diff --git a/blog/2015-11-27-autocallable/2015-11-27-autocallable.ipynb b/blog/2015-11-27-autocallable/2015-11-27-autocallable.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "using Gadfly"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Athena/Phoenix Simulation\n",
+    "\n",
+    "## Underlying simulation\n",
+    "\n",
+    "In order to price the autocallable bonds, we need to simulate the underlying assets. Let's go ahead and set up the simulation first, as this lays the foundation for what we're trying to do. We're going to use [JNJ](http://finance.yahoo.com/q?s=jnj) as the basis for our simulation. This implies the following parameters:\n",
+    "\n",
+    "- $S_0$ = \\$102.2 (as of time of writing)\n",
+    "- $q$ = 2.84%\n",
+    "- $r$ = [.49, .9, 1.21, 1.45, 1.69] (term structure as of time of writing, linear interpolation)\n",
+    "- $\\mu$ = $r - q$ (note that this implies a negative drift because of current low rates)\n",
+    "- $\\sigma$ = $\\sigma_{imp}$ = 15.62% (from VIX implied volatility)\n",
+    "\n",
+    "We additionally define some parameters for simulation:\n",
+    "\n",
+    "- `T`: The number of years to simulate\n",
+    "- `m`: The number of paths to simulate\n",
+    "- `n`: The number of steps to simulate in a year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "S0 = 102.2\n",
+    "nominal = 100\n",
+    "q = 2.84 / 100\n",
+    "σ = 15.37 / 100\n",
+    "term = [0, .49, .9, 1.21, 1.45, 1.69] / 100 + 1\n",
+    "\n",
+    "###\n",
+    "# Potential: Based on PEP\n",
+    "# S0 = 100.6\n",
+    "# σ = 14.86\n",
+    "# q = 2.7\n",
+    "###\n",
+    "\n",
+    "# Simulation parameters\n",
+    "T = 5 # Using years as the unit of time\n",
+    "n = 250 # simulations per year\n",
+    "m = 100000 # paths\n",
+    "num_simulations = 5; # simulation rounds per price"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining the simulation\n",
+    "To make things simpler, we simulate a single year at a time. This allows us to easily add in a dividend policy without too much difficulty, and update the simulation every year to match the term structure. The underlying uses GBM for simulation between years."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "simulate_gbm = function(S0, μ, σ, T, n)\n",
+    "    # Set the initial state\n",
+    "    m = length(S0)\n",
+    "    t = T / n\n",
+    "    motion = zeros(m, n)\n",
+    "    motion[:,1] = S0\n",
+    "    \n",
+    "    # Build out all states\n",
+    "    for i=1:(n-1)\n",
+    "        motion[:,i+1] = motion[:,i] .* exp((μ - σ^2/2)*t) .* exp(sqrt(t) * σ .* randn(m))\n",
+    "    end\n",
+    "    \n",
+    "    return motion\n",
+    "end\n",
+    "\n",
+    "function display_motion(motion, T)\n",
+    "    # Given a matrix of paths, display the motion\n",
+    "    n = length(motion[1,:])\n",
+    "    m = length(motion[:,1])\n",
+    "    x = repmat(1:n, m)\n",
+    "    \n",
+    "    # Calculate the ticks we're going to use. We'd like to\n",
+    "    # have an xtick every month, so calculate where those\n",
+    "    # ticks will actually be at.\n",
+    "    if (T > 3)\n",
+    "        num_ticks = T\n",
+    "        xlabel = \"Years\"\n",
+    "    else\n",
+    "        num_ticks = T * 12\n",
+    "        xlabel = \"Months\"\n",
+    "    end\n",
+    "    tick_width = n / num_ticks\n",
+    "    x_ticks = []\n",
+    "    for i=1:round(num_ticks)\n",
+    "        x_ticks = vcat(x_ticks, i*tick_width)\n",
+    "    end\n",
+    "    \n",
+    "    # Use one color for each path. I'm not sure if there's\n",
+    "    # a better way to do this without going through DataFrames\n",
+    "    colors = []\n",
+    "    for i = 1:m\n",
+    "        colors = vcat(colors, ones(n)*i)\n",
+    "    end\n",
+    "    \n",
+    "    plot(x=x, y=motion', color=colors, Geom.line,\n",
+    "    Guide.xticks(ticks=x_ticks, label=false),\n",
+    "    Guide.xlabel(xlabel),\n",
+    "    Guide.ylabel(\"Value\"))\n",
+    "end;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example simulation\n",
+    "\n",
+    "Let's go ahead and run a sample simulation to see what the functions got us!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
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+       "a.path.toString=z;a.path.clone=d});C.plugin(function(a,v,y,C){var A=Math.max,w=Math.min,z=function(a){this.items=[];this.bindings={};this.length=0;this.type=\"set\";if(a)for(var f=0,n=a.length;f<n;f++)a[f]&&(this[this.items.length]=this.items[this.items.length]=a[f],this.length++)};v=z.prototype;v.push=function(){for(var a,f,n=0,k=arguments.length;n<k;n++)if(a=arguments[n])f=this.items.length,this[f]=this.items[f]=a,this.length++;return this};v.pop=function(){this.length&&delete this[this.length--];\n",
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+       "return a};v.toString=function(){return\"Snap\\u2018s set\"};v.type=\"set\";a.set=function(){var a=new z;arguments.length&&a.push.apply(a,Array.prototype.slice.call(arguments,0));return a}});C.plugin(function(a,v,y,C){function A(a){var b=a[0];switch(b.toLowerCase()){case \"t\":return[b,0,0];case \"m\":return[b,1,0,0,1,0,0];case \"r\":return 4==a.length?[b,0,a[2],a[3] ]:[b,0];case \"s\":return 5==a.length?[b,1,1,a[3],a[4] ]:3==a.length?[b,1,1]:[b,1]}}function w(b,d,f){d=q(d).replace(/\\.{3}|\\u2026/g,b);b=a.parseTransformString(b)||\n",
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+       "function(){k.stop();var d=z(this.node,\"filter\");if(d)return(d=w(d).match(A))&&a.select(d[1])});k.on(\"snap.util.attr.filter\",function(d){if(d instanceof v&&\"filter\"==d.type){k.stop();var f=d.node.id;f||(z(d.node,{id:d.id}),f=d.id);z(this.node,{filter:a.url(f)})}d&&\"none\"!=d||(k.stop(),this.node.removeAttribute(\"filter\"))});a.filter.blur=function(d,f){null==d&&(d=2);return a.format('<feGaussianBlur stdDeviation=\"{def}\"/>',{def:null==f?d:[d,f]})};a.filter.blur.toString=function(){return this()};a.filter.shadow=\n",
+       "function(d,f,k,u,p){\"string\"==typeof k&&(p=u=k,k=4);\"string\"!=typeof u&&(p=u,u=\"#000\");null==k&&(k=4);null==p&&(p=1);null==d&&(d=0,f=2);null==f&&(f=d);u=a.color(u||\"#000\");return a.format('<feGaussianBlur in=\"SourceAlpha\" stdDeviation=\"{blur}\"/><feOffset dx=\"{dx}\" dy=\"{dy}\" result=\"offsetblur\"/><feFlood flood-color=\"{color}\"/><feComposite in2=\"offsetblur\" operator=\"in\"/><feComponentTransfer><feFuncA type=\"linear\" slope=\"{opacity}\"/></feComponentTransfer><feMerge><feMergeNode/><feMergeNode in=\"SourceGraphic\"/></feMerge>',\n",
+       "{color:u,dx:d,dy:f,blur:k,opacity:p})};a.filter.shadow.toString=function(){return this()};a.filter.grayscale=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {b} {h} 0 0 0 0 0 1 0\"/>',{a:0.2126+0.7874*(1-d),b:0.7152-0.7152*(1-d),c:0.0722-0.0722*(1-d),d:0.2126-0.2126*(1-d),e:0.7152+0.2848*(1-d),f:0.0722-0.0722*(1-d),g:0.2126-0.2126*(1-d),h:0.0722+0.9278*(1-d)})};a.filter.grayscale.toString=function(){return this()};a.filter.sepia=\n",
+       "function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {h} {i} 0 0 0 0 0 1 0\"/>',{a:0.393+0.607*(1-d),b:0.769-0.769*(1-d),c:0.189-0.189*(1-d),d:0.349-0.349*(1-d),e:0.686+0.314*(1-d),f:0.168-0.168*(1-d),g:0.272-0.272*(1-d),h:0.534-0.534*(1-d),i:0.131+0.869*(1-d)})};a.filter.sepia.toString=function(){return this()};a.filter.saturate=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"saturate\" values=\"{amount}\"/>',{amount:1-\n",
+       "d})};a.filter.saturate.toString=function(){return this()};a.filter.hueRotate=function(d){return a.format('<feColorMatrix type=\"hueRotate\" values=\"{angle}\"/>',{angle:d||0})};a.filter.hueRotate.toString=function(){return this()};a.filter.invert=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncG type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncB type=\"table\" tableValues=\"{amount} {amount2}\"/></feComponentTransfer>',{amount:d,\n",
+       "amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
+       "{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
+       "\n",
+       "]]> </script>\n",
+       "<script> <![CDATA[\n",
+       "\n",
+       "(function (glob, factory) {\n",
+       "    // AMD support\n",
+       "    if (typeof define === \"function\" && define.amd) {\n",
+       "        // Define as an anonymous module\n",
+       "        define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
+       "            return factory(Snap);\n",
+       "        });\n",
+       "    } else {\n",
+       "        // Browser globals (glob is window)\n",
+       "        // Snap adds itself to window\n",
+       "        glob.Gadfly = factory(glob.Snap);\n",
+       "    }\n",
+       "}(this, function (Snap) {\n",
+       "\n",
+       "var Gadfly = {};\n",
+       "\n",
+       "// Get an x/y coordinate value in pixels\n",
+       "var xPX = function(fig, x) {\n",
+       "    var client_box = fig.node.getBoundingClientRect();\n",
+       "    return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
+       "};\n",
+       "\n",
+       "var yPX = function(fig, y) {\n",
+       "    var client_box = fig.node.getBoundingClientRect();\n",
+       "    return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Snap.plugin(function (Snap, Element, Paper, global) {\n",
+       "    // Traverse upwards from a snap element to find and return the first\n",
+       "    // note with the \"plotroot\" class.\n",
+       "    Element.prototype.plotroot = function () {\n",
+       "        var element = this;\n",
+       "        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
+       "            element = element.parent();\n",
+       "        }\n",
+       "        return element;\n",
+       "    };\n",
+       "\n",
+       "    Element.prototype.svgroot = function () {\n",
+       "        var element = this;\n",
+       "        while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
+       "            element = element.parent();\n",
+       "        }\n",
+       "        return element;\n",
+       "    };\n",
+       "\n",
+       "    Element.prototype.plotbounds = function () {\n",
+       "        var root = this.plotroot()\n",
+       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
+       "        return {\n",
+       "            x0: bbox.x,\n",
+       "            x1: bbox.x + bbox.width,\n",
+       "            y0: bbox.y,\n",
+       "            y1: bbox.y + bbox.height\n",
+       "        };\n",
+       "    };\n",
+       "\n",
+       "    Element.prototype.plotcenter = function () {\n",
+       "        var root = this.plotroot()\n",
+       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
+       "        return {\n",
+       "            x: bbox.x + bbox.width / 2,\n",
+       "            y: bbox.y + bbox.height / 2\n",
+       "        };\n",
+       "    };\n",
+       "\n",
+       "    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
+       "    // does everything right.\n",
+       "    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
+       "    var events = [\"mouseenter\", \"mouseleave\"];\n",
+       "\n",
+       "    for (i in events) {\n",
+       "        (function (event_name) {\n",
+       "            var event_name = events[i];\n",
+       "            Element.prototype[event_name] = function (fn, scope) {\n",
+       "                if (Snap.is(fn, \"function\")) {\n",
+       "                    var fn2 = function (event) {\n",
+       "                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
+       "                            return;\n",
+       "                        }\n",
+       "\n",
+       "                        var reltg = event.relatedTarget ? event.relatedTarget :\n",
+       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
+       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
+       "\n",
+       "                        if (reltg != this.node) {\n",
+       "                            return fn.apply(this, event);\n",
+       "                        }\n",
+       "                    };\n",
+       "\n",
+       "                    if (event_name == \"mouseenter\") {\n",
+       "                        this.mouseover(fn2, scope);\n",
+       "                    } else {\n",
+       "                        this.mouseout(fn2, scope);\n",
+       "                    }\n",
+       "                }\n",
+       "                return this;\n",
+       "            };\n",
+       "        })(events[i]);\n",
+       "    }\n",
+       "\n",
+       "\n",
+       "    Element.prototype.mousewheel = function (fn, scope) {\n",
+       "        if (Snap.is(fn, \"function\")) {\n",
+       "            var el = this;\n",
+       "            var fn2 = function (event) {\n",
+       "                fn.apply(el, [event]);\n",
+       "            };\n",
+       "        }\n",
+       "\n",
+       "        this.node.addEventListener(\n",
+       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
+       "            fn2);\n",
+       "\n",
+       "        return this;\n",
+       "    };\n",
+       "\n",
+       "\n",
+       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
+       "    // This is a function to directly update element attributes without going\n",
+       "    // through eve.\n",
+       "    Element.prototype.attribute = function(key, val) {\n",
+       "        if (val === undefined) {\n",
+       "            return this.node.getAttribute(key);\n",
+       "        } else {\n",
+       "            this.node.setAttribute(key, val);\n",
+       "            return this;\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    Element.prototype.init_gadfly = function() {\n",
+       "        this.mouseenter(Gadfly.plot_mouseover)\n",
+       "            .mouseleave(Gadfly.plot_mouseout)\n",
+       "            .dblclick(Gadfly.plot_dblclick)\n",
+       "            .mousewheel(Gadfly.guide_background_scroll)\n",
+       "            .drag(Gadfly.guide_background_drag_onmove,\n",
+       "                  Gadfly.guide_background_drag_onstart,\n",
+       "                  Gadfly.guide_background_drag_onend);\n",
+       "        this.mouseenter(function (event) {\n",
+       "            init_pan_zoom(this.plotroot());\n",
+       "        });\n",
+       "        return this;\n",
+       "    };\n",
+       "});\n",
+       "\n",
+       "\n",
+       "// When the plot is moused over, emphasize the grid lines.\n",
+       "Gadfly.plot_mouseover = function(event) {\n",
+       "    var root = this.plotroot();\n",
+       "\n",
+       "    var keyboard_zoom = function(event) {\n",
+       "        if (event.which == 187) { // plus\n",
+       "            increase_zoom_by_position(root, 0.1, true);\n",
+       "        } else if (event.which == 189) { // minus\n",
+       "            increase_zoom_by_position(root, -0.1, true);\n",
+       "        }\n",
+       "    };\n",
+       "    root.data(\"keyboard_zoom\", keyboard_zoom);\n",
+       "    window.addEventListener(\"keyup\", keyboard_zoom);\n",
+       "\n",
+       "    var xgridlines = root.select(\".xgridlines\"),\n",
+       "        ygridlines = root.select(\".ygridlines\");\n",
+       "\n",
+       "    xgridlines.data(\"unfocused_strokedash\",\n",
+       "                    xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
+       "    ygridlines.data(\"unfocused_strokedash\",\n",
+       "                    ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
+       "\n",
+       "    // emphasize grid lines\n",
+       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
+       "    xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
+       "              .selectAll(\"path\")\n",
+       "              .animate({stroke: destcolor}, 250);\n",
+       "\n",
+       "    destcolor = root.data(\"focused_ygrid_color\");\n",
+       "    ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
+       "              .selectAll(\"path\")\n",
+       "              .animate({stroke: destcolor}, 250);\n",
+       "\n",
+       "    // reveal zoom slider\n",
+       "    root.select(\".zoomslider\")\n",
+       "        .animate({opacity: 1.0}, 250);\n",
+       "};\n",
+       "\n",
+       "// Reset pan and zoom on double click\n",
+       "Gadfly.plot_dblclick = function(event) {\n",
+       "  set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
+       "};\n",
+       "\n",
+       "// Unemphasize grid lines on mouse out.\n",
+       "Gadfly.plot_mouseout = function(event) {\n",
+       "    var root = this.plotroot();\n",
+       "\n",
+       "    window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
+       "    root.data(\"keyboard_zoom\", undefined);\n",
+       "\n",
+       "    var xgridlines = root.select(\".xgridlines\"),\n",
+       "        ygridlines = root.select(\".ygridlines\");\n",
+       "\n",
+       "    var destcolor = root.data(\"unfocused_xgrid_color\");\n",
+       "\n",
+       "    xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
+       "              .selectAll(\"path\")\n",
+       "              .animate({stroke: destcolor}, 250);\n",
+       "\n",
+       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
+       "    ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
+       "              .selectAll(\"path\")\n",
+       "              .animate({stroke: destcolor}, 250);\n",
+       "\n",
+       "    // hide zoom slider\n",
+       "    root.select(\".zoomslider\")\n",
+       "        .animate({opacity: 0.0}, 250);\n",
+       "};\n",
+       "\n",
+       "\n",
+       "var set_geometry_transform = function(root, tx, ty, scale) {\n",
+       "    var xscalable = root.hasClass(\"xscalable\"),\n",
+       "        yscalable = root.hasClass(\"yscalable\");\n",
+       "\n",
+       "    var old_scale = root.data(\"scale\");\n",
+       "\n",
+       "    var xscale = xscalable ? scale : 1.0,\n",
+       "        yscale = yscalable ? scale : 1.0;\n",
+       "\n",
+       "    tx = xscalable ? tx : 0.0;\n",
+       "    ty = yscalable ? ty : 0.0;\n",
+       "\n",
+       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
+       "\n",
+       "    root.selectAll(\".geometry, image\")\n",
+       "        .forEach(function (element, i) {\n",
+       "            element.transform(t);\n",
+       "        });\n",
+       "\n",
+       "    bounds = root.plotbounds();\n",
+       "\n",
+       "    if (yscalable) {\n",
+       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
+       "        root.selectAll(\".xfixed\")\n",
+       "            .forEach(function (element, i) {\n",
+       "                element.transform(xfixed_t);\n",
+       "            });\n",
+       "\n",
+       "        root.select(\".ylabels\")\n",
+       "            .transform(xfixed_t)\n",
+       "            .selectAll(\"text\")\n",
+       "            .forEach(function (element, i) {\n",
+       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
+       "                    var cx = element.asPX(\"x\"),\n",
+       "                        cy = element.asPX(\"y\");\n",
+       "                    var st = element.data(\"static_transform\");\n",
+       "                    unscale_t = new Snap.Matrix();\n",
+       "                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
+       "                    element.transform(unscale_t);\n",
+       "\n",
+       "                    var y = cy * scale + ty;\n",
+       "                    element.attr(\"visibility\",\n",
+       "                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
+       "                }\n",
+       "            });\n",
+       "    }\n",
+       "\n",
+       "    if (xscalable) {\n",
+       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
+       "        var xtrans = new Snap.Matrix().translate(tx, 0);\n",
+       "        root.selectAll(\".yfixed\")\n",
+       "            .forEach(function (element, i) {\n",
+       "                element.transform(yfixed_t);\n",
+       "            });\n",
+       "\n",
+       "        root.select(\".xlabels\")\n",
+       "            .transform(yfixed_t)\n",
+       "            .selectAll(\"text\")\n",
+       "            .forEach(function (element, i) {\n",
+       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
+       "                    var cx = element.asPX(\"x\"),\n",
+       "                        cy = element.asPX(\"y\");\n",
+       "                    var st = element.data(\"static_transform\");\n",
+       "                    unscale_t = new Snap.Matrix();\n",
+       "                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
+       "\n",
+       "                    element.transform(unscale_t);\n",
+       "\n",
+       "                    var x = cx * scale + tx;\n",
+       "                    element.attr(\"visibility\",\n",
+       "                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
+       "                    }\n",
+       "            });\n",
+       "    }\n",
+       "\n",
+       "    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
+       "    var size_attribs = [\"font-size\"];\n",
+       "    var unscaled_selection = \".geometry, .geometry *\";\n",
+       "    if (xscalable) {\n",
+       "        size_attribs.push(\"rx\");\n",
+       "        unscaled_selection += \", .xgridlines\";\n",
+       "    }\n",
+       "    if (yscalable) {\n",
+       "        size_attribs.push(\"ry\");\n",
+       "        unscaled_selection += \", .ygridlines\";\n",
+       "    }\n",
+       "\n",
+       "    root.selectAll(unscaled_selection)\n",
+       "        .forEach(function (element, i) {\n",
+       "            // circle need special help\n",
+       "            if (element.node.nodeName == \"circle\") {\n",
+       "                var cx = element.attribute(\"cx\"),\n",
+       "                    cy = element.attribute(\"cy\");\n",
+       "                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
+       "                                                        cx, cy);\n",
+       "                element.transform(unscale_t);\n",
+       "                return;\n",
+       "            }\n",
+       "\n",
+       "            for (i in size_attribs) {\n",
+       "                var key = size_attribs[i];\n",
+       "                var val = parseFloat(element.attribute(key));\n",
+       "                if (val !== undefined && val != 0 && !isNaN(val)) {\n",
+       "                    element.attribute(key, val * old_scale / scale);\n",
+       "                }\n",
+       "            }\n",
+       "        });\n",
+       "};\n",
+       "\n",
+       "\n",
+       "// Find the most appropriate tick scale and update label visibility.\n",
+       "var update_tickscale = function(root, scale, axis) {\n",
+       "    if (!root.hasClass(axis + \"scalable\")) return;\n",
+       "\n",
+       "    var tickscales = root.data(axis + \"tickscales\");\n",
+       "    var best_tickscale = 1.0;\n",
+       "    var best_tickscale_dist = Infinity;\n",
+       "    for (tickscale in tickscales) {\n",
+       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
+       "        if (dist < best_tickscale_dist) {\n",
+       "            best_tickscale_dist = dist;\n",
+       "            best_tickscale = tickscale;\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
+       "        root.data(axis + \"tickscale\", best_tickscale);\n",
+       "        var mark_inscale_gridlines = function (element, i) {\n",
+       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
+       "            element.attribute(\"gadfly:inscale\", inscale);\n",
+       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
+       "        };\n",
+       "\n",
+       "        var mark_inscale_labels = function (element, i) {\n",
+       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
+       "            element.attribute(\"gadfly:inscale\", inscale);\n",
+       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
+       "        };\n",
+       "\n",
+       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
+       "        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "\n",
+       "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
+       "    var old_scale = root.data(\"scale\");\n",
+       "    var bounds = root.plotbounds();\n",
+       "\n",
+       "    var width = bounds.x1 - bounds.x0,\n",
+       "        height = bounds.y1 - bounds.y0;\n",
+       "\n",
+       "    // compute the viewport derived from tx, ty, and scale\n",
+       "    var x_min = -width * scale - (scale * width - width),\n",
+       "        x_max = width * scale,\n",
+       "        y_min = -height * scale - (scale * height - height),\n",
+       "        y_max = height * scale;\n",
+       "\n",
+       "    var x0 = bounds.x0 - scale * bounds.x0,\n",
+       "        y0 = bounds.y0 - scale * bounds.y0;\n",
+       "\n",
+       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
+       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
+       "\n",
+       "    tx += x0;\n",
+       "    ty += y0;\n",
+       "\n",
+       "    // when the scale change, we may need to alter which set of\n",
+       "    // ticks is being displayed\n",
+       "    if (scale != old_scale) {\n",
+       "        update_tickscale(root, scale, \"x\");\n",
+       "        update_tickscale(root, scale, \"y\");\n",
+       "    }\n",
+       "\n",
+       "    set_geometry_transform(root, tx, ty, scale);\n",
+       "\n",
+       "    root.data(\"scale\", scale);\n",
+       "    root.data(\"tx\", tx);\n",
+       "    root.data(\"ty\", ty);\n",
+       "};\n",
+       "\n",
+       "\n",
+       "var scale_centered_translation = function(root, scale) {\n",
+       "    var bounds = root.plotbounds();\n",
+       "\n",
+       "    var width = bounds.x1 - bounds.x0,\n",
+       "        height = bounds.y1 - bounds.y0;\n",
+       "\n",
+       "    var tx0 = root.data(\"tx\"),\n",
+       "        ty0 = root.data(\"ty\");\n",
+       "\n",
+       "    var scale0 = root.data(\"scale\");\n",
+       "\n",
+       "    // how off from center the current view is\n",
+       "    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
+       "        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
+       "\n",
+       "    // rescale offsets\n",
+       "    xoff = xoff * scale / scale0;\n",
+       "    yoff = yoff * scale / scale0;\n",
+       "\n",
+       "    // adjust for the panel position being scaled\n",
+       "    var x_edge_adjust = bounds.x0 * (1 - scale),\n",
+       "        y_edge_adjust = bounds.y0 * (1 - scale);\n",
+       "\n",
+       "    return {\n",
+       "        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
+       "        y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "\n",
+       "// Initialize data for panning zooming if it isn't already.\n",
+       "var init_pan_zoom = function(root) {\n",
+       "    if (root.data(\"zoompan-ready\")) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    // The non-scaling-stroke trick. Rather than try to correct for the\n",
+       "    // stroke-width when zooming, we force it to a fixed value.\n",
+       "    var px_per_mm = root.node.getCTM().a;\n",
+       "\n",
+       "    // Drag events report deltas in pixels, which we'd like to convert to\n",
+       "    // millimeters.\n",
+       "    root.data(\"px_per_mm\", px_per_mm);\n",
+       "\n",
+       "    root.selectAll(\"path\")\n",
+       "        .forEach(function (element, i) {\n",
+       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
+       "        if (sw > 0) {\n",
+       "            element.attribute(\"stroke-width\", sw);\n",
+       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
+       "        }\n",
+       "    });\n",
+       "\n",
+       "    // Store ticks labels original tranformation\n",
+       "    root.selectAll(\".xlabels > text, .ylabels > text\")\n",
+       "        .forEach(function (element, i) {\n",
+       "            var lm = element.transform().localMatrix;\n",
+       "            element.data(\"static_transform\",\n",
+       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
+       "        });\n",
+       "\n",
+       "    var xgridlines = root.select(\".xgridlines\");\n",
+       "    var ygridlines = root.select(\".ygridlines\");\n",
+       "    var xlabels = root.select(\".xlabels\");\n",
+       "    var ylabels = root.select(\".ylabels\");\n",
+       "\n",
+       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
+       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
+       "    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
+       "    if (root.data(\"xtickscales\") === undefined) {\n",
+       "\n",
+       "        // index all the tick scales that are listed\n",
+       "        var xtickscales = {};\n",
+       "        var ytickscales = {};\n",
+       "        var add_x_tick_scales = function (element, i) {\n",
+       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
+       "        };\n",
+       "        var add_y_tick_scales = function (element, i) {\n",
+       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
+       "        };\n",
+       "\n",
+       "        if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
+       "        if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
+       "        if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
+       "        if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
+       "\n",
+       "        root.data(\"xtickscales\", xtickscales);\n",
+       "        root.data(\"ytickscales\", ytickscales);\n",
+       "        root.data(\"xtickscale\", 1.0);\n",
+       "    }\n",
+       "\n",
+       "    var min_scale = 1.0, max_scale = 1.0;\n",
+       "    for (scale in xtickscales) {\n",
+       "        min_scale = Math.min(min_scale, scale);\n",
+       "        max_scale = Math.max(max_scale, scale);\n",
+       "    }\n",
+       "    for (scale in ytickscales) {\n",
+       "        min_scale = Math.min(min_scale, scale);\n",
+       "        max_scale = Math.max(max_scale, scale);\n",
+       "    }\n",
+       "    root.data(\"min_scale\", min_scale);\n",
+       "    root.data(\"max_scale\", max_scale);\n",
+       "\n",
+       "    // store the original positions of labels\n",
+       "    if (xlabels) {\n",
+       "        xlabels.selectAll(\"text\")\n",
+       "               .forEach(function (element, i) {\n",
+       "                   element.data(\"x\", element.asPX(\"x\"));\n",
+       "               });\n",
+       "    }\n",
+       "\n",
+       "    if (ylabels) {\n",
+       "        ylabels.selectAll(\"text\")\n",
+       "               .forEach(function (element, i) {\n",
+       "                   element.data(\"y\", element.asPX(\"y\"));\n",
+       "               });\n",
+       "    }\n",
+       "\n",
+       "    // mark grid lines and ticks as in or out of scale.\n",
+       "    var mark_inscale = function (element, i) {\n",
+       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
+       "    };\n",
+       "\n",
+       "    if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
+       "    if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
+       "    if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
+       "    if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
+       "\n",
+       "    // figure out the upper ond lower bounds on panning using the maximum\n",
+       "    // and minum grid lines\n",
+       "    var bounds = root.plotbounds();\n",
+       "    var pan_bounds = {\n",
+       "        x0: 0.0,\n",
+       "        y0: 0.0,\n",
+       "        x1: 0.0,\n",
+       "        y1: 0.0\n",
+       "    };\n",
+       "\n",
+       "    if (xgridlines) {\n",
+       "        xgridlines\n",
+       "            .selectAll(\"path\")\n",
+       "            .forEach(function (element, i) {\n",
+       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
+       "                    var bbox = element.node.getBBox();\n",
+       "                    if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
+       "                        pan_bounds.x0 = bounds.x1 - bbox.x;\n",
+       "                    }\n",
+       "                    if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
+       "                        pan_bounds.x1 = bounds.x0 - bbox.x;\n",
+       "                    }\n",
+       "                    element.attr(\"visibility\", \"visible\");\n",
+       "                }\n",
+       "            });\n",
+       "    }\n",
+       "\n",
+       "    if (ygridlines) {\n",
+       "        ygridlines\n",
+       "            .selectAll(\"path\")\n",
+       "            .forEach(function (element, i) {\n",
+       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
+       "                    var bbox = element.node.getBBox();\n",
+       "                    if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
+       "                        pan_bounds.y0 = bounds.y1 - bbox.y;\n",
+       "                    }\n",
+       "                    if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
+       "                        pan_bounds.y1 = bounds.y0 - bbox.y;\n",
+       "                    }\n",
+       "                    element.attr(\"visibility\", \"visible\");\n",
+       "                }\n",
+       "            });\n",
+       "    }\n",
+       "\n",
+       "    // nudge these values a little\n",
+       "    pan_bounds.x0 -= 5;\n",
+       "    pan_bounds.x1 += 5;\n",
+       "    pan_bounds.y0 -= 5;\n",
+       "    pan_bounds.y1 += 5;\n",
+       "    root.data(\"pan_bounds\", pan_bounds);\n",
+       "\n",
+       "    root.data(\"zoompan-ready\", true)\n",
+       "};\n",
+       "\n",
+       "\n",
+       "// drag actions, i.e. zooming and panning\n",
+       "var pan_action = {\n",
+       "    start: function(root, x, y, event) {\n",
+       "        root.data(\"dx\", 0);\n",
+       "        root.data(\"dy\", 0);\n",
+       "        root.data(\"tx0\", root.data(\"tx\"));\n",
+       "        root.data(\"ty0\", root.data(\"ty\"));\n",
+       "    },\n",
+       "    update: function(root, dx, dy, x, y, event) {\n",
+       "        var px_per_mm = root.data(\"px_per_mm\");\n",
+       "        dx /= px_per_mm;\n",
+       "        dy /= px_per_mm;\n",
+       "\n",
+       "        var tx0 = root.data(\"tx\"),\n",
+       "            ty0 = root.data(\"ty\");\n",
+       "\n",
+       "        var dx0 = root.data(\"dx\"),\n",
+       "            dy0 = root.data(\"dy\");\n",
+       "\n",
+       "        root.data(\"dx\", dx);\n",
+       "        root.data(\"dy\", dy);\n",
+       "\n",
+       "        dx = dx - dx0;\n",
+       "        dy = dy - dy0;\n",
+       "\n",
+       "        var tx = tx0 + dx,\n",
+       "            ty = ty0 + dy;\n",
+       "\n",
+       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
+       "    },\n",
+       "    end: function(root, event) {\n",
+       "\n",
+       "    },\n",
+       "    cancel: function(root) {\n",
+       "        set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "var zoom_box;\n",
+       "var zoom_action = {\n",
+       "    start: function(root, x, y, event) {\n",
+       "        var bounds = root.plotbounds();\n",
+       "        var width = bounds.x1 - bounds.x0,\n",
+       "            height = bounds.y1 - bounds.y0;\n",
+       "        var ratio = width / height;\n",
+       "        var xscalable = root.hasClass(\"xscalable\"),\n",
+       "            yscalable = root.hasClass(\"yscalable\");\n",
+       "        var px_per_mm = root.data(\"px_per_mm\");\n",
+       "        x = xscalable ? x / px_per_mm : bounds.x0;\n",
+       "        y = yscalable ? y / px_per_mm : bounds.y0;\n",
+       "        var w = xscalable ? 0 : width;\n",
+       "        var h = yscalable ? 0 : height;\n",
+       "        zoom_box = root.rect(x, y, w, h).attr({\n",
+       "            \"fill\": \"#000\",\n",
+       "            \"opacity\": 0.25\n",
+       "        });\n",
+       "        zoom_box.data(\"ratio\", ratio);\n",
+       "    },\n",
+       "    update: function(root, dx, dy, x, y, event) {\n",
+       "        var xscalable = root.hasClass(\"xscalable\"),\n",
+       "            yscalable = root.hasClass(\"yscalable\");\n",
+       "        var px_per_mm = root.data(\"px_per_mm\");\n",
+       "        var bounds = root.plotbounds();\n",
+       "        if (yscalable) {\n",
+       "            y /= px_per_mm;\n",
+       "            y = Math.max(bounds.y0, y);\n",
+       "            y = Math.min(bounds.y1, y);\n",
+       "        } else {\n",
+       "            y = bounds.y1;\n",
+       "        }\n",
+       "        if (xscalable) {\n",
+       "            x /= px_per_mm;\n",
+       "            x = Math.max(bounds.x0, x);\n",
+       "            x = Math.min(bounds.x1, x);\n",
+       "        } else {\n",
+       "            x = bounds.x1;\n",
+       "        }\n",
+       "\n",
+       "        dx = x - zoom_box.attr(\"x\");\n",
+       "        dy = y - zoom_box.attr(\"y\");\n",
+       "        if (xscalable && yscalable) {\n",
+       "            var ratio = zoom_box.data(\"ratio\");\n",
+       "            var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
+       "            var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
+       "            dx = width * dx / Math.abs(dx);\n",
+       "            dy = height * dy / Math.abs(dy);\n",
+       "        }\n",
+       "        var xoffset = 0,\n",
+       "            yoffset = 0;\n",
+       "        if (dx < 0) {\n",
+       "            xoffset = dx;\n",
+       "            dx = -1 * dx;\n",
+       "        }\n",
+       "        if (dy < 0) {\n",
+       "            yoffset = dy;\n",
+       "            dy = -1 * dy;\n",
+       "        }\n",
+       "        if (isNaN(dy)) {\n",
+       "            dy = 0.0;\n",
+       "        }\n",
+       "        if (isNaN(dx)) {\n",
+       "            dx = 0.0;\n",
+       "        }\n",
+       "        zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
+       "        zoom_box.attr(\"width\", dx);\n",
+       "        zoom_box.attr(\"height\", dy);\n",
+       "    },\n",
+       "    end: function(root, event) {\n",
+       "        var xscalable = root.hasClass(\"xscalable\"),\n",
+       "            yscalable = root.hasClass(\"yscalable\");\n",
+       "        var zoom_bounds = zoom_box.getBBox();\n",
+       "        if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
+       "            return;\n",
+       "        }\n",
+       "        var plot_bounds = root.plotbounds();\n",
+       "        var zoom_factor = 1.0;\n",
+       "        if (yscalable) {\n",
+       "            zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
+       "        } else {\n",
+       "            zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
+       "        }\n",
+       "        var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
+       "            ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
+       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
+       "        zoom_box.remove();\n",
+       "    },\n",
+       "    cancel: function(root) {\n",
+       "        zoom_box.remove();\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
+       "    var root = this.plotroot();\n",
+       "    var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
+       "    var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
+       "    var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
+       "    var drag_action = zoomable ? zoom_action :\n",
+       "                      panable  ? pan_action :\n",
+       "                                 undefined;\n",
+       "    root.data(\"drag_action\", drag_action);\n",
+       "    if (drag_action) {\n",
+       "        var cancel_drag_action = function(event) {\n",
+       "            if (event.which == 27) { // esc key\n",
+       "                drag_action.cancel(root);\n",
+       "                root.data(\"drag_action\", undefined);\n",
+       "            }\n",
+       "        };\n",
+       "        window.addEventListener(\"keyup\", cancel_drag_action);\n",
+       "        root.data(\"cancel_drag_action\", cancel_drag_action);\n",
+       "        drag_action.start(root, x, y, event);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
+       "    var root = this.plotroot();\n",
+       "    var drag_action = root.data(\"drag_action\");\n",
+       "    if (drag_action) {\n",
+       "        drag_action.update(root, dx, dy, x, y, event);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.guide_background_drag_onend = function(event) {\n",
+       "    var root = this.plotroot();\n",
+       "    window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
+       "    root.data(\"cancel_drag_action\", undefined);\n",
+       "    var drag_action = root.data(\"drag_action\");\n",
+       "    if (drag_action) {\n",
+       "        drag_action.end(root, event);\n",
+       "    }\n",
+       "    root.data(\"drag_action\", undefined);\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.guide_background_scroll = function(event) {\n",
+       "    if (event.shiftKey) {\n",
+       "        increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
+       "        event.preventDefault();\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_button_mouseover = function(event) {\n",
+       "    this.select(\".button_logo\")\n",
+       "         .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_button_mouseout = function(event) {\n",
+       "     this.select(\".button_logo\")\n",
+       "         .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_zoomout_click = function(event) {\n",
+       "    increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
+       "    increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_track_click = function(event) {\n",
+       "    // TODO\n",
+       "};\n",
+       "\n",
+       "\n",
+       "// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
+       "// The constants a, b, and c are solved using the constraint that the function\n",
+       "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
+       "var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
+       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
+       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
+       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
+       "    return a * Math.exp(b * position) + c;\n",
+       "}\n",
+       "\n",
+       "// inverse of scale_from_slider_position\n",
+       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
+       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
+       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
+       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
+       "    return 1 / b * Math.log((scale - c) / a);\n",
+       "}\n",
+       "\n",
+       "var increase_zoom_by_position = function(root, delta_position, animate) {\n",
+       "    var scale = root.data(\"scale\"),\n",
+       "        min_scale = root.data(\"min_scale\"),\n",
+       "        max_scale = root.data(\"max_scale\");\n",
+       "    var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
+       "    position += delta_position;\n",
+       "    scale = scale_from_slider_position(position, min_scale, max_scale);\n",
+       "    set_zoom(root, scale, animate);\n",
+       "}\n",
+       "\n",
+       "var set_zoom = function(root, scale, animate) {\n",
+       "    var min_scale = root.data(\"min_scale\"),\n",
+       "        max_scale = root.data(\"max_scale\"),\n",
+       "        old_scale = root.data(\"scale\");\n",
+       "    var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
+       "    if (animate) {\n",
+       "        Snap.animate(\n",
+       "            old_scale,\n",
+       "            new_scale,\n",
+       "            function (new_scale) {\n",
+       "                update_plot_scale(root, new_scale);\n",
+       "            },\n",
+       "            200);\n",
+       "    } else {\n",
+       "        update_plot_scale(root, new_scale);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "var update_plot_scale = function(root, new_scale) {\n",
+       "    var trans = scale_centered_translation(root, new_scale);\n",
+       "    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
+       "\n",
+       "    root.selectAll(\".zoomslider_thumb\")\n",
+       "        .forEach(function (element, i) {\n",
+       "            var min_pos = element.data(\"min_pos\"),\n",
+       "                max_pos = element.data(\"max_pos\"),\n",
+       "                min_scale = root.data(\"min_scale\"),\n",
+       "                max_scale = root.data(\"max_scale\");\n",
+       "            var xmid = (min_pos + max_pos) / 2;\n",
+       "            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
+       "            element.transform(new Snap.Matrix().translate(\n",
+       "                Math.max(min_pos, Math.min(\n",
+       "                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
+       "    });\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
+       "    var root = this.plotroot();\n",
+       "    var min_pos = this.data(\"min_pos\"),\n",
+       "        max_pos = this.data(\"max_pos\"),\n",
+       "        min_scale = root.data(\"min_scale\"),\n",
+       "        max_scale = root.data(\"max_scale\"),\n",
+       "        old_scale = root.data(\"old_scale\");\n",
+       "\n",
+       "    var px_per_mm = root.data(\"px_per_mm\");\n",
+       "    dx /= px_per_mm;\n",
+       "    dy /= px_per_mm;\n",
+       "\n",
+       "    var xmid = (min_pos + max_pos) / 2;\n",
+       "    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
+       "                   dx / (max_pos - min_pos);\n",
+       "\n",
+       "    // compute the new scale\n",
+       "    var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
+       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
+       "\n",
+       "    update_plot_scale(root, new_scale);\n",
+       "    event.stopPropagation();\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
+       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
+       "    var root = this.plotroot();\n",
+       "\n",
+       "    // keep track of what the scale was when we started dragging\n",
+       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
+       "    event.stopPropagation();\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_thumb_dragend = function(event) {\n",
+       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
+       "    event.stopPropagation();\n",
+       "};\n",
+       "\n",
+       "\n",
+       "var toggle_color_class = function(root, color_class, ison) {\n",
+       "    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
+       "    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
+       "    if (ison) {\n",
+       "        guides.animate({opacity: 0.5}, 250);\n",
+       "        geoms.animate({opacity: 0.0}, 250);\n",
+       "    } else {\n",
+       "        guides.animate({opacity: 1.0}, 250);\n",
+       "        geoms.animate({opacity: 1.0}, 250);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.colorkey_swatch_click = function(event) {\n",
+       "    var root = this.plotroot();\n",
+       "    var color_class = this.data(\"color_class\");\n",
+       "\n",
+       "    if (event.shiftKey) {\n",
+       "        root.selectAll(\".colorkey text\")\n",
+       "            .forEach(function (element) {\n",
+       "                var other_color_class = element.data(\"color_class\");\n",
+       "                if (other_color_class != color_class) {\n",
+       "                    toggle_color_class(root, other_color_class,\n",
+       "                                       element.attr(\"opacity\") == 1.0);\n",
+       "                }\n",
+       "            });\n",
+       "    } else {\n",
+       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "\n",
+       "return Gadfly;\n",
+       "\n",
+       "}));\n",
+       "\n",
+       "\n",
+       "//@ sourceURL=gadfly.js\n",
+       "\n",
+       "(function (glob, factory) {\n",
+       "    // AMD support\n",
+       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
+       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
+       "            factory(Snap, Gadfly);\n",
+       "        });\n",
+       "      } else {\n",
+       "          factory(glob.Snap, glob.Gadfly);\n",
+       "      }\n",
+       "})(window, function (Snap, Gadfly) {\n",
+       "    var fig = Snap(\"#fig-3a6dd25ad25c4037a166889ee51bb151\");\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-8\")\n",
+       "   .init_gadfly();\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-11\")\n",
+       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-11\")\n",
+       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-12\")\n",
+       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-12\")\n",
+       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-16\")\n",
+       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-16\")\n",
+       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-16\")\n",
+       "   .click(Gadfly.zoomslider_zoomin_click)\n",
+       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
+       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-18\")\n",
+       "   .data(\"max_pos\", 111.58)\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-18\")\n",
+       "   .data(\"min_pos\", 94.58)\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-18\")\n",
+       "   .click(Gadfly.zoomslider_track_click);\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-19\")\n",
+       "   .data(\"max_pos\", 111.58)\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-19\")\n",
+       "   .data(\"min_pos\", 94.58)\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-19\")\n",
+       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-19\")\n",
+       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-19\")\n",
+       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
+       "     Gadfly.zoomslider_thumb_dragstart,\n",
+       "     Gadfly.zoomslider_thumb_dragend)\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-20\")\n",
+       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-20\")\n",
+       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
+       ";\n",
+       "fig.select(\"#fig-3a6dd25ad25c4037a166889ee51bb151-element-20\")\n",
+       "   .click(Gadfly.zoomslider_zoomout_click)\n",
+       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
+       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
+       ";\n",
+       "    });\n",
+       "]]> </script>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "Plot(...)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "initial = ones(5) * S0\n",
+    "# Using μ=0, T=.25 for now, we'll use the proper values later\n",
+    "motion = simulate_gbm(initial, 0, σ, .25, 200) \n",
+    "\n",
+    "display_motion(motion, .25)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Computing the term structure\n",
+    "\n",
+    "Now that we've got the basic motion set up, let's start making things a bit more sophisticated for the model. We're going to assume that the drift of the stock is the difference between the implied forward rate and the quarterly dividend rate.\n",
+    "\n",
+    "We're given the yearly term structure, and need to calculate the quarterly forward rate to match this structure. The term structure is assumed to follow:\n",
+    "\n",
+    "$d(0, t) = d(0,t-1)\\cdot f_{i-1, i}$\n",
+    "\n",
+    "Where $f_{i-1, i}$ is the quarterly forward rate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "forward_term = function(yearly_term)\n",
+    "    # It is assumed that we have a yearly term structure passed in, and starts at year 0\n",
+    "    # This implies a nominal rate above 0 for the first year!\n",
+    "    years = length(term)-1 # because we start at 0\n",
+    "    structure = [(term[i+1] / term[i]) for i=1:years]\n",
+    "end;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Illustrating the term structure\n",
+    "\n",
+    "Now that we've got our term structure, let's validate that we're getting the correct results! If we've done this correctly, then:\n",
+    "\n",
+    "```\n",
+    "term[2] == term[1] * structure[1]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Actual term[2]: 1.0049; Calculated term[2]: 1.0049\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example term structure taken from:\n",
+    "# http://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?data=yield\n",
+    "# Linear interpolation used years in-between periods, assuming real-dollar\n",
+    "# interest rates\n",
+    "forward_yield = forward_term(term)\n",
+    "calculated_term2 = term[1] * forward_yield[1]\n",
+    "\n",
+    "println(\"Actual term[2]: $(term[2]); Calculated term[2]: $(calculated_term2)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The full underlying simulation\n",
+    "\n",
+    "Now that we have the term structure set up, we can actually start doing some real simulation! Let's construct some paths through the full 5-year time frame. In order to do this, we will simulate 1 year at a time, and use the forward rates at those times to compute the drift. Thus, there will be 5 total simulations batched together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wIyHkB7bt2e90DjcSQjyxa1e7SqdzuJN81OkEbhSLedd4vTE1L42iKIqiKIqiuE4wGOplmuEzakrchmKakTsta8PFTudwI9MMP7ds2Ua1Gm89GEb4RaczuJFhhHTDCE9wOkdjUjUXimOYKZmIOjmdw42YuT3AqU7ncCNmdEtJ8arPvnqhHk4ncCMi0RTAGTXpnaq5UByTnBwt3rWryR+dzuFO/GI0mlDldAo3Skjge/v0aVfudA43YqYzaiKohtK8uSwoL/d84XQORVEURVEURVHqyrLCmZYV+ZvTOdzIMMIPqDlC6scwwrmWtTXF6RxuZBiRpU5ncCPTjFxkGOHHnc7RmNR9R8UxzFTJzDudzuFOtAfgMqdTuBERtqol1+uHiNWy4fUgJVUIgd1O51AURVEURVEUpa4KCrYkFxWt7ex0DjcqKlrfrqBgXXOnc7jR6tUbuzGzumpbD5a1vqfTGdyouHhbU9Pc2NHpHI1JvcEUx3i9Vb2k9P7C6RxuFIuJu7xezxk1nXBDEcJ+LhgsVfNc1IOU2gtOZ3CjaPSgzmyPdzpHY1JDURUn7ZZSWk6HcCMhxFfq/nf9CIH/JSfvjzmdw42I5BKnM7iREHIHMz53OoeiKIqiKIqiKHVVVLS+nWVFLnc6hxuZ5vqBq1dvVKui1oNhRG4uKChQq6LWg2GE1aqo9WBZmzoFg+vPqNuYquZCcYyUoiOAq53O4UbM4lKPR/Z2Ooc78T1eb6cEp1O4E41xOoEbMds+2xZDnc7RmFTNheIYIeRmKbW5TudwIyK5NBbz7nI6hzvRK9HoJjV1er3wTKcTuBGRFhaiKs/pHIqiKIqiKIqi1JVlhdMNIzTa6RxuZFkbrgkGI/2dzuFGlhV+qKSkJNHpHG5kmqHHnM7gRsFgqJdlhc6oRd9UzYXipBZCCL/TIdxIStmT+cxawrmhSImLysubqVvC9cAsBjudwY2kFK2lpHOdztGY1BtMcUw0mrAmMbH8GadzuJHHI1+rrOQKp3O4kZTaZF1vd9DpHG4khP0TpzO4kdfbxIxGK0NO51AURVEURVEUpa5MM9LXNMOPOJ3DjQwjcr9lhS9zOocbmWb45WXLNqrpv+vBNMNvOp3BjQoLN2RZVvhXTudoTOq2iOIYIk4EqJXTOdyJUwFKcTqFGzGjfUqKV9Wb1QMzqTqfehCCk6REC6dzKIqiKIqiKIpSVyUlJYkrVmxSVy7qwTBCqQUFW5KdzuFGxcWh9sxMTudwo8LCDerKRT2EQqGk4uKNLZ3O0ZjUpUHFMeXl3r5JSbaquagXut/rjap1WeqhqopeDgZLVcesHohY1VzUw969Iquqyv6l0zkak+pcKI4h4nJm3uR0Djcioq0A7XE6hxsRYW1ZWVQ6ncOd+GunE7gRszwAYIvTORRFOcPNnj1bczqDoihKfakrF4pjgsFIC8MI6aei7fz8fFePhOqZsvO679tuWet7qvvf9WNZGy7Oz2dX//twimGEBjudwY0KCra0tqwNZ9QMnapzoTiGmdOFEHefkrabRgedinYbgzlnekcCxnBOzjHfn1KKazSN1doi9SCl/E3LlqVqbZF6EWptkXrweKK9pJRqbRFFaQzMWimAxQ3d7pyCOcnE8ryGbrexCOIRAC7/fED7zGPto2lyRSzGaxsx1mmDiN/2eHZGnc7hRkTyDaczuBGz3ETEHzudozGpS4OKY3S982YAmxu63SYxTzfS3LyoF/UDIMAYDKCgtj0yMrqeUR9UDUnX019xOoNb6Xr6TKcznAr5K/Pegi1/nX3+sPCpaD8QSA8DOCVt/1CdNp2LiXdPfB7E1wLoXJUQbfPCCy/sqNk2YfSENQQ0B2DHn6E7psyasgQAxo8erwvQLAaaE+hrlnzb1Nem7nTgJZxxTHNjRyKZ6fenzWmoNpmZlq5aeB8z2jZUm42OkAEATPLsY+0SDK6/pLKyYpd32+IqfcREVcFfB6YZusfrLX+9b9++VU5ncRvTDI1prA5GDueIHMo55aN68qy8FFTiR5K0lwGE8/PzPVrzgy3svU12Z2dnxxriHIYR8hGhp66n5zVEe25w2twWIaLZnlh0EICyWrd7xIAps6Z2iv8v3rEAAAF6iSUemzprajpImiTo942V+UxHZLcD0KDrYyxaMb83gLEAOtYUdRYUFHgb8hynUnDutK4ABgIoB6glABR8OLP5kfvZthiUWLltiIB2QWNndDtmujkWa+WafxM/JMzitsY616WrLsjIs/JS8ovzm35Y8OFR74GG4inzpAKAENVXO5Mrfyxj4jqRXNW5oc5BJDox0yUN1Z4bnDadi+dnPf/Js/964Zu6HDPu3nFdAXSa+trU/wIAYmIGwDefinzK0YgoJKV8ta7HfbQ8t8/hMyzmluQeKs7ThGcIAwkE+Dml8ncAsE/ucs2bmkn0IGAjg18D0KJo/rSMhEp51LL0QsgPeH/oLAAZBXNmtq7PuYrnzOxysnndSAjxxK5d7SqdztEYGn5Is3y0Yds7NmaZ4a2iS7ks2t8j5aRTdR7yRFPjf5J6vpGfCqJHAbrDJuraUOeIxbxrhBBn1ARkp03n4ng4JpdMHD1h3cTR46c/eO+9zQDAw+gMYBMABoDSitINAFpNmjRJVZI3goyMtN2BQLpZ1+OE0K5YXJA3oOZx4h7hzzfyU/Pz85MA/ikAMHAWgR5ZvGrhQwJ8TUPmPqUIbSWwjUArAKSzpKdBuNecM31gzS6h/FeSaHNeFtkVAwFckijsl815U3rU5TTBudNaxMg+JcOAf+j8/i6fZGdTg1zu/iEz3vtbavdmOxvs1zcABALpSxqyve9H5wJ8FYEHAPjR0uULu5+a04jqzgVNRrTyLQDtGRgkILsuWLagQabszsrqsMPv7/J5Q7TlFqdNzcX3IZuGTfnnlPCk2yc1Q4J8sVwmPQVgXF3aMM3wPwH09nq1K7dt67SvRYvIcmbeHAikX2ua4QCAF4mwwO/3PWJZkbuYeTLAf9H19H8bRvgvRMiWku7KzEz7wjQjCwDZUtfTBxYWbugghPwAgKnrvh+b5vorAfEnZv5XIJD+rGWFf8WMW4jwoN/vW2xZkVnMfC5RbHhGxjnbLSuyCsBWXfddXVS0PsO2xcsAfajrab8xjPDtRPg5M54JBHxvmGbozwBdIYR2T0ZG5yLLCs9jRju/P21AMLiuDbMnF6AiXU+7x7IilzPzkwDe1HXfXw0j9DMiuoOZfhMIpH1omuGXAPhjMYzMyvJ9Y5qhlQDt1HXfMNOM9AX4VWbkBwK+BywrdAsz/YqInvP7014zjPDjRBjKzH8QAh2YMQygDrt3p52XnLy5eWJiLA/Al7ruu9MwQoOJ6Glmmh0IpD1lmuGJZfY3j0iOLgEwyrTW/6MqdmBwVXTfQ9Gk/SsBke6hJttjfLANABLAHwV5vwHwU8uK3MTMvwZ4qq6nv2Ka4d8DGC6lGJuZ2aXANMPvAeickhK98MABTxMi+ogZXwcCvtssa8PFzPJvRPyu35/+hGWFxjHTfQDl6HraXNOMTAd4gJTyxszMrhHTDH8CIKrrvssMY113Iu1NgD/V9fSfmmb4egAPE2GG3+/7h2VFHmXmkUQ8QWzMayuTWvpkStpDYpfRHUAMAERK+9eL5k2fJ8l7yf5YYncmBlFlUwBg0jKF1jQVwCWmGX4ewPlC0CjaPK+pPHvoFMkeDgTSLrUWzHpatgxcRQnNl2Bz3kzRpNU1phn+HcD/0PX0GaYZfgTAdcyYHAj4lplm5C2Au1VURC+TMimanGx/AiCs676bTHP9QEBMY+a5gUB6jmWF72PGOAB/0nXffwwj9CwRXcRs3xoInFNimuFFAJJ03XdhQcG6Lh6P9h+AVul62jjDCI8gwmPMeCUQ8E01zdCvAbqJSPzc7+/yiWWFX2dGTyFoiKYdKItGU5YB2KjrvuuDwUh/KXkmgPm67vutYYRGE9FEZnoyEEh72zTDfwVwqW3jzv79fV+aZmghs+ibmirP2bvX0wqw32eGEQj47res0FXM9Ecies3vT3vOMMIPEOFHAB7QdV++aYZfA9AnFku4qn//s3dZVmQlgG903TfSMEI6Ef2DmRYGAmkPmWbkDoB/RsRP+/3pb1lW+ClmXEYkRvv9XT43jHCuEGjj9/sGFBeH2kejNBdAUNd99xpGZAgRPwHw67qe/jfTDP8CwG1S0q8zM9M+Ms3IKwD30zQ5ol+/rqWWFV4lJbYHAr7hweDGflLarwC8yLMx7w3ZrNsNphkeAdCzup72L9OMPAHwECnlfZmZXS3TDM8BcLbfnzbQMNa2EsI7n5mLA4H0u00znA3gL0R4y+/3PW1ZkckAP8TMd+t6ep5hhF8kQkBKcU1mZpctphleDtBeXU8bunp1uLem4Z8AliRFFj1W2T5rFCc2H8fMUwKB9FmmGf4Dl22+1cPlD/W7aMhsy4r8l5k7eb1lFwBnNY1G7YX7opGzba4oI2hbGbKv19vs7wCGG0Z4AhHuYcbvAgFfrmlGZgCcpWmx6/v167bRNMOfAjio674rTHNdD0B7g4g+8fvTfm5Z4RuZ8RsiTPf7fS8Zxto/Rrn8lvLYN+UMfAPQ0OqP/ERADEtNbP2waYY/03XfrYYRuZCI/86M9wIB3+OmGRoL0I+J6DG/P22OZYWnMmMQEW72+30h0wx9DJCt677sYHDjMNuOTSeiObrum2QYoeuI6BGAXtD1tBdMM5yt6778unwn/dCddov3TBw94UBVQtR3eEHn4SbfNT5bCnpmyqypgXH3juuqSbF8yqyp7QDwxDsnpkPjFVNmTXVvMaCLWFY4E6A7/f60n9fluPyVC/OJuOXgAUP9Sws+7CelfR8gYhL8HwF8Skw3gKAz+CcA2gMIZw8cmn5qXkXDsuZN/zuBtCZU9dBB9u4CUHNZm3H4+5VJclLrV6hy++0AkkDYkjF8XMeazQUFM70JW+UMyfidPnLc5vz8HE+r8nYHmehW//Cx7wTnzxhOkkf0GzFuQqO+wB+A+Bd70s1+f/ta67NOF8F5M65lIFnEtMX9rr2/tCHaNIzI0kAg7dK6HGPOn3mRkHJWjKLnZw6fvD23JDdxePfhlcF505cAOCtjxLhah1znr8zbCKATgAoASQDeyx449IaTfQ3fOceqhWOYOZGAq8C0DsQTD9vMILyTPWDoqJM9j2lGLmLmYYGA7+GTbcstTvvbIg/ee2+zST+e1AkAJk2alGgLuosBEwCmvzx9PYg3TRo9/noAYI3HEeMdJ/OeSZKTo8UVFdof63LMR6s+6grwAJbomp+fnyRt+0IC3czgnwuifvG95A4u8/6BgffAOEBAh8ZYBdPKm962PucpyX3urM/nzPAH58zsRcC5TFzcffjkfQAMAFEQvQ3gAOK38ABgOxOXRFMvmAzCcjA9C0aHornT7qhp01tqPwbC7R5QawBoWd7uWgAesIxfApbcmUHdTvIlu1JCAt+bkdGu3Okcp1JJ7nOJBE4j4s6s2WMaqvaCmeo8EZRmc08A52jS0wsAUvZ4+8TrpDgLgF6Y+1ybI4/J+zSvLeIdCyDesS4D0OAzXBLzXWA+j4Ewk9yO+JXC7TWbwWiQWzHNm8uChATtrw3RllucNp2LifdMmDlx9IRNAJITqhKCE0dPzAOAylizFhyTCyaOnrCZ98m1ALwJWsKh1enI1u5jUM7EuydsJECHEI1WsHSm6969e+WgQZ1OaNjvsmXLmgCAxnIkgBAITUWzWHciMYSBDgQIMD8MADGWO7Ozs2OQeAmEsQwkLFqx6NDVqMUrF166aEVeg45SAQBE+Txr3owr63rYQfaOl4JNaPx7AL2Jqbh60z8AmBnDx44C4XMA6wBsAvMjgmBkZXUoZ+bXd6ZsfYAJ45loatG8GWMBgCA6A0iUJNsAABENiP9XXBnvAFEngM9piJftNn37pm8lInY6x6l0kD0PMFM6JDqD+LYeZ23r1RDtZmZ2qfviW4SeAEAkWgIAS+6WsrO8C0ApzGx7paVdVmgAACAASURBVPadL3BmpgQv/nrY47FEeBeEehUuH4ePiEYR8dfE2AagAIQNALbGT45uDfHDJD09vaJv3867TrYdNzltai6mvDJ1TG3P//21v2/A9/R4n3/teQOIzyugNK663Bap9O4dtGTFwrMBfoqJJoP5cbbtW5jo8E5CdfFawk4AuOy8oasBrM5fmTdVwO4EoBQABPODIGQBaNdQryWU/0rSgfKKAAi9rPnTwv5h478CAGP+dF9g2Ljw9x9N2QBLMHcD0J5JWw8ARDIkWXxWvc8mAgsQXotJ8YnW5OzrLCsy0vPViteyR4yPAZhu5U5rAeBKADOIuA0zANBoAB+BOX7FgvmmotwZPwOhMxhpBQUzvVlZY86o2SrPjNsidC2I0wDyApwqbM+TxbkzHuw7fOwXJ9NqfW6LMGp+/cuWACAFuqfu3tcbgGRN2wWb2x++/+JVC88n4DaK/4ioyB4w9J+LVswvEEK7oyHnvigoKPDukzvbEyCkLb4gTTYTkhczUw9BuF4CRSA0X7Iqtx1qOhv1pG6LKEojEkLGpMTBE9mXIboy8UsMyIOpsVlg/I1BkwFUV3pjPgj5ADjVk3rkL4QNgqjLooIFmbOLZycwIZuBFg15q+RAxcEeTNyKGReQxLNAvMPhsfmiE3h1XUB4H/FO7oGMlZu3AIAkioDxGQAw4z0A75+7snSWvnrrOnibrwVktO+oUd9OBEViBRgDqvdvC4BB3A0AGJwKIIp43cYDANIAeJJ3nJJfgz9oQuBANCpP6ysXALoAaANUdyrBw2zGUyf7b56I95/Ift9dF4e7AZAVyanx4aSMblosei8Iu23yCin4O518ItxC8e+mFdKmnxERa54m2wgQl5qXnpXDx15zpy72YWcm1XwHSq2YmbbboEUg/PeSAVduAvDn+J7e9kcem1+c37Qu5yKSMSJUnHxq91CdC8Ux/fp1DQYCaQ+dyL4EdAXQBMD24d2HVxIhCELT6m0rieUfYIuHAPwvKyvru7/EiTYwuI9guqH1gbNuAVAJwPvppx/U6QPi+0ib+oDpcgBdGNQbAPZWlp3DgtIBoHj27ITajjPmT/cB6ALmdwBoYORRTvyXWTSmbfSS/RkA+EeMfbPfiHH/pJwcSTk5cs2WbT/3+9MXfCdDxcHVIDSx5k6/BYCPgQ0ANY//HYkWDKys3vVsMM4HgJh95nUu/H7fqKysDqdtzYU5Z3pHAG0ABKufYgAC4BFr3pva8vM5M/z1bVvXfVefyH7BrLYXAvEONoCeUkvYA6JzAIBg9xAsO0e1xLW2x9OUEL9yMXv2bG3pqg8DgnE7ABDztMvPu3IhAGA/9gBgVFSkXrRi4Hdu8Xy04qNWdX0dzEyCeVQ8D7ZdfsHlmz2MjaI8YdlBac8hIgbTRwB2kpQtjmqgvLJOaxf5/enLdd33h7rmdDPVuVAcU1CwJbmoaO0xx+F/tDy3T82fiVEz2mMXANjA3pptTPwwa0kl2ecNWW5LOfGIZsDg9SA8DImeEJRFwBsAuDLZc9Jj2Ivef7EdAAigH4A+AEBAp4KCmV4hRXdIdLbmzbhKJu+aHJw34xdF86aPsuZP61lzvGC+DUCyR9PyAVRAYFXNtqyRY8r7jJgYqe28vXtntS4oWPedWQsD1/98DyTeJ8ITYNpCwH9AOCuekS8l0OFD3TwA9nJ1TUbBnJnJy2Y/0+Rk/z7cYPXqjd2Y+bT87DPnTB8oBIIAYgS6j8CvAfQ6AxEA0ajX20YKvqW+7VvW+p7H2hafZ6Ya0U0AsL/84A0AKqo8CTGWnAIAWtTuwQD2p3YYxJoniUlL/7gwt007X/MOUvK9zEgA0bzBg676pKa56mm4D0CI2wWJrMPPK8i+/USyL12+sPuSlfMHAEB+wYcXgOkWAN8wUAgAlw4aGsrOzq4Yft7wfQAweOCQ1QA2Sg0tgXjd1+KCvPhqy5JO4IrkYX83qxblLFm5ZHxdjnG70/INpriD11vVS0rvL461XQhx06JVCwYCABMCBOwCxTsXLOWemv1ipH2dnZW9AwCuOG9Y8KiGGGsANAGhm2DqCEIYwAGPnXDyE+RosUeXzX6mCRPGVj9TAYCb7IilAdQDhGsIPJ+JfwPw0wy8QhLzzXlTehTNm34nMd0AoLTPVWO+AfABQZ7QpGKxmLjL6/UcNfMoEZYA8LGQ86XEW2BuDo99PYCwBvtdxDtn2wG8C2AtSQwGAK/H7pyS1Oyoy7+nIyHs54LB0tOyIyWIswG0BLCxsh1ZkuhVQTwD8fqB7SzYB+CEJ5VbtGxRx8MfS6m9cKx9uWnl0KUr8zovXZnXmYDrC+fMOB+g10G0jTVPC8FSW7RsUUcBuzULYTORLUmDTaJbNKr1s5lbgjgVwF/h8d5Ryym2gDAGgg51LmYXz04QwN0ncquEBU9kiPeWrshLB3ApAx1AVEigZbXtT0RMwC4Czs5fuWBKpXf/CNi48ePC3DY1RaoniqQclSCa1rnY281U50Jx0m4ppXWsjcTUQWPxy/zP5vvA6AHmGcT4EgAoQe4FAAZkC7T43mnfOd65AIBzAPSSkjeD8LUt7X4nE37N+y81Y8LolKZNRiP+gQ4wPgVQFLO1AczcA98WjbYEUAwwA5QuSPszA68B6I/4KBBA0KtSEyfUuRBCfEWEoyr3K6V4F8AukljJHmwDKBnE1wG0rO+ICSaBXiLw/ANlB0cDtBGEezknRwgWOpF9hnQu8L/k5P2n6QydFB++SdiQlTUmmjFsbP4XB1otF4QlYGwn8J0AOn5/G4e1pn33qgCRXHLMfZl6SyAXkAsBdNa82p8BIOZJaA2QJCmRzHtu8EQriKFpIHwMojABHTWiCwWjJYBUJuzKDmTvOfoE2AKgJUkcmq229cHmAQDnXlxw3qEOx5IVCy4+/LBFK/MuyeEcwcCNADraxPcQx2uhiHkzE793rNfEwBZiuh6gCWDcRYSbojGtO6huxeBM3EbTNKMux7jdaTNaRHEfv98XAhCqbdviVfN7QnJfCVxAmtAADg4edNWhSmspE/YISBBQelSNxRG8Gn9pS0wFaAIzUmxNm+dhvpiIMxH/gsdHy+dn1HrV4wiFuc+18XDCC2D5yyqKPgegCTEeP7QDYRuDw0TkB1PNELtSAO1AWE8spjJ4Ohgjv22V3gWAfiu2Lqiptzgev7/LB7U9nzVyTHlw3vQXPCRXwhb7QETMGArwU/FT0Uxmce0Fo35xMDhv2nqArisa0PYWMIZBk++eyLndzu/3/cnpDKcMUTqIPwCwO/6QGIBdkvvci+XsTSLCH4H4PBjdh0/+3vVVli1b1qSK9t8I4Kma53Q9/fuG6vcC0FfYtgQAkrELmABbS2gO284j2JdSVeU1yeV7EfMmCmJ8A9B2YlzP4BtZcDGBWoD56I4FADA2A0gBof/SVR8GZLKnBAeqhjEhQWP6ab6RPwEJiHFZ1ZMLli24OkHD+ey1PxcxTL501QX7AO4IoIJANyA+h4bNjG+yBw495rTcEpgugE+rH14BoIkA3UB84p2L3JLcROzmVhXRbS+f6DGnA3XlQmlUS1fmHaqxKCjY0rqwMHz+kfvkfZrXlqQoANFFFP+QvJEZvz18n0/7f7ofwFIwav2SPdwlWVd9I2PeJwhYBvAfhmQN2csSJQAdmudBaNr3FqoVvv98BwDwsOfXAF8Hoo8BHoH4CIxDBV8MbCeIbxAvUjsHTMWaJi8CECSJzYhp7wGowqGOPZf1W7X1WQA40Y4FABQWrvcXFq5Pq21bjKLP9BkxMbIzZdt+VK+bQ0zbAaDf8DHriO3lAECgmnqORwG+hFgcunJRlDe1Qdek+CEJBsPD8vP5NP1hxVkAPyMlvrNIVvfhk/dBeF5F9QRR5Why1MRVRzro2R8AcO5Hy+cfGqpvWZGR33NITwD/Izt6IB4FHtvTRDJptoC9VkiOapKvAAAmEWOmfFvTioW0vQD8xFpLAKkQovbOBdHm6j95GHIWHYgOI4oXTzNwKypjvbis6iEAAxM9NJeIXtdintsIGAlmPwAQYzGAvgCaA1TI4uirf4e7fODQZQC+AlCMeEE5CBhW/f4+IZ79nlYAKEV0q/X9erpSnQul0eSW5CZKwqCax15vVRdNo6Om1k1MgB+EpmAcACOfgPKztNYLD98nh3IkyhKuIIH3T+Tcl19w+WZJeKkqCW8BAIRcB+Dqj1Z81CPfyE8l4ILvqzr3ap5Hvpg3JQ04tGyyDWCTBP2LAQbjAAAQYQcgt5LkcwB0YMKv+l41YS2I5ktgc79r7y8FYQGAfSC8Cojf1aVTUYNIXKlpWq3zs2QOn7wdALKzc2KEeIGohKyZdRBflrdZAQBMvKG6tR4AugDxIYHMTDKmHXOaZeuDF10xlfqx2DYmtWxZetotTlg8b2p7AO1iiH2hXz0u78jt/uH3b2LmHwPYD8R6FL3/YrvvDhn9Lo0wiIFkjyYOTTooJX51rP0J6AGm17xVB2t+6SMmPB9KYB1krEjY0YTEin2QoD0iWrl4e2TPK1EWywCGZsdAglsAaEVEtU42RZJrOhdgRj8G92Xmmi9sguBuArgd8SnzzwfQnIFRDCSAcF/1XnMRf+8yA5+TpMJjvZ7DzjyXiR5kQAIAg3sz0GJ2ce0jwI7kickWRKjSNG3Yiex/ulCdC6XRNNlD55PkAAAsXp7XXwi5GcDcI/eTkvoB+MxmrRsI7zCwurZbH9nZ2TFPZcX/TvT82VlXvjLUP7QMAOhAYh6AIg9kf45W3QXmjpqIBY51LBPuiEH7OwA/gAoQ/sHgx2xvk8W2J9Fg4t9XSZHCzN+QpC1MdCGAkH/E2OrhoryOiOO/khhLwFhIUj6ZMWLsUcupnwgiuTQWE18ed0emJwFESROHOhejRo2yAQASG7+zK3AhAHw+f8YfBLjWVVa/WDDzbBaxOq3A+sNDr0Sjm6qOv5+7xEhcD8AOrNx1zFlv/VeP/wBAITGelZ7Yis8Htvv9sfZlxpDq//6oejpuADyztn0/+WRuCwZaat7Y+0lV+7+2NW/81kis4nfE8mOi6Ftg9hAghaD/08DhUaNG2QeaaMuYBDyxCgDcGkAbEeNa10GRgregpj4JIBB6gpBGQLyTTNyLgY5giniiFUB8Ku+alX8vALCVpTAAlID5UwKXLh247PidCxl79rIBV+YSePqhcwPUpiz10OKXCwsXdjjm8SRSmbFX0+RRHb7TmepcKI0ityQ3EUzXsaDucwrmJEPwiH79upb6/WmLjtyXCB3ACF5x3hWl0hNbTsSfHavdiy669oQm9Ym3++2Uz9nZ2RXEWMUCfgJ+QkAHlqLWBZSqh2g2A3AtgFIQJjP4Q4/GS8pbtN10oFmbYpb0RtbIMeUM+bEdn0+CcFgVOgPriOK/vISkxQC+7Hf1hON3Do5B17uu7N+/89rj7Wd7Yl8x+DWS2HbktpiIbTjiqSFW7vRxzHiYCbV2tOyo7CkIZ9cz9g9CIJD29vHqdNwmOGfG+cR4EsCOE7gStgDxodObJKO2URk1BgK8A0Ci1yMCABAI+N6obcekyt03n7W3NNasNJJCUqbFNM9cFlpZ1tD7VnnLK6ZkDRmzF8RBBraR5MKauoqRWSPLWYhKb2UZmGkcAK0MsVo7F2Rrm5loMjP+Uv2UDqAdA18AiIJxLwNE4EWJB/dXEfAyGAdA2A1AEvCfmAdfglDERG+CsfZEZvvMPm/4JgCoSqT/A/A04iOtFgB8fm5JbuKiwnlpnqgMfFywoNb3BUluAcKOjIyuHx/vXKcT1blQTrkFyxa0TN7lGY54IdQ5KZx4BRGda1nhdMMIjT76CG5HRKUA8Elg1UbbFkd1QBqEwBow/wJAXwbaCsF9a9stNSXhsFVyaUmVV8z+6kDrVX2vmrDWlloL25PQXh85bjMA6CMmfq2PHLcZhM/A8lChmJftdbaIdy76rt5aRESfHHmeurCsDdcEg5H+x9svRcq1gvFeZTs6alROYNikHQDWgPjQr1di/AgAwBhk5U1vW5w/9TsTjTFxD8RXmm0U33fZvr4sK/xQSUmJ62+L5OfnfFs3IvhmxDvA2495wCFcCkDasdgoAtK+/M+Uo24HVg9BbQngv/H2ZRoAmGbosdpa9MrYUG9Vmcdj0+VgXAWPeDOmJcwjIs4ceZ8FAARaCKC0MoFKCPRtXQXzNwlVZUisLPMAOFgzz8SRtITouuysIfMvGzT0QSZ6GPECUgJ4K+KL+p1N4OmsiVeSqvZv8Bzc8z4RPQPAZNCwhFizXw3JGrKXmIrA/KnU7CXH/7v61lD/0LLsgUMfyB449CZmFAEYlbxL+4lma5eQED6bcejzIzh3WtdDLw/cUkActKxQnRd9czPVuVBOOW8CfEw8CkBnBvoR84Ng9AbQQghR22yB7WT8AxA5lCN3bthzajoX8SGq3kMPGX1q282Gp823+8jVWUPG7B01apS9ZMX88xmyHQHnHDnOniWeIzq0ABl6r9qxMamyyQYgXrh57vAxH51MdCllT2Yc+1Jste7DJ1c2TWmyqLb1Q6rH8S8myW8x8M/qpy9EfIIyQTb3l2Xad8bmM6gHo/GuXBQPanPcDlRdSYmLysubub6gs2VZ2+EAUDxvqg5CzYyR3zssGwCqF+gqzbx20hYA+6KJnruP3EfT7AsI9CUTfQYgKmS8toFZDK6tTWbZEQCYeDoA7wEkzaWY/E5HhCH/S4StWUPG7AXjq8MO/pqYpLeqvNBjRxcUzJlZ66yxl2QO315z9TE7a8gTBGwBsJEh/gXiZ6pP8nnb9btXguE7a//uLBucB4mdlw28clGz3cGLCt9/voPN/OmOyL7PL88avq6285wIImxD/NbM05LpfI7//Zz70fL5GSW5z51FRN9OAAjqTNB2S0kNvqrrD5nqXCinHDHSwagZjeEFcB6A7kT7Qsyx6bUc0hXMh2amPFQj0MCailYLgJoPOc4DkHXkpEEAIBkDqws2JYDVNc+z0O5iQlsG0i5def5XBQUFhzoq0fbi3Uopltc8ppwc2eva+w7dwjnZVTmZ5exo1HvM20WHS8++55hrGkjmleeOGL+GIP4CxG+dEGNq/CToz8TfGR0giHsQ0dlr3n+pWf3TnzhpN3wRHBEe/uqrdq5f54GIBhcvmNotRjS8Zjp3ML94/ANFKeFQvc0GgO+x8qa3BYCi+dPiRcKEXgCbZIsvCDBYxIdVM9Oh4s48Ky9l9uzZmpX7YicC2jDIBuABI5ydPepA5vB7v7NIWsbKbSsZFJ/HxWsfmolWSA7HvAlfeSvLOjbfvfHsBLKPO6qi+v3zDggLLxt45aJm1HomAdPQNPFfseSdPQB4QPxY6jcbt4GwNp6db/F4PB9eNvDK/JP+TGFsZ0AykEDA+STgA6OvRmJkBXl8XD2XyCefzG0BQn/JsWIptVdO6pwuozoXyilHDF/NOiDVNACJ2yq3tdD1c74+fN9PPpnbAkDXmFec8glnqu+7bwDjHQn6EwBo3thRIzCE4IvtBO98ZizwpLCRX5DfelFB7jnEPFxA9AWwEYT0Pbyn87dtj4lmjRyz41Rlz8zsGsnK6nDS7RNrK4iI+w3/yefVa4+8zeCaacLvBXDNmvdfala8YGr1AmjUA8ztKz3R7wzdre3S+skqyX0uEYKzG7pdXfcZo0bRKemwNrJe0hbnE9OhSaXAYtPxDhKStklwzX4bAZxLNvfOz8/xsKyZvZNbA9gR8+BLyfiUGTfm5+cnZWZ2Kahpx1shLmqT1vwtkvJ8sGxZkdL8gyop2jLx0trOSzk5UhP2PwCg39AJ3xYTE39ma97ZgmVbMM4johO6MmYD/2ZgGRB/Lw8eOHRCdt/sA9Ujur4gYIfHU/VHgP4df+HoDVCHk+3YAwAJbCNCzWdUP2IMAqivx64cxZJuB8c7F3aC1gvAdWD870RqpE4nqnOhNIZahy4mU7PzTDP8yOHPxRK9vwBQNSQw5LiXdxsEYQMJvOyx46MpWPLRwzsld4qJJvsOJqdM7TN4fBlk1RIhNYOBtgy+BswzCNhM0vY1SmYAhhG537LClx1/z+/X7+qffAVU3yIhrNQ070SimnkGKB1AyypPNCBt8XpBwUwv4v9fXkDgJwGgaM706wGgKkkb1dBrkxywE7qA0fc7tQUNwDTDLy9btrHRp/+25systWD4JPRmxi0Arkb1fCaa1z5uzUWCVllKLOLFvIxNAECgsS3L2/0VEDW3oVoDtGNI1pC9RFRIgLCT7S6mGT40f4aA7AHCTVWJieOFtJvbIsnIGjlmhwf8yNFnjet71YSjv2CZ39/aPPWpmk4An2DB8GUDrvwsWhUfbVZz5QUAiPhCEL/DRC8RqF/q5uIw5+QIMPoC3Hz27NnaibT/fVjSdpKcB6CMgBUcX4W2R5PyPecy4XpUX7mQQvQGQMnetF2WFT7mMN7TkepcKKceCR8YK6rn6Z8GIEpAFSPWnogO/eKtXg56IoDtDfHr4gRtjEH7KipoG4DPQNQ7f9WCnx2+AxN1iiUkagdT2onFKxdmI17Q6AWQBCCJiL4AEBbH6ESdGpwKUMrJtnL43/OaA63+1PeqH++KxqI1EwvVbPs5AwMTS/lxxCf/CgLobM2bfiMTbiuYM7M1MSY2S0485lDe+tBIpgFo07Ks/U8asl1mtE9J8Tb+Z5+Q11i500+qQ1jzxRifcwVdQBiO+JVAEwASpH3UqKAjdR8+uZKA6mJj2gQAzLiFCLeDEZ9VltC6eqQIuHoCNmLpY6ZDdT4M6gHwDibtUjBTRULSagDoO2LC1rq8poyrx+8emTWynICS+HlqryU6vEgSiP/bHXrh0G0AIGJ4NJg7bVj17bqLSAqLGO8AOFskJP2sqP/ZPQCkAKDeiXtPeiXgmEbbWCAfjPnMeAJEM4i5VUJluSBGdxB1WrZsWRNi9AMADxJiUuLo1VUdMrD3gFkD+2YdNQ1AQ1KdC+WUI3BnJvkUMxYPHjh0AjFfzkBhFZeVlZVtfih/5YJnFy/P6//xyoU+AKmEo4dNnrJskr7eFdoVuWLQFTsJ/CIYvcD0dP6KvLcXr1z4cmF8/HrnKm9iWwjupgkeivjcHPOqmyhlxFZJ4CsS8bkhPlr1Udf84vwGW869NoGA7y9+f9qchmyz5j50daHfLgCfAbABvo6AHQx+AEAeqos/CZgCgY6JZF8FoLdE/PK8OW9KjzXvv9TsZK84MMFXfZ7xxXNmdjmZtg4XCPiG+/3tyxqqvRMlGG2J8RsAqL4KVCdr3n+pWc+UHb+YPXu2FoX4LYBPABys3rwEwObuwyfXOtLiqCxkrwYAErwJNUuzM1qB+JzVudPOY4Zfgr7mnBzh0cR2YrldEzKtpGTFZZ/PnzoAAEDcjUFvE0tioUFjT61T+Z8oCf4awDZ5jNkvmeintT2fn5/jYeAOML0X9Va9AKBcepPzJGlbAbQgYBI0+1bEC0BtaPZRE/fVVUpVynaw/blkejF70NC525P3/FTIqDzUHyfWY7xjEQHjAZRlZqYvDQR8D39voyegf//+yQN7D3x4YJ8B5sA+A8oG9hlQMaDvgODAPgNzdF1PPdn2G5LqXCinHDM6QojNLDAfAKqXUg6zjbaxxIpLAPopEe5nkA7A5lPYuai+OvLtY4p+W9zFWAtCfwAaCDcS+J7y8gOTmGGzSBhKTN3BNBhEn4HwDgHlQEzPHjhiKxFZLHFBfn5+kpAyB2VVC/KN/FP2Zg+FQv/P3pmHV1Wd+//zrn1OJuYZFBlFhUASIAkKVQhTgIC2DrGD11vbWhCHa2t/HW9b2t7W3vZ20sqgrdbbW7XUOgGBMAW0MoQACRBAQEZB5jEhyTlnr/f3xz4nZIQTEpTafJ4nD8nea6+19yHZ+91rve/3G1c1gfQKsFlVn8Wzo37HwjNACGGFIpE3064o3a03DSwikrYz56nWjjj3V8QE0zuWdarzLb1o/qyBRQtmvlKzzLUmotIj/E2ia9w6HyyXQ3Hx0ZY1fw8+EoTOCIM3LZh9h/+I++dLH3ABnTHDVPhCA0TlBze1OLFOkC8j8h6w1+tbl4D+Ndr+9t3gbgPwB2P+psLX8J6KHwJxfpXVxg12aHNy/74tqV0/3e7o+6N9wfJVCDfd2IUe1jXTCpbMaYNyPSKLjFprjaE8wdbUTWkQxuhOlPmCJ0NfMzgVuDOiDluU+7+Vs3ZdT3VsA7QGrCqfFeW15Mz7S4Od7RG8JOz2qPxAkRPAIRW9uzHnCXDLLbeUZ6RnHR49bNwSgOzE7IAvFLyQA6V09rnBW8IJn2fy8tTX2KW4lJSUts55WY3oN1B5yRjNUuFmQX4O9rbYgP8rjbysBjF06NCL3n+ag4tmrij/+MebrYD2GvB94BNdeGGPnnCM78YYaeWtQwptVOxI0D97sthNw6b5M6tNRW7OmVPpmKiqkpGeVTmFWxEnG/DU94j867ju/WqckAqOwl0KyYj/7+qyU+FI5HgXsxCREbQMTBbRzwEjCFV8samuoyanT8ujfn+nCVeqf5TNKnY1KvMFeclRuxLYrFa3G6uRfBgLXBc2ggIl/TwxI62SLK6OVDW/2LRgzqDNbz5X3eTJyBBB7g2WmhGXOIuueA+9M4iM2rxgVqPfOAECgfNzN206ktAUfTUEhc4obUH/W5DPFC2cPaZo4cyLWndvzpnTF2BLWtcxInYkQjwwGAgqdh/KHtDSeELvGqOvRHsu8SdvGgRw0x1fPudTuxU4rD5SwEtSdFz3gM8NZanRZ1H3WzGBstOKDCb+prmI3usPuN9D6CmiO8S65VZMMKJ+e7nEaeg9NbIy4tvRoazziJ05T8UChH+HuosTGglAsGRIwZI5bQBCcf62gEVYC5TbsIZMuPz6KBe0P06J5X6g0SWhxYtmpkL1ZUVfsOxU1Tbx5WdU4FD8+VP728asz4yPd+vNRYmGmKD/lxjp54iblr8t/xdrthSssSK8xgAAIABJREFUWFe8rjC/OP+l/K0FowOBUOS+acKzG3vSB6QF0hPTdqf3T/8mF+5tdXHJY9IGpL2aPiD9b+mJqf+dNiDtoFNuykb1GhVXb4eNudhmmrkUQX/sdGDrmOFjDt6WOqEySVMwJ6y4NwdCJd2AcjwjoZEiLM9Iy4z6JnkxNs2f2Q41N1XdFracpnjBM10jN4gImcmZpSjzRXkeZDCwKCZQeo36fBHDIheYlZGacbwcdydI5ZvKyYSTXva91f+g0pTMXDGxKRE5TFUhoiYfgC1GzTH16+wKK6/GGne9KGtU7HbFRMbejJetH5Zfpq9Y/bRAOsL3gGTE7Ys/dB94+QIbF87qJUo2gDHUpXFC5YyGoQtQqOhMlMEKvy9e8ExKXcc06NKEXaWlwQb7uTQBnfHyI64XIU+svmysXFS+3qr7jc05s55QuA1vbf+PqGYbY0egZoPCNpDt/SY9dnbgxIfXXayvCPMK5iWI0cogOzHr4cMoG5MzHzoK+rSA9YXKzgj6BZQOCh39gZIAyi3WPXsapIUg3zBuyE98zAdGXdd4zr+Not+kxyrE2kN4jqWgMuS89ffbkjNrBCY03GulgwAMplNcRSicIKupwDm1rBeV7wdjpGqJ9kGFl4FtYE8FnWAx0L7mS0d91Le0p675dM1tvmCFA2CNCbn+WHWCFeCG3ogrPXnanN38JbH2spPUhw4d6kf5HKovri7eUGfVycZdG48BpCWmfRPR7wr6E1GTKPBzRH+Unpj6eF3HNewYvUNUbAs5f2MC59uu2LuiXmfdf3ohmWaubkRkvIo8XXO7RU+Ial+XAChvimh/i1xvVFY01djWSC9RrUwOK5g3J0GxdwIPWsw4XG7ZnDPn5KBJUyvFdKyjP/bDhwmn9p4vj+/yJ3+gfELQHycAat1hESngSTdPOpuXv6hSJCs7MTuQl597POzk+pRC73A53xUhJaVng6bVG4og6wdmTTtd9c1sc87slafij+9ue7pNSycm7g0An2O+64Z0khr9Mco1CHfjTVEDlKuaxxAV4Ff9E45PsK5kIJqlSJ6oZhK289YZM0xEttqWOuNV9fXNObM7A7kG85pV+54IP3QxTwG30QhSUno91pjjL4fivGdauucJJyTKMmv1HREyNbwvMePhkrqOE5X7FM5F3ubV0e8nT5peuWy4ef4zZy3SIJ+UeI3vqupWC+xCMXHbAc7EdlmZYM9tjS0vuQ5ogyeo1sa4oQ7AukDZBx+G5/YlpqKk/EBxaVn7lhjjBqPSXImC9UC3TfNn9kFIBrFW9VGM7gRKEXMdgIp2Qp0+mxfMPqeqv1U47aj8OXH94c3V5M+Fg0Z5F9XFQMKQrMeObZo/q0QcriNsS38xOpzv8pNN82a/lTRlWrXrU2Hixtd/88vBn/naafACZ597sq+qUp7Q1mdCbpkTrIhvffbYFoF/x5a38B1a8rXL/VBMwFwHtFCVzZdoKgLfFuVXa7cVRCzed6b3T+sD8j3gN4085sDareu+R9jE7aLnfKkGzTTTOKQrVdwMK7fCCUUrYmj5VTXyvCI3iLJ7ZHrmgbp6gQuZ8tHiuPQyVbLbYxw3DWhflPNcd1UyVfiiYm+uesyY1Akbbp2/5khsUCe0PnukAhQ1AsraUcMmVruOsHphVbYKnG9pOnxDVVagjGhIXsTydQtvjLZ9UdHuGzds2H9Jhc7LZVtp+401K3bOlZx/KyNjRmjwZ7522iqvqGrRgAlTPxxYcPgF9Sy+jwCtw1n/awQWCJqB9d5Ercg08fxZFqqRH0AVueTULhfUUY0O2ZIzZyyQJGLzWibEbkmePP1FkDU0wZR2UdH+Wz9qy3UtM8nAKYHjqvoEwnYgCGBLTGpdx2x/84+twvow3fAeALu82YULDMyavlqNk9OQcxF1u0L1kuvSFu2vyS3KbRFq1fLWirgWxx032AE4oqr3I6wWpL8vWLERx6lUTI0vP8dNLY7fK2pjUXdfrYEug6TJ008BuwT6q0qyoHcCfQVJVZUnUR1UPHdujCod1ZCuqo8DXQVOD5wyraimr4pYDrqWdwdNnr4gafL0v4U/gEPWmuj+dpS7MfrMpnlzKmdAN+c+cx0wyMTE/3tkaaZ/7JmOqIo6TnFFXCvUcRYA+EJlT4IcADkSanltk2u21CRtYFp3lDYWqvmYWE97pMPQ/kNrlfk25Bj1qoyimvVrDi6aaRLy8vLqXHtT0W6qtSWJrfJhjGnzeKu46wZELJbF6FsXG6N/wrFhF9tfa2xDL5ULMxeiZrj3b+gFhGwgHsunty6aU+0PrnBYlx6qmqJqw1Pw5gSG/6n5sLWuXVP1Z1S+oWKzPHEuexy46Zw9kR1twCDq3HIueKyWQmhdWGtudxxtcmnsCHUpGA7P/nqkMoGQmnd86rwGnjiST+z/gSwBsCJPgCxVZSsQQLimeNEf2gtMQOhpcR8nZPcAnSu1MUQrgzxVrkX0S0CCur79EYXRcBVR28uptKiKtfY7H7Xluip9EN636JvJkx/aYnw2X4RfAOVq5D82v/lcl505T7XePH9mpZFY0B/qjHcj/xOA1uEgLCK641y7Bs0aiGg3kAFVfy/dmLgB/gBPApOtE9PWG0+OJU+e/hZWHwX6x5WdifVXlN7onVu8NW4wDpVbAJ9YmlIwbj8ifQXtjydH7yr8wSf2j0CXUMLxYUbohOrdCF/Acz+txwlWV0V8f6p8AIdMWOTqYhTMm5OAcD0wWMUdtmnBnEGb5s/soyGTB/gEfdwf1NsArBPqBJQb5a/WOOutmIjYWBtFV+D4jmH8d13exwHn7fkPgFIRb1no48JIZXXSpdteyRNp5l8HmxAYXnNb3sa8tijtgi61ZiPeSV+9Io7OOdbqamsC3n7P66P+McSMv9j+mij0ErRKXb69DagARuM5KZ5FuDvoarVEQXG5Ec8p9d8CsS1XnGvd+beBGBZSg5P7zxVX/Tlj2PiCjLSJKwAEs1WExQqJZ+yJW6I7X03H7+seTVsRW2RMw98Wc4tyG62NAZA6Zer5xClTK6sDErMeLkTs3xEOuQTWOAnufyMcFJXvAQnWDQ7HW4aVFmL3Ja8/8iEQSGgZH3kjrDqDdK0qnwaIdyoOXdgsRwAxBwOdaAQisjAh4VyoMX1Ew9y5c53tb/6x1ab5M/ug3IRlt8/Rn4OnUBkk+DtgN+gU9YeeLVPfYjXh5FhAXe0scFKteRpA5YLLblUaKmUdrsaIPR062q9yLKUTysMYxoT8sf0BK+gxuOCe6w+eH+QLljkhf+y6ktadTMgXsw3D50BOC9ooI77qJ6jbFf4NT0vmPMrzFveFxKyHDwssF2ScKh0JJxsqskWROl9M4kyo1nZRdlljb6qrfVViHbrhBXeKmLaIe7eoJAN9gQDQS9SOAsCRLsBRsaFlgRhGWp95BS8ZWRGzGpUiqTh22bNlxcXFAURfAfniLYlDrq+rzeDrB3dat2XdBwhnTI2lQ6MyEjixftv6Wi96l3NMNDQHF800CcZQq+zQBgMDQI9HhG6qMkNm2NTUvvtTUnrONWcSDgKua81FH5aiJIanJOuk5rKJQE9FugNsXjB7HMg4Ub1LkZWKmSoh3w0g/yfoNVUFekTkJjxHyD7g7FORDXVlwl/spp4xbHyBWt4WuMHxcgsuiYFh4mq911eVlJQ+iwcN6rMpmrYRZugMExM0TSp0VZV4Qgtbxcf1HTLpsWOJGQ+XONi1AwsO/xqlxKIPAB8ozOw36bGK8PT1TgO/Ls57pqVBqr6RXQvEgZZW023wDLeI9ZnONIKUlJ5P9+vXr95EtKaif5tj11T4AuMFc6cK38Lo+1UVKodMeuyYwDbAQRkLMgy4TWfMMJvnz8zC2DEKu6zPngRA7KXW26NDvfwNx/GWBsJluR0FDEo7RWJUzPcJV1mE3XPPG2uHAQRiWgy1xneuIiZuLlAh6IsVXX1RJZNGgwT9PwUSgKMouaBzU7Ie2QGg6GsIDyJMxnvwrxN0raj937r6qkv3Q9FCUUnZmfNU67qOieB6SqFHUN43op2w5i41+i3QHaDLw2ebASDKOIRCc75TQWZyZumIEXftQ5gHujPoyquBUOjxQEVpo0pgHfzfBN531VmX3j/9m2k3pY1MS0xLSU9M/3z6gNTlMTG+fwcU5EkVnhg2IO2BYf2H9RuWmPZVhP8Q1f+qp+vLOeaSNAcXzTQNym15eXnVInMHkgWp9wFYWHjg2qKifVMyMjJCwCHj03rr5Le/+cdWIiQTlFry3BHNgv4tTtYMcHoBGcWLnrle0fmg5YMmT1+QnDVtdHLW1LWD7njwiKC7BO2vxnxqc86cvp5Ec+VbTRmO7LKuWc9lYIU8YLyKjLxU29yi3BYKgyxEFVxs2rT7tvXr9/ZvyPmMWD+iO9ZGtexyOfSb9FhFVZO0xKyHC2XGDIvI3wS5E+HHyVkPVdWr2Ioyyi2ViQr9N+XM/vnm+bPvBK5FWK3wVLUBxHPKtTiNmrkoLNzzQHFxcUxj+ogG1/p6GnhSxY4BHMXUcuFUtDD8bUJ4Q4eiYd2Gq8hPQB5T5Eeuz5wCLU1ae3Rn05xZODnU0hngH/9Y0JYq7sACH1qxL0k4uAgvB+7Cq3TBNf6gIq+ea9PpWZ8xqdtKOzxRl+vu5TLojgePoPo74ANF5zsttHL5sVVC/Gt4iaZtgCIVXalG/hbO1YgS8wEq3cqJmRgpdd3w5tO1cjBEtasiRzC60KoMRzQRZBjKrxATuVclb14w6zFUHwWdnZidfSG51uqPQbalTpl63uk+vqXvuvG31hyjIawuXn2y3FcxTIVfg/2COCwUZQ3WfktF8giYZwHyi/N/icrPFPmhit2q8G1Ufrh2W8Hv6uv7co65FM3VIs00FQNsrO0CXFjfFG5WpN43GhG3C8hoYB6wv4RAreCiaOHMG4OdnN2BI4FFQD9EBhFWx9z+5h9b3XTHl89tyZkztmjhzP3W6r8BS6oc3hkQ1zUvATEgu2v2r8IhVH4o6EmrtDOinRWZIHAc+HpQ3d1jbx57WWV2J/aeWdupZxvB1G3lHmHpuqV9fBX2SUX9EmVw4bpmmM8n2/HefGuRV5zX0jNxuoA/5Pa1RprcYOxSiM/9oQZNjPpavFR1uyKrBM0GuQNoheq31HuItXGMf3KZ41abGXItR40hgEQ3u1MfqnJPKNRhLt7UdpNQvOCZrhHZ603zZt+SNGXaasfVm1XoB9ITUGPdWiWExmiutfKT8I/rgDSj9hfAYNAdSZOm5QJsWTBrY81kxUWrFrVv4Yb01lsnN+DBCoh0REGMF1xUxMR1cnABgigVKuwcPPGhvZsWzCqsPMbId9TSRq8ZNz0Y3JUTCNk5E9InnGzQuA05ReMsU9WJvvMdXk6cfOGB3TvjgfJN82etQugnwu/FEhiYNXU5TIu6byN63EInVU0vs7FFwHa/47+roGDO7KpBkljbHi8f7JSgkWXfgPrlDRPSbuHkxoEKvwMCTknHZVXHSZo8ff3mBbOWA4iY7qp6G57C7WWzadOmUuAn4a/6sPnb8n8K/LS+Bvnb1n2xoces27quQTMvzTMXzTSatzfkdAI64A9eW3WbKlni2lyAwnmzar0xi8gea+2LAALrp6ROOV91f1Hu/7Yw1nzZf0TvBsJ/3FJZYVDhC44AUNWvijX/KXBrRMEvTHuUZUBa+OdajpFJkx56DngW5X7B/tSK3AL0wvLVgZOm/V+57Fp7GR8JEF42ETajtFvsyYjXYknBkjY+QiNUtC/wLhJdkqYx9i3XlTpnVPIK8jpSGvjJivzcOSvX5vYuKCjwzyuYl4Dh+itZHlsfgzIfPpA0+aH7kjPvr7a0pKJedrpwwVbdS6A7kjjhKydTx009U7W9H3NY0edV+dGmnFn1ziZdSvnTGPPkyZNdmnRZxBXnRxETNzF6b8G8OR0RjXiixABvW7+pFVwkrj22XtHXrOMORHgOL8fgFiAkytc9QzlRRfJWrlncb9mqZZV/RzF+M82N9X+QV5DXsP9Tqx2BMqx8dvm6xSN84j4B7FGVV0RkcUVI7wTwV7iV8vJJE6flJGdNe1kx3x+VnvnkhOFXLrAACJeHv1NtJiCCsNQRmYi121Wcoob6EIXUPQZ0ECVdjPXcfo2OjTtq06s1FGmPchKV00AsgCLfTM586KgaWYeSL8IreBLsxXWeq8++DhAK+bcbY16utf8TTHNw0Uyjca3/RgDHcseqVaviAdyg7w5gz6ibJ+QBOIYRNYVrkpJ6nho8uLf3dqR1iAm5JYMUHQ32btCwb4GtTEITdOS2137fAeEO0AygpzjBUVApDxyLYTHe2uwxrySsNoqsBMQTB9JRwHvnysoWiYhmZMxoVOKfquwC8Ad1CFQGYpX4sN1U5R5RuoHMVhhWtU3Ozpw6qxpOlu/81Bl329LIktCiVYvaR/aJLe+Np6HwVSs8di547NoE9Q9UMX0NH/3MRX3sKOmwCdiNl99SiXpv8LVInDJ1v0FWAd1RBm+aP7NPVRlogIIlc9qEypyLKpcmJ/d4JyNDmiyhM+y4me2tx4PC6DgJDVLoiVKC8Lq6vgdqlpECYRty/XnKhEeKPVl1WQwg8MygydMj/jUYbJ41ej/+4LWR3wnjefQkYIOTG3TC3uxVAcIwUfuGwleBYkX/BPqnSODQ/85HalVgDB7ce0WDxmoEEnL+Utf2eAnOTJw0basvoO8Fulw8CbwuQjH+I4CjIsOs6vXFc+fGoGRYauaNSXeMnASzFC+AOOkYdxVAybnzeRj2OsZ8HVgsnj5HLSLW8qmp1xxPTu6xpaHn+s9Mc3DRTOOx6kX/It+t8J3zst0NQ4HKgEFFhoSFaypZv2Z9UlHRnocAjGNrBRdGzZ3AEEHGWewEhXcF6QuwddGcbgg3BuJMP7z14mvxAoTnNy+Y9ZyESr4IoOjbwDtOaYfuiG6o6/TFhv0ZvCUGQfX7VcsuG4VY721V+CVAyHU+s6RgSZvK3SHtBkxRuMa17iaD7gq5prLyJv6UqbM23mcSMlH6v12wNKWgoMAf41D5gFGMV50AipBsHdPDWJOE1b5Ak8xcNIUxW3Z2tithuekwHwIq6Gv1HmQrK48EkdUSKqvmlBlT4Y4X1esvZm++ceO+n+3Zs6de2eL6KF70h/a1tuXMHrAptfNkz6WWgWGZ6oFWzL1AjIj8LGnSQ3cm3/5gvaZeEWVNR9zDeFPtZ6zq3qptyrv61grcL9At/pTvHgjLicOHoHUqndaFqgpKd2Cxt0U6AojojnfSV684uvfMRTUzCgv3/jrasRrLoDserHM5MpKk2f/OR05cTq5HeEbsJOAXMde7LY/fArSiSnDhzX7p51XtiaSsqZsl5OsNLCr3+XaAV5YtLlv6Z371cMCar1jVi5oIFhXtH1hYuPdLDT3Xf2aag4tmGo2IJAmcxYvuPwsgru0JsreykTJYMd035cysTG70nd76KJgbAKpKg4N301b066hsBZmckvXIDmN0ukKH4rlzY1xXB6J0QOXaKmP8HuQJhS+D/B7YXVpSXoToysTs7IDPmL/Xdf6BONkKlKvK74GQmtqls5eNsgsICWEbaSXFcS+UXVovI937PsY5riJFohcqJxS5vZ6euwG4Vr92zp6cjsjNy/MXZauqCPQBRFVfQUk1Sm8g2Vty0DoFmxqKPResVXp8Wfjs1/BcOQ8g/BY4IiF/vQ84q759eLoGAJ1F3Uq31J05T7VGZJLCNBH3ItepKWfPxjdIkA3AhkJf0hkzTEQSevPCmUmuaqGIzATKUVH1uV/AK5GcinACKLhYn9XOynBYlL3A6zhmM0Befu4XVqxdfM8Z2ytVoYd4yq+R5ZYOgryDpwcRFWHn4YRALL8BKkupFbNjhsywlyprVZUrpq3yUSJebs8BRXuq4lVzKYMjM4H2vPkSnqPqQfACHVFZU3WpTkVXiIimTpl6PHny9Itq9KhqW1X6XrELugppDi6aaTQmWPYVK/w/QZ4UGLBq1ap4X6jiRl+o4hTApgWzbwMdjNWvovKKqkpR7qzO4pbcV3H6SJ1vQq4yHXAU+8ukrGlvA7SMi98BSKjlmc6KDgQ6Gm/GIggcDaj5UVLWtF+LyjeBn4ckePPw7K+XOUb/DDBgwtQ667VTx009o+ibGN0OvO3Xy/cAqIkadqnKywqtc3bmxIrqICOVyWFIJLgQTp2OPX1clDxgeuQmJzC5LgdPV8/vB0Ii+m+gXxb0HkH+unJd7lcs9PaOlZfwtDz+H0a/gHK9IjcsX7so6jfduli2YUFPU8WbojEMynz4gCpPivLnpEkP/ULRN+p7YwVIvv3BPSWlZa1BngGwRiqDi3IbMxYYK9AzIhNdFzEx+qWkpC7n69tfH2r0zq1Du3Vvf77zF1fN/XW8Kn3wHj7xwGGEHESfAN4DUNU3Bk6aujTa/s2ZTkcEd3dS1kMPJE+ctiysSfIHFTvTgXsBBHkAuHX5usXfFeig2HxULvnQmjt3rjNDZxg1XA8cyEzOLLVWvu71yUlx7Y6oPgOVz0V7PVczCu+r6F8FTZTwCxHQZmvuzL6b5z8zHGESgOqFFw1XQtWSMRtSodKmjS2IiXF+1SQn/09Cc3DRTKOYO3euE1te2tLxSZ469mWFHhW+c+85bqBPi7KT3fbkvRCH6MtAJzzp565bF87pT1DuBuLizhXU0izw9Cr0cwCOmsqM9XCZ41lc21m9m0JnhSkgCxxsZuqUqccBBk2e9j9JWQ99Z8ikx44BVNUWqA8RXsRyFPQ1Le3cZMFFMMguxM4HaHHK6YzIQEWrJI6ZbgoWy1PZidkBLY15QaBDXn5uUl7+gq7AdSs25NZ6eKiQoOhPBN0BDAxPbwdVJUvQPgAGihXeAhJR2oWlpBEj9zTmmhzXmY5wUTfPhtC6RdwbhG3cfeicS7X3lqxkjsI+UR7YtGDmw0ULZt2lRu+DsPLiRSpKEhN7H25oEuC2137fASXdFfcGg0xo1SJ+iqpkANsQvRPYJar/i3KNwDKQ0ygvNmScxOzsgEUqf99jAwwXMCBtFKYLnFQlybs8/alCjEVWI/Ssb5nqnXfmt5uhM0yn3q0fHbnu5iOq/ErCRnPvDFu1FNij8GS5pSiacxwypMehS7e6+lGl2Fh9HaQ3iC88y3TMdSVdxXxT1Vsi8cmF8viI1sbl0Lt37/LExOuuaBLs1UZzcNFMo+jUr9V1cRXnJPboodbH3j+7B28WobsvFFCnvGzAubLyf0O5hko9ei110cniJWCicZ1eKMqZNXpzzux7I332b3FyMF6SX6hFy9j3agx5EGN7i0ov9fIHxoK+mZj1cCGNYHtJx8X4W+xBeanOrO/LZMLwCSdd46wROGvhNaAtVJUe1iQDe63rew4gIyOjXJW3RFgK/m8AqGu+W7VPVRWDPzVGWncC+S3eNLwidrwKU0BSgZ0jh2XuMYKnjSCeSZNAgMg0cD3krcsdWFPOfdGqRe2XFeT0zcvLi1PlSxKFfHK09M54oBxhNYSVPqMgKWvqZnXcLIXtIL8VeBVlAmHVxoifSV1s3Lg3p6jocIOUSitinFQ8rYobFIaryN0CWcD+pEnTV/or3M9af8sVeMsj20G3uyZULdmwZjJvndc1efqpvPzF/5OXn/sqqt0V1qFsEHQHon+o0XyLKY3ZoOBIafDFuvpzY53rb119azdUskE+BAZZlX3gCdmJMAs4EG31x8aN+1ZG0+5qR8X9W9C4Yd0Q2YayU2GtiAwAxuPJNGwzFdokS6SFhfs+tXHj3nrLPD+JNAcXVxH1VQZczfjOB6eJdYl3yyZkZ2e7RkPvifKCL1QeEHQMykPhjPk3Eb4F8izKdxHuFjH7pOL4IFEWqerMKt2OAw4A71cVZQJAWS3wZ8STqlV4yqkjGbShZGdnu8mZ95c2TIwnOsaljtuv8BNUOgEIdAHvrVJghBV+Omb4mEp9kApXp4K0iKytCzq2an8rCxY/bq3bNsa0XC8wPxww5I9KnbBSwmZYKiwCsFZ3AHtBfwAUgS4S6MFFEPQBTQhWcw71G5Nq1EwhIXgfSEetmuvSBGwrbb/x0q2qkzLhkeLkrIfuV5HP4S1FxONpVwRA6rW7N4aSYNA2aObCGO8zE9HJQDcsA4BrBfkDeMmFyZn3l6IsR9iuov+IzJxFcIPmrqpVPTVZunZpuJJHrwXpYNV0EuQIht8q5l6sqZa/oTArIyOjXOAQXpVTLaxID/G7vUD7IZIDINjKXIuQdZ7X8KxRNIjouWjbXs2kZD2yI6w8Wo5ntFdklAMo40AqRJmN8o+6KmYuBxEbEqH80i0/OTQHF1cRcWecFICcNTkXlaWtyrKCnI8tSWjV3F/H+2ygH4AojwHEnztZoOdjpjpuSPCSDlMQ/baBX5WUlD3tlHb4dvjwMqz+HihSyAfab5o/s922137fQbEPisqPxMucr4bjs0/iTev6gaO+BPu9aJY9Pm4y0jP/B9HZClahw5KCJW1Csf5ZCiIlMdWki723SH1ePEXEAuC68BIJAKrcI9jvpg9N+tPI9MwDVuRHLs79IqJ4N8hfquifAIIhWWeU0SArFHnCwl8UOlzMYVbVDBGYXnWbMZoOcjui3wRA9JoZOqPJ7h8N9ceoSvKkaa+CLAUQNBdYi2jbetsn98pOTb0m6pyLzTlz+oqlF54vTSZwFtGbgDhVW011U0Tm4XO3O8gbtToSuT3GqVskLffd3M4+E5oUriQaB9pBhM4qHM1Iy3wlI318IdjDeMFjhSBzpTTmeQBFn1BoV6d5oDU9jeptIO2siyfopORHdo8dNvbE22mrovYFSUnp1bCy16uY8JLVQWCJqq5WkRMKQxQ2Dpr80HSF5U01VnJy7zUpKb0uJnz1iaM5uLiKMC7pAAmOuSOa9nnrcu821ldzqvQjo1XL2FuNG4rcLDsX5c7qHFdeYtqWb++O2hgVuRd4Jykjry4RAAAgAElEQVRr+jMDJz307vDsr5clZmcHUP4MekB9MTvFiZ+ftO7IbXg3za8GY327QHobcRdYYVnNMRMnPLxL0cib1w8SMx4uqdnmqkXsGkFnAed8qvfhJemtDMufV8NV3w8FblKVlwFU/FUFfnoYJ/5AQcH7bQBGp43/2dhhY3cAiLDfqqwbkzphA0DmiMyjI4dl7slIy9wyOn38Mtc4uYDTsXfbm1esXXxP3sa8yofw2xtyOv3jH2+2AjsQ0R55BbmV5k6CpqFkABGdEf/wjcO7NkVJapMQlgZHmYuwBGhXX9P16w9cr6oGYGV+7nUXZgxqszn3mevUuner6H+EBdmMoCvxyma31ZrpCjmvDxw//YPE/CPVnEpX5C96EBgtSJ3Bhd+v01TlQb/VVXhLgh0R7Ytqpd+O+vgQ2IHK4fM29GBGRkY5QGvT8XUFJa682mxN3rrFD3vVJTyC8DbWKRbYMHJY5oqq7WZIdeXPi1FUtLvJcm2uEnYErCxRsavVU+X1SXgp0bi+vKYapLj4aMvCwgNXTHr/aqQ5uLiKUEhcuTa3dySZ6GLMK5iXIMpfBB0+r2BeQs39NX0+morinNmVUtaKf7JxgxceQK72F8O1jmUqcCZp4tS/q/Cjmn1YcZ8G2UebGwJum5v6RkysEHkCtI3A8cSshw/7XLOg5rHhkTYDZwZNmvZsk1/gFSQjbeKKjPQJj4CuFdXvAqjI03W1HTts7ImR6ZkHwL4HlGK5e/naRck5O3NiFbolmE7D/H7fbTWPU3S/IbS1vnMYlzruDFAmqgtU9FcarKhc/nCt/8ZQTOxnw8mhgmXLsnWL0r1+5UZgDZ7TYwWAz9XJtjRQr57ER8xhAMfnLLPGfQ1IiPhG1MQY96lNm47EA8SePzXWGPeO+mZyNCSPq9AL5KwgfwRQNQUBa25Mynqolqz7oDsePCIiWlWqOy9/cYoizwKxIlxXMyBbum5pH+NJoCcpOgBYJZ6C5GhFLgQpcbGHBSkWdNmkmydVGnKlpqYGDRyv6qjr6Vnod0T08wrXCMwzFeYIMKOhyaxVsdb5p/qbuySqL6ROmXo+edLDOxG7NrxtD9Svs3E5BINlKaru9Eu3/OTQHFxcTQg9VXQsmMRLNW1hY9MUYhRiWmtc5dtEQUGBf+manAG0CD7c1Kc3d+5cJ4D5aiQ3RLET/IGyVpH9qvJl9RL9ngDmiIgmT3qo1tRiStYjO1TkWRvf7X3TorOXL6FsBDqB5mv4zaGqpXdVRDQP2NeYm+THiSAnFK5BWD86bfxFRYvEx3sCfxHRz4pIYfwpZ46BctG4QrDH6jhky6hhEw/Wsf0CykE846frjMiIyu2uvVGRx/B0JAoAx6iMD5fC9kR4EPgAYSHwgVHuFZUm0c1oLGr1CLB+wISpH4q03gsQNLH15DdIYevWZa7OmGHiykvvNZa7Ovdq+92qD/2ihTNv3P7mH1uBPCgq/QR+bs63nw9YUX07dcrUqJdV9IIuBQqT5HzFj/IKcm/KK8i96e0NOZ0cdd9UGIz3fwKwWmG9Qmuxocpll4zEjBJE11ijtTQVFNkq1lbqo6zIX/JlhW4aVj9V5WhGRkZoVHrmRcWeLoWIXpaJ39VKoKvzBnhLJMmTpq8B3latFNVrMkTktAi1jOs+yTQHF1cRgvQAMxZ0wMXWs1euXtIf9AuRn92wuyHAWT2Z7hPnl6D3NfX53djq+PUiclvHvUc/v2nB7Pmi2k/UgsgBVIoFbgfpDlKKNS9crK+kiVP/npLSd0dSUp9nAETIB6xiXlZPeKpe4gmtUbROw65/BqxX9oYol/QtOfb+2T2q8hwQyUn4vArbhg7t/dfk5N5rah0gknvJoEs4KLBB4Kgq6REdDYUbgYHA9qBP7hD0LWDssrXLOgNxZa67H0946a/AMoXhJkovlCuOOEdE5WcAyZn3lwrsDFodsTn3mVrLEIMH9/xu7969y4vSuySLuok+NzBO0WxKA/Pz1i6alr/0T18Qy1tBf+BZoBXCCJBt4Sqi9463PHLJBOKqydkCaWExtWJgsiIP4PJZsXwt5DqfwfvMK7VMFA4gUgS4lMVXS7YUMUtLJVjVnC+83eYrDI/ka6louqjMFE+JElVzPKrP8RKkpPT6elP0c7VQS+FT9AeY6uqoTUFyco8tKSm9nm/qfq9mmoOLqwSvxE97KDoGaDFyzbA6M/rzivNaqmP/IfAggMB+Y9zK4EJUM9QTgBma+25uLQ2JRqFmkGBvcFz3XtAsABVfAGSbiH4bOAPaFsukpClTL6r5LyJaUHCo44YNe28BsJi1Aq/7hCWRNc/66DfpsQqUly7W5mpGbPiGL/Zvl2qbnZ3tZgwbX4Br+4f1Cfyq+taGDbuTN2zY3bNm+4y0zGj8Cz4A/hBSZwDQalnBst4ARvRGAFH2jx8y/lDQOI8CKUbsL4HQxGETz6G8otiNgr4LxAGjIqJcEV+ZjwM/wcOO78ISgorkiWWEDUmtsttNm/ZOzMtTn1HJEGu7JZQc9wMDBa4TkV85rvttkBtUibhAHjyecDiy/v5cNH4zcSfNXVWWJluqkAe8DlSENUeygPtE+TzeTJEnNy9sMyoHVHkV+LBmPs5tQ8duqWnwB6BW3gHujsPpt2rVqniBLyLsUvTlcIMmCS6KivZNaYp+rlaSJk1feTL+aNSqqtFSXLyn68aN+9Mu3bJxTB41efvkUVkfTh6V9YH3NXnUlR6zPpqDi6uAvLw8n7QIvhgWOeoAYB1Tp4umlAYy1NPBV2AlaKFVeq/Iz/1dXv6i5xA+H25a4vdL1LLAUaE6UKxtEeMGxkU2WZGzqM3bVtphIfAScHR7Wfv8+ju5gN8f6OE4kg2QIBWFGP1xDIH3FbnkrMTJFkcvupxwVWPYDvwlI23iimgPybhl4l5FvGBEWS9ixjuOk3Q5w4vqPuNzXx07bOwJQd4zGkr1upUbAVR0OYRLaJXnw0HHGRHRWLfV3IzUCTu0RezLCKfUkzVPBih3Sh5dvnbRS8vXLYoqIbkpOd/Ff6h/5lcvvOVbux7RuwTpV7Ot6/Jo+/ZHYlFNBHUcG1LgnMIPFBIcayMVWD7X+Atcw9jKgEK9KpxLISJ32jbnrw3PCrVEdQXIeyiLgCIgVSEBGCnobuBdlF2Kfr8ipO9mpI1bDlpL3bO+WSljzEEgThx6lztnbwD8KvYDhVnAMY0JNUlwYS3faIp+rmYaa1ZYF8GguR7sp5u637pwsWnzVyzo7n3NX/FRjFkXzcHF1UBCYIKilW9YAgFHpcvK/NxaU7oK6SK8DFporfxMlYWCfF3hMZCvoPQHzghsxLPxbhKWFeT0FaSvioTUUw38L2t8JzHmOMiq7OxsFyPvoPJStGWFxtiDwHzwZiMGTZy+qd+kxyp8xnfJErArcQP4qPBKCzMbvGwl6v4dQA3HROzKUMhc1tKQOvLn24ZMOgag6GJBHli5ZnE/CHsfOLxZZdTDwE3AaYDhw4eXiYhmJGaUiOo7gIrQaUX+ogdE9Eci8jmj5iNP8kxNnRqs8eBdD1wnaB16F/JCMPhBAE963Yp1aXnu+NhuH37wVkz5uXJjQ/HBGC9HuqxVhx07z3U4EJmFiEYHJWdnTixKpgn5lizfmNMDaInKe66YVdb1PYx4fiMKVuAkyquq+luE/1SVDzNHZB4VETUqP472+q0b9PJvlF4iZjBw2MHZPTp9QjFQFHJ8TRJcEIWCajO1EXH2Oo7NvXTLTw7NwcXHzMr83OsQvo2XzFUcfnt5R7E3WGRkHYckqtWtiil0Y9hirLMyfCyC7lBPCXOvFYrQCwZZlyIvb4YvYshUF6K+e63jDC5LaGfVmFBIAk+daXfNbuv43ykpPb8OIOCTd43yp2jHHDSoz5Hk5J51lJt+5V9KJjdaRg2buBphmwnp0ZSUPvlDh153WfoeGamZlUtW6r2JZ7hGH8CTgp6VMXRClX7tYYXWiBdcVMWqWQnsBumqyK/xlkmgzgf6R4tzvuNmIKhIt5r7Bg/u+bfYgxvbAykV8a0PAKHY8rNPVvgCT7QoPeGzxkfAH3dEBUL+2O6de7d53iYEbotWfybulO/m8CxkP+P6nwFa4tiSsWljd48ZPuagKpsEzorwpBvyJYXU99Tb6WsW4tq1xtFKW/aRwzLrdVKtRVm8FzwInwX7S4HlI9PGbQRQdHVmcmZp1H1dhMGDe/3TLkd+nCQnd/8gKanP2x/FWD7Miskjs96fPGrSrNtH3N7q0kdcGZqDi48ZKwwDhgMozPQHy4eAHEbMXfVYKacgFInqu+MGj/vQlvt2ouwSZG5IfVOMsghhh7Gy2kDUyXZdKrp16ljaqdYUcgR/oGyKYvq7xu+Ux7UOlbbqE7LG17ukZYcXIvbkqeOmnhk4ZVpUHgUABQXv9ygs3JcdbftmQOCFEn/oaGHh7vGbN+++rGWRav2JewSIFfgiyDOj0zOrlcs51lkX/raWDLI6doWq5gPjFFqHT/DUxx1cLN6w+Jpw8uV2oFZwUVi471ET7x8IdKuIbdnaFWcJMEqER4y1PtfnR5RC64sJueIMV+V+I8yNd5xJeXl5vksFGSJUlmejmgX4Qo5TqWxprbtUheVqdeuY4WMOjr157JEZMsOOunnCvtiK1hev9KmHsObFOZShIB0VNl/Ya2oF8JdLUdHeT/yyyJVg/foD12/cuOeyl0VCodAkDYW+Uu1LtVZOnViZOG/Fgn5ugh2sKu2tP/SLxp355dMcXHzcqNxMOFNclEOf+tQd5wROhK2xq3kRhKWDeweCsgEn5k0R0YyMjJDj6G0VQX107LCxOzypYJaqcT8Eor7JqxvqYo1TZwls0VvP9U4oOZVkbNBY8RWWJbTzh2L924AO5a7U9P6IGr/f6SjCLZd7/L8iIes8PyV1ynlVk2ytUyuhs6Eca1EamSXqpkKtN+WRt4zbpvAfQUdqlTaf2H22SJC5QLKorgIQmKfI0LqcXBvDirULo/49iQnZLO87KUJqBxeqOtFqqKfibA/54lr7sCsAg2IVdqs4m/3BkjkhJ3Y/nsdEEOiA6tO0rBgVh9OvqmJqTQR7Q/jbvZEhE86XVQYXY2+etFWVbUa0WuKyiOjw4cPLor3O2uNyDO/rRWtlQ2T78RanV9d/VMOwVj7RCZ1XCsexXUEuO6Hz79/4yU9f/95/P1f166UHv1mrDHze2/P2ALpw4cKzwGyQqGevm5orIrTUTHSszM+9znpBBADW9a0DsOgJ8VK3qgUXfj/Xo5zOHJF5tOr221InVLp4jkrLXLIyP/c6a0wLVTrP0BkmGgU+K3Q2qonAqzX3qRP6tD8USgj64wjFxa0T1I+SJOiOaA2P6iI2tnxnebl/1uUe/6/I2GFjTwCo2rnBYFyjp7qzE7MDK/Jzzyq0DicW1mJ0euZTdR7r5da8sWLNotFqTBLoYHXNLDH27ryC3Dvz8heVKrQenT5hbmPOMS8vL04l8Fre2tz/zBiW+ceLtV20alF7i7kFeE6xm0TlCwVL5rRJHTf1TKSNr2zPTHVD411/bDsVFhOy6zAcQfU+nzo7bMX58T7jvn8uptW7oGdU+JGovAHSUZQX1dHHRP2T8orzfpORmFFNITavIK8jNvAV4KCKPi5WBqtwzac+dUd1Tw7V7UHHd9kum3WhsFHgxZFp4+dXzT/JTmw6Iz5VeaKp+vpXQtVusdYXtYdLTbYsXH6yZrRuRKotVd4+4vZWQX+wzcIVCz+YOHFiLGXcr9AoQ8fG0Dxz8TGwdN3SPgAWzVa4FdGvCeyOmFeJGO8BYuidszMnNm/doscBHLiRKIRYRqZnHsCJOQz4bll1S8eoTkpNF4V7Ny2Y+auinNn3V90l4lWdBGJbolbeUCtPC5y1Io1KULrpppvOpaT0bdIb7L8KQ4b02Zeaek2TJOkpnBQ4FIipPXMRDaNunpA3Km3cUwJHaOXbosJaUXkFZKFB/rxiXe7EyrFUZfm6hQ2SkNaEijFAV4Tf5K3LHVhzf1VNiTif9hX0Ju+67JsK+2MDVS3uQU9t/ymqj5THtGyrlu/7gu5mUf1y0uTpSxOnTN0fkxB6pSLkHHLjW8zFHzt6dNqEN4HTKCUK14jSC9FPaUloxMrVS/pXHd9S0UOhtaosB91uXd9zrjH/r+Y5i9W3w2qpTYaFp1akrV5wJcXlhgzp0eRlmv8KDB7c+/Tl5kgB+GLM+44jb1f9wtFqSe0ar+0czKLJI7MOOmVml4A/FBv62ILBjyS4+Nztn+uSPTF7xKVbfvJRVXHUfcD7SSYIWHyxf1Lk9SqtPCc+pX/CKd83VeVXy9fkDkXl56oaVQlmxuCM0wInY302uooRoTPQH+Tr4q0TXzgb8fWviG1lg3Et5o8eNi43Y9j4P4TEGYzKm/X0FhWFhfsSCwv3/mdj+vhXZePGfQ8WFe29pEx8VAgnFH7TmKQ/74EmhRmJGSVYzUXYaUO+7gqvqfLaO+/MbwewsiD3PkN1C/koOu8psBz076Ly/ZpeIPGnnSmeJwqgzvWEcx5Ssh7ZIWK2WmxKpO2m+TPbITIoFN/20xUJrWJjQuU7+t/5yImKrs7iSJvEjIdLUqdMPR7vdMnNGJxx2vuI5BDwK28MuUeVkca4o13HPhp/ypkfOda40gMIgl0kJXHvjxk+5mBdQUTGLRP3NugziIJ30lb/oyE+IZdDYeHel69k/59UNmzYn9qYfJWEBF/fhFb+26p+tWrtr7byMG/pvP3zVywYOH/lgmvnr1hw3fwVC+7Lzc392JLjr2hwcfvtt7e6Z9I9f3dDocOIVqraZWdl/1/2pLt/eCXHvloJqx1Ozn03t7PAbQjvZAzOOI1cqJ/3ci4i6CMCRgzfUTTmeIuz/xXNOF7lh76vIsPr2r9p/sx2BfPmdCzKea574aLfJ6rqNQCKPgmmUkZ4bvHcGNAbrT+2qESpVAUdmzZ29+j08Y1KFBPRWBGp1zSqmYuhbT1b9qboip0hY55rdD/hv3HX+P5mlMwxw8ccVNFXgbhQrO9Leety56qVH6gyDiDsABoN3VH2Ypw/KnqXMW6ljsbiDYuvQfn3YGzMUAAV7YvS7u2CRd0AQo6/1CAPVl6q4buIL1Datvt5kCOR5YpaSo14nh2Vx6GHfIHg7wQO4SVhx6qSLJ5sdxrA8vzFY4AvAEvlfOzcugzpriRXOrAAUJU69XeauTjGaJy19ZvpfRK5osFFXCjmv4AeRm31unfldVRuv5JjX62IE+oJJMf4+Q7CdkFfgOrKiq7oCZB1AAqdUUqAOxV5Pdr10w4l3a73u8EFqNxT1341ZkyMoZto6B7j+v4B8jjCIcF5WbH91i/9860Anc+0eEI01Clg5IdVzZKagvfe61kUDPq/15R9/qvQtq0+HQweW9Q0vcmappiiD6tEMjZt7G7PdA3EtWuB04J8HuUehOuBrqtWrYr3uzr+Un3m5eX5BKZYoehY/Kl8wBrVyuDCH9JbgclgfrBsbe5o4HqAkJXPAJxv0eGMQt+5c+c6RfNnDRSVz4iGVrhuWTdoyDKQFt966+RTYRGsA4CCDBQYArRZvm7x7UZ0iAijReWFjzqw+KiIjy/7xFiuf5ScOtVjTVmZE9WLYV3Et445mdA6Zm/Vr/i2sVc8mGwMV3hZRD4tKo+/svDvG6tudR23GKHJBJ6uVooWzJ6w4c2nq0X6jjU98apDHldl+cjUzP+reZzFOa6iswHPlU9YAYiIRG0aJKID/OdLDolQZ3mpWB2P2JtdcR703oIxWFYlZU3dXBHf6kNfsGyBqkpMqHwE4pTeeuvdjTI8auYqxsolPU6i4ry/VvJYxs2TPlB0qsIQgQAwD5Byf8kQFa1zVq0aLStGAZ2kNObZcGC9Q5FxVapRfgqAkmGEZcC9AvsFJq7Iz50T8sVMBEyf+FPtQMcCfc+17rJB0QeAfXWMWDci3qyM6/uBqnkQWAtcq+hZQMXqIFXaK/raqGHjLynr3kwzDcE40l6M9Kr6pa65qnMmr3S1SFf16aFag6ovVrF12iF/khD0RZ/je7towaxDJzv33aFQqOrdUAUCbsj3P3UlX7UItDh2utPpv8SfdI6qEQOERHWyWvtBtGNbtL8vWHJAad96+brFI0an/X/2zjs8qjL745/z3plJp4UuJXSkJSEIUgU73RYs667tt+padtfddVUQdhaxu+wqugrbdNddldjooChIFQ1kQu9J6J0A6TP3Pb8/JmBogoEIYj7Pc59k7n3bvTN35tz3Ped7rp5f9rgKV2Pp74hbz0KhAZ+qeRagKLJKRERhXlz6528/6IjTQcWUy9HvVLRqlZMI8lPgkYpo/0ImN1ce9nprrSb8Y31GFMYHM05d6tSc7Gm9iqn5UZ7de1CRNSEjD3msbWpUR+tRy38nw7QCGyjVcUCQFYq2nTdvSrVZ6bMcbEkzwqGi2QrNJCzk9YaAX8GHMYC4EY69A5UhVjSvxBf7G9RGoPzxtE9OQ/MAruh2xdZZK2Yd0PySd4DOApvDkximuqBxCBe0AFxhYdRk4ETifpV8C9Wrb7pUVfsC5ZupFbYI5qj71HHsaSkhnysq2rhYa6x24djpR2tv4SiRlwuP9PSxXnbamgg3oRoSazdjpBrhL790oOBwdMixHI5196t/ql/8dlL6pOhYfPtRTtu4EKGN47p5wCxBfwccMS5WTBrbyMU2RiDoi95eHBFbJaIoLzDpqqzArEUzrrPq1hIUx3WfigjmVSuMrvLaGV2Mk45RC6w9/XOq5BtEZAfIcaqZ5aFfi37FZ6Odk9GpU6fgrK9mzBf4+qpOV236fNH0n6jIEsPJU1vPXDSzpVPgbFRKWhm+0VJRWADcGPR5uokbrIdQKOivLBwwmJtBe/tCcX8pdg4tQ/gf4aymhao8DzhqvPsUezi1+mmH6fXp3P9IGGGftn3ypn459c0o4/wSZCci21Bto0o+SIUY4ucPWhndVQ5UbR5hp+ByERMX0UCF5LL7rFjnzEdWcVTotIrCn1TlldT+qfcApPZLvfbmvqljFX4voqMrsu9zjXeX+yjh61ukjlPiLSmIR4kS2C3woVWZUra8/+Kex+VjOOygNbDTwAKUtyyeLenpY70ADzfve9KZn9UT/hGHSiJiGwD/ABrCYSdPUE+oTmlRzYuJDxZHxsbkVa3dsvfXXT9FGKXiCCLqCRZWcx0vBTHV3joLl+Q4OnRosjopKeFPFdH2hU5SUuP/JCY2mnuux3G6iPBnzfc+BXB5l2szBZZZSJg1a1bkicp7NHSRG1XURpTWhJU2w/uLS/4Nul/E9EHss4I86wtVeVs97iJr9AULY7t161bYp8s1kxEdCmBFgoBBmO0aj4PIQzbkaUCBb/KJ+j4d+l3a76DC58BOtcwR6CJCDb3AZy6SkxN+fupSlRxLcnKTQHJyQoU8pJ2vVKhxkTY17U0RfUoszwMITFMhVUR//d6U99+tyL7PNWLNbUBhcWTs68URVaKjCg9uFWGtRT5T2ITYI8aFv2WPptb99qlGo7xSZ0eWx7uTjgDVTEHn37fqfpxu/KoPX40vdor7INpWVC5SkS0obWZ99cmdMbbFjFmzZkUWe+K6A1jHu9d6vBcBWDGuQh+grYiutcbzkRVDyBd1oPcl/XaezWtzmNWrV8cFAhtanrpkJceyZMnGxunp205Pw+Q8oPcl13x6zLJJQMAQG7ynbLk5S6bWArAONYxjOgkkqtgjCdp69hywXzC7BP0FSE3Uzu7WrVvhFR375/RJufqrYESpDwZgQs5nwKH82Fp7QL/aG58w2/VGRPtMXP7eLZfvOFOnSwOTRHSnEbtfoYZCytlKbX6+smTJpuNUISs5NRkZWdUWL97cvLz1vTG+nIho36KjtpiY89ppuMIdQt6b8v4YjaWOurRQS+tdBbtrvzfl/TEV3e+5YsH40VEZ015PQLQ1yJ/yq9QKhiKitjrBosZOMLhdxP239YTmNdyz9oigihVSEG21YPzoqKVT/jpt2ZQ37j+23ep7NnS1HptgxPZQv9+IcRtEYDoMb9kj0d+i25E4/mCEMxCkPVCCSgd1nC0AYnWMryT/8gizZ4iq/T1AUURMJEqhCmN2xxyog+hvSptZfbBmg4cOVatHfnS1jypKlKe4OLKFiOcXFdH2hY6IGeL1Bn+w0ukiZAJFqDYou991zc2zVsyKNUoNgduBaiU+83XZMgrTStOVa4HazG/aFC2r1XFZ16tWYXxNgxEx8UURVVYj5hfFvjhvhIm/o1WrnSecMfku+EJxM9WShZrDy1NVjcruM233fEZEK2cay4GIaWfMYX2j747H5zR2vE6XspvP2PNaYft78TZNS0tz02akrU+bnrZm9uzZ57W1dabExUTdZCwjRZhZYuVZVbkr5PU9JhARc2hn9d6drv3iio79cwrVe+s3teQSRC6Oi456UpBOij5x+Miyya91y5j0RguQR1w31NQqDy+7pPaUtv2Sr23XL/EOEQZY43zwSIOuUeGmGCSGFGA0RqvX2rEm2VidGl24N9ZYF6OhB72honoAwYjYWITMPp2u/tWQtkNKgo4ZD6hV2dAz5ZodIU/Ebmuc4xJWnS2CQXePKmct78GPCRGbaYx7+tEO5xuuBgSWgB41+yIqqRwsqqlKDZQ+iiw4VtzLWMYC+UD2qcKj+3Tqswfh9oK4WrcDhUGv9w8O3gnR0YfO+HuoW7duhTgR76ux+4EQgDoXtnFhjFZGjZUD1zU7QL8+dcmTchDYVXZTU3FKrGeDCrV8hvRLLfq24+Onpp3x08P5wosfflj70Rtu2OUi94nqpSW+yB6B+VtrNRucUHN7se/rfF/VjAYlua3XTxsTBxwEvX/J1FemvPvIe0WRcZHXFh0qqquCB9WXEHkufdLYmp0G3rdHxTzpGC1CaZu3fX+3ag3iG4M0vviKNlp4sNCumrFsr1qtXTUuotnQdl12EwLcvMAAACAASURBVF6jbqJGXhPVAlRejz20Y5e4LioSiizK6yyqFEfGBkMen1eFJYdnJq5Kvmr77PRPx6I2XUR01lczpoJU2DRvp07NNgGbKqr9C5mkpKafnLrU+UuRJTPS0S9BmkJYuXb24pndrbU9jEMtqxIvgGKP8yu57NKr181aNGM5hu3HNXwCel9yzbTZi6b3ca1n7ZWXXnlWl/j6dOqzZ9aiT3IxOgpluOs6F7RxkZiY8NK5HsMPkVLp73LLf0dWiawCclQWVGvMWU0OeLap2JkLkf87ZrsP1b+UavQPr9C+v2d86r72wYLpYwx0RaTkYJX6rWo0qzVYXcvGoK21LLLBgZKC4p0F6rkvXENaePBOiDSe2zvdfGn7mBqxNVW1s6POO8C+SE+oSfqksdFAH5TrgaioGrGD1NpCDcfnS1SVKKdln4trV61XDWvdKZ6SiJsknEk10i0JrrIuM4C63mBRU49bQsgbMcG44Qc21xs1BcjH6pFcASKiRvUZKYgYD6Cik1CtMOMiENh8UWZmzjdZFgc9XedbildShqVLN/ZavDj74nM9jvJybbdr91lLJkodgNmLP2kl1s4QMGCuFEMNAJQTanCIsEis/O90++vd5dq5hw2LQCDrrhUrVvjOwmkAYER3Eu37E/DivM7z9p+tds9HAoGs+871GH6IZGRkJQQCWdec63F8n1TozMX4KeOPE4gCuGXATdNdKw9XZN/fNxfFx1x7UXFuDKi4jjcPwx0RNeNChfsOCsHo6bZm9N7NgZyDNRvXvnus/97XgOooXXvd17tt7RZ1SYzrVZi362Dep3+aXnLDCzdvtSoXeY2bCGIAC+RGREc0OrTr4NSY6lVewaPTd6/fuavttR1qa8iSu21/Ixx+ZSFeoDg5Y++OlW3a7HFj9m5AtUHIG0lhVPU0b8n2wdbxFBVHRr8PxCMcJcx1WFkRwFdc/MlhWeWKQMStA3I5MInrnk0g5CYDH52qXiXguqaLxyOrgVWnLHyeYkRXKdJGVWX2V590UcEF3aMij6JsQfhYTMQJjQsrMr6KqfFVefpVldRQKH48YVGvM+bwPZOenj7i+5DgPpeomtuAsed6HD80REwDVe0FlCvZo8frXa/oUaGnvpA5r10MzonC17uT358t0ONc9F0RpKenexu6+2OjCw+IxVAUU70mSp/ImlW75+cWYGpWjxVomLszLzqiakRrVhx6BxC1WlK7Rd0qAPUbVY/avnJ7vBsX/UjQeIos5meCvK7of0RtO1RvzpwYmLvwX3M/+ezlaZcV5haSvzdvjBihdqu6AERGR7QQkGBRcL/4/bbtkCElKvJ40PE9XOyLeTPPRH6qRq4tiK62wKKfCTJrT/bB1Sc7rx49Bh8iL3LeyY6fKcGgb5Prajgdtxu6AsNFFdXXhYaq/cR13aXnehxnwiETWqEQ90X6J79GuAf4B8hClOpAe7X6UZ9OfU44c9an01ULyub++C44DmP27atz1rU9yjueHxLGULksUg68XrsezMflre+J8jb3RvkuK7tJFd957dB5TgZ348AbW+NyXquLfRd27Vr904vIZ0O1xhwoUep7DV4Ai9m+69BebajxYsQU16zZIDI2irZ9kwap1aJNi7O2N0pp0gRExagc2nXA+K7qecs6X+1GbYq2XQLghtz0UYPnrE0jbdWIVj1HojQzjlwXKg6uzfjP4lcTujRfX7V+tbcAU6d1PY8bdNm0OOuIhduh730flPpUHE5M9dnni6bXubzztTs+WzLln0OGDPnW9+Gk4Xqpfh9p/jN68itNGR7+8VCupKyoUr+nk2hQcwXjjk8oVQl07Ng089Slzm8GdhpYMOurGZtUGQ2EMGaoWPtTBURYGjLOSbPunkkEU4cOCdPKW/fHTmJi40qHznLQtm2THcCOUxa8gKhQ4+Lmfql/OXafitTE1QEgZ3Vq7aE7HhqD6GCgYYkvWGvcuHFHnngeuPOBJIO8qVBVkLVq9bbX/v3a3lMdO10OlXBrKMrLpHXF5OaV0K9DDZpFhEK7v1q5Mq96/UgWL4/Tg4d87hU9IyjJoUqdKhTsyy/KeP+rGlXrVSty4qL3xVWNrO+rEft7T/tWL+QEI7fFe+Kq1A4ejF78vy+bt225vWtbb9eVNkRzhObWqsfjc/7mz56de6O59evI2Chv5590XxVTM6aNW+JmrfxkWb3DYzvRl3AwMpwq/YqO/csfbVDsXM6gp7KYOHzNqQufmIyMDS2MMVcmJia8gTzdB/iEwSO7UOLZiLG3smNPBnBB66GUl4yMnFRj7ObExCZfnuuxnAmCfKXY/caamy/rfNW6WV/N6A3gOqFBV3Xsf8bJ1E5ERkbOM9Wq2ZFNmjT5VofzSo4nEMgenZSU8JtTl6ykLJmZm9qp2s5JSQn/LE/9iCrRRQp5Zfd5vJzXS3AVrdCZdMzWQVQ9gj6iMWH1vLOFiIz3hIJdCIeoHYVB/qGWP7725mtNEBsQIyNP59jpYgqLWxRYwzuTV/HJ3CwOGZ8cCLJ+49frrNO8UWu7defkknenUBhYGyoRE1Jr9+7euKtaKGir7t+6b8OemPg9irB10OB9Ur+2FL8zperenL3s1UizbUfhXS50sK7nJ4SdNWsBSyNrRL0HYPKqbw4F3VCjlMZtajSKBy8Liw8VR55IYOswx4b2nTZ+v6H/s9UZ+FwjrCSAKZ/fTG9/JIAxTtyqbQdS6P9sD6AO0B9r/Hj1GoQbUH2Dgc+0LlcfFzyaAKbWuR7FmXLZJVfdUhziyssuvXodgKpuAraekeF7SjTp4MGo81o6+XxFVSrMB+tCRlWrqZ5Bsk6VSEFiy25uUH68icvGT03rXZHtl2XMm2PmAjx054NH7f/F3b9oiqXBa/9+LbzeFTJv4Ogi4MFvO/Zd+t4wcf4rOcbzRGFRo3iArXsL3W2uW7PG7de3wHXz92/dsyTW2hu2T1+0e0O76+OLo6Pf2LZgVjVPj043b9nvZpuqdfpWL9pOyBv1qAHsnn0x+1Z6iUu4BO/tg2u4L/3jWlSTQdJV9Heien1S/4fWArQdMqTkvUcenN76yvYNRWnjFc+7wM3RHm8d4NCZXNPjWOIMwBPMQqUH0A3lOu4d+8g3SxcqcKrpahXinrkFeHPfPl1599+nC8bOKT1YA7gaS0lpam7AvkXq6N6k/abwrJ7LDx79WzDoOysOieeS0pm1bySzxeSAVpifD4DPp3e3aVOnoCL7uFBRlVtPXaqSY6la1aYXFHhWlre+cTxZVtwFZfeJdSodOs8lHqUhsAVQgJ1FOzcB8Q8//HDEtx37Ln0c+mp92sH0VdU77d88Q9Ruzlm8IRBfq0pNqsQ66uqEmdu0dkiMXZVn6ny43uZsi6g2jOG/Nr67bqy5LrnrlflOpGRLXNAIrVV1P/kFn+6fu2yLs2v3RNOqCficflI1roGJilwTNe7p3SZknynb//KpS59H7S9V5c7E6x6chJKlrvuLI8Jap8krU6ee+LxT/bEMGJmIlYexTgPU3AGSCsSyY1dTbng6vAzT75nEsIFxDP38dbl3rJeB/poMeuYuRAcB9Hnp3foFJaG7gWJgM8jXgEF0YJnanSkqqHxaOobk5Ca5nTrVv+B+IMV1cwS+qMg+2rZtsqOiVGcvdDp2bFTu5Fs/Zpo0aVLUtm3Dcued8cZENImIju5WdvPERp10cmBA7wGvDujdXwf2HnjOUgSc9ZmLIf1STzsZ0PipaQPOdv8VRUZG9jARbVRUFHp006bm+S1bZv9VVfYkJycMu+n9f0VOvvuB4r57Vl0TY4tXdsza60J7Ni7dRPqWwnUH6150l2t3HTxgog9Mm7khv279asUX1Y6704pSt+VFEVPmbLTZ63fRPjE49/ouLT5MevmFtntWrk+rVz1nfSbSP6JTorURXiPx1fIbeGOe7/DBf7cNVh5LSko4kJmZ/UZBMFQwvXjniH5VG20LBLLGLnr5r4u3Lvjy4S5337o+cMP/kqw1aR07Np4ZCOTcCzbFGPN8hw6NNwYCWc/9d+Gqji/c3/eaZcs2VdtWkv+vQCDry6SkJs9lZmZ33LT30K8b1qgyOWnE275qsRF/LCoJNmhdt+aiwOadXQ5fl/rVYrtv25enaTMCl08ObKz5Fn+8K+PprEgRnigJ2czOnZr9tcfFjX73s25tGv/h44I623Pze/o8Tt5XgayxL01L3zIxI+tA+4Y116LEfbl+12hXQ+9Eep1gUdD1AXgcs/fGTs1fHDoq6+XExCbvfjhz6U+a1ozrBfqPpKQmXwcCOb8BbWlt6MmOHVvszszMfk2VwqSkhN+lp29o5PGYYaqSmZyc8NclS7IuM4bbjJGPO3RImBYIZN0FXCqioxMTm64JBLKfAq0dCu19KCqqUUQwmP8nkM1JSQmjli7d3N7a0EOqMic5OeG/GRlZg0XoZ6282bFjwsJAIOsRoLXj6Ij27ZvuDASyXwFCSUkJvwkENl8EoRFgliclNR6Tmbmpp6p7u6pMTE5OmJKRkXWHCN1cV/6SkpKwKiMjyy9CvapV+dXevRHG4yn+s4jZlpjY+I+BQE5bsL8UMVFg/wfGq2oHqJp/Jyc3np+ZmfMrVdvGWuePHTs22paRkfUXESQpqcmv0tOz63k86hcxKxMTG7+ckZHTXcT+TMSZkpjYaGIgkPNTsD1EnDGJiY2WBwI5I8BeFApFPFK1ar6bn+95BdiRlNTkD4sXZ1/sOPprVRYmJzd5MyMju5+IDjZG/9uhQ9M5gUD2Q6DtRbxPJSY22BIIZI8GfElJCQ8tW7axjuvKSJA1SUkJozMzsy5V5S5Vprnu3imuemMDgayxjqOvtW/fdGkgkP0kaMPIyOLfbd/eqrB69ezXQHYlJSUMD+emMb8FWZSUlPDPzMysa1W5XpV3kpObzM7IyH5ARBNDIft0p07NNmVkZL9oDNcHg74OQLTHU/y0iKxLTEx4KRDY2BnkHpAZSUkJH2ZmZt2iSh9VXk9ObhLIzMx6QpWEUMj+PiWl6aHMzOzXVc3e5OTGQxcv3tzccUKPisjXiYkJf8/IyLlKxN4kIu8lJiZ8XqoP0VFEnktMTMgKBLKeB1M1Kanx/StWbK4RDIaeVTUbkpMbv7B0aU6KtfZeVTMzOblxWkZGTqqIvdIYM65Dh8aLA4Gcx8A2NcY83qFD4/2BQM4bYHOTkpo8npmZ3URVHweWJCU1GZuZmXOFqh2iat5PTm78aUZGzs9FbCfHcV5o377RhoyMnGdEbHxiYsIvAoHsKiI8L0J2YmKTZwOB7GTQ+4HPk5KavBcIZN8gon+21rkpObnR1xkZ2Y+KaPNQKGJYp07192RkZP8VyE9OTnh0yZKNjY2RoSIEEhObvB4IZPcBvUWEjxITm0zPzMy+R1U7g/1TUlKztZmZ2aNUtdb+/QkP1qu3JqqoKOIlVdmUnJzw9JIlGxONkQdU5Yvk5IT/ZWRkXSdCXxH+lZjY5MtAIPs3oK1E3OGJic13BQLZrwLFSUkJv122bH1D13WeBFmWlJTw6tKlG3tZKz9RlQnJyQlTMzKy7hShqzH8uUOHJqsDgeyRoHViYkK/LCiI86gWjgazNSmp8ciw34T7MJi5SUmN387M3DRI1e2vKm8lJycsyMjI+rUIF4dC4u/UKWF7IJD1soixiYmNHwkEcgaI6B9A/pOY2PiVQCCnB9ifipjJiYmNJ2VkZCUlJzc57Sy930a/y/v1UtUaolRotuNTcfaXRSpQdKk8hITNTniGQgCtE1mnEejeMWPGFP/i7l+c9Nix7VjLh16vifZ4DhSmpmKXLTPjVKUYwOeL2x7foumoLWuzbui1d2NKUa4R7819gx9/tdO7e29hCqINnajI/+6jSkitDszaepB6dWKjVgc2F7dod5GzbPWu0L7c4qKsmeuTSnbah1Ju6T63dpt2O+/7b+aBwYNNju+em8TdsmM7DeombncLk3ILd07p5vXkizTRZcs2/W1z0cHGCj2MiZyrWjiu6yMP7X9//qLGObO+cNrcNHic47hbuM5fzRhmglmsGrETwHU9/3vlk8BtL04KtPjbiMsPgGdpfV/Ux1zjryEiWY+nzd39f5e1XYbqu7n5RU0BVm7bkwzgOLLPa5zcnQcKeyDEjJywMDt796EWON4Bl458t32dqtEx+/KLr8fvfyN92c6LQ1bn7ziQ/3tga0nIveiNz5d9tmzzvkvjYyL3H8gPTlFxqxh0jThm9EU1qkRv3Ln/ToXdER7PezNXbEq6pGXdpfQfmXLzq5NHJNSs8lleUXEyAwvv7fdSdPH03984LiaGwzLQ/7DWlkbARO0xJjhO5HBq8tBKY3zjHMduBVBlluOYpT5f4TYAEfOeiEakpKS4s2dTFB9fOC4Y1AKAkpKSTT6fZ1xJSWh3uK7ztePoluJiJ7v0EzLFGM+c2Fh72AnxX9ZaC1BQwL7YWDMuGDQHAIJBzyqfT8eVlOjhp8AvjDHLi4pKtpaOI80YIr/+ulEQoHXrTeNCIQoBiopKNkdH+8a5rh1gDLiuLPZ4zLaSEm9O+HMamuo4nnkFBXJYzOlNLX1Gj4/X/YcOmXGuGzoYfv+9a3y+4LhQKKx0aa07x+NxVpaUBDeXfubf93hM1MaN9YpTU9FlyzaNs5ai8DkEt3q9vnGua/eG2yLD5zM7Q6GSTeHXznSv1y7My9O94bbsW8YYAxAbKwcOHTLjrA0dCp+Td11UlDvOWrOjU6dOwfT0De8ZY2oVF4dyStv+wOs10du3tyrs3Ru37L0XGRncXlISNU5V9wF4PARc1+w+PA6Px8xQ1UUQsSf82v7HWpMEULVq/qHCQt84VTcPwOv1rnddO85aswPAGHe+qned69rs8PvCRyImBvYWAGqMGQemJDyOiB2ua8uMw12q6uxTDV9LY8ynQLpqxK7w/aP/VQ0HlcHBPGNij4wD2GiMGafq7AzXdRaKsBHYGH5tJ4CJcZy8/PBr/gae0iWyyF3GFI8DSt//4DJjvLki7pZwv3wGZokxkTvDr807gA9Q192b7/PVGgc2P/wZ1yzHMeNEgrvCbTmLVN1NPp9sKB3HRBEntmrV/EOl4/i7MTYUfr8jd3s839x7IqEVIt4j9x7wuTEm4PMVl6qsmneN0YjevXFnz25VGB+/6ci9JyI5xpgj956I52tj7ObCwsP3njvZGM8XUVGhw/fePw7fe8XF0Xt9vuCRey8Ucld5PL6y995sY8wyn6+wdFzynjES2bx58+Ds2bjx8d/ce8FgcLPP5xkXCrl7Sj+X6R6P2frNOOwUYzxz4+Pt/vD141+qYZ9La+0hY0y6amhaeBze1eF7Tw6rzJa2cWK80dHbFVaU3RdUPS7SL7VralSRLXjB4wsOCpZ4b/i2Niua81o+tDw8dOeDeSW+YELZaJGH7npgiSijxrz51w8fvPPBF4wSO+at1x441bHvzDX+Gvdt//qjOsUHuxfcev1XLy0u6AocAGJ+v+nz7i80uKIAo0svbhYvt13XVkeO/kIeuLPTkJf/EZiJE+qCymTQeUwa3huAAaN+89zjvbsAQ4C9QHxpT8VFwWA1f58+Rc/Nn3+7WFtFofHjPXs+dngoI1r2fBNh08g1c0cAMOipQRS785jh31fadmeMDaJmCcJjzz1++bTiopJb/3B576EMGpVKx9AHvqUR831esySvIJQC1oJcAngQ2QL6PEo9kCtA40F3gnQDikq3YqA26I0g/0F4G+V+kDGgDxC+maoAr2KYiaUW4v0cSvqgUhWoDmxFVFEZj+gCVNYDdwEu4SU9CzjgacykxytlxCuppJILkiVpoz8DLi+7T9Duyam/PcoPY8Bl/f4kwrJJs6e+OaB3/yLBNJg0e9I5eeC/YHwuHrrrwbEP3fngFiDaV+Jb+tCdDx1RQhPXuUcR/0N3PLhZIAlj/nA6x74zM/6wf3Ktix9X1V/GfvLZKwgBoCqQk5UZv5gpw5YDczdszj20c3fetqDK/pdv6JvGlCf2M3H4dOBrkDYADHo+DpHfutYeDjU8bFgcAoKRPt9NL86Z0wHVW1TED/Q+aizCJhFpDEDfVyKw0h2vcyODnn4CVBD9LThtSwsP2bsvr/WefUWlad/1ks7Bph27JNW75Kc3tLsvKsL5C7iDUB0M7AX9CUUR/wImgbYG6oF0J2ysRqE6A+FxwJSGHEehDAYOoPop8CTQLNLnffmuXq1nIKGVGLuRiY8doijyM7wyk5TQq9SrNZGgOxMIotITGAxMBqaB/gpYCuxGgkeywv5YWLZsY5309A1Vz/U4fogsXry5uapeMN993yeZmRtbnesx/BBZsWJXbHh5tHw0SOxVtfEl1+wvu1VvkXJUuoR+ffp1QaTtpNlT3zzjAZ8FKlxE6+b+N7dT3CGikkB4+u0I701Nu+Vs9fPqv147qeb9mH+PyQA6fNdj3x3RrT39ixz/vEXQuwbVzBIcOwXYmEba4Sms0SXBUELQtZuBXx1d3/4fmGUMHtkedauA1n/7o+Vt77ixA9aqGiOg+i7QFnjBNeZL4EogAqhx/asfxn/44PX7ws5qklMqNwvsq4V42gE9Ue3MwKfvBbkI6AYEUU1Zn3MgtWG9uGbPLljQ/IlnP+9YrZpTp0tSQycmystt17WL/sftA/YAUxn4TA8mDg2reqb6l1Dk8RJOf70caAe6CeG/qN1ZarvWBv4O/B+wBBNaTJAv8Hh6fjHsphmRjucn/3r0pkdIHR+ObPnk0Xwgnw/DFwQIAlEMfCoHpBHGjmLCiMOS0GMYOGo8VgbQ95mFTBt6QSeNKksoZH7m9cpqoFLU6DtijPvK0qU7UzlB2Hol3461zjjgsnM9jh8awWBhkqr2BYaVp37ujo0l4pjqZfe5IXuUDowTfgDrMKB3/+zSXRGKu2TwFX2vnfDZtHJHqpSXCrXeb+530wBVm4HKZQqpqlRT6KxwowoXZpy532/9YP1rZ+9h6tC1iC4gvF4aZtKwiXR0x4y9ZcCHYEYcVTfSrgNAnamofRBg9Yb9Azbk7OfrzO1SVOyuKgqFfqnwKVAPuD4YdPcDuxFxWiTE/un3Uz9NfH7Bgs6qslaqxLb1J/SOxGtqgbYDuRhwgATAi2osMAugqCh4dZXYiJpi7SRjJKlGtag+MVFeduzOs80bV00B8H/5ZRUmDf1GLjzNXwISXgdUeZHw0s0cJg2fzCG7jHAaahf0GeAdRDYy0b+Naf6DiNztEw6AXRtu69uVQoEsABzn2OWP5Qj34LHjT1H/AkOywf5ojKmziwSqVCm8YBSCv09EdPGpS1VyLCKSK8KG8tYvys8tLDq0j7JbsCj3KBG4SbOnvDRp9pT6k2dPSZg8e0oCUCw4Hc+FYQEVbFxY5A+gfxw/Ne0y4IAKd2oMLQReQWVdRfZ9HvElVr4xLhDFX5rcaNLQo0PuwnLae1C9CEgFslW1zvvT1vDRJ2uDEz5ZY/x9+hQ5qhOB/MDKXXs+X7ip7trs/etDrlKjWvQdubnF96i1T0U9dnfIk9KuJpE6AtepDdIQqAZMQGVieCgsQuRnwOcx0b6qsdFeB2jdObF+fOP6VRq4rmXmvOx1InL/8wsXXhzpunccd3ZqNwAuEpwKMheV+QDM9hcBK0FuY9LwLDyhX6McycDKxGE7k5ObrUtMbPL6aV1Fld8DuXw49BgJ3dBfQRcBvbnGX+O02roASE5unPZDV+c8VyQnNx5aqc5ZPirVOctHYmKj5eVV5wTw+CI2Gp9v/lFbVMSPV+dChNaCE36iVFzHOpFpaWlu1Z3VHwcdUpF9nz/ol4huPHW5I6wiHOfvQWQKsG7/gSIL/C2waldTrvfXHjZ6vhQVBT9Om7IqflFg+5Jpn6/vmrFiByVBtzAuxtcfuNq0ajbASWm3goZ1n7gyb+M9VeIiws67orOYPPQ60PuxspqJw3YSGT2gbfOay/MKSg5syNnPgCuaExcbIUUldvPydbuHAges645VOKKW+dy8eeHEcyIbUf7EJP8ehFdw7PxvTkW+YNKw8Pv/kX8X9WqOLnui6enbai5Zkt31tK7K5Ce/An3tOJGuSf49OJ7bgO1EOh2/w3X+QbNkycbEJUs2Nj7X4/ghsnRpdt9Zs/S8Tvp0vpKZmTPw1KUqOZYVK7LqZmRsuqS89U2Et6nj9XUvuxn99sRlk2dPiTxXzpxQ0Q6dSknICYX7EHZZYxsBjFs8LgTEVGjf5wsH7TJcs+y0y6uOR5yfA/OwuhyRRxC5lpTQw6oECTovB63b9K2Pl093rW4oKCx5ZvvugkWTZ66f91Vg2/aaNaIP/+AMNa2bRjq3Dcpt1qh6/6aNqoV9GizrQJRJw8ei5o8A/t9280ZFeVpGx0ZeumpD7h3BErdg4eItWLVLmTj8Q2C2QFdRbQHwwvz5ScDPw+OVNTi8CsDEYbOYMHz5kXMRee+oczsmCZnXW9LIceQ7GJnucblqAPj4iWzQz7Byzem3dZZI9fsY+FSTw5Lm3xci5mrHcc6Sr9CPC9fl4Ro1dn4nobxKwljL7871GH6IBIOmOdjryt2ApQQoLLt5PHpeC8FVtPW+zGMlCViNylxE/zCk/5BRQH+s/jiU3mb7Q8Da0y7vddP4aPguBj21ECNLmTAsHGo0ERj09GrgFpCVWTkHBHQJKe5HrKgytbi48NL05Ts/6JbSQFzLXlGNNo4087ZozEX5BbSPrLEisGLXS6DfLEdNeWI/QEQw2F9E1g7t1m01sHr3I284a9bvebp7p0YTAIqCwSERHs/1IvL8s/PmPWpV+6Ea9lSOiko7Wpq7zMzCxCcWwMlTyBhjt1rrnLboGpP8J7fC1XyK0WFc/aKfTx7NJ3V0FIUFNYhqueM0/DnKT5GvK9gBxHjfIuzU+r0gYr8IhbzlVvz7cSP/Cga3/OCl088NelYTTv5YEHGyjSmZceqSJybkeHzAUYrLQZHzWkqiQo0LhecE8xj6YAAAIABJREFU4gBCNvSsxziTQT8HdhujN1Zk3z9YPvKHBWusZOKGjvmxsitAUlAdjrACNe/iH26BQlJHf7ljV17s2HcC2qFV7X9f3Dx+QI1qkS0AfB1a0XBPXm0mDXvzxRkzYg5s79vMk3vgkH/Dgl0AxphEha8O9+JOWvG/vvH7btEldSYC+Pv0Cb0wa9Zc6/UmCDwFKCK+l2bNqvm7Pn2+Zdrt2yWW27dvuhPY+d0v0gkwno/Q4JP4ioaQOv7fFK29D8N+itZUYdBTnxDhZp1pivgTY68G7sSxC/kejYukpKZfnbpUJSciOblx2rkeww+V5OSE/53rMfwQSUxssIVwqokfDRWyLDKkX+q01P6pg4jh0/emvJ8G8OGMD7ePn5aWoi7xbTq3rfvulPfnVkTfFwxeM51p/oNH7bOSefgoShJGv7mG4dmDjJwtB8ykz9fv33+g8Ju6xhAbH1v1uXnzGrlWR3pqVZ9sPZ4ja6eq2k5VlwP4wfzEE3jzktxNVxe//p+H/WD8bXvX/X2fPjuAuYTD954Fvg56vTcB+GfNOnZJ4LQs6vT0DY0CgZyz43sz8bFDwNuIGUTxmv5AD5Q+IJeg8iZFnvLrl3wrchVQE5WWFdP+iQkENl69bNnGymWRchAI5Dy8bt26ymWRcpCZmV25LFIOFi/e3DwjI6v8yyJGNihmTtnNxfOjdOisJcoEySc7tX/qiFsG3VL/8IG0GWn7/IejJSo5OR8N3XvcPkeXAoQVMpnGhGELjy6gT4T/2JYbNuXG5eWX7AsWldjg6o0FxjE+YD0x0b8xCRe1Bu4f0brHwFRSHaANUhpS2qJXD1WuBj4Avc1t3fNntsRNAXi8R4/LxONJfKx796dUNU3gef+sWZERHk9fAP/FvdsBMrxVj9NKxe71OjVFOD2HztNBZC5ob5AXgYZAM+Bq4FJgKIOe6Q4qDHrqGyGgM/GV6PtKBOhhJ9Lv1bhQNYnWOpUOneVAVfsWFMRVOnSWA2ul0qGzHDiOrVuqcFwuQuo0c8X0Krspcl5/hivEuBg/Na2TCp0QnSbwqA25Oan9Uj9I7Zd6FReg5Pj3RomTCWzG8gEpoQHHLTsccucA+SB9N+Tkttyzvyh7XVbuhMJX3v4YaxXwYgyebskriYqIR+WxtrfXGQg0UtfNAHCNdlT4yrruYypsEOWfGK19uIvHLr10i4ioffGvbwPRUV7vjSLSCsC17vMjWvXcJ8gz96akeE91OhERRetUQ6cXino6qLMBqIZSk7BxUZfwslzYUFM7lYFPT0HNI0DYOIjz9C93f5LXmLBuSAjR0hTxKgwe2b78J3F6qNrxwaB34alL/rh4duHChFOVEWHYmjV1KkNRy4Gq/PZcj+GHiKpdbq3zr3M9ju+TCosWSZuStnj8lPfvLXKK64M8LNBU4JMh/VPXDuk/5HfXX399/KlbqeQopg3djei7iJ3IiWZ/ws6jLwOhHbvzC3ftyVtReCj0E6e4aJXmHhLdl1tEfmGxadro4phXRjTw3X9bd9/Ay0eiunhor17hpEDIT0SYOmr9gg2up+R2IKRq6hzbVfGaLZcC2Qq/RrWWPyUlWuBKVFYDMfXzIhNPdTqtW7c+lJTU7PSdXU9FStEWIBd0JlAHqA8sRJlAWO2zBOgLehMDRo3DOfAmcDX9/HXp66/ynfoa9HwcHrc1EAR9F5V2DBz1ZwaNuh/XdD+qbN9XTn8KPnX8aYnLdezYNKdTp/rnVZLAc42qinHdU+YFSkpKyBgyRCpFtMpBx46N0k9dqpJjSU5ukpuS0nB9eeu74hwIibOt7GatOa9XACpcX3/ixImHxk8d/8b4qWnJaugCOgfVP3qLPVtPXbuS41B9i3q1T+6vIqHXgNnFxe64zNU730z7zZBCx7gzNfcgoQVLIks+nP4pIrfpjl1TnLYtcOrXaifGTC3TQWPFpAM8s3zRToUpYrS2v23XGiNa97rPn5ISDSBCLxF5G+ike3Pb2WD1D4Ei4917GaqfWjG3PXlxrxbfdiqBQE7bcDrts4Tfb1H5C8pMwsnTooEVGJkPTEJ4H2EtsB1hACK3gLbH8bXCeE9veeZ6f23uHetFSx4FGQL8C9HRhBOt/QqVZxBNOFI+9bmqeA4mkzreIXV01HFtHUvxum+SE6X6fccdLyUjI+fnmZnZl5/s+I+BsenpR82OvThnTnOFfv4VK0563QACgex/LliwOerbylRyYgKB7HfO9Rh+iCxZsqnTmfirqEpVkPplN9fY8zo/zvc6OEc1AkwE4CAET1mhkuOZNGLFsXoRRzHRvw3RhYgu3vDqw58DUOgN2M3brV2/aYu7fO2Sx7t3fzdv2MtDi19+MxT6etlBVEcD+Hv39gDxjnWPRHCIagDlZzbk+RrVv5Af1XlYy64XqUqv4PQvXAAV6WHqxl8DLPevWFGC8A7KnWL1+qEX96x3sqGKaISInN0ZLDf4Z9QsBMIy5arLCelnlITuxjFPEjQ9mPRke5BS6XXpCvY5HO1xXFsDn2sU/jvqm8imkKclO3bfBvJzlN5Y+ZCDdhnwOeElv2pAkyPli0MtUGlJ8dqVFBUcvYwR8vyMgc8ck6dBv1FBLXKuPvmJajWQ09KKebh534jDRuGFxL7i4p5lX7vGdAIujsjN/fuLM2ac9NqoUjcmxntefzGfr6hK/VOXquRYjNFIa6l+6pInQdgkyNdHb8557dBZ4Q4ht/W/rXpQgz8TuM8qFyu6FOWRYk/x2xXd948W0S+xemRN2Z89u2hkVvIWzdn2U83LjwAYtW7OqidN93a6cdPynP/+owjA3eHeLmDyIqOPGBdqzDpRjac0K6uq+alXSLNocnD63Jqeq3pYE18t2tSpWWyzty4EMNZmWONUF3jEUdkCnDB8bc2axplNm25ffaJj5SYcYbOcgU9/iFAbwwomP5lzXDmjK/lmUvFSlPD18vsNAacJVqqjoakMeOrvIHfQ/5ldXFIyn8U0RHUkSF0AbHAFs/0hUkcPoKggDegG0gq/3+D3WyytEL0WpSVQQup4p4zuRmuwg/H7L2OxcxeYDFTDBsW9Y71s330j4Sywx1Gtmo7Zu3fPaU3txzt5AzkUUwBMAxQQf9uu1f0rFh7RyfC37tWNkOb61889J3kIyoOoDiBs1IVfi9wKGFH9qRsX9waw4ET1fL7oIW3a1Cr4noZ5QREVVTjgXI/hh8j+/Y2+jIjYUu68LK44jYCjHEJFz2+V2Qqz3m/pf1O3If2GvBXS4DaB5xFZgkqPtKlpiWnT0l6fOHHioYrq+0ePz2bgVjvqR9tdmzVvm925cOSqLz49vG9XbNFGtSq1D0Q0AjBKW2DHS0s/OZItMjcU8wGwSpB/Aqro3Yr0ARw9mJcQnDRLAJwuSROA+QD+dQvCjqdQ1yhH+x98X0S2eA6rv8MXWnrC48asAT6DIyZGF1L9PhZ778aV9SiTgZqIPAFaC2M/YInnpyjNQBoRVskLEdtmOxAOBRb9LciVQBMWe35J/5EpCC2BVETnAh7y1zcrM4pWQFcWO++A/AX0C6AWA0fdyvbdKUDzs3EprHCnNfriiFY9bgIY3qpnNxtyBh8+nkqqY1U/s448x1lwuB69YMH3teTQ67n09LJp57tqOFy6BGt7f09jqKSSSk5ARelcLLMq80G7K4xwHE+D8VPG3z5+2vj5p65dyRmT5i9h2i+Ly+6SnXv+M27x4qOWU8KvdZ3HOO0BrGpT0KMktsesn1ZsjDPkj2vm3IMwF0DhisPHQ7O/DBsXia0PmNsGH35/FfTD8D96+4hWPU84S9WqVU6i1xt8+gzP9sSkDXHpFEojzZ93wuMfDd2Lde5F9E/AGiCKIqcH6LXAPMIOoW8j8j7wOlALZBTCzYAF/QBYcZT658Tha5g0bAnhrLV/xjH/B3QHPKj8DZiJ0VsBSPXHAklAMUgqYfW92NKW/o5yJWGH1BOSmysPe721rv22S3BvSorX37pHb5RrUNqAjHq8fY/qKI8rMuhwuVbNtyYAHtAe/ha9ep68xRPzcPO+RxxWn50797dBa796du7cK5+dO7d6qVT8WUdVBWgpRUUtXpo1q+bTc+bUA+JF5EkVuQ944JkFC1oDPL9gQeey9UpKCsYvXbrzglsm+j4oLIw6fUXdSo5QvfqmS6Oi3HL7l1mRzcDispt49bxeFqkQ40JhAyLXjp+a1iJtatqL70x6p9Kr/VxTzzPzRLsFSbdCRwARaW6Q49IC+1fNXg4gyuEsnMmlf5/Sg3kH1LUHVKRL5JVdj6SQV0f/JvA24VDNIcOb90r2J/SOLLv2ryrFqnpEz8Pftq2PsxmqfCo9lSlPbMS4zyCyDNgP8hnQD+R1YDoSehxH7qc4YiiwDNUGQDtgFdY8T2ToeD8NAOEVoAjlfsKGWCHi/Rjl34hez3X+ahQ6twJbUL0DWAk8TjiaZSlQjPAIUP+I38fxneSC5p/4WJi6hyKvtCpT+Wb5s6WvRPaJ0F/gan+zbrX9YMLGpa5HmWiNfudp73iTlwjwzKJF8SLylMLFIjJKREZba3/5Xds7HV6YP78eEKfQMuT1/p9jzGJAq0dELFSPZxJQ31j7xfPz5sVh7R9fmDWr7qgFCy56acGCWiLsyM8Pntee9ucrIj+StA1nGWulyBj2l7e+qja0kFJ2CwZ/hDoXaVPTrhs/ZfwMwuu7lZwH+GfPPqGVqyIbBJr42/aOBVrictI1d7XMAQkQ/twsNmvm+hF5yjhmuUAH4Jbn5s+/HcCR3HWukZGo3ANsEke/Up992ebHpB5uLymp8YqkpIRRRzoIxV/rb97z4rNzxqfJx/5cVNeDjAeygChEchC5k4n+bXw0dC+fPJqPcDcQnqUQZjBl2PKTzopMfHIOwjWEP//7galMfOwQjl0PNCDkXInISyCzqF97AippTHryJUQmIvoY4ZkUB4hE3ROmuE5Obvy3xMSEz090zN+q1+UjWvVcjZj3CM+IlNV0yCNswHldj/Oebd3rERUuE5UvwyJk330ZyzV0BpCSksFAlIqMBToCtyBy29Pz57fzq57t75q2AIhcLPAAUAtYel+nTsGhXbrsFRgG7AGGKPRSn2+IR7WTVW2UlJRwd7duDQu/pe1KTkJSUsKt53oMP0Q6dmyUnpiY8FJ560v4e2Rz2c0ae14byJUe0z9yFM1WoZEbcj8Atm6rWrDuZGVHrps7RdX+B1CFv/jBHsoL/tXCBMI/vPGojhm9YEEN/4oVJaNWzVk3cu2c9xAeA4Iqei9q7xnWsutFAKtXr44LBDa0BPC37nWDCje6Hs5BeKWsQeQ/WO0P/IwIs5yJw47OeTLxyXTgN4ASCr14yiYnPjkHNA3ROzGEU82X2K1ADUQGAVUQNjDuviBu3HMAKP/E9SwibORMB3IRvRe//7j7dMmSjY3T07fVPHa/H4wr+gThJZU4YKlRuREoBhaYkNsM9GVRxgoko/qAws+taIaqTQeSSmeQThvRUuNC5DpggwN/A4KEDZmVRnWIb+7crgCjFiy46Pkvv2zwXdo/lufmzWur8AagWHu5Qm2gD3DkfXmsR49nRSRTVe8FolU1FdXXgUaBQHby+PF6WnoilRzNkiWbOp3rMZxN/LNmfS9P/xkZWdUWL95cbh8qV6S6K9Kw7GaMOa9/v8/rwVVS8TghvkJJEbhcVW881i/jWHJt3GuI3PXUmrlvA/x5y8JC9Xr/SdjA+ASoVmLtmLJ1Rq6e+wGwjrB6aBcjnj7+hN7ViosjW4h4fuFP6B1pVf+mqrcI3P78hAn1n58z54rjOq8oHDObiUPnM2X4KiY9+R/SHj9wwnKTnnwFmMhU/47Ta9j7KBOHT2TCk+GohUJ2UPo0DQSxuhHgiH/MpKHTw5lqdSMiXxNOhBZFuue4cF4RM8TrDR6nzaGteo0R5TLQe4B8g73Hv3bOVFU+Na5zs3/Dgl0Gpgv6nsIioCkQgzoZO+MKVwJb3FCNu0/v/MLOoCgdS504+6N646PdumUCK1HdCGQLDDfG/PnZhQsTPNZmayj0/Om2fyJU5AGgqYrMQaQzsP7xHj3mPd6jx1FRSVY1u/T4PqAHUE9VG6rydKtWO8sv+/4jRsI+ShcMkV7vPf4VK3z+WbMiX5g/P+m5efPaPjd/fi84XkflTBAx7Yxx7zpb7f0QqDQufuT4189d6Rq3rYFbn1o778SRFWUYs35a8cjVc94qu29oly57PSK/scb8ivD0Xbfja8oahBsBRLkSr+0QDLp7ti5K32h9djBgjEgmShMrzlQVeWV4t4Hl1uL/Tnz8RPZplxUZcepCpUx6fNNRr8MKqm8RjjR5GStZxzR+eBkxC/RrlP+GX5qj/S78fiNiM41xjwqxHdGye19FHwAWjFwzLw2RT1gzf0npuP/jXz97C0BhMHJhXmR0xlNr5l5jlC7AJmftF/PHLV4cRPlAYJg/oXfksIu7nTJ3SctmO+sjNMyfl9ETKMq/b/hzI1r1fASR6cBGILu0aIpY24Ow/0cb/5dfVgEYvWBBjVP1UZbx48c7Ym0SsFfgz4AH1b+dsLDITMLRQBMlPJWMGNNQRKZFRx86r53hzleM0UnnegxnFdW7Iw8c6BTl892r1nYDnv5/9s47vIoy++OfM3NvCqGjKKCAtMCySFUguYlkrZRghbX3vlZUenCEQBIb1lVc++ru/sBKs2KAe28ApSNSpUnvJaTdO+/5/XETDBBaQMHdfJ5nnid37jvv+84kmTlz3nO+B9XPswKBujsLCm4+UcO4rrUR9IfyT5OfQaeW3sLyv1m4rII/EMMX5ax2lvg/4jhiZB5PTFw9MCFhMfAuUD/D77+y9Peq+snQxf6vFL5DuMHY2rpDh8Zrpj/74mZEh4J+HPXKkMeseme8Q3RUa6C5p+k5/zmuE/stGDvoiAbYYRHGAW+i+gZx9sqyG9nLiQ7PJjf8JrASzK/Gxd2jvMz2XN2mTaOvW7VqtP9cxBoEgJosAEvcp53iVNvcvaF9D4WsFd/uKkk3dpb6vwd9vKSdYn2Kqlej3QyPsdOdZr6yg1aBrOnTz7I8Wh+oypq1b7B5W5EYt76IDC968d0miKwgJuYpIp4DC9U3gHVAy5hw+Nv0nJx6IdWM0n2OzM6u/syUKeccOFam39/ayc72rKhb91NEOiNyR7+EhLGoToqy7TfKmt+AxMTvUH3PUv3YqDpElu7qt2nT4OWmTZsWlnXM/zoHpPYeROvWDZ/NCgR6Hq7Nqcah5pvh9z+GyPkY829VHV7sEetJRAjvLYVrT9Qc2rc/e3nbtud8Vt7jVaSxwUouvakeHNDZ44Iek3p06b62xwXd1/Xo0v2bK7pc1vC4Jn4cVBgXFZxQRGQVES/EfuqSm6rkfQzgad54MuBBeXjCmx83qNG4QSrQDLXmWDEx3WKeeniDxFXC/LzGkqpxjdLik06OTsZvRXSzIMoLjE9bdsjll5iiaYxxcos9HZOIuPQjcuAbNw9HGfD11B8Ti9dwBeDxcy+JA84X5RWryJsN4CwKlmT3MHLttEMGMA5dEhhT8vOwpVN+wJLhCo8o3GhE/GnNfAe9wTnZ2R4TCo2XSjH9PJ3b4un5lwbhFb9Ux/AQltXTnb+km1m7eVbBPfeEUC0JPI0FPgPeAFp5jJmjqvst7RRGR3c0tt3kwLFEZFyM1/s1kApIlIhfRJTY2Kv7JCQc8txcr3eAiY31F4bD7wPrVPXcuXNX9545c6Z3ZHZ29UMd979IZjDYRwoKFjwdCCQVp/oexKSZy+5TeFFVJSsQOKXVOh1Va8TUqYkKNxz43cjs7Ooi8jgwnUgwcGUiQcKbBYYoXAokZQUCVU7EXObNW3vW/Pkrkk9EX4dDvHLb+MkTzho/ZcJZiP4Qxsr8rcc8FBXGRQUnFtW1QIFC7wy/vwaAM3161VEzZ4YBou7q1VriKgE0nvP6mx/sWvPLX4F8Pa3qbCAVy/ob+QW7zMYtRVGXX+TaZ9e5pX8rX/llc081xvR2GT/48HV1xjhF+34W612EO6p0H5QQnc8DZ+bveQBo+9OcpTf++N7bNw9pnnSJ07JlVOWigtbA7qeW+h90Vk0+roqfYeEjSjJj0MVElC8ByMjJaQIQ5fVeIiKtPe3+nOrt1RWpFItZt7HI8m73L1pU+ztV9RQ5Lz5vcistC0/JeQZ4GdhaUKNGn/4+3wPAxcDPQHxGMHgeRB4Gasz9KtIqMxCYXRJ3E+v1pmikym2KwJ0CX/ZJSNgO0L9Dh7INtGIGdeq0qX+HDruclJSwikwBGu50Cx/wes+KKvJ6L3dmzjxmvQsnO7vykVv9AVHtqnC2gUlPB4NXZvr9++kyPB0MNl4R2jkUaJgZCLQzkbf8U5aoQOA8y7I+Beo/n5MTW5Kx9NLEidGFXu9MYJ3AJcAoIn+LYSCnn883DJgIRKvqCal9pOo2dF3r0nIfb1lhY1mh0puqfZCnedy349YAOI4jYHlEZPPxzPt4qDAuKjihiMg84GmgpsDfAKJDoWuygsHvMgMBn1Svlhyb1VftVs3XhTy2D5Ht3gb1bo7L6vcCUB3VJtjWTGKjPyAm2o5+6Ja7vGH7LSc+6VyAtCbJbQ8z/H8fYwcGG+3d/tl5O3757Lwda5xme7fEAqz894fn/vzVt4NClt3DhGp0N6qJCke1pnukwl4jFvk3oPod0VEovCaQXOwZQddvfmRIfNISnf3jXQDem64IA0Walx/WDVvmOwsXFo1hjAvMM2oaAHFF748bW/BIeg6q6U7LlkUA/X2+QI2YmGRU54rqswBxfv+fBXqieiPQVi1rnJOd7VH4C5E4lXzXmLGxu3dfUZ5LKfAx8OOMvM3nf7ln+TiFnpX27q13rP3Eer1HzhY6AqeagVKcNVHyv7VbVV9EZFhGMPiXEVOn/inT77/ZqD6xzS08DUBE7hB45LfMtnCys8sddPvMV1/FWSIPEfFKdCo0ZlFsMHh+ViCwYG/VqqOB2qje0c/n26Oqnwt8ArwiIpMBjGof4IvizK5jm3dxPBHsE3vjx4KtYbDKvyyieFC8pTcRt0zvUo8LekyaOfmH3ah2MyGeLO+Yx0uFcVHBCaVvYuLPtsirwFZE7s8MBFoCrYEGQDZQj9hoiXrwxm3RD9+8Lu6VJ6/wOg/tQeR8gduApURFzVgTY91r1mx4WaK9GtW9y5UGnk5rnvSAZev9ELk596KX7bTo8ueTd7bHj9OwS3UnPvGQ7uWBLZLq3LD+hx6+bStP921bUbll7sZdtvJVpZ3b2oIUodyiIh8S0ZSYdLixMoPB5Mxg8L6YHTtuK/EqHQqrauVXKo3oM7oQfQfYVamo4J9Z06a1wLjXAracVecKs31XWDy2J/R/Ex9zF/080123eV/sgyU6ENhrWbYPYYfZs/fvm37YsF9sxD0dOoQQeQxolTVtWgtXJKX4q7YKqwErxuuNB3oBz6jITQOTk7c81K1bueIlCoqKJqnqv1XYpZHU1avUto8pLfb5nJyaCvdkBQKdyjOH4j5iYzyeSeXxmvxWxNr2BUTibjYg8hQiZwGI6seWZY1E5D3gHoFpRETfrgHiK0dFHbNxdrRU8nrLHdDtxsU9B/RC9R0AgQYKryn8WaCnikzpn5Q0D6AwHA6IyL8skfe8Iv8AGJiUtDQ6FLoeaJTp9/8NfjUUDkfW9OlnRbvuxSWfMwOB2zL8fmddOP/5b3b+/Et5z+dPTdrUbvenTrtLb/XqNSxTYG/8lPEXbtizsYbAl9i8Xt4xj5cK46KCE84TiYmbEXkUqCMwSkS6SyQKexKAWb/5R7HtaOvsuvWio2Jex5iOiCzLj4kJArNUde4b99wTKhruPB5euuozu0WTTUCCfc7ZI6zWza8BiLXti5r/aX1zY8LdS4/ttOxckxOp8vkb43rD8YqVVvLZActp2aUJRKrU2q7cIsqGfNtbEKUupxfmVrlm45ydiESvqVl3vde4VSQSy9BO0AUlaXQHkhkM3oXqeFQzgaHWEd7IYl9Mi6J6tR6VXk9XEX3Pql2rk9m28y2rVo1a1mk1rpXTa4bV6BsCzyx4f+Nr4fGTMxb5PW/v62BxYKrCFGfR5B8FGQ/UqDbqxZZDmvmuLBZsA8AWWQpUN8Y8LZbVkWLBL0vkH6jOA+4BziqIickakJj4cTkvc+TapqTkRtv23zvE1v6MiCeEYtXVo6bIda8DUPig9LXODATKvNGXuOJLV2kNue41iJwfW1DwdlnH/N6oqqhlXY7qeBEZRfHbOxEjYiuRpQNQTbsort73xYbG6QBh1d8sJkpFjidGob2K3N1ow4a7UP0eyAPaiMhzQCHGzCtp6KSkhPMSEub3TUycWzp+59GUlJ0qMg2RpzJzclplTJsWf8Q5u25rUU2BSBCyJfKoiDyp0LlGTJWys5qOggXL523+4acZVUtvq9etWnOo9rNmzQqB9abISartRIVxUcFvhAXTFMYqdAaq5YdC72FZHyv4w7Pm3Nho/fqWHpGleSbcCuiL6nNOhw55qjob254D4CxcWGS3a/m61axhaNGSM2tE332tJ+re66tnjP/ibmPbF2Psrop0KT2uCXkcp2lCawAnPrHDgYJQIwKBbiVxA6cCIjRTuMOJT/7LkGa+r0180lQNh4cDmPXhVEtoCnyefVrTq1F+Aqz4PZv/+nOlWtb0uLqnAyyPO01VJNfTPXE7qh9lTZ16RVYgsE/sKCMYPA/VV0o+ArUN3FGWS3vEjBnF1W/1ViA2JioqXaIqjfT27lZHqsZ1IjqKmKy+V4uIx65V7ae+iYn9xjDGdb75z9ji5RAAHDCi5hUA0UhBO4P1LCLvGdd90Gnpq+808130RGLiZlW9V1QTVfUyInVcAJYgMhV4ENVPnQ4dTkgV0z4JCfm1rKizLRG/wuqSN/Rj4Hrg30ADVX0ZIstMxQJi+73djszOrh4TDF47YurURBMX901WIBB5o7X0lebJAAAgAElEQVSsRkCuRgrX7cczwWDtcp5auckKBtOAB7CsH1yP55WCatWWCPyIZV0LzAPWofp9P59vuIXV1nbdLynOLBO44Wje6MuDGnPEOjdljZ0VCKQAf7ZUp/Xu3dvVSEzFGhUZb4wZDtyMZc0pfYwjUqbapaj+ANTCmLdtYy468qS1NRGvGIj0VWhpjObl7yz86E9R1Q8qrXAiufLCK2t1u7BbM4BevXrZYG5VmPlbjnk4KoyLCn4TGq5bt8pjTJrARQLvOCkpYRs+V5F+Q7KGzevdu7dbndhL7MgDZHOBx/Pv4kMn9+/ced8/YWFRUQ4iZ7QPPPwT1aoU4fUg1Sq/IrtzO4lqmkCi07BLDEBaswuSEB50bbub08x3kYv1jAnXfN1p0rEqwNPZ2Wda8LEYc8+h5j2kme9K2L8YV1n0otdBCo+ZgcDjo0ePPiblR4lUTd1p0G8Q6QAkKtJ7SLOkCxHrLhVtosrSmVNHTURkPMIMYMOP1eutXRxbo8q3pzXjm9PjZUt01U+9V3QfApyuljXygLe+B1F9T2Cgqo4CNgokxXq9PTNzcvbTJLHC4eszAoE7iUTLv4DqwzF/HzLRbtMCLEtQ8oBHgS0iMkNEDgoqK2Ho0uCXAIWGoEQUNeOBeSiPmrAsNsVS417L+gqoCuRb8CmAEfm5IBR6ys3NvUVE7j6Wa3okjLEfQmRksWLnfUebEeCoWoi0xbIyiYiDtcrIyWkbs2NHU1VtP2rmTG9WMPh2ibei0ONJQvVhy7ImaqRi68iMnJy2RvUcIlkKjTOmTWsIkXX6DL//IteYcUeKiTmRFI//FCCIzBvYseM2p2XLIrWs6/snJCxAZJHAKIX7RUQty737ieTk+cALAAopmYHAkQMVU519arJHWpLbh0jHI12LzECgLLG94ai+1M/nWwJQGBv7H1RXuyL3DkhK2tHf5xvdPyHhk6OaA5QYIR2MyDWHa6iqonAZ0KK4YN81azfuWftl9s/vDP/HjMpxdnS5Y3WMWFsMsqz0Fra8buk2YQnHWWH5d2qX7uvzt+StQmgUdsP3lXfM46XCuKjgN6F3797uE8nJ8/v5fNn9fL6+EFkuGZiYOK2kzZ2J7Va7ql8DXzqdOu0GGJCUNKv0A8tJSclVka+Aau6k2a3Ymx9GxIsxLYisEce50eHrBzXrXE/E/ANAlDuNyBsCXYDbjCf6uiy//zb1el8AYlC92enSZb+3dqdl55ppzZNbITI4rXnSrTXs3L1DmvluOvC8Hj/3kri05km3tmy+6fIDv0Pk7hV16x51/YAn45NeU+QRhJFE6omMFDQd2I2QBnoRynk2uhjAUjMe5T2DJP5UqdZVII/m1GzUbUtU5T0rr7lqO5GH9VtAQ4XGACP8/o6ieo3a9mv9fL5XBiQl7VDVm4CQigzCdfe74anqncXLWN9Gh0JDBYJAewCMUUGHSCTYbXo/n+9Ib0UKkLHcv6XIZcjQJf46lmtfB9QEYgVpCBGNFIF3jDEX54VC04CdljHLnZSU3NBDw3f38/n2HO01PRratz97ed+EhC8Lw+FXFc4Evnpp4sTDGpOjR4+2Y4PB+4GYgqion1EdBuwWY95D9SJEOu8oLHwGuDUmGOwPICLdihVCqwK7gSZizHcCNym8CVQR1x3zbDDYICYcfkBExiJyfvTu3b95iqejaqmqiOveVbwrUGnnzlkl3/dPSFgAgOoiI/LlgKSkWQCtWzdaAlBQo0Z/IjL1MZSkSh8Wz3e9Xht9a+oL/76jqMgcMcjw2ezs04Dq0du3l+lZchYujMoIBP4qcE/pJSdH1VJoi23vq8TsdOiQpzB9cELCviytwxnFpTHGzCWyNDRTVBMyA4F34GCPibNwYVRWMJgokfiyva7rPrV1R/6at/8zr+rUmes8YWMuaDPg03IX8BTldESalt48Zn8J+3HfjlszfsqE9uMmT6g7fvKEs8dlT+j9lf+rDeUd83ipMC4qOGksWLDi3PioGq1VNftw7Yzr3o3qsMHDH/tFPPY4jEGrVI6lauV7EfncQjp78NyvsFKQl4BzijdAF6P6msnde7vCXykqKkSktubGfFHaO6Fh7zPFLtB2ojwP2CBdD/RgxBXmvy5GM43qfgF9TnZ2DKqNgEcy/P5+GX7/RWWtw5fEGwxumtxC4U4gFrFmKvq6MfLRU0sCaZZlJwKdAC/gxev+BLBwaZ1pxsjk9CVTV84ddkuneek3LGbcoC+qV4uZ1KZ9/ft/2bhnOhHVSsSYDhnB4CWWyFggNCAhIfIG1vWl6AFJSd+q6i1E3sQ7ZwYC/TMDgcdHzJhRS+BcVK/AmGcfTUnZaVQHIzIbCOuaDZ9JOPxhv6SkvxdEgt2Omozl/i0AzvLJa1W1L+iLRmi47/rHxDw+MDn5JyclJYzqjBKDwkC5MkMOx7x5q0bPnLm+kpOSkiuqdyh0zKta9eqy2mYGAl2dmTMrrahbd6TCs8AWp0OHvP5JSe8j8jLQCpFnUG2K6sNAEar3ZU2deqHCDcUemy8QGQxcRESgaVxhYuIYIu76DmHVRUC6wI3ANst1j7ky7dHy0sSJ0VmBwFWxweDUrGBwDBEv1ChgYVmBsh6RYGFCwj6jY+7cVeMBnJYti4xldQP8luy/NHkQ3TNq1Kwe06pZo9Nf6tzurFFRXuvezGDw11ipXgd7+4o8nsbFP15Z2niAyIO90tatDYAsRFJCsbFtRkyderqqSpTf3xyIioLlpY8R2y5XvE6TjRuXWCIXC4wl8v94S/qUKWdnBYMfF0ve48ycWSl2x47rJLIc8g0iS8Wyes5ZuKlhXmG4GujlQKxzzZ9eLc8cIifAetAFpbcCN+we+cCTR4VxUcFJpbYneqPHsqYcrs2g5OQN/ZOSXgUoqBx3a3j2wlyxhNjnB7YA/VAhRUUuEWHiT0vO6AMstSrFhFCWezq3e0psawyWfb7mFVD41se2FIVUqle7oIYnN6NYQ0MUvcpz/rnLqVF1AZG3sQKEK2vYuXMGxyfvU4xUSETkDEH3i5KP9XovIRI0hohkisg3Ag+MyMlpXtLGadilunHdSwEsS18D1ipMsKyiWXah5430ZVMXwb4S94tB1qF6u7Nw2naAMYxxS9oAzMrfdln6lCln33Vdm3o1qsZ4Fy/btrf/i+sXo3oLIueL6gdEinotBaDrS9HYu/8GMCAp6d8Cw4gEv2YYw8VWUVGJC3VvbG5uAKDxhg1+hRHAV9GNzr6hb0rKRoh4lI72d3wgw5YGnle1PxY4N6158vUOWCV6FX3jE6sofDMkPvnhIc2TBlCOKq3HQvXY2P8AYVTbl/W9wC0x+fl9iKg1RqvI9yXfWRFhLog8dFxgJzAakbOwrKdEZDzQV+GbgqKi1xqtXz8NKEAkxxExlsgzxccVAC/08/k+EdigcGdGMHhe6b+d4yHD76+RFQh0yvT7U/OqVu2n8IrC+cDVwK4oyxooImVWQ348MXH1oeIRBiYkLFbbvkWh48js7OqjZs70ZuTkHJwqbrk9enVrQZU4bxXLElssiVbVUSVLQhQseYiuTtXSh9iWdRmAiPx1Ta7VqvR3WYHAs8bjuUwiGWi1bMsabFnW9KeDwTds234Y+PZAYbV9nphjpHfv3m7fxMS5qroaeF9hgce21wBXFhYbgTFFRY2NyG0q0k5Vf1BjnjWKWbtxj4eI/P2Zlkh+jcox5dafcVXqGqxWpTeP7Tmli++d0vXgK/jvpli++uFjOcbp1Gn3k/FJ/7H63tXJim/0hOlxyZ+tcV/HgTa2XHlsDGPcNEnKsK+45CGPxxa7Q6tr7A6t1lNYFFX47D92EgrPUJFa0fde296s2fCIfP6NjvjyqzPJK6hu1a5VWUdPeM/9cVE/1m4BsV4DWliYfmnNfK+JyMNAfQCD1AUYMXXq6SLS3kBvgXdVdbeIDMLot2rJg5YxN40ePfqsn+vUqVp4Z//GonYyEb2FFqJMVEtfKTEe9keWIvrd0CWBfx74TYbf3+ir3F92K9zsse2Ha1WL3GN+XrOzOgW7rs8tOOOHyrH2XiK6Id+78Cqp6QNh9x2AS+qwuhTGPHnO+vWfr6xX72FVzbIsLoFiVVWRe0veYnu/s9GD96dpGf0u5nBKmMeK7d06w4RrWqL6Ybhp8hyKjaYYrNfC/xizDNRBKUTwOA27xBDlnotN2Fnkn328Y7du3bB3yc/3dOgQygoE3leRdo6qFR0IJAv4sKz2ohqlcAEiVxExIF4WkX2S9H0vuGBZZiDwBZAETFHVTyyIUZHrNGIUvdff59vjzJw5yvH5wgBZgcAT+QkJf4dI2naG3z/IEvm+n8+XDaCwEfiTqH4v8DZwR1nn4EyfXtVTWFjNa1mpIdv+PFyt2hanZcsiJzvb46Sk7FdzwhK5QeFJREriHjaL6h0q4gAL+iQkbB8xY8aHR3Pt2rRpuJ9XZUDnzqsyA4G1hV7vmMKCgl0C6ymOU8jIyWky4OlJ2rRBzdvPObsahYVhNm3JXVy9WmzzqlWi64kxtwBPgVyMx1MbGMDlQzvyedr3aPBiIqJWHTZt230RkRiVCCIXoPooEEC1UCEXEUvhTlRR1f77TbpnVhXG9juupTVLdbWKvKwiUhyrM1ZE/pUZCFyhrrtSRJKNMXvnLNqaM2bswrmtW54R2rBpzzbQdJDXjOr4h//qe+B45vBHo8JzUcFJY9myZdEzZqytdazHGeQbFXsSUCnuipS7LEx7VcY7y6b6ATxXX1oYlXxedU/yea0lLra73aZFV3fOwk/ZsP1ys233O0R535YzTxdpWE9i+t3ziBUTfXl4QvYWgRmehHY7Kzt9zhu6NPiF5RadizABpDuRG3EKymJgmUA9JzvbY4n0FtXPBG4Qkam2yFcUFG7NGzLyPxS/taysW/dDEfG7ajVR9PK0+KQvgNqCfjhscWDWIU5zqaUy/sCdWYFAXbGs8xXelkjl0aeBT1zXbF+3cXcSIg+nv+i/NRRyPwLG9E9K6jgoY8oUYDiRCqhNQR4junB079693X6JiS8ZYy762r/yi6+mrBgpqjc/9YJ/KqnDAvQY2hp7dzfUPr+wevUTWrDKWbiwSCIy4IWWTTIUB+TCteHv5/YF9QPRgO16TAsDbxvln8WpxsfFwoUrzyy9Zm5UPwTaxQSDOcVxD8NQba3QjsjDfi+wqr/P91D/hISc0n0Zr/cmFZnsGnNXf5/vHRWZj8itwMfhcPhLiKz5l7Tv5/O9UtoTUBgOP1diWBSzRKEPAAekeWYGAi1Hjx5tZwaDyTHh8GSvbf9bRV71GLM2ZseOhzKDwfuiPZ6DKtpqxEA5jeIYGBW5rl9S0j8tuNWK/F0zsGPHbUdz7WbPXlNWPEgmsIuIJ+QvGYHAvZmBQEtRHUCY9FpVYzqEw2aLf+bauaO/XnjhvMWbMUZVVYtl5aU10Ice6akYawKp6XcrNBWRMYBYkd9D6TiHxsBEVZ3daMOGS/snJfVS1XZElq3AsvZbEoGix47m3A6H5boLz1m/fs6AxMQfjOr1/X2+ocayWgGXisigvQWh7XkF4W3f+FcMxLLeW7Bkyzu79xamExP3L6BXlMdatnDhL+X+21WRVRrJwNu3eaK8FYXLKqigLPLyvC1jYtxjltctCkV9ZTc8+wsi8r23R7+Zmb/TVL4G0MycnFbRXbs8ql7POYgIkSWOauFLk291lk2dai2dOgaRgNm6Y3vhyHeQqpWt8PR5seHArNkKU+yz6tQxlvW3zECgvrN8xm7rTPsKYL3AFaj0tyy9yrLMzd5eXc+JtqybEXnZrNmQG5o5/199ExI+zu0zYnh43uIabNjymtmyfT6wReGvQGOJpJU2EMFV1cecpYFvD3WOFny/vvJe/4H7jerdonqBgLSJPu2d/klJ/fr7fFd7PPbzYVdzUG2P8NiaDbmbxOPpF+lMzwEKQEcDRUSCC7uRmv4jV46oNXDIdzO+y1ldI3v6msR+T0/6OL/AfQQkEbEuxpLrwWpZoqx5IglZvKDKCFRvSYtPmiRivgU2GMs93/LsuAj0RQCxzfsITVD+ZFzvcZetLiqSt+fP37RPwEpEVhAJuuxIpAz9noJQ6E+q2gPVq8W2Eyrt3l3mEsXAjh23ierfBiUnbxARJSZmQUFR0WcFiYm9B19wwRFFkw70MmBZoyyYS2SJ5ayMnJy2mYHAh5l+/xvAJyvq1BmA6mgiQmOdVfVxIgGHmaj2FZFBpWt+ZAUCdwBtUF0kMEhhbLTINIC+Pp+/b2Li3GO5diL67wP39ff5Rrkez9+IGGENJZJOnIXqTVHR9mV/anZ6nKp+7f9+fe+tb/dbP+fHTVkvvDMToFG7AW++3Lh+9Zr161YbhTAWqFmtSsz9wOlWODzIGA2dcVrceZmBwMCsYNDJysk5H6gWHQrdWDM29vHevXu7AMVZIE+Ix3N2YVHR5ziOheNYdM+ogUofUkcc1xLT4ykpW0vGGpiUtBQiy0JrNuzuY1zdOG3muq3pL0/7ZOeuguqg5xnkZdq5oxjTJ59xgz+/sl3TjUVFbrmNHBWroYrVufQWLgqd0isPp/TkKvjvxhh3j2VZS4/1uKwV3+5Kt9N+8oo8rar3iWXd9fLyL54ePXq0vcKY94F6EnmzWawiTVCdUJKN4oBxOndeKA/2/1A3b7s99K+xW0MzFtRGzYuqGiMRxchoYJKTnd3Ca9un593Z7yrbG7PEvrGnVeWCjr8Ubt9eRSpX9mDbb7lzF+0s+vuHtTTshp56/T+dUfW5Py1fB5xVkDWqfeyzA94UkWtQbWpVr9JSd+a+/dPiM+4urQlRFguXnDGhrDYScXGfXcXyfndmVPTCfdfSsj4kcnNPAex3/2/O6tDng9fSc/i5qJ6L6A+o9S6wDswvIGmATcjcQhXvdtBOwCbwPgp6H5FMnIdQrQPUAh1RqiT8CWHEIv8GJz5xosF6SiBUPP83hv0UXAjgQB+NT66icKVl3E7Gsn9AuRF47vhGlrlVq+bvu7YFiYlrY4LBlYhk90tIuDMzELjGSUkpAA7lVdqP/j7fPjGjI9U6OWJfCQkLnOxsT4zXOw0oEGMGAVcVG8ohRIYCU4AaiDwywOd7LTMQaEpkKeJ1AIWBwAPFP9+q8J5Evp9hGbOoj89X7uUtES3zmgzq1GnTiECgt8d1lxnbng90B2gVX1ubNqxuROT1wo/7LgNY/9Yj/UlNv6QobFp2blvvunp1KkdZIvGDn5u6Jxw241s0rXVxXkFo09DnFqy//bpzljdrVKMFEW9ODTXmEWDVoykpO8uaR79OndYCkJr+DMJFWO5OoAqYe+iZ/iljB08t77nvo4sTQxWrFeOG/PD3f86JaVC36qrV63a1JJKG+iQQy+cDFvH5r/8vj3ZrP0WEDofs8wgImkfE4NyHa6wyY2FOFf4wSoYVVFAaVZWng8E6Bv4qcHF/n69bpt+fishrrshlIdteUyk/v5J6vVZ+YuLGA4PSBjdNbiG2XmkZ4lwx76cvCS7J8PtriMgmIg/WaFT/IZZVGdV7it79ZKT31qsuFHhDVe83S1dVL3z9X5V1dy5EgvIigQ/K8tirup2f/8lEP0JLq/Zpz3DP1ekxDRpsL0gbudZs3Pzg0MWBcRAJtDNeb5SIhI/kllZVycrJeRPV2wUm9vP5uh/UyHEsZnouR2QAUA/V1xCpCXo7om9Q6GYSZVcjxl1HflR7xO0EMpDIW/tLKLsQbgGaABOIPCB+ptgNDbodY7/BhIEHeVTKy93t23vr5Ma9Lpb7D1RiwmHzS/rynP3EhtKa+joOWxaY8WR80j8UbrfCbh3n55x9BZl60csewxjXadalubN08uLyzCMzGHywoHr1Ub+Fh6Y8ZAUCdxhVFZG3iiWsOyHyorGsKQJhMebVfomJl4mIZvj9Nfr7fDuzgsFVQH2F+QN8vtZPB4NtjOp0Y1ltvHv2/LI3KkqqgedQD+YTRWYg8LxC7aUrtnds0rBGfduSwoLExOr7/Q/2GHbXFZfFD+7Upu6+jKofl2zZ+sFnP9726O3nZc1fvKXRpJzVn3o81iW3XPXnao0b1epnqZYYlR/09x2cJg7A5ekJmPBc8GwGKiF8jdIIqAMUAg9isZrPB0eWt1KdSoxzyhZo6+qchdfblLGDfl226uVEUeB5Ergfkd4Y7YtQWlyrM+MGTz+or+Nkwswpk4jU2NmHGhJ7nH9BziEOOelUeC4qOGnMnLn+NMsqatquXcNpR269P8V56uszgsF5qD5WvPNiVP2DfL4fi5vtPtTx6cumLhrQJGlrdNgOpa/y74SIazUrELjvnPXr311Zt+4UFblLVQGaRN18xRaN6Ef0R6Squ2TFLN2d60XYKYaACgUWMs8VrTogc8COtHjf8wJPmM1bH9Nho7YychBSrerpBRs2TS6Zg1pWcyscfl0ibvBbDne+WYHAIERuADAwb/bsFa2Bne3aNVq9r5HjGOBTUtMrA+8jDAb1ADtwPa/zVdp2oCR4dBrdnJXYngdQ7mb84E/pOewyVIYVX+FXsfQ+isK78HjfA+0BYmOZGsAJMy7emDUrxCGCFksYtiwwA0Aq5z2subE+47WTiATFMrBFUh3b3XAdS3neWG4f4IiCW/Pnr+q6bVuDb1JSZN+ShJ2b+7aTmHhKGBYAXsv6tCActkUkbFTfEsv6yisy/fGEhNUAmcHg30u0GgYkJe0YAGRMm3YBrvu+QKIzfXpVEw5/ILBsYEJCuQyuspg3b3Vq69YNyo6/cRyL2bFxS1bvWOWfvvLNFWv2TEt/LLkImHpQxonNyp9X7ajTqU1dFLZZkNeiSa2699/Ydm+NGpWqr9ucq8B14bDhg89/CsV4bd8VlzWbU1jk/rl2zZjvyxwfwHAbeOoDP4OOY+zgNFKHv0KkbtFm4E0M6+j1fGvGPFoAI74jddg7GPung4xmj+fvqPag+/BzmTAock8p8D4KOhCVsaBfIPwC7CFyr/kawvPLmtbChSvPLCqyz27btv5RFRf8b6DCuKjgpOH1FtUH6U2kGFK5sIuKfjJeb71iieu7gP5HPKiYEu2F0vTz+d4CyAoEcvg1DfJstazziLjJmyqsdv3fZ6OELI89wlk8uXRapgAMWxJ4G3h7SHxypqAZZvM2y2p2zpxKn72zF4k4DK1IKt25QLPMYPCT/omJnx9ysiIpCo8InC0iC0SsSyxLFhMp8HUAGgBZADRBZQKi+UwYsOKgZhOdjfQa3YwxkbVkQu6PeDyAjCLGzmFM/4iLv+fQoag1H3ic/QSTVE70UsnhcGbNykuLT/pRDKlO0+Qtrmhdy2hLFbl/SDPfFpSridQiOeycXJcHa9bcNJVINgIAT1x66d7fePrHRElJ+axA4OMmGzdO79Wrlykt/NRo3bqDgn0HdO686ulg8DajOiM6IiHfXGHoiZyXMTwOlG1czPZeiIZefG/0/O+M0WeAz21hsmtZ3x3UNsqdnp8fenXrjvy7Tqse84mB/F25RS3q16ueAdQJh6U38A7I7sLC8KuFheH09z5aMLdF49P25u4tPOj/FqA4nfUmQDF2XSb03wmihEc4eMxuXF7H5iOgAwV515CasQa0JchILH2f0kbzJc/EQeFlgGDpLXRx0qgRFUfYPAGynvEDryB1+IeofoCwCLwuMVFbGNOnzCWnUMhqAqYrR1m5+EBUrOWK2T/1VOSUDuisMC4qOGmo2ptEzME3nmPgiS5dNmUFg3tF9Usgr5/P99JRWxeHmxu8R+RNp2uj9esvWVGnzgJPOHxZkdd7vseYvWb7nrOwrc3OwskH6j3s92ArwB0eo9aZZuv26+w2LVZHBYP9HNWs6GDwMTXGEpE9RESU7lDVsQcqBzqqVvFbX2s1Zohl21td1/3Jts36UMgq+yY7Lm0lqemvgzYitlIa+fm9DnmiJYYFwBfOWlLTv6R96H6cQb++aY4dMgd0LqnDuwEd6Dl8Jqqb0BGTiXVeZIzzu73xW7BChb5G9MaIZSMA6xF5EajhxPsudZYEvjxcHyI6xuPZFvpdJnz89CkJJCxNWfsgkt6aGQjMFnhAVe/wWNZBRsjxIGL+dcgvVc8DWhijNQGDG36oX1LSxjLbjnFyR4we/fhnG3LfO61WnGu5bptqVeLeA3cWsGT5y/d+RGp6IlCfcYNH0GPYXELu9MUrt49S45ZdCdn2ngcaDXzPhAE7YEBk/xcDt1DyIXX4KNAOIOeBuRD4FyJ+VB16OVFswRBnN8Uu6ILKVuBD4DGqeC4kbLYDVREdHPnTG/YUe9yfmewc8SGvataKUO54DxdpAtYF+/XpMaf087si5qKCPzwZgcA8gSaqOnRAUlLWiez76WCwTd/ExLlZU6de2C85eV9J87Tmya3yo2JWPDv/66N64x027Lmp3pSOdYGzgVeJKCOuAt4Xy5qgxnyDyGDjupMq5+b+XKIzkRUI3KIRZccbbZEzCsPh6LAxGw7KMjiQyI3SYrJTQBfHczQ3QACuyGjIZwNWHaLPyhR4VgETgWTgDOAVxg1+gu7DWlDJ/ZkCux7j0lYe1VjlYEh88g2gQ1UYKcqLwKqwhc9jWABUAaJEeUW89oAyDL//CbL8/vtV5FXxeM7eF+B4ouk12iZ3o4eO20M4jqHnsHjUGgHaFVgARDNucJuj7W7EjBm1BnbsuC0zEFghMLyfz/cW3Ya3weJixg/6VaK+x/ALEf0U9AlU1jN+8K9elNT0gcBwhOcZO/jQmRmp6aOBVCAGkZsIVRmDZ/dqLL0R196LmFuBu4B3CVv98JiNwBbgDJAZjBvY+ff02AF8PiswCXT/mAvLJF7R9tSNuahIRa3gpDFz5s/1585d3fvILQ+PiMxEddqJNiwASlL1ShsWAMMWT11wtIYFgNWi8XAigZNRwKMSWQo6U1VfOGft2llEVCJfsixrWl7VqkFVlaezs89UaKQRkaatTyQmbh58wQW/OCkp4cc0I94AACAASURBVLlzV1yyYMGKcw854BiniMlORBHwaA0L4JCGRaTPXFSCxITvB/mESJpvRHjLko/J9z6MchepQ8876vGOkaFLpn5oeexLN8XljRL0I0SGjljk32Ahtys8LGi6CneZsDlkOfO5c1c/uGzZssPWEvkjkx8b+67Aj7+FYTFv3qrH6ZnegYKlz2Pv7sZszyJSM+ujMho0EWQ+MeEkVJ4/ln5LApqNMTeVLE0SF/oJ293/bX/8oEnA9yCvIRwgSiXnAduwrRGHHWxP+GZgCWgWHibxxUOFwGRc/lLszbgNCGP0mWKvx/Mo7UHWI/pJeQyLWbN+aTJnzspyS9kXieQWYW0tvRUY+5TOFqkwLio4aXi99mkidD4BXU1XCJyAfn4zQrWqTYmyrOaoPgLMid29OwW4eEBS0o7evXu7EinONpVIcGfjrJycB4zX+xxwh8CHqnpd6f5UrdbG2A1+9xMR8whjnFws/YiI56UlqenXAS0QHYHI42D56T78yJUyy4mzcPLyN2bNConHvW/o4qnvAThLpo4dtsQ/SpYEngT2gl56YHG6ElS1a15elVPapXw8OB065BnVW4+7o17Px9LNObP0rt154StQJgEPYemjKM3AnQaci7ALzEDGOEWMH/R+2Z0enoHJycF9H8Y4RXw+ZMZBjcLhq4DJwIWkOpVIHdaDHsOfBO2C6O18OvDwgmCTnQJcuRXjyeKTQZHCXiKfIXI9Qipgg17FhLSI1P6ecBrjB69DzXuEqr5YnvOybXNmsfFTXiojnFZ6sy3rlH5+n9KTq+C/m+jogmWq4deOtx/bdWdYIsEjtzx5OCkpBX0SErb3T0p6UeCCh7p1K+zv8+0ziFzXTXeNubYgFLpUIAfV54HrgXoKvwxIStpPcEvVjA6FvOUOhC03JUsenw+ahujDwAdElnkMkRguLxCNpf1JHRZRTOyRXq/szo6PMmTT1QFjFdr1AHHXmlZlHIYIg5YsOaPcdR7+CJRUMT0uCvJa47ETgEgmCJD2ac6nRDxwoJJUnDURESQz+izj0o4rhuqo+MLZjchzgIt6Pgf5N6IO4BIdP+Go+pg4aG4kLqOY6KZjgE3A+aiOZlzar7EqJR7A2Pi0Yi/HMaNqfjTGfqc8xwIYZJWqzCi9FQmndEBnRcxFBX94Ro8eba+uVi3mVIv4Ly+ZgcDjwDOo3iIiDbyW9eyJrOtxQukxtDViTQVeIZIGWg1lCkIKIIj0RbU94wYfUxXV42VIfJIf5f2hS/3/+D3H/a8iddiDqDRD5B1QH4XRbxFVNBzR2yNqmrITtZ+G0LlgDcFYl+73wP7N55c+l0iJcwAX4T+MHXxjufvrOawnSi8k6v7jrUVyovlw9vRJyP46FxYm8bq2CadszMV/rWuwglOf+fNXNjdGurdp0/C4FBeLI+f/KwwLANfj+acnFMrLr1nzP4cSdZo7d/VNIrKqdev6J0xzolyMHzKPLk4tJjthUtN/BJ7EMn3B/guq96L6LLD1905bFWWuChlD4pOe2OFWbvXy8i/2vXHOnbvqpbw8u19CwtmnpsF2qhBRlLwS9BqgFlFF0Yg+jMg9RDd9q1Sm0RquGLaIcYN+P8MCAH0PZCiR59gwkHnH1V10/AT46UvG9Dvh2U9z5qxsA5LYtm3D8pdd/4NRYVxUcNJQlUoictbJnsepxqBOnTYBfz9cG1U9U4TfVGnxqCkJGLXMClx7CeOGzAHm0HP4B8DlqI6ie3pzJrDosP2cQIzoHEHuB6rU8uy5eEh88hlgzNAlgXdUaRIX57UcsJzIck4FB9LLqUyBXEokA6cKAKJDzqxWKX9jIf/cL4UZ4LO0nw/u5Ddmj/syVTy1EeIZO/jwQZxHQ+ScDivLX15ErMqqWlbRt6OiyJJ1BxpPdlh/k7meKCqMiwpOGkuWNJjXqNGGE6Ye+L9E9er68rZtW0+tm4vaq7DMkn2fxw7aRK/Rb1Gw9FrE6gG/n3FhGzPb2HY3lBsM8kRxJkN4cJOEqVFRlXqP6d27jhuf9BxL/Jf/XnP6Q1FoNQVigTGoRCPaE9iSW1D0IR/1PzU8PpOdMOhAUke0PdlTORI7dtSfHh29ttxxMIrUK64eu4+wbe8nqnXlJZfUDhd531boAIQR+U4qy73jxo0rW978N6YioLOCCio4MYwduBmVOfvtG9PbBXkT0Xu4PD2By4d2pOfQ3/xh4CzLmTt0sf8rVf0WJZmIINo6y7b+8dENNww0YfcdgVSnaXJy8SH/m/Fnlw+7iF5OZXoOu2zfvi6OB7XqA+tpH74W9E3gXexwuxfuTB550uZaJqKMGzT7ZM/it0aVrQg/77dZ1n4vF4VFHgv09Vz2nuXGmsYYrUauORGaguWiwrio4KQRH7+6tdcbGn6y5/FHZOdOedDrPf2yI7f8PRElbB1cRj4m9lOgEoYpGGs6aj3xe83ILvKMBVai+jcVeQ0kxS0o7A/UBX4wlv7f3e3be9OaJ/3tkJ30Gm0f8rvfkb7xiVVOeKdGHqHQuwiV2/ftq+q9HcEBNuI4hg7hCRi7D585O9ufUe/TEz6H/wFq1FjTKTbWHVze43flh07bkVfUeL9tb9F+f5cTJ0/cOG7yxPGTJ08Of/HFF4VYMsUoZx//7MtHhXFRwUlDVQpV9fA56RUcAtkJeuoFsUZEh/ZnTJ98sK4jsgz7AnDUyo3Hi7Nq8k7LE+4wdGngU4zMQJgaf+XlOQ0v6drJEvuvwJln5sZ9LKq9e9FrfyOiq1OV1BHNKVzyAD2Gdfm95nwQ3Uck0WNE5xixHztojkSKtznNkrs5zX3NjqnfnllVgE6oxoI0BCIpp6pPobRBWFy8z5RkgYjo+uM6l/8xBjRJOj2t2QVJxkiBZfG7Bbx26dIlRlRvFZVPfq8xD6Qi5qKCk0abNg0WAgtP9jz+iLRt2+CPlWLZvsjPLM98POEMwp5b6Tk8Zb9S1ieCri9F88VDhTiOxYyoWiWGTokehl0ldxZ7qlx9Q8ZjW4uP2DokPikXNBVEWzbfdOGY5hlL+WzAKnoOT0P1ajCtUTEIq0Eb78t4KRmrNL2cyrjeKvuEmU4UlmkP9DUqm1s02+hnKfvUYnvRy/aYTQ+7mKaC1Bscn3xd+pKpRye/rkXjQDYg+gTKP0lNH8gscoGIcJaRjw48pE2bhtcduK+CQ+OxtJeIeeWz62/oujEur1wCXABdm8XXrhEds6v0vvlbtzcADkpF7dKli6cycf8y8NWEKePLLjL3O1BhXFRw0li8eHGVggJvnTZtGi892XP5ozF79ooGxsTs7dCh7tYjtz4FcBxDj2ED+NTZTM/0bKATcGKNC3vXnfQc9gsz5UFs/Sewn0qkM2tWHpA3d+6qtkuXNpjfu7e4QF9VuonQzai5irApIDU9FtUbEP07Rj5CeAw4h57DkxjLVFKHd0V273WadJzrLJ+xe98ABZ4LQc+n5/Acxg46OjGnIzCgSdLpGdDSVlMd9Axj8Vf41bho2WxDA4PcIiI1ULDQ6UOa+8aA9YNlWx/vq69y9ygvb9wTItWpRExVpSj3XIy0JcZTH6KKKMjzACVLlIryOLFNJx84n9mz13Ro167+zBNxbv/tOO3bV3Jz5XJAUMY3rdY0HWY9VZ6+Ppj342bggIJtclBF5F69etn5W/Z+oLB5wuQJv9vyY1lULItUcNIoLIxpKuK572TP44+IiNXb6w2dCOn034/xaROLf/oO1W7H1VeP9PMj2hnAFcMa02PYbYjcgcqnCBchesigUVWGx8dvigEYusT/mrHdB7ZHxQwAuSfWhHoAt6A8zti0vhRFj8SiO+gUlGfoOaxnjaK96Z23rbzC2FEHrKFLV+AeVB+le0YjemZVITX9oeM4S/F65KozCvecd+sv388DJCSe3oPjk88Z0izpQgAjVgoRT8NokAXA6ajch+q7BUaGDIm/IIVez8eyYfO79BjaGrwXUJD3KMa6HpGvGdN/V3GZ8I+AMSA/oLzJ+MHPH5RuCojocWnS/K/gxCfWNbmVHAEfyBg7yrvxoucyo8rdoWCIVFzetxnVA3VjJH/z3rcFKZgwecJJv69WeC4qOGmEQu5Wj8fz+0tY/xcgYuZZFmWXsz7lsb8E93l6Dj+DsYM2HfPhVzq1CfMlqSM+QNJ34XIdQmMARNaiugToTqoziHHOQWl4IvJFpUp7ItocjmMNn+1thtHLByz/Zk/twj2NV1eq+QnjB70OwNdP7AVyuOSZ7kQXzhHD5703zKV6KL+5K5ancjdnfO7EJ/30GhlDYX53lFpACpb7JWp2Aq3pOewrwrbi0asZOygjMosji4oNapx8lqr6Wu3Z8Kd6Bbs8q2Jrvhmt7h1nFuyai7BlSHzSc6DDgV/27A3fUyXO809BVyjWStDHgXtClqRQkLcI5HpEEsH4QW4EwqgpVf/F+gB1T0ekMdbBb8T7Wll60tzsfwScFl3+7Cya/KPBvh/0CWCD5bEGeuPiBoL+UN5+t+cWWRyQ0SRG9/vc4y89zsPozcCGHl26/0LkgOxxkyfcVN5xj4cKz0UFJ40OHRqvadOmweiTPY8/Im3aNPq6VatG80/2PMrF2P4/A1tRHcUVwyJGQY/hN5Oa/iJXOrXLPObuUd59noqQ525gA+hdKNcDpyHyKiJrQUdj7F4gceDNLKurNm0avNy0adNIvMQsbxtU30dIWFmpVlzr3evNmYW7DhaE+vqJveC56IyiPXtrF+USbcKVbDVRdQu2Tznr4scfoiBvJapVQNYTebNsCtoOUFT+D9vMRHUEPdN7gQqp6bdzqVPzcJcpeEajvxVY3pT43M122LIfrZZb9ODySqf5JVLb4ywiQms1VlWq8c3ItdPyRVhtkHFLK9femufx/su1rDiPMR0a520tkcRuUGxYADJ+vzog7Yv8iDsR436Law6pdNm6dcNnDzfn/3Vc476Y1szXp9i4AyTNWTh5+d7tW9Patj3ns99y7PHfjf9+/OQJMm7yhLrjJ084a/zkCWedLMMCKjwXFZxEFixYcYYx9p9bt24w6citKyjN3Lkrzndd7/b27c9efrLncuyIwvAeoGMJyyPAg2C6glyLay8Gfi1ml5pZnz0F6/+/vfsOb6u6+wD+/Z0reWeSPTyCEzuYWJITdpyEFchiK1BGy2jhZYS+b8vbFkqCIIzS9i2lIW9b4GV1ADHQkrAKLRgctm3p2pg6dmLJTsggZMdL0j2/9w9ZNAkzauKbi3+f5/GTR+Mefa+kc3Ry77nnYP3m+Zh3xxpkLHsfXU3ngeheMKrBGATiq5A+/vvoWvUSSOXg+Ru3Yd4dTwJ8Fc5YvAbLF+41kC4YbPVb1ua/Trn9uWxo/X0wjQCwq77/qH7nbjAxont78f2fkzrQ8tLWVne/9TuN9O3BgWML8zq36pmbV1mdhvvX/5d7bAiMtwAuBqvzQHo+iGoAHgXG3T37/T6Yl+GM26vAlIk01wmYe9u9gOGBS72BuDUJGnVwqTFgPblje+v3M3Usw9KqPS3W+fCS1ZXd8wede/snadnTAb5hSLRz9i5Xmvnk6LIjEFwOzdy6Nn1A6wvDin41LNr+Vla82zh9cyOO2xppXJM15C4AfgDTwfgFlHpi750LaAAdCASCaDjiC+f8CAYjF/p8+X8OAKoBfqpAxaE1kVsvurH45MNc8Vju4tVvBAHg5onTxpPmk0B0PJi3gyizC9YyAJhfUUHnU3Raaem4N7681M8XjfMaUnv/XjPzIb1wmXQuhG20VqMBzMUeA9TE18OsprtcuhGAAzsXAFb8tBZnLL4eoF9j7u0rQXx8YlwDfQ9n3v4cnr15LQCA4hegnysK0AUAv4uuRgtQ+YjFnsCLgZ248vdubPw4GxXzLcy5600Ag3rK/wHm3R4E0wOYt7gLKxb+/l8vzpe53WNegOV6EMCZAFYC+j8a+o884ZwNobNGdO8+9aaJ5SPv/GfVBgAITJhRrMnya8vaPja+fbwCz3pjcO4b/xVZe1S/aNerAPTNq1/+nbK4/+3jZ67Tz930NoC34V9moOvDQSBjLkD9wPwDAKeC6WYAFsDDQOoygAGt14EwPAtR7uC0NAC7SnducANA/aBRl7yy8rGdAMDvq9feL88Nvz8wtwxuugIxfgI9l/YyuVr+PPao8qhyDd3pyjgDoHCOFc09bkvEwIqb/4C5t2+Hwi+x4uYv/oFLdDK+BF0VKCl5CrFB0ybSxmcCPOPoQFNln5plN1B0wqgd7Xqbm7tXwqDYouKpP92Y3fmS2qWXgkiD0KKgbmTg5J+venMXADBb+Vqr0wCk1LnY2R0/HMC0Pe9j8CH9+31IhxPfbLFYWptSUTktkgJm/bJlHSJri6QqTjUwMBaExwEiEM0D87PQeAFn3nk+nr3pw57ZNecAbAE4HEQDwajFi4HEVRr3XxUDsBwAeuZi+NdcAitu/gPm3T4FoP8B+H7MuzUTKwId74Q3/OmaR16tBDAZwN3Qxs/x/M3bADRQUXkhgNNdmt5fOKH8BkMbKzVZHyIx4j8O0B2BVVUvYdVK3AO8DsBYVDTtfWXxPQDSLllfc8yjAH487pQBmR/+ZkCgYWUbgGmYd/uZmBx/C4HASsy9YzeIJ2LFzVdg3h2tANLAPBrAvXM2ffifLtYf1/cfqYZFdxFAS19Z+dinE1dVoMJKyw6cBeBdxPiyT/f1nDtG3tfZNSyqXBcA+CdAm8H83ebsYUtP2dw0I1AyNRfNL24IrHqzGkh0mFLpFOjOnb9ma/CjrLABjH4WWacD+MpyAoXlRwRWV324v693KGI2bumXTRMBKk7cQQ+O2J3dBuIpYCxRLuOmQEPl7kDJjPeS27jdenU0ahzU0yKHGhlzIWwzZcqoT8rK8mVAZwrKysaZZWXjvnDgnSO8cHMrABOJxaK2YvlN9SDjVABHwrIuxtzbfwDgFDAuANTJAF4EUz7AnzuW4nORsQRAJubdeTrg/hXm3HHk1Q+/eh4zZ4FxLCi+ZM9lwpnRRIQnAR5NhIctw7oHQCeAbgJ+tTGn/TOXEirwFYqMIwB8kLt7y/hFE8pPTnd3v6fjFPj0SZPjKwKPVPZfVDTtCjz3018gI3594vQQ/xmaTgFw709XPf/Libs3YXz75qHnbKg7zKX1XzbmtC/c9/WiLwXqADwG8NtIjO+IIoqLt7vS7wEwGsQ/APMf8dzC5s3d6bMZeE3HaYUF9eNP95Pit/rhN35UdEK/PWf+DBRPnXBD6czshUXlJ9w8ftrE5LuYnLzr2cuvGcOM88G4GMDbIMxZOGH6UYuKp30nWcYNpTOz982sDTxxc+HxhwPAouLyc7/WZ3eIYmIfQMVMfBWI/oOIXgI4MYEZ42/Jy38DDZWfDrguKSnY6PPlpj6gsyO2dWtHLLLn3/YufUgvutc359MXh4RgcM14pdQpHk/Bb7/62WJPwWCrXym91uMpeMfuLP+WuXd8G9DFIGKsuPmnAIB5t/8DwIkAVQP8yqf3pyqxFHwJgG4AOwbnZBiZ6WlzP3rk+s+8d4HiacejS32INH08E09n4EcA/wQgv2Lj4i/73/4tReV/YKAUiT8A2HrbqqohSHQAsKi4/H4wvseM723XOX9YsvrFbvh/NgAVP9kBAIuKpn0f4F8r4nla0/EK/GCgaWXL577YWXflY9jgj7B+yxSQdS5AP+x55E5Mji/Eu8hJHt0JFE+bqbV+DERu5TKOATa36fjgbWC6nAjHa+Yti5uqbruhdGZ2VrTzSTDmAIgDuEmx8TCr+A+1xuZYOh4eOjK3dkfr2nwATyoyfqLZWtXz3Cxm9i5uWmkuLJp6vUGu5VZcD1q8+o3goqLyMwH8FUAXgR5i8Dy1qio/8DVXpF00ofy/bmuquidQeEz/veYVsUFgxgyX3mDtYkNPWfzhm59OABgoKj+PQbNuXfXGd9Hzee/JNNuOZNZHe735D6XyutMf+Ms/CDhpz/sU0wmvXnnWZybROlRI50LYxjQjZQBd4vHk/ZfdWZwmGIz8t1LU6PHkOfzSwJ4rQOb8bOCnRxD8ywx0NTUDKACRB8t/+u9dFTNn8UQoOhPAhWC888qPzy0Y1n/QWR7PiM+bPp2wx49DYOIJx2K4u5o3Wnff2lh1Az7nhyNp4YRpPyHiuwA8DOAUAEMYuNJgfstiDCVF7wD4E4C5xPSkBv3RUGyBrYiGugjA9QzULV5VNWe/9m/uHSeD+GUAClATseKmz3SAAiUzcqy4tYKAbij1P9D6ZSR+3BWAncxqLhE/DvAgEH4Chg/A+QA+AjAaQBYRnmCo86H1z25rqvopAF5YPG0SMVcz8BaYt0Kr28ngX4DoRTB/XzGu14SHATwA4JKe928kG/rIPX+cgcRsoxMnfnwStDVHdbsCgUjljkVFU28CKNAFPTiDjEdVvPvS3uxg+OE3ios23Wi41BOw4mO0VsNB/NA/V43ov+9g1kDJjJxPJy3bRyjUOpWZZ/l8+Sl1lKfd/8yjYJq0530a1tUrrzrv3VTK6w3SuRC2qa5en5We3nHYpEmFa+3O4jT19S3Du7u5a8qUw3d89bMdaN7tiwDetPdAzH9TzyyVNTVrC8vKxrQQ0dc+rHzz+GkTb29+40uXjL+xsHyoW9Fy5d4yXccHz2JCCTEWA2gHuBVMNUpHr9dG2msAypD4ca8BIwuEEgAAc+C2ppX7N4vjzF9kI6P7l2Bs/bKjPD0dgXcBfABgJwO7CDwAoBORGKsyCIR7bmus+sHC4qmTiektAG5mBEDoIuBuV1ZW1aLg3/YaWLioaNoVirBNMz8N4BMAGQCiif3GWAAdG3M6Bo7q6peOeKy/hloBcJrF1ul3NL39EQAEJkybrRUvAqMEQA7A94JUEMz3IDEg5zEGXw/Glbc1VfXa1PcLi6ddSMy/7dmvUQBcYL73tqaVN+xPOQ0NH+fEYt0DvN6xH6WSo+ieZf/APkcuSNEJjd/3y5ELIYT4pgvkz8gIRCq7ACBw+PHDLLcxH4yzCRi6zco5asnqF7sDxTPyWesbmfjKns3eY+ATAmYrwB9YVfWZNT2+0rzAEMQH7/rMeif7uKVo6mIG/VixURpoqmwMFE+doZn+E8B0AGkALrxtVdWzALBowgmzQGoxM1+xuGmlecuE8odJ8dOBxpXP7VvuDaUzs7O6OxsAbgPoeGLcTcCzWuEY1uqdxU2vfzreIFA8baZmfhFAFRG/TRoPaKJGAG4AqwAUAIggcZTjXhBpMN8BoBHMhdt0v7wlq1/80v08EBYVlS8EeD5AEwEYzPwMETXctqrqFnzJEayDQToXQuyH+vqW0njcuMDny7vJ7ixOEwq1LiDSzR5PwUt2Z3Ea04wsi8XSLp0yZdRnZu88GAIlxw22Ymkli5ter0re95NJUwelRekjABYYi1RT1b3W+KlHGYZrU6CxMnKwsvjhNyYWbzzrtsaqp/+Vb0ZhPB4zFNSfFBunB5oqP12vZuGE8u8ubqp6EEh0IC565LfLfGWHf+5pm8CEqVMbmka+PbFo401qVdUdgS8ZUxEoKi/VzC+DyAC4HqBxAMDARQboMA3+CwCVHMcBAAFA6aLyrdC46LbmqgOydssX5pswdZwmWg2AGfg7sf71bU1vvoQUOxWmGT6WmU71evMXp7L9gJ/96SHonqNbPdjAgp0/vui9L9rGbn3iUtRrL722kYABSIxKB0AX3/fIfZUAcM2l13gV6BEGBhCoiTVfuPSxpbIMeC/QWrmUQqbdOZyImTOIlNvuHE6kNXLcbtVr/7HqWZW1as/7fla/ctuiomnPATyKwa8GAI3mlQf9/HkFKiw04uk97ws0VK4GgIXF5c8EVlXutRCe0VT16QDEX9a93H4RuXK+qOxA08qVAIBV+Mof0MCqqrobC8snuRRdQYS7wFgC4NnFTVVvAsAtE6Y9yISjDffWT09FBQC9iNAIhVt/PO6Ulenu6KXKpf+C7th2xDNVIFJ5wC7N1qTuA+NdEL8CRtttTW+++O+Ux6xczJyR6vY7YjoPwNF7hzy0f78P6XAHErnUUUseXLJu3/sV6P9Y49aljy39y3WXXXM3KXUbgGttiNjneDz5tQBq7c7hRD5f/i/szuBUPl/+v7do2gGioB/SpI5dvKrqkJjG3UoMvNxLYJ+jDz5f3vQD9Xp3ra7avLCovArA06zU44sbX//0snRyb1lA0cOODTQ0RPfaSKMRhIvT3d1NAFyWRXNhpD1PSscCJcc93tOR2y+Bomknaeg8ldP5ZMfuDCMd6iGAZypFswONVa8EDsARfq83byWAlf9GETsB7L0OT/zrjxmyQ5/pXHyeqy+/ehw0xix9bGlicpO4+h0MfhfSuegVzc3N6Vu3ZuYcc8wYOVK0n4LB8EDLSo/21qH9b5KGhvCII47I30T05QuHHfQcq0a+Mv7ItiB6+fz9F0nOSPplamvbRpWV5a4/UK/ZuGrEOxWo8GOf96CnU/GZ2SyZ+bdEahhBbyPGQg16B8ApILZ03BiInmXjFxTOSv+icRlXTp7svr+mJhYomTFCx61nNNgHUIZuz1qQQfR7MJ8KYFeg8Y1XAHDgAHw+4XA4o6PDlVVSMna/Oz8AgJ3R/iAM3+s+okN6nqo+07nguK687tJrCeCXs1TXj37+0EO7XIyxDKxDz5dnU9emtuHZww5bsGBB+pIlS/b6YoZCkXMAGtLRof5w3HFjuk2z7btEeqfHU/BEdXVkpMtF84h4tceT/2ptbYtHKeMYAG96vXkNoVDLTMDI1zr6l7Ky8ZtDodaLmZHp8+U90NjY2K+rK/NbRLTR48ldXl/fdrhl8clEus7jKXgnGIwcT0RHEvErHk9+OBgMn0WkhhGl/8njGdEeCrVeycy7fb78P9fXtwy3LONMrdFSVpb397q6tZO01scppd4uLR1bX1vbeopSGGcY1rOTJo3bFAxGLiSiHK83737T3JjN3H0Rs/7Y5yv4q2lGbarplwAAH49JREFUCpjpVK25vqws/+3EOUNVahj0j0mTcteYZtsZzDzC7c78c0nJsN3BYOv3iNDp9eb90TRXD2N2n8WMsM+X90riGm8+Xmvr3bKycaZptp7MjMO7uhDOyLBmm2b4PWbVz+PJfbCmZkOGyxW7GODNXm/+X4LBcD6RmsmMBp8v781QqOVowPBalnp18uSxq02zdR4zRnZ1RZ849tjxO00zcgUzRb3evD9UV68f4nLFzgF0q9db8Lfa2tYjlMJUgN/3evODoVDkRIDGa03PlZXlrg+FwucDasC2bbkP5edHXDt2qG8T8RaPJ//p6uo1uS6X63Qi+qfHk1sVDLYdRcQ+IF7p9R7eFAxG5hDRaGa9zOcr2B4KRS5nZsvnK3i0oWHt4FhMn6cUry0tzX+xpiYy0TConIirPZ782tra8HSlVBGgnvd6x34UCrXOBzAwHt/8cGZmJsViOZcSYavHk/dUff3qsZblngVon9sde6GurnW91phMZL3u8YxbFQxGZhPRGKVQUVqaty0YDF8KAD5fwSN1da2DtIafmdf5fPkvmGZLEbMxXWuqLSvLra6ra5mmtVFsGLEXJ00qXGuarecxY3B2dvTRdesKrUGD2i4HsN3rzVsWCq0dDeg5zLrJ5yuoDIUiPoCOsiyumjw5/5+mGT6dWeV2dxtPH3PMmC2m2fptZnZ5vfkPVVevGeByuc4HsN7rzXsuGFwznsh1IjMFfb7c90Oh1qkAjtDa+ltZ2bjWZN0bMEA/9v77+bEJE9quSNa92tq2UUrxXICbvd7814LBsJdIHf2vuhc+DVB58bj7mSlTRn0SCrVeEo3yj+rqNh37zjvNRkZG2gVE2ODx5K2oqVlbaBj6JCJtejwF7waDrScQoYRZv+zzFURCocjZAA2Nx91/nDx5ZKdptn2PSO/yeAoeb2gIj4jF1BlEWOPx5P2jvr6l1LKMY4noLY8n94NgsPVUIhQQxf7q8RR+HAxGLiKibK837/7ElQSdFzLrTT5fwbN1da3jtMYpybpXWxs5TimapBT+Xlqa1xIMhs8kUsOTda+nDWj3+fL/tG/dS7YBhmG9M2nSuLpk3XO79fKSkoKNyTbA48l9oKZmQ6bLFbt43zaAmT/w+fLfMs3wMVrzH+rr22bt2QZkZHQ+XlxcvMs0I99lpi6vN++PtbXNQ5VKOxuwIl7vuJdDodYSACcw6/d8voKQaUZOYqbCeJxXVEyp2GCa4QuYVf+mptz/GzNmXVpWlr4E4E+83vxnamtb8pQyTgPwodebtzIYbLuFiH3MceNZ/xVllGXUcNwaoC0rcEvRtFGFc08DoGILV+x6eO4fnliXnm6dS6TbPJ6Cl+469ow/ais+4xczzn9p14b1Z7nS0zqZOZ6WnX1P57btC0ipX4+dNrVi6wcf3Nrc3JzW3p72nX3rXrINqK1tm6IUl+1R9/ZpA8KXMYN9voJHtm+nkwDrurq6yJLS0vwX6+rCxVqraUqhprQ0ryYYDOf7fAWR3vo97A2HdM/nQCGLZt33yNLxFFM+IhrcoTN+bncmAcTjsXZAN9mdw5nUBkBvtjuFEylFbf37d/bZBbf+HUrhkJoVNrC6cl3J+f47BxcWvulKz3iQwddE/lF5/vr3qk9UpK5v+vOfhgDA1nAkxw+/0b1jx1nR3e3Dd21YfzkIbUecd/Yt029ffMvJd995DzMuAYAJZ82tPeqOvxzg/aQdAKdeX+Px1YhZr+/116EP6YXL+tzVItd/+5oTtaJf3ffIUt/Vl189ztDqnfseWTocAF93yXUFMPjd+x5Z+vnLPgshhDikBACFwvJiKB6siY4D+HCArkDiyHziPy+Ebaz0FWSpahD9J2vesalfx9P319TE9iorf8bAAzkw9ID5j/v+AdBel6JC4QT877WH7KWo3/jTIj+6/PJ+nSp7wJIHl6xbsGBBurVLfxtACAB++9BvW6677Jp1Cy695uwlj/zvM2zw1Yqx/9eYi5TU1bUOsiyd5/MVhOzO4jSm2VJkWa5dB/L8d19hmm3lW7eOffvEE+mQ/p/foSgYDM/w+Qoq7c6xpwCg8a9F0VYCwMKi8hARfwTGnQxaT4yZZBmPMLhyceMbXzgx28HqWFRXrx/idsdHeDy5HxyM8g9F3/jTIt3xfoM4rl+67tJrP+KdejUAd5qRlpyHH2QZVzAocN13rl1LgBdK3WJj3D6FmQuUUt/56meKfWmtzjAMnmx3DifSWt84ePCmdLtzOJPav9lDbbJ4VdXvbmtcuUKtWulZvKrqNABPAzzIYLZlsL7LFSvWWn8r5QJ2d3dhV/fuvf52WHK1iJ3ufezeNgBHftHjSx5bEsS/FhoSvYjZ2ESkX7U7hxMZhn43FlMy5iIFRFzhcm2JffUzxb6I9J/tzrA/Aj2X0jLxy2D6+xcuBHeQMet1RJ+9+uVr05QB4r3nGKHYZw4OzJ0xZwnAZwI0lqCGrqhc8cm+z+kt3/jOhTh09cyzn9Jc+31daem41BuqPs7rLfjMfA7i6/F6Cw7cWi+9yND4e2c83bZL3nuuBImkXIC2wgD2Hl9hfXZAJzOWxbV1p9twNaf8WgfIN/60iDh0VVevye257FLsp1CoZWZ9fYsccUtBKNS6oLm5WU6LpMA0I/u1YNehItC0suXulr/btshfTc3awmAwfFbKBbR3F6C9+/i9/qKxzxwceP7156v+VvW3r5yvpDdI50LYxu02hhDhOLtzOBGz8mht5Nmdw4mYeVZHRz85apsCrWme3RmcyDD0CICOSnX7otGDMyaOHYI9/w4fPuiQ7iBLBRO2IaKwZelH7c7hRErp5Zbl2mV3DidSSt21devwg76q5jeTlgHvKYjH3Y1udzzlK1GevvG8N7Iz0j7e8771W3ZuPuF+mUxaCCGEEL1g7ow5u+fNmDfEzgxyWkTYpq4uXBwKRX741c8U+wqFWi8xzbZyu3M4USgU+c1bb62V1XhTEAxGHrA7gxMFg2FvMBjpU4cZpHMhbMNMWUQ0xu4cTsTMIwAeaHcOJ2JGYXa2W9q+lNAEuxM4EZHKATDqYL/OvOlzfj93xpx1ALIAXTd3+uy/HezXFOKQs2wZG9XV67PszuFE4XA4o7q62m13DidqaPg4h5n73NIHB0JjY2M/uzM40WuvsUuOlgkhhBBCCOFEphkpM83We+zO4UTBYOS/TbNVLgtMQTAYecE0N2bbncOJgsHW1+3O4EShUOvUYDByh905epOcdxS2YaZuZrZt1jxno+0At9udwomIsLG9PXZIr8twqCJiWSgvBVpTl1LYZncOIYQQQgghxP5qbGzsFwqtkdHnKaitbcmrrl5v63XsThUKRXzLlrFhdw4nqq1tm2J3BicKBsMDa2rWFtqdozfJaRFhm+7ujPFErqvtzuFERGq+2x2TqdNTwIw7ioo2Zdidw4mI+H/szuBEROpIpazL7M7Rm2T6b2GnbVpr0+4QTqSUWiXnv1OjFFZmZe36zIqS4qsR6Uq7MziRUvoTZnxgdw4hhBBCCCHE/qqvbxlumq0n253DiUKhlqP72jncAyUYbPXLBGSpCQYjF9qdwYlMc92YurqWaXbn6E0y5kLYRms1GsBcu3M4EbOa7nLpiXbncCa+zO0ek2Z3Cmeiq+xO4ETMVr5lqdPsztGbZMyFsE0sltamVHSZ3TmciFm/bFlIeQnnvswwsGTLFllyPRVK4Zd2Z3Ait1uvjkaNv9qdQwghhBBCCLG/TDNSEAyGL7U7hxOZZtsZdXWtk+3O4USmGbmpubk53e4cThQKhW+1O4MT1dWFi00z/C27c/QmGXMh7DRIKeWxO4QTaa2LmA/+Es7fRFpjakdHPzklnAJmNcPuDE6ktRqiNR1pd47eJBVM2CYWS2tMT+/4ld05nMjl0o91d3OX3TmcSGvjeq93eKfdOZxIKetKuzM4kdudGYrFusN25xBCCCGEEELsr/r6ltJgsPVOu3M4USjUusA0w6fbncOJTDOyrLp6fZbdOZwoFIo8Z3cGJzLN8LGhUGSh3Tl6k4y5ELbRWrmUQqbdOZyImTMAJRNBpUBr5LjdiuzO4UTM1M/uDE7ErFzMkPVshBBCCCGEcJzm5ub0d99dd5jdOZwoGAwPlEP7qWloCI9gZjlykYLa2ja5QikF4XA4o6Fh7WC7c/QmOS0ibNPR4S7JyLButjuHM9H33O6YrMuSgmiUHqqr2yQdsxQQ8eN2Z3CiHTvUlGjU+qHdOXqTdC6EbbS2dgG6ye4czkQRQG+2O4UzUah//07L7hRORMQ1dmdwIiLaToQ1ducQQgghhBBC7K+6utZBwWDYa3cOJzLNliI5/50a02wrf+01lgkEUxAMhmfYncGJqqvXDzHNtj41Q6ecFhG2YeYCpdR37M7hRFqrMwyDZW2RFGitbxw8eJOsLZISJWuLpMDlihVrrfvU2iLSexe2YTY2EelX7c7hRIah343FlIy5SAERV7hcW2J253AiIv1nuzM4EbNeR4Q37M4hhBBCCCGE2F/V1WtyQ6HW+XbncKJQqGVmfX1Lqd05nCgUal0gS66nxjQjN9idwYlqatYWBoPhs+zO0ZtkzIWwjdttDCHCcXbncCJm5dHayLM7hxMx8yxZcj01WtM8uzM4kWHoEQAdZXeO3iQVTNiGiMKWpR+1O4cTKaWXW5Zrl905nEgpddfWrcO77c7hTPoWuxM4UTzubnS749vtziGEEEIIIYTYX3V14eJQKNKnpsQ9UEKh1ktMs63c7hxOFApFfvPWW2tlNd4UBIORB+zO4ETBYNgbDEautTtHb5IxF8I2zJRFRGPszuFEzDwC4IF253AiZhRmZ7ul7UsJTbA7gRMRqRwAfWrSOxlzIWyzalWeOW7chka7czjRwIG8ZMuWT2R9jBSkpWXNP+KIoR1253CizMzOuXZncKJt23LfSU9fJ+uyCCGEEEII4TimGSkzzdZ77M7hRMFg5L9Ns1UuC0xBMBh5wTQ3Ztudw4mCwdbX7c7gRKFQ69RgMHKH3Tl6k5x3FLZhpm5m3mJ3Dmei7QC3253CiYiwsb09pu3O4UREvN7uDE6kNXUphW125xBCCCGEEELsr+rq9Vn19avH2p3DierrW4ZXV68ZYHcOJ6qpWVvIzHLUNgWm2VJkdwYnamj4OCcUWjva7hy9SSqYsI3bHS3W2v0Du3M4UTyuvu12u6bZncOJlLJ+U1e3Sea5SIHWxv12Z3CiWKzTy2xdY3eO3iSXogo7bdNam3aHcCKl1Co5/50apbAyK2tX3O4cTkSkK+3O4ERK6U+Y8YHdOYQQQgghhBD7q76+Zbhptp5sdw4nCoVajq6pWVtodw4nCgZb/dXV1W67czhRMBi50O4MTmSa68bU1bX0qdOYMuZC2EZrNRqAzPiXAmY13eXSE+3O4Ux8mds9Js3uFM5EV9mdwImYrXzLUqfZnaM3yZgLYRul9EdaG8/ZncOJiPTr8bh7q905nIkejsXWRe1O4Uz8e7sTOBGREVEq+je7cwghhBBCCCH2l2lGCoLB8KV253Ai02w7o66udbLdOZzINCM3NTc3p9udw4lCofCtdmdworq6cLFphr9ld47eJGMuhJ0GKaU8dodwIq11EXPfWsL5QNEaUzs6+skp4RQwqxl2Z3AirdUQrelIu3P0JqlgwjaxWFpjenrHr+zO4UQul36su5u77M7hRFob13u9wzvtzuFESllX2p3BidzuzFAs1h22O4cQQgghhBBif4VCrSWhUORmu3M4UTDY+j3TjJxkdw4nCoUiD7311lqZ/jsFoVDkcbszOFFtbdsU04zcYHeO3iSnRYRtiDgdoMPszuFMPBCgbLtTOBEzRmRnu2W8WQqYScb5pEApztAag+zOIYQQQgghhNhfzc3N6e++u06OXKQgGAwPrK5en2V3DidqaAiPYGayO4cT1da2yZGLFITD4YyGhrWD7c7Rm+TQoLBNR4e7JCPDkjEXKaHvud0xWZclBdEoPVRXt0k6ZikgYhlzkYIdO9SUaNT6od05elOfH3NxzaXXeBXoEQYGEKiJNV+49LGlW+zO1RcQcYfWWGd3Dicioo0Abbc7hxMRYXV7e0zbncOZuMnuBE7ErHcDtN7uHKIXXXfptTXXfvvaswHgusuuufu6S69bancmIYQQwsn69GmRqy+/ehyAMUsfW/pXAEBc/Q5gv72p+o66utZBwWDYa3cOJzLNliI5/50a02wrf+017vNHbVMRDIZn2J3Biaqr1w8xzTaZobOvcDHGMrAOAAPApq5NbcOzhx22YMGC9CVLlnTv+dyamshEt5uyotHNdZMnT47X17eVMVO3x5P7QUPDxzmW1VnEzFs9nvxwQ0N4hGWp0cyxtR5P4cf19W2HM/NA5vRGj2dEe319S6nWyuXx5Nc2NDSkWVbOJGZrt8czblVDw9rBlqULmI1NHs+YdfX1q8cyu4cBaCktzdtWVxcuBlR2NLq5bsqUKbHE+hIqWlo6tj6ZA8C20tK8lvr6luHMxhgia92kSeM21dW1jgMwKJmjrm7tJECnlZbm1SRzALq9tLSgsa6udRCAcUSxjydNKlxrmuvGEFnDkzlMs6WIyMgxjN31JSUlUdOMlCml45MmjaszzY3ZRN3FRLR90qTcNaa5ehiRe6xh6I9KSgo2mmakgIgGa41MpdS5ptkWJ+L0SZNya2tqalxpaUNLYzHumDw5/5/BYHigYajDkzlCobWjldIjDEOFS0rGbg2F1kxQytUvMzP6wfjx47tNM1JmWdoqKxtnVlevz0pLi01M5qitbR7qcqXlJnMEg+F8w1CHpaV1NhUXF+8yzbYje3IEKyuhDjuszZPMUV29ZkBamqswGo1vnjLl8Lba2rZRLheP7Ow0IsccM2ZLMke/frqhoKCgKxSK+LTWuqxsnPnWW2szc3L0EbGY2jF58tjV1dXrh6SlxfKiUV4/ZUr+hmSOjo5o87HHjt8ZCrWWKIWMxsbcEAAUF7d543F0lpXlffjOO839s7LSxlsWzjUMfru2tg0uF4+MRt2tU6aM+iQYXDPeMFz9d+9WHx5//NjOZOfN5ysIhcPhjF27VIllxXf6fIc3J3PE47ShrCx3fW1tS57LZQyJRuOrp0w5fEdtbesRLhcyGxtzQ34/uL6+zac1urzevIZ/5dBbfL6CSHV1ZGRaGo2Kx6NtZWXjN9fUrC10u/WAZI7a2haPUkp5vfnBZA6t47u83sOb3n133WGZmVa+1mqj1zv2o+rqNblpaa6hyRzJurdlS645YwasPeteY2Njv2g0c8K+dS+ZI1n3olH3P6dMGdVRX99Sall81+DBm05rbm42OjvTjty37iVzJOueZek1Pl/B9j3rXrIN2LfuJXMk616yDdi37tXXt5QyG+49614yR7LuJduAfeteMkey7u3ZBiTrXrINSNa9fdsAw8hcVVI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+       "a.path.toString=z;a.path.clone=d});C.plugin(function(a,v,y,C){var A=Math.max,w=Math.min,z=function(a){this.items=[];this.bindings={};this.length=0;this.type=\"set\";if(a)for(var f=0,n=a.length;f<n;f++)a[f]&&(this[this.items.length]=this.items[this.items.length]=a[f],this.length++)};v=z.prototype;v.push=function(){for(var a,f,n=0,k=arguments.length;n<k;n++)if(a=arguments[n])f=this.items.length,this[f]=this.items[f]=a,this.length++;return this};v.pop=function(){this.length&&delete this[this.length--];\n",
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+       "return a};v.toString=function(){return\"Snap\\u2018s set\"};v.type=\"set\";a.set=function(){var a=new z;arguments.length&&a.push.apply(a,Array.prototype.slice.call(arguments,0));return a}});C.plugin(function(a,v,y,C){function A(a){var b=a[0];switch(b.toLowerCase()){case \"t\":return[b,0,0];case \"m\":return[b,1,0,0,1,0,0];case \"r\":return 4==a.length?[b,0,a[2],a[3] ]:[b,0];case \"s\":return 5==a.length?[b,1,1,a[3],a[4] ]:3==a.length?[b,1,1]:[b,1]}}function w(b,d,f){d=q(d).replace(/\\.{3}|\\u2026/g,b);b=a.parseTransformString(b)||\n",
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+       "function(){k.stop();var d=z(this.node,\"filter\");if(d)return(d=w(d).match(A))&&a.select(d[1])});k.on(\"snap.util.attr.filter\",function(d){if(d instanceof v&&\"filter\"==d.type){k.stop();var f=d.node.id;f||(z(d.node,{id:d.id}),f=d.id);z(this.node,{filter:a.url(f)})}d&&\"none\"!=d||(k.stop(),this.node.removeAttribute(\"filter\"))});a.filter.blur=function(d,f){null==d&&(d=2);return a.format('<feGaussianBlur stdDeviation=\"{def}\"/>',{def:null==f?d:[d,f]})};a.filter.blur.toString=function(){return this()};a.filter.shadow=\n",
+       "function(d,f,k,u,p){\"string\"==typeof k&&(p=u=k,k=4);\"string\"!=typeof u&&(p=u,u=\"#000\");null==k&&(k=4);null==p&&(p=1);null==d&&(d=0,f=2);null==f&&(f=d);u=a.color(u||\"#000\");return a.format('<feGaussianBlur in=\"SourceAlpha\" stdDeviation=\"{blur}\"/><feOffset dx=\"{dx}\" dy=\"{dy}\" result=\"offsetblur\"/><feFlood flood-color=\"{color}\"/><feComposite in2=\"offsetblur\" operator=\"in\"/><feComponentTransfer><feFuncA type=\"linear\" slope=\"{opacity}\"/></feComponentTransfer><feMerge><feMergeNode/><feMergeNode in=\"SourceGraphic\"/></feMerge>',\n",
+       "{color:u,dx:d,dy:f,blur:k,opacity:p})};a.filter.shadow.toString=function(){return this()};a.filter.grayscale=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {b} {h} 0 0 0 0 0 1 0\"/>',{a:0.2126+0.7874*(1-d),b:0.7152-0.7152*(1-d),c:0.0722-0.0722*(1-d),d:0.2126-0.2126*(1-d),e:0.7152+0.2848*(1-d),f:0.0722-0.0722*(1-d),g:0.2126-0.2126*(1-d),h:0.0722+0.9278*(1-d)})};a.filter.grayscale.toString=function(){return this()};a.filter.sepia=\n",
+       "function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {h} {i} 0 0 0 0 0 1 0\"/>',{a:0.393+0.607*(1-d),b:0.769-0.769*(1-d),c:0.189-0.189*(1-d),d:0.349-0.349*(1-d),e:0.686+0.314*(1-d),f:0.168-0.168*(1-d),g:0.272-0.272*(1-d),h:0.534-0.534*(1-d),i:0.131+0.869*(1-d)})};a.filter.sepia.toString=function(){return this()};a.filter.saturate=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"saturate\" values=\"{amount}\"/>',{amount:1-\n",
+       "d})};a.filter.saturate.toString=function(){return this()};a.filter.hueRotate=function(d){return a.format('<feColorMatrix type=\"hueRotate\" values=\"{angle}\"/>',{angle:d||0})};a.filter.hueRotate.toString=function(){return this()};a.filter.invert=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncG type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncB type=\"table\" tableValues=\"{amount} {amount2}\"/></feComponentTransfer>',{amount:d,\n",
+       "amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
+       "{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
+       "\n",
+       "]]> </script>\n",
+       "<script> <![CDATA[\n",
+       "\n",
+       "(function (glob, factory) {\n",
+       "    // AMD support\n",
+       "    if (typeof define === \"function\" && define.amd) {\n",
+       "        // Define as an anonymous module\n",
+       "        define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
+       "            return factory(Snap);\n",
+       "        });\n",
+       "    } else {\n",
+       "        // Browser globals (glob is window)\n",
+       "        // Snap adds itself to window\n",
+       "        glob.Gadfly = factory(glob.Snap);\n",
+       "    }\n",
+       "}(this, function (Snap) {\n",
+       "\n",
+       "var Gadfly = {};\n",
+       "\n",
+       "// Get an x/y coordinate value in pixels\n",
+       "var xPX = function(fig, x) {\n",
+       "    var client_box = fig.node.getBoundingClientRect();\n",
+       "    return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
+       "};\n",
+       "\n",
+       "var yPX = function(fig, y) {\n",
+       "    var client_box = fig.node.getBoundingClientRect();\n",
+       "    return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Snap.plugin(function (Snap, Element, Paper, global) {\n",
+       "    // Traverse upwards from a snap element to find and return the first\n",
+       "    // note with the \"plotroot\" class.\n",
+       "    Element.prototype.plotroot = function () {\n",
+       "        var element = this;\n",
+       "        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
+       "            element = element.parent();\n",
+       "        }\n",
+       "        return element;\n",
+       "    };\n",
+       "\n",
+       "    Element.prototype.svgroot = function () {\n",
+       "        var element = this;\n",
+       "        while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
+       "            element = element.parent();\n",
+       "        }\n",
+       "        return element;\n",
+       "    };\n",
+       "\n",
+       "    Element.prototype.plotbounds = function () {\n",
+       "        var root = this.plotroot()\n",
+       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
+       "        return {\n",
+       "            x0: bbox.x,\n",
+       "            x1: bbox.x + bbox.width,\n",
+       "            y0: bbox.y,\n",
+       "            y1: bbox.y + bbox.height\n",
+       "        };\n",
+       "    };\n",
+       "\n",
+       "    Element.prototype.plotcenter = function () {\n",
+       "        var root = this.plotroot()\n",
+       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
+       "        return {\n",
+       "            x: bbox.x + bbox.width / 2,\n",
+       "            y: bbox.y + bbox.height / 2\n",
+       "        };\n",
+       "    };\n",
+       "\n",
+       "    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
+       "    // does everything right.\n",
+       "    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
+       "    var events = [\"mouseenter\", \"mouseleave\"];\n",
+       "\n",
+       "    for (i in events) {\n",
+       "        (function (event_name) {\n",
+       "            var event_name = events[i];\n",
+       "            Element.prototype[event_name] = function (fn, scope) {\n",
+       "                if (Snap.is(fn, \"function\")) {\n",
+       "                    var fn2 = function (event) {\n",
+       "                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
+       "                            return;\n",
+       "                        }\n",
+       "\n",
+       "                        var reltg = event.relatedTarget ? event.relatedTarget :\n",
+       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
+       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
+       "\n",
+       "                        if (reltg != this.node) {\n",
+       "                            return fn.apply(this, event);\n",
+       "                        }\n",
+       "                    };\n",
+       "\n",
+       "                    if (event_name == \"mouseenter\") {\n",
+       "                        this.mouseover(fn2, scope);\n",
+       "                    } else {\n",
+       "                        this.mouseout(fn2, scope);\n",
+       "                    }\n",
+       "                }\n",
+       "                return this;\n",
+       "            };\n",
+       "        })(events[i]);\n",
+       "    }\n",
+       "\n",
+       "\n",
+       "    Element.prototype.mousewheel = function (fn, scope) {\n",
+       "        if (Snap.is(fn, \"function\")) {\n",
+       "            var el = this;\n",
+       "            var fn2 = function (event) {\n",
+       "                fn.apply(el, [event]);\n",
+       "            };\n",
+       "        }\n",
+       "\n",
+       "        this.node.addEventListener(\n",
+       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
+       "            fn2);\n",
+       "\n",
+       "        return this;\n",
+       "    };\n",
+       "\n",
+       "\n",
+       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
+       "    // This is a function to directly update element attributes without going\n",
+       "    // through eve.\n",
+       "    Element.prototype.attribute = function(key, val) {\n",
+       "        if (val === undefined) {\n",
+       "            return this.node.getAttribute(key);\n",
+       "        } else {\n",
+       "            this.node.setAttribute(key, val);\n",
+       "            return this;\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    Element.prototype.init_gadfly = function() {\n",
+       "        this.mouseenter(Gadfly.plot_mouseover)\n",
+       "            .mouseleave(Gadfly.plot_mouseout)\n",
+       "            .dblclick(Gadfly.plot_dblclick)\n",
+       "            .mousewheel(Gadfly.guide_background_scroll)\n",
+       "            .drag(Gadfly.guide_background_drag_onmove,\n",
+       "                  Gadfly.guide_background_drag_onstart,\n",
+       "                  Gadfly.guide_background_drag_onend);\n",
+       "        this.mouseenter(function (event) {\n",
+       "            init_pan_zoom(this.plotroot());\n",
+       "        });\n",
+       "        return this;\n",
+       "    };\n",
+       "});\n",
+       "\n",
+       "\n",
+       "// When the plot is moused over, emphasize the grid lines.\n",
+       "Gadfly.plot_mouseover = function(event) {\n",
+       "    var root = this.plotroot();\n",
+       "\n",
+       "    var keyboard_zoom = function(event) {\n",
+       "        if (event.which == 187) { // plus\n",
+       "            increase_zoom_by_position(root, 0.1, true);\n",
+       "        } else if (event.which == 189) { // minus\n",
+       "            increase_zoom_by_position(root, -0.1, true);\n",
+       "        }\n",
+       "    };\n",
+       "    root.data(\"keyboard_zoom\", keyboard_zoom);\n",
+       "    window.addEventListener(\"keyup\", keyboard_zoom);\n",
+       "\n",
+       "    var xgridlines = root.select(\".xgridlines\"),\n",
+       "        ygridlines = root.select(\".ygridlines\");\n",
+       "\n",
+       "    xgridlines.data(\"unfocused_strokedash\",\n",
+       "                    xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
+       "    ygridlines.data(\"unfocused_strokedash\",\n",
+       "                    ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
+       "\n",
+       "    // emphasize grid lines\n",
+       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
+       "    xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
+       "              .selectAll(\"path\")\n",
+       "              .animate({stroke: destcolor}, 250);\n",
+       "\n",
+       "    destcolor = root.data(\"focused_ygrid_color\");\n",
+       "    ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
+       "              .selectAll(\"path\")\n",
+       "              .animate({stroke: destcolor}, 250);\n",
+       "\n",
+       "    // reveal zoom slider\n",
+       "    root.select(\".zoomslider\")\n",
+       "        .animate({opacity: 1.0}, 250);\n",
+       "};\n",
+       "\n",
+       "// Reset pan and zoom on double click\n",
+       "Gadfly.plot_dblclick = function(event) {\n",
+       "  set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
+       "};\n",
+       "\n",
+       "// Unemphasize grid lines on mouse out.\n",
+       "Gadfly.plot_mouseout = function(event) {\n",
+       "    var root = this.plotroot();\n",
+       "\n",
+       "    window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
+       "    root.data(\"keyboard_zoom\", undefined);\n",
+       "\n",
+       "    var xgridlines = root.select(\".xgridlines\"),\n",
+       "        ygridlines = root.select(\".ygridlines\");\n",
+       "\n",
+       "    var destcolor = root.data(\"unfocused_xgrid_color\");\n",
+       "\n",
+       "    xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
+       "              .selectAll(\"path\")\n",
+       "              .animate({stroke: destcolor}, 250);\n",
+       "\n",
+       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
+       "    ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
+       "              .selectAll(\"path\")\n",
+       "              .animate({stroke: destcolor}, 250);\n",
+       "\n",
+       "    // hide zoom slider\n",
+       "    root.select(\".zoomslider\")\n",
+       "        .animate({opacity: 0.0}, 250);\n",
+       "};\n",
+       "\n",
+       "\n",
+       "var set_geometry_transform = function(root, tx, ty, scale) {\n",
+       "    var xscalable = root.hasClass(\"xscalable\"),\n",
+       "        yscalable = root.hasClass(\"yscalable\");\n",
+       "\n",
+       "    var old_scale = root.data(\"scale\");\n",
+       "\n",
+       "    var xscale = xscalable ? scale : 1.0,\n",
+       "        yscale = yscalable ? scale : 1.0;\n",
+       "\n",
+       "    tx = xscalable ? tx : 0.0;\n",
+       "    ty = yscalable ? ty : 0.0;\n",
+       "\n",
+       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
+       "\n",
+       "    root.selectAll(\".geometry, image\")\n",
+       "        .forEach(function (element, i) {\n",
+       "            element.transform(t);\n",
+       "        });\n",
+       "\n",
+       "    bounds = root.plotbounds();\n",
+       "\n",
+       "    if (yscalable) {\n",
+       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
+       "        root.selectAll(\".xfixed\")\n",
+       "            .forEach(function (element, i) {\n",
+       "                element.transform(xfixed_t);\n",
+       "            });\n",
+       "\n",
+       "        root.select(\".ylabels\")\n",
+       "            .transform(xfixed_t)\n",
+       "            .selectAll(\"text\")\n",
+       "            .forEach(function (element, i) {\n",
+       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
+       "                    var cx = element.asPX(\"x\"),\n",
+       "                        cy = element.asPX(\"y\");\n",
+       "                    var st = element.data(\"static_transform\");\n",
+       "                    unscale_t = new Snap.Matrix();\n",
+       "                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
+       "                    element.transform(unscale_t);\n",
+       "\n",
+       "                    var y = cy * scale + ty;\n",
+       "                    element.attr(\"visibility\",\n",
+       "                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
+       "                }\n",
+       "            });\n",
+       "    }\n",
+       "\n",
+       "    if (xscalable) {\n",
+       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
+       "        var xtrans = new Snap.Matrix().translate(tx, 0);\n",
+       "        root.selectAll(\".yfixed\")\n",
+       "            .forEach(function (element, i) {\n",
+       "                element.transform(yfixed_t);\n",
+       "            });\n",
+       "\n",
+       "        root.select(\".xlabels\")\n",
+       "            .transform(yfixed_t)\n",
+       "            .selectAll(\"text\")\n",
+       "            .forEach(function (element, i) {\n",
+       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
+       "                    var cx = element.asPX(\"x\"),\n",
+       "                        cy = element.asPX(\"y\");\n",
+       "                    var st = element.data(\"static_transform\");\n",
+       "                    unscale_t = new Snap.Matrix();\n",
+       "                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
+       "\n",
+       "                    element.transform(unscale_t);\n",
+       "\n",
+       "                    var x = cx * scale + tx;\n",
+       "                    element.attr(\"visibility\",\n",
+       "                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
+       "                    }\n",
+       "            });\n",
+       "    }\n",
+       "\n",
+       "    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
+       "    var size_attribs = [\"font-size\"];\n",
+       "    var unscaled_selection = \".geometry, .geometry *\";\n",
+       "    if (xscalable) {\n",
+       "        size_attribs.push(\"rx\");\n",
+       "        unscaled_selection += \", .xgridlines\";\n",
+       "    }\n",
+       "    if (yscalable) {\n",
+       "        size_attribs.push(\"ry\");\n",
+       "        unscaled_selection += \", .ygridlines\";\n",
+       "    }\n",
+       "\n",
+       "    root.selectAll(unscaled_selection)\n",
+       "        .forEach(function (element, i) {\n",
+       "            // circle need special help\n",
+       "            if (element.node.nodeName == \"circle\") {\n",
+       "                var cx = element.attribute(\"cx\"),\n",
+       "                    cy = element.attribute(\"cy\");\n",
+       "                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
+       "                                                        cx, cy);\n",
+       "                element.transform(unscale_t);\n",
+       "                return;\n",
+       "            }\n",
+       "\n",
+       "            for (i in size_attribs) {\n",
+       "                var key = size_attribs[i];\n",
+       "                var val = parseFloat(element.attribute(key));\n",
+       "                if (val !== undefined && val != 0 && !isNaN(val)) {\n",
+       "                    element.attribute(key, val * old_scale / scale);\n",
+       "                }\n",
+       "            }\n",
+       "        });\n",
+       "};\n",
+       "\n",
+       "\n",
+       "// Find the most appropriate tick scale and update label visibility.\n",
+       "var update_tickscale = function(root, scale, axis) {\n",
+       "    if (!root.hasClass(axis + \"scalable\")) return;\n",
+       "\n",
+       "    var tickscales = root.data(axis + \"tickscales\");\n",
+       "    var best_tickscale = 1.0;\n",
+       "    var best_tickscale_dist = Infinity;\n",
+       "    for (tickscale in tickscales) {\n",
+       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
+       "        if (dist < best_tickscale_dist) {\n",
+       "            best_tickscale_dist = dist;\n",
+       "            best_tickscale = tickscale;\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
+       "        root.data(axis + \"tickscale\", best_tickscale);\n",
+       "        var mark_inscale_gridlines = function (element, i) {\n",
+       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
+       "            element.attribute(\"gadfly:inscale\", inscale);\n",
+       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
+       "        };\n",
+       "\n",
+       "        var mark_inscale_labels = function (element, i) {\n",
+       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
+       "            element.attribute(\"gadfly:inscale\", inscale);\n",
+       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
+       "        };\n",
+       "\n",
+       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
+       "        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "\n",
+       "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
+       "    var old_scale = root.data(\"scale\");\n",
+       "    var bounds = root.plotbounds();\n",
+       "\n",
+       "    var width = bounds.x1 - bounds.x0,\n",
+       "        height = bounds.y1 - bounds.y0;\n",
+       "\n",
+       "    // compute the viewport derived from tx, ty, and scale\n",
+       "    var x_min = -width * scale - (scale * width - width),\n",
+       "        x_max = width * scale,\n",
+       "        y_min = -height * scale - (scale * height - height),\n",
+       "        y_max = height * scale;\n",
+       "\n",
+       "    var x0 = bounds.x0 - scale * bounds.x0,\n",
+       "        y0 = bounds.y0 - scale * bounds.y0;\n",
+       "\n",
+       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
+       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
+       "\n",
+       "    tx += x0;\n",
+       "    ty += y0;\n",
+       "\n",
+       "    // when the scale change, we may need to alter which set of\n",
+       "    // ticks is being displayed\n",
+       "    if (scale != old_scale) {\n",
+       "        update_tickscale(root, scale, \"x\");\n",
+       "        update_tickscale(root, scale, \"y\");\n",
+       "    }\n",
+       "\n",
+       "    set_geometry_transform(root, tx, ty, scale);\n",
+       "\n",
+       "    root.data(\"scale\", scale);\n",
+       "    root.data(\"tx\", tx);\n",
+       "    root.data(\"ty\", ty);\n",
+       "};\n",
+       "\n",
+       "\n",
+       "var scale_centered_translation = function(root, scale) {\n",
+       "    var bounds = root.plotbounds();\n",
+       "\n",
+       "    var width = bounds.x1 - bounds.x0,\n",
+       "        height = bounds.y1 - bounds.y0;\n",
+       "\n",
+       "    var tx0 = root.data(\"tx\"),\n",
+       "        ty0 = root.data(\"ty\");\n",
+       "\n",
+       "    var scale0 = root.data(\"scale\");\n",
+       "\n",
+       "    // how off from center the current view is\n",
+       "    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
+       "        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
+       "\n",
+       "    // rescale offsets\n",
+       "    xoff = xoff * scale / scale0;\n",
+       "    yoff = yoff * scale / scale0;\n",
+       "\n",
+       "    // adjust for the panel position being scaled\n",
+       "    var x_edge_adjust = bounds.x0 * (1 - scale),\n",
+       "        y_edge_adjust = bounds.y0 * (1 - scale);\n",
+       "\n",
+       "    return {\n",
+       "        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
+       "        y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "\n",
+       "// Initialize data for panning zooming if it isn't already.\n",
+       "var init_pan_zoom = function(root) {\n",
+       "    if (root.data(\"zoompan-ready\")) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    // The non-scaling-stroke trick. Rather than try to correct for the\n",
+       "    // stroke-width when zooming, we force it to a fixed value.\n",
+       "    var px_per_mm = root.node.getCTM().a;\n",
+       "\n",
+       "    // Drag events report deltas in pixels, which we'd like to convert to\n",
+       "    // millimeters.\n",
+       "    root.data(\"px_per_mm\", px_per_mm);\n",
+       "\n",
+       "    root.selectAll(\"path\")\n",
+       "        .forEach(function (element, i) {\n",
+       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
+       "        if (sw > 0) {\n",
+       "            element.attribute(\"stroke-width\", sw);\n",
+       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
+       "        }\n",
+       "    });\n",
+       "\n",
+       "    // Store ticks labels original tranformation\n",
+       "    root.selectAll(\".xlabels > text, .ylabels > text\")\n",
+       "        .forEach(function (element, i) {\n",
+       "            var lm = element.transform().localMatrix;\n",
+       "            element.data(\"static_transform\",\n",
+       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
+       "        });\n",
+       "\n",
+       "    var xgridlines = root.select(\".xgridlines\");\n",
+       "    var ygridlines = root.select(\".ygridlines\");\n",
+       "    var xlabels = root.select(\".xlabels\");\n",
+       "    var ylabels = root.select(\".ylabels\");\n",
+       "\n",
+       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
+       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
+       "    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
+       "    if (root.data(\"xtickscales\") === undefined) {\n",
+       "\n",
+       "        // index all the tick scales that are listed\n",
+       "        var xtickscales = {};\n",
+       "        var ytickscales = {};\n",
+       "        var add_x_tick_scales = function (element, i) {\n",
+       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
+       "        };\n",
+       "        var add_y_tick_scales = function (element, i) {\n",
+       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
+       "        };\n",
+       "\n",
+       "        if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
+       "        if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
+       "        if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
+       "        if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
+       "\n",
+       "        root.data(\"xtickscales\", xtickscales);\n",
+       "        root.data(\"ytickscales\", ytickscales);\n",
+       "        root.data(\"xtickscale\", 1.0);\n",
+       "    }\n",
+       "\n",
+       "    var min_scale = 1.0, max_scale = 1.0;\n",
+       "    for (scale in xtickscales) {\n",
+       "        min_scale = Math.min(min_scale, scale);\n",
+       "        max_scale = Math.max(max_scale, scale);\n",
+       "    }\n",
+       "    for (scale in ytickscales) {\n",
+       "        min_scale = Math.min(min_scale, scale);\n",
+       "        max_scale = Math.max(max_scale, scale);\n",
+       "    }\n",
+       "    root.data(\"min_scale\", min_scale);\n",
+       "    root.data(\"max_scale\", max_scale);\n",
+       "\n",
+       "    // store the original positions of labels\n",
+       "    if (xlabels) {\n",
+       "        xlabels.selectAll(\"text\")\n",
+       "               .forEach(function (element, i) {\n",
+       "                   element.data(\"x\", element.asPX(\"x\"));\n",
+       "               });\n",
+       "    }\n",
+       "\n",
+       "    if (ylabels) {\n",
+       "        ylabels.selectAll(\"text\")\n",
+       "               .forEach(function (element, i) {\n",
+       "                   element.data(\"y\", element.asPX(\"y\"));\n",
+       "               });\n",
+       "    }\n",
+       "\n",
+       "    // mark grid lines and ticks as in or out of scale.\n",
+       "    var mark_inscale = function (element, i) {\n",
+       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
+       "    };\n",
+       "\n",
+       "    if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
+       "    if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
+       "    if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
+       "    if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
+       "\n",
+       "    // figure out the upper ond lower bounds on panning using the maximum\n",
+       "    // and minum grid lines\n",
+       "    var bounds = root.plotbounds();\n",
+       "    var pan_bounds = {\n",
+       "        x0: 0.0,\n",
+       "        y0: 0.0,\n",
+       "        x1: 0.0,\n",
+       "        y1: 0.0\n",
+       "    };\n",
+       "\n",
+       "    if (xgridlines) {\n",
+       "        xgridlines\n",
+       "            .selectAll(\"path\")\n",
+       "            .forEach(function (element, i) {\n",
+       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
+       "                    var bbox = element.node.getBBox();\n",
+       "                    if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
+       "                        pan_bounds.x0 = bounds.x1 - bbox.x;\n",
+       "                    }\n",
+       "                    if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
+       "                        pan_bounds.x1 = bounds.x0 - bbox.x;\n",
+       "                    }\n",
+       "                    element.attr(\"visibility\", \"visible\");\n",
+       "                }\n",
+       "            });\n",
+       "    }\n",
+       "\n",
+       "    if (ygridlines) {\n",
+       "        ygridlines\n",
+       "            .selectAll(\"path\")\n",
+       "            .forEach(function (element, i) {\n",
+       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
+       "                    var bbox = element.node.getBBox();\n",
+       "                    if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
+       "                        pan_bounds.y0 = bounds.y1 - bbox.y;\n",
+       "                    }\n",
+       "                    if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
+       "                        pan_bounds.y1 = bounds.y0 - bbox.y;\n",
+       "                    }\n",
+       "                    element.attr(\"visibility\", \"visible\");\n",
+       "                }\n",
+       "            });\n",
+       "    }\n",
+       "\n",
+       "    // nudge these values a little\n",
+       "    pan_bounds.x0 -= 5;\n",
+       "    pan_bounds.x1 += 5;\n",
+       "    pan_bounds.y0 -= 5;\n",
+       "    pan_bounds.y1 += 5;\n",
+       "    root.data(\"pan_bounds\", pan_bounds);\n",
+       "\n",
+       "    root.data(\"zoompan-ready\", true)\n",
+       "};\n",
+       "\n",
+       "\n",
+       "// drag actions, i.e. zooming and panning\n",
+       "var pan_action = {\n",
+       "    start: function(root, x, y, event) {\n",
+       "        root.data(\"dx\", 0);\n",
+       "        root.data(\"dy\", 0);\n",
+       "        root.data(\"tx0\", root.data(\"tx\"));\n",
+       "        root.data(\"ty0\", root.data(\"ty\"));\n",
+       "    },\n",
+       "    update: function(root, dx, dy, x, y, event) {\n",
+       "        var px_per_mm = root.data(\"px_per_mm\");\n",
+       "        dx /= px_per_mm;\n",
+       "        dy /= px_per_mm;\n",
+       "\n",
+       "        var tx0 = root.data(\"tx\"),\n",
+       "            ty0 = root.data(\"ty\");\n",
+       "\n",
+       "        var dx0 = root.data(\"dx\"),\n",
+       "            dy0 = root.data(\"dy\");\n",
+       "\n",
+       "        root.data(\"dx\", dx);\n",
+       "        root.data(\"dy\", dy);\n",
+       "\n",
+       "        dx = dx - dx0;\n",
+       "        dy = dy - dy0;\n",
+       "\n",
+       "        var tx = tx0 + dx,\n",
+       "            ty = ty0 + dy;\n",
+       "\n",
+       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
+       "    },\n",
+       "    end: function(root, event) {\n",
+       "\n",
+       "    },\n",
+       "    cancel: function(root) {\n",
+       "        set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "var zoom_box;\n",
+       "var zoom_action = {\n",
+       "    start: function(root, x, y, event) {\n",
+       "        var bounds = root.plotbounds();\n",
+       "        var width = bounds.x1 - bounds.x0,\n",
+       "            height = bounds.y1 - bounds.y0;\n",
+       "        var ratio = width / height;\n",
+       "        var xscalable = root.hasClass(\"xscalable\"),\n",
+       "            yscalable = root.hasClass(\"yscalable\");\n",
+       "        var px_per_mm = root.data(\"px_per_mm\");\n",
+       "        x = xscalable ? x / px_per_mm : bounds.x0;\n",
+       "        y = yscalable ? y / px_per_mm : bounds.y0;\n",
+       "        var w = xscalable ? 0 : width;\n",
+       "        var h = yscalable ? 0 : height;\n",
+       "        zoom_box = root.rect(x, y, w, h).attr({\n",
+       "            \"fill\": \"#000\",\n",
+       "            \"opacity\": 0.25\n",
+       "        });\n",
+       "        zoom_box.data(\"ratio\", ratio);\n",
+       "    },\n",
+       "    update: function(root, dx, dy, x, y, event) {\n",
+       "        var xscalable = root.hasClass(\"xscalable\"),\n",
+       "            yscalable = root.hasClass(\"yscalable\");\n",
+       "        var px_per_mm = root.data(\"px_per_mm\");\n",
+       "        var bounds = root.plotbounds();\n",
+       "        if (yscalable) {\n",
+       "            y /= px_per_mm;\n",
+       "            y = Math.max(bounds.y0, y);\n",
+       "            y = Math.min(bounds.y1, y);\n",
+       "        } else {\n",
+       "            y = bounds.y1;\n",
+       "        }\n",
+       "        if (xscalable) {\n",
+       "            x /= px_per_mm;\n",
+       "            x = Math.max(bounds.x0, x);\n",
+       "            x = Math.min(bounds.x1, x);\n",
+       "        } else {\n",
+       "            x = bounds.x1;\n",
+       "        }\n",
+       "\n",
+       "        dx = x - zoom_box.attr(\"x\");\n",
+       "        dy = y - zoom_box.attr(\"y\");\n",
+       "        if (xscalable && yscalable) {\n",
+       "            var ratio = zoom_box.data(\"ratio\");\n",
+       "            var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
+       "            var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
+       "            dx = width * dx / Math.abs(dx);\n",
+       "            dy = height * dy / Math.abs(dy);\n",
+       "        }\n",
+       "        var xoffset = 0,\n",
+       "            yoffset = 0;\n",
+       "        if (dx < 0) {\n",
+       "            xoffset = dx;\n",
+       "            dx = -1 * dx;\n",
+       "        }\n",
+       "        if (dy < 0) {\n",
+       "            yoffset = dy;\n",
+       "            dy = -1 * dy;\n",
+       "        }\n",
+       "        if (isNaN(dy)) {\n",
+       "            dy = 0.0;\n",
+       "        }\n",
+       "        if (isNaN(dx)) {\n",
+       "            dx = 0.0;\n",
+       "        }\n",
+       "        zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
+       "        zoom_box.attr(\"width\", dx);\n",
+       "        zoom_box.attr(\"height\", dy);\n",
+       "    },\n",
+       "    end: function(root, event) {\n",
+       "        var xscalable = root.hasClass(\"xscalable\"),\n",
+       "            yscalable = root.hasClass(\"yscalable\");\n",
+       "        var zoom_bounds = zoom_box.getBBox();\n",
+       "        if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
+       "            return;\n",
+       "        }\n",
+       "        var plot_bounds = root.plotbounds();\n",
+       "        var zoom_factor = 1.0;\n",
+       "        if (yscalable) {\n",
+       "            zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
+       "        } else {\n",
+       "            zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
+       "        }\n",
+       "        var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
+       "            ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
+       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
+       "        zoom_box.remove();\n",
+       "    },\n",
+       "    cancel: function(root) {\n",
+       "        zoom_box.remove();\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
+       "    var root = this.plotroot();\n",
+       "    var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
+       "    var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
+       "    var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
+       "    var drag_action = zoomable ? zoom_action :\n",
+       "                      panable  ? pan_action :\n",
+       "                                 undefined;\n",
+       "    root.data(\"drag_action\", drag_action);\n",
+       "    if (drag_action) {\n",
+       "        var cancel_drag_action = function(event) {\n",
+       "            if (event.which == 27) { // esc key\n",
+       "                drag_action.cancel(root);\n",
+       "                root.data(\"drag_action\", undefined);\n",
+       "            }\n",
+       "        };\n",
+       "        window.addEventListener(\"keyup\", cancel_drag_action);\n",
+       "        root.data(\"cancel_drag_action\", cancel_drag_action);\n",
+       "        drag_action.start(root, x, y, event);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
+       "    var root = this.plotroot();\n",
+       "    var drag_action = root.data(\"drag_action\");\n",
+       "    if (drag_action) {\n",
+       "        drag_action.update(root, dx, dy, x, y, event);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.guide_background_drag_onend = function(event) {\n",
+       "    var root = this.plotroot();\n",
+       "    window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
+       "    root.data(\"cancel_drag_action\", undefined);\n",
+       "    var drag_action = root.data(\"drag_action\");\n",
+       "    if (drag_action) {\n",
+       "        drag_action.end(root, event);\n",
+       "    }\n",
+       "    root.data(\"drag_action\", undefined);\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.guide_background_scroll = function(event) {\n",
+       "    if (event.shiftKey) {\n",
+       "        increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
+       "        event.preventDefault();\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_button_mouseover = function(event) {\n",
+       "    this.select(\".button_logo\")\n",
+       "         .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_button_mouseout = function(event) {\n",
+       "     this.select(\".button_logo\")\n",
+       "         .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_zoomout_click = function(event) {\n",
+       "    increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
+       "    increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_track_click = function(event) {\n",
+       "    // TODO\n",
+       "};\n",
+       "\n",
+       "\n",
+       "// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
+       "// The constants a, b, and c are solved using the constraint that the function\n",
+       "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
+       "var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
+       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
+       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
+       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
+       "    return a * Math.exp(b * position) + c;\n",
+       "}\n",
+       "\n",
+       "// inverse of scale_from_slider_position\n",
+       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
+       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
+       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
+       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
+       "    return 1 / b * Math.log((scale - c) / a);\n",
+       "}\n",
+       "\n",
+       "var increase_zoom_by_position = function(root, delta_position, animate) {\n",
+       "    var scale = root.data(\"scale\"),\n",
+       "        min_scale = root.data(\"min_scale\"),\n",
+       "        max_scale = root.data(\"max_scale\");\n",
+       "    var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
+       "    position += delta_position;\n",
+       "    scale = scale_from_slider_position(position, min_scale, max_scale);\n",
+       "    set_zoom(root, scale, animate);\n",
+       "}\n",
+       "\n",
+       "var set_zoom = function(root, scale, animate) {\n",
+       "    var min_scale = root.data(\"min_scale\"),\n",
+       "        max_scale = root.data(\"max_scale\"),\n",
+       "        old_scale = root.data(\"scale\");\n",
+       "    var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
+       "    if (animate) {\n",
+       "        Snap.animate(\n",
+       "            old_scale,\n",
+       "            new_scale,\n",
+       "            function (new_scale) {\n",
+       "                update_plot_scale(root, new_scale);\n",
+       "            },\n",
+       "            200);\n",
+       "    } else {\n",
+       "        update_plot_scale(root, new_scale);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "var update_plot_scale = function(root, new_scale) {\n",
+       "    var trans = scale_centered_translation(root, new_scale);\n",
+       "    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
+       "\n",
+       "    root.selectAll(\".zoomslider_thumb\")\n",
+       "        .forEach(function (element, i) {\n",
+       "            var min_pos = element.data(\"min_pos\"),\n",
+       "                max_pos = element.data(\"max_pos\"),\n",
+       "                min_scale = root.data(\"min_scale\"),\n",
+       "                max_scale = root.data(\"max_scale\");\n",
+       "            var xmid = (min_pos + max_pos) / 2;\n",
+       "            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
+       "            element.transform(new Snap.Matrix().translate(\n",
+       "                Math.max(min_pos, Math.min(\n",
+       "                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
+       "    });\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
+       "    var root = this.plotroot();\n",
+       "    var min_pos = this.data(\"min_pos\"),\n",
+       "        max_pos = this.data(\"max_pos\"),\n",
+       "        min_scale = root.data(\"min_scale\"),\n",
+       "        max_scale = root.data(\"max_scale\"),\n",
+       "        old_scale = root.data(\"old_scale\");\n",
+       "\n",
+       "    var px_per_mm = root.data(\"px_per_mm\");\n",
+       "    dx /= px_per_mm;\n",
+       "    dy /= px_per_mm;\n",
+       "\n",
+       "    var xmid = (min_pos + max_pos) / 2;\n",
+       "    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
+       "                   dx / (max_pos - min_pos);\n",
+       "\n",
+       "    // compute the new scale\n",
+       "    var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
+       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
+       "\n",
+       "    update_plot_scale(root, new_scale);\n",
+       "    event.stopPropagation();\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
+       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
+       "    var root = this.plotroot();\n",
+       "\n",
+       "    // keep track of what the scale was when we started dragging\n",
+       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
+       "    event.stopPropagation();\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.zoomslider_thumb_dragend = function(event) {\n",
+       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
+       "    event.stopPropagation();\n",
+       "};\n",
+       "\n",
+       "\n",
+       "var toggle_color_class = function(root, color_class, ison) {\n",
+       "    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
+       "    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
+       "    if (ison) {\n",
+       "        guides.animate({opacity: 0.5}, 250);\n",
+       "        geoms.animate({opacity: 0.0}, 250);\n",
+       "    } else {\n",
+       "        guides.animate({opacity: 1.0}, 250);\n",
+       "        geoms.animate({opacity: 1.0}, 250);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "\n",
+       "Gadfly.colorkey_swatch_click = function(event) {\n",
+       "    var root = this.plotroot();\n",
+       "    var color_class = this.data(\"color_class\");\n",
+       "\n",
+       "    if (event.shiftKey) {\n",
+       "        root.selectAll(\".colorkey text\")\n",
+       "            .forEach(function (element) {\n",
+       "                var other_color_class = element.data(\"color_class\");\n",
+       "                if (other_color_class != color_class) {\n",
+       "                    toggle_color_class(root, other_color_class,\n",
+       "                                       element.attr(\"opacity\") == 1.0);\n",
+       "                }\n",
+       "            });\n",
+       "    } else {\n",
+       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "\n",
+       "return Gadfly;\n",
+       "\n",
+       "}));\n",
+       "\n",
+       "\n",
+       "//@ sourceURL=gadfly.js\n",
+       "\n",
+       "(function (glob, factory) {\n",
+       "    // AMD support\n",
+       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
+       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
+       "            factory(Snap, Gadfly);\n",
+       "        });\n",
+       "      } else {\n",
+       "          factory(glob.Snap, glob.Gadfly);\n",
+       "      }\n",
+       "})(window, function (Snap, Gadfly) {\n",
+       "    var fig = Snap(\"#fig-0378e04b897742b597befd2e8e1c169e\");\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-8\")\n",
+       "   .init_gadfly();\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-11\")\n",
+       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-11\")\n",
+       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-12\")\n",
+       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-12\")\n",
+       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-16\")\n",
+       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-16\")\n",
+       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-16\")\n",
+       "   .click(Gadfly.zoomslider_zoomin_click)\n",
+       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
+       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-18\")\n",
+       "   .data(\"max_pos\", 111.58)\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-18\")\n",
+       "   .data(\"min_pos\", 94.58)\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-18\")\n",
+       "   .click(Gadfly.zoomslider_track_click);\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-19\")\n",
+       "   .data(\"max_pos\", 111.58)\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-19\")\n",
+       "   .data(\"min_pos\", 94.58)\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-19\")\n",
+       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-19\")\n",
+       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-19\")\n",
+       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
+       "     Gadfly.zoomslider_thumb_dragstart,\n",
+       "     Gadfly.zoomslider_thumb_dragend)\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-20\")\n",
+       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-20\")\n",
+       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
+       ";\n",
+       "fig.select(\"#fig-0378e04b897742b597befd2e8e1c169e-element-20\")\n",
+       "   .click(Gadfly.zoomslider_zoomout_click)\n",
+       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
+       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
+       ";\n",
+       "    });\n",
+       "]]> </script>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "Plot(...)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_motion = ones(5) * S0\n",
+    "full_term = vcat(term[1], forward_yield)\n",
+    "for i=1:T\n",
+    "    μ = (full_term[i] - 1 - q)\n",
+    "    year_motion = simulate_gbm(full_motion[:,end], μ, σ, 1, n)\n",
+    "    full_motion = hcat(full_motion, year_motion)\n",
+    "end\n",
+    "\n",
+    "display_motion(full_motion, T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Final simulation\n",
+    "\n",
+    "We're now going to actually build out the full motion that we'll use for computing the pricing of our autocallable products. It will be largely the same, but we will use far more sample paths for the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time to run simulation: 5.34s"
+     ]
+    }
+   ],
+   "source": [
+    "full_simulation = function(S0, T, n, m, term)\n",
+    "    forward = vcat(term[1], forward_term(term))\n",
+    "\n",
+    "    # And an S0 to kick things off.\n",
+    "    final_motion = ones(m) * S0\n",
+    "    for i=1:T\n",
+    "        μ = (forward[i] - 1 - q)\n",
+    "        year_motion = simulate_gbm(final_motion[:,end], μ, σ, 1, n)\n",
+    "        final_motion = hcat(final_motion, year_motion)\n",
+    "    end\n",
+    "    return final_motion\n",
+    "end\n",
+    "\n",
+    "tic()\n",
+    "full_simulation(S0, T, n, m, term)\n",
+    "time = toq()\n",
+    "@printf(\"Time to run simulation: %.2fs\", time)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Athena Simulation\n",
+    "\n",
+    "Now that we've defined our underlying simulation, let's actually try and price an Athena note. Athena has the following characteristics:\n",
+    "\n",
+    "- Automatically called if the underlying is above the **call barrier** at observation\n",
+    "- Accelerated coupon paid if the underlying is above the **call barrier** at observation\n",
+    "    - The coupon paid is $c \\cdot i$ with $i$ as the current year, and $c$ the coupon rate\n",
+    "- Principle protection up until a **protection barrier** at observation; All principle at risk if this barrier not met\n",
+    "- Observed yearly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean of simulation 1: $103.2805; Simulation time: 5.59s\n",
+      "Mean of simulation 2: $103.3796; Simulation time: 5.05s\n",
+      "Mean of simulation 3: $103.4752; Simulation time: 5.18s\n",
+      "Mean of simulation 4: $103.4099; Simulation time: 5.37s\n",
+      "Mean of simulation 5: $103.3260; Simulation time: 5.32s\n",
+      "Mean over 5 simulations: 103.37421610015554\n",
+      "Present value of Athena note: $95.00, notional: $100.00"
+     ]
+    }
+   ],
+   "source": [
+    "call_barrier = S0\n",
+    "strike = S0\n",
+    "protection_barrier = S0 * .6\n",
+    "coupon = nominal * .07\n",
+    "\n",
+    "price_athena = function(initial_price, year_prices, call_barrier,\n",
+    "        protection_barrier, coupon, forward_structure)\n",
+    "\n",
+    "    total_coupons = 0\n",
+    "    \n",
+    "    t = length(year_prices)\n",
+    "\n",
+    "    for i=1:t\n",
+    "        price = year_prices[i]\n",
+    "        if price ≥ call_barrier\n",
+    "            return (nominal + coupon*i) * exp((prod(forward_structure[i:end])-1)*(t-i))\n",
+    "        end\n",
+    "    end\n",
+    "\n",
+    "    # We've reached maturity, time to check capital protection\n",
+    "    if year_prices[end] > protection_barrier\n",
+    "        return nominal\n",
+    "    else\n",
+    "        put = (strike - year_prices[end]) / strike\n",
+    "        return nominal*(1-put)\n",
+    "    end\n",
+    "end\n",
+    "\n",
+    "forward_structure = forward_term(term)\n",
+    "price_function = (year_prices) -> price_athena(S0, year_prices,\n",
+    "    call_barrier, protection_barrier, coupon, forward_structure)\n",
+    "\n",
+    "athena = function()\n",
+    "    year_indexes = [n*i for i=1:T]\n",
+    "    motion = full_simulation(S0, T, n, m, term)\n",
+    "    payoffs = [price_function(motion[i, year_indexes]) for i=1:m]\n",
+    "    return mean(payoffs)\n",
+    "end\n",
+    "\n",
+    "mean_payoffs = zeros(num_simulations)\n",
+    "for i=1:num_simulations\n",
+    "    tic()\n",
+    "    mean_payoffs[i] = athena()\n",
+    "    time = toq()\n",
+    "    @printf(\"Mean of simulation %i: \\$%.4f; Simulation time: %.2fs\\n\", i, mean_payoffs[i], time)\n",
+    "end\n",
+    "\n",
+    "final_mean = mean(mean_payoffs)\n",
+    "println(\"Mean over $num_simulations simulations: $(mean(mean_payoffs))\")\n",
+    "pv = final_mean * (exp(-(prod(forward_structure)-1)*T))\n",
+    "@printf(\"Present value of Athena note: \\$%.2f, notional: \\$%.2f\", pv, nominal)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Phoenix without Memory Simulation\n",
+    "\n",
+    "Let's move into pricing a Phoenix without memory. It's very similar to the Athena production, with the exception that we introduce a coupon barrier so coupons are paid even when the underlying is below the initial price.\n",
+    "\n",
+    "The Phoenix product has the following characteristics (example [here](https://www.rbccm.com/usstructurednotes/file-780079.pdf)):\n",
+    "\n",
+    "- Automatically called if the underlying is above the **call barrier** at observation\n",
+    "- Coupon paid if the underlying is above a **coupon barrier** at observation\n",
+    "- Principle protection up until a **protection barrier** at observation; All principle at risk if this barrier not met\n",
+    "- Observed yearly\n",
+    "\n",
+    "Some example paths (all assume that a call barrier of the current price, and coupon barrier some level below that):\n",
+    "\n",
+    "- At the end of year 1, the stock is above the call barrier; the note is called and you receive the value of the stock plus the coupon being paid.\n",
+    "- At the end of year 1, the stock is above the coupon barrier, but not the call barrier; you receive the coupon. At the end of year 2, the stock is below the coupon barrier; you receive nothing. At the end of year 3, the stock is above the call barrier; the note is called and you receive the value of the stock plus a coupon for year 3.\n",
+    "\n",
+    "We're going to re-use the same simulation, with the following parameters:\n",
+    "\n",
+    "- Call barrier: 100%\n",
+    "- Coupon barrier: 70%\n",
+    "- Coupon: 6%\n",
+    "- Capital protection until 70% (at maturity)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean of simulation 1: $106.0562; Simulation time: 5.72s\n",
+      "Mean of simulation 2: $106.0071; Simulation time: 5.85s\n",
+      "Mean of simulation 3: $105.9959; Simulation time: 5.87s\n",
+      "Mean of simulation 4: $106.0665; Simulation time: 5.93s\n",
+      "Mean of simulation 5: $106.0168; Simulation time: 5.81s\n",
+      "Mean over 5 simulations: 106.02850857209883\n",
+      "Present value of Phoenix without memory note: $97.44"
+     ]
+    }
+   ],
+   "source": [
+    "call_barrier = S0\n",
+    "coupon_barrier = S0 * .8\n",
+    "protection_barrier = S0 * .6\n",
+    "coupon = nominal * .06\n",
+    "\n",
+    "price_phoenix_no_memory = function(initial_price, year_prices, call_barrier, coupon_barrier,\n",
+    "        protection_barrier, coupon, forward_structure)\n",
+    "\n",
+    "    total_coupons = 0\n",
+    "    t = length(year_prices)\n",
+    "\n",
+    "    for i=1:t\n",
+    "        price = year_prices[i]\n",
+    "        if price ≥ call_barrier\n",
+    "            return (nominal + coupon + total_coupons)*exp((prod(forward_structure[i:end])-1)*(t-i))\n",
+    "        elseif price ≥ coupon_barrier\n",
+    "            total_coupons = total_coupons * exp(forward_structure[i]-1) + coupon\n",
+    "        else\n",
+    "            total_coupons *= exp(forward_structure[i]-1)\n",
+    "        end\n",
+    "    end\n",
+    "\n",
+    "    # We've reached maturity, time to check capital protection\n",
+    "    if year_prices[end] > protection_barrier\n",
+    "        return nominal + total_coupons\n",
+    "    else\n",
+    "        put = (strike - year_prices[end]) / strike\n",
+    "        return nominal*(1-put)\n",
+    "    end\n",
+    "end\n",
+    "\n",
+    "forward_structure = forward_term(term)\n",
+    "price_function = (year_prices) -> price_phoenix_no_memory(S0, year_prices,\n",
+    "    call_barrier, coupon_barrier, protection_barrier, coupon, forward_structure)\n",
+    "\n",
+    "phoenix_no_memory = function()\n",
+    "    year_indexes = [n*i for i=1:T]\n",
+    "    motion = full_simulation(S0, T, n, m, term)\n",
+    "    payoffs = [price_function(motion[i, year_indexes]) for i=1:m]\n",
+    "    return mean(payoffs)\n",
+    "end\n",
+    "\n",
+    "mean_payoffs = zeros(num_simulations)\n",
+    "for i=1:num_simulations\n",
+    "    tic()\n",
+    "    mean_payoffs[i] = phoenix_no_memory()\n",
+    "    time = toq()\n",
+    "    @printf(\"Mean of simulation %i: \\$%.4f; Simulation time: %.2fs\\n\", i, mean_payoffs[i], time)\n",
+    "end\n",
+    "\n",
+    "final_mean = mean(mean_payoffs)\n",
+    "println(\"Mean over $num_simulations simulations: $(mean(mean_payoffs))\")\n",
+    "pv = final_mean * exp(-(prod(forward_structure)-1)*(T))\n",
+    "@printf(\"Present value of Phoenix without memory note: \\$%.2f\", pv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Phoenix with Memory Simulation\n",
+    "\n",
+    "The Phoenix with Memory structure is very similar to the Phoenix, but as the name implies, has a special \"memory\" property: **It remembers any coupons that haven't been paid at prior observation times, and pays them all if the underlying crosses the coupon barrier**. For example:\n",
+    "- Note issued with 100% call barrier, 70% coupon barrier. At year 1, the underlying is at 50%, so no coupons are paid. At year 2, the underlying is at 80%, so coupons for both year 1 and 2 are paid, resulting in a double coupon.\n",
+    "\n",
+    "You can also find an example [here](https://www.rbccm.com/usstructurednotes/file-781232.pdf).\n",
+    "\n",
+    "Let's go ahead and set up the simulation! The parameters will be the same, but we can expect that the value will go up because of the memory attribute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean of simulation 1: $108.8612; Simulation time: 5.89s\n",
+      "Mean of simulation 2: $109.0226; Simulation time: 5.90s\n",
+      "Mean of simulation 3: $108.9175; Simulation time: 5.92s\n",
+      "Mean of simulation 4: $108.9426; Simulation time: 5.94s\n",
+      "Mean of simulation 5: $108.8087; Simulation time: 6.06s\n",
+      "Mean over 5 simulations: 108.91052564051816\n",
+      "Present value of Phoenix with memory note: $100.09"
+     ]
+    }
+   ],
+   "source": [
+    "call_barrier = S0\n",
+    "coupon_barrier = S0 * .8\n",
+    "protection_barrier = S0 * .6\n",
+    "coupon = nominal * .07\n",
+    "\n",
+    "price_phoenix_with_memory = function(initial_price, year_prices, call_barrier,\n",
+    "    coupon_barrier, protection_barrier, coupon, forward_structure)\n",
+    "\n",
+    "    last_coupon = 0\n",
+    "    total_coupons = 0\n",
+    "    \n",
+    "    t = length(year_prices)\n",
+    "\n",
+    "    for i=1:t\n",
+    "        price = year_prices[i]\n",
+    "        if price > call_barrier\n",
+    "            return (nominal + coupon + total_coupons)*exp((prod(forward_structure[i:end])-1)*(t-i))\n",
+    "        elseif price > coupon_barrier\n",
+    "            ####################################################################\n",
+    "            # The only difference between with/without memory is the below lines\n",
+    "            memory_coupons = (i - last_coupon) * coupon\n",
+    "            last_coupon = i\n",
+    "            total_coupons = total_coupons * exp(forward_structure[i]-1) + memory_coupons\n",
+    "            ####################################################################\n",
+    "        else\n",
+    "            total_coupons *= exp(forward_structure[i]-1)\n",
+    "        end\n",
+    "    end\n",
+    "\n",
+    "    # We've reached maturity, time to check capital protection\n",
+    "    if year_prices[end] > protection_barrier\n",
+    "        return nominal + total_coupons\n",
+    "    else\n",
+    "        put = (strike - year_prices[end]) / strike\n",
+    "        return nominal*(1-put)\n",
+    "    end\n",
+    "end\n",
+    "\n",
+    "forward_structure = forward_term(term)\n",
+    "price_function = (year_prices) -> price_phoenix_with_memory(S0, year_prices,\n",
+    "    call_barrier, coupon_barrier, protection_barrier, coupon, forward_structure)\n",
+    "\n",
+    "phoenix_with_memory = function()\n",
+    "    year_indexes = [n*i for i=1:T]\n",
+    "    motion = full_simulation(S0, T, n, m, term)\n",
+    "    payoffs = [price_function(motion[i, year_indexes]) for i=1:m]\n",
+    "    return mean(payoffs)\n",
+    "end\n",
+    "\n",
+    "mean_payoffs = zeros(num_simulations)\n",
+    "for i=1:num_simulations\n",
+    "    tic()\n",
+    "    mean_payoffs[i] = phoenix_with_memory()\n",
+    "    time = toq()\n",
+    "    @printf(\"Mean of simulation %i: \\$%.4f; Simulation time: %.2fs\\n\",\n",
+    "        i, mean_payoffs[i], time)\n",
+    "end\n",
+    "\n",
+    "final_mean = mean(mean_payoffs)\n",
+    "println(\"Mean over $num_simulations simulations: $(mean(mean_payoffs))\")\n",
+    "pv = final_mean * exp(-(prod(forward_structure)-1)*(T))\n",
+    "@printf(\"Present value of Phoenix with memory note: \\$%.2f\", pv)"
+   ]
+  }
+ ],
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+  "kernelspec": {
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+   "name": "julia",
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+    <text x="8.81" y="43.19" text-anchor="middle" dy="0.35em" transform="rotate(-90, 8.81, 45.19)">Value</text>
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diff --git a/blog/2015-11-27-autocallable/_notebook.md b/blog/2015-11-27-autocallable/_notebook.md
new file mode 100644
index 0000000..7d7e2d4
--- /dev/null
+++ b/blog/2015-11-27-autocallable/_notebook.md
@@ -0,0 +1,459 @@
+```julia
+using Gadfly
+```
+
+## Underlying simulation
+
+In order to price the autocallable bonds, we need to simulate the underlying assets. Let's go ahead and set up the simulation first, as this lays the foundation for what we're trying to do. We're going to use [JNJ](http://finance.yahoo.com/q?s=jnj) as the basis for our simulation. This implies the following parameters:
+
+- $S_0$ = \$102.2 (as of time of writing)
+- $q$ = 2.84%
+- $r$ = [.49, .9, 1.21, 1.45, 1.69] (term structure as of time of writing, linear interpolation)
+- $\mu$ = $r - q$ (note that this implies a negative drift because of current low rates)
+- $\sigma$ = $\sigma_{imp}$ = 15.62% (from VIX implied volatility)
+
+We additionally define some parameters for simulation:
+
+- `T`: The number of years to simulate
+- `m`: The number of paths to simulate
+- `n`: The number of steps to simulate in a year
+
+
+```julia
+S0 = 102.2
+nominal = 100
+q = 2.84 / 100
+σ = 15.37 / 100
+term = [0, .49, .9, 1.21, 1.45, 1.69] / 100 + 1
+
+###
+# Potential: Based on PEP
+# S0 = 100.6
+# σ = 14.86
+# q = 2.7
+###
+
+# Simulation parameters
+T = 5 # Using years as the unit of time
+n = 250 # simulations per year
+m = 100000 # paths
+num_simulations = 5; # simulation rounds per price
+```
+
+```
+5
+```
+
+### Defining the simulation
+To make things simpler, we simulate a single year at a time. This allows us to easily add in a dividend policy without too much difficulty, and update the simulation every year to match the term structure. The underlying uses GBM for simulation between years.
+
+
+```julia
+simulate_gbm = function(S0, μ, σ, T, n)
+    # Set the initial state
+    m = length(S0)
+    t = T / n
+    motion = zeros(m, n)
+    motion[:,1] = S0
+    
+    # Build out all states
+    for i=1:(n-1)
+        motion[:,i+1] = motion[:,i] .* exp((μ - σ^2/2)*t) .* exp(sqrt(t) * σ .* randn(m))
+    end
+    
+    return motion
+end
+
+function display_motion(motion, T)
+    # Given a matrix of paths, display the motion
+    n = length(motion[1,:])
+    m = length(motion[:,1])
+    x = repmat(1:n, m)
+    
+    # Calculate the ticks we're going to use. We'd like to
+    # have an xtick every month, so calculate where those
+    # ticks will actually be at.
+    if (T > 3)
+        num_ticks = T
+        xlabel = "Years"
+    else
+        num_ticks = T * 12
+        xlabel = "Months"
+    end
+    tick_width = n / num_ticks
+    x_ticks = []
+    for i=1:round(num_ticks)
+        x_ticks = vcat(x_ticks, i*tick_width)
+    end
+    
+    # Use one color for each path. I'm not sure if there's
+    # a better way to do this without going through DataFrames
+    colors = []
+    for i = 1:m
+        colors = vcat(colors, ones(n)*i)
+    end
+    
+    plot(x=x, y=motion', color=colors, Geom.line,
+    Guide.xticks(ticks=x_ticks, label=false),
+    Guide.xlabel(xlabel),
+    Guide.ylabel("Value"))
+end;
+```
+
+### Example simulation
+
+Let's go ahead and run a sample simulation to see what the functions got us!
+
+
+```julia
+initial = ones(5) * S0
+# Using μ=0, T=.25 for now, we'll use the proper values later
+motion = simulate_gbm(initial, 0, σ, .25, 200) 
+
+display_motion(motion, .25)
+```
+
+![](./2015-11-27-autocallable_6_0.svg)
+
+### Computing the term structure
+
+Now that we've got the basic motion set up, let's start making things a bit more sophisticated for the model. We're going to assume that the drift of the stock is the difference between the implied forward rate and the quarterly dividend rate.
+
+We're given the yearly term structure, and need to calculate the quarterly forward rate to match this structure. The term structure is assumed to follow:
+
+$d(0, t) = d(0,t-1)\cdot f_{i-1, i}$
+
+Where $f_{i-1, i}$ is the quarterly forward rate.
+
+
+```julia
+forward_term = function(yearly_term)
+    # It is assumed that we have a yearly term structure passed in, and starts at year 0
+    # This implies a nominal rate above 0 for the first year!
+    years = length(term)-1 # because we start at 0
+    structure = [(term[i+1] / term[i]) for i=1:years]
+end;
+```
+
+### Illustrating the term structure
+
+Now that we've got our term structure, let's validate that we're getting the correct results! If we've done this correctly, then:
+
+```
+term[2] == term[1] * structure[1]
+```
+
+
+```julia
+# Example term structure taken from:
+# http://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?data=yield
+# Linear interpolation used years in-between periods, assuming real-dollar
+# interest rates
+forward_yield = forward_term(term)
+calculated_term2 = term[1] * forward_yield[1]
+
+println("Actual term[2]: $(term[2]); Calculated term[2]: $(calculated_term2)")
+```
+
+```
+Actual term[2]: 1.0049; Calculated term[2]: 1.0049
+```
+
+### The full underlying simulation
+
+Now that we have the term structure set up, we can actually start doing some real simulation! Let's construct some paths through the full 5-year time frame. In order to do this, we will simulate 1 year at a time, and use the forward rates at those times to compute the drift. Thus, there will be 5 total simulations batched together.
+
+
+```julia
+full_motion = ones(5) * S0
+full_term = vcat(term[1], forward_yield)
+for i=1:T
+    μ = (full_term[i] - 1 - q)
+    year_motion = simulate_gbm(full_motion[:,end], μ, σ, 1, n)
+    full_motion = hcat(full_motion, year_motion)
+end
+
+display_motion(full_motion, T)
+```
+
+![](./2015-11-27-autocallable_12_0.svg)
+
+### Final simulation
+
+We're now going to actually build out the full motion that we'll use for computing the pricing of our autocallable products. It will be largely the same, but we will use far more sample paths for the simulation.
+
+
+```julia
+full_simulation = function(S0, T, n, m, term)
+    forward = vcat(term[1], forward_term(term))
+
+    # And an S0 to kick things off.
+    final_motion = ones(m) * S0
+    for i=1:T
+        μ = (forward[i] - 1 - q)
+        year_motion = simulate_gbm(final_motion[:,end], μ, σ, 1, n)
+        final_motion = hcat(final_motion, year_motion)
+    end
+    return final_motion
+end
+
+tic()
+full_simulation(S0, T, n, m, term)
+time = toq()
+@printf("Time to run simulation: %.2fs", time)
+```
+
+```
+Time to run simulation: 5.34s
+```
+
+## Athena Simulation
+
+Now that we've defined our underlying simulation, let's actually try and price an Athena note. Athena has the following characteristics:
+
+- Automatically called if the underlying is above the **call barrier** at observation
+- Accelerated coupon paid if the underlying is above the **call barrier** at observation
+    - The coupon paid is $c \cdot i$ with $i$ as the current year, and $c$ the coupon rate
+- Principle protection up until a **protection barrier** at observation; All principle at risk if this barrier not met
+- Observed yearly
+
+
+```julia
+call_barrier = S0
+strike = S0
+protection_barrier = S0 * .6
+coupon = nominal * .07
+
+price_athena = function(initial_price, year_prices, call_barrier,
+        protection_barrier, coupon, forward_structure)
+
+    total_coupons = 0
+    
+    t = length(year_prices)
+
+    for i=1:t
+        price = year_prices[i]
+        if price ≥ call_barrier
+            return (nominal + coupon*i) * exp((prod(forward_structure[i:end])-1)*(t-i))
+        end
+    end
+
+    # We've reached maturity, time to check capital protection
+    if year_prices[end] > protection_barrier
+        return nominal
+    else
+        put = (strike - year_prices[end]) / strike
+        return nominal*(1-put)
+    end
+end
+
+forward_structure = forward_term(term)
+price_function = (year_prices) -> price_athena(S0, year_prices,
+    call_barrier, protection_barrier, coupon, forward_structure)
+
+athena = function()
+    year_indexes = [n*i for i=1:T]
+    motion = full_simulation(S0, T, n, m, term)
+    payoffs = [price_function(motion[i, year_indexes]) for i=1:m]
+    return mean(payoffs)
+end
+
+mean_payoffs = zeros(num_simulations)
+for i=1:num_simulations
+    tic()
+    mean_payoffs[i] = athena()
+    time = toq()
+    @printf("Mean of simulation %i: \$%.4f; Simulation time: %.2fs\n", i, mean_payoffs[i], time)
+end
+
+final_mean = mean(mean_payoffs)
+println("Mean over $num_simulations simulations: $(mean(mean_payoffs))")
+pv = final_mean * (exp(-(prod(forward_structure)-1)*T))
+@printf("Present value of Athena note: \$%.2f, notional: \$%.2f", pv, nominal)
+```
+
+```
+Mean of simulation 1: $103.2805; Simulation time: 5.59s
+Mean of simulation 2: $103.3796; Simulation time: 5.05s
+Mean of simulation 3: $103.4752; Simulation time: 5.18s
+Mean of simulation 4: $103.4099; Simulation time: 5.37s
+Mean of simulation 5: $103.3260; Simulation time: 5.32s
+Mean over 5 simulations: 103.37421610015554
+Present value of Athena note: $95.00, notional: $100.00
+```
+
+## Phoenix without Memory Simulation
+
+Let's move into pricing a Phoenix without memory. It's very similar to the Athena production, with the exception that we introduce a coupon barrier so coupons are paid even when the underlying is below the initial price.
+
+The Phoenix product has the following characteristics (example [here](https://www.rbccm.com/usstructurednotes/file-780079.pdf)):
+
+- Automatically called if the underlying is above the **call barrier** at observation
+- Coupon paid if the underlying is above a **coupon barrier** at observation
+- Principle protection up until a **protection barrier** at observation; All principle at risk if this barrier not met
+- Observed yearly
+
+Some example paths (all assume that a call barrier of the current price, and coupon barrier some level below that):
+
+- At the end of year 1, the stock is above the call barrier; the note is called and you receive the value of the stock plus the coupon being paid.
+- At the end of year 1, the stock is above the coupon barrier, but not the call barrier; you receive the coupon. At the end of year 2, the stock is below the coupon barrier; you receive nothing. At the end of year 3, the stock is above the call barrier; the note is called and you receive the value of the stock plus a coupon for year 3.
+
+We're going to re-use the same simulation, with the following parameters:
+
+- Call barrier: 100%
+- Coupon barrier: 70%
+- Coupon: 6%
+- Capital protection until 70% (at maturity)
+
+
+```julia
+call_barrier = S0
+coupon_barrier = S0 * .8
+protection_barrier = S0 * .6
+coupon = nominal * .06
+
+price_phoenix_no_memory = function(initial_price, year_prices, call_barrier, coupon_barrier,
+        protection_barrier, coupon, forward_structure)
+
+    total_coupons = 0
+    t = length(year_prices)
+
+    for i=1:t
+        price = year_prices[i]
+        if price ≥ call_barrier
+            return (nominal + coupon + total_coupons)*exp((prod(forward_structure[i:end])-1)*(t-i))
+        elseif price ≥ coupon_barrier
+            total_coupons = total_coupons * exp(forward_structure[i]-1) + coupon
+        else
+            total_coupons *= exp(forward_structure[i]-1)
+        end
+    end
+
+    # We've reached maturity, time to check capital protection
+    if year_prices[end] > protection_barrier
+        return nominal + total_coupons
+    else
+        put = (strike - year_prices[end]) / strike
+        return nominal*(1-put)
+    end
+end
+
+forward_structure = forward_term(term)
+price_function = (year_prices) -> price_phoenix_no_memory(S0, year_prices,
+    call_barrier, coupon_barrier, protection_barrier, coupon, forward_structure)
+
+phoenix_no_memory = function()
+    year_indexes = [n*i for i=1:T]
+    motion = full_simulation(S0, T, n, m, term)
+    payoffs = [price_function(motion[i, year_indexes]) for i=1:m]
+    return mean(payoffs)
+end
+
+mean_payoffs = zeros(num_simulations)
+for i=1:num_simulations
+    tic()
+    mean_payoffs[i] = phoenix_no_memory()
+    time = toq()
+    @printf("Mean of simulation %i: \$%.4f; Simulation time: %.2fs\n", i, mean_payoffs[i], time)
+end
+
+final_mean = mean(mean_payoffs)
+println("Mean over $num_simulations simulations: $(mean(mean_payoffs))")
+pv = final_mean * exp(-(prod(forward_structure)-1)*(T))
+@printf("Present value of Phoenix without memory note: \$%.2f", pv)
+```
+
+```
+Mean of simulation 1: $106.0562; Simulation time: 5.72s
+Mean of simulation 2: $106.0071; Simulation time: 5.85s
+Mean of simulation 3: $105.9959; Simulation time: 5.87s
+Mean of simulation 4: $106.0665; Simulation time: 5.93s
+Mean of simulation 5: $106.0168; Simulation time: 5.81s
+Mean over 5 simulations: 106.02850857209883
+Present value of Phoenix without memory note: $97.44
+```
+
+## Phoenix with Memory Simulation
+
+The Phoenix with Memory structure is very similar to the Phoenix, but as the name implies, has a special "memory" property: **It remembers any coupons that haven't been paid at prior observation times, and pays them all if the underlying crosses the coupon barrier**. For example:
+- Note issued with 100% call barrier, 70% coupon barrier. At year 1, the underlying is at 50%, so no coupons are paid. At year 2, the underlying is at 80%, so coupons for both year 1 and 2 are paid, resulting in a double coupon.
+
+You can also find an example [here](https://www.rbccm.com/usstructurednotes/file-781232.pdf).
+
+Let's go ahead and set up the simulation! The parameters will be the same, but we can expect that the value will go up because of the memory attribute
+
+
+```julia
+call_barrier = S0
+coupon_barrier = S0 * .8
+protection_barrier = S0 * .6
+coupon = nominal * .07
+
+price_phoenix_with_memory = function(initial_price, year_prices, call_barrier,
+    coupon_barrier, protection_barrier, coupon, forward_structure)
+
+    last_coupon = 0
+    total_coupons = 0
+    
+    t = length(year_prices)
+
+    for i=1:t
+        price = year_prices[i]
+        if price > call_barrier
+            return (nominal + coupon + total_coupons)*exp((prod(forward_structure[i:end])-1)*(t-i))
+        elseif price > coupon_barrier
+            ####################################################################
+            # The only difference between with/without memory is the below lines
+            memory_coupons = (i - last_coupon) * coupon
+            last_coupon = i
+            total_coupons = total_coupons * exp(forward_structure[i]-1) + memory_coupons
+            ####################################################################
+        else
+            total_coupons *= exp(forward_structure[i]-1)
+        end
+    end
+
+    # We've reached maturity, time to check capital protection
+    if year_prices[end] > protection_barrier
+        return nominal + total_coupons
+    else
+        put = (strike - year_prices[end]) / strike
+        return nominal*(1-put)
+    end
+end
+
+forward_structure = forward_term(term)
+price_function = (year_prices) -> price_phoenix_with_memory(S0, year_prices,
+    call_barrier, coupon_barrier, protection_barrier, coupon, forward_structure)
+
+phoenix_with_memory = function()
+    year_indexes = [n*i for i=1:T]
+    motion = full_simulation(S0, T, n, m, term)
+    payoffs = [price_function(motion[i, year_indexes]) for i=1:m]
+    return mean(payoffs)
+end
+
+mean_payoffs = zeros(num_simulations)
+for i=1:num_simulations
+    tic()
+    mean_payoffs[i] = phoenix_with_memory()
+    time = toq()
+    @printf("Mean of simulation %i: \$%.4f; Simulation time: %.2fs\n",
+        i, mean_payoffs[i], time)
+end
+
+final_mean = mean(mean_payoffs)
+println("Mean over $num_simulations simulations: $(mean(mean_payoffs))")
+pv = final_mean * exp(-(prod(forward_structure)-1)*(T))
+@printf("Present value of Phoenix with memory note: \$%.2f", pv)
+```
+
+```
+Mean of simulation 1: $108.8612; Simulation time: 5.89s
+Mean of simulation 2: $109.0226; Simulation time: 5.90s
+Mean of simulation 3: $108.9175; Simulation time: 5.92s
+Mean of simulation 4: $108.9426; Simulation time: 5.94s
+Mean of simulation 5: $108.8087; Simulation time: 6.06s
+Mean over 5 simulations: 108.91052564051816
+Present value of Phoenix with memory note: $100.09
+```
diff --git a/blog/2015-11-27-autocallable/index.mdx b/blog/2015-11-27-autocallable/index.mdx
new file mode 100644
index 0000000..8f3ceac
--- /dev/null
+++ b/blog/2015-11-27-autocallable/index.mdx
@@ -0,0 +1,20 @@
+---
+title: Autocallable Bonds
+date: 2015-11-27
+slug: 2015/11/autocallable
+authors: [bspeice]
+tags: []
+---
+
+import Notebook from './_notebook.md'
+
+For a final project, my group was tasked with understanding three exotic derivatives: The Athena, Phoenix without memory, and Phoenix with memory autocallable products.
+
+<!-- truncate -->
+
+My only non-core class this semester has been in Structure Products. We've been surveying a wide variety of products, and the final project was to pick one to report on.
+Because these are all very similar, we decided to demonstrate all 3 products at once.
+
+What follows below is a notebook demonstrating the usage of [Julia](http://julialang.com) for Monte-Carlo simulation of some exotic products.
+
+<Notebook/>
\ No newline at end of file
diff --git a/docusaurus.config.ts b/docusaurus.config.ts
index f0b410d..048f86f 100644
--- a/docusaurus.config.ts
+++ b/docusaurus.config.ts
@@ -80,6 +80,7 @@ const config: Config = {
     prism: {
       theme: prismThemes.oneLight,
       darkTheme: prismThemes.oneDark,
+      additionalLanguages: ['julia']
     },
   } satisfies Preset.ThemeConfig,
   plugins: [require.resolve('docusaurus-lunr-search')],