Erna

Scale and align images in a neat row, aka the magazine layout.

Devlog

Commits: 1

Rename the layer in the logo SVG document

deleted file mode 100644
index 332bb07..0000000
Binary files a/erna-logo.png and /dev/null differ
index d09c058..de84ee6 100644
--- a/erna-logo.svg
+++ b/erna-logo.svg
@@ -7,9 +7,9 @@
=   id="svg8"
=   inkscape:version="1.4.3 (0d15f75042, 2025-12-25)"
=   sodipodi:docname="erna-logo.svg"
-   inkscape:export-filename="erna-logo.png"
-   inkscape:export-xdpi="96"
-   inkscape:export-ydpi="96"
+   inkscape:export-filename="erna-logo-512.png"
+   inkscape:export-xdpi="1536"
+   inkscape:export-ydpi="1536"
=   xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
=   xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
=   xmlns="http://www.w3.org/2000/svg"
@@ -84,7 +84,7 @@
=    </rdf:RDF>
=  </metadata>
=  <g
-     inkscape:label="Layer 1"
+     inkscape:label="logo"
=     inkscape:groupmode="layer"
=     id="layer1">
=    <rect

Commits: 1

Implement a image_rows (plural) helper

It's already useful for keeping the gutter consistent, but in the future I might implement more styling options (like styling each cell or an individual image), and then it will shine even more.

index 225bdd5..9d19036 100644
--- a/erna.typ
+++ b/erna.typ
@@ -78,3 +78,20 @@
=        )
=    })
=}
+
+#let image_rows(gutter: 0pt, ..args) = context {
+    let rows = args.pos().map(row => {
+        if type(row) == array {
+            row
+        } else {
+            (row,)
+        }
+    })
+
+    grid(
+        gutter: gutter,
+        columns: 1,
+        ..rows.map(row => image_row(gutter: gutter, ..row))
+    )
+}
+
index 14a0b71..95b850e 100644
Binary files a/preview.pdf and b/preview.pdf differ
index c2eaba9..726637b 100644
--- a/preview.typ
+++ b/preview.typ
@@ -1,4 +1,5 @@
-#import "erna.typ" as erna: image_row
+#import "erna.typ" as erna: image_row, image_rows
+
=
=#show heading.where(level: 1): content => {
=    pagebreak(weak: true)
@@ -9,7 +10,14 @@
=
=#show raw.where(lang: "typ"): content => {
=    content
-    eval(content.text, mode: "markup", scope: (image_row: image_row))
+    eval(
+        content.text,
+        mode: "markup",
+        scope: (
+            image_row: image_row,
+            image_rows: image_rows,
+        )
+    )
=}
=
=#grid(
@@ -95,5 +103,18 @@ It's possible to place several images in a vertical stack. This is useful when m
=)
=```
=
-= TODO: Multiple rows
+= Multiple rows
=
+```typ
+#image_rows(
+    gutter: 4pt,
+    // Row 1
+    ("780x300.jpg"),
+    ( // Row 2
+        "360x480.jpg",
+        "300x600.jpg",
+        // A stack in a second row
+        ( "500x480.jpg", "960x250.jpg" ),
+    )
+)
+```

Commits: 1

Thanks to Fana, Mark_Hilton and Astrusia for early feedback on https://forum.typst.app/t/erna-a-typst-library-to-scale-and-align-rows-of-images/8375?u=tad-lispy

new file mode 100644
index 0000000..332bb07
Binary files /dev/null and b/erna-logo.png differ
new file mode 100644
index 0000000..d09c058
--- /dev/null
+++ b/erna-logo.svg
@@ -0,0 +1,142 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<svg
+   width="32"
+   height="32"
+   viewBox="0 0 32 32"
+   version="1.1"
+   id="svg8"
+   inkscape:version="1.4.3 (0d15f75042, 2025-12-25)"
+   sodipodi:docname="erna-logo.svg"
+   inkscape:export-filename="erna-logo.png"
+   inkscape:export-xdpi="96"
+   inkscape:export-ydpi="96"
+   xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
+   xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
+   xmlns="http://www.w3.org/2000/svg"
+   xmlns:svg="http://www.w3.org/2000/svg"
+   xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
+   xmlns:cc="http://creativecommons.org/ns#"
+   xmlns:dc="http://purl.org/dc/elements/1.1/">
+  <title
+     id="title1">Erna Logo</title>
+  <defs
+     id="defs2" />
+  <sodipodi:namedview
+     id="base"
+     pagecolor="#ffffff"
+     bordercolor="#666666"
+     borderopacity="1.0"
+     inkscape:pageopacity="0.0"
+     inkscape:pageshadow="2"
+     inkscape:zoom="15.839192"
+     inkscape:cx="9.2492092"
+     inkscape:cy="13.889597"
+     inkscape:document-units="px"
+     inkscape:current-layer="layer1"
+     inkscape:document-rotation="0"
+     showgrid="false"
+     units="px"
+     scale-x="1"
+     inkscape:showpageshadow="0"
+     inkscape:pagecheckerboard="1"
+     inkscape:deskcolor="#d1d1d1"
+     inkscape:window-width="1920"
+     inkscape:window-height="1011"
+     inkscape:window-x="0"
+     inkscape:window-y="0"
+     inkscape:window-maximized="1">
+    <inkscape:grid
+       id="grid1"
+       units="px"
+       originx="16"
+       originy="16"
+       spacingx="1"
+       spacingy="1"
+       empcolor="#0099e5"
+       empopacity="0.30196078"
+       color="#0099e5"
+       opacity="0.14901961"
+       empspacing="5"
+       enabled="true"
+       visible="false" />
+  </sodipodi:namedview>
+  <metadata
+     id="metadata5">
+    <rdf:RDF>
+      <cc:Work
+         rdf:about="">
+        <dc:format>image/svg+xml</dc:format>
+        <dc:type
+           rdf:resource="http://purl.org/dc/dcmitype/StillImage" />
+        <dc:creator>
+          <cc:Agent>
+            <dc:title>Tad Lispy</dc:title>
+          </cc:Agent>
+        </dc:creator>
+        <dc:title>Erna Logo</dc:title>
+        <dc:date>2026-04-06</dc:date>
+        <dc:rights>
+          <cc:Agent>
+            <dc:title>All rights reserved</dc:title>
+          </cc:Agent>
+        </dc:rights>
+      </cc:Work>
+    </rdf:RDF>
+  </metadata>
+  <g
+     inkscape:label="Layer 1"
+     inkscape:groupmode="layer"
+     id="layer1">
+    <rect
+       style="opacity:1;fill:#eeefea;fill-opacity:1.0;fill-rule:evenodd;paint-order:markers stroke fill"
+       id="rect8"
+       width="32"
+       height="32"
+       x="0"
+       y="0" />
+    <rect
+       style="opacity:1;fill:#fdcc4e;fill-opacity:1;fill-rule:evenodd;paint-order:markers stroke fill"
+       id="rect7"
+       width="15"
+       height="15"
+       x="17"
+       y="17" />
+    <path
+       id="path4"
+       style="font-weight:bold;font-size:20px;line-height:1.25;font-family:'FiraCode Nerd Font';-inkscape-font-specification:'FiraCode Nerd Font, Bold';fill:#2f2f30;fill-opacity:1.0"
+       d="m 24.132812,19.353516 c -0.601708,0 -1.284969,0.06833 -2.050781,0.205078 -0.758973,0.136752 -1.491039,0.338802 -2.195312,0.605468 l 0.748047,2.154297 c 0.547008,-0.184615 1.095569,-0.324195 1.642578,-0.419922 0.547008,-0.102564 0.995031,-0.154296 1.34375,-0.154296 1.435898,0 2.152343,0.557345 2.152343,1.671875 v 0.410156 h -1.466796 c -1.682052,0 -2.988054,0.320156 -3.917969,0.962891 -0.923077,0.642735 -1.384766,1.555375 -1.384766,2.738281 0,1.005128 0.332846,1.829921 0.996094,2.472656 0.670085,0.635897 1.566132,0.953125 2.6875,0.953125 0.676923,0 1.338708,-0.118105 1.988281,-0.357422 0.649573,-0.246153 1.184468,-0.619994 1.601563,-1.11914 0.252991,0.451281 0.597549,0.789984 1.035157,1.015624 0.444444,0.218802 0.988124,0.358382 1.630859,0.419922 l 0.666016,-2.132812 c -0.252993,-0.09573 -0.436495,-0.235306 -0.552735,-0.419922 -0.109402,-0.184615 -0.164063,-0.48232 -0.164063,-0.892578 v -4.267578 c 0,-1.244445 -0.390435,-2.193199 -1.169922,-2.849609 -0.772649,-0.663247 -1.969331,-0.996093 -3.589844,-0.996094 z m 0.841797,6.318359 h 0.798828 v 1.763672 c -0.198291,0.382906 -0.488188,0.680612 -0.871093,0.892578 -0.382906,0.205128 -0.748938,0.308594 -1.097657,0.308594 -0.458119,0 -0.816341,-0.117131 -1.076171,-0.349609 -0.252992,-0.239316 -0.38086,-0.567282 -0.38086,-0.984375 0,-0.533333 0.212787,-0.937433 0.636719,-1.210938 0.423932,-0.280342 1.087671,-0.419922 1.990234,-0.419922 z" />
+    <rect
+       style="opacity:1;fill:#25476a;fill-opacity:1;fill-rule:evenodd;paint-order:markers stroke fill"
+       id="rect6"
+       width="15"
+       height="15"
+       x="17"
+       y="0" />
+    <rect
+       style="opacity:1;fill:#9fabcf;fill-opacity:1;fill-rule:evenodd;paint-order:markers stroke fill"
+       id="rect5"
+       width="15"
+       height="13"
+       x="0"
+       y="19" />
+    <path
+       style="font-weight:bold;font-size:20px;line-height:1.25;font-family:'FiraCode Nerd Font';-inkscape-font-specification:'FiraCode Nerd Font, Bold';fill:#2f2f30;fill-opacity:1.0;stroke-width:0.953805"
+       d="M 3,30.815216 V 20.426087 h 2.7 l 0.215218,1.203261 q 0.694564,-0.782609 1.467391,-1.16413 0.772826,-0.381522 1.760869,-0.381522 1.330434,0 2.093479,0.811956 Q 12,21.707609 12,23.184783 v 7.630433 H 8.908694 V 24.075 q 0,-0.958695 -0.215216,-1.330435 -0.205435,-0.381521 -0.782609,-0.381521 -0.48913,0 -0.93913,0.313043 -0.440218,0.313044 -0.880435,0.870653 v 7.268476 z"
+       id="path2" />
+    <path
+       style="font-weight:bold;font-size:20px;line-height:1.25;font-family:'FiraCode Nerd Font';-inkscape-font-specification:'FiraCode Nerd Font, Bold';fill:#eeefea;fill-opacity:1.0;stroke-width:0.918375"
+       d="M 19.681308,14.011031 V 12.014154 H 21 V 5.9858457 h -1.318692 v -1.978039 h 3.588728 l 0.546315,2.251197 q 0.536896,-1.290436 1.365789,-1.921525 0.838312,-0.631087 2.043973,-0.631087 0.508639,0 0.904248,0.08477 0.395607,0.07536 0.744118,0.216642 l -0.555735,4.182139 h -1.865008 v -1.846173 q -0.84773,0.150713 -1.488238,0.923089 -0.640508,0.762959 -0.98902,1.883848 v 2.8634453 h 1.978039 v 1.996878 z"
+       id="path1" />
+    <rect
+       style="opacity:1;fill:#ad2c34;fill-opacity:1;fill-rule:evenodd;paint-order:markers stroke fill"
+       id="rect4"
+       width="15"
+       height="17"
+       x="0"
+       y="0" />
+    <path
+       style="font-weight:bold;font-size:20px;line-height:1.25;font-family:'FiraCode Nerd Font';-inkscape-font-specification:'FiraCode Nerd Font, Bold';fill:#2f2f30;fill-opacity:1;stroke-width:0.998818"
+       d="M 6.3601259,4.1985815 V 7.609929 H 11.277385 V 9.9661142 H 6.3601259 V 13.592592 H 12.291569 V 16 H 3 V 1.8014185 h 9.281325 l -0.338061,2.397163 z"
+       id="text1" />
+  </g>
+</svg>
index da40a8e..14a0b71 100644
Binary files a/preview.pdf and b/preview.pdf differ
index 333a558..c2eaba9 100644
--- a/preview.typ
+++ b/preview.typ
@@ -12,7 +12,13 @@
=    eval(content.text, mode: "markup", scope: (image_row: image_row))
=}
=
-#title[Erna]
+#grid(
+    columns: 2,
+    align: left + horizon,
+    gutter: 12pt,
+    image(width: 42pt, height: 42pt, "./erna-logo.svg"),
+    title[Erna]
+)
=
=#v(1fr)
=

Commits: 5

Remove stale comments, unwrap contexts

There were nested context blocks, but it seems that I can just prefix a whole function body in context and it works.

index 633687d..225bdd5 100644
--- a/erna.typ
+++ b/erna.typ
@@ -37,57 +37,44 @@
=        // Too low
=        solve(step: step + 1, max_steps: max_steps, tolerance: tolerance, height, max_height, available_width, cells)
=    }
-
-    
=}
=
-#let image_row(gutter: 0pt, ..args) = {
-    context {
-        let cells = args.pos().map(paths => {
-            if type(paths) == array {
-                cell(gutter: gutter, ..paths)
-            } else {
-                cell(gutter: gutter, paths)
-            }
-        })
-
-        layout(parent => {
-            let gutters_width = (cells.len() - 1) * gutter
-            let available_width = parent.width - gutters_width
-
-            // Theoretical minimum height, as if horizontal gutters where not there
-            let min_ratio = cells.fold(0, (sum, cell) => sum + cell.aspect_ratio)
-            let min_height = available_width / min_ratio
-
-            // Theoretical maximum height would be the minimum height and all the gutters
-            let max_gutters = cells.fold(0pt, (current_max, cell) => calc.max(current_max, cell.gutters_height))
-            let max_height = min_height + max_gutters
-
-            // TODO: Add to state for debugging purposes
-            // [#(
-            //     cells: cells,
-            //     available_width: available_width,
-            //     min_height: min_height,
-            //     max_height: max_height,
-            // )]
-
-            let widths = solve(min_height, max_height, available_width, cells)
-
-            // TODO: Add to state for debugging
-            // [widths #widths]
-
-            grid(
-                gutter: gutter,
-                columns: cells.len(),
-                ..cells.enumerate().map(((index, cell)) => {
-                    grid(
-                        gutter: gutter,
-                        columns: widths.at(index),
-                        ..cell.images
-                    )
-                })
-            )
-        
-        })
-    }
+#let image_row(gutter: 0pt, ..args) = context {
+    let cells = args.pos().map(paths => {
+        if type(paths) == array {
+            cell(gutter: gutter, ..paths)
+        } else {
+            cell(gutter: gutter, paths)
+        }
+    })
+
+    layout(parent => {
+        let gutters_width = (cells.len() - 1) * gutter
+        let available_width = parent.width - gutters_width
+
+        // Theoretical minimum height, as if horizontal gutters where not there
+        let min_ratio = cells.fold(0, (sum, cell) => sum + cell.aspect_ratio)
+        let min_height = available_width / min_ratio
+
+        // Theoretical maximum height would be the minimum height and all the gutters
+        let max_gutters = cells.fold(0pt, (current_max, cell) => calc.max(current_max, cell.gutters_height))
+        let max_height = min_height + max_gutters
+
+        let widths = solve(min_height, max_height, available_width, cells)
+
+        // TODO: Add to state for debugging
+        // [widths #widths]
+
+        grid(
+            gutter: gutter,
+            columns: cells.len(),
+            ..cells.enumerate().map(((index, cell)) => {
+                grid(
+                    gutter: gutter,
+                    columns: widths.at(index),
+                    ..cell.images
+                )
+            })
+        )
+    })
=}

Re-organize and improve the preview

index 08e02a8..3e0eb86 100644
--- a/preview.typ
+++ b/preview.typ
@@ -1,44 +1,53 @@
-#import "erna.typ": image_row
+#import "erna.typ" as erna: image_row
=
=#show heading.where(level: 1): content => {
=    pagebreak(weak: true)
+    v(120pt)
=    content
=    v(1em, weak: true)
=}
=
+#show raw.where(lang: "typ"): content => {
+    content
+    eval(content.text, mode: "markup", scope: (image_row: image_row))
+}
+
=#title[Erna]
=
-Erna places her images in neat rows.
+#v(1fr)
=
+#figure(
+    caption: [Erna places her images in neat rows],
=
-#image_row(
-    gutter: 12pt,
-    ("780x300.jpg", "500x480.jpg", "960x250.jpg"),
-    ("300x600.jpg", "360x480.jpg"),
+    box(radius: 20pt, clip: true, image_row(
+        gutter: 12pt,
+        ("780x300.jpg", "500x480.jpg", "960x250.jpg"),
+        ("300x600.jpg", "360x480.jpg"),
+    ))
=)
- 
+
+#v(1fr)
+
+= About Erna
+
=Erna is a Typst library for aligning images in neat rows. It's easy to use. I made it for a friend who struggles with aligning images in LibreOffice. It's named after her. With Erna (the library), you can:
=
=- Cram as many images as you see fit!
=- Bravely mix sizes, resolutions and aspect ratios. Let the computer figure it out!
=- Got some wiiide images? Stack 'em up!
=
-Erna's promises:
+Erna promises to:
=
=#set list(marker: sym.checkmark.heavy)
=
-- To fill all the available width. \
-- Make the images align vertically. \
-- Preserve aspect ratios.
+- fill all the available width;
+- arrange the images in a rectangle;
+- preserve all aspect ratios.
=
-#pagebreak()
+TODO: Diagram
=
-#show raw.where(lang: "typ"): content => {
-    
-    content
+Brought to you by #link("https://tad-lispy.com/")[Tad Lispy].
=
-    eval(content.text, mode: "markup", scope: (image_row: image_row))
-}
=
== Basic use
=
@@ -70,14 +79,17 @@ It's possible to place several images in a vertical stack. This is useful when m
=```typ
=#image_row(
=    gutter: 6pt,
-    "360x480.jpg", 
-    ("500x280.jpg", "780x300.jpg"),
+
+    // A stack of three
+    ("500x280.jpg", "780x300.jpg", "960x250.jpg"),
+
+    // A single image
+    "360x480.jpg",
+
+    // A stack of two
=    ("960x250.jpg", "500x480.jpg"), 
=)
=```
=
== TODO: Multiple rows
=
-= TODO: Diagram
-
-Brought to you by #link("https://tad-lispy.com/")[Tad Lispy].

Write a readme with the layout diagram

Also add exported PDF files. They should be built with in continuous integration, but I don't have it set-up on Codeberg yet.

new file mode 100644
index 0000000..7d5b99a
Binary files /dev/null and b/420x800.jpg differ
new file mode 100644
index 0000000..feeea5c
--- /dev/null
+++ b/README.md
@@ -0,0 +1,20 @@
+Erna
+=====
+
+
+Erna places her images in neat rows. It's a Typst library.
+
+![A diagram showing two rows of images scaled and aligned in a neat square. In the second row, the middle column is a stack of two images. There is a gutter (whitespace) between all the images. Arrows and labels explain various aspects of the layout.](diagram.png)
+
+
+Erna is a Typst library for aligning images in neat rows. It's easy to use. I made it for a friend who struggles with aligning images in LibreOffice. It's named after her. With Erna (the library), you can:
+
+  - Cram as many images as you see fit!
+  - Bravely mix sizes, resolutions and aspect ratios. Let the computer figure it out!
+  - Got some wiiide images? Stack 'em up!
+
+Erna promises to:
+
+  - [x] fill all the available width;
+  - [x] arrange the images in a rectangle;
+  - [x] preserve all aspect ratios.
new file mode 100644
index 0000000..28974ca
--- /dev/null
+++ b/diagram-rows.typ
@@ -0,0 +1,28 @@
+// Part of diagram.svg
+// TODO: investigate https://typst.app/universe/package/larrow to make the whole diagram in Typst
+
+#import "erna.typ" as erna
+
+#set page(height: auto, margin: 100pt)
+
+#let gutter = 20pt
+
+#grid(
+    gutter: gutter,
+    
+    erna.image_row(
+        gutter: gutter,
+        "500x480.jpg",
+        "360x480.jpg",
+    ),
+
+    erna.image_row(
+        gutter: gutter,
+        "300x600.jpg",
+        (
+            "500x280.jpg",
+            "780x300.jpg",
+        ),
+        "420x800.jpg"
+    ),
+)
new file mode 100644
index 0000000..816c1ac
Binary files /dev/null and b/diagram.png differ
new file mode 100644
index 0000000..2120e36
--- /dev/null
+++ b/diagram.svg
@@ -0,0 +1,298 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<svg
+   class="typst-doc"
+   viewBox="0 0 595.2755905511812 609.9639894214444"
+   width="595.2755905511812pt"
+   height="609.9639894214444pt"
+   version="1.1"
+   id="svg17"
+   sodipodi:docname="diagram.svg"
+   inkscape:version="1.4.3 (0d15f75042, 2025-12-25)"
+   xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
+   xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
+   xmlns:xlink="http://www.w3.org/1999/xlink"
+   xmlns="http://www.w3.org/2000/svg"
+   xmlns:svg="http://www.w3.org/2000/svg">
+  <defs
+     id="defs17">
+    <marker
+       style="overflow:visible"
+       id="Stop"
+       refX="0"
+       refY="0"
+       orient="auto"
+       inkscape:stockid="Stop"
+       markerWidth="1"
+       markerHeight="1"
+       viewBox="0 0 1 1"
+       inkscape:isstock="true"
+       inkscape:collect="always"
+       preserveAspectRatio="xMidYMid">
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gmFxTI1GibVwTnzSVIYCjAAFI3pdqqMMTxzx5oItu8Zpk8gIORS2DBk3LgLzRs9BmL3MT3POBTgQbCnimViccxBoScjucDAPegH4JuOTU6VKaSTNRY0lTbDDBpK2j6eRjHarxjPKsITESPTVt3HIrSMqmtIQ3CwFJR0MCKdglSZgF4qKuIbztIJ4qdqkRJ9pwM05RpDA709lpIbdpBGaqZJuIEsJXkHvTQHsOA20geCRwaYREO5scDPGT4oCDRbSR3x5FI4gq7TnHI7c1FaRhkZiWJJJPJPmpqojtw3bils9C7QBmmSEmD2pxNCWUhsVcTYZjl9Nw/f6VcrOwwJA5JxjPirQyKaQpB4oBVmZSQCR4pGishVsGgaMxzArjHOe+aAlIwKGg4qblsxiPCgBidwHNQqK9YgxINOQbQeLYSB2o0PIPJU5FTpXlseK7K8UAyLpSKRhW5JNNJ9CQMUjJ3THPPamRVScn+tMMSOVXnzQC5m5pkcglGO9AGL+pk0jLy2+/LEjv2ppRitkRyXBI2nGPnHFA0GItppkbj24AAGfJ+aVVBH9q81KoTlMbR929TPbHGKlchdgrBQEUbRyR5pbPT3p0bPRuCABAT3Iz3pbGhRCfIoGnpFHo7Ngzn9XmnE0qmY2O3GSMdqNkJFECDmkYgiA7UzZMYaNnLDcCAFwcn/AGpkXeLgmgBFGjwefcPI8U02JJxQQ0VxtOKegfhnBPBqLFyrGKTcoGBU6Xs7HGpSriMkHi9uR4rRlQGUjyM57UgnGGxxSMykh7UjSMpxzU2KxoZlqKtj1mA78UHEg5Yd6RvB9oyTx9acKmYLSe5hM6qqQdvVkcKv9T3rWemOVixtdE/NJ/8AddOVRyA85ABP7Yqk+R+TobWIk3IsEw//AOcn/IFLR7VV1ol9a5E9pMmO52HH9aSorWt+aitIE0Xioq4C0ZFI0TwMU4kJicHFVCpcjJGM7qpJiE5Q7jz4FVKmwwpOBiqjOwyjcc1aNIuAaD0DPEiojLJlmzlcdqWzkIS5BzS2ekon5709loZnYqSASB3NGwrZ2yalULryacTRxDuFUkOS2wO3NB7IyRFScVOlSoqWx3paPZ+1XjNIzROBz3NIyU7b8e76Y+KcSBu9NSAcZHP1pkUkk3Z5p6LZffhsUwciYYGP3pA6h+DSUZgJVm4ByMcjNAeMeRigidwuFJBNUkOCQoeTnNKnBJrgGpq4WGGJ5rNrEhhanapE1j3RmTcvBAxnk0tqkPIsbKnpqy4UBgWzk/P0p7LVNLEcUDQEyHbgCntOiog+SM0bGmAhBOM0bGhASBVRNGRA8bEnBHYfNUlH0nwSMjIwcHuKnagJY2wMknaMAHwKNjRZlx5qojQDNsY9jTIza3YWWP1UMkaH9GcZH3pWnFra3HOew+KhpFrDNleaJT0Yxzgn+lVMkXEORFBODmjyHixkBQAD25peReKKvyaPIeIyRvKCEUtS2egWBBwRjHFQpggmmFPrGtjToEFuy+o/Z3UlR9vmtMeP5qMuTXUavdSXerFnmuXfJz7jx/TtWsxjLyqC2WqtbFIrx0APtVZCMU/Ebb9+GvTX8Y1w3ourmWxswE3TE5MoPIU/A4/rUwtbd/i9oAz2oULuoCuvtC0zUlIuLZNx/wAaDa39RSslOWz01DWOhpbeNp9Pf1o1GTG36/2+ayy4/wBNcOT9tLkhw21gV5wfpWbXYDxxhmUucAcEDuacKlSMjFMkMbQRgHPzVRNZVME4/rVQjEPfmnKiw2EGMirlRpgpQJC0y8VKirxgjvmgBLlAQDwe9MtIsxxQNFZU7mkqBRjBolKw9BzxWkZ0y0Ade3NMiU9ngE4qacIGLacUlJWtwojzULkEM29+DzQegpFwc5zmhJWQcmqhFnUENj+9PZaBCcj5oI2ojEKFWb1MncuOAPFA0ZiyBSOHEkAHFIxVl2KSAM/WqKkJ5A8u4jK55AOM0yLgqqMW3epkbcdsef8AapOAsMkYOeOamrguSyIAirtGCR3b6morTESOJm71DSJhMNipVFpZxZFOEsREMdqZlpwF8UJ0W3Qelt9Ml92d4PijY0gqq2cg48UCxnYvxVRFGjiLIcDgVe2aax84Pj5qVRGW3U9iKRkJbXA4PNGxot+TZ84B4p+ReIkFptbkcUvI5ifSPkYFTtpMVkqooG39XnnNLyVIYVW258UbGojICD3BGM8eKVyHizA5DEdsijZXEUwqewo2nxEVXiGUNLyHjCzLjc8hAA5JNOW26K9NZ1HqRJWe1tI24P8A3DyCPtXVhh4ubPPaplkklOZIfUx3Lf7fFaIFhCpG2zaQ2CPpTgHSf07dwqEk8du+aVJ9A9KaLb6FoFra26BQsYJPyT3NZtGwKeMs2KYTV8jOcgUANr9EfaVbHzikehfzcSgFnUL8k4pk0/rDp1pN+pWSZJH86NR3/wDMP96yzx+Y1489dVooto2hOPU9XPgcVk3KSQFD809loOYbwuECkDHHmqlTpFEOKe06ERMGjY0aR39P0y3szux9aqVFxFZB6YbcMnx8VSdE5VoBZxQYJQkmgGo7eF4QDjdjk/BppJNAxViFyB3OO1KnKXMBHikdEh9rc5q8azsWURVxx28VaUZUBBqacVskQ3mo2trsEh7dhSXDiHbzU7VpP1MiqibAZWBppLEZJPxQAix5p7LQsX1pbOQ2rYHFLapiZhyeaNjQxUnx/WmnQDoUclfIxT2WirRknFLapE4rc5Jx/WptVMTUdrgg7cgHsfNZ2tJikYypPtx9qnataA7yDNB7W1oQFAFUnaxC5XNB7J3hT1DsBC480i2QK5Pag5RlQH9GcY80j2zsAHJzVRnXlO3jPP0q0MmbGc9/JNI48H3DmpqomVV03AKpUYxk5Y/NTaqRHYPIpbPSSxDGRjH3pbOQ1bxqDmltchwQCQjHels9Dsh9Pbzgf0FGy0W9PAzSG0ETKs3GAcHnmqkK07bh5BsRCzDnAFPSNishVwu3k+KWhuNb6quYkWGwZtryZc5O3OOMV0cM1usOW/DWFe1sYpJxFtIXAy2cVvGAa3EU8YOchqexpgybGRc+ztQEpXaJPUwcD/F9RQH0ZpOsCfR7SZAHVoVYY+1ZbaSE+ourY9B0g3skLOxcIkZO3JP/AMUS7FmltHrkNv0+mp3zJAvpCRlZv0+cfenbIJLXOrn8W5oZJ5WEYiBxFEVPvHjHHJI5pbl9H45T38q7X+u21G+0trRmWORlSaP/AAnccEH9qcKx1XpjVJNQ0ENcc3FuTFNnzjz+45plGma1+RF/N+WkZoix2hP0g+RXNyaldXFu4qZhuFZyttBtGpFOVOg/TA8U9iYsgAUeQ8Ro1DHG4D6mqmSbizx2Jq5kz8S0wA7U/IvEHbkUeQ8WCrOxLck+aPIvF5Y+aqVNgsUMfqr6qMyZ9wU4NUjQTQDnApVULtDhs4zRKVicbbWPGOewq5UaFLBgaVokKuo3HioXGow8cUqqHo2GOalacyFI433IQ+cANyMfIq4il2UkZoIJhig9BAxk8s+cHOF7Hx5o2ehIlqdnIfiiyKnatHIkEYY4ByPNEosSDqpq5UUvIQxyDz8UWiRhVBXO08Hk1Nq5D1qqlGXYCT5NRa0mJ6O2GKzayPSwBFJHBIxTiaoniPrnHbNaSMatLNDwACTTKLNMlKSicsBbLEdqCKlCKBHlyKRpYLeKIVQ2EEmrRoJycGgPRk4paOUdcAc1FipUXfFRVbZifJpKlWNvgipq4tYghA2DBwBg+frQabRkg8nB7inIQXo8kEAgirxiLS80IUlh3rSYs7kzDK8bbo2KsRjIp6TtaaZpF3qsgEYZY84MzDj/ANayyyk6+TXuofhva3ujXUU05lnaMm3Yrj037g1vxy+6yyy304Fe2t7BLPa3OTPG53b+5wec1tPTMtb+lNdTmGIRR7vaq5wPtmnBVpFZy3zCGFdzZxTS6pD0PBF0OfzkZW7jQv2/Vxjn/Wls9L/8NZo7jpC0zgvGDG2fkHFQqemw6vomnarDGl9axzrE/qIG7Bvn696DVGqafDqGnzabLJ6aSDCkAe34qNtJ66c+1Lp260fTPU1a6jMKn09+FJdQMDAOSDjjg0rw4ZZeXyvH6jPHHxvppHUsMVpZW7WAcxq2Y3bll4+RWsmmOVmXbv34eStddI2ss8QEzRKsj/8A7MDuaERqeq6M2hyvAkcwtTI7RtIO25icZ/fiublxu9uvhznjr5VZIPastNtsZ296NHtFjQECTSNJCacTUwA5IzjiriaC/uHNPZaYRaUyPxECr8U/IvF4pjmnMk+KSMBVTJFwSKhwT2NPyLxLugxxR5DxAdOM8cU5kVwQAHOSRxxin5J8Q2HNLZ6alGhHNUUMjheKnStoMcDmqiWN+RQJA3ORipqkFRfb7cEdznvRsaPQxqxGBikqH44wOKlQuOMUQqVm47VcRQlBzStVIYThcZ4PioXDtsyqeaNHMljHIMVPiqZA3L5BxVSJuSuWIl92KqM6sbN5LdiyjkjHNLZybNrubLEcnvU7XIDKmSaNjRcoOc00sCMGgbSAAGKaUHAxRAC0BJGQRkZH2qkpC3AWnotsemRU2HKGwOTxUWKlGiii9EEFvVzyPGKmxUvaxhbKohCgLxkADz5+aitcVliKPb6b5z3FGlSihwRVyItRIJBIBOK0kY2gshbvmtJEWntL0G4v5kf0mW2B98hHGPgVlycnjjdKmNrfk2QPHDGoWNFwoHiuG8muSRpMfstF6juntOn7+eE4dLd2U/BwcGvW+HJXzLc3LXdzI0rlps5LMeSDVlCx0LVYIXu7WP1rRnwZIzuCn647Ug6P+HvTwiibU9XRoraMbwzcCQ+B9aZNwuevtOjnEcseYnfZndnj5xRobB6IuFiXUrdFRY4719mDkbTgj/Woy9qxbk1zlCe4H9KW1aV95b/ml3KoDjsRUWbXjdKjV+l//Emmfk70yIVO6N14IP8AxTw3Cy1VfoX4bpYQzWV/JHcQygA+znjPb681Vt2UxkxroOmWMGm2MVrbjCRrgVSIreotV02Oxmtbm4tFlZSEW4Pt3fX4o0W3Nntrgv8AzNMmSNsbLq0/mxMPkjkgfv8AtWeXHPhrjy2e0L6wutOm9O5THkMDkEfSs7hprOSX0TLGl4rmQZY7qi4qmQiMcE44HenIdqavnntT0m17nNGhK93Pj9qixcrINI2d2T9Kco0820MSudueM05U6eLnbwaradAsxwaNjQWC+cEcUbLQb8c5zRsaDLc09p8WthMVrKy0IqYFPZaqLxllNGz0XC84FIJmLBIOD9qShEh4pGahj2qePtSBpMngDmkpKQqiKUk3EjkY7U4kphpCx/yjJyf2pkko81NXEyAq5zRBUfVK9jWsjG05bzSbS2CVHc/FLxEzNI4mcKSOfJo8VeTOzJ4A444/1qacp22iz3rPKtsYa9LAqNr0FIgGfg04m9E/SaRn2KWCqWOPAHmrjOhj3HA7ngCq0jaLYBIOR9DT0W0BRobSPOPt805C2KseRzVJYkTFTTgAUFqiqg8cWDkVNi4OoxWdjTGmYwWUAADHn5pRZhGI71pjUZHY0ZrcyemQgOC/jNaxjfZ/TNHm1Ul1wkK/rc/6Cpzzvcns8cZ7rdXVLeyWCMbVjVcYrm5brj8VYzeWyUr4lV/GK87lz1lMnRhjvG4mNQjXU9Entzk742RgPtXs8HJM8OnFyYeOT5/l6M1QXFj60RiN1I4LMf0BTzn9hXTcpMd1ljjbdRsd8urdF2r21uTNo9wAHcgFoT5B+lZ8fJMuq05OPx7j2o9ZrrOjxWEk/oeghYSLHwzc4HHatvTBpZSViWYsHB8980w338PtVs9P9axuLtfz91L6iRMpzjA5/sayz3trhrTe7jU2tyA7bifpisrnptMNrfSy0y+tIcA/4TWmLLJaiaHsuM+KpCCsFYsxyT/aiHaJ6qDk0yci/FXTZZdRhvktXeH08GRBna2eDgUyaBHca9phN3I8wtzKAwEhUqDk529x9wKSp0uG60srK39S6lWeRTuG9S5P7mnuF432hpvVllr1yUit/QkIJAHY478Vllprjtbqu4E5HFZ2NZWJHEZZVZWDAZOKSvaKtRCsHT34AwKZTphjtqdLlR9Qu2SRmp0qV4nHelo9vb+KchbYDZp6TsJz35p6GwCSPNIIZJ4zxQNsNnPFMbUAaqlZ6HJiEIKlvUycg9sf+809loB5Qoo2NBocHjzVSlR0X5opQdVB7UlG4oxikbLJjtQAiM8UyAYYPFA0ySFPcE/SpNgybl84q8YjKoIyGVRIxCZ5IHOK0jLZmN8bkRmKE+eMimQyvgjmkZ6B/IJ544rLJrgtLRQRWOTpxh0oAtQspcA7MYH3qsWeSplYhjWsY2vRnnI7jtirkRai3JOf61Wk7TV52tBDk+ir78Aee2aBtOODcu7xnHenotmApQEA8EYNIBGPNTVR70COWBAqVCIhH2qaqRPbWVrTEYOBU7aSPepuNOUWLjTtIvLwQnekcUzYUM/J+wrbdmO2GpctOg2dnFYacbeHJVc8nuTU61hSt3YHLIHCpjkpkH5x3FcueXlrH9xtjjrd/sgx7ofHavMzu/trpxmuxLW4MDjcfb2b/mt/pPqP4su/SeXi850jrWJUjt41G1m3E17HJluajk4sdW2tA6x61tOmLu3s7q1FylwMyKf8vY0sMN3Z5Zamitx0RaapaJqnTtyqwXChxBISACfAPj7GtZy+PWTK8Xl3GuatpN9oFjPe6nZt6aD2kSqdzZ+hqp9Rhb4z2m/T5yeV9OeRX97Nq352Nys+7cpB/TjtVeyk0790NeTdRaTFd3jq86MUcKOM+Kx8O2kz606CsQRQFG0CtYyTXdjtyfNMDDaVx/rQSg6r1w6FozXkUHrMGAxnt9acJznqnqvWJ7nTJNNS7ijuI1lMW0YJyeM0BV9U6Nfi4uOodQlWCCSLLQucFGxjAA78+am34aYzrdcuL211d/z42XLcnfhPueCR+1G9DW28dG6Rb29j+eAJllLKOOAufGfms7e1ydNlZCDStVHmjX0w24789seKzrSJR4C525Oe57UTI9bSZ8uTtAyc8U5U+Og5GxTIL1MHikpPeWpHt4tg80QMK4LcU0pvEwJBGCPHmnothNHlaNFtEwMqK5HtbODn4p6KVArzRo9tbBwDUnsNpG7CgdBNKeVIHfPbmnCr0cuGGOD4qommmdg2WzuPfPemQ8MhJFLZnkY4pbPQ+NyYxznvRs5ETEaW1eIDQnPFT5H4IGA47VUqbiC8ZUH6VpjWWUK4bNaxlTQnkZEVuQowKCGQlhSM3BkGssm2C6s3OAMc5rDJ04rANkVMVaUuHUE7gSPgGrjPJTTnkjFa4scg4CzyiNe7fNaxjTCjOR5zTSONyptB9tAThYIrAqCT2J8UAykLTISKBsLGMj4qaqCRq8w2hd23k4qFwVFBGDgKTk1nWkZnjRD/ACySPrWWTTEHuMVDSAySNBtKxmWR2CRxj/Gx7CjHG5XUGWUxnlW59P6FBpc2nPMolvQzySTH/M36gPpziurnxwxmP9ac/B/L45XL5bkr5MiZ81ljlu3ErNapWOaOOZoZR9UNcmHJjhnePP8A4dGWGWWHnj/yr7klZXAHK/3B7GvN+p3jlf6/9nXwyWS/v/3RjkDjP9ayw5JlFZYXEuJJotTWKQq1vKn8s45DDuCftXpfR813/Hl/ww5cfLHyjkH4xaZd/wActbhB/wBPNEFLY4DKT/sa9Xis7ji5J8qzpjrC56c9OP1TJbEgTQkZGPkfBquTCZT+08eVxv8AToFl110/1Kstk0yxs+U9OcABx9M8V5/JxcmPbvw5ML00jUPwv1K1vPV0m5iuIWJ9sh2so/0NdPH9VL1k5+T6a+8XSuhdEl6es5IbmVGmd9x2nitZyS1leKz23y2vPzDenICGXzjvWmOW2Vx1DxChcg4P1NWiTZRrq3B2Ndwq57DcKNwarjHV0OtWV88mo3DNEzuyMSGVlz2UHgfvmnNJrWbm6bVtUtI7jUGtbe0jWEtEO5OT2B5Oc5+tBxPr++tbu00+zgv3lEUShmkyAfrk+ePio321k6a7oOj2Op6rbWG5mU+6ZyuMgeB9PrS2dx06iIIY4FEeEK+0RgYAFZ1cYIzS2qRgoCO1Ra0kTSAtGSpGFGSDUqCaLg98+KJSsLyxk1cqbABlGppkETPcikaDnPHbNAeMZgl2llb6qaaYZVy3Oc5plXiuBQQeMCmAz3pk11LdvTzSN5YDncB2+aQ0F+VOW45oPTD2ZQjBzxnjwaNjSaxuxy5JP1p7HiPHHg1NqpDkQ+anapDcY5pbVIZEW6p8lTEZLSIxuX3+p/gx2/eluK8aG1pxwKJRcSk9p7e1a45MMsNq57fFbY5McsENuK0lY2CxnHAo0Nn4eeWY8DjAqMo0xqwhbH6c4+axrox9LCAq4YM4TC5GfJ+KhdLXCsVJx3o2Vivlj+nPmrmSMsSxjwSa1mTDLEaIsMDAq4zpzcNnPeqSFuxQIJFcOuQDxS2enlJZwNwG49z2FSqDpw7Dd24yvY1FXDEa7qzrWCtCWJY9z3rKtJEo7CS5uUgt42eRjjb9anxtuorymM3W0WPTFppeoWt3e3MLXcZIWIHOwspGa6ceLwm3NeXzy1+jMfrrBNPFEsssbcyMTjAPIUD4H964d55bnz/4jv5ebC2fGP8A5WD3G6MXETBlIDgjsRWXNnlhblPcRxYTL7ahdKLmATRH3D3KRWP1E/m45yYe2nBf4s7hkAXN1biZB/Nj4ZfkeRWHl/Px+U/Kf/dNfH+LO431SLn0TvTlW5Vs8YrzOT/pXc+fl1Y/fNX4GSdJBtJwf9K24vqJb71WeXFZ3Cmq6Vb6xam1vIg4Odp+uCM17XD9Rcp/bkywn/DjGrdFy6Rb6jPJfW7CzZcx5O5g3b969Lj5Zm4eTi8d9tDNtIAzjGO+Aa3Zbdf/AA9N7F0xPqt9qMlxZxKQtv3KY+vzXJzcONvTq4eXKTs3Yde6Jey3KWz3CTRxl495wJCPA+tRPp7j3au88y60d0L8QNSuLnZPpk0NqP0ysvOPrRly/wAc6uxjw/yXvHTeJrm36hsgY5JwGGD6TlGH3+K0/knJNw+KXgtmTn2p/h5BZkRWd/dNdyEkepPn0x+3jnzVZ8snU9s8OK5W23onq9jdR20WlXes/mcD2b48hD//AFisv5sp3tpODD1poUsctjAbUp60gnYs8UeSrceTz471rjy+X9MrwzG79nNP6AudQT808/px5wBJnk+P2p48mVmhnx4S7jdNC6di6eVgs5nlclmcjjkY4Hiq2y0s3Hc/NSqPHdIS7klj3JqbVyPFPb2qK0jyoWOFBJxk4qTexmjZgvGC1OVNgTwDaGBHft5p7TpARkZo2ei8sZzinKWgipFVtOhYSc0bGjZbKY4x3p7LRdzinCsA3pzuDZpoVcIyuKVXIYSIc5xgVO1aS9NQe1Gz0l6Ssef60tnpF4EBwvajY0H6QFFEEjXnioqodiQZGalchyNRiko2qKOxBoDJTjtQC8lsXBwBVSpV0tsApJHNXjkzyxV0kICvkkMOwA71vjk5ssQ0RlUtwMHGPNaysbDtq+GOVDcEYNRkvBa2yZUVhk6sDqRc1ltpoRogQc5+lGxolPB3OKcpWEyhGR81rjWGUEig38ADPetsaxyg5m9JT6fBxjtVys9FAjE5xQciQjJNTs5Blh54qbVSGIo2BIxwRg1Fq5D0EIAzWdrWRaWGk3Opyenbpx5dv0iljhcvQyzmCy1S/sug7EMIzc384ID/AB/wK6uPjmPpycnJcnK31e8n1P8APj33RckF89znsK1yx+3URjfG7df03UYr3SEYER+kg9VCMFePj+wz3xmsePjky7PmyuWO4pbfV00/VmtXGLKZ8IWP/bY/4Sfr3+hNYfW/T7+/D26fpOXK4eOXx8rmKUWFyYm/+nk/Qf8AKfivn+Pk/wBNyeF/C+v6evnh/Ph5T8ojdbrOb14uQTyKnn3wZ/yYelcOubDwyYzFcRmSLmJuXUd0Pz9vmll4c+Hlj6+f6/stZceWsvf/ALz9EZoSjFHPH+FhXk8nFcMvHL/h14Z+U3AUv57V/TlOV8Gnx/U8vFdW9Ky4MOWeWPtr3VPR8PUVvPNYXRt7uYLvycpJt7Ajx+1ez9J/krh77n/l53P9Lcpr1XG9S0nUdCv1tr+zdWLYVsZV/sfNfQ8P1HHzY+WFeVnxZcd1lG6XPV46c0GPSl031LmdfcB+j/kmon/Utu+m93xySzsj0n0dqcF/HqVzstEzu9Jl3MQfHPajPkxzx1O04YZY3fp0trnGQiIo+grCYY/pveTL9i2wnkG+OQgg984rSYzXTO535Zb1FlIZju+c1FxkqplbPaDxLIAWXkHuvGaU48d70v8Alz1rbzKxJzWkkZ20OUBxyOfn5q9s9FHHNOZH4oSLiJW3Kc59o7inspO2EYYqaqCgAoT4qVht7NpRjuI547UjejwcgkDjPNIBsR3zSCBIPFAZVcijZyIvBuolFhZ7fH3z2qpU6eEWwciqlLTOGYYUE05SsLSHFOVLCupXmq2ixUwukbKDkgAbsGiiCG4AGQaixpKwLr3AnBo0NipcJ6bAglzjac9qR7SEheqmJXJ7gMN+Qvk4qtImSUIJBb+lRY0lNxOahezcbgLndzntS0cpqOUEUj2IJM0GzvyKABLErKMA585pkr57fk1pjWWUJSQnOeSa2mTDLHsxDGqoODvz3zxilaMYtrYYTNY5V0Yw2pxWTQbbvJA4FNOwZFGCDVyItKSQgHOKuIYKLuJRCF8AnNXMkXFAxbjVzJncREgz2FPYmI6WoHcVO1aFWBA3v7UtjS207QLrUps28ZWH/wDa4wv7fNHjb6LzkbRadJaaEw87zspw5VsDPxxVzix+U3lyqy1W8g0HRJp4owqxJhFUefFaSfEZW/LgWqanc6nqUs9zIxc85PYVcmkNl6R6OfXdt6bp4PTbKlV7/Y0WnJtvet6XLpOzVbIvIYwFu1xnevYuB5YDP9azym149Vrd/ZxPCpsoPWiuvaP8Qhzzs/8A68k1wfV/UZ4Y9e6+k/xmPBcLM+te5+z9k80UY0nU23sRiCfw/wD5Sf8AMP715vNx4/Ucd/bDHKcXJvD18f7CxXEiSGyu+ccIx8ivIx5Mpf4eV3ZceNn8vGCry2V4Ap4Y8HPFY4XPg5tY/LS4483H2tHkguYW7ZX9W3nH1r0OS8fLjZfhwTDPjy6+SktoksWxiCCPaw81x5/TzWr/AMVthzWXapf8xp7nI3J81xeOXHXbLhzR6dtP1W3NveRI6H/C4yM/P0ro4/qLjd71WHJ9Pf1tp7dJ20Otwz3M3rpE2YYg244+v/rXr8f1XP8AUYfx4/8ANc14ePj++/8AltcwDjfn3nuPivZxxmOMx/Ty7lvK0uy4XOcmnobGtpzCmCMqTRvUKzYnq+vcAtwDxU73VSaghAGRkcGmGAQV7U5RoGZcAkUWiQqUOM4olFCcDBqkghipOMfuKRxMOQPpSVA2lAoCHqAtmgI713DcSF8kUaLYfqDc2wkrngmlVQaKXA5qFGUdSKcIN9pIIpgJyCpp7IA7k5U4yPBpykWZM5qpU0MF0yFOBVbRprqMfnmqqZEtxIpKEjHtLGnobSEvt2gDvnNIWmIXAIyauRlaZlkUpt4Jp2DGpxH296yrfEePioaCZIpBOOQjFIz1pdm2LFo924eacuhZt5GyfvRDohI205EbBdN/mnotlnhzniqlRZsMLg8U9p0etm4+lZ1pDavk1GlbGV+M/FVIm0PJYnI71cRag67RTpx5QCtRtWmUiGeauVNhyJIRA2c+pnj4qpUa7Tt7eW6mEMEbSSHsFFE3fQtmM7bnpXScFuVmvis0g7IB7R/zWuOGvbDLPav6u6t/haS2FkfSlAAL7e2fitZGVvwZ6HmmbpiGad9zSszDnJxnjP1oGMOdVTJH05dSNEkpVCQrjI/pSO+nAZ2A1FQUwpHfNXvtMnTuvRDL/wCGrUKTkLznvU08WyHDKVIyDxikpo+o2D9L3Ml1GSdJlbLqAT+XzksVA+TgfSubm4plLPiunj5da/r/AO9/tnVXtbvTvRkXCkA5XvE3cAY/x9qw4Pp95a/TP6r6jLDv9qa3vzPL/DtRKi+jGYpMgeqP9m+R+9ef/lP8b4zzxm3q/wCO+umc1eqLcNLLaGVRuZOHX4r5zKZcmHl+vb2+OYY5+N+fRS1umDZjcq3kVjjc+O7jfl4pr7vTDajeafIWVPUgY5aPPb6j4ro4ea/jaX+m4ueavWX7/wD6OuvQTAEbW+UcYI/5p555y71uMMvoOTj+P+YrrzMvNmnubuxPtWq+j+my5+Tx+E8nP/BhvNjT7FLMO7O0szn3yN3P/FfV8PBhw46xjwOf6jLmy79HeSK1YolByDnOPFIw9pA+lSqPbgPNRauCo68bs484olGhUG8e1sZ7Zq4VAmfICgHPmjZyMJtZck1UTSky7WIFMg9u5W5AwM8+aE+g2Y/NJUAk70AAvigPKS9MJYwanRxnJAzS0e0kmx5paG2fX570xt71ARQbHBpkiAQQR3FOUtAMPcc09lprfpENmtGVHSMBee5pltjHtI8UHAXGBmiC15Jvk1pGVMJJuOBzRRD8LDz8cVnY2xppDxWemqYYZ+tIx4GUSKzAYB70A5PIsiAAgkGlRjEE7ZogqTer6Yco3pjs23j+tbSVjlUBINp5GaekysHnOalYDEKTSMSBs5GaVgh6FSRU6M0EKDkU5EsOyByVXaPC5zVEhK3qMxVQoJyAPFKnj0h/lATBxg896lW01RhIyMQpXvmqkTavND0G51U7mDRW3mQ+ft81rjhtjlya6jdodIgs7P8AL2bGDJG+RRlm+ea2k16YW2+1hkAUw4d+JpuH12SG1t3CnapYeSf+TxTl6Trt0Hoiyu7Hpa3t72IxSRjG0/FI41TrrqGYa42kq+2KONSyjkS552nnjtRj3SrXLHp6416+txaxcI49Y44RT5q6Udr0uxXTLCK1TkRrjIGKhUmjwNBvSxxzRNHKqujDBVhkGgOdavp8vStwHXMmjyNhC3a1djlnfyeO39KzuNl8sPa8bLPDP01HVraYTRy+oXhl/mQTklSB/wDsb4PwKf8ALOSael/jf8X/ADcnV6WWh9QNJMYpm3E+0uVwJPg48H6ee4r5f6r6bHHLLk4O58z/APce7z/T5/T2cXN/+OX7/q/2NeqlvO8i42nkYNeLNW2O/htzxkpL+IkHkCRP8ucVeHFjL3G/+n/4qjTWY77UjZwQxbt4j3k55zjsK9T6f/G55yXftxfVf5HH6a3GXelvpur22prItvH6LW7enJH9R5/fvXucXBjwYzGPmOf6nL6nO55VaiRQymLcPbyW+a02w0JGyB19TJTPu298VMv7VpPcmWK5A8Zo2JGJTGQuxSOOcnOTSpyFjjd9KiqiO7AxmpVt5Llo8jPFVjSock4Y9+fmqJFZSq4GKqEh6zB9wJDDzTCAkHzQSEkiu2QNopkXmYAnByKQLEFjQEoyUPNMbHYptBDZJ70aPaDE7ceKNJtCJwKNDaO44zUqgq5OKSomDzxQbO7Yct/i7UyQYgnNA0o9nHNdEjl2kVHp/Wq0WyzMRmjR7KTSYBpaGyHrsH70yOW8/I5oEWcMx+amrx6OCfC96ixpKys/NLStmIpgfNTozkbFjx/akcplR47UHezM2r3n8LGn7gLf/wDnk1rM7rTHLjm9q1Bk04jRoJ7aKcBdAd3Bz4NSuIRqUPFLZ6WMDnbU7GjSN7eTQWmUjM8xAjaRm4VVHmrnab0vbHo/ULlQ0u23U/5+T/QVc47fbK8sno3qegadoGlSXt1610yD9K+0ZrScMRlzZKDpmYdT6+qmxjjsYU3MsYwCfG4+a08MZ8MvPK+66kgSJAkahVUYAAwBTDxagINIAKQV0ujafdXn5uaBZJsg7m57dqDN3BEUEjeFUmgPnG41SbVOsri5mOQ7dj4A4GP2o4xlOnROhNUitNVksdpLXBBBHzV5RnjXTM4PNQ0VepdSabpFxDBdTqskpwq5plbpaLKJEDKQQeaRh3Ecc8DxTRrJE4wysMgj7Ug5V1Hok3ThlaKJ7zRJ23SRbvejYwoLHsgrl+o4ty2PT+h+sy4c5ljdZT/z/X9RPS7DTZtO2wyC4t3JAkyczMOM577VPY/Svn8+Hl4+bzl/2/8Al7H1X+Qn1GFyz/8Aif7f2oLnVrC21ltKvr1njdkjV1b3IScZPzjI+9ejj/jeHkk5Ljq1wcH+Z5eL13prWuLd2eoXtj+bwbYlXLSiPP27k5FdPF9Bwzu4q5v859Xl1Mp/xD/TmmWU+mXB0/d+bic+jdSBse4c5HyK6eW444zTzMM8uTO3P9LnQtC/g8cxeb1p5mBdsYAx2AFc2Wfk1mOl1jaM0tnpj1MCgMiXb5oCJm8UB4Nk1Oj2g3GaNAIK8sgSMZY9hmnILdF8n1CPOeRVyJ2I5A7Aj71Wi2EX70tDYW8nPyBRobA9Yjsf7UyQaQmgbGhwaDSlUY4oIANTNPIK8mqnpFYbtS0JQqnS5R0IqdLiRPFGjLs2c98+KAiHOPNAV0b54rskcO3icg0aGyr4INBk5YyScVJwjJbndkUj0LbxspzRs5FlEcDNI4OrFs4zxSOMmTFJRiKXMYTYN27O/wA4+KBFtaPgjPP1qLFSn927tUqgMrErg+KcGg4gdx+lXKiw6i5j3ZGM4xVbLT3pVFqpGFgYuAFJJ8AVntprpb2mh3UoXdsi3nCq59x/bvWuPHle2GfNjj0vT0hdQxrsjW4lby77UT9u5rbHhk9sMubK+myaPocWmxK0m2S4A5YDAH2HitZjJ6ZW2+1tmmGtdaaZe6voxtLLlpGCtlsADPf60Js2Y6c6dtenNPEEAzIwHqOf8RoEi3ZqDD35pAnqGo22m24nupVjjLhMn5NA9GomBQEdjyKAxcFRBIX/AEBDu+1BvnHWBHD1PPNHEbdN2FiYEMAB5+/enjCt3HQvw70qaXURqNzA6xhCYW8Hwf8AaqtRI2brfqQ6Lp/o2hDX8uNi4zgeTUybXa5XYSNrvUdvb6tLKJ8gRbl4POcH6U0u7RbY4ERQMAADFSuASX0Me9XkUMi7mGeQKQ25H+JvWvoW72dlMu5iY5EI/wAJHeinO3OOn+qNXsdOufTkY4CxeqVLFU5O3PgVhlx4W+nRjnnoxpcVpfXlowuRdand3KJ6eD7Mnv8A8n+lVjnd2XFGfHPCXHL/AIbJfWOk6r1lqU95eR+pcXHp29uhBZscbm+BxUZZW3qNMcZjO66Nd6NbaX0nZx2UYSOB8MccsSO5pc03hsuK6z0pN3NcjpeLginQGzcUQAOzDkdhTJES5WmTIlI7UDbJkz5phjfnGBggYyKIHkGxj2581cSjO4wBnmnsicsnbFADAZhkUaLYTffn4o0NvbQYwd3uz2o0JWUcxnNI4MzZWltoCqsz4wR5qoijbSo5qomsYzxTJ5V55o0cqTYHaosXKg7YFLR7LM4wfmjQ2g0ijGCe3PHmjQ2qlkKrz3rq25NJpJnNA0ycHigPCJecjxU1URSBSxyKSnja4OQKSoi8ZQUgxG7YIBxmgh0VDswGzg7s/wC1CoOmMgUjPwNikaxR9wG1eTxxSOIyK6OVcYYeKk5dvIvNLatHYEGCXyBjIwKqbqb17WNhYXmpsFsrR3UnHqsNqCrx4rfbLLnxn49mNfktelrAQQTrLq78lwOIx9Piujj4scXNyc2WfVbL0VpMUVgmqSmSW7uFyzyNnH2+Kus5+22bqDY3UBjNAezxzQESwoAbMM96A1+46psYUkkyfTjmMLSEe1WAz3ohbaJedQwdY2ENtezflAl4pQLyZCM/2o0TqXrRQJGGYDdgLnzSUrup9TfSun57yEj1EIwD96IL1HB9VufzmoLcXEgO9w0jZNXUz07x05NZHRIGsCxt9vBJJ/1qFRyfrHXZLvX7r1o/TWE+mnGDj5NVPSb7afLrckmpW0lrkPDwr+c0Hrp9CaNKY9MtIriYNMYwDk8k45qTnRTXtAhvEu7u3LreS25hGGwD9aXo/b5o6jtb631AxXsbo7E4Lc5wccGjapNOpfh9pFvJ0p/9Pky7t5Izn4P7cVzXG+e66pl/09RT3GnW+h6nrms2ki3F5GnshjUgW24YLHtz3xVY42zxRllN3P8A8NF6Rin1TrS2lkdg7zbmYVtep0w93dfUJszfaFcWg/U6ZXPyOR/pUXHeNi/LWW3Pwv8AO9Nzg5xXDrt2b629jaxGeR8UhtDeO1MIOjbN+PbnvT0RXaSxAogTQEH3HCnzjtVSJ2iWPijQ2kh+tB7HILqSAAABmqgKSA/b60JLTJslZAwfBwCOxpk8rlAapIX/AHHwWC58ntSP4RzgUURL2enncd+79OPFT8HEt3twPPeob4+nkkYtyxJAwM/FaY+mWU1RHl9uOOfmrQ9EVJ5pkI2357UCBsRzjgVNXC0snelDKu/1ooiIkOKNHst6atxzj581e2XiC2FPHaiZH4sBzmq8k6NRSKe4zx80bLRmHZsI2e4n9WfFAHMYNKrlL3SZHNIVXhcE4oOQ1AORmkZiVEUkqc4oCcMuammficngVO1SLCy0651GbZAhdv8AEx7D7miS5dQZZY4ztC6njstTWyt1M04wjbTuBbziujHg+a58vqetR1LQ+nreysYmuF9afYMl+dvnAFazGT057lcu6t7hmtrOT8tCrOq+xBwM1ROU2vROs67rL3uqbYYJJSXAb3EfSjfXSZi6rbRR2drHbxjCRqFX7UKSaZUUsxwB3NAVFj1NYahq0un28m+WP9WKC2us0GwWOKAXubmO2gkmkbCIMsaWw5rqv4lLvmjsoywxhGPYnPn9qZdue3ms3UlpLaSSkQPIZWQdtxOaYkN6fpF3bXGma1eKqaYzmRnGSFVT5+57VNp6dheGDWp9O1NJmMMALogPDEjGTRRIp/xGuzbdIOc59SVR/qf9qNizbjaXC3zRq2MsQDgdqreys0+gum9MTSenba1SUuoXcGP1qTjmHWnSN9Dq09zbRy3FvOS7HGSD5/anL8FVf0f0lO9+b+eyaS3gVnw4xuYDgf1p2idh6FrNzF1UL3Up5ABISInYhVyeeKNFXbY7+C8t2e3kjdVHuYNwKmxcrl/4mW+mxWh1GdledISsUQxg7s+6o0uZQp+FWt28WgSWl3crE6nMRkPGD4p+Pyny0Dp2nXt9f9ZwBd8skSlApzuwSeDT1ot7aJaPdaD1Kfy8YSRHOFNVotuzaN1dd3nRV3e2sLG7swEwRwx81Hpc7ipnLSyB8YZuSPqa8692vR1qBsxA74IoSCWJ7UiYy2O/FMxIigjPBL571U9JsZ/UpAFVC0gUf09oxjOcUyCTIbnilo9jjt3oDz4I5pgr6fJbB2jzTiaDMQTgVRSAYJ8ZoDxFIPSyySgbzkjPOOf60hGFbIxWddGPpg/qBAxVRnl7ZLHnNXEIrLtbvVEaikjJPqbiMHG358UbEjDH6ZA8Ulk7gjORSIrmmEc0gEhJ7nAx3xU7XoF8YJz7vjFKU9JQoZZFQYGTgZpzIvFP0yjEVUyTcTFuSp81pKzsPxsCKKche4IzipVCfGaDFRSF3fWkbJbeGBJGBxx35oIe2hklkEcSl2PYDuaXvoeu63vTOk4rOyGoa5P+XhA3CEH3N960w4be8mGfPrrE1JBe9UWqQdPMllpKttkPZ3+TXRMZj1I5rbl7N/kunOiLiW8dg05QBUJzgjvj70x1G5WN9Hf2MV1H+iRQRQcF30B7fxQEC/1pBrHXeo/kOlrnEwieUbFP3pUaaB+Frb+opXc4IjxjPc5oispp1l9SuP48lglq5g9IyPOf0g54X71SVgW4pBR9TW8l5oV1DHIyErn29z9KA+f5d0VzIkmVkRyCPrT2Iqrq6SRzliB5xSVIf1nri9venINCiURWkQ2kDuw8ZqVSOnfhjrx1PQTbSSAywADGMcYpxOtU5+JtzDH0f6UjhZ5GzGvzjv8A60F8uNaLKLS/iupc+gh94A5YdiB+1OHlG76319d3l/nT5TFaxgIiYwCB5pyIrqPTWsR6noEN4+MJHhifoOaKI1bUfxQ0+wvzDZ2zSoO5AABOeaJBtz7rPVP43qIvLWAC1VQpKJjB+Cf96cGj/RF7qGoB9FlZ49OuiY/XRv0EDJA+tKnIqurtRs7vV7uzab/p7cLBE5ORhRjk0T0WvlrtqTbQiMSAjtuQ8H7Uw3DpvqybQ7q/ab3P+XIViPcSf0g/vU09NxboeHXtbj1e9UNHLbwvsHAZ8e7P9KjK29RphJ7rerTSLDSdJkRYUjhA3sFGM4pSah73emhXMRednHZiSK875ej/AEA6GNQCnPfJp+kFyOSfmgtIlcg0wl6MiRCTA2FtoOfNPXRbm9DqvsqoTO0ADDZPmqhaAnUbcjuKCiKSKYNuDuU/qz4p/Bs8EUgVlzkgE4oJBbd3QMADkkAZ54+n71UIISyW29VABYbTkZIo3otbL7u+aAg8gyKBGUbvWVdGPoX2lgDVYs8mJQEwfmqTC4IJNOUaFRscUbEiZY4wDSii0hB79qpJfHNARoNkRkL2rC1tpB4wW/5qbkeko4aJT0L6RwCRj4qpU2JqD571pjkzuKW4r3PHzV+RTEvPMe5U4+oolGi8Tq74amRtNuMUASGItOIwN7HGAvknxU6G9TbpulaDa9N6LPrd/Asl1Gm9Y/8AJ9PvXRhxyd1ycnJcnL9Y6q1DqLVFMkxBdtsaA4VMmt4xdq6P0j+A9OLGJS8jrvJzkZqTkcU6hu7vVerZrTeZJZJyiDx3p76Eju+g2z6dodpbTNl44wDUnIeaUZyDQYZnLHANIINcxxuokkVWc4UE9zTDSfxTsb3UdNsbezheUmXJCjPg96mnj7V/4b9NalpWoS3l7bhAyAKGP6RRDybPqGq6nH1vZWccTmweFi7heA33qto02UyADtQYTSJtJY8Ac0jcQ/EC+s11pP4bHHHMASZEbuDngj5oEaBK000hjWJ3bb7Qg5/pQpXvJmX1HQOpySBxk/tSU6H+EWqrHrU1kUJMseRjxg8mhN9tg/GKGUjTJ9/8rBQKPnvmmme2u9W26Q9PdOPaw+jA9qWbIxufPJ+ppQVqIu3jYArgDtmr2l2v8O5s9EXEofeTvO1eSKQjk905e7ckEEuc8fWqJc9EQSahr1zaqsTwzQFX9QA4HyM0qqNuv9O0joDQJJmxJqMqtHCw8sexA8VntetRxrTbJNR6hgtr8SCN5grqDzyadokb+/4XalHcGCyINqJPa0hxgfSjy1C8batNQ6B0vTIPzM9+yyAoCzngYPI+prHLkvlqN8eLHxtroXTOv6fqkMcFpOrbE9qeQO2avG35Z2T4XetsV0SUA/rIX+9HLdYU+HHfJGklfTl3Y7VxR35FpwZeKETonJFtGaWgX3Y4oGmVVmHHbxTIwp9uCeaZMIpZsD+9VCoU3CH60yCVBtHGDQEDJjIoBcygSc0EKlwqAg+fNOUrCl1J6r7h2xS2NE5A23ODjtmgBqCDk/FP4KDqC3aobToREDNhnCD5NXjOmWV7DkORQIEijcMnAJ7/ABQYgxkgHIHmg4yxIX+9Eg296SNAWJ92M5q4i3sju80lIO43dsAjimDTKAcZ4rjtdUEubYwxxhkZZHXdzjBU9iKNaTvdBiR2OB3okMVlKjB70/QQ53VcpaRlbcqjGKe02K66kbABYnHAq5UUrHIwYGtIhZRSggUHGwaW6aLp911Bdx8QrttlccPIfP7Vpx497Yc2f/bDPS/XKdRxtod8zPcXzN6zk4CLjjFb7c2tNYbUtK0HUZrO1tFumSXaZ5DngHxThV17p3rSx1pvytucenGMg8ZpaOUpcdJWUnV8OtghVReU+vzUq02uSfIG05FAC9YnuaDBvr9NM0u4vph7IlLYoDiWpdd3+qdRWN7EpVLd8rEG/Vz/AMUj8XetKunu9Jtri4j2ySIGKkdiapMMmQDNIF3mXfkY4pbVorLee/C80tnMS17qCWVlJPJyAM4z3o2NPm/XtSF1rV1PENgLkgDnFMRV/nLhZleF3EnbK96DG1DSL7S4beS8geIXCl03DGRS2ay6Cu3tOs7IxZPqNsOPg0UnYvxM0x9R6UWeFS0ltIJCB3x2NBSduW6z1FHedNaLpro7XNr6m52PAUngf2p40ZYhWGnRanAUWU/mmZVjiCZ3k/WrZ9umLqsX4ddOWdjJbKb6cbpEDZA8ZNL2Y8vSGg9Vaf8An7L/AKe5ddxZW/xHnkUXcEkp7pHo+10S2T1Y45rtWLGYDBx8d6ja5Gm/i3pepXeoWa2kM0sJwoK9gxonR3YPQv4eXlrrMF/qIIMOHCnkE/8AvFK05HW764FvEzZ4UVGWWl4Y7fOvXPVd5q92bNmKRxOxKj78Z/anx4a7LPPfS9/Bgyt1NO+/2iMKQaqpjumtXCDT0gzlywOPpWXPdYNvpsd57avKMknFc89OnL2RlYBjQkCUblODQSvmOXJAwKkw1nZAR3HxTlKpLc+7IB+uaohxcDj5pk9I6uCOSfGD2o2cgLA7eO9Eo0E8ZQtyD9vNBaV7tlzQTGTQTBYbfrTIGVyV2KTjvigBRqWYjtRThqPK5GeDwfrUxpfSfAdSBnByQ3Y1rJ0xy9hyENuYKFBPYdhSogG7FJSe4beO9AZlvWmgjiZUAj4DBQCfuaoibTEe3dgVSQDLnNJcQLA0jGjuN3Pc1xadOzbyozezdtwMbu9V/smf2mjbfcKcDzNuOafs4wMHNOEGy7uKcTarruHB75rSRnaDFHxyKsl/oGjtq2qW9tGDgnL/AGHeqxnlUcmXjisfxhvghsdBtFUIgBIXwewrp/pxz21Kz0u56Q1mO51PYkccDLC6L/3GIJx9Tz3pRTUYbvNw7OSCzZpylY2HR9QuLC6W4tZWRhyxz4qkuraB11a6iphviIZce0E9x5qbDxradN1GC9i9W3lV427EHtUr9leoepbHpuCK4vGO2RioxQSrsup7HrvRNS0+zQGYRnYjeaA0Lp7oTUNPu31PW8WtlZt6jbh+vHIApKvptek/io+q9QLZxWLLbvhUwe3yT9KradV0b8yGi3gg5+KRyF2m9pyKnapAowGYksKUNzj8UJb5TGYHLWqL71jP6T8tVQrHHX3ht3YHOeKY23v8LrK0veoibvAwvs3R7geR/fxSpQp+IWvX1/rk9nfwhY7Z2EKhdp2+P2og7ax09qyaTrlteSRho0YbgO+PkUVT6L6e1dNaguldQUDewH/EjDI/3FEK+3JeudESx6ouhbLGlvGin0gOdpGTj+9OJ8nQeiultO0HQ11S42y3Dr6qytn2LjIoJy3qjW7nWtWnuZpmeMSMIl8KufFUSz6J6lvLDU4dONwEtZWySR5pVUd6s1X0VI8jNTIeyt4iXE4QgFU8Eeai3tpjOhQyRRk8DApbORr+r6pCLGaYNuRFJOPOKxzy703wx62+cZ7DUdX1a5lS1ly8pzle3NdPlMZquaS5dx3H8N+mYtHtUu2g9OeRAGG4nnyefn4rPy3elzDU3Vwbme6vb6aRg0frFYgDnCrx/rmsee96dH00mrf2WvJVRlKvkkZOPFRPTS91Vz3HPegmFkyvemnQDEGp0Zdkd2wik/ahIJ9RTg5GOMU4SQYjnNBbGiYkHmmcom7LUKSb2puVsMDkYqdr0qnKlmLE7yauemN9oBxuoTWVjSa4CGRUB7s3YUy2DIcKVGCM5oAKttJpU8fZyBC55qY1Znwnatp6YX2Wy0jYHNBbSa3CgsrZwPIxRo5S7OTnOMn4GKlRZpMZ5pwFJZfdVp0gJeO9JUQ9SkpmGXBrGYq8j8Um4U/EvIyjtsKnGM57c0eI8kgwxilpW2VBz9KNHtmRTjI/Y1UibVfIWYkVU6QLbW7TSJDEpaRyAqjyafs/U3XZ+itDsdKt3VHSa+GBM452H4rqww8Y4s8/Kq3VtG6UPUsEN4TNqUjblUnPPfNXpnsr1D0tpnVuqxN+eDR2LbWgXtnzU6OVp93+E1vNrMk4k9K0ySEQ80u1e2gapCtlq01vEGS3jYiMt3cA96uUrAru/nMaGSQkRptjx4H/ALNFKCaP1nqWiWzQWz5XJYFj2yKlawlv9X69sI7RMPcWSbwmf1jyfvU2njG2/hNoGq6Tqlxf31u9vGY9qh+Mn7UvJXjb26RraTazot3YK4QzoVDEZxU+dV4R893+naroF9JDLFNGUYqsqgjP2NVMpSs17dd6O6gSLpi2j1K9VbkcbXb3EZ44pWlJptkdwsibgcqe3NLatJI25HVQjMQcANg0QOHPr91091bqBntTNvLJ6M7lsc/XNaRlYq43t9TuYILvFtCZCzyIC2M/C/0oEruPSHTOn9P2iTwTiX1lyGdME/apv7VGjfi507//ALkMA2AhXbfy37UpdVVnTkMzpLcGRIhEhx7Ac4q0yuvfhR1JBJcJpLKRKVfDE9xwQKn0Ntn6m6Ql17qWzuBLstDEVuCBycHgfvk05Rrtd9RWkn/hO7tLEMrrDtQJ3wPFGxpwq10fUL5WCWzYDbdz8An4HyaraPGkY727sLmFscW8haNHHAJ7nH7Cj2NPobpzUZrjp2C8uGVpnQcKeM1GV1GmGPlRjdCGNndgD3YnsK5/J0+Pw1rqPqy1tNOSNJ9892fTgRD7nycZ+1PHdTlrGKXXupNM6V01ba5CzXRjGLcf71E4ss8ttby4YY6C6Q6htdWt5ria0W0tYiCXYBU/aneO43Wyx5JlN66R1v8AEmGRf4V07mW6lPpCVRwvjIrXDDXdYcmfl1G0aQn8O0uG2I3useGZvLHufvmubLPu12YYaxkJ3bHcamelX2RliIK7j+oZ48VSUcOnBP8ASgaGiiBAJGfNOJtP6Y8UN00ckYIY8H4pdejxl9mr/TraZJJ0GxvOavxmmNy77a20YIOOSD4paRtmIjBUCjR40QMgl2eTRZ00xvek1kSKC4R4RIzrhGJ/Qc96mLy38VTNnfuIzg9jVxlQZX3yswQKGOQq9hQljaysVcEEeDSOIO2Rjz96YDX3soUksTjGKV9Hj7WtqhCZxWW28nQF3kNW2Nc+U7AicxkkZHg/atIzRebgqDwaVqpCkpIBNSsi8hqoRdyW5qi0i4MTYLAnAPFI4EX545+/FGj2s47JlOGFTCMRwFSfiqiTMcJZSSQBzz9cZ7UaG0djAgkcVNi5TAUlRUnGGUgc5ohkSv8AMNVRIttNkbTNNu9TjhMt0CILRAM5kbz+wrTine2P1GWp4uo9D6d/A9BRLqYSX059Wdicksa6ZHHFXqGh2cHWTa9c6iqsYysUR/zY4oCn6D0PUrHV9TvNQm2+u5ZIt2cjPekcbzJCSSSKNHtzPr3UtH0gFBYQzXzAgB17ZpSHtxt3mvbjaiZPJ2LTEgK2F5JH6i20mzOM7ak21/hiinrKDdcNEyg4Vf8AF9KVVj7bV+IvVut6TrRtLSVUgYZQp3+oNTJs8rfSt6Z/FK805kt9VjM0btn1c8rmncRMkeqeqOoNK6m/MlQdOmIeBGG6ORD96Uxh+Vl9tK1LVDe6hLdDfGHbIAb9I+KqTSd7q803r7V7DTDaW8okxyrOeR/zU2Kl/Sut+sddtb782l9J6m4nBORzVaLSyvOsbfqG9ik1eySJwMNcW495PyfmiQe/bZtH/DaXWo1u7HVYZLV8ENj3A/UUvIeH9qzqefqbRNWFpd6hPthG2FlO0FfoBTnadWM6T11fW7rbauf4hYOMPFNzwfNGhKb6oteir/SxeaVFNb3DDbHHEBgnnuKJBuNS6Q1AaP1PZXTA+yUA8+DwaKcj6Vif1oN6nuNwqVVhJs7lfH1zUyq0iYbf2AQxnacqAg4NPZSNG6z/AA8l13U4LiwaOHAAdcYHfvinMtFcd9tpgtotH0+105SXkiQZNY8ufw34MOtuYfiR1RcpfjRoMpEAHmcNjd9PtVceHzU8ufxHO11i4/i0d9IxkeHHpKewx2A+la6mtMd97bbo3Sk+us2vdQXBMeDI0bdyB/pWV5ZL44tpw2/dkrNSurzqnUF0zQ7dorCM7UiXgY+TVyTGbqLl5XU9Oo9GdDWOh6NcX80SzX8Kfq77WPxSttlo145SLiE5j571w6ehiBPGP61WJX2Wkg7McYpkCYwTwOKYZXKDANBVOBC0vGSfp3pWW+jxulgz+pbOrOSR4qpemOc7qp0wK88itzj5qr1GfH3dUjdMsF7Iq9ge1OdxF+3Kx6z/AJl0WPIxxRl6acd3dnrnBUHaBgYqJWtimmfJPP0ps9E2GKBoRZEcMZSeF4x80ETc5zTCEOd4qcvSsfbYrWVhaCIsdgOQv1rDbo0SuhukrfH058p2AwxGCR34HFaSs9BLFvJG5VwCcmle1TorLytTFEZF9prSJAA9vamEGiHpli3PxTLYOygbbx+Tz4rnmTo8HjZ8Hiq8k+ERFrg8iq8k+KYtEIO7OR+nFK0TFOO08Yqdr8UZ7bCninKXiSSyLNnFPexJpLVetY+ndKOjW1ujXJzIZiM7Ce2PrXVxdYuLn7zUPS/VV/HrttJdX0rRO/v3NmtYwsWf4i39zJ1WHPqfl0VTHgkAj6GiDSq07qrVrG7Fwt27E4BDHPFMO76ZqP57SLeckMzoCWHalTjVur+loeoowygfmRwGPGBUKa03T/TvQUEWoXjm5uR7Qp5ycUa37G9CS6rH1npT6d07aRwT5y5K4wD3waqyT0JbROmvw/bo+V9c1FxM8anao+aUm/Y8tNT1m3n17XLi/eBtjHIDNjA+lG8Ycxyy7UN9Hb2sxWRPaBwVHJo8sR4ZT2OnVct1aQ6Zdy7rGM4UPFuZR9Km39Kxn7bjpmj9Dataqpx62PdltprmuWcrqxw47Ol5p/TXR2nepGCj7gRtlwcfY05bfkvGY/DXD0BoN+8q2GqiK4WTKK4BGPgg1fnlEeErV7/8Otd0++MKWxuY8Eh4zxVTllTePKNu6YsuoLHTklk1L+EQ27BWilXHqfX70bnwXjflL8RI9P6hngmsNUtXuoYv5il9u79+1VKmxzO6t3tLj0ZXUthTmNw68jPcfeqTIJDA0scnphyEG4sDjFBrbofSo9Y6vsLZz/LEgdgfIHNKivp2C0RY22gAYwAKeitVt1asJC/KgmsssWmOQtpGWYIwOB2OKeMGVQ1XVrbSLWWe4YIiDJY9z9BTpRps/VcE+hzavaxyzTSuY4EIxk/8CsbjPLt0Y53w6c4t9LsNV1K6veodT96n3R243M30+wqssrJrFnjhN7yqrlt9NTqiCPTInlST9KsASPqaPuyx7P7ccpp12zsYjpzx3o/kMuHGcVjhO+nRnZZ2tNAsdAhkEdhHbrx3Tua6fG/LkuUnpfa1NBp2hyQwqqvMQAB3+9LksxxLjlyzapCxrjr0Ym+G880AFxxiggtopk8qDPuP7U4i1G2untb1Wjxu7cinLrtFkvVHkk2O7MctJyaV/Z33pQQXLRaoyD/E1XlN4seO6zY1ZDDcqznhxmqw9J5prJPTwEJfnYSdpPxU5tOH0cncGI81lK6dbihnceoewq4ysBLZFCUVwSfj604RaQ+4imSURy4+lLL0rH2urf8A7fJxXPrt0b6Bf3Oc1tj6YZe0DGM96tGmJYoxCCC3qluR4xSvpUhVrcsppQyEseCQa0idBbMAnFUNAvH7s5pkHsoJ0VQD4rjldukyikdquIBeH4p7GnkiAPPagaMLEBnHbxmkAZ4wfFBsQQKT2qpU2OeXnTOoa7qWqahCVFtDMY2Z2wOK7eOdPO5b91UNtbySXSWsCl5C+1dvk1oyb7+JDpZ6Xo1k0g/NxJiRRz4+aNjTS/Sk/KJc7GETsVDHsSO4pk7B0r1Da2XRltLdTJGAxQDOSaVE6B6s6yt7DRBPZTAyynEZ+3elpW3JXvbzqzX41vJgqMQD7uAPpQHZYhpH4f6KnpBBcSRr7j3al67p63dRoOsfiPqWoiSG3YiNicioy5P02w4PmqiDUG9JItRgnkiLA+w4OPjNc+7buV2yYzHVg+nWsd5dKCxWHdsjWRgTycgGnfK+mc8J7bvD0OhiBkt4ye+QKz+9f/T/AEjD0FFeXYaGYoUOWXinMsr0m44TteXHQVncx7ZnYMBgMjYIpTc+RbL8KS5/CqHHqW2o3KzZ4O/tVfyWRn/HK2XRtB1bT4hHcap+YRQAu9ef61nbb21moS6z6fvde0g2yMu9TuUg96rDO40ssJlOnFbvpjVtLnC3NtIrE4D4yMfeurHklcuWFnttXTbaRaRmy1S3R4uQZtvuOcf6U6jH32c138O4/Qe80jUY/wAu3LIW5AqZkvw+Wr6JdR6HrKXljc7mgIVmPGc96orNvqPRZor/AEq2uomLRyIGBq4xOzxI68hcD5FGhLpqd91hoVjqAsTdp6+SG/8ALj5okPyaF+Lck91pFrcWj77J+7L/AJj9amyKlrV9D6f6k1yxhtoyltpscZRXkBB57kD5+tY3PGXfy3xwys18N2tukNP0Hpm+tLRC88sDB5n5Zjj58CsLncst10TCTGyNL/DfSPSh/iNzAF7gSsf8Oa6OS99MOGTW6j+IXVYvJVstNldYomxIUPDVXHhrtHJnvo3+FjzNq9zdzMWEMO4An4rVhF/o/UMnU+rarM2RFC6pFGT+kc5rl5/h1/S/LYIxsGa53XApDlifrQHhgDmmQDtycUggXIFOVFJXU4WbMQwOOKr2yvVMtJ6hViMe2pvppZ8qG5Jj1YSA4Ga1xu8HNZrNLXJ2lWFvAp8Z83eqJaPK9mAuSfFRne18fWIzSMg2uMGsfl14+lTcnc/FaT0yy9oYHp5J5z2poCJIPAzQQDA5pwh7RcyDii+jx9r2KPEfasa3+CsilWz4NaY+mOXtBgwYgggjwfFUUjygt+rmptXIkUwhoh2Ky4jBbIq06LtGcYFPY0gYTTlTp4QcU9lpuUMme9crsM9+1VEpBT8UB7bTCYzQEXQspOM470E9B7SR80QVyXVeobuO0k0ZDtie6eaQjuxzxXoYenl5z7qxoQnXU7V4CVJlVCQeRn/0Bq2S7/EnQ7221w3YWWS1IBDnkLStVGpRzEJjjtggU4Qst5L+TSH1CY0JIWgDSXiXHTJgluGaWGXdFEF8Huc0gtugumku70arqMgitrZg4UnBkP0qcrqbaYY+V0veq1/8Q3klxNd+jAvEcffAFct5ba7seGYzbVLbRP5+IZtw+cUrmeON23aw0+B7T8vfS7025DAYINZW99OjW5rJUXmg6WD61hqbJco25UfsSPFb4cl+XJy8Uv4upWXUFrN05DcKGaTZh1HJyO9VnZGfHLb2a0Hcbea7dDGZj7VI5AqNyYru7ksWaslsBxigPb+O9I9IGQg5BpHIBIElUq8auPORmls9Nfvul9HvJC7wsj/KHFVOTKFePG96VFx0Yigm01e4izkbGwRg1c5bEXilazP+Fkru7xXsffODkZq59R+0XgvxXbukbGfTel7O0k/VEuDg5zXVjdzbks1dLG/uTHaTMGXeEO3ccDPimT5o195Pzs9zKU9ZmL+xge5NUh0vosPqPSOnG8UNGCz7SMhueP6d64ufPvTt4MOt1tecDA4HgCsJXSFLhkYMOCMGmcce6v6hfSrI9N2g2OuPUkHwc8Cuvjm5tx8nX2tQghItiXOc/NbRhXVfwp05bjSNRiY+2dfT3fcUJgXSGlnR9b1+x3BhDKq7h571zc/w7PpvluI2hGyCSe1c8dXYLnCY4+9ALu/jNIB+oyqy+D3oAEj8GiJquuXJXbngHOKuMcjVqTJEuTmpya4TeJLWgkYUge6r4u4y5pou2LrSt3lac6yLL7sNrjp/asG5lzRfZ4eit7NvvHkUDAPArG9V0Y1XMgEbkod5Iw2e1XjekZzsuyOE3bTt+aaA+AvNIwSuTThHtPjLTAAUX0eLYlgCxZYeKy+W3wq3jJcj4rSMr7YMJzimJBI4FD7Wb2/IFJU9ByA7TiiHpXvGTniq2nQYhJJ4o2Hni9vaiJDUADBp7JskA4rLTo2dQUEYHIAwPvRsJemMUB4xkDNADK+00wa0bTjf6iIz/wBtVLOa04sPLLTLm5PDFyHqXpK8huor23QyQXBcjGSR7yK7J08+3a8/D7pm/wD46s91bsEhH+MearadOrdTWkF107eR3BCp6Z5I7UB86G3Vc7T9qcIvOuwKOT8nxQFnoWoLpDz3UtsJ4NuCjDhj4oOLS0kvLoNqVx/LM7fyowMBVrk5c93Ud30/HqeVWsFuHPvOSfmud2QcCK1jJ2jPjFEmyuUgEmoiaFowwBA7CqmCP5d9NW0u9Nz1CsRwRuxg1tljrDbmxz3np2Hp3RX0TT2kZtyXD7lHxUZT7ZTxv3WNkjmyorNo801ByMGYfNKnI8Ju3FIaeZ+KDDEpUEY70t6PWw2BYk1KtBmDceBzSCPoEY+vNMtM/h/1NNrGq61ayZMUU+If/KoG3H9s16fH+MeXyfnVp1vO1t0zeGIAsy7cnxVJ+Hzw9tdapNHa28LPJM+wYB7mnaUjtmlwjRLC205Tn8tGIz98c/3ry+TL769Ljx+yHBccZpStNI+ruNVKenJOttJtpeu09e4EEc0O4tjPIBrr4r9jj5p97XI1R29OLLLnCk1vHNXQbe5vemPw4hmt12yTz53jwAe1OEuemNTtdb0yTVYYvSu7iU/mVHYsBgH965Of4df029VayuQMCsHUAXZlNB6C2sQSFJxycUghIzYyck/WgE5XNKIyJSMOc1pGORjTpwqlfNTnGnDfhWa1LP7kLsImOSvgmq4ukfUV7TZgbB4sc07+RYTeCwsbr8vAV3YNTfa8NTFjCMAQ2Se9TmrjRlj/AJZxSxaZQrJP/J9Hb3qt96R49FJIyvcGhIbcCqhVeaHDuYsRStPGNhmQLCSe2KzjRRiFvULeDVyloYQ5HbijZyJGA7TgVOz0XNufNEo08tmCnaq2WkfyOCeKWxoF7PgjFOUrCjWBLGnsvFfJCUPNS0MpigCpigDbQQMA9uc0FEih20AB1xmg42/RNPFjosk7cSzLn7DxXbw4eOO3nc+flnohpFksWh2sMsSlgvII+ua0jJYwwore1QPsKZEepLFr7Qru1jba0kZANFEj5tnimju2tipMits2gdzT2TrOj/h3a3nSMEV3/LlkcSyPj3Y+KR6LdT9IabZadaopxawZLIP1SN4zWeeWo148PKtdd/WZBtCxxjCL9K469CQaOktTaneTwXLKqk5Fa4Yxz8mV21i0vpm1MJIxAJwa6LjPFyzK+SWksbXqIFoTJ7iByRgnsaeWvEY7mT6KWUt0vZfO6sMvwjXGf9SvQudtYuiJk5bJ7eaA9LsLj0sgY5zRlr4GO9dsxhhUqF2kntQHvTNI0ljpaG0xEDS0NstEAhx8U9J20T8P0uNJ64urK5RhJIWYkdufcP7GvTwmsY8vO/dXWL+1gvoTBPCskTdw1CorrPp3S9OnNxBaIjnBz8YGKQjXZFL3UshyQzk/3ry8vytephNYyJKpxRFJoMGmbTOtdJs7jW9HvLyXZC0nouB3Oa6+G9acfPPuladr2hNoWozra5e2D4icc/Xv9K6ca5M5qrK9bU36EsGkeQoZJCgHBXGMGqiKuvw0UL0y5x7jO2a4+e/c9D6afY3JUDEisY2oiWBm3lSF2jJzTmOyuWixh25POPP1qYoncqAcr58fFAV8q8U4jIhMuM4qoxyRhnC3GdoUfSnlNwuPLxoupRC5gDDmpw6quXV7KWEZTcD2FVkXF+hmVAwYD7/WlLtWU10kr/zTgYB8Vnne2nGPktxSlXQAga5C45qp+yvrSc0OIznzVbRIrniIxxRKLi2LRgiRZbvU2qkWVwxeLbUSq0Alvkiq2NGhbjHaptOJi3G2kaEsBcKCB7RgcU97KRBbfFPZs+gCTxk0DQLQqc8UQBSQRs2UTaMDIznmnspjRytI0OxolAq5wD4pgzGeKNkmTximFnoelC/uvUlX+TGcnPk1tw8fld305ufl8Zqe1pq+orFdW1hERmRwu0fFdVveo48cet01JFhjxwKpLwwq88UAlfPvXavalVY+2vR9M6ZcavHem0T107Niph3S61C5SytgvACinbosZtpetyC40+S6mVtinOBzgfNc9ymd065jePHbUh6csZkhIdD2IrKzV03xsym4mrqY40EahlJy4Jy33oOGDZQTNyFc4xuxRLoWbaZ1FoUelRvdq3vZuK6OPLfTk5MPHtddH2kF9pgupYQ0ynAbFTye9K4ta3XWxHt6ds8jGG/5qc+sIeHedRjHFYbdGhlQHOaewkIuaQECYpGIgpkMsYNMJCIeeKNFagQFyMUiQZgqknsBk0Bd2dpYXEcWowxxs8ihhKByeMV6WPp5l9jMcyYHikr4Bu/ZaSse+04qc7rG1WE3lI1dIMjtXmPTTW3pyDbxgp6ErQvxSR4+nraZCQ0VwGBHfzXR9P705/qO5K1vo7qtY4nttUiW6jklBX1BkLk8muuRw22OiddR6fbdGQT29uBF2j29gG71Uidqb8Oot3TG8Dh5nNcXN+T0Pp/wbaE2sc1nGo6KSpAJwe9MtslAEwR/60grprUZPY/alTl2r7i1O3gUomqqeIgEEVUZ5QhJEMjbn61pKx0aSV0RQM5ByDUb1Wsm4E25NzY5J5ovcPDqsLG7oCBWeN00ymxI4HLjINTbLV4zUP8A5VlAbFCgIozPcZRf0+areindNm0JHu5pbVotLZfSiUaO2duEgGCxkycjxii6sLuLGOPcOaUAvpY7UHBEz5qT0Kq5FMmWGVA2qMeQO/3p72UBZcChRaQkE0AAtk0GyBxSIZgNuB2+1VUwu6UGzGCTQR2Jc96INrLTtKmv5faNsQPL1rx8Vzv9MeXmmE/tealqFpoOnFUIXaufvXbuYTUcM3nlutX6bE2s68up3AIRATGD/rWWGXlm25MPHBudwVU963rmhCaZVHfmls5C6EzPgfvSM/BbLAhYgZpyFtquqB77U9mT6UfJx5Nc/Nnrp08GG+wL3TvzOm3EAH60K1z43VdV7lci0OO80y8ms7gExiQgA/eunOSzbn4rZ02JoMHchrndJq2QjHmnBRLzSrfU4fTuFyBVy6ZZTy9p6VY2ujW/oRD2k5ot3eymOppuauX0m3jzkAk1PLl1occ7tYTKjmud0GIjmqlKmRgjgVSXitBsqtIbMR1UKjEZFNATLwaDAZMhvjHNSbWumevLGy0qx0mVWEokeINngYY4r0cPxjzcvyrfLe5jkLHOfoKADqUqLAsbMA8pwq/bmsua/ZWvBPvVqoFGCK4pNO7bJCjGD9+KBNsnGwjAp/A+WlfiTamfoy5YLuMbK/25rTiuskcs3g5HooSQEBRlRmu2OHJ1/RbL/wAb9DHTWl9Oe2b2H5+9UyhjozTZNM0J7KVcSQXEiMPs1cXL+VejwfhFrOSrnHY1lttroe1O5TmnEWGCpbAHJ8UCAtDmpUTniznFBe1ZNZ7gTRKmxXy2BHIFVtPiE9m8ZU5HNLZyaHnsgttuAz5zRs9D6ZZ+vCDt81nfbSej8umGN8EDI+KmyyqllZmt9ts7N3xRDY02yUW5bH6jTgMtbAntQcDNkD4pHtKO0A7AfenE0VYsHtQQxiymaNnECo3HC4FKqg6Jx2pxNYMdMgpF4pHCkkec0KLmI57UthMRHHakEjye1as4gwwO1EDMUTE5AJBOB96NDcbJpWiet/MujsRf8Pk10cXBb3k5uX6jXWJzVtfsdEsyqMq4GAorpuUwmo5Zjc7utHcXPUNyLm7LJbf4Y/8AN9648+S2u3j4pjFd1Z1PL01PpcVo3piSUept/wAgrT6f3az+q9SOkWzve20dycrG6gjPFdTkhdxGZMZ9g7mpUatVjQlgePmnE0PU71YbUtu2iiiRSafE7wGWTO52Lc/FcWfeT0OOeOOjU08NlF6szBV7DPmjDHeQzy1ja0nVNPha7kvI1/7hz27VrnKji1rarkVURRnuaysbSlr7VRp8JI/wjJNVjizzy0Rt+srd2WP1VJc1pcGc5Y2q1s2vZYUXJLnJPwKy9Ndt1Fr6UKxLjCis7DlgfpEeKz0vYiRmqkGzCJxTTsQRZp6LafpEc44p6LaaRjPJwKJC2IBTkLaLjHFFOUs44b7VCpXz7exkuSG2t60hB+Pca9HD8Y87L8q338LtZv5tQuLGeUyxqu4Fm5FOlGzTX13efiJLaOcWtna7lA8s57/2rn571I6OD8qvK5tOvYe45qFRMNxVBWa/ai+6ev7fGd8LYH1xTxurtOU3jY4V07p1zLFc3iI3owsEcjwTXfK4L6d8/DbSRp+grJJFsmlYk/bxVMouNWhii1AkJt9Rdxx5NcvNJ5O36e3xU1ymT2rnrqj1qMZolFhscU0IsRigFztIoIJkQg0Qy7QgnGKnKnIWvrUG1ZlHK80tnoG0eO40xkJyy8Gi0a6XWl2qpajAqYDE20MFOKKIQ1GMmDYvmprSGoIvSto0+BVRNvaYQE0B7ZgdqDZEeEPFELaG0Y7c5oIfZ/LoOAmPJpKlFCbRTibXmXimnYDDvUrhcpmkYewCg0wvHagE8jvWzJj9XA704GxaTaQ2Nub6+ICjlQ3b710cWEk8snJzclyvjiq9R6luLyaS30tc7j7pDwBT5ObXUTx8G+6rIdHBm/MX0zXEvfDdhXNcrfbrxxk9LMYUcDAHgVOlNa1LpW71/rTTrmRANPt03EnncwPauzgx+3bh+oy3np0C4mlYCGJcRqMZFbWssZPlGMgkRY+5NEFOpGkUJPGBRot7azJI2q6q6ZzbQ9/gmsOTP4jo4sPmrVQMbQMYrB0Of9f30k+pafpUDEe71JMHwK14Z3tlzX7ZiJfXghso4j+oKBV5Ucc1FKZPVuAMcYrOtcWj9W3k6z+lhlBPetuORzc2Va5a2N5PG9xbQu6QkbmX/D8VtbHPN7fRPQdnN/Corq7UiUoBz4rhyvfTuxl123D0wak9omD6UtHtkQ0aG01hx4p6LYojpyFtnZT0TG2lo9sYoCLCkIBIvtP2qVRwvq+3jsddlt48qikcY8kAn/WvQx/GODL8q2j8JtOuJNVub4xn0gmNx/2p1MbsbUr1tqVxtIVraJQ2O5y1cvP7jq4PlYYINYOjaJXJpK2yFNA2n6QdCCOCMGjRbcW0OOXT/wARJNJaTbbfmSWiP6WGeM/1ruwu8Y4uTrp9F2kSJCgVQFAGAK0jJXa+gE0DY7giufn+HV9N8xQXLbefNctrsiFm+c570sRkbPaqQC3Y96YKyFqQQDHFI0lyTU0zBhEkTKfIpDbUJpH0q/aIg7Haoi96jdNNJNohPxVz0zqN2vO6lThB5GubhIx2XuanR71FpIML9R4qvhO3k7UHtLZ7c5o0NoFsLQAlbc2KQNHhBTGwlI3Uj2KaZIEZoLYLrSVsBuDSVsBjgmgbZVuKY2r9+a2ZpxSiKVXIzjxTnVKzc0ZvdRbUoBBMB6YP6RV5clymkYcUxu0YPThj2xqFH0rJqmZaCZWQA88j60Gh0nry6lbXkQyfRuXQH6Zru4+sJHn8nedq/vL8afYSXDAEIua0jKuQar+Kl9I5W2hWMB/1fIpp7radF60m1rp8KR/1JbYcVOdkm14S26bLp1uLK0WLOW7sfk1w27u3fJqaHubyK0tnnlYKqjJJpHr9uIya3LrPW9zdwksinav2rqxnji58r55L26aWWTD96hrBbTbGrMVBJ7E+KRxWdRaZHq8SqrKrnjtzVY5eKM8fJsXRn4eRacq3N0WYtztJ4P3FZ58lyVhxzDt0qGNY0CqAoHgVEi7dmFHFUhMKKCZCigJDApwnsigMg5FAe2ZHajQ29so0GClLRwF4/FTo96cu6g6Nu+ouotSlspFKxbSPhjjsD+1d06mnFbu2ujdJ6E2h6LDZu5aQDLH4+lFKLO/QApwM881hz/Do4fkiUBFYNwwozS0aYQGjQ2mFFGhtyjqPRpV/Ey3u4A2Hlh9TaOwPGf7V08V+1z807dtlu4dO0t7qY4jhTcT9q3jmvTU9O6xtOrTdRwRlHtJBwfKnz/asef06fpvdTu1riyd2Idio3tkZFGJZnvTycCrRtFocjtQNhG1zyRSGwHgIPApCJpb89qk9mkt8CnotqTqDSWnjWWNcspzUXHV20xy60f0Xc9muRjAxVz0jL2ZuUyGFTYMaXsbcLucjnNLGDKm3TirsRKgi1GlyiBeO1GhsCYYpU4HbLl+aJBfRuUfSqqYjGmcnFEh2vHljxigtsAU9DbzJxS0coDw9yTR4n5FHiyTjtS0e2ViwPb2+tMRShs1omskEjimJXo85P0oMcZIpBIZBoDMkiRQSSOcKqkn+lVhj5ZSJzy8cbWjfhzrqwa1Pp7ACOWR5R8k5813zXp5u62n8SNeFloTxIxDSjA+opwq4M1w0rCNFJdjgAeaDkde/DXRZrDSzeXalZJCdiN4FcfLnu6jr4cNTdb+khfOP0ryT8VnjjcrqNblJN1zj8QurbKSxk0y2lYy9iV811Y8cxjly5LldfCq/D2zjWylnkg95OQxFVl6GHtsNygT1JmHLcAVk32rHnbKwxgs54AFIm59OdLhdl7fqPVx7U8fes7drk17biqjGB2FKQbTC0y2yDigCKeKCSBphjdQGQwpBkGmQimqhM5oCJYUjAnw0Mg/8pqVSFOibIW/S9qcZeVnkZj3JLGuyeo4b7rYyuDzTEV9229hjsM1z83w6OH5JlawbhkUjSUGnAnmmTTOtb86FLHqkaBnkiMI+jA5U/wCtbcPuxjzTrbcp1TWOlVhnkZTLCGO3ycZrol05tWuYfh/Y3tp1Nq8htpUtGTG5lIGQ3FY83cb8HV03y6YGuLJ3Yo2XLUsBksVxitYyGUAigMsg2n4oKE2UF6mqhkRDaKek7ECALzT0NozQLNbsp7UrjuCXVVWlTFZ5bYqQFPFRjfhWX7N3CcmiwY0rp7mQuuOxowPOH2T2mr0zBGFqdLia4o0C9xUVWKFr+o0YnTEhFVpMZjGFNOQUOUefIpWCIo4PamBSRigi0hHNI4XBGaSxlAxQGqq/H2q0jq/FMmVA5JPNBwUGgJg5pBr3U+pm2m06yjG57mcKy/8Al81vwTvbD6jLWOmj9KRNYdcXaxIWjjkZcnwM11OP23Pq6wi12wSOSUxlOQai5LmO4pejeh4Ybs31wfWVD7MjiseXk3034uPXddQs7RriVYUwo/0FY4Y3K6a55eM21H8SOrl0G2GkaeP50gwz1244zGajizyyyvbizCWacvKS8rn+tUTrOhzGz0KGIoA5UZrLKt+OftJY7zWrn0bSM7V4LnsKyyzkazG1uOidMW2mYlmAluPLHxWdu/atSemxAighFOKZJFqAgX5pBkSUbMUOMUyezmgPDNASzigMiUCnsaZ9UGjZaDaTilacCkkAhkLHA2nmlFelvpdqlnpttAhJWOMAGu2TUcAxx7mLcCkpWSNyQcd6x5W3F8h1i2YKjmjQlR3YGKUpoGQCjZ6aV+Idu97aabGqM6fmgXA+K14rqsuaW49I9VdV3ehatYWNrEi28KKxL/8A5AR4+K6cZK5bbK37RdSi6g6Za5gQKzoQU+CKWWO8bDwy+6Vr9zIcAV5uT1MUrIkDNGJZLGNxjvzVxnTKNVRNTkkASnfRQqvucVCzYYYxVoeZh6ZFAgFpOCXQ0sadgNxtgmEiLyTyam6lOdpyOGTPkiiiE9NdRNKo5zU4Lz9H3k4xWjPRVn5wfFTVRJX4pAC5kGPrU1cDt5ME0QUeS43tk4GB4qrdlJplZv5dGxoKWbigaASYBu/eplMx6vt71RFXm70GVE/uqaZhZuO9Aa1GcnFWjZjgcCgtpjJFAlTGfNCts+osaMWOABnNCduf2ctx1P8AiNFLACbaxfGfGa7eLHxxcfLn5ZN6v9HsNNu5LyFUR2/WfmqtTjGuM0ut6qttCdsQPvbwBWOeWm2GO262sKW8KQxjCKMCuZvBrm4/J2klxvKhFySPir47rJOc3HGOp4JdY1mO4iV5kbkPjiu2TTjyu70tNP6Yg0+JLq7ZWmHIFTlkvDD5q7061m1a+WFfbH/ix4FYZZab4zbodjZwafbLDAoUDv8AWsWm/g0GoCQNMkw1OEzuoINnoDAekBVk4phMSUBkSUyeL/Wg4GZPrUqZWSgJFgRTJXa0ZP4LeeicSCMkGnj+ULL8a221nxYwjHdBkj7V27cUgcmJW2KePOKlUVtwfTnZTXNy37nRx/iH6gx3qNrYaQDsc0U4A8+KlcLyT5Pig4pte1n+FaeLs2xuFSQbwBkqp7mqxvac51sjcdMW/X0FrqNndejJEMFmGdy/FdHFbpy8uM303/pHpsdN6fJaiczb23ZIx4raMdKbXLQ2+oypjCsd6/Y15/PjrKvQ4M94FbcsvFZYtacR8VcQKs1MtMyze3FFpSMQycnnijEUYS5qkpF/aCexoBGaQwS717Gp9KjN5Putww+KWXo5Ccl/6dmXPgUpT12jotwZYnlK4zRid7h8ze7vVbLTBGefNBBeoQcfFBlp5eamqiEE3JpQCPLk0UkfXwMZpgGWckYGaDCEv3o0NieudvJoATTZzQCpm92c0qYyyNjvRotqtRg1bLYynxTGxFbBxmg4OpBFINR691ptJsfyMJ/6qcZ47qK6eLivuufk5fiLT8Ounm0Pp/8AiM6n8zde9ge4Hit6whbqXUJZZhAuSznAHyayyrfGLzQ9N/h2nqrcyv7nP1rlyy3W+M1FonBqTElRbm1lgcAq6FSDTgafHGdI0FYY7YSMjMO3I5rrmXTCYxRPJdXL+rcKyID7V+aR7dB6dsVsdPEjD+bIMk1z5XdayaizafmpNOObNAHWQ0yTEhzRsJbhimA2akWmN1GxpkMcfSg9MhyKNlpISU9hPcSKAGzUjYWTBolAnqZ+P2p7LRXUfVm0u5it1BmeNlUHsTjtTxui1tnoTULy+02Wx1GPZc2LiGQg5DHGRg/YiuzG+U25Mp45WNvVERDhcGnpLX9WYpegdsrXLzfk6+H8SnrHGM1ltrpj1SaNjQb/ADQIWbdzxQYRUsCGGQfBpGtunZFtp5EJWOPbnHYVtw3usebHeq2uyuorhMxMGHfOe9dWN25c8bj7I9Q2P5i0Fwoy8Xf6rWX1GHljv9NPp8/HLV+Wqx43cVw4u2jdhVJRyc96DCmmxS2cgkMnsJzTlKwZZeeae06S9TPnFPZaK3Mg9MnNTVSF1m9S1Iz2pb6VIRvGZLBsDPNRFG9NlxZDIx9KueisE9fElAs6MrLkVSSs8pQ5Hmls9EpJCx71NqtPRPz3pbAkz+moJ8jOKZTss92QcrxVwaAN25OdxzS7GnluHIxnIpbGhwwZBk4NNPoFmO9lJxgHnNEivRfdk0tAdZSFxxTBcrmqY6ZCOhIP9KBGdxyM80jHjJyAO5IoK01f/htDqWrprN7cMye1jE3nHYV6E3pw+8l3feja6STlVQAAD4AovpU9tJsLeO/1J79xlEJEf/NcnJl8Orjny2NeRWLRIUHpNTzTLTWUna6vtQg8QyD/AErp459jDK/dQfQa41OFWRfTyBjGaq+ibmAFUKOwGK5HQCy80BJODQNDK+KZCrJg5oGmGl4pbPSPqUDTIbNBM78DGaZvBuaCSDUEmH4phg0gHnmkaQNMJpQHukIHTW9eYyAo06EJjt7BXZw/i4+X863BgMDNaM1FrsH/AG5R49prn58fl0/T33FRt+AcVzOlIJQEvTO3J7dqeiRMQx2oCBjFIwZ7VZ4JImyFdSpwcUtnF703HDp9mlskrOygL7jnA8V18PUcvNvK7bMNroQRkEYIroczXJunJFuHkt5EZCchDwR9K5Mvp7LvF14/UdayJ3FlLbL/ADY2XPY+P61nlhcfbTHOZeiTrjNZ1pCE7cY8571m0gkJwgqoVFLYqk6YMhoGgp3Agcn4qdnIr9PnWb1AO1TFWAXl4AjRHgU5fgtHrNwbMEVUCLuAc0DRi3myufpVRNiE43KaVEIlvdioWnCQTz2ogZuGMjZ8eKr2WtEX7keKuEDgBCfOaRwWH3cVGjHdPbiqJDYShHnwaBpBYsA5HNLZ6eEZpbGhhCPFWx0yY8eKQQwAe1MGbZN9zCuM5YVWM3UZeq2K719Vu/yRUkKAMV22/DlmPW2qdaXpkgS0tyd0hwAKnK9HhN1nTrcWllFDjBVRn71xW7rsnU0eVsUjE38UKjG+gmutZzRavdlRiKfDbhXRw5bx0w5Jq7W2l2IMqvuyVbNa6ZbWrttlZT4Ncd6rrx7m3i2RSPSOaCRL480Bn1+KNh71s0bD284pkkJcCgPermmkWNsigC0Bndig9M7vae9Bdh55PIHHmiCsCTALHsO5oOAvrNlbwtJJMAq9zRuK8a2DpSOGS3uNQiOVvHDgn4AAH+ldnF1i4uXvPpfsQT37VoyVer2YvbTDytGI2DMV7lfIqM8JlO2mGdw9KLV3i0fT45bBzdL5QnLY+lZ58c19q8eTLf3KCXraC3ZI30q5Lv8AK4rKY35jW5fpZ/xq2EKzXED26sON1GWOjxytYt9f065uGgSfLjxipk2rKyD3epabZbRcX0UbNyqk8n9qLh/YmaVvdWt1xbzo5+M81Hjb6aeWvat17WX6ZvbK8mRltZWKSOOynxn+9b8cy+XPyZRtHTvVVprav+WmRwjbSQ1dW3LpYXFxcwagAil7eQZLZ/Qai2y9NcZjcbb7NXaLdafJGT7sZGOTmnlj5Y6RjfHLbUgrywu6DgZ/UdvP79q4rx5OycmKpsrfVbzUJYrizhhiB9jeuGLfXAqZxW+l3mxi3uNOFjGGnn2r/mEZI/fHatf4LGf+oxpaY20bxxm9h9WTlFz3/wCKV4rDnPi89rc+lu9FsfNTlhZFY5426lUt7dPGrRsOCcH6VjuX1W2qhamK2CMrZB4o0FZ1CrJgpn3HxTx6qcvS00zMWnKrtyao4IxyM1JpwPtBz2qomjmQbTmmStmfDnFZ1cRjlYEc0oo0W9uTVRKulY7yfFVs48BuFAFhQ54pEsY7cleaNhMW/FFDHoDdjHNI0DAASMU9EDHICKcqLim3IyKC0Vc4/agaFtLho7uAoMn1AMVpxfnGfLNYVs0otsvK6Ayd84rtunFNtHLC+6ld+6QA4+9c/LdR0cU7XW34rnbsjIpAQcrQsMqRSJCaJnjJB5FacV1Uck3iY0rCSqD5rrjlpzVI/Sug47OM1zc+OsnTwZbx0XR6xjavM3FMukCCaCrAU4oJJIyalUGSI04WnjDVFpH0SKC0kqstA0Oj/NGwy0qKvJFVMbStkR9RDj3DntT8Mi85rYd5I9qgfAZByRWuPD+2WXL+lNLr38ThktLC3kZ84Y4xiozwyvUVhnPdV1noyx6ifz04MS+4x54/ejHjmM3VXkuXUdI0G4tJbNY7dwFQfpQ/pB7V08feO3LydZaX0SrswDVsxGjjZWBGeMEfIphyfqeCXpq9YRrNJazZaF152/K/tXLnhlL07OPPG46y9tetpdU1qbMKttQ/qk4xWWMyy9tbZjPtbjHaW8toiXdwsxjOXXuBW+Ml+XNcrNiXsWlW9obm0hUSD/GExj96vKSRGFytUVnq2htdGeSL81ffpDMu7Z9qjGYtMssp2sovycE09+0chLLnCrjnxgVUmMTcsr7avJdS67p2pW2s2N+oyPy8Qzubk888DFZzHV3te9zVUejLqnS7oRdxpBkkjdltvgsB/pWl5pJ6TOC2723zSeqrm5u5Ba3M1xvXCLIoCHj4rCcuVy1HRcJMNUQvqjoY7rWTvjYOI7JCXcDxkmt85MnPxZ3D0NHq1nqEkzanatFKVCRx3TAtLjzt7fvWfljLrI5x5ZTcCt+otPh9t7NHaT2znZbqpwE4+gyfNaY+umeU1dU3N15ZxwN+XR7g5C4xgZPyT2pfyTXRzhvyrbjq/Ql0/wDOXS29vIZSrqI97NjwPHxUfy6+Gn8G7ZKH/FLLrGNrXT7u7txHjEyjCnPwuefHapuM5bZlD+7im5QH6S1qxYsdYgumbG2O4yhP9Cawy/x+N7x6rXH66/8AdDVxa3Wn6Wx1C0Mcsb8vCN0ZXwfkVPJjlxyeUt/2Xx8kzt7DllttS04NDJHK6DkA8ipwzxzm4vKWGNOtJPywMnHwKsxnhIpGEwYsvPYfFPZaDmkKgjNPaSg3O2aiqhhITxxSitisj7cU0l2tyfFOBJbdgvajYNwQ7cZoCxRQq0QklXJpjbDKM58inobAcgsT80woIrjgc1Cjaze2mnTBw4oGjOmRH+JRELkLlsVt9PPv2w+o/HS7ulUwSyDjiuyuKNP0pSJ7mVu7vXJzXuR18M6q6UjFYbbyPbgKNjSaMKAngGmlkJ4onVF9JLasmCDjHIxXdrpxn7uJ7rTkmC5MffFZcuO8dtOLLWWlWprmdNGRN3AGTTk2nek9nFBvBBQHsqp5YClo4Ijg8A5NGhax+ahG7ceV7inqluGV2MoIIwR5pyb9D0ptV6g03SnCXE3vJ7Crx4cqyy5ccVTP1pppuBBBLu4ySBWn8Mx7qceXzuo1jV+rri6vPSsM7V74Penq0+jOh6pe3++2ZnUjncx5WlhuZjPVw1rtbG8u7cGC8mEhf9G7jNbb/Tn8deyrap/C5mW2nQSv+tFXJrK59t8cOvZxZY71Ek9VY42GLjPf9qUymV0MsMsJulekdctOnertWRrljpzhCsjknkDsK6cJ1py537tuhaf1XZz688FvdCaGWMFPo2Tn+2KNXY3LF9caklteWx5/nt6f0zjIqbdVWM3GqfiBcobIRRgC4BEkYY8HnBrLks3ptxY3Xk5vHdajviiUqryE4VjtzWUw36a3PrstYamk19iTUDbRliGcg4OKmfb16FssX9zrek31tJYmbUGjjI5jXYG/t2rTy38bZya69JI2nabFGmiLDHdOMF5mDkDuf3rWY32z899Ke51u+sLv/wC6zzBuCS3A+cU/48S87+13031PeSW08MUEcmfdJeSHBiB454OftTkkK9/Ky0LS4dOW6/MvDfQTDf8Apz255B/2pXVmjx8vJp111SkWon8iIbZEY7wEC47+3muaY/NdeVlnQg67d3kNpbRrcupQTqeSfsO1O3O9FMcZNqp7TUbZ7fU7tLlbp90gnaUuuCMgHHbx3qfHd1F3Ozt7+K3eopHKtnGLyMHdNwBL45+TWkxmEmO+mVzuf3Wdl5LeaeG4lmnFraZBVUf3ufjH+9XMpfSNX5W1/Y6ff6NpcFuw3O5PpCPMhP7fajc9HJ+mxaHFadIGJ53xNKp3qF3OD9SeQPoKiZ7vQywuu2v69rGpG6C292BcMC3HtAHgk574rnmOfrPKuuZ8XvHCLDpz8R3t5VsL8LcTjEfqv7snyOO4rbiyyxmrNxjzcWGVuWF1S/UluyandapoJaGdWzNatGUH3H/Bqs+HHO7ZcfLlj0uuj+ql12NrV4HjuYUBkO0gHxWFwyxurHVM8cpuVs5QNUqQFvyeKNFsjc2/uoAUFuPUqTWUcI+KchJm2z4qtJtSWzAAzijReQU6KnihUoSNkgVJnB2pkmlMIu2KAQklIcjFBtbVsLinSjK3GOMmko3DcK3mgmx9Ow+pJLcEcBdqmur6edWuT6m9yH9V/l2TgLywreuaNUt12KfHNcXN+Tu4PxHEmOxrBuyZSaCpiBWkbaoyfirkqLZEmuYIGZJJFDL3GarwqPKKm56niiMoiTfsGaJFSxDStauL1km5CE/pzXVHNlG4w9Q29htjuFHpy8fen5aT4W+nrqytbq3lutOkBbG70yai8ON7i5yZSarXotYijIF2pibt80v9Pl8D+fH5En1hAVFuUYHvmqx+n/abz/8Apa/qHWfoTvbxhWcccCozmGHUaYXPKbqvstYvJ73/AKkbYif1bqMJnvudDLLHXVbpY3GmRghpn9QDPet5gwueutk59U097opEZHb+lO46hTPd6UE99eyX0qT6iILY8KUOSp+tThZMdRWcty7qje3DmW6uLmOcRcgu36hWuumUvewm1LSryBQ0JhdT/wDjXG4feomCv5PkpJBbRXJ/hJbc4yWmPaleO1c5ZPQkUNxcITHfhJxn2jjd+9PHGROVuXewbx9SiljlvJiVi/SCc0a+C3803BHqGqtvhjiVUGTITjFZeHi6Jy+Xo/baS9rA23Vrcyzvsb1GyB+1GOM/KIyztmqodY0sW1wsdjfR3E+SXR8ouR/lJrTz8fbOcfl+JTRW1hbnbYRTSTI5kb0xwrHjg/FaTKWbRcLLp1DQeotZ1xYLe7so44bcZNw0oDiQA/pHn7VF1VYyxsGp3SXUVncanOLIwZErbAySk4xgnkD9qnx3d1dz1LJ8qu90/pu2voL++vpp5dmIpA3Cg54wAMee9LKyXtOPlZuLCx6I0CO1F5Z20OZP5gNwvqAqeT+rgZq8cZrcTlld9ldc0nTZ+n3Z4PyzO+YYEm4bnA4HGO9LWp0fl5XtqF1oNxbW0EtuLdDtyQZNrvzU5cmM6yq8cL7xi66f6Zhm0mV7oeoXBVhKo2w89wc8k+P71UvlNxNll1T7aLp2nWIS9dQSdkMMIAYnHGSo5NTqqlhHSLhbe7ngknuFctteMuDswMccH6Uxr9KTqq60l7ho4bWNxgq0jIBlh5H+9OYwede6f1cXWly2H5i0s33KsTekBgeefJNO4pmRPV7nVf47LFNbyGEErsiJYSAdiccVlJPbXfwrBZXty35uSYWQ34A3YBGclvrRMcfQty9mrfT9ENq0s2q7LrHqBIlyTzxk5xzj+9OSFu6EMEf8cgudKC+oqN6CySge75P75qZe1Wdde1BqS6vaCR7kXQkZi75OAT+/6hmiYQpkVlt57q1kuGlSJguCpOdx+lVNTpO7XundKu4tQtb1bd5hFKGaMHbwD5Jq7Zeim5G49W9SWplW6NhcRNgDIYbZOe3B+4zR7pT0b6a1mxlsLpYg1pcSt6sQQbiIx2B5yT3qT02rp/WoNYSWKOQvLAdsjsAu4/QVz8nHq9Oni5NzVXfAFZNlZcsPUOKVNm2K5GO/mkO1lCM8VUKmdgFUz2g7BRQSpvZe9TVSl7Z9zUtLiwEgA70w8svJpbCMj5FPYkJMfcalTXyuBg1RQFgA+M04GY5NvajRWui9OQldDj3d39xrt4prF5/Ld5AakWkZgre1BVVMiiisrmeNp8BYy3eufPjuWW3Tx8kxx0VaW3h1JbSZ3GeQwHBqf9Or/UwzJC0V4npj1ITyfmrx4JGeXPcp0Ym1Sxtr+MxMUJXac9hW1k+GMtad1RPZRXTf9UZJ5RztPaotnpcmu2pyvLa2wX1XMkh5x3xSyxlPHKz5G0LWp7TURB7/AEm8kdqrWux5bum2arcRazYrBFdBblDuUZxk0vbSTSx0PWP4RZia8m3bRhgO5pTezyx67U2u3+m3l0LvSb05k5eJwfbW+OTkzwk9UrpdpcszXVzG7pjslPuVPVnovNIy6kJ47YRxsO7Dk1OpL0ryti2ea0Fislw8aA/4VXk1GXLPUnbXHg63b0hJqlq9v7i6wYwGA5p4cuVmrC5OLDHuZKi/6gsJbIxJ+YBH+XjNGWfl0jHHx7Us2pRi59OOFtk6A7ifNGHU6Vlq0lBcxmZxOWG04KfNabZaM2a6ldzyPbWLsoOFYjAH79qnyVMflaaboOsTXG+5mto4y2H9wYqPsKcpaXzWVhpaKbSG4u7jklvSwMDvUb7aSdbkAk0V79WW7kFsZG/TIQGUYJz/AGpfySXRzjtm76NLo80VlHbQGX0GYB3iX3kf6YqfLfaph1qIa5aaZ0xHZ3Fxcme7RhIlqI8bj8t8D/Wtcb5TbDKeN01ufUm1nUptRuYk3yAcsuCo7YWi8cvtU5LJqNo0/VLSK3RiyW6qNjj08qy+D/alcbPR45S+zVndvqcsdjZlMljIJbdUQIPk/AoxnzRlZJqNiXQ9MiVDqN36zg7gsbFgT9sAUbkT3SN10jo+pXy3NrLHHIDxbzBlif4BwaUsp2WT0W1a81fp/Vo7qWS0jKxe2JZCFUeCB5HjHPaq79Fqa2SstUuNQuJL+6kllwuRsXf6hXOcL3A/ancRMlBroOo3C3kVrNIsvKCIEH4wfrxWdll1pcss3tcdLpqdlZXDXE6WJ3AiEgliO3zyf+aeOoWW7Fieqrm0vT+Yt90sDg4iX27cY3MTzn74qvGXuFMr6rYZNTtLqye7FvZyO5VjuIzjxz81K/joomhWmsx+rc2rouMqJpAhPGeP/ihPtr56XQanAtrZo0anEiMp9MAZJZmOMf3p7L+iGswSabB/DLa8U24JU3iSghz3IIA3Yrnsn41tv/uaq11c2Mk1rcWckzRkq+ckKSPB+oq5hr0Xludvae4t5JUktVHqITmRlJUd+xp2WTsbi1jFjDCkq+uG2naJGBC5HgZ4qMsrbqLxx+3b3rF7X8o7NcDJ2IZMKvPPB5+tOd0usZQ3sNPnkiWZ/wAvG6EpIZMj7EDsea0k2zt0Hb3s2h6hcQwwveRupUOq5JUj6fFGWIxyWmjrqmsWstsbOG+tIlIXA2SKcdxnzU2ZfAlxn+xK3u7XRtQiZ9OMKxwtyOWkJ4YGr1+yuXxCmlXs2najHqUM7BWcfqxkjPIx81GfjZ2vDyl3I6/a3yX1ol1GSVk5GRXHl1dO3HubJXb4cnNJTFpKd/elCXFu/mqhUw9wFFUjRWSfd5pbLRCVfUzzQchZd0bHFJcSM7VJpxyNSArMTRs9Asfce1BqZIGSLaxyfmrSA0aZO6iASGFJJY1UcFgKqe05enUEZEs47eFCpVQtd86mo8291S3qPIZ41U7VXv8AWotXJ0BoN9DFpjrfusYRyAWOM1eNmkZTtQdT9TaVYXQWO3MjMudwA2j7nxSytnqDGb9krXq6x1MRja8E5X25OUP3xUzPftXjr0r7rqDTvfHfQS+vGSJAi+0fvR42qmUiqXT9Iv7hLuxuTLMBzG7d/p9DRot77U89ndC+3xxqyEnjfyKJBvZM6leW0yvgepFIDuJzx5FXJuJt1T+v6msk9pcxmMrtyTGMEH61OOM+VXO/FKxa0kmRIm4DkHdx+9VJr0VzuXVMxa9GqSxwpHDIwycL3Hj/AJo3aUkV8HUOsxyqPzZxngD4oJsOo9SS2dpCNu4AZckAj9qB66a1e6k19bsEkUxZ3YzjFEn6G/2Siae5kjhtXk5/oKfZTVq+fQZJrUZvSZwwUxrjnPx81Mxk7qt31Gw6Nomi3VssV07rJCoUq8qowfzgHuBii9dw5d9U3b9PaRHctczwr6aSYeaSUKvY8fU9qmZ6irj9xe4tV1WSO2g1FxbKpClYdkf7EkZ++Ke/1E6/Y9kbLRNRubW4M05jhBi2HcJG/wD5HenZddFLN9rbQrC+vdQe5aOS2tpc7pDwWXGQFBGO48A4zSnHqdqy5d/iH1ksGjaSiWOnxvucepdTlTJkcgZOT+9Kz4gl+apOnta0sJ/1UTB/WDK4Y8Ljnz3zjj4ox45YMs62PW9ZsYVt4pRFdQzRghnTeEY54JzwD3GTU5Y2ejxyny1nU004Wq3WnQs9yp2yJEcr/wCn71eGds1U5Yze4R3WcY2XEiqTwPcThs9iB2FVc4Uxq40S4bS9SeWVofyrxhZQj7vac4YcDgEVPlj472PG3LUgt31Xbte/ltPlWRsEB5EwOPI57Vnu3016nts+xJDHJIkgPAXC5y3ntTmNT5yEOqptPuLmGyZmuryAEOFAbZkZC+P98VtLrpjrfbTZbO4jWb0Y2XcdpEDYKg+CeKfsek+mtUGlteT38jyOoCwAj9Dc8/Ss88flphl8ADVbe4upXeGMq7b2LKe+c5yM4+9Y3HU6bTLfs3FFNc3Dypdj80CNgkcBfT78/J7Vvx68Iwzl8qu2u71LhGjFo0e0F1lhLHd8kAfIp6ns5b6WXT8N489zc3cXoLv3AbQoP/8AIz2yKVuy9LqLqaG0sLm0kVbgTIzALgMWPkgntxR8Cd1zGCdoddgDxBI1cGUFPULA9/H6f7VlNa22ylnRKUA6sDZ6l6zGTuI+M9j24ouW53PQwlxt1fa1k0iIwJIbRgre9JEYbseM57558nxSucl9nMLZ3DF10vZhfzMmo/lULDaCmJD275x3PmnM58wrhe5jV1qFrFZdLQQaXbxSBRvnmlxukHOcZ5zijzl7E476c5vdkExTc+JJCXiB5UeAKrGeMiMu7Ql9OM7pJZxGGyf5hBIqvZemy9K2j2V7JNLbOkOwzR3LO36B3H/mFO+kSd9j9aSeqjX95ZvDHMUeIryTwc9u3IBxRDu2tWMljrUkFlI7WTf9uMY3K2fn4OanLCW7aYclk06hoc3p6AAzbng9gEY3bx9B4rl5MPl08eep40S9juVUSSQuiMOCayuOU9xrM8cvVDtH2nNJSyS7wfgUbLTD3Jc4zVQqlFKMc0tlofC4NMaKuoyQKRhNEaVgSRdowaSo9JKEFEhkJLz3mmFTDfFco55Par0z2k0ykkHOaNJ8jmjSRfxGJZDwHBp8c+6Dkv2XTfdU1mHSYllflTyceBXdbpwyNY1Tq0tbNPZjbEzBMD9THv8AtU7PXXTXr2cXOh3s08DRSqymFw5y3PIxV42M8pWoNGslrOL55IINpaNnjJ3v8VV3eqJqXcUdnEZJdyXrxXKe5FbhT8j6VPh+j89+1/dML/0/U32zOMOV9wz5II8UToFY9MbSJY7xZBPCH4kh5/qPFLWzl0lf3Rjuiwu/S3+9T3Rs/wClKbXqX+lMBeLev6qJtPORyD9qcstTcbHpGjlVo51Kk8BlqkKtLeaNpE3hRjIz5FAix0rRru+WVrOEyLF7pZD+hB9SeBSPfwaudBCQCR7+1UqfcEct/oOaWV1N6PGbutpDR7yZMLfWkkTDlRIcn+1ZY82NuvS7x07pOkaTa3QNxE12w75fEf8ATua28aiZT5Wl8tnEqizaztWPCxgnI+uf+am3Xs5N+i2k2N7f34gBnaTfiSTb7UX5471VpYz9HH06Aavc2Xq+q0KblGBGWfvjk+PNVvpOu0BdXNjdK8svqkR74sptRD3Ixj/5pST2d2Jp2pXmoWLLJIq3F5PtVmcAIMjGAew/5p67G+m+WGkaTYwKJTI93vCyzEsZGI78/wCEc9hip3stabBFJBbyG3ih9TIzGjnOw58ftQTVOr4/4/MkFoqPc7yoWRuNqjkYz+rJ/saqf2BYdD0y1SNXtVLLAyyTK26JWHntwfOT/Slsa6J2HT9m9xcNLqEE4uVZEGMKQ2Rk9vntjvStlVJZdkra5uNItngtLISoFMYYQSbQ4OM/B5peKvPrYEHS1rfWEkd5bvb3zneJSXXAxk7gfFLUnQ3b21q9ht4kl0yK8jE8eNpBZlZfjdjkeajLGfPprxZXf2+1r01oNvaXButXgt51ZAyKJG3HzuwcDFH4/wCx78/9yXUWq3Et2t03qtAjFECnnb4Hxxz2qcs5lNY1pxcPjb5xHT5LO8T86ly0RAwIcYYsD2OeDxVzlncynbLP6ezVxvV/+9m9Ta2tbWOSaW6a3uTxbBASo+rY7eac5N9RN4dd2k5VguLd4LO9iZh7mhlXbIBj6/qFXM99WM7x3HuUlBYtczruUCJ8rgezP7ee1PxnvReV9OkdH9OJqVos80cts0GUT0lADEY559xx/Slr9CW/LYl6K9GNnSe4M+P+60e7k8ZA8d6Wqfk1G80K5sHKalqB3rIdgPuJHz34qMuSY3tphxXOdKs6cL22L2khttwCvKy7WfJ5APjx2/3qP5vP4aThnHd29lv4NbwmOyuLqZppQQVAyzr3HckDtxWfl+ms71s5L0zdwywyW9rb28efSXPcD689/wC/+2urrbLeO9B3ESWMjfmbuPA9wPpllHfABJBABB7d6m49d+1zOTdnpVwLa6prEcEl0WHDJJllJ+nwPuavDG63WXJlN6jd4tTTT9NnsbkpdJndGXXKuMD2gjuR2JoyxmMLHK53bTLaS2/NXj2dsGGSyLMmWQH+5xSv7PG/BfUo86ZI6xQrchvcAp5P79qrCyJ5Mb+lbD1FrF3aw2j7HhjYNtfzjsK06+Gc/VbH1hrzarpUESW8Syq/qSKXBIPwKid9L6nda8TavGHs7RoZ0ADPuzk/JFVMekXLsxbdXWlrZR2u2SN3O+QoTlHB8ZPbHiouHl7aeevS20zrK6hlaGb07+0flsPgn7A9iPpSywutexjlPfpsNjqVveBQhWN27IXGW+1cmWPfTtwz37ObirFSfcKie9Lv7DLsWrTTPZiGbb3zmpVL0cSckZpG9vIP370oNPFs80UAvKQST3NBwhcXByeaoE1YOC24DmnobUtlYXkoLqVkkQ4AU5H9a3/jtcn8sWnpGCJ7mQoxQYEee5+Se1P+LpP8natiu5rt3kJjwFBWOLwOck/NVjhJ7Tlnatra6WfSzCZRNcb/AOWsgznP0z2rX4ZbGTRJzdIl4FEj8mNAFJ+/gCjQ3asL6MLp7RWf5dAvMjMeML2HPmqmom99Nc1e80ieOOESRvGRiRQSSv1AHnNLV2e5po76ZNPcMFQsVYiPjDMng4qre0zHrZ6HT7u2EYkRoFZuWc5A+48Ub76GuhbuSa2gY2zxPFMuGTP17j60dU5ue1IsTQ3IeV5FgfllZe/7GlZFS5SC3l9DFNH6cb+kT4PIHxUaXMuhXe1dnkjJRGXgOM8/Sluz2esb6ISCKWRYARNg5GOP6mrnfaMvtumwWl/cW+kTadA5MUxBkhVMJ9B81cjK0slrae97+4ZbkoRHHEOD9OOKNHtR3lm8EYe1lLKB71Vs7TWescquZXFaaQ8zWTSyOQ27Ckn9I81eM0WdmV6MNJBp+pWtzK0dzM5DhlP6fIGf96Xsp0uI7iLUNbmmHp2yyZ9QiV9ucZJwD809SCW0Ow1H8ol6ISplkI2T7NzIoJzgnsDxTs3Cl7C1XrC/mktlislmeJQqO0e7jjPbg/0rLKW9NsLJ3VRPHcy3aXLymA7Q2FGSpx4rSS6Y2x0bo/XLkwXf525M4t037yMyMOB3+M4oupNiTdWUQuGufziSh1kHqKCRlee3z8VBhajFHpKtqcqtGjSFVVAAHY8EnvgkcUrlZFSSkLbqyTVnhsLa1VAxKiOSTEeMjg58/wDxSk2L0tnjjNrcSS6fJhHBRIsky9sY49q58/Sr1IUtrOmyRdQXlz+YSO0khiUfl1kEZ4J7jGR3HnzSs62co+rw2eoWKQWTyy3JtivEvMRAOcH4PGfmmXr25xfaHPaqs0FvczvtDyj0vdGx8EA5x9SKrSdqu6u5EaI3UEkSnAfex2jBz2P7VGWLXDPXUWM2tXN80YtLqBAvuLLgYH+nHxzXLlqd2OvjzutSpTSzrOIbZbSaZQGkYoOF854xn9qMe92pzy+MWEGssJsrFLCThQvICfIP0p/ZrafLL17QidUmWxmtTIcHMxfa6AjgDGPvirmtbRd32s/yjQEQ2uUVMencOmMfXJ/1rosmeOnPLccmy9KajFpaq0aPcXv+OWebjae+0cDFTjhMPUO5XL3VjrfWusmCRLP8pEpjYqwuMk4+D898CnvcGpGmy2utXds17O6bFAd2Q+ow+u0nx23fSs7htpM/E/ft0/GkNq97600SK4CuR6rHkhgB/wDFEwnxBc7fZefXzcT29lZfl4jEyhGAByTxtDDwfilZfS8bNbA1LqC/WWVbm6SOVSAqbduzHBAHc5xUT5kXl13VDLql1dIsT3OwIdxdrcLkc5zjP9K1mOp6Y3Ld9oWt/wCpdLuEL7v+5KvAK+ftWs9MbvZaW+v4GM73DiCCUtFEzEjBOOx+1Z5TyjXC+NEOswwTNcOgX1Bgek/Zu2RmouG5qLmerbS//iCfe8kKI9qkpYpKQzHPbPzVTDHXcK55b3L0Wn1RmnZLYAl8cbRx9M4p62Vy7MziaC3t7i4uYtzMA0Q/VnnmjuTovftC6uxYSsbT3B/a0hbsfgmlx55Xe4efHjqWK+0gtzKxmjM2Oe/B58VXfwOt9vQNL6siw+0RtiNyf0j4qM+u61475fbo3ZS3356OYyEBXDFu57881PlJNfsXj3fL1p1K31i1vZhAlxH63+Jd2T/75rmylldMu57MhPd37UJ0OiAc+aRmoVGO4pHHnYA1NVHgcLg04YEo3HFAI3FuQM80wCg2rjFVtNhY3+z0002wIKrszg7f3+K73mQo8AZFaWcvI/OFB296V0YcKTwepdsmbYDY3twu34pa+RvZrTLqea/W5tLeMWiMQWGAQB9fvT2Wl++o7m3yRwyNKAwUOdw+x/8ASnvQ1arNUsrOWxvLxJXjmjG4xb94YZA44GPtV4aTltqToqQF0iRXznJ4H3qkGdIurXbcfz2VlXKOre0t9fpSqsVDqGoXF5vkccycEJzjH96WjV1y1xMI4o4FQpzuRv1Glob+BYzrFxcGPYf8pBGQT8VGWUntphjb6bfoOj6Q6XD65ewQtDgenxuOf9f6VPlMvxVrx/JR9QxwT3Dx6PG62ca7izfqIHc/Srxw17Rln5elDAEtog247nOEXH6v3ouVlkkGOMyltpqKK8llVGVkVid3u/2q+2fRrUoGs79pIPVWNEx6h/8AyHueaVujk2Ws5Nkct2kaRJt9zOe5+g8mjchyWgz6teXeyAEJGMBEQBV796m9qk10lHH+a1JY3fCwgNkDuaePpOXVXMckvMGnNmRjyu0dsY89z3p0QxFZXbQ8Bd3KiNcBmJznin6Jn+H3mYbWW3kjZxuBPAK9zzU7h6voMzy2UkZiHt2cMyDJ+1VE+l30vclFv7aW9jgiuQq4dPryfpSsthyyVfQo9q0cUjLIQxBcMDsXOMnH2rP17X1fRu5lWUQkyg2sTEyWzw7gx55Vvn7iq3E6EsbHRknAjxHNvSVWjhCvGw5wSo5+vHY0bko70avmlnWUQBZJUkCrIGZm2HJ4Hgjtz8UU5VRqkTvM1xa2cb3TRiKORJG3ytjHJHA+396Vt1qHjJ7bJoOlXGj2kU9+sMM12PfByxQceaNWexuX00nqzVZU1aWO0MrRyLuaWNc8Z/T27DH96nLGqxzkatPcyX2lujLFFEpGBL/3G5+wz48U8Jos7v0lbaXDAomuFVIXj3qkR27sEitLJeqjG2dwneWkMq7bf1YAScyEknHxUTCRdy37BIGnuIk1C4ZWABdV4Hfg570XCWJmWllBY219BJLfauVbblUReWYcjA/9an+PS/OX2jJfXEtr6Ru2IDBc7SuR5xV4466Rld9ofnLy2l9S0mRNg25bB/aryTjDNvq1sZoFuFluLvcSyrkLzzgr9Pt5rLy7a+N0v7K1Wa2S5n1OS3iunbEMakMrjsc+ADVb7TrpqOrM9pePDcuVnf3yzZyxHcEn61VvXScZ93aGmX9tZL67W5uJmPAwfbg8H6Vl3bprdSCrqMKXc1ybQXXqqVb1CfYPpnnP1q+p6R3l3Xrub13MtlarDauR/JLF8YH+Y80bHjFdJc3s1qLaCFViB2jauWbP/vxT2UkWam6bTdt7bOyWmF3yDDITwMjuR/pU616VvdVUYhubd4SQWR8qM85zzyfFPWondtSZBvG6BbcwxHkj2vngffvU4xeVU/5ciQMkm4htoAOCapCc0Yhl3sJGEmR3/wB/NGj3FnplgklwiTBntJccquGU/IHOf3pWW+jlxnsHUrK5triSADAQZUldrEeOPFO3XRTHfb1tiFxBcN6YZAynbn3fBrPkx214uTxWsLJJa3A4iY4CMxwWIpTj9WHebcsvuo3EVwphWDfbOo3MykDePv37/NEwk+BeTfy3zRtTjv8AT4C0qm5VcSJ5z81hnhY3485VqrnHas2nswjcc0laYZiaVOF5LkgEUzShlBGSeaNE9NKpFPRbVzXCqxFPQ2GurXEukwwKrwuP1A+4n7mu7yuu3mWTfSkGsSWcqAoMA7gM0t6Go9FPfa3JJvMi2qqXmKjhR8/U0+6OovrMtp2lx2axiBLhGaTfkSAePHn4qpOk29qSO5ke2uGtYnuFB3O+dpU5wMZHapqoBq2qSWWhRwRP6bXjEOwbJ2qMkf1I/pVYJzafearKYFjViqtwec5q9oHS6GlpGSVIK524/Ufr8ikYWl3cUs77oyigZYjnjNEoPLumANtbmR9wA+tMaNXo/KsIVkkEq/rCHG0/A+tT79q/H0RkEZX1m2+sjHLHPP1NOST0m3avMu+aQQs5aTh9hPI+MUrvZz0PEhMyyyholhIBjPg0vGb2ryutNhsprd7B5sKELYVHHO6r2jRJtL1rWog0VtLcMJNpZVIUD5qO72rqdK+86cvbd0t7iOW3P+V0Pf8A3zRo9nNK6amuGd5g5QQnYu3cc54P0paV5RGN1imLXQjUEYwE9vY4wB37VbI/ZboLZ5raSKNmAw7n3L88d6DiejaxJaalHdSv60aO2WP9+/3pZehj1T3UF+h2Tm5VljLCKGN/cA2Tn696nD9NM587at+ekGDIWEWdwi8n96pDYILEPodzctO1ncYJQSYIIwCMeckHFLy7HhNezvTk2o2cYtx75LpFkkWQZATkAcDufJz5qbe9Kk622S8un0+zSeWxX0f+3LskH6u3Hx/TFK9CTaen2dwl1dJFfRT3PpepFbuxB8fqx2JGKcnXY+emta1c6vFdG4vPThhDeyG3kyAR7QSfPJPnvVe50n17N6R1JfqkEs9wYfy7e1E24lXySO5wKiZa+2+1+MvfwPqX4i3M8rRuFnjQ7Y8cbh5Jx2q9X5TufCguRbzehexXMlvGEAEUhJ9x7584znnFObnSbqq/VtQa6lSe4cpbxLtQquQTjsMd6L1DndHu9Yn1SUSthYyoWPK9gBjGP2z+9PWy3oGNbqZTCU3e3LMfb/elobKPF2AG7bwcEE1STVnZQ31tK6qd0WMMCMnjt3H/ALFK1Ui96Z6PvtYgknt5Y/SUnksTg/GMc5+aNjWiN3p6jWpYb68hiRMxyYUsMr8DHejXRb7Vl3d6fJrLCzjWCEFYxIqkFsY93Hk/fzXPZd/06ZZrr2b1fOiGWCxvnljdQ4RWyVJOcn7Zx3p+UnReO2uyXpniyVc84MjfqI71puXpnrXZ21tLtoxNLu9DACkMQCDRsSftfxWiSQ7lwp24KsCc58g0lK9mniRgbYvDggt/hP2FBFdHv57C/wDWtoxI0fbcBx84B81V1pM2nruvS6g0yKnpxscSISCxOfnils9KQWV6sZl2OSoyc8kilqHumraWfVrGaJk9kSe1sY58jP7Ua0Uu0dIgsob+D89NtgJBIY07SkXPUkEFnqJhts7QvnnBPIx/aiXfYs10ro91q8Ui3P5okZIHtCfQ/NPdLT2p3ZgELsWlmYZDEnPHgn4+lJU9Ktr6ecB5JgpHZY19x/enRJfgFzvjeVmZHDjGST/elBehrfV5bOGVYCwMg2lmOSf+KNH5dM6bqE9o5kgd+CMjPalZ12WN76roeidUGQbNTQx5Psc9qwz4u9x08XNqarbFmR0DKwKkZBFc+nVL+kJZ1XjNLRq95dznn96JBaJGSTxVISlJ2FacTtTS7jI2AaBtU6lO8BjDSmMOMOyndt+ftXY4R72xt454baGaKWWaD1BI5IZVwSSAOCarXyjyvpHp/XUt7eSylcRx5wCeMc9yfJpS7VcT99q9pcmRsi4G7aPVfgjH/FPZaUgvmus2ltGIIIzlpF4ULU731FyanbXNY1I38/tOyKICOL7eT+9aTqMr3SECvJKsUgzGCWJ+BRsaSnuUnnB2fp4QMOCKR+oftZOMALg9wOKuIMC5ito1jJPJyrbwT9aPR+2JzHcsxEkgRUySeP3qdnYEoV0jZpW9EkkjtjmmQSkiVhFiMkFAdvOM8/vU630reh7uKP0liWQ7I+fd3JqvSfaw6eurkXkCwaYlzCh3spGSSOf/AGKW1adXkW6kt1Z5WQlQQu3AUkdsA1IJ28tzYkvJtuFzsKOBtI+1LditSqjXprS0mv7EMjIvu3eocFfjHkirnaPTQxIklxtMTGPaQFc9vrVEetNDvJp4JYoyWnGYxgYxnGee3al1Dmx7TTQ2ppbzzwwIMk7Dkknx/wA/elTxKzWCCKWWBh62TsAG4nv8dqNlpW6clu0kc0zSe39RT9WfpxT60SxE1xYwStBdOzMhWMYGVB+vzRrobWHTRur+2he1b+ZbARSxq5D4yTuyfHP9qysu2suOl7rWsrZxhAyTXrvl4g28IMccHjNVjjddllZvoFYLi10qbVFuxJdSsDNMgw6Ajzj74yOOKuIu41XVFbUVNyPWc5wspY4+tBQfpbpO61TVysjOLVYy8koYHAzjIB+tLXZ7um9P+HqWsPpRxvI0g/8AqQQ20fIXI/pR3s5rTUL2ys9MYW11K02SVZ4nAPfOGBzinCUk0kay5jgZLU8iN/k+foMUeyXekW9lcRMbmVkiVCylgf7U/U6Hs2PRGlzzxQ4SOPLmXIbaR+oAjBGfFTv9nr9Nba6eVFWJJIg5wGAwzAf6CnspHS+m+l9OuOnbV4/UUl2MknGXIJHHkCpuO1TLXS26l1K46c0CS406NleMIv6xtx/zz96LbJ0MZLe3JJNUu9R9W4lWFHzuMi4G7PfPz5rKXXy11f0SllvGgUr/ANhckllAz37f3rTW0S6Ky3b3LY5uTt/Wc5A+KWpPg5bQYLK4v0LRFQikBtp4H0p70Wtt/sdPkWGKKEOwVeQxHuHg0QVG5iDSNbYaLad3u4Lf07UaEL3Mwt7T0miAQ8e1uB9c0jhXSrQJP66qTGzgbhgn68UCqjXrRIr8JDHy8p4HB5NMltLciDR7gOmHC+3Pf44ogqm/KmGytgJRskjM0qKcYYnz/apt3VY9Qo0f5m3dXcZhkGM/Uf37VSPhstnHBJHNdakm+KWLEcmeEYcY/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+                   transform="matrix(1 0 0 1 338.1834828785937 100)"
+                   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"
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+                       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"
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+                       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+       y="197.10948">Equal</tspan><tspan
+       sodipodi:role="line"
+       style="font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;font-size:18.765px;line-height:1.25;font-family:'Caveat Brush';-inkscape-font-specification:'Caveat Brush, Normal';font-variant-ligatures:normal;font-variant-caps:normal;font-variant-numeric:normal;font-variant-east-asian:normal;text-align:center;stroke-width:16;fill:#000080;stroke:#ffffff;stroke-dasharray:none;paint-order:stroke fill markers"
+       x="543.70557"
+       y="220.56573"
+       id="tspan24">height</tspan></text>
+  <path
+     style="fill:none;stroke:#000080;stroke-width:2;stroke-linecap:butt;stroke-linejoin:miter;stroke-dasharray:none;stroke-opacity:1;marker-end:url(#ArrowWideRounded)"
+     d="m 528.89349,360.68272 c -5.52919,-65.25209 -59.40926,-37.1211 -94.349,-38.81788"
+     id="path26"
+     sodipodi:nodetypes="cc" />
+  <text
+     xml:space="preserve"
+     style="font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;font-size:18.765px;line-height:1.25;font-family:'Caveat Brush';-inkscape-font-specification:'Caveat Brush, Normal';font-variant-ligatures:normal;font-variant-caps:normal;font-variant-numeric:normal;font-variant-east-asian:normal;text-align:center;fill:#000080;stroke:#ffffff;stroke-width:16;stroke-dasharray:none;paint-order:stroke fill markers"
+     x="510.9133"
+     y="374.33774"
+     id="text21"><tspan
+       sodipodi:role="line"
+       style="font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;font-size:18.765px;line-height:1.25;font-family:'Caveat Brush';-inkscape-font-specification:'Caveat Brush, Normal';font-variant-ligatures:normal;font-variant-caps:normal;font-variant-numeric:normal;font-variant-east-asian:normal;text-align:center;fill:#000080;stroke:#ffffff;stroke-width:16;stroke-dasharray:none;paint-order:stroke fill markers"
+       x="510.9133"
+       y="374.33774"
+       id="tspan25">Constant</tspan><tspan
+       sodipodi:role="line"
+       style="font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;font-size:18.765px;line-height:1.25;font-family:'Caveat Brush';-inkscape-font-specification:'Caveat Brush, Normal';font-variant-ligatures:normal;font-variant-caps:normal;font-variant-numeric:normal;font-variant-east-asian:normal;text-align:center;fill:#000080;stroke:#ffffff;stroke-width:16;stroke-dasharray:none;paint-order:stroke fill markers"
+       x="510.9133"
+       y="397.79398"
+       id="tspan26">gutter</tspan></text>
+  <path
+     style="fill:none;stroke:#000080;stroke-width:2;stroke-linecap:butt;stroke-linejoin:miter;stroke-dasharray:none;stroke-opacity:1;marker-end:url(#ArrowWideRounded)"
+     d="m 372.5815,544.66996 c -59.98873,-2.86714 -77.24113,12.94099 -77.24113,-23.32439"
+     id="path27"
+     sodipodi:nodetypes="cc" />
+  <text
+     xml:space="preserve"
+     style="font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;font-size:18.765px;line-height:1.25;font-family:'Caveat Brush';-inkscape-font-specification:'Caveat Brush, Normal';font-variant-ligatures:normal;font-variant-caps:normal;font-variant-numeric:normal;font-variant-east-asian:normal;text-align:center;fill:#000080;stroke:#ffffff;stroke-width:16;stroke-dasharray:none;paint-order:stroke fill markers"
+     x="377.9888"
+     y="551.4953"
+     id="text23"><tspan
+       sodipodi:role="line"
+       id="tspan23"
+       style="font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;font-size:18.765px;line-height:1.25;font-family:'Caveat Brush';-inkscape-font-specification:'Caveat Brush, Normal';font-variant-ligatures:normal;font-variant-caps:normal;font-variant-numeric:normal;font-variant-east-asian:normal;text-align:center;fill:#000080;stroke:#ffffff;stroke-width:16;stroke-dasharray:none;paint-order:stroke fill markers"
+       x="377.9888"
+       y="551.4953">A stack</tspan></text>
+  <g
+     id="g28"
+     transform="translate(0,10.5)">
+    <path
+       style="fill:none;stroke:#000080;stroke-width:2;stroke-linecap:butt;stroke-linejoin:miter;stroke-dasharray:none;stroke-opacity:1;marker-start:url(#Stop);marker-end:url(#Stop)"
+       d="M 99.999997,74.411043 H 495.27559"
+       id="path28" />
+    <text
+       xml:space="preserve"
+       style="font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;font-size:18.765px;line-height:1.25;font-family:'Caveat Brush';-inkscape-font-specification:'Caveat Brush, Normal';font-variant-ligatures:normal;font-variant-caps:normal;font-variant-numeric:normal;font-variant-east-asian:normal;text-align:center;fill:#000080;stroke:#ffffff;stroke-width:16;stroke-dasharray:none;paint-order:stroke fill markers"
+       x="266.33884"
+       y="80.303253"
+       id="text19"><tspan
+         sodipodi:role="line"
+         id="tspan19"
+         style="font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;font-size:18.765px;line-height:1.25;font-family:'Caveat Brush';-inkscape-font-specification:'Caveat Brush, Normal';font-variant-ligatures:normal;font-variant-caps:normal;font-variant-numeric:normal;font-variant-east-asian:normal;text-align:center;fill:#000080;stroke:#ffffff;stroke-width:16;stroke-dasharray:none;paint-order:stroke fill markers"
+         x="266.33884"
+         y="80.303253">Fill the width</tspan></text>
+  </g>
+  <g
+     id="g208"
+     transform="matrix(0.56952201,0,0,0.56952201,237.37873,244.3475)">
+    <path
+       style="fill:none;stroke:#000080;stroke-width:2;stroke-linecap:round;stroke-linejoin:miter;stroke-dasharray:none;stroke-opacity:1"
+       d="m 455.18563,602.46401 c 51.38754,-4.52405 91.71573,-2.54261 131.08993,0"
+       id="path29"
+       sodipodi:nodetypes="cc" />
+    <text
+       xml:space="preserve"
+       style="font-size:28.7529px;line-height:1.25;font-family:'Caveat Brush';-inkscape-font-specification:'Caveat Brush, Normal';fill:#000080;stroke:#ffffff;stroke-width:4;stroke-dasharray:none;paint-order:stroke fill markers"
+       x="362.92322"
+       y="595.62079"
+       id="text28"><tspan
+         sodipodi:role="line"
+         id="tspan28"
+         style="fill:#000080;stroke:#ffffff;stroke-width:4;stroke-dasharray:none;paint-order:stroke fill markers"
+         x="362.92322"
+         y="595.62079">Erna, by tad-lispy.com</tspan></text>
+  </g>
+</svg>
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index 0000000..3b896e6
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index feeea5c..27fcf9e 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@ Erna
======
=
=
-Erna places her images in neat rows. It's a Typst library.
+Erna places her images in neat rows. It's a [Typst](https://typst.app/) library.
=
=![A diagram showing two rows of images scaled and aligned in a neat square. In the second row, the middle column is a stack of two images. There is a gutter (whitespace) between all the images. Arrows and labels explain various aspects of the layout.](diagram.png)
=
@@ -18,3 +18,6 @@ Erna promises to:
=  - [x] fill all the available width;
=  - [x] arrange the images in a rectangle;
=  - [x] preserve all aspect ratios.
+
+
+See the [preview PDF document](./preview.pdf) document for more information.

Add the diagram to the preview

index 3b896e6..da40a8e 100644
Binary files a/preview.pdf and b/preview.pdf differ
index 3e0eb86..333a558 100644
--- a/preview.typ
+++ b/preview.typ
@@ -44,9 +44,7 @@ Erna promises to:
=- arrange the images in a rectangle;
=- preserve all aspect ratios.
=
-TODO: Diagram
-
-Brought to you by #link("https://tad-lispy.com/")[Tad Lispy].
+#image("diagram.png", height: 1fr)
=
=
== Basic use

Commits: 2

Implement the divide and conquer solution

Stacks and gutters work together!

index 699bf9f..595359b 100644
--- a/playground.typ
+++ b/playground.typ
@@ -526,7 +526,7 @@ The height of two images in the stack combined would be
=
=It checks! Both cells have approximately the same height.
=
-Now the question is how to scale it arbitrary number of cells. For each cell there would be an additional unknown $w_i$, but also an additional equation $h = w_i r_i + g_i$
+Now the question is how to scale it arbitrary number of cells. For each cell there would be an additional unknown $w_i$, but also an additional equation $h = w_i / r_i + g_i$
=
=== Divide and conquer approximation
=
@@ -536,9 +536,205 @@ In very broad terms:
=
=1. Identify the minimum possible height $h_min$ (that would be the height without any gutters). The current implementation of `image-row` function can calculate this.
=2. Identify the maximum possible height $h_max$ (that would be $h_min + max(g_i)$, so all the gutters without shrinking the content, which would be a case in a single cell containing a stack)
-3. Solve the $h = r_i w_i + g_i$ equations using $h = (h_min + h_max) / 2$ (i.e. midpoint)
+3. Solve the $h = w_i / r_i  + g_i$ equations using $h = (h_min + h_max) / 2$ (i.e. midpoint). This can be re-written as $w_i = r_i (h - g_i)$
=4. If $sum_(i=o)^n w_i approx w$ then that's it.
=5. If $sum_(i=o)^n w_i < w$, set $h_min := h$ and solve again.
=6. If $sum_(i=o)^n w_i > w$, set $h_max := h$ and solve again.
=
=Do it at most 100 times or so. Consider some small length, like 1pt to be sufficiently precise.
+
+To make it work:
+
+TODO: Uniform representation of single and stack cell.
+
+Single:
+
+```typst
+(
+  content_ratio: 0.5, 
+  gutters_height: 0pt, 
+  images: (image-1) # Always an array
+)
+```
+
+Stack:
+
+```typst
+(
+  content_ratio: 0.5,
+  gutters_height: 24pt,
+  images: (image-1, image-2)
+)
+```
+
+#let image_ratio(image) = {
+    let size = measure(image)
+    size.width / size.height
+}
+
+#let cell(gutter: 0pt, ..args) = {
+    let paths = args.pos()
+    let images = paths.map(path => image(path))
+    let gutters_height = (images.len() - 1) * gutter
+    let aspect_ratio = 1.0 / images.map(image => 1.0 / image_ratio(image)).sum()
+
+    (
+        images: images,
+        gutters_height: gutters_height,
+        aspect_ratio: aspect_ratio,
+    )
+}
+
+A cell with a single image:
+
+#context cell(
+    gutter: 12pt,
+    "500x480.jpg",
+)
+
+A cell with multiple images:
+
+#context cell(
+    gutter: 6pt,
+    "300x600.jpg",
+    "500x480.jpg",
+    "360x480.jpg",
+    "780x300.jpg",
+)
+
+Now, with the uniform data structure to represent each cell, we can make a new `image_row_dc` function (for divide and conquer).
+
+#let solve(step: 0, max_steps: 64, tolerance: 1pt, min_height, max_height, available_width, cells) = {
+    let height = (min_height + max_height) / 2
+    let widths = cells.map(cell => cell.aspect_ratio * (height - cell.gutters_height))
+
+    let solved_width = widths.fold(0pt, (sum, width) => sum + width)
+    let deviation = calc.abs(available_width - solved_width)
+
+    // TODO: Add to state for debugging purposes, like seeing how many steps it took
+    // [+ #solved_width × #height #widths]
+
+    if deviation < tolerance or step > max_steps {
+        return widths
+    }
+
+    if solved_width > available_width {
+        // Too high
+        solve(step: step + 1, max_steps: max_steps, tolerance: tolerance, min_height, height, available_width, cells)
+    } else {
+        // Too low
+        solve(step: step + 1, max_steps: max_steps, tolerance: tolerance, height, max_height, available_width, cells)
+    }
+
+    
+}
+
+#let image_row_dc(gutter: 0pt, ..args) = {
+    context {
+        let cells = args.pos().map(paths => {
+            if type(paths) == array {
+                cell(gutter: gutter, ..paths)
+            } else {
+                cell(gutter: gutter, paths)
+            }
+        })
+
+        layout(parent => {
+            let gutters_width = (cells.len() - 1) * gutter
+            let available_width = parent.width - gutters_width
+
+            // Theoretical minimum height, as if horizontal gutters where not there
+            let min_ratio = cells.fold(0, (sum, cell) => sum + cell.aspect_ratio)
+            let min_height = available_width / min_ratio
+
+            // Theoretical maximum height would be the minimum height and all the gutters
+            let max_gutters = cells.fold(0pt, (current_max, cell) => calc.max(current_max, cell.gutters_height))
+            let max_height = min_height + max_gutters
+
+            // TODO: Add to state for debugging purposes
+            // [#(
+            //     cells: cells,
+            //     available_width: available_width,
+            //     min_height: min_height,
+            //     max_height: max_height,
+            // )]
+
+            let widths = solve(min_height, max_height, available_width, cells)
+
+            // TODO: Add to state for debugging
+            // [widths #widths]
+
+            grid(
+                gutter: gutter,
+                columns: cells.len(),
+                ..cells.enumerate().map(((index, cell)) => {
+                    grid(
+                        gutter: gutter,
+                        columns: widths.at(index),
+                        ..cell.images
+                    )
+                })
+            )
+        
+        })
+    }
+}
+
+Here's a row with gutter and a stack in the middle cell:
+
+#image_row_dc(
+    gutter: 12pt,
+    "300x600.jpg",
+    ("780x300.jpg", "500x480.jpg", "780x300.jpg"),
+    "360x480.jpg",
+)
+
+Same, without the gutter:
+
+#image_row_dc(
+    gutter: 0pt,
+    "300x600.jpg",
+    ("780x300.jpg", "500x480.jpg", "780x300.jpg"),
+    "360x480.jpg",
+)
+
+With a large gutter:
+
+#image_row_dc(
+    gutter: 20pt,
+    "300x600.jpg",
+    ("780x300.jpg", "500x480.jpg", "780x300.jpg"),
+    "360x480.jpg",
+)
+
+Only one stack in a box (kind of usesless, could just be images)
+
+#box(
+    width: 200pt,
+    stroke: 1pt + blue,
+
+    image_row_dc(
+        gutter: 20pt,
+        ("780x300.jpg", "500x480.jpg", "780x300.jpg"),
+    )
+)
+
+In a box, only singular cells:
+
+#box(
+    width: 300pt,
+    stroke: 1pt + blue,
+
+    image_row_dc(
+        gutter: 2pt,
+        "780x300.jpg", "500x480.jpg", "780x300.jpg",
+    )
+)
+
+Two stacks:
+
+#image_row_dc(
+    gutter: 3pt,
+    ("780x300.jpg", "500x480.jpg", "780x300.jpg"),
+    ( "300x600.jpg", "360x480.jpg"),
+)

Create a preview document and a module to import

The module is called Erna, after my dear friend who inspired me to figure this out.

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index 0000000..2517109
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index 0000000..503ed6f
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new file mode 100644
index 0000000..633687d
--- /dev/null
+++ b/erna.typ
@@ -0,0 +1,93 @@
+#let image_ratio(image) = {
+    let size = measure(image)
+    size.width / size.height
+}
+
+#let cell(gutter: 0pt, ..args) = {
+    let paths = args.pos()
+    let images = paths.map(path => image(path))
+    let gutters_height = (images.len() - 1) * gutter
+    let aspect_ratio = 1.0 / images.map(image => 1.0 / image_ratio(image)).sum()
+
+    (
+        images: images,
+        gutters_height: gutters_height,
+        aspect_ratio: aspect_ratio,
+    )
+}
+
+#let solve(step: 0, max_steps: 64, tolerance: 1pt, min_height, max_height, available_width, cells) = {
+    let height = (min_height + max_height) / 2
+    let widths = cells.map(cell => cell.aspect_ratio * (height - cell.gutters_height))
+
+    let solved_width = widths.fold(0pt, (sum, width) => sum + width)
+    let deviation = calc.abs(available_width - solved_width)
+
+    // TODO: Add to state for debugging purposes, like seeing how many steps it took
+    // [+ #solved_width × #height #widths]
+
+    if deviation < tolerance or step > max_steps {
+        return widths
+    }
+
+    if solved_width > available_width {
+        // Too high
+        solve(step: step + 1, max_steps: max_steps, tolerance: tolerance, min_height, height, available_width, cells)
+    } else {
+        // Too low
+        solve(step: step + 1, max_steps: max_steps, tolerance: tolerance, height, max_height, available_width, cells)
+    }
+
+    
+}
+
+#let image_row(gutter: 0pt, ..args) = {
+    context {
+        let cells = args.pos().map(paths => {
+            if type(paths) == array {
+                cell(gutter: gutter, ..paths)
+            } else {
+                cell(gutter: gutter, paths)
+            }
+        })
+
+        layout(parent => {
+            let gutters_width = (cells.len() - 1) * gutter
+            let available_width = parent.width - gutters_width
+
+            // Theoretical minimum height, as if horizontal gutters where not there
+            let min_ratio = cells.fold(0, (sum, cell) => sum + cell.aspect_ratio)
+            let min_height = available_width / min_ratio
+
+            // Theoretical maximum height would be the minimum height and all the gutters
+            let max_gutters = cells.fold(0pt, (current_max, cell) => calc.max(current_max, cell.gutters_height))
+            let max_height = min_height + max_gutters
+
+            // TODO: Add to state for debugging purposes
+            // [#(
+            //     cells: cells,
+            //     available_width: available_width,
+            //     min_height: min_height,
+            //     max_height: max_height,
+            // )]
+
+            let widths = solve(min_height, max_height, available_width, cells)
+
+            // TODO: Add to state for debugging
+            // [widths #widths]
+
+            grid(
+                gutter: gutter,
+                columns: cells.len(),
+                ..cells.enumerate().map(((index, cell)) => {
+                    grid(
+                        gutter: gutter,
+                        columns: widths.at(index),
+                        ..cell.images
+                    )
+                })
+            )
+        
+        })
+    }
+}
new file mode 100644
index 0000000..08e02a8
--- /dev/null
+++ b/preview.typ
@@ -0,0 +1,83 @@
+#import "erna.typ": image_row
+
+#show heading.where(level: 1): content => {
+    pagebreak(weak: true)
+    content
+    v(1em, weak: true)
+}
+
+#title[Erna]
+
+Erna places her images in neat rows.
+
+
+#image_row(
+    gutter: 12pt,
+    ("780x300.jpg", "500x480.jpg", "960x250.jpg"),
+    ("300x600.jpg", "360x480.jpg"),
+)
+ 
+Erna is a Typst library for aligning images in neat rows. It's easy to use. I made it for a friend who struggles with aligning images in LibreOffice. It's named after her. With Erna (the library), you can:
+
+- Cram as many images as you see fit!
+- Bravely mix sizes, resolutions and aspect ratios. Let the computer figure it out!
+- Got some wiiide images? Stack 'em up!
+
+Erna's promises:
+
+#set list(marker: sym.checkmark.heavy)
+
+- To fill all the available width. \
+- Make the images align vertically. \
+- Preserve aspect ratios.
+
+#pagebreak()
+
+#show raw.where(lang: "typ"): content => {
+    
+    content
+
+    eval(content.text, mode: "markup", scope: (image_row: image_row))
+}
+
+= Basic use
+
+At the heart of it is the `image_row` function. It takes a list of image paths, and scales them to equal height and to fit the available width, while preserving aspect ratio of each image. No cropping!
+
+```typ
+#image_row(
+    "300x600.jpg", "360x480.jpg", "500x480.jpg",
+)
+```
+
+
+= With gutters
+
+You can put some space between the images with the `gutter` option.
+
+```typ
+#image_row(
+    gutter: 4pt,
+    "300x600.jpg", "360x480.jpg", "500x480.jpg",
+)
+```
+
+
+= With stacks
+
+It's possible to place several images in a vertical stack. This is useful when mixing portraits and landscapes. Just group the paths in a parentheses to create a stack.
+
+```typ
+#image_row(
+    gutter: 6pt,
+    "360x480.jpg", 
+    ("500x280.jpg", "780x300.jpg"),
+    ("960x250.jpg", "500x480.jpg"), 
+)
+```
+
+= TODO: Multiple rows
+
+= TODO: Diagram
+
+Brought to you by #link("https://tad-lispy.com/")[Tad Lispy].

Commits: 2

Describe the stacks and gutters as linear equations

index f9341b2..89fb03b 100644
--- a/playground.typ
+++ b/playground.typ
@@ -381,3 +381,149 @@ But adding a gutter messes the height of the stack:
=    ("780x300.jpg", "500x480.jpg", "780x300.jpg"),
=    "360x480.jpg",
=)
+
+== Solution for stacks and gutters
+
+To solve the image row with stacks and gutters we need to think of the problem as a set of $n$ equations with $n$ unknowns, where $n$ is the number of cells in a given row.
+
++ The total width $w$ of the row is known.
+
++ The height $h$ of every cell is equal.
+
++ We must solve for the width of each cell $w_i$
++ The aspect ratio of each cell content $r_i$ can be computed from the ratios of stacked images
+
+
++ For each cell the gutter height $g_i$ is known ($(s_i - 1) * g$) where $s_i$ is the number of stacked images in cell $i$ and $g$ is the given gutter size.
+
+Sum of all widths equal the total width
+
+$
+    w_0 + w_1 + ... + w_n = w
+$
+
+The height of each cell is the same
+
+$
+    h = g_i + w_i / r_i
+$
+
+
+The stack ratio $r_i$ can be calculated as inversion of the sum of inverted aspect ratios of each image.
+
+$
+    r_i = (sum_(j=0)^s r_j^(-1) )^(-1)
+$
+
+Where $s$ is the number of stacked images and $r_j$ is the ration of a stacked image $j$. Inverted ratio $r^(-1)$ is a ratio of height to width, as opposed to regular width to height. Conceptually it's like stacking the images on top of each other, scaling them to have equal width and measuring the aspect ratio of the whole stack.
+
+Let's try to solve it for a simplified set of images.
+
++ 300 ÷ 600 (portrait)
++ 800 ÷ 400 (landscape)
++ 600 ÷ 200 (landscape) <resolutions>
+
+#let total_width = 360pt
+#let gutter = 12pt
+
+
+The last two should be stacked, so there are two rows. Let's say the gutter is #gutter and available space is #total_width.
+
+
+$
+    g = #gutter
+$
+
+For simplicity let's already subtract the vertical gutter from the total width.
+
+$
+    w = #total_width
+$
+
+We can treat each cell as a stack: `(0) (1, 2)`. First cell is a stack of one image, second is a stack of two.
+
+The content ratio of first cell is simple
+
+$
+    r_0
+    = 300 / 600
+    = 0.5
+$
+
+
+The content ratio of the second is
+
+$
+    r_1
+    = 1 / ((800 / 400)^(-1) + (600 / 200)^(-1))
+    = (1/2 + 1/3)^(-1)
+    = 1.2
+$
+
+Now it's possible to solve the set of two equations to find $w_0$ and $w_1$:
+
+$
+    cases(
+        h = g_i + w_i / r_i,
+        w_0 + w_i = w
+    )
+$
+
+Becasue $h$ is the same for each cell we can write it as:
+
+$
+    cases(
+      g_0 + w_0 / r_0 = g_1 + w_1 / r_1,
+      w_0 + w_i = w
+    )
+$
+
+Substituting known values:
+
+$
+    cases(
+        0 + w_0 / 0.5 = 12 + w_1 / 1.2,
+        w_0 + w_1 = 360 \
+    )
+$
+
+Let's reduce the first equation to express $w_0$ in terms of $w_1$.
+
++ $2w_0 = 12 + w_1 / 1.2$
++ $w_0 = 6 + w_1 / 2.4$
+
+Now we can use it in the second equation and solve it:
+
++ $(6 + w_1 / 2.4) + w_1 = 360$
++ $(14.4 + w_1) + 2.4w_1 = 864$ (×2.4)
++ $3.4w_1 = 849.6$
++ $w_1 approx 250$
+
+Knowing the approximate value of $w_1$ and the total width $w$ we can calculate $w_0$ as the remaining length
+
++ $w_0 + w_1 = w$
++ $w_0 + 250 = 360$
++ $w_0 = 110$
+
+Let's check if the height's add up. Again, the image resolutions are are:
+
++ 300 ÷ 600 (portrait, 1:2)
++ 800 ÷ 400 (landscape, 2:1)
++ 600 ÷ 200 (landscape, 3:1)
+
+If the first image width $w_0$ is 110pt, then the height is
+
++ $h = w_0r_0^(-1) + g_0$
++ $h = 110 × 2 + 0$
++ $h = 220$.
+
+The height of two images in the stack combined would be
+
++ $h = w_1r_1^(-1) + g_i$
++ $h = 250 × 1.2^(-1) + 12$
++ $h = 250 × 10/12 + 12$
++ $h approx 220$
+
+It checks! Both cells have approximately the same height.
+
+Now the question is how to scale it arbitrary number of cells. For each cell there would be an additional unknown $w_i$, but also an additional equation $h = w_i r_i + g_i$

Describe "divide and conquer" approach to stacks and gutters

index 89fb03b..699bf9f 100644
--- a/playground.typ
+++ b/playground.typ
@@ -527,3 +527,18 @@ The height of two images in the stack combined would be
=It checks! Both cells have approximately the same height.
=
=Now the question is how to scale it arbitrary number of cells. For each cell there would be an additional unknown $w_i$, but also an additional equation $h = w_i r_i + g_i$
+
+== Divide and conquer approximation
+
+The equations above seem to work, and I can solve them on paper, but I don't (yet) know how to program a solver in Typst. In the mean time I want to try a "divide and conquer" approach to approximate the height and widths.
+
+In very broad terms:
+
+1. Identify the minimum possible height $h_min$ (that would be the height without any gutters). The current implementation of `image-row` function can calculate this.
+2. Identify the maximum possible height $h_max$ (that would be $h_min + max(g_i)$, so all the gutters without shrinking the content, which would be a case in a single cell containing a stack)
+3. Solve the $h = r_i w_i + g_i$ equations using $h = (h_min + h_max) / 2$ (i.e. midpoint)
+4. If $sum_(i=o)^n w_i approx w$ then that's it.
+5. If $sum_(i=o)^n w_i < w$, set $h_min := h$ and solve again.
+6. If $sum_(i=o)^n w_i > w$, set $h_max := h$ and solve again.
+
+Do it at most 100 times or so. Consider some small length, like 1pt to be sufficiently precise.

Commits: 3

Describe the stacking feature

index ef39395..5b2a3da 100644
--- a/playground.typ
+++ b/playground.typ
@@ -289,3 +289,17 @@ Now we have everything we need! Here are some examples.
=+ Multiple rows, each in an array
=+ Automatic vertical spacing
=+ In a box, to prevent page breaks inside
+
+== TODO: Stack of images within a row
+
+When some images are wide and some narrow, it might be desirable to stack several wide ones on top of each other. In that case, the length of each stacked image should be equal, and the combined height (including gutters) should be equal to the height of every image in the row. In the code, a stack should be represented as an array of paths, like this:
+
+
+```typst
+#image-row(
+    gutter: 4pt,
+    "left.jpg",
+    ("middle-top.jpg", "middlep-bottom.jpg"), // <-- a stack
+    "right.jpg",
+)
+```

Set page height to automatic

Pages are broken on h2 elements.

index 5b2a3da..6800ddb 100644
--- a/playground.typ
+++ b/playground.typ
@@ -1,3 +1,5 @@
+#set page(height: auto)
+
== Layout Playground
=
=#let images = (
@@ -59,8 +61,8 @@ Sum the aspect ratios of all the images to get the total aspect ratio of the row
=#let total_aspect_ratio = aspects.map(a => a.ratio).sum()
=
=#rect(
-    height: 100% / total_aspect_ratio,
-    width: 100%,
+    height: 300pt / total_aspect_ratio,
+    width: 300pt,
=    align(center + horizon)[1 × #calc.round(digits: 3, total_aspect_ratio)]
=)
=

Implement partial solution for stacks

It works as long as there are no gutters. Gutters mess up the height.

index 6800ddb..f9341b2 100644
--- a/playground.typ
+++ b/playground.typ
@@ -305,3 +305,79 @@ When some images are wide and some narrow, it might be desirable to stack severa
=    "right.jpg",
=)
=``` 
+
+
+#let describe_image(path) = {
+    let content = image(path)
+    let size = measure(content)
+    let ratio = size.width / size.height
+    return (path: path, content: content, ratio: ratio, ..size)
+}
+
+#let aspect_ratio(item) = {
+    if type(item) == array {
+        // It's a stack! Sum the inverted ratios, then invert it again
+        return 1.0 / item.map(i => 1.0 / i.ratio).sum()
+    } else {
+        return item.ratio
+    }
+}
+
+#let image-row(gutter: 0pt, ..paths) = {
+    context {
+        // An item is either an image or a stack
+        let items = paths.pos().map(path => {
+            if type(path) == array {
+                return path.map(describe_image)
+            } else {
+                return describe_image(path)
+            }
+        })
+
+
+        let total_ratio = items.fold(0, (acc, item) => acc + aspect_ratio(item))
+        let total_gutter = gutter * (items.len() - 1)
+
+        box(layout(parent => grid(
+            columns: items.len(),
+            column-gutter: gutter,
+
+            ..items.map((item) => {
+                let width_fraction = aspect_ratio(item) / total_ratio
+                let final_width = width_fraction * (parent.width - total_gutter)
+
+                if type(item) == array {
+                    grid(
+                        columns: 1,
+                        gutter: gutter,
+                        ..item.map(stacked => {
+                            let scaling_factor = final_width / stacked.width * 100%
+                            scale(stacked.content, scaling_factor, reflow: true)
+                        })
+                    )
+                } else {
+                    let scaling_factor = final_width / item.width * 100%
+                    scale(item.content, scaling_factor, reflow: true)
+                }
+            })
+        )))
+    }
+}
+
+This works now without gutters
+
+#image-row(
+    gutter: 0pt,
+    "300x600.jpg",
+    ("780x300.jpg", "500x480.jpg", "780x300.jpg"),
+    "360x480.jpg",
+)
+
+But adding a gutter messes the height of the stack:
+
+#image-row(
+    gutter: 20pt,
+    "300x600.jpg",
+    ("780x300.jpg", "500x480.jpg", "780x300.jpg"),
+    "360x480.jpg",
+)

Commits: 6

Setup DevEnv with typst and tinymist (Typst LSP)

new file mode 100644
index 0000000..c5d670d
--- /dev/null
+++ b/.envrc
@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+
+if ! has nix_direnv_version || ! nix_direnv_version 3.1.0; then
+  source_url "https://raw.githubusercontent.com/nix-community/nix-direnv/3.1.0/direnvrc" "sha256-yMJ2OVMzrFaDPn7q8nCBZFRYpL/f0RcHzhmw/i6btJM="
+fi
+
+export DEVENV_IN_DIRENV_SHELL=true
+
+watch_file flake.nix
+watch_file flake.lock
+if ! use flake . --no-pure-eval; then
+  echo "devenv could not be built. The devenv environment was not loaded. Make the necessary changes to devenv.nix and hit enter to try again." >&2
+fi
new file mode 100644
index 0000000..f03ce8a
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+.devenv
+.direnv
new file mode 100644
index 0000000..fdbdf50
--- /dev/null
+++ b/flake.lock
@@ -0,0 +1,849 @@
+{
+  "nodes": {
+    "cachix": {
+      "inputs": {
+        "devenv": [
+          "devenv"
+        ],
+        "flake-compat": [
+          "devenv",
+          "flake-compat"
+        ],
+        "git-hooks": [
+          "devenv",
+          "git-hooks"
+        ],
+        "nixpkgs": [
+          "devenv",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1767714506,
+        "narHash": "sha256-WaTs0t1CxhgxbIuvQ97OFhDTVUGd1HA+KzLZUZBhe0s=",
+        "owner": "cachix",
+        "repo": "cachix",
+        "rev": "894c649f0daaa38bbcfb21de64be47dfa7cd0ec9",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "ref": "latest",
+        "repo": "cachix",
+        "type": "github"
+      }
+    },
+    "cachix_2": {
+      "inputs": {
+        "devenv": [
+          "devenv",
+          "crate2nix"
+        ],
+        "flake-compat": [
+          "devenv",
+          "crate2nix"
+        ],
+        "git-hooks": "git-hooks",
+        "nixpkgs": "nixpkgs"
+      },
+      "locked": {
+        "lastModified": 1767714506,
+        "narHash": "sha256-WaTs0t1CxhgxbIuvQ97OFhDTVUGd1HA+KzLZUZBhe0s=",
+        "owner": "cachix",
+        "repo": "cachix",
+        "rev": "894c649f0daaa38bbcfb21de64be47dfa7cd0ec9",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "ref": "latest",
+        "repo": "cachix",
+        "type": "github"
+      }
+    },
+    "cachix_3": {
+      "inputs": {
+        "devenv": [
+          "devenv",
+          "crate2nix",
+          "crate2nix_stable"
+        ],
+        "flake-compat": [
+          "devenv",
+          "crate2nix",
+          "crate2nix_stable"
+        ],
+        "git-hooks": "git-hooks_2",
+        "nixpkgs": "nixpkgs_2"
+      },
+      "locked": {
+        "lastModified": 1767714506,
+        "narHash": "sha256-WaTs0t1CxhgxbIuvQ97OFhDTVUGd1HA+KzLZUZBhe0s=",
+        "owner": "cachix",
+        "repo": "cachix",
+        "rev": "894c649f0daaa38bbcfb21de64be47dfa7cd0ec9",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "ref": "latest",
+        "repo": "cachix",
+        "type": "github"
+      }
+    },
+    "crate2nix": {
+      "inputs": {
+        "cachix": "cachix_2",
+        "crate2nix_stable": "crate2nix_stable",
+        "devshell": "devshell_2",
+        "flake-compat": "flake-compat_2",
+        "flake-parts": "flake-parts_2",
+        "nix-test-runner": "nix-test-runner_2",
+        "nixpkgs": [
+          "devenv",
+          "nixpkgs"
+        ],
+        "pre-commit-hooks": "pre-commit-hooks_2"
+      },
+      "locked": {
+        "lastModified": 1773440526,
+        "narHash": "sha256-OcX1MYqUdoalY3/vU67PEx8m6RvqGxX0LwKonjzXn7I=",
+        "owner": "nix-community",
+        "repo": "crate2nix",
+        "rev": "e697d3049c909580128caa856ab8eb709556a97b",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nix-community",
+        "repo": "crate2nix",
+        "type": "github"
+      }
+    },
+    "crate2nix_stable": {
+      "inputs": {
+        "cachix": "cachix_3",
+        "crate2nix_stable": [
+          "devenv",
+          "crate2nix",
+          "crate2nix_stable"
+        ],
+        "devshell": "devshell",
+        "flake-compat": "flake-compat",
+        "flake-parts": "flake-parts",
+        "nix-test-runner": "nix-test-runner",
+        "nixpkgs": "nixpkgs_3",
+        "pre-commit-hooks": "pre-commit-hooks"
+      },
+      "locked": {
+        "lastModified": 1769627083,
+        "narHash": "sha256-SUuruvw1/moNzCZosHaa60QMTL+L9huWdsCBN6XZIic=",
+        "owner": "nix-community",
+        "repo": "crate2nix",
+        "rev": "7c33e664668faecf7655fa53861d7a80c9e464a2",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nix-community",
+        "ref": "0.15.0",
+        "repo": "crate2nix",
+        "type": "github"
+      }
+    },
+    "devenv": {
+      "inputs": {
+        "cachix": "cachix",
+        "crate2nix": "crate2nix",
+        "flake-compat": "flake-compat_3",
+        "flake-parts": "flake-parts_3",
+        "git-hooks": "git-hooks_3",
+        "nix": "nix",
+        "nixd": "nixd",
+        "nixpkgs": [
+          "nixpkgs"
+        ],
+        "rust-overlay": "rust-overlay"
+      },
+      "locked": {
+        "lastModified": 1774052327,
+        "narHash": "sha256-gQhiHj8q5NAa8jGTmoaS8FRgo8bVoAL2difjmcLtdgo=",
+        "owner": "cachix",
+        "repo": "devenv",
+        "rev": "43c650cae3ca65b6095819e4613614c242588cd7",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "repo": "devenv",
+        "type": "github"
+      }
+    },
+    "devshell": {
+      "inputs": {
+        "nixpkgs": [
+          "devenv",
+          "crate2nix",
+          "crate2nix_stable",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1768818222,
+        "narHash": "sha256-460jc0+CZfyaO8+w8JNtlClB2n4ui1RbHfPTLkpwhU8=",
+        "owner": "numtide",
+        "repo": "devshell",
+        "rev": "255a2b1725a20d060f566e4755dbf571bbbb5f76",
+        "type": "github"
+      },
+      "original": {
+        "owner": "numtide",
+        "repo": "devshell",
+        "type": "github"
+      }
+    },
+    "devshell_2": {
+      "inputs": {
+        "nixpkgs": [
+          "devenv",
+          "crate2nix",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1768818222,
+        "narHash": "sha256-460jc0+CZfyaO8+w8JNtlClB2n4ui1RbHfPTLkpwhU8=",
+        "owner": "numtide",
+        "repo": "devshell",
+        "rev": "255a2b1725a20d060f566e4755dbf571bbbb5f76",
+        "type": "github"
+      },
+      "original": {
+        "owner": "numtide",
+        "repo": "devshell",
+        "type": "github"
+      }
+    },
+    "flake-compat": {
+      "locked": {
+        "lastModified": 1733328505,
+        "narHash": "sha256-NeCCThCEP3eCl2l/+27kNNK7QrwZB1IJCrXfrbv5oqU=",
+        "rev": "ff81ac966bb2cae68946d5ed5fc4994f96d0ffec",
+        "revCount": 69,
+        "type": "tarball",
+        "url": "https://api.flakehub.com/f/pinned/edolstra/flake-compat/1.1.0/01948eb7-9cba-704f-bbf3-3fa956735b52/source.tar.gz"
+      },
+      "original": {
+        "type": "tarball",
+        "url": "https://flakehub.com/f/edolstra/flake-compat/1.tar.gz"
+      }
+    },
+    "flake-compat_2": {
+      "locked": {
+        "lastModified": 1733328505,
+        "narHash": "sha256-NeCCThCEP3eCl2l/+27kNNK7QrwZB1IJCrXfrbv5oqU=",
+        "rev": "ff81ac966bb2cae68946d5ed5fc4994f96d0ffec",
+        "revCount": 69,
+        "type": "tarball",
+        "url": "https://api.flakehub.com/f/pinned/edolstra/flake-compat/1.1.0/01948eb7-9cba-704f-bbf3-3fa956735b52/source.tar.gz"
+      },
+      "original": {
+        "type": "tarball",
+        "url": "https://flakehub.com/f/edolstra/flake-compat/1.tar.gz"
+      }
+    },
+    "flake-compat_3": {
+      "flake": false,
+      "locked": {
+        "lastModified": 1767039857,
+        "narHash": "sha256-vNpUSpF5Nuw8xvDLj2KCwwksIbjua2LZCqhV1LNRDns=",
+        "owner": "edolstra",
+        "repo": "flake-compat",
+        "rev": "5edf11c44bc78a0d334f6334cdaf7d60d732daab",
+        "type": "github"
+      },
+      "original": {
+        "owner": "edolstra",
+        "repo": "flake-compat",
+        "type": "github"
+      }
+    },
+    "flake-parts": {
+      "inputs": {
+        "nixpkgs-lib": [
+          "devenv",
+          "crate2nix",
+          "crate2nix_stable",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1768135262,
+        "narHash": "sha256-PVvu7OqHBGWN16zSi6tEmPwwHQ4rLPU9Plvs8/1TUBY=",
+        "owner": "hercules-ci",
+        "repo": "flake-parts",
+        "rev": "80daad04eddbbf5a4d883996a73f3f542fa437ac",
+        "type": "github"
+      },
+      "original": {
+        "owner": "hercules-ci",
+        "repo": "flake-parts",
+        "type": "github"
+      }
+    },
+    "flake-parts_2": {
+      "inputs": {
+        "nixpkgs-lib": [
+          "devenv",
+          "crate2nix",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1768135262,
+        "narHash": "sha256-PVvu7OqHBGWN16zSi6tEmPwwHQ4rLPU9Plvs8/1TUBY=",
+        "owner": "hercules-ci",
+        "repo": "flake-parts",
+        "rev": "80daad04eddbbf5a4d883996a73f3f542fa437ac",
+        "type": "github"
+      },
+      "original": {
+        "owner": "hercules-ci",
+        "repo": "flake-parts",
+        "type": "github"
+      }
+    },
+    "flake-parts_3": {
+      "inputs": {
+        "nixpkgs-lib": [
+          "devenv",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1772408722,
+        "narHash": "sha256-rHuJtdcOjK7rAHpHphUb1iCvgkU3GpfvicLMwwnfMT0=",
+        "owner": "hercules-ci",
+        "repo": "flake-parts",
+        "rev": "f20dc5d9b8027381c474144ecabc9034d6a839a3",
+        "type": "github"
+      },
+      "original": {
+        "owner": "hercules-ci",
+        "repo": "flake-parts",
+        "type": "github"
+      }
+    },
+    "git-hooks": {
+      "inputs": {
+        "flake-compat": [
+          "devenv",
+          "crate2nix",
+          "cachix",
+          "flake-compat"
+        ],
+        "gitignore": "gitignore",
+        "nixpkgs": [
+          "devenv",
+          "crate2nix",
+          "cachix",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1765404074,
+        "narHash": "sha256-+ZDU2d+vzWkEJiqprvV5PR26DVFN2vgddwG5SnPZcUM=",
+        "owner": "cachix",
+        "repo": "git-hooks.nix",
+        "rev": "2d6f58930fbcd82f6f9fd59fb6d13e37684ca529",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "repo": "git-hooks.nix",
+        "type": "github"
+      }
+    },
+    "git-hooks_2": {
+      "inputs": {
+        "flake-compat": [
+          "devenv",
+          "crate2nix",
+          "crate2nix_stable",
+          "cachix",
+          "flake-compat"
+        ],
+        "gitignore": "gitignore_2",
+        "nixpkgs": [
+          "devenv",
+          "crate2nix",
+          "crate2nix_stable",
+          "cachix",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1765404074,
+        "narHash": "sha256-+ZDU2d+vzWkEJiqprvV5PR26DVFN2vgddwG5SnPZcUM=",
+        "owner": "cachix",
+        "repo": "git-hooks.nix",
+        "rev": "2d6f58930fbcd82f6f9fd59fb6d13e37684ca529",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "repo": "git-hooks.nix",
+        "type": "github"
+      }
+    },
+    "git-hooks_3": {
+      "inputs": {
+        "flake-compat": [
+          "devenv",
+          "flake-compat"
+        ],
+        "gitignore": "gitignore_5",
+        "nixpkgs": [
+          "devenv",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1772893680,
+        "narHash": "sha256-JDqZMgxUTCq85ObSaFw0HhE+lvdOre1lx9iI6vYyOEs=",
+        "owner": "cachix",
+        "repo": "git-hooks.nix",
+        "rev": "8baab586afc9c9b57645a734c820e4ac0a604af9",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "repo": "git-hooks.nix",
+        "type": "github"
+      }
+    },
+    "gitignore": {
+      "inputs": {
+        "nixpkgs": [
+          "devenv",
+          "crate2nix",
+          "cachix",
+          "git-hooks",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1709087332,
+        "narHash": "sha256-HG2cCnktfHsKV0s4XW83gU3F57gaTljL9KNSuG6bnQs=",
+        "owner": "hercules-ci",
+        "repo": "gitignore.nix",
+        "rev": "637db329424fd7e46cf4185293b9cc8c88c95394",
+        "type": "github"
+      },
+      "original": {
+        "owner": "hercules-ci",
+        "repo": "gitignore.nix",
+        "type": "github"
+      }
+    },
+    "gitignore_2": {
+      "inputs": {
+        "nixpkgs": [
+          "devenv",
+          "crate2nix",
+          "crate2nix_stable",
+          "cachix",
+          "git-hooks",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1709087332,
+        "narHash": "sha256-HG2cCnktfHsKV0s4XW83gU3F57gaTljL9KNSuG6bnQs=",
+        "owner": "hercules-ci",
+        "repo": "gitignore.nix",
+        "rev": "637db329424fd7e46cf4185293b9cc8c88c95394",
+        "type": "github"
+      },
+      "original": {
+        "owner": "hercules-ci",
+        "repo": "gitignore.nix",
+        "type": "github"
+      }
+    },
+    "gitignore_3": {
+      "inputs": {
+        "nixpkgs": [
+          "devenv",
+          "crate2nix",
+          "crate2nix_stable",
+          "pre-commit-hooks",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1709087332,
+        "narHash": "sha256-HG2cCnktfHsKV0s4XW83gU3F57gaTljL9KNSuG6bnQs=",
+        "owner": "hercules-ci",
+        "repo": "gitignore.nix",
+        "rev": "637db329424fd7e46cf4185293b9cc8c88c95394",
+        "type": "github"
+      },
+      "original": {
+        "owner": "hercules-ci",
+        "repo": "gitignore.nix",
+        "type": "github"
+      }
+    },
+    "gitignore_4": {
+      "inputs": {
+        "nixpkgs": [
+          "devenv",
+          "crate2nix",
+          "pre-commit-hooks",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1709087332,
+        "narHash": "sha256-HG2cCnktfHsKV0s4XW83gU3F57gaTljL9KNSuG6bnQs=",
+        "owner": "hercules-ci",
+        "repo": "gitignore.nix",
+        "rev": "637db329424fd7e46cf4185293b9cc8c88c95394",
+        "type": "github"
+      },
+      "original": {
+        "owner": "hercules-ci",
+        "repo": "gitignore.nix",
+        "type": "github"
+      }
+    },
+    "gitignore_5": {
+      "inputs": {
+        "nixpkgs": [
+          "devenv",
+          "git-hooks",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1709087332,
+        "narHash": "sha256-HG2cCnktfHsKV0s4XW83gU3F57gaTljL9KNSuG6bnQs=",
+        "owner": "hercules-ci",
+        "repo": "gitignore.nix",
+        "rev": "637db329424fd7e46cf4185293b9cc8c88c95394",
+        "type": "github"
+      },
+      "original": {
+        "owner": "hercules-ci",
+        "repo": "gitignore.nix",
+        "type": "github"
+      }
+    },
+    "nix": {
+      "inputs": {
+        "flake-compat": [
+          "devenv",
+          "flake-compat"
+        ],
+        "flake-parts": [
+          "devenv",
+          "flake-parts"
+        ],
+        "git-hooks-nix": [
+          "devenv",
+          "git-hooks"
+        ],
+        "nixpkgs": [
+          "devenv",
+          "nixpkgs"
+        ],
+        "nixpkgs-23-11": [
+          "devenv"
+        ],
+        "nixpkgs-regression": [
+          "devenv"
+        ]
+      },
+      "locked": {
+        "lastModified": 1773936165,
+        "narHash": "sha256-iL6V03FP1vLJ/YJr0KHcNP+0lyyM9pT4rnRSk57DSYc=",
+        "owner": "cachix",
+        "repo": "nix",
+        "rev": "185e962dbc1b4925f5da3d05725a11e2ecea4a14",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "ref": "devenv-2.32",
+        "repo": "nix",
+        "type": "github"
+      }
+    },
+    "nix-test-runner": {
+      "flake": false,
+      "locked": {
+        "lastModified": 1588761593,
+        "narHash": "sha256-FKJykltAN/g3eIceJl4SfDnnyuH2jHImhMrXS2KvGIs=",
+        "owner": "stoeffel",
+        "repo": "nix-test-runner",
+        "rev": "c45d45b11ecef3eb9d834c3b6304c05c49b06ca2",
+        "type": "github"
+      },
+      "original": {
+        "owner": "stoeffel",
+        "repo": "nix-test-runner",
+        "type": "github"
+      }
+    },
+    "nix-test-runner_2": {
+      "flake": false,
+      "locked": {
+        "lastModified": 1588761593,
+        "narHash": "sha256-FKJykltAN/g3eIceJl4SfDnnyuH2jHImhMrXS2KvGIs=",
+        "owner": "stoeffel",
+        "repo": "nix-test-runner",
+        "rev": "c45d45b11ecef3eb9d834c3b6304c05c49b06ca2",
+        "type": "github"
+      },
+      "original": {
+        "owner": "stoeffel",
+        "repo": "nix-test-runner",
+        "type": "github"
+      }
+    },
+    "nixd": {
+      "inputs": {
+        "flake-parts": [
+          "devenv",
+          "flake-parts"
+        ],
+        "nixpkgs": [
+          "devenv",
+          "nixpkgs"
+        ],
+        "treefmt-nix": "treefmt-nix"
+      },
+      "locked": {
+        "lastModified": 1773634079,
+        "narHash": "sha256-49qb4QNMv77VOeEux+sMd0uBhPvvHgVc0r938Bulvbo=",
+        "owner": "nix-community",
+        "repo": "nixd",
+        "rev": "8ecf93d4d93745e05ea53534e8b94f5e9506e6bd",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nix-community",
+        "repo": "nixd",
+        "type": "github"
+      }
+    },
+    "nixpkgs": {
+      "locked": {
+        "lastModified": 1765186076,
+        "narHash": "sha256-hM20uyap1a0M9d344I692r+ik4gTMyj60cQWO+hAYP8=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "addf7cf5f383a3101ecfba091b98d0a1263dc9b8",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "ref": "nixos-unstable",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "nixpkgs-src": {
+      "flake": false,
+      "locked": {
+        "lastModified": 1773597492,
+        "narHash": "sha256-hQ284SkIeNaeyud+LS0WVLX+WL2rxcVZLFEaK0e03zg=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "a07d4ce6bee67d7c838a8a5796e75dff9caa21ef",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "ref": "nixpkgs-unstable",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "nixpkgs_2": {
+      "locked": {
+        "lastModified": 1765186076,
+        "narHash": "sha256-hM20uyap1a0M9d344I692r+ik4gTMyj60cQWO+hAYP8=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "addf7cf5f383a3101ecfba091b98d0a1263dc9b8",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "ref": "nixos-unstable",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "nixpkgs_3": {
+      "locked": {
+        "lastModified": 1769433173,
+        "narHash": "sha256-Gf1dFYgD344WZ3q0LPlRoWaNdNQq8kSBDLEWulRQSEs=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "13b0f9e6ac78abbbb736c635d87845c4f4bee51b",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "ref": "nixpkgs-unstable",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "nixpkgs_4": {
+      "inputs": {
+        "nixpkgs-src": "nixpkgs-src"
+      },
+      "locked": {
+        "lastModified": 1773704619,
+        "narHash": "sha256-LKtmit8Sr81z8+N2vpIaN/fyiQJ8f7XJ6tMSKyDVQ9s=",
+        "owner": "cachix",
+        "repo": "devenv-nixpkgs",
+        "rev": "906534d75b0e2fe74a719559dfb1ad3563485f43",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "ref": "rolling",
+        "repo": "devenv-nixpkgs",
+        "type": "github"
+      }
+    },
+    "pre-commit-hooks": {
+      "inputs": {
+        "flake-compat": [
+          "devenv",
+          "crate2nix",
+          "crate2nix_stable",
+          "flake-compat"
+        ],
+        "gitignore": "gitignore_3",
+        "nixpkgs": [
+          "devenv",
+          "crate2nix",
+          "crate2nix_stable",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1769069492,
+        "narHash": "sha256-Efs3VUPelRduf3PpfPP2ovEB4CXT7vHf8W+xc49RL/U=",
+        "owner": "cachix",
+        "repo": "pre-commit-hooks.nix",
+        "rev": "a1ef738813b15cf8ec759bdff5761b027e3e1d23",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "repo": "pre-commit-hooks.nix",
+        "type": "github"
+      }
+    },
+    "pre-commit-hooks_2": {
+      "inputs": {
+        "flake-compat": [
+          "devenv",
+          "crate2nix",
+          "flake-compat"
+        ],
+        "gitignore": "gitignore_4",
+        "nixpkgs": [
+          "devenv",
+          "crate2nix",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1769069492,
+        "narHash": "sha256-Efs3VUPelRduf3PpfPP2ovEB4CXT7vHf8W+xc49RL/U=",
+        "owner": "cachix",
+        "repo": "pre-commit-hooks.nix",
+        "rev": "a1ef738813b15cf8ec759bdff5761b027e3e1d23",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "repo": "pre-commit-hooks.nix",
+        "type": "github"
+      }
+    },
+    "root": {
+      "inputs": {
+        "devenv": "devenv",
+        "nixpkgs": "nixpkgs_4",
+        "systems": "systems"
+      }
+    },
+    "rust-overlay": {
+      "inputs": {
+        "nixpkgs": [
+          "devenv",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1773630837,
+        "narHash": "sha256-zJhgAGnbVKeBMJOb9ctZm4BGS/Rnrz+5lfSXTVah4HQ=",
+        "owner": "oxalica",
+        "repo": "rust-overlay",
+        "rev": "f600ea449c7b5bb596fa1cf21c871cc5b9e31316",
+        "type": "github"
+      },
+      "original": {
+        "owner": "oxalica",
+        "repo": "rust-overlay",
+        "type": "github"
+      }
+    },
+    "systems": {
+      "locked": {
+        "lastModified": 1681028828,
+        "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=",
+        "owner": "nix-systems",
+        "repo": "default",
+        "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nix-systems",
+        "repo": "default",
+        "type": "github"
+      }
+    },
+    "treefmt-nix": {
+      "inputs": {
+        "nixpkgs": [
+          "devenv",
+          "nixd",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1772660329,
+        "narHash": "sha256-IjU1FxYqm+VDe5qIOxoW+pISBlGvVApRjiw/Y/ttJzY=",
+        "owner": "numtide",
+        "repo": "treefmt-nix",
+        "rev": "3710e0e1218041bbad640352a0440114b1e10428",
+        "type": "github"
+      },
+      "original": {
+        "owner": "numtide",
+        "repo": "treefmt-nix",
+        "type": "github"
+      }
+    }
+  },
+  "root": "root",
+  "version": 7
+}
new file mode 100644
index 0000000..74cf48c
--- /dev/null
+++ b/flake.nix
@@ -0,0 +1,39 @@
+{
+  inputs = {
+    nixpkgs.url = "github:cachix/devenv-nixpkgs/rolling";
+    systems.url = "github:nix-systems/default";
+    devenv.url = "github:cachix/devenv";
+    devenv.inputs.nixpkgs.follows = "nixpkgs";
+  };
+
+  nixConfig = {
+    extra-trusted-public-keys = "devenv.cachix.org-1:w1cLUi8dv3hnoSPGAuibQv+f9TZLr6cv/Hm9XgU50cw=";
+    extra-substituters = "https://devenv.cachix.org";
+  };
+
+  outputs = { self, nixpkgs, devenv, systems, ... } @ inputs:
+    let
+      forEachSystem = nixpkgs.lib.genAttrs (import systems);
+    in
+    {
+      devShells = forEachSystem
+        (system:
+          let
+            pkgs = nixpkgs.legacyPackages.${system};
+          in
+          {
+            default = devenv.lib.mkShell {
+              inherit inputs pkgs;
+              modules = [
+                {
+                  # https://devenv.sh/reference/options/
+                  packages = with pkgs; [
+                    typst
+                    tinymist
+                  ];
+                }
+              ];
+            };
+          });
+    };
+}
new file mode 100644
index 0000000..d4c65e2
--- /dev/null
+++ b/playground.typ
@@ -0,0 +1,11 @@
+= Layout Playground
+
++ First thing
++ Second thing
+
+Here?
+
+There?
+
+#circle(fill: red, width: 100% )[#align(center)[Hello there]]
+

Describe the problem

index d4c65e2..b166f27 100644
--- a/playground.typ
+++ b/playground.typ
@@ -1,11 +1,17 @@
== Layout Playground
=
-+ First thing
-+ Second thing
+#let images = ((60, 120), (120, 200), (100, 40), (300, 250))
=
-Here?
+We have #images.len() images.
=
-There?
+#for ((width, height)) in images {
+    rect(width: width * 1pt, height: height * 1pt, align(center + horizon)[#width × #height])
+}
+
+The goal is to put them in a row, so that:
+
++ The whole available width of the page is used.
++ All images have the same height.
++ The aspect ratio of every image is preserved.
=
-#circle(fill: red, width: 100% )[#align(center)[Hello there]]
=

Implement the solution and describe the math

I haven't figured out yet how to get the actual dimensions of the parent container. There are layout and measure functions, but they confuse me.

index b166f27..340560d 100644
--- a/playground.typ
+++ b/playground.typ
@@ -1,11 +1,25 @@
== Layout Playground
=
-#let images = ((60, 120), (120, 200), (100, 40), (300, 250))
+#let images = (
+    (60, 120, red),
+    (120, 200, green),
+    (100, 30, blue),
+    (300, 250, yellow)
+)
=
=We have #images.len() images.
=
-#for ((width, height)) in images {
-    rect(width: width * 1pt, height: height * 1pt, align(center + horizon)[#width × #height])
+#let rects = images.map( image => {
+    let (width, height, color) = image
+    rect(
+        width: width * 1pt,
+        height: height * 1pt,
+        fill: color,
+        align(center + horizon)[#width × #height])
+})
+
+#for r in rects {
+    r
=}
=
=The goal is to put them in a row, so that:
@@ -15,3 +29,127 @@ The goal is to put them in a row, so that:
=+ The aspect ratio of every image is preserved.
=
=
+== Step 1
+
+Calculate individual aspect ratios as $w / h$.
+
+#let aspects = images.map(size => (
+    width: size.at(0) * 1pt,
+    height: size.at(1) * 1pt,
+    ratio: size.at(0) / size.at(1))
+)
+
+#table(columns: 2,
+    [*Size*], [*Aspect ratio*],
+    ..for (width, height, ratio) in aspects {
+        ([#width × #height], [#calc.round(digits: 3, ratio)])
+    }
+)
+
+== Step 2
+
+Sum the aspect ratios of all the images to get the total aspect ratio of the row of images.
+
+#let total_aspect_ratio = aspects.map(a => a.ratio).sum()
+
+#rect(
+    height: 100% / total_aspect_ratio,
+    width: 100%,
+    align(center + horizon)[1 × #calc.round(digits: 3, total_aspect_ratio)]
+)
+
+== Step 3
+
+Take available width of the parent container (e.g. page - margins).
+
+// #let available_width = layout(size => size.width)
+// FIXME: This gives content, I want a Length. For now let's just assume it:
+#let available_width = 300pt
+
+#let straightedge = line(length: available_width)
+
+Available width: #available_width
+#straightedge
+
+For each image:
+
++ calculate the `fraction` of the width it can take, as `image_aspect_ratio / total_aspect_ratio`.
++ calculate the `final_width` as `available_width * fraction`
++ calculate the `scaling_factor` as `final_width / image_width`
++ scale the image by the `scaling_factor`
+
+
+
+#box()[
+    #grid(
+        columns: images.len(),
+    
+        ..rects.enumerate().map(((index, this_rect)) => {
+            let aspect = aspects.at(index)
+            let image_aspect_ratio = aspect.ratio
+            let width_fraction = image_aspect_ratio / total_aspect_ratio
+            let final_width = width_fraction * available_width
+            let scaling_factor = final_width / aspect.width * 100%
+
+            scale(this_rect, scaling_factor, reflow: true)
+        })
+    )
+]
+#straightedge
+
+Adding gaps (gutter) between images is not difficult. Just subtract the width of the gutter (its length × number of images - 1) from the available space.
+
+#let gutter = 2pt
+
+
+#let total_gutter = gutter * (rects.len() - 1)
+#box()[
+    With gutter of #gutter
+
+    #straightedge
+    #grid(
+        columns: images.len(),
+        column-gutter: gutter,
+
+        ..rects.enumerate().map(((index, this_rect)) => {
+            let aspect = aspects.at(index)
+            let image_aspect_ratio = aspect.ratio
+            let width_fraction = image_aspect_ratio / total_aspect_ratio
+            let final_width = width_fraction * (available_width - total_gutter)
+            let scaling_factor = final_width / aspect.width * 100%
+
+            scale(this_rect, scaling_factor, reflow: true)
+        })
+    )
+]
+
+#let gutter = 16pt
+
+
+#let total_gutter = gutter * (rects.len() - 1)
+#box()[
+    With gutter of #gutter
+
+    #straightedge
+    #grid(
+        columns: images.len(),
+        column-gutter: gutter,
+
+        ..rects.enumerate().map(((index, this_rect)) => {
+            let aspect = aspects.at(index)
+            let image_aspect_ratio = aspect.ratio
+            let width_fraction = image_aspect_ratio / total_aspect_ratio
+            let final_width = width_fraction * (available_width - total_gutter)
+            let scaling_factor = final_width / aspect.width * 100%
+
+            scale(this_rect, scaling_factor, reflow: true)
+        })
+    )
+]
+
+
+*TODO*: Pack it as a function
+
+*TODO*: Use actual images (figure out how to get their dimensions or aspect ratios)
+
+*TODO*: How to get the actual width of a parent container

Improve the document layout

Put the "images" in a grid, break page on h2

index 340560d..fbfd23b 100644
--- a/playground.typ
+++ b/playground.typ
@@ -18,9 +18,11 @@ We have #images.len() images.
=        align(center + horizon)[#width × #height])
=})
=
-#for r in rects {
-    r
-}
+#grid(
+    columns: (auto, auto),
+    gutter: 10pt,
+    ..rects
+)
=
=The goal is to put them in a row, so that:
=
@@ -28,6 +30,10 @@ The goal is to put them in a row, so that:
=+ All images have the same height.
=+ The aspect ratio of every image is preserved.
=
+#show heading.where(depth: 2): body => {
+    pagebreak(weak: true)
+    body
+}
=
=== Step 1
=

Add some images from placedog.net (cute)

Get the dimensions of sample 2 images.

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Binary files /dev/null and b/300x600.jpg differ
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index 0000000..64dd9f1
Binary files /dev/null and b/360x480.jpg differ
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index 0000000..1dfa8ab
Binary files /dev/null and b/500x480.jpg differ
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index 0000000..d0c7387
Binary files /dev/null and b/780x300.jpg differ
index fbfd23b..cb902a2 100644
--- a/playground.typ
+++ b/playground.typ
@@ -154,8 +154,26 @@ Adding gaps (gutter) between images is not difficult. Just subtract the width of
=]
=
=
-*TODO*: Pack it as a function
+== Use actual images
+
+#let i = image("300x600.jpg")
+
+#box[
+    The following image size is #context measure(i)
+
+    #i
+]
+
+#let i = image("500x480.jpg")
+#box[
+    The following image size is #context measure(i)
+
+    #i
+]
+
+== *TODO*: Pack it as a function
+
+
+== *TODO*: How to get the actual width of a parent container
=
-*TODO*: Use actual images (figure out how to get their dimensions or aspect ratios)
=
-*TODO*: How to get the actual width of a parent container

Implement the solution

Works with actual images and fills the parent container. Wrapped in a image-row function.

index cb902a2..ef39395 100644
--- a/playground.typ
+++ b/playground.typ
@@ -171,9 +171,121 @@ Adding gaps (gutter) between images is not difficult. Just subtract the width of
=    #i
=]
=
-== *TODO*: Pack it as a function
+== How to get the actual width of a parent container
=
+#context layout(size => [
+    The parent layout can only be accessed inside a `context`. Here the width is  #size.width. It is best to wrap the whole computation in ```typst context layout(size => { ... })``` expression.
+])
=
-== *TODO*: How to get the actual width of a parent container
+== Pack it as a function
=
+#let image-row(gutter: 0pt, ..paths) = {
+    context {
+        let images = paths.pos().map(path => {
+            let content = image(path)
+            let size = measure(content)
+            let ratio = size.width / size.height
+            return (path: path, content: content, ratio: ratio, ..size)
+        })
+
+
+        let total_ratio = images.fold(0, (acc, item) => acc + item.ratio)
+        let total_gutter = gutter * (images.len() - 1)
+
+        layout(parent => grid(
+            columns: images.len(),
+            column-gutter: gutter,
+
+            ..images.map((item) => {
+                let width_fraction = item.ratio / total_ratio
+                let final_width = width_fraction * (parent.width - total_gutter)
+                let scaling_factor = final_width / item.width * 100%
+
+                scale(item.content, scaling_factor, reflow: true)
+            })
+        ))
+    }
+}
+
+Now we have everything we need! Here are some examples.
+
+=== 4 images, no gutter
+
+#image-row(
+    "300x600.jpg",
+    "500x480.jpg",
+    "360x480.jpg",
+    "780x300.jpg",
+)
+
+=== 2 image, gutter of 2t
+
+#image-row(
+    gutter: 2pt,
+    "300x600.jpg",
+    "780x300.jpg",
+)
+
+=== 1 image, gutter set, but has no effect
+
+#image-row(
+    gutter: 2pt,
+    "780x300.jpg",
+)
+
+=== Inside a figure
+
+
+#figure(
+    caption: [Some lovely dogs here])[
+        #image-row(
+            gutter: 4pt,
+            "360x480.jpg",
+            "780x300.jpg",
+            "300x600.jpg",
+            "500x480.jpg",
+        )
+    ]
+
+#pagebreak()
+
+=== Inside containers of various sizes
+
+#for width in (40%, 60%, 85%) {
+    align(center, rect(
+        width: width       
+        
+    )[
+        #layout(size => [This container is #size.width wide])
+        #image-row(
+            gutter: 4pt,
+            "360x480.jpg",
+            "780x300.jpg",
+            "300x600.jpg",
+            "500x480.jpg",
+        )
+    ])
+}
+
+
+#pagebreak()
+
+=== Multiple rows
+
+#image-row(
+    gutter: 4pt,
+    "300x600.jpg",
+    "780x300.jpg",
+)
+#v(4pt, weak: true)
+#image-row(
+    gutter: 4pt,
+    "360x480.jpg",
+    "500x480.jpg",
+)
+
+== TODO: Implement `image-rows` function
=
++ Multiple rows, each in an array
++ Automatic vertical spacing
++ In a box, to prevent page breaks inside