30]\n)))","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"If you don't want to have separate legend groups, you can merge them using the order keyword in the legend config.","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"draw(\n    split_spec,\n    scales(\n        secondary = (; palette = [:gray70, :gray30])\n    );\n    legend = (; order = [[:Color, :secondary] => \"Legend\"])\n)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/figure.jl\"","category":"page"},{"location":"gallery/gallery/customization/figure/#Figure-tweaking","page":"Figure tweaking","title":"Figure tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"You can pass attributes to the underlying Makie.Figure using the figure keyword of draw.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"df = (x=rand(100), y=rand(100), z=rand(100), c=rand([\"a\", \"b\"], 100))\nxyc = data(df) * mapping(:x, :y, layout=:c)\nlayers = linear() + mapping(color=:z)\nplt = xyc * layers\ndraw(\n    plt,\n    figure = (;\n        figure_padding = 10,\n        backgroundcolor = :gray80,\n        size = (800, 400)\n    )\n)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"You can also add a figure title, subtitle and footnotes.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"fg = draw(\n    plt,\n    figure = (;\n        figure_padding = 10,\n        backgroundcolor = :gray80,\n        size = (800, 400),\n        title = \"Figure title\",\n        subtitle = \"Some subtitle below the figure title\",\n        footnotes = [\n            rich(superscript(\"1\"), \"First footnote\"),\n            rich(superscript(\"2\"), \"Second footnote\"),\n        ]\n    )\n)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"The appearance of these elements can be modified further, for all options check the draw function.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"draw(\n    plt,\n    figure = (;\n        figure_padding = 10,\n        backgroundcolor = :gray80,\n        size = (800, 400),\n        title = \"Figure title\",\n        subtitle = \"Some subtitle below the figure title\",\n        footnotes = [\n            rich(superscript(\"1\"), \"First footnote\"),\n            rich(superscript(\"2\"), \"Second footnote\"),\n        ],\n        titlecolor = :firebrick,\n        titlealign = :right,\n        footnotefont = :bold,\n    )\n)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/mapping/#Mapping","page":"Mapping","title":"Mapping","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Mappings determine how the data is translated into a plot. For example, this mapping maps columns weight and height to positional arguments 1 and 2, and age to the markersize attribute of the Scatter plotting function:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"mapping(:weight, :height, markersize = :age)","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"mapping","category":"page"},{"location":"layers/mapping/#AlgebraOfGraphics.mapping","page":"Mapping","title":"AlgebraOfGraphics.mapping","text":"mapping(positional...; named...)\n\nCreate a Layer with positional and named selectors. These selectors will be translated into input data for the Makie plotting function or AlgebraOfGraphics analysis that is chosen to visualize the Layer.\n\nA Layer created with mapping does not have a data source by default, you can add one by multiplying with the output of the data function.\n\nThe positional and named selectors of mapping are converted to actual input data for the plotting function that will be selected via visual. The translation from selector to data differs according to the data source.\n\nTabular data\n\nWhen a mapping is combined with a data(tabular) where tabular is some Tables.jl-compatible object, each argument will be interpreted as a column selector. Additionally, it's allowed to specify columns outside of the dataset directly by wrapping the values in direct. The values can either be vectors that have to match the number of rows from the tabular data, or scalars that will be expanded as if they were a column filled with the same value.\n\nmapping(\n    :x,                        # column named \"x\"\n    \"a column\";                # column named \"a column\"\n    color = 1,                 # first column\n    marker = direct(\"abc\"),    # a new column filled with the string \"abc\"\n    linestyle = direct(1:3),   # a new column, length must match the table\n)\n\nnothing\n\nIf no data is set, each entry of mapping should be an AbstractVector that specifies a column of data directly. Scalars like strings for example will be expanded as if they were a column filled with the same value. This is useful when a legend should be shown, but there's only one group.\n\nmapping(\n    1:3,               # a column with values 1 to 3\n    [4, 5, 6],         # a column with values 4 to 6 \n    color = \"group 1\", # a column with repeated value \"group 1\"         \n)\n\nPregrouped\n\nWith data(Pregrouped()) * mapping(...) or the shortcut pregrouped(...), each element in mapping specifies input data directly, like with nothing. However, in this mode, data should be passed in pregrouped. Categorical variables should come as a vector of categories, while numerical variables should come as a vector of vectors of values, with as many inner vectors as there are groups in the categorical variables.\n\npregrouped(\n    [[1, 2, 3], [4, 5]], # two grouped vectors, of length 3 and 2\n    color = [\"A\", \"B\"]   # a vector with two categorical group values\n)\n\n\n\n\n\n","category":"function"},{"location":"layers/mapping/#Aesthetics","page":"Mapping","title":"Aesthetics","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The structure of a mapping is always directly tied to the signature of the plotting function (or analysis) that it is being connected with. What visual aspects of the plot the positional or keyword arguments affect depends on the plotting function in use.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"To be used with AlgebraOfGraphics, a plotting function has to add a declaration which aesthetics (like X, Y, Color, MarkerSize, LineStyle) its arguments map to. This mechanism allows AlgebraOfGraphics to correctly convert the raw input data into visual attributes for each plotting function and to correctly create and label axes, colorbars and legends.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Aesthetics can also change depending on attributes passed to visual.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"For example, for a BarPlot, args 1 and 2 correspond to the X and Y aesthetics by default. But if you change the direction in the visual, then axis labels shift accordingly because the aesthetic mapping has changed to 1 = Y, 2 = X:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (; name = [\"Anna\", \"Beatrix\", \"Claire\"], height_meters = [1.55, 1.76, 1.63])\nm = mapping(:name, :height_meters)\nspec1 = data(df) * m * visual(BarPlot)\nspec2 = data(df) * m * visual(BarPlot, direction = :x)\n\nf = Figure()\ndraw!(f[1, 1], spec1)\ndraw!(f[1, 2], spec2)\nf","category":"page"},{"location":"layers/mapping/#Hardcoded-aesthetics","page":"Mapping","title":"Hardcoded aesthetics","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Most aesthetics are tied to specific attributes of plot types, for example like AesColor to strokecolor of Scatter. There are a few aesthetics, however, which are hardcoded to belong to certain mapping keywords independent of the plot type in use.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"These are layout, row and col for facetting, group for creating a separate plot for each group (like separate lines instead of one long line) and dodge_x and dodge_y for dodging.","category":"page"},{"location":"layers/mapping/#Dodging","page":"Mapping","title":"Dodging","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Dodging refers to the shifting of plots on a (usually categorical) scale depending on the group they belong to. It is used to avoid overlaps. Some plot types, like BarPlot, have their own dodge keyword because their dodging logic additionally needs to transform the visual elements (for example, dodging a bar plot makes thinner bars). For all other plot types, you can use the generic dodge_x and dodge_y keywords.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"They work by shifting each categorical group by some value that depends on the chosen \"dodge width\". The dodge width refers to the width that all dodged elements in a group add up to at a given point. Some plot types have an inherent width, like barplots. Others have no width, like scatters or errorbars. For those plot types that have no width to use for dodging, you have to specify one manually in scales.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Here's an example of a manual width selection:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (\n   x = repeat(1:10, inner = 2),\n   y = cos.(range(0, 2pi, length = 20)),\n   ylow = cos.(range(0, 2pi, length = 20)) .- 0.2,\n   yhigh = cos.(range(0, 2pi, length = 20)) .+ 0.3,\n   dodge = repeat([\"A\", \"B\"], 10)\n)\n\nf = Figure()\nplt = data(df) * (\n   mapping(:x, :y, dodge_x = :dodge, color = :dodge) * visual(Scatter) +\n   mapping(:x, :ylow, :yhigh, dodge_x = :dodge, color = :dodge) * visual(Rangebars)\n)\ndraw!(f[1, 1], plt, scales(DodgeX = (; width = 1)), axis = (; title = \"width = 1\"))\ndraw!(f[1, 2], plt, scales(DodgeX = (; width = 0.75)), axis = (; title = \"width = 0.75\"))\ndraw!(f[2, 1], plt, scales(DodgeX = (; width = 0.5)), axis = (; title = \"width = 0.5\"))\ndraw!(f[2, 2], plt, scales(DodgeX = (; width = 0.25)), axis = (; title = \"width = 0.25\"))\nf","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"A common scenario is plotting errorbars on top of barplots. In this case, AlgebraOfGraphics can detect the inherent dodging width of the barplots and adjust accordingly for the errorbars. Note in this example how choosing a manual dodging width only applies to the errorbars (because the barplot plot type handles this internally) and potentially leads to a misalignment between the different plot elements:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (\n   x = repeat(1:10, inner = 2),\n   y = cos.(range(0, 2pi, length = 20)),\n   ylow = cos.(range(0, 2pi, length = 20)) .- 0.2,\n   yhigh = cos.(range(0, 2pi, length = 20)) .+ 0.3,\n   dodge = repeat([\"A\", \"B\"], 10)\n)\n\nf = Figure()\nplt = data(df) * (\n   mapping(:x, :y, dodge = :dodge, color = :dodge) * visual(BarPlot) +\n   mapping(:x, :ylow, :yhigh, dodge_x = :dodge) * visual(Rangebars)\n)\ndraw!(f[1, 1], plt, axis = (; title = \"No width specified, auto-determined by AlgebraOfGraphics\"))\ndraw!(f[2, 1], plt, scales(DodgeX = (; width = 0.25)), axis = (; title = \"Manually specifying width = 0.25 leads to a mismatch\"))\nf","category":"page"},{"location":"layers/mapping/#Pair-syntax","page":"Mapping","title":"Pair syntax","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The Pair operator => can be used for three different purposes within mapping:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"renaming columns\ntransforming columns by row\nmapping data to a custom scale","category":"page"},{"location":"layers/mapping/#Renaming-columns","page":"Mapping","title":"Renaming columns","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndata((; name = [\"Anna\", \"Beatrix\", \"Claire\"], height_meters = [1.55, 1.76, 1.63])) *\n   mapping(:name => \"Name\", :height_meters => \"Height (m)\") *\n   visual(BarPlot) |> draw ","category":"page"},{"location":"layers/mapping/#Transforming-columns","page":"Mapping","title":"Transforming columns","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"If a Function is paired to the column selector, it is applied by row to the data. Often, you will want to also assign a new name that fits the transformed data, in which case you can use the three-element column => transformation => name syntax:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndata((; name = [\"Anna\", \"Beatrix\", \"Claire\"], height_meters = [1.55, 1.76, 1.63])) *\n   mapping(:name => (n -> n[1] * \".\"), :height_meters => (x -> x * 100) => \"Height (cm)\") *\n   visual(BarPlot) |> draw","category":"page"},{"location":"layers/mapping/#Row-by-row-versus-whole-column-operations","page":"Mapping","title":"Row-by-row versus whole-column operations","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The pair syntax acts row by row, unlike, e.g., DataFrames.transform. This has several advantages.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Simpler for the user in most cases.\nLess error prone especially\nwith grouped data (should a column operation apply to each group or the whole dataset?)\nwhen several datasets are used","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Naturally, this also incurs some downsides, as whole-column operations, such as z-score standardization, are not supported: they should be done by adding a new column to the underlying dataset beforehand.","category":"page"},{"location":"layers/mapping/#Functions-of-several-arguments","page":"Mapping","title":"Functions of several arguments","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"In the case of functions of several arguments, such as isequal, the input variables must be passed as a Tuple.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"accuracy = (:species, :predicted_species) => isequal => \"accuracy\"","category":"page"},{"location":"layers/mapping/#Helper-functions","page":"Mapping","title":"Helper functions","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Some helper functions are provided, which can be used within the pair syntax to either rename and reorder unique values of a categorical column on the fly or to signal whether a numerical column should be treated as categorical.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The complete API of helper functions is available at Mapping helpers, but here are a few examples:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"# column `train` has two unique values, `true` and `false`\n:train => renamer([true => \"training\", false => \"testing\"]) => \"Dataset\"\n# column `price` has three unique values, `\"low\"`, `\"medium\"`, and `\"high\"`\n:price => sorter([\"low\", \"medium\", \"high\"])\n# column `age` is expressed in integers and we want to treat it as categorical\n:age => nonnumeric\n# column `labels` is expressed in strings and we do not want to treat it as categorical\n:labels => verbatim\n# wrap categorical values to signal that the order from the data source should be respected\n:weight => presorted","category":"page"},{"location":"layers/mapping/#Custom-scales","page":"Mapping","title":"Custom scales","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"All columns mapped to the same aesthetic type are represented using the same scale by default. This is evident if you plot two different datasets with two different plot types.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"In the following example, both Scatter and HLines use the Color aesthetic, Scatter for the strokecolor keyword and HLines for color. A single merged legend is rendered for both, which does not have a title because it derives from two differently named columns.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf_a = (; x = 1:9, y = [1, 2, 3, 5, 6, 7, 9, 10, 11], group = repeat([\"A\", \"B\", \"C\"], inner = 3))\n\nspec1 = data(df_a) * mapping(:x, :y, strokecolor = :group) * visual(Scatter, color = :transparent, strokewidth = 3, markersize = 15)\n\ndf_b = (; y = [4, 8], threshold = [\"first\", \"second\"])\n\nspec2 = data(df_b) * mapping(:y, color = :threshold) * visual(HLines)\n\ndraw(spec1 + spec2)","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"If we want to have separate legends for both, we can assign a custom scale identifier to either the strokecolor or the color mapping. The name can be chosen freely, it serves only to disambiguate.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"spec2_custom_scale = data(df_b) * mapping(:y, color = :threshold => scale(:color2)) * visual(HLines)\n\ndraw(spec1 + spec2_custom_scale)","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Each scale can be customized further by passing configuration options via scales as the second argument of the draw function. More information on scale options can be found under Scale options.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"As an example, we can pass separate colormaps using the palette keyword:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"spec2_custom_scale = data(df_b) * mapping(:y, color = :threshold => scale(:color2)) * visual(HLines)\n\ndraw(\n   spec1 + spec2_custom_scale,\n   scales(\n      Color = (; palette = [:red, :green, :blue]),\n      color2 = (; palette = [:gray30, :gray80]),\n   )\n)","category":"page"},{"location":"changelog/#Changelog","page":"Changelog","title":"Changelog","text":"","category":"section"},{"location":"changelog/#Unreleased","page":"Changelog","title":"Unreleased","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added ability to include layers in the legend without using scales by adding visual(label = \"some label\") #565.","category":"page"},{"location":"changelog/#v0.8.8-2024-09-17","page":"Changelog","title":"v0.8.8 - 2024-09-17","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Fixed aesthetics of errorbar so that x and y stay labelled correctly when using direction = :x #560.\nAdded ability to specify title, subtitle and footnotes plus settings in the draw function #556.\nAdded dodge_x and dodge_y keywords to mapping that allow to dodge any plot types that have AesX or AesY data #558.","category":"page"},{"location":"changelog/#v0.8.7-2024-09-06","page":"Changelog","title":"v0.8.7 - 2024-09-06","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added ability to return ProcessedLayers from transformations, thereby enabling multi-layer transformations, such as scatter plus errorbars #549.\nFixed bug where mergesorted applied on string vectors used isless instead of natural sort #553.","category":"page"},{"location":"changelog/#v0.8.6-2024-09-02","page":"Changelog","title":"v0.8.6 - 2024-09-02","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added bar_labels to BarPlot's aesthetic mapping #544.\nAdded ability to hide legend or colorbar by passing, e.g., legend = (; show = false) to draw #547.","category":"page"},{"location":"changelog/#v0.8.5-2024-08-27","page":"Changelog","title":"v0.8.5 - 2024-08-27","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added presorted helper function to keep categorical data in the order encountered in the source table, instead of sorting it alphabetically #529.\nAdded from_continuous helper function which allows to sample continuous colormaps evenly to use them as categorical palettes without having to specify how many categories there are #541.","category":"page"},{"location":"changelog/#v0.8.4-2024-08-26","page":"Changelog","title":"v0.8.4 - 2024-08-26","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added fillto to BarPlot aesthetics #535.\nFixed bug when giving datalimits of density as a (low, high) tuple #536.\nFixed bug where facet-local continuous scale limits were used instead of the globally merged ones, possibly leading to mismatches between data and legend #539.","category":"page"},{"location":"changelog/#v0.8.3-2024-08-23","page":"Changelog","title":"v0.8.3 - 2024-08-23","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Fixed incorrect x/y axis assignment for the violin plot type #528.","category":"page"},{"location":"changelog/#v0.8.2-2024-08-21","page":"Changelog","title":"v0.8.2 - 2024-08-21","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Enable use of LaTeXStrings and rich text in renamer #525.\nFixed widths of boxplots with color groupings #524.","category":"page"},{"location":"changelog/#v0.8.1-2024-08-20","page":"Changelog","title":"v0.8.1 - 2024-08-20","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added back support for Hist, CrossBar, ECDFPlot and Density #522.","category":"page"},{"location":"changelog/#v0.8.0-2024-07-26","page":"Changelog","title":"v0.8.0 - 2024-07-26","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Columns with element types of Union{Missing,T} are not treated as categorical by default anymore, instead T decides if data is seen as categorical, continuous or geometrical. If you relied on numerical vectors with missings being treated as categorical, you can use :columnname => nonnumeric in the mapping instead.\nBreaking: AbstractString categories are now sorted with natural sort order by default. This means that where you got [\"1\", \"10\", \"2\"] before, you now get [\"1\", \"2\", \"10\"]. You can use sorter, the categories keyword or categorical arrays to sort your data differently if needed.","category":"page"},{"location":"changelog/#v0.7.0-2024-07-16","page":"Changelog","title":"v0.7.0 - 2024-07-16","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: The palette keyword of draw linking palettes to keyword arguments was removed. Instead, palettes need to be passed to specific scales like draw(..., scales(Color = (; palette = :Set1_3)))\nBreaking: All recipes need to have the new function aesthetic_mapping defined for all sets of positional arguments that should be supported, as can be seen in src/aesthetics.jl. This breaks usage of all custom recipes. Additionally, not all Makie plots have been ported to the new system yet. If you encounter missing plots, or missing attributes of already ported plots, please open an issue.\nBreaking: All custom recipes that should be displayed in a legend, need to have legend_elements(P, attributes, scale_args) defined as can be seen in src/guides/legend.jl. AlgebraOfGraphics cannot use the same default mechanism as Makie, which can create a legend from an existing plot, because AlgebraOfGraphics needs to create the legend before the plot is instantiated.\nBreaking: Pregrouped data cannot be passed anymore to the plain mapping(...) without any data(tabular). Instead, you should use pregrouped(...) which is a shortcut for data(Pregrouped()) * mapping(...).\nBreaking: Contour and Contourf generally do not work anymore with visual(). Instead, the contours() and filled_contours() analyses should be used. Contour can still be used with categorical colors, but not with continuous ones.\nBreaking: All colormap properties for continuous color scales need to be passed via scales now, and not through visual. This is to have central control over the scale as it can be used by multiple visuals simultaneously.\nHorizontal barplots, violins, errorbars, rangebars and other plot types that have two different orientations work correctly now. Axis labels switch accordingly when the orientation is changed.\nPlotting functions whose positional arguments don't correspond to X, Y, Z work correctly now. For example, HLines (1 => Y) or rangebars (1 => X, 2 => Y, 3 => Y).\nIt is possible to add categories beyond those present in the data with the categories keyword within a scale's settings. It is also possible to reorder or otherwise transform the existing categories by passing a function to categories.\nThe supported attributes are not limited anymore to a specific set of names, for example, strokecolor can work the same as color did before, and the two can share a scale via their shared aesthetic type.\nThere can be multiple scales of the same aesthetic now. This allows to have separate legends for different plot types using the same aesthetics. Scale separation works by pairing a variable in mapping with a scale(id_symbol).\nLegend entries can be reordered using the legend = (; order = ...) option in draw. Specific scales can opt out of the legend by passing legend = false in scales.\nLabels can now be anything that Makie supports, primarily Strings, LaTeXStrings or rich text.\nLegend elements now usually reflect all attributes set in their corresponding visual.\nSimple column vectors of data can now be passed directly to mapping without using data first. Additionally, scalar values are accepted as a shortcut for columns with the same repeated value.\nColumns from outside a table source in data can now be passed to mapping by wrapping them in the direct function. Scalar values are accepted as a shortcut for columns with the same repeated value. For example, to create a label for columns x and y from a dataframe passed to data, one could now do mapping(:x, :y, color = direct(\"label\")) without having to create a column full of \"label\" strings first.\nThe numbers at which categorical values are plotted on x and y axis can now be changed via scales(X = (; palette = [1, 2, 4])) or similar.\nContinuous marker size scales can now be shown in the legend. Numerical values are proportional to area and not diameter now, which makes more sense with respect to human perception. The min and max marker size can be set using the sizerange property for the respective scale in scales.","category":"page"},{"location":"changelog/#v0.6.11-2022-08-08","page":"Changelog","title":"v0.6.11 - 2022-08-08","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added paginate for pagination of large facet plots. ","category":"page"},{"location":"changelog/#v0.6.8-2022-06-14","page":"Changelog","title":"v0.6.8 - 2022-06-14","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added choropleth recipe to supersede geodata for geographical data.","category":"page"},{"location":"changelog/#v0.6.1-2022-01-28","page":"Changelog","title":"v0.6.1 - 2022-01-28","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Support level in linear analysis for confidence interval.\nReplaced tuples and named tuples in Layer and Entry with dictionaries from Dictionaries.jl.\nSplit internal Entry type into ProcessedLayer (to be used for analyses) and Entry (to be used for plotting).","category":"page"},{"location":"changelog/#v0.6.0-2021-10-24","page":"Changelog","title":"v0.6.0 - 2021-10-24","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Default axis linking behavior has changed: now only axes corresponding to the same variable are linked. For consistency with row/col, layout will hide decorations of linked axes and span axis labels if appropriate.\nCustomizable legend and colorbar position and look.\nCustomizable axis linking behavior.","category":"page"},{"location":"changelog/#v0.5-2021-08-05","page":"Changelog","title":"v0.5 - 2021-08-05","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Axis(ae) has been replaced by ae.axis.\nBreaking: Legend(fg) has been replaced by legend!(fg) and colorbar!(fg).\nlegend! and colorbar! API allows for custom legend placement.","category":"page"},{"location":"changelog/#v0.4-2021-05-21","page":"Changelog","title":"v0.4 - 2021-05-21","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Removed deprecations for style and spec (now only mapping and visual are allowed).\nBreaking: Analyses now require parentheses (i.e. linear() instead of linear).\nBreaking: Rename layout_x and layout_y to col and row.\nBreaking: Rename wts keyword argument to weights.\nBreaking: categorical has been replaced by nonnumeric.","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/statistical analyses/density_plots.jl\"","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/#Density-plots","page":"Density plots","title":"Density plots","text":"","category":"section"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\n\ndf = (x=randn(1000), c=rand([\"a\", \"b\"], 1000))\nplt = data(df) * mapping(:x, color=:c) * density(bandwidth=0.5)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"df = (x=randn(1000), c=rand([\"a\", \"b\"], 1000))\nplt = data(df) * mapping(:x, color=:c) * density(bandwidth=0.5) * visual(orientation=:vertical)\n\"Not yet supported\" # hide","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"\"Not yet supported\"","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"API/types/#Types","page":"Types","title":"Types","text":"","category":"section"},{"location":"API/types/","page":"Types","title":"Types","text":"AlgebraOfGraphics.AbstractDrawable\nAlgebraOfGraphics.AbstractAlgebraic\nAlgebraOfGraphics.Layer\nAlgebraOfGraphics.Layers\nAlgebraOfGraphics.ProcessedLayer\nAlgebraOfGraphics.ProcessedLayers\nAlgebraOfGraphics.Entry\nAlgebraOfGraphics.AxisEntries","category":"page"},{"location":"API/types/#AlgebraOfGraphics.AbstractDrawable","page":"Types","title":"AlgebraOfGraphics.AbstractDrawable","text":"AbstractDrawable\n\nAbstract type encoding objects that can be drawn via AlgebraOfGraphics.draw.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.AbstractAlgebraic","page":"Types","title":"AlgebraOfGraphics.AbstractAlgebraic","text":"AbstractAlgebraic  <: AbstractDrawable\n\nAbstract type encoding objects that can be combined together using + and *.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.Layer","page":"Types","title":"AlgebraOfGraphics.Layer","text":"Layer(transformation, data, positional::AbstractVector, named::AbstractDictionary)\n\nAlgebraic object encoding a single layer of a visualization. It is composed of a dataset, positional and named arguments, as well as a transformation to be applied to those. Layer objects can be multiplied, yielding a novel Layer object, or added, yielding a AlgebraOfGraphics.Layers object.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.Layers","page":"Types","title":"AlgebraOfGraphics.Layers","text":"Layers(layers::Vector{Layer})\n\nAlgebraic object encoding a list of AlgebraOfGraphics.Layer objects. Layers objects can be added or multiplied, yielding a novel Layers object.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.ProcessedLayer","page":"Types","title":"AlgebraOfGraphics.ProcessedLayer","text":"ProcessedLayer(l::Layer)\n\nProcess a Layer and return the resulting ProcessedLayer.\n\nNote that this method should not be used anymore as processing a Layer can now potentially return multiple ProcessedLayer objects. Therefore, you should use the plural form ProcessedLayers(layer).\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.ProcessedLayers","page":"Types","title":"AlgebraOfGraphics.ProcessedLayers","text":"ProcessedLayers(layers::Vector{ProcessedLayer})\n\nObject encoding a list of AlgebraOfGraphics.ProcessedLayer objects. ProcessedLayers objects are the output of the processing pipeline and can be drawn without further processing.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.Entry","page":"Types","title":"AlgebraOfGraphics.Entry","text":"Entry(plottype::PlotType, positional::Arguments, named::NamedArguments)\n\nDefine plottype as well as positional and named arguments for a single plot.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.AxisEntries","page":"Types","title":"AlgebraOfGraphics.AxisEntries","text":"AxisEntries(axis::Union{Axis, Nothing}, entries::Vector{Entry}, categoricalscales, continuousscales)\n\nDefine all ingredients to make plots on an axis. Each categorical scale should be a CategoricalScale, and each continuous scale should be a ContinuousScale.\n\n\n\n\n\n","category":"type"},{"location":"API/recipes/#Recipes","page":"Recipes","title":"Recipes","text":"","category":"section"},{"location":"API/recipes/","page":"Recipes","title":"Recipes","text":"choropleth\nlinesfill","category":"page"},{"location":"API/recipes/#AlgebraOfGraphics.choropleth","page":"Recipes","title":"AlgebraOfGraphics.choropleth","text":"choropleth(geometries; transformation, attributes...)\n\nChoropleth map, where regions are defined by geometries. Use transformation to transform coordinates (see Proj.jl for more information).\n\nwarning: Warning\nThe transformation keyword argument is experimental and could be deprecated (even in a non-breaking release) in favor of a different syntax.\n\nAttributes\n\nAvailable attributes and their defaults for Plot{AlgebraOfGraphics.choropleth} are: \n\n  alpha               1.0\n  clip_planes         MakieCore.Automatic()\n  color               :gray25\n  colormap            :batlow\n  colorrange          MakieCore.Automatic()\n  colorscale          identity\n  cycle               [:color => :patchcolor]\n  depth_shift         0.0f0\n  highclip            MakieCore.Automatic()\n  inspectable         true\n  inspector_clear     MakieCore.Automatic()\n  inspector_hover     MakieCore.Automatic()\n  inspector_label     MakieCore.Automatic()\n  joinstyle           :miter\n  linecap             :butt\n  linestyle           \"nothing\"\n  lowclip             MakieCore.Automatic()\n  miter_limit         1.0471975511965976\n  nan_color           :transparent\n  overdraw            false\n  shading             NoShading\n  space               :data\n  ssao                false\n  stroke_depth_shift  -1.0f-5\n  strokecolor         :black\n  strokecolormap      :batlow\n  strokewidth         0\n  transparency        false\n  visible             true\n\n\n\n\n\n","category":"function"},{"location":"API/recipes/#AlgebraOfGraphics.linesfill","page":"Recipes","title":"AlgebraOfGraphics.linesfill","text":"linesfill(xs, ys; lower, upper, attributes...)\n\nLine plot with a shaded area between lower and upper. If lower and upper are not given, shaded area is between 0 and ys.\n\nAttributes\n\nAvailable attributes and their defaults for Plot{AlgebraOfGraphics.linesfill} are: \n\n  color       :gray25\n  colormap    :batlow\n  colorrange  MakieCore.Automatic()\n  fillalpha   0.15\n  linestyle   \"nothing\"\n  linewidth   1.5\n  lower       MakieCore.Automatic()\n  upper       MakieCore.Automatic()\n\n\n\n\n\n","category":"function"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/no_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/#Using-mapping-without-tabular-data","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"Sometimes it's easier to specify columnar data directly within mapping rather than first storing it in some tabular data source and accessing it by column name. Note that you can also use scalar values which will be treated like columns with repeated elements. In the example below, we specify color = \"marker\" instead of the more verbose color = fill(\"marker\", 3).","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"using AlgebraOfGraphics, CairoMakie\n\nx = 1:100\ny = sin.(range(0, 2pi, length = 100))\n\nplt = mapping(x, y, color = repeat([\"high\", \"low\"], inner = 50)) *\n    visual(Lines) +\n    mapping([20, 28, 51], color = \"marker\" => scale(:secondary)) *\n    visual(VLines, linestyle = :dash)\n\nfg = draw(plt, scales(secondary = (; palette = [:gray80])))","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/data/#Data","page":"Data","title":"Data","text":"","category":"section"},{"location":"layers/data/","page":"Data","title":"Data","text":"data","category":"page"},{"location":"layers/data/#AlgebraOfGraphics.data","page":"Data","title":"AlgebraOfGraphics.data","text":"data(table)\n\nCreate a Layer with its data field set to a table-like object.\n\nThere are no type restrictions on this object, as long as it respects the Tables interface. In particular, any one of these formats should work out of the box.\n\nTo create a fully specified layer, the layer created with data needs to be multiplied with the output of mapping.\n\nspec = data(...) * mapping(...)\n\n\n\n\n\n","category":"function"},{"location":"layers/data/","page":"Data","title":"Data","text":"using AlgebraOfGraphics\ndf = (a = rand(10), b = rand(10))\ndata(df)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/applications/time_series.jl\"","category":"page"},{"location":"gallery/gallery/applications/time_series/#Time-series","page":"Time series","title":"Time series","text":"","category":"section"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"using AlgebraOfGraphics, CairoMakie\nusing Dates\n\nx = today() - Year(1) : Day(1) : today()\ny = cumsum(randn(length(x)))\nz = cumsum(randn(length(x)))\ndf = (; x, y, z)\nlabels = [\"series 1\", \"series 2\", \"series 3\", \"series 4\", \"series 5\"]\nplt = data(df) *\n    mapping(:x, [:y, :z] .=> \"value\", color=dims(1) => renamer(labels) => \"series \") *\n    visual(Lines)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"x = now() - Hour(6) : Minute(1) : now()\ny = cumsum(randn(length(x)))\nz = cumsum(randn(length(x)))\ndf = (; x, y, z)\nplt = data(df) *\n    mapping(:x, [:y, :z] .=> \"value\", color=dims(1) => renamer(labels) =>\"series \") *\n    visual(Lines)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"dates = Date(2022, 1, 1):Day(1):Date(2022, 1, 31)\ntrend = cumsum(randn(31))\ndf = map(1:2000) do _\n    idx = rand(1:31)\n    date = dates[idx]\n    observation = trend[idx] + 2 * rand()\n    return (; date, observation)\nend\nplt = data(df) * mapping(:date, :observation) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/draw/#Drawing-Layers","page":"Drawing Layers","title":"Drawing Layers","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A AlgebraOfGraphics.Layer or AlgebraOfGraphics.Layers object can be plotted using the functions draw or draw!.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Whereas draw automatically adds colorbar and legend, draw! does not, as it would be hard to infer a good default placement that works in all scenarios.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Colorbar and legend, should they be necessary, can be added separately with the colorbar! and legend! helper functions. See also Nested layouts for a complex example.","category":"page"},{"location":"layers/draw/#Scale-options","page":"Drawing Layers","title":"Scale options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"All properties that decide how scales are visualized can be modified by passing scale options (using the scales function) as the second argument of draw . The properties that are accepted differ depending on the scale aesthetic type (for example Color, Marker, LineStyle) and whether the scale is categorical or continuous.","category":"page"},{"location":"layers/draw/#Categorical-scale-options","page":"Drawing Layers","title":"Categorical scale options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"All categorical scales take the same properties, independent of aesthetic type.","category":"page"},{"location":"layers/draw/#palette","page":"Drawing Layers","title":"palette","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The palette decides which attribute values are passed to Makie's plotting functions for each categorical value in the scale.","category":"page"},{"location":"layers/draw/#Color","page":"Drawing Layers","title":"Color","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A Symbol is converted to a colormap with Makie.to_colormap. ","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n    mapping(:x, :y, color = :z) *\n    visual(BarPlot)\ndraw(spec, scales(Color = (; palette = :tab10)))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"It's also possible to directly specify a vector of colors, each of which Makie.to_color can handle:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\nusing CairoMakie.Colors: RGB, RGBA, Gray, HSV\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n    mapping(:x, :y, color = :z) *\n    visual(BarPlot)\ndraw(spec, scales(Color = (; palette = [:red, :green, :blue, RGB(1, 0, 1), RGB(1, 1, 0), \"#abcff0\", \"#c88cbccc\", HSV(0.9, 0.3, 0.7), RGBA(0.7, 0.9, 0.6, 0.5), Gray(0.5)])))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"If you want to use a continuous colormap for categorical data, you can use the from_continuous helper function. It automatically takes care that the continuous colormap is sampled evenly from start to end depending on the number of categories. Any colormap that Makie understands can be passed, including named colormaps such as :viridis.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"This example shows the difference in behavior when the two-element colormap [:red, :blue] is used with or without from_continuous:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n    mapping(:x, :y, color = :z) *\n    visual(BarPlot)\n\nf = Figure()\nfg1 = draw!(f[1, 1], spec, scales(Color = (; palette = [:red, :blue])))\nlegend!(f[1, 2], fg1)\nfg2 = draw!(f[1, 3], spec, scales(Color = (; palette = from_continuous([:red, :blue]))))\nlegend!(f[1, 4], fg2)\nf","category":"page"},{"location":"layers/draw/#Marker","page":"Drawing Layers","title":"Marker","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A vector of values that Makie.to_spritemarker can handle.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n    mapping(:x, :y, marker = :z) *\n    visual(Scatter, markersize = 20)\n\ndraw(\n    spec,\n    scales(\n        Marker = (; palette = [:rect, :circle, :utriangle, :dtriangle, :diamond, :hline, :vline, :star5, :star6, :hexagon])\n    )\n)","category":"page"},{"location":"layers/draw/#LineStyle","page":"Drawing Layers","title":"LineStyle","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A vector of values that Makie.to_linestyle can handle.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = repeat('A':'E', inner = 2))) *\n    mapping(:x, :y, linestyle = :z) *\n    visual(Lines, linewidth = 2)\n\ndraw(spec, scales(\n    LineStyle = (; palette = [:solid, :dash, :dot, (:dot, :loose), Linestyle([0, 1, 2, 3, 4, 8])])\n))","category":"page"},{"location":"layers/draw/#X-and-Y","page":"Drawing Layers","title":"X & Y","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The \"palette\" values for X and Y axes are by default simply the numbers from 1 to N, the number of categories. In some circumstances, it might be useful to change these values, for example to visualize that one category is different than others. The palette values are normally assigned category-by-category in the sorted order, or in the order provided manually through the categories keyword. However, if you pass a vector of values, you can always use the category => value pair option to assign a specific category directly to a value, while the others cycle. Here, we do this with \"Unknown\" as it would otherwise be sorted before \"X\".","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (; group = [\"A\", \"B\", \"C\", \"X\", \"Y\", \"Unknown\"], count = [45, 10, 20, 32, 54, 72])\n\nspec = data(df) * mapping(:group, :count) * visual(BarPlot)\n\ndraw(spec, scales(X = (; palette = [1, 2, 3, 5, 6, \"Unknown\" => 8])))","category":"page"},{"location":"layers/draw/#Layout","page":"Drawing Layers","title":"Layout","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Normally, with the Layout aesthetic, rows wrap automatically such that an approximately square distribution of facets is attained. You can overwrite these values, however, to place axes at manually chosen positions:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (;\n    group = repeat([\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"], inner = 20),\n    x = randn(160),\n    y = randn(160)\n)\n\nspec = data(df) * mapping(:x, :y, layout = :group) * visual(Scatter)\n\nclockwise = [(1, 1), (1, 2), (1, 3), (2, 3), (3, 3), (3, 2), (3, 1), (2, 1)]\n\ndraw(spec, scales(Layout = (; palette = clockwise)))","category":"page"},{"location":"layers/draw/#categories","page":"Drawing Layers","title":"categories","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The categories keyword can be used to reorder, label and even add categorical values.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Some reordering and renaming can be done using the sorter and renamer helper functions applied directly to columns in mapping. However, this works less well when several data sources are combined where not all categories appear in each column. Also, no categories can be added this way, which is something that can be useful if the existence of categories should be shown even though there is no data for them.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"New labels can be assigned using the value => label pair syntax.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; group = [\"A\", \"C\", \"D\"], value = [1, 3, 4])) *\n    mapping(:group, :value) * visual(BarPlot)\n\nf = Figure()\n\ndraw!(f[1, 1], spec, scales(\n    X = (; categories = [\"A\", \"B\", \"C\", \"D\"])\n))\ndraw!(f[1, 2], spec, scales(\n    X = (; categories = [\"D\", \"A\", \"C\"])\n))\ndraw!(f[1, 3], spec, scales(\n    X = (; categories = [\"A\" => \"a\", \"C\" => \"c\", \"D\" => \"d\"])\n))\n\nf","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"You can also pass a Function to categories which should take the vector of category values and return a new vector of categories or category/label pairs.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"For example, you could add summary statistics to the facet layout titles this way, by grabbing them from a dictionary computed separately.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nsummary_stats = Dict(\"A\" => 1.32, \"B\" => 4.19, \"C\" => 0.04)\n\ndf = (;\n    x = randn(90),\n    y = randn(90) .+ repeat([0, 5, 10], inner = 30),\n    group = repeat([\"A\", \"B\", \"C\"], inner = 30)\n)\n\nspec = data(df) * mapping(:x, :y, col = :group) * visual(Scatter)\n\ndraw(spec, scales(Col = (;\n    categories = cats -> [\n        cat => rich(\"$cat\\n\", rich(\"λ = $(summary_stats[cat])\", font = :italic))\n            for cat in reverse(cats)\n        ]\n)))","category":"page"},{"location":"layers/draw/#Continuous-scale-options","page":"Drawing Layers","title":"Continuous scale options","text":"","category":"section"},{"location":"layers/draw/#Color-2","page":"Drawing Layers","title":"Color","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Continuous color scales can be modified using the familiar Makie attributes colormap, colorrange, highclip, lowclip and nan_color. By default, colorrange is set to the extrema of the encountered values, so no clipping occurs.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = [1:4; NaN; 6:10])) *\n    mapping(:x, :y, color = :z) *\n    visual(Scatter, markersize = 20)\n\ndraw(spec, scales(\n    Color = (;\n        colormap = :plasma,\n        nan_color = :cyan,\n        lowclip = :lime,\n        highclip = :red,\n        colorrange = (2, 9)\n    )\n))","category":"page"},{"location":"layers/draw/#MarkerSize","page":"Drawing Layers","title":"MarkerSize","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The range of marker sizes can be set with the sizerange attribute. Marker sizes are computed such that their area, and not markersize itself, grows linearly with the scale values. ","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 10:10:100)) *\n    mapping(:x, :y, markersize = :z) *\n    visual(Scatter)\n\nf = Figure()\n\ngrid = draw!(f[1, 1], spec, scales(\n    MarkerSize = (;\n        sizerange = (5, 15)\n    )\n))\nlegend!(f[1, 2], grid)\n\ngrid2 = draw!(f[2, 1], spec, scales(\n    MarkerSize = (;\n        sizerange = (5, 30)\n    )\n))\nlegend!(f[2, 2], grid2)\n\nf","category":"page"},{"location":"layers/draw/#Legend-options","page":"Drawing Layers","title":"Legend options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The legend keyword forwards most attributes to Makie's Legend function. The exceptions are listed here.","category":"page"},{"location":"layers/draw/#order","page":"Drawing Layers","title":"order","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"By default, the legend order depends on the order in which layers and mappings have been defined, as well as whether scales are categorical or continuous.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (;\n    x = 1:12,\n    y = 1:12,\n    z = 1:12,\n    group1 = repeat([\"A\", \"B\", \"C\"], inner = 4),\n    group2 = repeat([\"X\", \"Y\"], 6),\n)\n\nspec = data(df) *\n    mapping(:x, :y, markersize = :z, color = :group1, marker = :group2) *\n    visual(Scatter)\n\ndraw(spec)","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"You can reorder the legend with the order keyword. This expects a vector with either Symbols or Vector{Symbol}s as elements, where each Symbol is the identifier for a scale that's represented in the legend.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Plain Symbols can be used for simple reordering:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"draw(spec; legend = (; order = [:MarkerSize, :Color, :Marker]))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Symbols that are grouped into Vectors indicate that their groups should be merged together. For example, consider this plot that features two color scales, but one for a scatter plot and one for a line plot.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf_a = (; x = 1:9, y = [1, 2, 3, 5, 6, 7, 9, 10, 11], group = repeat([\"A\", \"B\", \"C\"], inner = 3))\n\nspec1 = data(df_a) * mapping(:x, :y, strokecolor = :group) * visual(Scatter, color = :transparent, strokewidth = 3, markersize = 15)\n\ndf_b = (; y = [4, 8], threshold = [\"first\", \"second\"])\n\nspec2_custom_scale = data(df_b) * mapping(:y, color = :threshold => scale(:color2)) * visual(HLines)\n\ndraw(spec1 + spec2_custom_scale)","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"You can group the two scales together using order. The titles are dropped.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"draw(spec1 + spec2_custom_scale; legend = (; order = [[:Color, :color2]]))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"If you want to add a title to a merged group, you can add it with the group => title pair syntax:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"draw(spec1 + spec2_custom_scale; legend = (; order = [[:Color, :color2] => \"Title\"]))","category":"page"},{"location":"layers/draw/#Figure-options","page":"Drawing Layers","title":"Figure options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"AlgebraOfGraphics can add a title, subtitle and footnotes to a figure automatically. Settings for these must be passed to the figure keyword. Check the draw function for a complete list.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = pregrouped(\n    fill(1:5, 6),\n    fill(11:15, 6),\n    [reshape(sin.(1:25), 5, 5) .+ i for i in 1:6],\n    layout = 1:6 => nonnumeric) * visual(Heatmap)\n\ndraw(\n    spec;\n    figure = (;\n        title = \"Numbers in square configuration\",\n        subtitle = \"Arbitrary data exhibits sinusoidal properties\",\n        footnotes = [\n            rich(superscript(\"1\"), \"First footnote\"),\n            rich(superscript(\"2\"), \"Second \", rich(\"footnote\", color = :red)),\n        ],\n    ),\n    axis = (; width = 100, height = 100)\n)","category":"page"},{"location":"","page":"Home","title":"Home","text":"CurrentModule = AlgebraOfGraphics","category":"page"},{"location":"#Introduction","page":"Home","title":"Introduction","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"AlgebraOfGraphics defines a language for data visualization. It is based on a few simple building blocks that can be combined using + and *.","category":"page"},{"location":"","page":"Home","title":"Home","text":"This package can be installed typing","category":"page"},{"location":"","page":"Home","title":"Home","text":"julia> import Pkg; Pkg.add(\"AlgebraOfGraphics\")","category":"page"},{"location":"","page":"Home","title":"Home","text":"in the Julia REPL.","category":"page"},{"location":"","page":"Home","title":"Home","text":"See the Tutorial to get started.","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/legend_merging.jl\"","category":"page"},{"location":"gallery/gallery/scales/legend_merging/#Legend-merging","page":"Legend merging","title":"Legend merging","text":"","category":"section"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"using AlgebraOfGraphics, CairoMakie\n\nN = 40\n\nx = [1:N; 1:N]\ny = [cumsum(randn(N)); cumsum(randn(N))]\ngrp = [fill(\"a\", N); fill(\"b\", N)]\n\ndf = (; x, y, grp)\n\nlayers = visual(Lines) + visual(Scatter) * mapping(marker = :grp)\nplt = data(df) * layers * mapping(:x, :y, color = :grp)\n\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/statistical analyses/regression_plots.jl\"","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/#Regression-plots","page":"Regression plots","title":"Regression plots","text":"","category":"section"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"using AlgebraOfGraphics, CairoMakie\n\nx = rand(100)\ny = @. randn() + x\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayers = linear() + visual(Scatter)\ndraw(layers * xy)","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"x = rand(100)\ny = @. randn() + 5 * x ^ 2\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayers = smooth() + visual(Scatter)\nfg = draw(layers * xy)","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/continuous_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/#Continuous-scales","page":"Continuous scales","title":"Continuous scales","text":"","category":"section"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"using AlgebraOfGraphics, CairoMakie\n\nx = 1:100\ny = @. sqrt(x) + 20x + 100\ndf = (; x, y)\nplt = data(df) *\n    mapping(\n        :x,\n        :y => log => \"√x + 20x + 100 (log scale)\",\n    ) * visual(Lines)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"x = 1:100\ny = @. sqrt(x) + 20x + 100\ndf = (; x, y)\nplt = data(df) *\n    mapping(\n        :x,\n        :y => \"√x + 20x + 100 (log scale)\",\n    ) * visual(Lines)\ndraw(plt, axis=(yscale=log,))","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"x = 0:100\ny = @. 0.01 + x/1000\ndf = (; x, y)\nplt = data(df) *\n    mapping(\n        :x,\n        :y => \"y\",\n    ) * visual(Lines)\nfg = draw(plt, axis=(yscale=log,))","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"API/functions/#Functions","page":"Functions","title":"Functions","text":"","category":"section"},{"location":"API/functions/#Drawing-functions","page":"Functions","title":"Drawing functions","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"draw\ndraw!\ncolorbar!\nlegend!\npaginate\nscales","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.draw","page":"Functions","title":"AlgebraOfGraphics.draw","text":"draw(d, scales::Scales = scales(); [axis, figure, facet, legend, colorbar])\n\nDraw a AlgebraOfGraphics.AbstractDrawable object d. In practice, d will often be a AlgebraOfGraphics.Layer or AlgebraOfGraphics.Layers. Scale options can be passed as an optional second argument. The output can be customized by passing named tuples or dictionaries with settings via the axis, figure, facet, legend or colorbar keywords. Legend and colorbar are drawn automatically unless show = false is passed to the keyword arguments of either legend or colorbar.\n\nFor finer control, use draw!, legend!, and colorbar! independently.\n\nFigure options\n\nAlgebraOfGraphics accepts the following special keywords under the figure keyword, the remaining attributes are forwarded to Makie's Figure constructor. The title, subtitle and footnotes arguments accept objects of any kind that Makie's Label or text function can handle, such as rich text.\n\n- `title`\n- `subtitle`\n- `titlesize::Union{Nothing,Float64}`\n- `subtitlesize::Union{Nothing,Float64}`\n- `titlealign::Union{Nothing,Symbol}`\n- `titlecolor`\n- `subtitlecolor`\n- `titlefont`\n- `subtitlefont`\n- `titlelineheight`\n- `subtitlelineheight`\n- `footnotes::Union{Nothing,Vector{Any}}`\n- `footnotesize::Union{Nothing,Float64}`\n- `footnotefont`\n- `footnotecolor`\n- `footnotealign`\n- `footnotelineheight`\n\n\n\n\n\ndraw(p::PaginatedLayers; kws...)\n\nDraw each element of PaginatedLayers p and return a Vector{FigureGrid}. Keywords kws are passed to the underlying draw calls.\n\n\n\n\n\ndraw(p::PaginatedLayers, i::Int; kws...)\n\nDraw the ith element of PaginatedLayers p and return a FigureGrid. Keywords kws are passed to the underlying draw call.\n\nYou can retrieve the number of elements using length(p).\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.draw!","page":"Functions","title":"AlgebraOfGraphics.draw!","text":"draw!(fig, d::AbstractDrawable, scales::Scales = scales(); [axis, facet])\n\nDraw a AlgebraOfGraphics.AbstractDrawable object d on fig. In practice, d will often be a AlgebraOfGraphics.Layer or AlgebraOfGraphics.Layers. fig can be a figure, a position in a layout, or an axis if d has no facet specification. The output can be customized by passing named tuples or dictionaries with settings via the axis or facet keywords.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.colorbar!","page":"Functions","title":"AlgebraOfGraphics.colorbar!","text":"colorbar!(figpos, grid; kwargs...)\n\nCompute colorbar for grid (which should be the output of draw!) and draw it in position figpos. Attributes allowed in kwargs are the same as MakieLayout.Colorbar.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.legend!","page":"Functions","title":"AlgebraOfGraphics.legend!","text":"legend!(figpos, grid; order = nothing, kwargs...)\n\nCompute legend for grid (which should be the output of draw!) and draw it in position figpos. All kwargs are forwarded to Makie's Legend constructor.\n\nThe order of scales represented in the legend can be changed with the order keyword. By default, legend sections are ordered the same as they appear in the plot specification. Assuming three scales Color, MarkerSize and custom exist in a spec, you can pass a vector to reorder them like [:MarkerSize, :custom, :Color], or merge multiple entries together with a nested vector like [[:MarkerSize, :custom], :Color], or give merged sections a title with the pair syntax [[:MarkerSize, :custom] => \"Merged group\", :Color].\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.paginate","page":"Functions","title":"AlgebraOfGraphics.paginate","text":"paginate(l; layout=nothing, row=nothing, col=nothing)\n\nPaginate l, the Layer or Layers object created by an AlgebraOfGraphics spec, to create a PaginatedLayers object. This contains a vector of layers where each layer operates on a subset of the input data.\n\nThe PaginatedLayers object can be passed to draw which will return a Vector{FigureGrid} rather than a single figure.\n\nThe keywords that limit the number of subplots on each page are the same that are used to specify facets in mapping:\n\nlayout: Maximum number of subplots in a wrapped linear layout.\nrow: Maximum number of rows in a 2D layout.\ncol: Maximum number of columns in a 2D layout.\n\nExample\n\nd = data((\n    x = rand(1000),\n    y = rand(1000),\n    group1 = rand(string.('a':'i'), 1000),\n    group2 = rand(string.('j':'r'), 1000),\n))\n\nlayer_1 = d * mapping(:x, :y, layout = :group1) * visual(Scatter)\npaginated_1 = paginate(layer_1, layout = 9)\nfiguregrids = draw(paginated_1)\n\nlayer_2 = d * mapping(:x, :y, row = :group1, col = :group2) * visual(Scatter)\npaginated_2 = paginate(layer_2, row = 4, col = 3)\nfiguregrid = draw(paginated_2, 1) # draw only the first grid\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.scales","page":"Functions","title":"AlgebraOfGraphics.scales","text":"scales(; kwargs...)\n\nCreate a Scales object containing properties for aesthetic scales that can be passed to draw and draw!. Each keyword should be the name of a scale in the spec that is being drawn. That can either be a default one like Color, Marker or LineStyle, or a custom scale name defined in a mapping using the scale function.\n\nThe values attached to the keywords must be dict-like, with Symbols as keys (such as NamedTuples).\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Mapping-helpers","page":"Functions","title":"Mapping helpers","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"pregrouped\ndirect\nrenamer\nsorter\nnonnumeric\nverbatim\nscale\npresorted","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.pregrouped","page":"Functions","title":"AlgebraOfGraphics.pregrouped","text":"pregrouped(positional...; named...)\n\nEquivalent to data(Pregrouped()) * mapping(positional...; named...). Refer to mapping for more information.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.direct","page":"Functions","title":"AlgebraOfGraphics.direct","text":"direct(x)\n\nReturn DirectData(x) which marks x for direct use in a mapping that's used with a table-like data source. As a result, x will be used directly as data, without lookup in the table. If x is not an AbstractArray, it will be expanded like fill(x, n) where n is the number of rows in the data source.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.renamer","page":"Functions","title":"AlgebraOfGraphics.renamer","text":"renamer(arr::Union{AbstractArray, Tuple})\n\nUtility to rename a categorical variable, as in renamer([value1 => label1, value2 => label2]). The keys of all pairs should be all the unique values of the categorical variable and the values should be the corresponding labels. The order of arr is respected in the legend.\n\nExamples\n\njulia> r = renamer([\"class 1\" => \"Class One\", \"class 2\" => \"Class Two\"])\nAlgebraOfGraphics.Renamer{Vector{String}, Vector{String}}([\"class 1\", \"class 2\"], [\"Class One\", \"Class Two\"])\n\njulia> println(r(\"class 1\"))\nClass One\n\nAlternatively, a sequence of pair arguments may be passed.\n\njulia> r = renamer(\"class 1\" => \"Class One\", \"class 2\" => \"Class Two\")\nAlgebraOfGraphics.Renamer{Tuple{String, String}, Tuple{String, String}}((\"class 1\", \"class 2\"), (\"Class One\", \"Class Two\"))\n\njulia> println(r(\"class 1\"))\nClass One\n\nIf arr does not contain Pairs, elements of arr are assumed to be labels, and the unique values of the categorical variable are taken to be the indices of the array. This is particularly useful for dims mappings.\n\nExamples\n\njulia> r = renamer([\"Class One\", \"Class Two\"])\nAlgebraOfGraphics.Renamer{Nothing, Vector{String}}(nothing, [\"Class One\", \"Class Two\"])\n\njulia> println(r(2))\nClass Two\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.sorter","page":"Functions","title":"AlgebraOfGraphics.sorter","text":"sorter(ks)\n\nUtility to reorder a categorical variable, as in sorter([\"low\", \"medium\", \"high\"]). A vararg method sorter(\"low\", \"medium\", \"high\") is also supported. ks should include all the unique values of the categorical variable. The order of ks is respected in the legend.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.nonnumeric","page":"Functions","title":"AlgebraOfGraphics.nonnumeric","text":"nonnumeric(x)\n\nTransform x into a non numeric type that is printed and sorted in the same way.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.verbatim","page":"Functions","title":"AlgebraOfGraphics.verbatim","text":"verbatim(x)\n\nSignal that x should not be rescaled, but used in the plot as is.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.scale","page":"Functions","title":"AlgebraOfGraphics.scale","text":"scale(id::Symbol)\n\nCreate a ScaleID object that can be used in a mapping to assign a custom id to the mapped variable. This variable will then not be merged into the default scale for its aesthetic type, but instead be handled separately, leading to a separate legend entry.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.presorted","page":"Functions","title":"AlgebraOfGraphics.presorted","text":"presorted(x)\n\nUse within a pair expression in mapping to signal that a categorical column from the data source should be used in the original order and not automatically sorted.\n\nExample:\n\n# normally, categories would be sorted a, b, c but with `presorted`\n# they stay in the order b, c, a\n\ndata((; some_column = [\"b\", \"c\", \"a\"])) * mapping(:some_column => presorted)\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Theming","page":"Functions","title":"Theming","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"set_aog_theme!\nAlgebraOfGraphics.aog_theme\nfrom_continuous","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.set_aog_theme!","page":"Functions","title":"AlgebraOfGraphics.set_aog_theme!","text":"set_aog_theme!(; kwargs...)\n\nSet the current theme to a predefined and opinionated theme, as defined by the unexported internal function AlgebraOfGraphics.aog_theme.\n\nTo tweak the predefined theme, use the function Makie.update_theme!. See the example below on how to change, e.g., default fontsize, title, and markersize.\n\nFor more information about setting themes, see the Theming section of the Makie.jl docs.\n\nExamples\n\njulia> using CairoMakie, AlgebraOfGraphics\n\njulia> set_aog_theme!()                # Sets a prefedined theme\n\njulia> update_theme!(                  # Tweaks the current theme\n           fontsize=30,\n           markersize=40,\n           Axis=(title=\"MyDefaultTitle\",)\n       )\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.aog_theme","page":"Functions","title":"AlgebraOfGraphics.aog_theme","text":"aog_theme(; fonts=[firasans(\"Medium\"), firasans(\"Light\")])\n\nReturn a NamedTuple of theme settings. Intended for internal use. The provided functionality is exposed to the user by the function set_aog_theme!.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.from_continuous","page":"Functions","title":"AlgebraOfGraphics.from_continuous","text":"from_continuous(x)\n\nMark a colormap as continuous such that AlgebraOfGraphics will sample a categorical palette from start to end in n steps, and not by using the first n colors.\n\nYou could also use cgrad(colormap, n; categorical = true), however, this requires you to specify how many levels there are, which from_continuous detects automatically.\n\nExample:\n\ndraw(scales(Color = (; palette = from_continuous(:viridis))))\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Ticks-helpers","page":"Functions","title":"Ticks helpers","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"datetimeticks","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.datetimeticks","page":"Functions","title":"AlgebraOfGraphics.datetimeticks","text":"datetimeticks(datetimes::AbstractVector{<:TimeType}, labels::AbstractVector{<:AbstractString})\n\nGenerate ticks matching datetimes to the corresponding labels. The result can be passed to xticks, yticks, or zticks.\n\n\n\n\n\ndatetimeticks(f, datetimes::AbstractVector{<:TimeType})\n\nCompute ticks for the given datetimes using a formatting function f. The result can be passed to xticks, yticks, or zticks.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Internal-functions","page":"Functions","title":"Internal functions","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"AlgebraOfGraphics.scientific_eltype\nAlgebraOfGraphics.scientific_type\nAlgebraOfGraphics.plottypes_attributes\nAlgebraOfGraphics.compute_attributes","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.scientific_eltype","page":"Functions","title":"AlgebraOfGraphics.scientific_eltype","text":"scientific_eltype(v)\n\nDetermine whether v should be treated as a continuous, geometrical, or categorical array.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.scientific_type","page":"Functions","title":"AlgebraOfGraphics.scientific_type","text":"scientific_type(T::Type)\n\nDetermine whether T represents a continuous, geometrical, or categorical variable.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.plottypes_attributes","page":"Functions","title":"AlgebraOfGraphics.plottypes_attributes","text":"plottypes_attributes(entries)\n\nReturn plottypes and relative attributes, as two vectors of the same length, for the given entries.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.compute_attributes","page":"Functions","title":"AlgebraOfGraphics.compute_attributes","text":"compute_attributes(pl::ProcessedLayer, categoricalscales, continuousscales_grid, continuousscales)\n\nProcess attributes of a ProcessedLayer. In particular,\n\nremove AlgebraOfGraphics-specific layout attributes,\nopt out of Makie cycling mechanism,\ncustomize behavior of color (implementing alpha transparency),\ncustomize behavior of bar width (default to one unit when not specified),\nset correct colorrange.\n\nReturn computed attributes.\n\n\n\n\n\n","category":"function"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/presorted_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/#Pre-sorted-data","page":"Pre-sorted data","title":"Pre-sorted data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"using AlgebraOfGraphics, CairoMakie, DataFrames","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"Sometimes we have datasets that have an inherent order we want to preserve. For example, this dataframe has countries sorted by some_value.","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"countries = [\"Algeria\", \"Bolivia\", \"China\", \"Denmark\", \"Ecuador\", \"France\"]\ngroup = [\"2\", \"3\", \"1\", \"1\", \"3\", \"2\"]\nsome_value = exp.(sin.(1:6))\n\ndf = DataFrame(; countries, group, some_value)\nsort!(df, :some_value)\n\ndf","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"<div><div style = \"float: left;\"><span>6×3 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">countries</th><th style = \"text-align: left;\">group</th><th style = \"text-align: left;\">some_value</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"String\" style = \"text-align: left;\">String</th><th title = \"String\" style = \"text-align: left;\">String</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: left;\">Ecuador</td><td style = \"text-align: left;\">3</td><td style = \"text-align: right;\">0.383305</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: left;\">Denmark</td><td style = \"text-align: left;\">1</td><td style = \"text-align: right;\">0.469164</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: left;\">France</td><td style = \"text-align: left;\">2</td><td style = \"text-align: right;\">0.756226</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: left;\">China</td><td style = \"text-align: left;\">1</td><td style = \"text-align: right;\">1.15156</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: left;\">Algeria</td><td style = \"text-align: left;\">2</td><td style = \"text-align: right;\">2.31978</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: left;\">Bolivia</td><td style = \"text-align: left;\">3</td><td style = \"text-align: right;\">2.48258</td></tr></tbody></table></div>","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"When we plot this, the categorical variable countries is sorted alphabetically by default:","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"spec = data(df) *\n    mapping(:countries, :some_value, color = :group) *\n    visual(BarPlot, direction = :x)\ndraw(spec)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"We don't want this, because we have purposefully sorted the dataframe to visualize which countries have the highest value. To retain the order, we can use the presorted helper.","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"spec = data(df) *\n    mapping(:countries => presorted, :some_value, color = :group) *\n    visual(BarPlot, direction = :x)\nfg = draw(spec)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"We can also mark multiple variables as presorted, note how the order in the color legend shifts when we do the same for group:","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"spec = data(df) *\n    mapping(:countries => presorted, :some_value, color = :group => presorted) *\n    visual(BarPlot, direction = :x)\ndraw(spec)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/basic visualizations/lines_and_markers.jl\"","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#Lines-and-markers","page":"Lines and markers","title":"Lines and markers","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#A-simple-scatter-plot","page":"Lines and markers","title":"A simple scatter plot","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"using AlgebraOfGraphics, CairoMakie\n\ndf = (x=rand(100), y=rand(100))\nxy = data(df) * mapping(:x, :y)\ndraw(xy)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#A-simple-lines-plot","page":"Lines and markers","title":"A simple lines plot","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"x = range(-π, π, length=100)\ny = sin.(x)\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayer = visual(Lines)\ndraw(layer * xy)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#Lines-and-scatter-combined-plot","page":"Lines and markers","title":"Lines and scatter combined plot","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"x = range(-π, π, length=100)\ny = sin.(x)\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayers = visual(Scatter) + visual(Lines)\ndraw(layers * xy)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"x = range(-π, π, length=100)\ny = sin.(x)\ndf1 = (; x, y)\ndf2 = (x=rand(10), y=rand(10))\nlayers = data(df1) * visual(Lines) + data(df2) * visual(Scatter)\nfg = draw(layers * mapping(:x, :y))","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/statistical analyses/histograms.jl\"","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/#Histograms","page":"Histograms","title":"Histograms","text":"","category":"section"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\n\ndf = (x=rand(0:99, 1000),)\nplt = data(df) * mapping(:x) * histogram(bins=20)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"df = (x=randn(1000), c=rand([\"a\", \"b\"], 1000))\nplt = data(df) * mapping(:x, color=:c, stack=:c) * histogram(bins=20)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"df = (x=rand(1000), y=randn(1000))\nplt = data(df) * mapping(:x, :y) * histogram(bins=20)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/wide_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/#Wide-data","page":"Wide data","title":"Wide data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\n\ndf = (a=randn(100), b=randn(100), c=randn(100))\nlabels = [\"Trace 1\", \"Trace 2\", \"Trace 3\"]\nplt = data(df) *\n    density() *\n    mapping([:a, :b, :c] .=> \"some label\") *\n    mapping(color=dims(1) => renamer(labels))\ndraw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"df = (a=rand(100), b=rand(100), c=rand(100), d=rand(100))\nlabels = [\"Trace One\", \"Trace Two\", \"Trace Three\"]\nlayers = linear() + visual(Scatter)\nplt = data(df) * layers * mapping(1, 2:4 .=> \"value\", color=dims(1) => renamer(labels))\ndraw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"The wide format is combined with broadcast semantics.","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/#Axes-are-linked-when-they-correspond-to-the-same-variable","page":"Wide data","title":"Axes are linked when they correspond to the same variable","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"df = (\n    sepal_length = 1 .+ rand(100),\n    sepal_width = 2 .+ rand(100),\n    petal_length = 3 .+ rand(100),\n    petal_width = 4 .+ rand(100)\n)\nxvars = [\"sepal_length\", \"sepal_width\"]\nyvars = [\"petal_length\" \"petal_width\"]\nlayers = linear() + visual(Scatter)\nplt = data(df) * layers * mapping(xvars, yvars, col=dims(1), row=dims(2))\ndraw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/#Axes-can-be-fully-linked-or-fully-unlinked","page":"Wide data","title":"Axes can be fully linked or fully unlinked","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"draw(plt, facet = (; linkxaxes = :all, linkyaxes = :all))","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"fg = draw(plt, facet = (; linkxaxes = :none, linkyaxes = :none))","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/operations/#Algebraic-Operations","page":"Algebraic Operations","title":"Algebraic Operations","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"There are two algebraic types that can be added or multiplied with each other: AlgebraOfGraphics.Layer and AlgebraOfGraphics.Layers.","category":"page"},{"location":"layers/operations/#Multiplication-on-individual-layers","page":"Algebraic Operations","title":"Multiplication on individual layers","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"Each layer is composed of data, mappings, and transformations. Datasets can be replaced, mappings can be merged, and transformations can be concatenated. These operations, taken together, define an associative operation on layers, which we call multiplication *.","category":"page"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"Multiplication is primarily useful to combine partially defined layers.","category":"page"},{"location":"layers/operations/#Addition","page":"Algebraic Operations","title":"Addition","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"The operation + is used to superimpose separate layers. a + b has as many layers as la + lb, where la and lb are the number of layers in a and b respectively.","category":"page"},{"location":"layers/operations/#Multiplication-on-lists-of-layers","page":"Algebraic Operations","title":"Multiplication on lists of layers","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"Multiplication naturally extends to lists of layers. Given two Layers objects a and b, containing la and lb layers respectively, the product a * b contains la * lb layers—all possible pair-wise products.","category":"page"},{"location":"layers/introduction/#Introduction","page":"Introduction","title":"Introduction","text":"","category":"section"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"Layers are the key building blocks of AlgebraOfGraphics. Each layer is the product of the following elementary objects.","category":"page"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"Data (encoding the dataset).\nMapping (associating variables to plot attributes).\nVisual (encoding data-independent plot information).\nAnalyses (encoding transformations that are applied to the data before plotting).","category":"page"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"Data, mappings, visuals and analyses can be combined together using Algebraic Operations to form one or more layers.","category":"page"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"The output of these algebraic operations can be visualized, as shown in the section Drawing Layers.","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/discrete_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/#Discrete-scales","page":"Discrete scales","title":"Discrete scales","text":"","category":"section"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"By default categorical ticks, as well as names from legend entries, are taken from the value of the variable converted to a string. Scales can be equipped with labels to overwrite that. You can either use the renamer function to apply relabeling or reordering to some column on the fly, or you use the categories keyword for the respective scale in the draw call.","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"note: Note\nThe categories keyword (added in v0.7) also allows adding categories not present in the data.","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"df = (x=rand([\"a\", \"b\", \"c\"], 100), y=rand(100))\nplt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt, scales(X = (;\n    categories = [\"a\" => \"label1\", \"b\" => \"label2\", \"c\" => \"label3\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"The same operation can be done with renamer as well which modifies the input data","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) *\n    mapping(\n        :x => renamer(\"a\" => \"label1\", \"b\" => \"label2\", \"c\" => \"label3\"),\n        :y\n    ) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"With categories, you can add further categories that might be missing from your data","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt, scales(X = (;\n    categories = [\"a\", \"missing\", \"b\", \"c\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"The order can also be changed:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt, scales(X = (;\n    categories = [\"b\" => \"label b\", \"a\" => \"label a\", \"c\" => \"label c\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"Or with renamer:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) *\n    mapping(\n        :x => renamer(\"b\" => \"label b\", \"a\" => \"label a\", \"c\" => \"label c\"),\n        :y\n    ) * visual(BoxPlot)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"When categories come from different datasets, you can either apply the same renamer to multiple mappings, or set the ordering at one place in categories:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"df1 = (; x = rand([\"one\", \"two\"], 100), y = randn(100))\ndf2 = (; x = rand([\"three\", \"four\"], 50), y = randn(50))\nplt = (data(df1) + data(df2)) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"draw(plt, scales(X = (;\n    categories = [\"one\", \"two\", \"three\", \"four\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"You can also pass a function to categories, which has to return a vector with (optionally labelled) categories in the desired order. For example, strings are by default ordered alphabetically:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"df = (; name = [\"Anna Coolidge\", \"Berta Bauer\", \"Charlie Archer\"], age = [34, 79, 58])\nplt = data(df) * mapping(:name, :age) * visual(BarPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"Instead of specifying the order manually, we could use a function to order by last name:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"draw(plt, scales(X = (;\n    categories = cats -> sort(cats; by = name -> split(name)[2])\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"Or even combine this with a relabeling function that shortens the first name to the initial:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"function initialed(name)\n    a, b = split(name)\n    return name => \"$(first(a)). $b\"\nend\n\ndraw(plt, scales(X = (;\n    categories = cats -> initialed.(sort(cats; by = name -> split(name)[2]))\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/layout/faceting.jl\"","category":"page"},{"location":"gallery/gallery/layout/faceting/#Faceting","page":"Faceting","title":"Faceting","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-grid","page":"Faceting","title":"Facet grid","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = let\n    N = 100\n    x0 = rand(1:10, N)\n    i = rand([\"α\", \"β\"], N)\n    j = rand([\"a\", \"b\", \"c\"], N)\n\n    x = map(zip(x0, j)) do (xx, jj)\n        shift = jj == \"a\" ? -2.9 : jj == \"c\" ? 2.9 : 0.0\n        xx + shift\n    end\n\n    y = map(zip(x0, i)) do (xx, ii)\n        shift = ii == \"α\" ? -3.9 : 3.9\n        xx + 2 + shift + rand()\n    end\n\n    (; x, y, i, j)\nend\n\nplt = data(df) * mapping(:x, :y, row=:i, col=:j)\n\ndraw(plt)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-grid-with-minimal-axes-linking-needed-to-remove-ticks","page":"Faceting","title":"Facet grid with minimal axes linking needed to remove ticks","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, facet=(; linkxaxes=:minimal, linkyaxes=:minimal))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-grid-with-unlinked-x-axes","page":"Faceting","title":"Facet grid with unlinked x-axes","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, facet=(; linkxaxes=:none))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-wrap","page":"Faceting","title":"Facet wrap","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = (x=rand(100), y=rand(100), l=rand([\"a\", \"b\", \"c\", \"d\", \"e\"], 100))\nplt = data(df) * mapping(:x, :y, layout=:l)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-wrap-with-unlinked-axes","page":"Faceting","title":"Facet wrap with unlinked axes","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, facet=(; linkxaxes=:none, linkyaxes=:none))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-wrap-with-specified-layout-for-rows-and-cols","page":"Faceting","title":"Facet wrap with specified layout for rows and cols","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, scales(Layout = (; palette = [(1, 1), (2, 1), (3, 1), (1, 2), (2, 2)])))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Adding-traces-to-only-some-subplots","page":"Faceting","title":"Adding traces to only some subplots","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df1 = (x=rand(100), y=rand(100), i=rand([\"a\", \"b\", \"c\"], 100), j=rand([\"d\", \"e\", \"f\"], 100))\ndf2 = (x=[0, 1], y=[0.5, 0.5], i=fill(\"a\", 2), j=fill(\"e\", 2))\nlayers = data(df1) * visual(Scatter) + data(df2) * visual(Lines)\nfg = draw(layers * mapping(:x, :y, col=:i, row=:j))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Caveats","page":"Faceting","title":"Caveats","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"The faceting variable must be non-numeric. If the source is numeric, you can convert it with nonnumeric.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = (x=rand(100), y=rand(100), l=rand([1, 2, 3, 4, 5], 100))\nplt = data(df) * mapping(:x, :y, layout=:l => nonnumeric)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Pagination","page":"Faceting","title":"Pagination","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"If you have too many facets for one figure, you can use paginate to split the data into several subsets given a maximum number of plots per layout, row or column.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"Note that pagination is considered an experimental feature. In the current implementation, scales and layouts are not synchronized across pages. This means that, e.g., linked limits on one page are not influenced by limits of other pages. The exact synchronization behavior can be subject to change in non-breaking versions.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = (x=rand(500), y=rand(500), l=rand([\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\"], 500))\nplt = data(df) * mapping(:x, :y, layout=:l)\npag = paginate(plt, layout = 4)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"PaginatedLayers with 2 entries (layout = 4)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"The object returned from draw will be a Vector{FigureGrid}.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"figuregrids = draw(pag)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"2-element Vector{AlgebraOfGraphics.FigureGrid}:\n FigureGrid()\n FigureGrid()","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"You can either extract single figures from this vector...","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"figuregrids[1]","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"or use draw with an optional second argument specifying the index of the page to draw.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(pag, 2)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"EditURL = \"penguins.jl\"","category":"page"},{"location":"generated/penguins/#Tutorial","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This is a gentle and lighthearted tutorial on how to use tools from AlgebraOfGraphics, using as example dataset a collection of measurements on penguins[1]. See the Palmer penguins website for more information.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"[1]: Gorman KB, Williams TD, Fraser WR (2014) Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis). PLoS ONE 9(3): e90081. DOI","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"To follow along this tutorial, you will need to install a few packages. All the required packages can be installed with the following command.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"julia> import Pkg; Pkg.add([\"AlgebraOfGraphics\", \"CairoMakie\", \"DataFrames\", \"LIBSVM\", \"PalmerPenguins\"])","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"After the above command completes, we are ready to go.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using PalmerPenguins, DataFrames\n\npenguins = dropmissing(DataFrame(PalmerPenguins.load()))\nfirst(penguins, 6)","category":"page"},{"location":"generated/penguins/#Frequency-plots","page":"Tutorial 🐧","title":"Frequency plots","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Let us start by getting a rough idea of how the data is distributed.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"note: Note\nDue to julia's compilation model, the first plot may take a while to appear.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using AlgebraOfGraphics, CairoMakie\nset_aog_theme!()\n\naxis = (width = 225, height = 225)\npenguin_frequency = data(penguins) * frequency() * mapping(:species)\n\ndraw(penguin_frequency; axis = axis)","category":"page"},{"location":"generated/penguins/#Small-intermezzo:-saving-the-plot","page":"Tutorial 🐧","title":"Small intermezzo: saving the plot","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"If you are working in an interactive enviroment with inline plotting support, such VSCode or Pluto.jl, the above should have displayed a bar plot. If you are working directly in the console, you can simply save the plot and inspect it in the file explorer.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"fg = draw(penguin_frequency; axis = axis)\nsave(\"figure.png\", fg, px_per_unit = 3) # save high-resolution png","category":"page"},{"location":"generated/penguins/#Styling-by-categorical-variables","page":"Tutorial 🐧","title":"Styling by categorical variables","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Next, let us see whether the distribution is the same across islands.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_frequency * mapping(color = :island)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Oops! The bars are in the same spot and are hiding each other. We need to specify how we want to fix this. Bars can either dodge each other, or be stacked on top of each other.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_frequency * mapping(color = :island, dodge = :island)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This is our first finding. Adelie is the only species of penguins that can be found on all three islands. To be able to see both which species is more numerous and how different species are distributed across islands in a unique plot, we could have used stack.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_frequency * mapping(color = :island, stack = :island)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/#Correlating-two-variables","page":"Tutorial 🐧","title":"Correlating two variables","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Now that we have understood the distribution of these three penguin species, we can start analyzing their features.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"penguin_bill = data(penguins) * mapping(:bill_length_mm, :bill_depth_mm)\ndraw(penguin_bill; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We would actually prefer to visualize these measures in centimeters, and to have cleaner axes labels. As we want this setting to be preserved in all of our bill visualizations, let us save it in the variable penguin_bill, to be reused in subsequent plots.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"penguin_bill = data(penguins) * mapping(\n    :bill_length_mm => (t -> t / 10) => \"bill length (cm)\",\n    :bill_depth_mm => (t -> t / 10) => \"bill depth (cm)\",\n)\ndraw(penguin_bill; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Much better! Note the parentheses around the function t -> t / 10. They are necessary to specify that the function maps t to t / 10, and not to t / 10 => \"bill length (cm)\".","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"There does not seem to be a strong correlation between the two dimensions, which is odd. Maybe dividing the data by species will help.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Ha! Within each species, penguins with a longer bill also have a deeper bill. We can confirm that with a linear regression","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * linear() * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This unfortunately no longer shows our data! We can use + to plot both things on top of each other:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * linear() * mapping(color = :species) + penguin_bill * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Note that the above expression seems a bit redundant, as we wrote the same thing twice. We can \"factor it out\" as follows","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * (linear() + mapping()) * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"where mapping() is a neutral multiplicative element. Of course, the above could be refactored as","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping()\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We could actually take advantage of the spare mapping() and use it to pass some extra info to the scatter, while still using all the species members to compute the linear fit.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping(marker = :sex)\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This plot is getting a little bit crowded. We could instead show female and male penguins in separate subplots.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping(col = :sex)\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"See how both plots show the same fit, because the sex mapping is not applied to linear(). The following on the other hand produces a separate fit for males and females:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping()\nplt = penguin_bill * layers * mapping(color = :species, col = :sex)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/#Smooth-density-plots","page":"Tutorial 🐧","title":"Smooth density plots","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"An alternative approach to understanding how two variables interact is to consider their joint probability density distribution (pdf).","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using AlgebraOfGraphics: density\nplt = penguin_bill * density(npoints=50) * mapping(col = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"The default colormap is multi-hue, but it is possible to pass single-hue colormaps as well. The color range is inferred from the data by default, but it can also be passed manually. Both settings are passed via scales to draw, because multiple plots can share the same colormap, so visual is not the appropriate place for this setting.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"draw(plt, scales(Color = (; colormap = :grayC, colorrange = (0, 6))); axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"A Heatmap (the default visualization for a 2D density) is a bit unfortunate if we want to mark species by color. In that case, one can use visual to change the default visualization and, optionally, fine tune some arguments. In this case, a Wireframe with thin lines looks quite nice. (Note that, for the time being, we must specify explicitly that we require a 3D axis.)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (type = Axis3, width = 300, height = 300)\nlayer = density() * visual(Wireframe, linewidth=0.05)\nplt = penguin_bill * layer * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Of course, a more traditional approach would be to use a Contour plot instead:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (width = 225, height = 225)\nlayer = density() * visual(Contour)\nplt = penguin_bill * layer * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"The data and the linear fit can also be added back to the plot:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = density() * visual(Contour) + linear() + mapping()\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"In the case of many layers (contour, density and scatter) it is important to think about balance. In the above plot, the markers are quite heavy and can obscure the linear fit and the contour lines. We can lighten the markers using alpha transparency.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = density() * visual(Contour) + linear() + visual(alpha = 0.5)\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/#Correlating-three-variables","page":"Tutorial 🐧","title":"Correlating three variables","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We are now mostly up to speed with bill size, but we have not considered how it relates to other penguin features, such as their weight. For that, a possible approach is to use a continuous color on a gradient to denote weight and different marker shapes to denote species. Here we use group to split the data for the linear regression without adding any additional style.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"body_mass = :body_mass_g => (t -> t / 1000) => \"body mass (kg)\"\nlayers = linear() * mapping(group = :species) + mapping(color = body_mass, marker = :species)\nplt = penguin_bill * layers\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Naturally, within each species, heavier penguins have bigger bills, but perhaps counter-intuitively the species with the shallowest bills features the heaviest penguins. We could also try and see the interplay of these three variables in a 3D plot.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (type = Axis3, width = 300, height = 300)\nplt = penguin_bill * mapping(body_mass, color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * mapping(body_mass, color = :species, layout = :sex)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Note that static 3D plot can be misleading, as they only show one projection of 3D data. They are mostly useful when shown interactively.","category":"page"},{"location":"generated/penguins/#Machine-Learning","page":"Tutorial 🐧","title":"Machine Learning","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Finally, let us use Machine Learning techniques to build an automated penguin classifier!","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We would like to investigate whether it is possible to predict the species of a penguin based on its bill size. To do so, we will use a standard classifier technique called Support-Vector Machine.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"The strategy is quite simple. We split the data into training and testing subdatasets. We then train our classifier on the training dataset and use it to make predictions on the whole data. We then add the new columns obtained this way to the dataset and visually inspect how well the classifier performed in both training and testing.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using LIBSVM, Random\n\n# use approximately 80% of penguins for training\nRandom.seed!(1234) # for reproducibility\nN = nrow(penguins)\ntrain = fill(false, N)\nperm = randperm(N)\ntrain_idxs = perm[1:floor(Int, 0.8N)]\ntrain[train_idxs] .= true\nnothing # hide\n\n# fit model on training data and make predictions on the whole dataset\nX = hcat(penguins.bill_length_mm, penguins.bill_depth_mm)\ny = penguins.species\nmodel = SVC() # Support-Vector Machine Classifier\nfit!(model, X[train, :], y[train])\nŷ = predict(model, X)\n\n# incorporate relevant information in the dataset\npenguins.train = train\npenguins.predicted_species = ŷ\nnothing #hide","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Now, we have all the columns we need to evaluate how well our classifier performed.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (width = 225, height = 225)\ndataset =:train => renamer(true => \"training\", false => \"testing\") => \"Dataset\"\naccuracy = (:species, :predicted_species) => isequal => \"accuracy\"\nplt = data(penguins) *\n    expectation() *\n    mapping(:species, accuracy) *\n    mapping(col = dataset)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"That is a bit hard to read, as all values are very close to 1. Let us visualize the error rate instead.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"error_rate = (:species, :predicted_species) => !isequal => \"error rate\"\nplt = data(penguins) *\n    expectation() *\n    mapping(:species, error_rate) *\n    mapping(col = dataset)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"So, mostly our classifier is doing quite well, but there are some mistakes, especially among Chinstrap penguins. Using at the same time the species and predicted_species mappings on different attributes, we can see which penguins are problematic.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"prediction = :predicted_species => \"predicted species\"\ndatalayer = mapping(color = prediction, row = :species, col = dataset)\nplt = penguin_bill * datalayer\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Um, some of the penguins are indeed being misclassified... Let us try to understand why by adding an extra layer, which describes the density of the distributions of the three species.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"pdflayer = density() * visual(Contour, colormap=Reverse(:grays)) * mapping(group = :species)\nlayers = pdflayer + datalayer\nplt = penguin_bill * layers\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We can conclude that the classifier is doing a reasonable job: it is mostly making mistakes on outlier penguins.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This page was generated using Literate.jl.","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/applications/geometries.jl\"","category":"page"},{"location":"gallery/gallery/applications/geometries/#Geometries","page":"Geometries","title":"Geometries","text":"","category":"section"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"using AlgebraOfGraphics, CairoMakie\nusing GeometryBasics\n\ngeometry = [Rect(Vec(i, j), Vec(1, 1)) for i in 0:7 for j in 0:7]\ngroup = [isodd(i + j) ? \"light square\" : \"dark square\" for i in 0:7 for j in 0:7]\ndf = (; geometry, group)\n\nplt = data(df) * visual(Poly) * mapping(:geometry, color = :group)\nfg = draw(plt; axis=(aspect=1,))","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/index.md\"","category":"page"},{"location":"gallery/#Gallery","page":"Gallery","title":"Gallery","text":"","category":"section"},{"location":"gallery/#Basic-visualizations","page":"Gallery","title":"Basic visualizations","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"columns is-multiline bulma-grid-card-section\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Lines and markers\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Basic linear and scatter plots.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Statistical visualizations\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Violin plot, boxplot, qqplot.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"</div>","category":"page"},{"location":"gallery/#Statistical-analyses","page":"Gallery","title":"Statistical analyses","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"columns is-multiline bulma-grid-card-section\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Density plots\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Visualizing kernel density estimation of data.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Histograms\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Computing 1- and 2-dimensional histograms.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Regression plots\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Linear and nonlinear regressions.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"</div>","category":"page"},{"location":"gallery/#Data-manipulations","page":"Gallery","title":"Data manipulations","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"columns is-multiline bulma-grid-card-section\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Renaming and transforming variables\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Transforming data before generating the plot.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Using mapping without tabular data\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Passing columnar data directly to `mapping`.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Pre-grouped data\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Working with arrays of arrays.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Pre-sorted data\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Preserving the original order of categorical data.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Wide data\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Working with data in the wide format.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"</div>","category":"page"},{"location":"gallery/#Scales","page":"Gallery","title":"Scales","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"Some advanced keywords to tweak the plot","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"columns is-multiline bulma-grid-card-section\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Discrete scales\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Sorting and renaming categorical variables.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Continuous scales\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Applying nonlinear transformations.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Custom scales\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Custom palettes and custom attributes.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Secondary scales\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Categorical and continuous scales in the same plot.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Multiple color scales\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Categorical and continuous scales in the same plot.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Pre-scaled data\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Pass data to the plot as is.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Legend merging\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Multiple scales for the same variable.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Dodging\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Dodging groups to avoid overlaps.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"</div>","category":"page"},{"location":"gallery/#Customization","page":"Gallery","title":"Customization","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"Some advanced keywords to tweak the plot","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"columns is-multiline bulma-grid-card-section\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Axis tweaking\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Setting axis attributes.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Figure tweaking\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Setting figure attributes.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Legend tweaking\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Setting legend attributes.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Colorbar tweaking\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Setting colorbar attributes.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"</div>","category":"page"},{"location":"gallery/#Layout","page":"Gallery","title":"Layout","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"columns is-multiline bulma-grid-card-section\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Faceting\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Generating a grid of plots.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Nested layouts\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                AlgebraOfGraphics plots within a Makie figure.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"</div>","category":"page"},{"location":"gallery/#Applications","page":"Gallery","title":"Applications","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"Example applications with different types of data","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"columns is-multiline bulma-grid-card-section\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Geographic data\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Antarctic coastline.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Geometries\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Visualizing geometries.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"<div class=\"column is-half\">\n    <div class=\"card bulma-grid-card\">\n        <div class=\"card-content\">\n            <h3 class=\"is-size-5 bulma-grid-card-text\">\n                Time series\n            </h3>\n            <p class=\"is-size-6 bulma-grid-card-description\">\n                Visualizing time series data.\n            </p>\n        </div>\n        <div class=\"card-image bulma-grid-card-cover\">","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"        </div>\n    </div>\n</div>","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"</div>","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/new_columns_on_the_fly.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/#Renaming-and-transforming-variables","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"using AlgebraOfGraphics, CairoMakie\n\ndf = (x=rand(100), y=rand(100), z=rand(100), c=rand([\"a\", \"b\"], 100))\nlayers = linear() + mapping(color=:z)\n\nplt = data(df) * layers * mapping(:x => (x -> x^2) => L\"x^2\", :y => L\"This variable is called $y$\")\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"Use a Tuple to pass combine several columns into a unique operation.","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"plt = data(df) * layers * mapping(:x, (:x, :y, :z) => (+) => L\"the new variable $x + y + z$\", layout=:c)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/colorbar.jl\"","category":"page"},{"location":"gallery/gallery/customization/colorbar/#Colorbar-tweaking","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: Source code)","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"To tweak the position and appearance of the colorbar, simply use the colorbar keyword when plotting. For example","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"df = (x=rand(100), y=rand(100), z=rand(100))\nplt = data(df) * mapping(:x, :y, color=:z)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"fg = draw(plt, colorbar=(position=:top, size=25))","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"To change the colormap, you have to modify the corresponding scale. Usually, this will be the Color scale.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"note: Note\nBefore AlgebraOfGraphics v0.7, you would change the colormap by passing it via visual. This was changed so that each color scale, which can be used by multiple plot layers, has a single source of truth for these settings.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"draw(plt, scales(Color = (; colormap = :thermal)))","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"Other continuous color parameters are highclip, lowclip, nan_color and colorrange.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"draw(plt, scales(Color = (;\n    colormap = :thermal,\n    colorrange = (0.25, 0.75),\n    highclip = :cyan,\n    lowclip = :lime,\n)))","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/custom_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/custom_scales/#custom_scales","page":"Custom scales","title":"Custom scales","text":"","category":"section"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"using AlgebraOfGraphics, CairoMakie\nusing Colors","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"A palette maps categorical values to particular attribute specifications (e.g. the first value maps to green, the second maps to red, and so on).","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"x=repeat(1:20, inner=20)\ny=repeat(1:20, outer=20)\nu=cos.(x)\nv=sin.(y)\nc=rand(Bool, length(x))\nd=rand(Bool, length(x))\ndf = (; x, y, u, v, c, d)\ncolors = [colorant\"#E24A33\", colorant\"#348ABD\"]\nheads = ['◮', '◭']\nplt = data(df) *\n    mapping(:x, :y, :u, :v) *\n    mapping(arrowhead = :c => nonnumeric) *\n    mapping(color = :d => nonnumeric) *\n    visual(Arrows, arrowsize=10, lengthscale=0.4, linewidth = 1)\nfg = draw(plt, scales(Marker = (; palette = heads), Color = (; palette = colors)))","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"To associate specific attribute values to specific data values, use pairs. Missing keys will cycle over values that are not pairs.","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"x = rand(100)\ny = rand(100)\nz = rand([\"a\", \"b\", \"c\", \"d\"], 100)\ndf = (; x, y, z)\nplt = data(df) * mapping(:x, :y, color=:z)\ncolors = [\"a\" => :tomato, \"c\" => :lime, colorant\"#988ED5\", colorant\"#777777\"]\ndraw(plt, scales(Color = (; palette = colors)))","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"Categorical color gradients can also be passed to palettes. You can use the from_continuous helper function to wrap continuous colormaps which you want to sample from start to end in n steps, where n is the number of categories you are visualizing.","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"x = rand(200)\ny = rand(200)\nz = rand([\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\"], 200)\ndf = (; x, y, z)\nplt = data(df) * mapping(:x, :y, color=:z)\ndraw(plt, scales(Color = (; palette = from_continuous(:cividis))))","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/dodging.jl\"","category":"page"},{"location":"gallery/gallery/scales/dodging/#dodging","page":"Dodging","title":"Dodging","text":"","category":"section"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"using AlgebraOfGraphics, CairoMakie\nusing Colors","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"Some plot types like barplots natively support a dodge attribute which avoids overlap between groups that share the same coordinates.","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"df = (; x = [\"One\", \"One\", \"Two\", \"Two\"], y = 1:4, err = [0.2, 0.3, 0.4, 0.5], group = [\"A\", \"B\", \"A\", \"B\"])\nplt = data(df) * mapping(:x, :y, dodge = :group, color = :group) * visual(BarPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"Other plot types like errorbars do not have a dodge keyword, however you can dodge them using AlgebraOfGraphic's hardcoded dodge_x or dodge_y mappings. These will only shift the data away from the category centers but will not change other plot attributes (like dodging a barplot makes narrower bars). They are therefore mostly appropriate for \"width-less\" plot types like scatters or errorbars.","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"If you combine errorbars with a barplot, AlgebraOfGraphics will apply the barplot's dodge width to the errorbars automatically so they match:","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"plt2 = data(df) * mapping(:x, :y, :err, dodge_x = :group) * visual(Errorbars)\nfg = draw(plt + plt2)","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"If you only use \"width-less\" plot types, you will get an error if you don't set a dodge width manually. You can do so via the scales function:","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"df = (\n    x = repeat(1:10, inner = 2),\n    y = cos.(range(0, 2pi, length = 20)),\n    ylow = cos.(range(0, 2pi, length = 20)) .- 0.2,\n    yhigh = cos.(range(0, 2pi, length = 20)) .+ 0.3,\n    dodge = repeat([\"A\", \"B\"], 10)\n)\n\nf = Figure()\nplt3 = data(df) * (\n    mapping(:x, :y, dodge_x = :dodge, color = :dodge) * visual(Scatter) +\n    mapping(:x, :ylow, :yhigh, dodge_x = :dodge, color = :dodge) * visual(Rangebars)\n)\nkw(; kwargs...) = (; xticklabelsvisible = false, xticksvisible = false, xlabelvisible = false, kwargs...)\n\ndraw!(f[1, 1], plt3, scales(DodgeX = (; width = 0.25)); axis = kw(title = \"DodgeX = (; width = 0.25)\"))\ndraw!(f[2, 1], plt3, scales(DodgeX = (; width = 0.5)); axis = kw(title = \"DodgeX = (; width = 0.5)\"))\ndraw!(f[3, 1], plt3, scales(DodgeX = (; width = 1.0)); axis = (; title = \"DodgeX = (; width = 1.0)\"))\n\nf","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/prescaled_data.jl\"","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/#Pre-scaled-data","page":"Pre-scaled data","title":"Pre-scaled data","text":"","category":"section"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"using AlgebraOfGraphics, CairoMakie\nusing Colors\n\nx = rand(100)\ny = rand(100)\nz = rand([colorant\"teal\", colorant\"orange\"], 100)\ndf = (; x, y, z)\nplt = data(df) * mapping(:x, :y, color=:z => verbatim)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"Plotting labels instead of markers","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"x = rand(100)\ny = rand(100)\nlabel = rand([\"a\", \"b\"], 100)\ndf = (; x, y, label)\nplt = data(df) * mapping(:x, :y, text=:label => verbatim) * visual(Makie.Text)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/layout/nested_layouts.jl\"","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/#Nested-layouts","page":"Nested layouts","title":"Nested layouts","text":"","category":"section"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"All AlgebraOfGraphics plots can be inserted in any figure position, where the rest of the figure is managed by vanilla Makie. For example","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"fig = Figure(; size=(800, 600))\nax = Axis(fig[1, 1], title=\"Some plot\")\n\ndf = (\n    x=rand(500),\n    y=rand(500),\n    i=rand([\"a\", \"b\", \"c\"], 500),\n    j=rand([\"d\", \"e\", \"f\"], 500),\n    k=rand(Bool, 500),\n    l=rand(Bool, 500)\n)\nplt = data(df) * mapping(:x, :y, col=:i, row=:j, color=:k, marker=:l)\n\nsubfig = fig[1, 2:3]\nag = draw!(subfig, plt)\nfor ae in ag\n    ae.axis.xticklabelrotation[] = π/2\nend\nlegend!(fig[end+1, 2], ag, orientation=:horizontal, tellheight=true)\nfig","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"The above also works in more nested situations.","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"f = Figure(; size=(800, 600))\nax = Axis(f[1, 1], title=\"Some plot\")\nsubfig = f[1, 2]\nax2 = Axis(subfig[1, 1])\n\ndf = (\n    x=rand(500),\n    y=rand(500),\n    c=rand([\"a\", \"b\", \"c\"], 500),\n)\nplt = data(df) * mapping(:x, :y, color=:c)\n\ndraw!(subfig[2, 1], plt)\nf","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"It is also possible to let Makie control the axis and plot directly on top of it.","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"f = Figure(; size=(800, 300))\nax1 = Axis(f[1, 1])\nax2 = Axis(f[1, 2])\n\ndf = (x=rand(100), y=rand(100), c=rand([\"a\", \"b\", \"c\"], 100))\nplt = data(df) * mapping(:x, :y, color=:c)\n\nscatter!(ax1, rand(10), rand(10), color=:black)\ngrid = draw!(ax2, plt)\nlegend!(f[1, 3], grid)\n\nf","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/applications/geographic.jl\"","category":"page"},{"location":"gallery/gallery/applications/geographic/#Geographic-data","page":"Geographic data","title":"Geographic data","text":"","category":"section"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"using AlgebraOfGraphics, CairoMakie\nusing Shapefile, ZipFile\nusing Downloads","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"Antarctic coastline. Data from the SCAR Antarctic Digital Database[1].","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"[1]: Gerrish, L., Fretwell, P., & Cooper, P. (2021). Medium resolution vector polygons of the Antarctic coastline (7.4) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/747e63e-9d93-49c2-bafc-cf3d3f8e5afa","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"# Download, extract, and load shapefile\nt = mktempdir() do dir\n    url = \"https://ramadda.data.bas.ac.uk/repository/entry/get/add_coastline_medium_res_polygon_v7_4.zip?entryid=synth%3Ae747e63e-9d93-49c2-bafc-cf3d3f8e5afa%3AL2FkZF9jb2FzdGxpbmVfbWVkaXVtX3Jlc19wb2x5Z29uX3Y3XzQuemlw\"\n    r = ZipFile.Reader(seekstart(Downloads.download(url, IOBuffer())))\n    for f in r.files\n        open(joinpath(dir, f.name), write = true) do io\n            write(io, read(f, String));\n        end\n    end\n    Shapefile.Table(joinpath(dir, \"add_coastline_medium_res_polygon_v7_4.shp\"))\nend\n\n# Draw map\nplt = data(t) * mapping(:geometry, color = :surface) * visual(Choropleth)\nfg = draw(plt; axis=(aspect=1,))","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"}]
 }