kietdlam/planner

## planner
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    {
     "data": {
      "text/plain": [
       "fast_mpc_controller! (generic function with 1 method)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "include(\"../KinematicMPC.jl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "path_d2c (generic function with 1 method)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "include(\"../Utilities.jl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "convert_to_continuous (generic function with 1 method)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "include(\"../GraphPlanner.jl\")"
   ]
  },
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       "    function createElem(tag, attr, content) {\n",
       "\t// TODO: remove jQuery dependency\n",
       "\tvar el = $(\"<\" + tag + \"/>\").attr(attr);\n",
       "\tif (content) {\n",
       "\t    el.append(content);\n",
       "\t}\n",
       "\treturn el[0];\n",
       "    }\n",
       "\n",
       "    // A widget must expose an id field which identifies it to the backend,\n",
       "    // an elem attribute which is will be added to the DOM, and\n",
       "    // a getState() method which returns the value to be sent to the backend\n",
       "    // a sendUpdate() method which sends its current value to the backend\n",
       "    var Widget = {\n",
       "\tid: undefined,\n",
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       "\tlabel: undefined,\n",
       "\tgetState: function () {\n",
       "\t    return this.elem.value;\n",
       "\t},\n",
       "\tsendUpdate: undefined\n",
       "    };\n",
       "\n",
       "    var Slider = function (typ, id, init) {\n",
       "\tvar attr = { type:  \"range\",\n",
       "\t\t     value: init.value,\n",
       "\t\t     min:   init.min,\n",
       "\t\t     max:   init.max,\n",
       "\t\t     step:  init.step },\n",
       "\t    elem = createElem(\"input\", attr),\n",
       "\t    self = this;\n",
       "\n",
       "\telem.onchange = function () {\n",
       "\t    self.sendUpdate();\n",
       "\t}\n",
       "\n",
       "\tthis.id = id;\n",
       "\tthis.elem = elem;\n",
       "\tthis.label = init.label;\n",
       "\n",
       "\tInputWidgets.commInitializer(this); // Initialize communication\n",
       "    }\n",
       "    Slider.prototype = Widget;\n",
       "\n",
       "    var Checkbox = function (typ, id, init) {\n",
       "\tvar attr = { type: \"checkbox\",\n",
       "\t\t     checked: init.value },\n",
       "\t    elem = createElem(\"input\", attr),\n",
       "\t    self = this;\n",
       "\n",
       "\tthis.getState = function () {\n",
       "\t    return elem.checked;\n",
       "\t}\n",
       "\telem.onchange = function () {\n",
       "\t    self.sendUpdate();\n",
       "\t}\n",
       "\n",
       "\tthis.id = id;\n",
       "\tthis.elem = elem;\n",
       "\tthis.label = init.label;\n",
       "\n",
       "\tInputWidgets.commInitializer(this);\n",
       "    }\n",
       "    Checkbox.prototype = Widget;\n",
       "\n",
       "    var Button = function (typ, id, init) {\n",
       "\tvar attr = { type:    \"button\",\n",
       "\t\t     value:   init.label },\n",
       "\t    elem = createElem(\"input\", attr),\n",
       "\t    self = this;\n",
       "\tthis.getState = function () {\n",
       "\t    return null;\n",
       "\t}\n",
       "\telem.onclick = function () {\n",
       "\t    self.sendUpdate();\n",
       "\t}\n",
       "\n",
       "\tthis.id = id;\n",
       "\tthis.elem = elem;\n",
       "\tthis.label = init.label;\n",
       "\n",
       "\tInputWidgets.commInitializer(this);\n",
       "    }\n",
       "    Button.prototype = Widget;\n",
       "\n",
       "    var Text = function (typ, id, init) {\n",
       "\tvar attr = { type:  \"text\",\n",
       "\t\t     placeholder: init.label,\n",
       "\t\t     value: init.value },\n",
       "\t    elem = createElem(\"input\", attr),\n",
       "\t    self = this;\n",
       "\tthis.getState = function () {\n",
       "\t    return elem.value;\n",
       "\t}\n",
       "\telem.onkeyup = function () {\n",
       "\t    self.sendUpdate();\n",
       "\t}\n",
       "\n",
       "\tthis.id = id;\n",
       "\tthis.elem = elem;\n",
       "\tthis.label = init.label;\n",
       "\n",
       "\tInputWidgets.commInitializer(this);\n",
       "    }\n",
       "    Text.prototype = Widget;\n",
       "\n",
       "    var Textarea = function (typ, id, init) {\n",
       "\tvar attr = { placeholder: init.label },\n",
       "\t    elem = createElem(\"textarea\", attr, init.value),\n",
       "\t    self = this;\n",
       "\tthis.getState = function () {\n",
       "\t    return elem.value;\n",
       "\t}\n",
       "\telem.onchange = function () {\n",
       "\t    self.sendUpdate();\n",
       "\t}\n",
       "\n",
       "\tthis.id = id;\n",
       "\tthis.elem = elem;\n",
       "\tthis.label = init.label;\n",
       "\n",
       "\tInputWidgets.commInitializer(this);\n",
       "    }\n",
       "    Textarea.prototype = Widget;\n",
       "\n",
       "    // RadioButtons\n",
       "    // Dropdown\n",
       "    // HTML\n",
       "    // Latex\n",
       "\n",
       "    var InputWidgets = {\n",
       "\tSlider: Slider,\n",
       "\tCheckbox: Checkbox,\n",
       "\tButton: Button,\n",
       "\tText: Text,\n",
       "\tTextarea: Textarea,\n",
       "\tdebug: false,\n",
       "\tlog: function () {\n",
       "\t    if (InputWidgets.debug) {\n",
       "\t\tconsole.log.apply(console, arguments);\n",
       "\t    }\n",
       "\t},\n",
       "\t// a central way to initalize communication\n",
       "\t// for widgets.\n",
       "\tcommInitializer: function (widget) {\n",
       "\t    widget.sendUpdate = function () {};\n",
       "\t}\n",
       "    };\n",
       "\n",
       "    window.InputWidgets = InputWidgets;\n",
       "\n",
       "})(jQuery, undefined);\n",
       "</script>"
      ]
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     "output_type": "display_data"
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       "<script charset=\"utf-8\">(function (IPython, $, _, MathJax, Widgets) {\n",
       "    $.event.special.destroyed = {\n",
       "\tremove: function(o) {\n",
       "\t    if (o.handler) {\n",
       "\t\to.handler.apply(this, arguments)\n",
       "\t    }\n",
       "\t}\n",
       "    }\n",
       "\n",
       "    var redrawValue = function (container, type, val) {\n",
       "\tvar selector = $(\"<div/>\");\n",
       "\tvar oa = new IPython.OutputArea(_.extend(selector, {\n",
       "\t    selector: selector,\n",
       "\t    prompt_area: true,\n",
       "\t    events: IPython.events,\n",
       "\t    keyboard_manager: IPython.keyboard_manager\n",
       "\t})); // Hack to work with IPython 2.1.0\n",
       "\n",
       "\tswitch (type) {\n",
       "\tcase \"image/png\":\n",
       "            var _src = 'data:' + type + ';base64,' + val;\n",
       "\t    $(container).find(\"img\").attr('src', _src);\n",
       "\t    break;\n",
       "\tdefault:\n",
       "\t    var toinsert = IPython.OutputArea.append_map[type].apply(\n",
       "\t\toa, [val, {}, selector]\n",
       "\t    );\n",
       "\t    $(container).empty().append(toinsert.contents());\n",
       "\t    selector.remove();\n",
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       "    }\n",
       "\n",
       "\n",
       "    $(document).ready(function() {\n",
       "\tWidgets.debug = false; // log messages etc in console.\n",
       "\tfunction initComm(evt, data) {\n",
       "\t    var comm_manager = data.kernel.comm_manager;\n",
       "        //_.extend(comm_manager.targets, require(\"widgets/js/widget\"))\n",
       "\t    comm_manager.register_target(\"Signal\", function (comm) {\n",
       "            comm.on_msg(function (msg) {\n",
       "                //Widgets.log(\"message received\", msg);\n",
       "                var val = msg.content.data.value;\n",
       "                $(\".signal-\" + comm.comm_id).each(function() {\n",
       "                var type = $(this).data(\"type\");\n",
       "                if (val[type]) {\n",
       "                    redrawValue(this, type, val[type], type);\n",
       "                }\n",
       "                });\n",
       "                delete val;\n",
       "                delete msg.content.data.value;\n",
       "            });\n",
       "\t    });\n",
       "\n",
       "\t    // coordingate with Comm and redraw Signals\n",
       "\t    // XXX: Test using Reactive here to improve performance\n",
       "\t    $([IPython.events]).on(\n",
       "\t\t'output_appended.OutputArea', function (event, type, value, md, toinsert) {\n",
       "\t\t    if (md && md.reactive) {\n",
       "                // console.log(md.comm_id);\n",
       "                toinsert.addClass(\"signal-\" + md.comm_id);\n",
       "                toinsert.data(\"type\", type);\n",
       "                // Signal back indicating the mimetype required\n",
       "                var comm_manager = IPython.notebook.kernel.comm_manager;\n",
       "                var comm = comm_manager.comms[md.comm_id];\n",
       "                comm.then(function (c) {\n",
       "                    c.send({action: \"subscribe_mime\",\n",
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       "                    toinsert.bind(\"destroyed\", function() {\n",
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       "                               mime: type});\n",
       "                    });\n",
       "                })\n",
       "\t\t    }\n",
       "\t    });\n",
       "\t}\n",
       "\n",
       "\ttry {\n",
       "\t    // try to initialize right away. otherwise, wait on the status_started event.\n",
       "\t    initComm(undefined, IPython.notebook);\n",
       "\t} catch (e) {\n",
       "\t    $([IPython.events]).on('status_started.Kernel', initComm);\n",
       "\t}\n",
       "    });\n",
       "})(IPython, jQuery, _, MathJax, InputWidgets);\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using Gadfly\n",
    "using DataStructures\n",
    "using Compose\n",
    "using Ipopt\n",
    "using JuMP\n",
    "using Reactive\n",
    "using Interact\n",
    "using Base.Collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "set_default_plot_size(10*1inch, 5*1inch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(anonymous function)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second = xs -> xs[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "tags": [
     "worksheet-0"
    ]
   },
   "source": [
    "### A* Path Planner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "tags": [
     "worksheet-0"
    ]
   },
   "source": [
    "### Case 1: A* + MPC for stationary obstacles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start point: PlainPoint(11,8)\n",
      "goal point: PlainPoint(20,22)\n"
     ]
    }
   ],
   "source": [
    "srand(9474153)\n",
    "square_dimension = 15.0\n",
    "width = 25\n",
    "height = 25\n",
    "width_half = round(width/2)\n",
    "height_half = round(height/2)\n",
    "goal_point = PlainPoint(rand(width_half:width), rand(height_half:height))\n",
    "start_point = PlainPoint(rand(1:width_half), rand(1:height_half))\n",
    "\n",
    "println(\"start point: \", start_point)\n",
    "println(\"goal point: \", goal_point)\n",
    "\n",
    "obstacles = Obstacle[]\n",
    "n_obstacles = 15\n",
    "ob_width = 3\n",
    "ob_height = 3\n",
    "\n",
    "n_horizon = 8\n",
    "\n",
    "for n in 1:n_obstacles\n",
    "    start_x = rand(1:width - ob_width)\n",
    "    start_y = rand(1:height - ob_height)\n",
    "    end_x = rand(start_x+1:start_x+rand(2:ob_width))\n",
    "    end_y = rand(start_y+1:start_y+rand(2:ob_height))\n",
    "    ob = Obstacle(start_x, end_x, start_y, end_y)\n",
    "    \n",
    "    # checks to see if the obstacle is in the start or end position\n",
    "    if is_legal(width, height, [ob], goal_point) && is_legal(width, height, [ob], start_point)\n",
    "        push!(obstacles, ob)\n",
    "    end\n",
    "end\n",
    "\n",
    "node_eval = node -> simple_node_eval(goal_point, node)\n",
    "empty_q = PriorityQueue()\n",
    "start_node = construct_node(nothing, nothing, start_point, 0)\n",
    "\n",
    "successor_f = s -> point_successor(obstacles, width, height, s)\n",
    "\n",
    "res = universal_search(goal_function(goal_point), successor_f, node_eval, start_node, empty_q,\n",
    "                        Set{PlainPoint}())\n",
    "\n",
    "if size(res, 1) == 0\n",
    "    println(\"NO RESULT!!!\")\n",
    "end\n",
    "\n",
    "\n",
    "# prepend!(res, [start_point])\n",
    "\n",
    "res_cont = convert_to_continuous(square_dimension, res)\n",
    "\n",
    "# mm = zeros(width, height)\n",
    "# mm[goal_point.x, goal_point.y] = 320\n",
    "# pp_to_points!(mm, res)\n",
    "# mm[start_point.x, start_point.y] = 3\n",
    "\n",
    "mm_obstacles = zeros(width, height)\n",
    "mm_obstacles[start_point.x, start_point.y] = 3\n",
    "mm_obstacles[goal_point.x, goal_point.y] = 1\n",
    "\n",
    "for obstacle in obstacles\n",
    "    for j in obstacle.start_y:obstacle.end_y\n",
    "        for i in obstacle.start_x:obstacle.end_x\n",
    "            if i > 0 && i < width && j > 0 && j < height\n",
    "#                 mm[i, j] = 2\n",
    "                mm_obstacles[i, j] = 2\n",
    "#             else\n",
    "#                 println(\"Obstacle out of bounds!!!: \", obstacle)\n",
    "            end\n",
    "        end\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
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    {
     "data": {
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      "text/plain": [
       "Image{PNGBackend}(IOBuffer(data=Uint8[...], readable=true, writable=true, seekable=true, append=false, size=34978, maxsize=Inf, ptr=34979, mark=-1),CairoSurface(Ptr{Void} @0x0000000000000000,960.0,480.0,#undef),CairoContext(Ptr{Void} @0x0000000000000000,CairoSurface(Ptr{Void} @0x0000000000000000,960.0,480.0,#undef),Ptr{Void} @0x00007f8858d7d430),960.0000000000001,480.00000000000006,AlphaColorValue{RGB{Float64},Float64}(RGB{Float64}(0.0,0.0,0.0),0.0),AlphaColorValue{RGB{Float64},Float64}(RGB{Float64}(0.0,0.0,0.0),1.0),[],LineCapButt(),LineJoinMiter(),true,[],[],[Property{FillPrimitive}=>nothing],true,false,nothing,true,true,3.7795275590551185,nothing,nothing)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw(PNG(10*1inch, 5*1inch), spy(mm_obstacles, Coord.Cartesian(ymin=1,ymax=width,xmin=1,xmax=height)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false,
    "tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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       "\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",
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       "\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",
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       "    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",
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       "                            return;\n",
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       "                        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",
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       "\n",
       "                    if (event_name == \"mouseenter\") {\n",
       "                        this.mouseover(fn2, scope);\n",
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       "                    }\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",
       "\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.stopPropagation();\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",
       "\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.stopPropagation();\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-83d60fd3231945e4a1423f273aa64a66\");\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-4\")\n",
       "   .mouseenter(Gadfly.plot_mouseover)\n",
       ".mouseleave(Gadfly.plot_mouseout)\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-83d60fd3231945e4a1423f273aa64a66-element-7\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-7\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-8\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-8\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-51\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-51\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-51\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-53\")\n",
       "   .data(\"max_pos\", 233)\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-53\")\n",
       "   .data(\"min_pos\", 216)\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-53\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-54\")\n",
       "   .data(\"max_pos\", 233)\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-54\")\n",
       "   .data(\"min_pos\", 216)\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-54\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-54\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-54\")\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-83d60fd3231945e4a1423f273aa64a66-element-55\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-55\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-55\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-62\")\n",
       "   .mouseenter(Gadfly.plot_mouseover)\n",
       ".mouseleave(Gadfly.plot_mouseout)\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-83d60fd3231945e4a1423f273aa64a66-element-65\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-65\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-66\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-66\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-70\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-70\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-70\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-72\")\n",
       "   .data(\"max_pos\", 233)\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-72\")\n",
       "   .data(\"min_pos\", 216)\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-72\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-73\")\n",
       "   .data(\"max_pos\", 233)\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-73\")\n",
       "   .data(\"min_pos\", 216)\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-73\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-73\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-73\")\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-83d60fd3231945e4a1423f273aa64a66-element-74\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-74\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-83d60fd3231945e4a1423f273aa64a66-element-74\")\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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