{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "using Plots\n",
    "x = [\"x1\", \"x2\"]\n",
    "y = [0.2, 0.7]\n",
    "bar(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "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",
       "\n",
       "\n",
       "// When the plot is moused over, emphasize the grid lines.\n",
       "Gadfly.plot_mouseover = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\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",
       "    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",
       "// Panning\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\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",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    root.data(\"dx\", 0);\n",
       "    root.data(\"dy\", 0);\n",
       "    init_pan_zoom(root);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onend = function(event) {\n",
       "    var root = this.plotroot();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_scroll = function(event) {\n",
       "    if (event.shiftKey) {\n",
       "        var root = this.plotroot();\n",
       "        init_pan_zoom(root);\n",
       "        var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n",
       "        new_scale = Math.max(\n",
       "            root.data(\"min_scale\"),\n",
       "            Math.min(root.data(\"max_scale\"), new_scale))\n",
       "        update_plot_scale(root, new_scale);\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",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "    var min_scale = root.data(\"min_scale\"),\n",
       "        scale = root.data(\"scale\");\n",
       "    Snap.animate(\n",
       "        scale,\n",
       "        Math.max(min_scale, scale / 1.5),\n",
       "        function (new_scale) {\n",
       "            update_plot_scale(root, new_scale);\n",
       "        },\n",
       "        200);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "    var max_scale = root.data(\"max_scale\"),\n",
       "        scale = root.data(\"scale\");\n",
       "\n",
       "    Snap.animate(\n",
       "        scale,\n",
       "        Math.min(max_scale, scale * 1.5),\n",
       "        function (new_scale) {\n",
       "            update_plot_scale(root, new_scale);\n",
       "        },\n",
       "        200);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_track_click = function(event) {\n",
       "    // TODO\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_mousedown = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_mouseup = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "// compute the position in [0, 1] of the zoom slider thumb from the current scale\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
       "    if (scale >= 1.0) {\n",
       "        return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n",
       "    }\n",
       "    else {\n",
       "        return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_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) {\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;\n",
       "    if (xpos >= 0.5) {\n",
       "        new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n",
       "    }\n",
       "    else {\n",
       "        new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n",
       "                        Math.log(min_scale));\n",
       "    }\n",
       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
       "\n",
       "    update_plot_scale(root, new_scale);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragstart = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "\n",
       "    // keep track of what the scale was when we started dragging\n",
       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragend = function(event) {\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",
       "\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",
       "\n",
       "\n",
       "// When the plot is moused over, emphasize the grid lines.\n",
       "Gadfly.plot_mouseover = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\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",
       "    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",
       "// Panning\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\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",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    root.data(\"dx\", 0);\n",
       "    root.data(\"dy\", 0);\n",
       "    init_pan_zoom(root);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onend = function(event) {\n",
       "    var root = this.plotroot();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_scroll = function(event) {\n",
       "    if (event.shiftKey) {\n",
       "        var root = this.plotroot();\n",
       "        init_pan_zoom(root);\n",
       "        var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n",
       "        new_scale = Math.max(\n",
       "            root.data(\"min_scale\"),\n",
       "            Math.min(root.data(\"max_scale\"), new_scale))\n",
       "        update_plot_scale(root, new_scale);\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",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "    var min_scale = root.data(\"min_scale\"),\n",
       "        scale = root.data(\"scale\");\n",
       "    Snap.animate(\n",
       "        scale,\n",
       "        Math.max(min_scale, scale / 1.5),\n",
       "        function (new_scale) {\n",
       "            update_plot_scale(root, new_scale);\n",
       "        },\n",
       "        200);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "    var max_scale = root.data(\"max_scale\"),\n",
       "        scale = root.data(\"scale\");\n",
       "\n",
       "    Snap.animate(\n",
       "        scale,\n",
       "        Math.min(max_scale, scale * 1.5),\n",
       "        function (new_scale) {\n",
       "            update_plot_scale(root, new_scale);\n",
       "        },\n",
       "        200);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_track_click = function(event) {\n",
       "    // TODO\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_mousedown = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_mouseup = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "// compute the position in [0, 1] of the zoom slider thumb from the current scale\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
       "    if (scale >= 1.0) {\n",
       "        return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n",
       "    }\n",
       "    else {\n",
       "        return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_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) {\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;\n",
       "    if (xpos >= 0.5) {\n",
       "        new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n",
       "    }\n",
       "    else {\n",
       "        new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n",
       "                        Math.log(min_scale));\n",
       "    }\n",
       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
       "\n",
       "    update_plot_scale(root, new_scale);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragstart = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "\n",
       "    // keep track of what the scale was when we started dragging\n",
       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragend = function(event) {\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\", \"Gadfly\"], function (Snap, Gadfly, Gadfly) {\n",
       "            factory(Snap, Gadfly, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly, Gadfly) {\n",
       "    var fig = Snap(\"#fig-9980017814cd4e328fef5f3b11a24989\");\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-4\")\n",
       "   .drag(function() {}, function() {}, function() {});\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-8\")\n",
       "   .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",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-11\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-11\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-12\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-12\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-16\")\n",
       "   .data(\"mouseover_color\", \"RGB{U8}(0.804,0.361,0.361)\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-16\")\n",
       "   .data(\"mouseout_color\", \"RGB{U8}(0.416,0.416,0.416)\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-16\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-18\")\n",
       "   .data(\"max_pos\", 113.75)\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-18\")\n",
       "   .data(\"min_pos\", 96.75)\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-18\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-19\")\n",
       "   .data(\"max_pos\", 113.75)\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-19\")\n",
       "   .data(\"min_pos\", 96.75)\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-19\")\n",
       "   .data(\"mouseover_color\", \"RGB{U8}(0.804,0.361,0.361)\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-19\")\n",
       "   .data(\"mouseout_color\", \"RGB{U8}(0.416,0.416,0.416)\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-19\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ".mousedown(Gadfly.zoomslider_thumb_mousedown)\n",
       ".mouseup(Gadfly.zoomslider_thumb_mouseup)\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-20\")\n",
       "   .data(\"mouseover_color\", \"RGB{U8}(0.804,0.361,0.361)\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-20\")\n",
       "   .data(\"mouseout_color\", \"RGB{U8}(0.416,0.416,0.416)\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-20\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-26\")\n",
       "   .drag(function() {}, function() {}, function() {});\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-30\")\n",
       "   .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",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-33\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-33\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-34\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-34\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-38\")\n",
       "   .data(\"mouseover_color\", \"RGB{U8}(0.804,0.361,0.361)\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-38\")\n",
       "   .data(\"mouseout_color\", \"RGB{U8}(0.416,0.416,0.416)\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-38\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-40\")\n",
       "   .data(\"max_pos\", 113.75)\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-40\")\n",
       "   .data(\"min_pos\", 96.75)\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-40\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-41\")\n",
       "   .data(\"max_pos\", 113.75)\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-41\")\n",
       "   .data(\"min_pos\", 96.75)\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-41\")\n",
       "   .data(\"mouseover_color\", \"RGB{U8}(0.804,0.361,0.361)\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-41\")\n",
       "   .data(\"mouseout_color\", \"RGB{U8}(0.416,0.416,0.416)\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-41\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ".mousedown(Gadfly.zoomslider_thumb_mousedown)\n",
       ".mouseup(Gadfly.zoomslider_thumb_mouseup)\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-42\")\n",
       "   .data(\"mouseover_color\", \"RGB{U8}(0.804,0.361,0.361)\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-42\")\n",
       "   .data(\"mouseout_color\", \"RGB{U8}(0.416,0.416,0.416)\")\n",
       ";\n",
       "fig.select(\"#fig-9980017814cd4e328fef5f3b11a24989-element-42\")\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": [
       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xscalable yscalable\")]),Compose.ListNode{Compose.ComposeNode}(Compose.Table(2x3 Array{Array{Compose.Context,1},2}:\n",
       " [Context(AdhocContainerPromise)]  …  [Context(AdhocContainerPromise)]\n",
       " []                                   []                              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xscalable yscalable\")]),Compose.ListNode{Compose.ComposeNode}(Compose.Table(2x3 Array{Array{Compose.Context,1},2}:\n",
       " [Context(AdhocContainerPromise)]  …  [Context(AdhocContainerPromise)]\n",
       " []                                   []                              ,2:2,1:1,nothing,nothing,nothing,Any[],Compose.UnitBox{Float64,Float64,Float64,Float64}(0.0,1.0,100.0,-1.0,Compose.Measure{Compose.MeasureNil,Compose.MeasureNil}(2.0,Compose.MeasureNil(),Compose.MeasureNil(),0.0,0.0),Compose.Measure{Compose.MeasureNil,Compose.MeasureNil}(2.0,Compose.MeasureNil(),Compose.MeasureNil(),0.0,0.0),Compose.Measure{Compose.MeasureNil,Compose.MeasureNil}(2.0,Compose.MeasureNil(),Compose.MeasureNil(),0.0,0.0),Compose.Measure{Compose.MeasureNil,Compose.MeasureNil}(2.0,Compose.MeasureNil(),Compose.MeasureNil(),0.0,0.0)),0,false,false),Compose.ListNull{Compose.ComposeNode}()))),0,false,false,false,false,nothing,nothing,0.0,symbol(\"\")),Compose.ListNull{Compose.ComposeNode}())),0,false,false,false,false,nothing,nothing,0.0,symbol(\"\")),Compose.ListNull{Compose.ComposeNode}()),0,false,false,false,false,nothing,nothing,0.0,symbol(\"\")),Compose.ListNull{Compose.ComposeNode}())),0,false,false,false,false,nothing,nothing,0.0,symbol(\"\"))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Plots; using Compose\n",
    "p1 = plot(rand(10))\n",
    "p2 = scatter(rand(100))\n",
    "compose(context(),\n",
    "    (context(0.6,0,0.4,0.4), Gadfly.render(p2.o[2])),\n",
    "    (context(0,0,1,1), Gadfly.render(p1.o[2])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "using Plots\n",
    "import Contour\n",
    "default(size=(500,300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "typeof(cs) = Array{Contour.ContourLevel,1}"
     ]
    }
   ],
   "source": [
    "n = 100\n",
    "x = sort(randn(n)); y = sort(randn(n))\n",
    "cs = Contour.contours(x, y, x * y', 5)\n",
    "@show typeof(cs) length(cs)\n",
    "#xys = [Contour.coordinates(c.lines) for c in cs]\n",
    "for clevel in cs\n",
    "    @show length(clevel.lines)\n",
    "    #for (x,y) in Contour.coordinates(clevel.lines[1])\n",
    "    #    @show x y\n",
    "    #end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = [0.996311623950869,1.014779586168885,1.0619429011645203,1.0705643632344357,1.0757346819029472,1.2352537493424733,1.3083367492687343,1.3362429344485531,1.3382013214101371,1.381189378592954,1.387758007376257,1.4330616775674128,1.4330873131641844,1.4802974364117303,1.5983182860133243,1.7395968844958194,1.8898088744099915,1.9983557145685487,2.0028308618853106,2.0112886298409127,2.0289470759898443,2.074888219238593,2.10414834798257,2.1510008635888065,2.2151888265553907,2.241346798167681,2.3299639292951215,2.3944690872276184,2.593030479782728,2.6035782354641266,2.620319713774897,2.6412138619889283,2.69457070468019,2.712098273036215,2.844711254707502,2.9183936196692386]"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Plot{Plots.ImmersePackage() n=1}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "y = [-1.9760705052937122,-1.9401080205046,-1.8539433824658917,-1.8390132174981266,-1.8301743425134045,-1.5938280011038843,-1.5047976106082777,-1.4733713185043547,-1.4712151173905417,-1.4254251043953374,-1.4186781871955039,-1.3738292252100077,-1.3738046496438667,-1.3299908286965345,-1.2317834510179533,-1.1317461141241336,-1.0417889559256392,-0.9852009828969108,-0.9829996389796655,-0.9788659792335812,-0.9703466578644496,-0.9488617246538046,-0.9356669248431247,-0.9152864824451101,-0.8887648721269691,-0.8783924093228624,-0.8449839027191308,-0.8222206854422591,-0.7592591099567633,-0.7561831587594401,-0.7513518307788163,-0.7454080271591599,-0.7306477468752341,-0.7259257652070719,-0.6920850089486591,-0.6746115400271908]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x, y = Contour.coordinates(cs[2].lines[2])\n",
    "@show x y\n",
    "plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Nothing to be done\n",
      "INFO: METADATA is out-of-date — you may not have the latest version of GeometricalPredicates\n",
      "INFO: Use `Pkg.update()` to get the latest versions of your packages\n",
      "INFO: Cloning VoronoiDelaunay from https://github.com/JuliaGeometry/VoronoiDelaunay.jl\n",
      "INFO: Computing changes...\n",
      "INFO: No packages to install, update or remove\n",
      "INFO: Package database updated\n"
     ]
    }
   ],
   "source": [
    "Pkg.add(\"GeometricalPredicates\")\n",
    "Pkg.clone(\"https://github.com/JuliaGeometry/VoronoiDelaunay.jl\")\n",
    "Pkg.add(\"Contour\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Plots.jl] Switched to backend: gadfly"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Plot{Plots.GadflyPackage() n=1}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Plots.jl] Initializing backend: gadfly\n"
     ]
    }
   ],
   "source": [
    "using Plots; gadfly()\n",
    "default(size=(500,300))\n",
    "n = 100\n",
    "x = randn(n)*3\n",
    "y = randn(n)*3\n",
    "z = Float64[sin(x[i]) + cos(y[i]) for i in 1:n]\n",
    "scatter(x,y,z=z,c=:heat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zippoints (generic function with 2 methods)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using VoronoiDelaunay\n",
    "tess = DelaunayTessellation(n)\n",
    "tmin, tmax = min_coord, max_coord\n",
    "twidth = tmax - tmin\n",
    "function squash(a)\n",
    "    amin, amax = extrema(a)\n",
    "    v = similar(a)\n",
    "    for i in eachindex(a)\n",
    "        v[i] = tmin + twidth * (a[i] - amin) / (amax - amin)\n",
    "    end\n",
    "    v\n",
    "end\n",
    "function zippoints(x, y)\n",
    "    x, y = squash(x), squash(y)\n",
    "    Point2D[Point(x[i], y[i]) for i in eachindex(x)]\n",
    "end\n",
    "function zippoints(x, y, z)\n",
    "    x, y, z = squash(x), squash(y), squash(z)\n",
    "    Point3D[Point(x[i], y[i], z[i]) for i in eachindex(x)]\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Base.Uint64 is deprecated, use UInt64 instead.\n",
      "WARNING: Base.Uint64 is deprecated, use UInt64 instead.\n",
      "WARNING: Base.Uint64 is deprecated, use UInt64 instead.\n",
      "WARNING: int(x::AbstractFloat) is deprecated, use round(Int,x) instead.\n",
      " in depwarn at deprecated.jl:73\n",
      " in int at deprecated.jl:50\n",
      " in _mssort! at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:1133\n",
      " in push! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:634\n",
      " in include_string at loading.jl:266\n",
      " in execute_request_0x535c5df2 at /Users/tom/.julia/v0.4/IJulia/src/execute_request.jl:177\n",
      " in eventloop at /Users/tom/.julia/v0.4/IJulia/src/IJulia.jl:141\n",
      " in anonymous at task.jl:447\n",
      "while loading In[5], in expression starting on line 2\n",
      "WARNING: int(x::AbstractFloat) is deprecated, use round(Int,x) instead.\n",
      " in depwarn at deprecated.jl:73\n",
      " in int at deprecated.jl:50\n",
      " in _mssort! at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:1133\n",
      " in push! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:634\n",
      " in include_string at loading.jl:266\n",
      " in execute_request_0x535c5df2 at /Users/tom/.julia/v0.4/IJulia/src/execute_request.jl:177\n",
      " in eventloop at /Users/tom/.julia/v0.4/IJulia/src/IJulia.jl:141\n",
      " in anonymous at task.jl:447\n",
      "while loading In[5], in expression starting on line 2\n",
      "WARNING: int64(x) is deprecated, use Int64(x) instead.\n",
      " in depwarn at deprecated.jl:73\n",
      " in int64 at deprecated.jl:50\n",
      " in _exact_intriangle! at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:576\n",
      " in _exact_intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:648\n",
      " in _sz_intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:672\n",
      " in intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:701\n",
      " in findindex at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:268\n",
      " in _pushunfixed! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:282\n",
      " in _pushunsorted! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:627\n",
      " in push! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:635\n",
      " in include_string at loading.jl:266\n",
      " in execute_request_0x535c5df2 at /Users/tom/.julia/v0.4/IJulia/src/execute_request.jl:177\n",
      " in eventloop at /Users/tom/.julia/v0.4/IJulia/src/IJulia.jl:141\n",
      " in anonymous at task.jl:447\n",
      "while loading In[5], in expression starting on line 2\n",
      "WARNING: int64(x) is deprecated, use Int64(x) instead.\n",
      " in depwarn at deprecated.jl:73\n",
      " in int64 at deprecated.jl:50\n",
      " in _exact_intriangle! at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:577\n",
      " in _exact_intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:648\n",
      " in _sz_intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:672\n",
      " in intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:701\n",
      " in findindex at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:268\n",
      " in _pushunfixed! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:282\n",
      " in _pushunsorted! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:627\n",
      " in push! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:635\n",
      " in include_string at loading.jl:266\n",
      " in execute_request_0x535c5df2 at /Users/tom/.julia/v0.4/IJulia/src/execute_request.jl:177\n",
      " in eventloop at /Users/tom/.julia/v0.4/IJulia/src/IJulia.jl:141\n",
      " in anonymous at task.jl:447\n",
      "while loading In[5], in expression starting on line 2\n",
      "WARNING: int64(x) is deprecated, use Int64(x) instead.\n",
      " in depwarn at deprecated.jl:73\n",
      " in int64 at deprecated.jl:50\n",
      " in _exact_intriangle! at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:580\n",
      " in _exact_intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:648\n",
      " in _sz_intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:672\n",
      " in intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:701\n",
      " in findindex at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:268\n",
      " in _pushunfixed! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:282\n",
      " in _pushunsorted! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:627\n",
      " in push! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:635\n",
      " in include_string at loading.jl:266\n",
      " in execute_request_0x535c5df2 at /Users/tom/.julia/v0.4/IJulia/src/execute_request.jl:177\n",
      " in eventloop at /Users/tom/.julia/v0.4/IJulia/src/IJulia.jl:141\n",
      " in anonymous at task.jl:447\n",
      "while loading In[5], in expression starting on line 2\n",
      "WARNING: int64(x) is deprecated, use Int64(x) instead.\n",
      " in depwarn at deprecated.jl:73\n",
      " in int64 at deprecated.jl:50\n",
      " in _exact_intriangle! at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:584\n",
      " in _exact_intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:648\n",
      " in _sz_intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:672\n",
      " in intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:701\n",
      " in findindex at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:268\n",
      " in _pushunfixed! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:282\n",
      " in _pushunsorted! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:627\n",
      " in push! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:635\n",
      " in include_string at loading.jl:266\n",
      " in execute_request_0x535c5df2 at /Users/tom/.julia/v0.4/IJulia/src/execute_request.jl:177\n",
      " in eventloop at /Users/tom/.julia/v0.4/IJulia/src/IJulia.jl:141\n",
      " in anonymous at task.jl:447\n",
      "while loading In[5], in expression starting on line 2\n",
      "WARNING: int64(x) is deprecated, use Int64(x) instead.\n",
      " in depwarn at deprecated.jl:73\n",
      " in int64 at deprecated.jl:50\n",
      " in _exact_intriangle! at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:589\n",
      " in _exact_intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:648\n",
      " in _sz_intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:672\n",
      " in intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:701\n",
      " in findindex at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:268\n",
      " in _pushunfixed! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:282\n",
      " in _pushunsorted! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:627\n",
      " in push! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:635\n",
      " in include_string at loading.jl:266\n",
      " in execute_request_0x535c5df2 at /Users/tom/.julia/v0.4/IJulia/src/execute_request.jl:177\n",
      " in eventloop at /Users/tom/.julia/v0.4/IJulia/src/IJulia.jl:141\n",
      " in anonymous at task.jl:447\n",
      "while loading In[5], in expression starting on line 2\n",
      "WARNING: int64(x) is deprecated, use Int64(x) instead.\n",
      " in depwarn at deprecated.jl:73\n",
      " in int64 at deprecated.jl:50\n",
      " in _exact_intriangle! at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:592\n",
      " in _exact_intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:648\n",
      " in _sz_intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:672\n",
      " in intriangle at /Users/tom/.julia/v0.4/GeometricalPredicates/src/GeometricalPredicates.jl:701\n",
      " in findindex at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:268\n",
      " in _pushunfixed! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:282\n",
      " in _pushunsorted! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:627\n",
      " in push! at /Users/tom/.julia/v0.4/VoronoiDelaunay/src/VoronoiDelaunay.jl:635\n",
      " in include_string at loading.jl:266\n",
      " in execute_request_0x535c5df2 at /Users/tom/.julia/v0.4/IJulia/src/execute_request.jl:177\n",
      " in eventloop at /Users/tom/.julia/v0.4/IJulia/src/IJulia.jl:141\n",
      " in anonymous at task.jl:447\n",
      "while loading In[5], in expression starting on line 2\n"
     ]
    }
   ],
   "source": [
    "a = zippoints(x, y)\n",
    "push!(tess, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for tri in tess\n",
    "    println(tri)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "Julia 0.4.0-rc4",
   "language": "julia",
   "name": "julia-0.4"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.4.0"
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 },
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 "nbformat_minor": 0
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