M-S-H/2010 Cohort Analysis.ipynb

## 2010 Cohort Analysis.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2010 Cohort Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "using DataFrames, DataArrays, Gadfly, Colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "set_default_plot_size(985px, 600px)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = readtable(\"begfresh2.csv\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert NA values to 0 in semester enrollment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i=1:9\n",
    "    sym = symbol(\"SEM0$(i)\");\n",
    "    data[isna(data[sym]), sym] = 0;\n",
    "end\n",
    "\n",
    "for i=10:12\n",
    "    sym = symbol(\"SEM$(i)\");\n",
    "    data[isna(data[sym]), sym] = 0;\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert NA values to 0 in grads:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[isna(data[:GRAD4]), :GRAD4] = 0;\n",
    "data[isna(data[:GRAD5]), :GRAD5] = 0;\n",
    "data[isna(data[:GRAD6]), :GRAD6] = 0;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[isna(data[:BACDEG]), :BACDEG] = 0;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform semseter entered to a more usable value (ex: 20063 to 2006)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[:ENTRYSEM] .-=3;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[:ENTRYSEM] ./= 10;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "years = collect(2006:1:2010);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "year_names = [];\n",
    "for y in years\n",
    "    push!(year_names, \"$(y)\");\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "colors = [\"red\" \"blue\" \"green\" \"orange\" \"purple\"]# \"black\" \"gray\" \"lightseagreen\"];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "semesters = [];\n",
    "gpas = [];\n",
    "\n",
    "for i=1:9\n",
    "    sem = symbol(\"SEM0$(i)\");\n",
    "    gpa = symbol(\"CUMGPA0$(i)\");\n",
    "    push!(semesters, sem);\n",
    "    push!(gpas, gpa);\n",
    "end\n",
    "\n",
    "for i=10:12\n",
    "    sem = symbol(\"SEM$(i)\");\n",
    "    gpa = symbol(\"CUMGPA$(i)\");\n",
    "    push!(semesters, sem);\n",
    "    push!(gpas, gpa);\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retention Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "layers = Array(Layer, 0);\n",
    "\n",
    "for (i, year) in enumerate(years)\n",
    "    retention = [];\n",
    "    \n",
    "    d = data[data[:ENTRYSEM] .== year, :];\n",
    "    \n",
    "    for sem in semesters\n",
    "        push!(retention, mean(d[sem])*100);\n",
    "    end\n",
    "    \n",
    "    theme = Theme(default_color=parse(Colorant, colors[i]))\n",
    "    append!(layers, layer(x=semesters, y=retention, Geom.line, Geom.point, theme));\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "f:e,unbind:b(this.node||document,d,e,f||this)}));return this};a[\"un\"+d]=A[\"un\"+d]=function(a){for(var b=this.events||[],e=b.length;e--;)if(b[e].name==d&&(b[e].f==a||!a)){b[e].unbind();b.splice(e,1);!b.length&&delete this.events;break}return this}})(v[y]);A.hover=function(a,b,d,e){return this.mouseover(a,d).mouseout(b,e||d)};A.unhover=function(a,b){return this.unmouseover(a).unmouseout(b)};var r=[];A.drag=function(b,d,f,h,n,p){function u(r,v,w){(r.originalEvent||r).preventDefault();this._drag.x=v;\n",
       "this._drag.y=w;this._drag.id=r.identifier;!q.length&&a.mousemove(e).mouseup(l);q.push({el:this,move_scope:h,start_scope:n,end_scope:p});d&&k.on(\"snap.drag.start.\"+this.id,d);b&&k.on(\"snap.drag.move.\"+this.id,b);f&&k.on(\"snap.drag.end.\"+this.id,f);k(\"snap.drag.start.\"+this.id,n||h||this,v,w,r)}if(!arguments.length){var v;return this.drag(function(a,b){this.attr({transform:v+(v?\"T\":\"t\")+[a,b]})},function(){v=this.transform().local})}this._drag={};r.push({el:this,start:u});this.mousedown(u);return this};\n",
       "A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind(\"snap.drag.*.\"+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\\s*url\\((.+)\\)/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;\"svg\"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z(\"filter\");z(u,{id:k,filterUnits:\"userSpaceOnUse\"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on(\"snap.util.getattr.filter\",\n",
       "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(\"#img-7d4922df\");\n",
       "fig.select(\"#img-7d4922df-4\")\n",
       "   .drag(function() {}, function() {}, function() {});\n",
       "fig.select(\"#img-7d4922df-9\")\n",
       "   .init_gadfly();\n",
       "fig.select(\"#img-7d4922df-11\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-7d4922df-11\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-7d4922df-12\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-7d4922df-12\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-7d4922df-35\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-7d4922df-35\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-7d4922df-35\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#img-7d4922df-37\")\n",
       "   .data(\"max_pos\", 229.61)\n",
       ";\n",
       "fig.select(\"#img-7d4922df-37\")\n",
       "   .data(\"min_pos\", 212.61)\n",
       ";\n",
       "fig.select(\"#img-7d4922df-37\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#img-7d4922df-38\")\n",
       "   .data(\"max_pos\", 229.61)\n",
       ";\n",
       "fig.select(\"#img-7d4922df-38\")\n",
       "   .data(\"min_pos\", 212.61)\n",
       ";\n",
       "fig.select(\"#img-7d4922df-38\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-7d4922df-38\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-7d4922df-38\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ";\n",
       "fig.select(\"#img-7d4922df-39\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-7d4922df-39\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-7d4922df-39\")\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": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(\n",
    "    layers, \n",
    "    Guide.manual_color_key(\"Key\", year_names, colors),\n",
    "    Coord.Cartesian(ymin=10,ymax=100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layers = Array(Layer, 0);\n",
    "\n",
    "for (i, year) in enumerate(years)\n",
    "    retention = [];\n",
    "    \n",
    "    d = data[data[:ENTRYSEM] .== year, :];\n",
    "    \n",
    "    for sem in semesters\n",
    "        enrolled = d[d[sem] .== 1, :]\n",
    "        push!(retention, size(enrolled)[1]);\n",
    "    end\n",
    "    \n",
    "    theme = Theme(default_color=parse(Colorant, colors[i]))\n",
    "    append!(layers, layer(x=semesters, y=retention, Geom.line, Geom.point, theme));\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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       "f:e,unbind:b(this.node||document,d,e,f||this)}));return this};a[\"un\"+d]=A[\"un\"+d]=function(a){for(var b=this.events||[],e=b.length;e--;)if(b[e].name==d&&(b[e].f==a||!a)){b[e].unbind();b.splice(e,1);!b.length&&delete this.events;break}return this}})(v[y]);A.hover=function(a,b,d,e){return this.mouseover(a,d).mouseout(b,e||d)};A.unhover=function(a,b){return this.unmouseover(a).unmouseout(b)};var r=[];A.drag=function(b,d,f,h,n,p){function u(r,v,w){(r.originalEvent||r).preventDefault();this._drag.x=v;\n",
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       "A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind(\"snap.drag.*.\"+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\\s*url\\((.+)\\)/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;\"svg\"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z(\"filter\");z(u,{id:k,filterUnits:\"userSpaceOnUse\"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on(\"snap.util.getattr.filter\",\n",
       "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",
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       "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(\"#img-5221a0ee\");\n",
       "fig.select(\"#img-5221a0ee-4\")\n",
       "   .drag(function() {}, function() {}, function() {});\n",
       "fig.select(\"#img-5221a0ee-9\")\n",
       "   .init_gadfly();\n",
       "fig.select(\"#img-5221a0ee-11\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-11\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-12\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-12\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-35\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-35\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-35\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-37\")\n",
       "   .data(\"max_pos\", 229.61)\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-37\")\n",
       "   .data(\"min_pos\", 212.61)\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-37\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#img-5221a0ee-38\")\n",
       "   .data(\"max_pos\", 229.61)\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-38\")\n",
       "   .data(\"min_pos\", 212.61)\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-38\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-38\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-38\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-39\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-39\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-5221a0ee-39\")\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": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(\n",
    "    layers, \n",
    "    Guide.manual_color_key(\"Key\", year_names, colors))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Incoming Characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layers = Array(Layer, 0);\n",
    "layers_var = Array(Layer, 0);\n",
    "\n",
    "for (i, year) in enumerate(years)\n",
    "    d = data[data[:ENTRYSEM] .== year, :];\n",
    "    act = mean(d[:ACTCOMP]);\n",
    "    actvar = std(d[:ACTCOMP]);\n",
    "    hsgpa = mean(d[:HSGPA]);\n",
    "    hsgpavar = std(d[:HSGPA]);\n",
    "    \n",
    "    theme = Theme(default_color=parse(Colorant, colors[i]));\n",
    "    append!(layers, layer(x=[act], y=[hsgpa], Geom.point, theme));\n",
    "    append!(layers_var, layer(x=[actvar], y=[hsgpavar], Geom.point, theme));\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "                        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(\"#img-8113917b\");\n",
       "fig.select(\"#img-8113917b-4\")\n",
       "   .drag(function() {}, function() {}, function() {});\n",
       "fig.select(\"#img-8113917b-9\")\n",
       "   .init_gadfly();\n",
       "fig.select(\"#img-8113917b-11\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-8113917b-11\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-8113917b-12\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-8113917b-12\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-8113917b-30\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-8113917b-30\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-8113917b-30\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#img-8113917b-32\")\n",
       "   .data(\"max_pos\", 229.61)\n",
       ";\n",
       "fig.select(\"#img-8113917b-32\")\n",
       "   .data(\"min_pos\", 212.61)\n",
       ";\n",
       "fig.select(\"#img-8113917b-32\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#img-8113917b-33\")\n",
       "   .data(\"max_pos\", 229.61)\n",
       ";\n",
       "fig.select(\"#img-8113917b-33\")\n",
       "   .data(\"min_pos\", 212.61)\n",
       ";\n",
       "fig.select(\"#img-8113917b-33\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-8113917b-33\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-8113917b-33\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ";\n",
       "fig.select(\"#img-8113917b-34\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-8113917b-34\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-8113917b-34\")\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": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(\n",
    "    layers, \n",
    "    Guide.manual_color_key(\"Key\", year_names, colors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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