{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script charset=\"utf-8\">(function ($, undefined) {\n",
       "\n",
       "    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",
       "\telem: undefined,\n",
       "\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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<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",
       "\t}\n",
       "\tif (type === \"text/latex\" && MathJax) {\n",
       "\t    MathJax.Hub.Queue([\"Typeset\", MathJax.Hub, toinsert.get(0)]);\n",
       "\t}\n",
       "    }\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",
       "                       mime: type});\n",
       "                    toinsert.bind(\"destroyed\", function() {\n",
       "                        c.send({action: \"unsubscribe_mime\",\n",
       "                               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 Color\n",
    "using Gadfly\n",
    "using Interact\n",
    "using ProgressMeter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wigner's semicircle law\n",
    "\n",
    "Quite possibly the most famous result in random matrix theory.\n",
    "\n",
    "Take a $N\\times N$ real symmetric matrix with Gaussian entries and find its eigenvalues. The eigenvalues are distributed according to the semicircle law."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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"
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       "                        this.mouseover(fn2, scope);\n",
       "                    } else {\n",
       "                        this.mouseout(fn2, scope);\n",
       "                    }\n",
       "                }\n",
       "                return this;\n",
       "            };\n",
       "        })(events[i]);\n",
       "    }\n",
       "\n",
       "\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\n",
       "        if (Snap.is(fn, \"function\")) {\n",
       "            var el = this;\n",
       "            var fn2 = function (event) {\n",
       "                fn.apply(el, [event]);\n",
       "            };\n",
       "        }\n",
       "\n",
       "        this.node.addEventListener(\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
       "            fn2);\n",
       "\n",
       "        return this;\n",
       "    };\n",
       "\n",
       "\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
       "    // This is a function to directly update element attributes without going\n",
       "    // through eve.\n",
       "    Element.prototype.attribute = function(key, val) {\n",
       "        if (val === undefined) {\n",
       "            return this.node.getAttribute(key, val);\n",
       "        } else {\n",
       "            return this.node.setAttribute(key, val);\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.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attr(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attr(\"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.attr(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attr(\"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",
       "    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",
       "        root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        root.select(\".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",
       "    root.select(\".xlabels\")\n",
       "        .selectAll(\"text\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.data(\"x\", element.asPX(\"x\"));\n",
       "        });\n",
       "\n",
       "    root.select(\".ylabels\")\n",
       "        .selectAll(\"text\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.data(\"y\", element.asPX(\"y\"));\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",
       "    root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
       "    root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
       "    root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n",
       "    root.select(\".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",
       "    root.select(\".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",
       "            }\n",
       "        });\n",
       "\n",
       "    root.select(\".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",
       "            }\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",
       "    // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n",
       "    // will be clipped.\n",
       "    root.select(\".xgridlines\")\n",
       "        .selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                element.attr(\"visibility\", \"visible\");\n",
       "            }\n",
       "        });\n",
       "\n",
       "    root.select(\".ygridlines\")\n",
       "        .selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                element.attr(\"visibility\", \"visible\");\n",
       "            }\n",
       "        });\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\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#fig-cb563905c10841efb8aec62c810aca33\");\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-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-cb563905c10841efb8aec62c810aca33-element-7\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-7\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-8\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-8\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-14\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-14\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-14\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-16\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-16\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-16\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-17\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-17\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-17\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-17\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-17\")\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-cb563905c10841efb8aec62c810aca33-element-18\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-18\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-cb563905c10841efb8aec62c810aca33-element-18\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "    });\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot(...)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n=500\n",
    "M=randn(n, n)\n",
    "M=(M+M')/√2n\n",
    "\n",
    "xg = linspace(-2, 2, 100)\n",
    "plot(Coord.Cartesian,\n",
    "    Guide.xlabel(\"Eigenvalue\"), Guide.ylabel(\"Spectral density\"),\n",
    "    layer(x=xg, y=√abs(4 - xg.^2)/(2π), Geom.line,\n",
    "        Theme(default_color=color(\"black\"))\n",
    "    ),\n",
    "layer(x=eigvals(M), Geom.histogram(bincount=round(Int,√n), density=true))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wigner's original paper (1955)...\n",
    "\n",
    "... was actually not on Gaussian random matrices, but rather, other matrices motivated from nuclear physics.\n",
    "\n",
    "<img width=700 src=\"wigner55head.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This paper considers 3 random matrix problems surrounding this type of matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5x5 Symmetric{Int64}:\n",
       " -2  -1   0   0   0\n",
       " -1  -1   1   0   0\n",
       "  0   1   0  -1   0\n",
       "  0   0  -1   1  -1\n",
       "  0   0   0  -1   2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function wigner55(n::Int, N::Int=n, v::Real=1.0)\n",
    "    T = promote_type(typeof(v*1), typeof(n))\n",
    "    H = zeros(T, 2n+1, 2n+1)\n",
    "    for i=1:2n+1\n",
    "        H[i,i] = i-1-n\n",
    "    end\n",
    "    for m = 1:N, j=1:2n+1-m\n",
    "        H[j,j+m] = v*(2randbool()-1)\n",
    "    end\n",
    "    Symmetric(H, :U)\n",
    "end\n",
    "wigner55(2, 1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wigner studied three problems:\n",
    "\n",
    "1. The bandwidth $N = 1$, dimension $n \\rightarrow \\infty$, with nonzero diagonals.\n",
    "2. $n = N$ and $v$ are all finite, with zero diagonals.\n",
    "3. $N \\rightarrow \\infty, n \\rightarrow \\infty, v \\rightarrow \\infty, v^2/N = $ some constant $q$, with nonzero diagonals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 1. $N=1$, $n\\rightarrow \\infty$, nonzero diagonals\n",
    "\n",
    "<img src=\"wigner55_4.png\" />\n",
    "\n",
    "## Eigenvectors (Eq. 5b)\n",
    "\n",
    "...\n",
    "\n",
    "<img src=\"wigner55_5b.png\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Slider{Float64}([Input{Float64}] 2.5,\"v\",2.5,0.0:0.25:5.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Slider{Int64}([Input{Int64}] 21,\"k\",21,1:41)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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",
       "}(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",
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       "    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",
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       "                            return;\n",
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       "\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",
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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, val);\n",
       "        } else {\n",
       "            return this.node.setAttribute(key, val);\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.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attr(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attr(\"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.attr(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attr(\"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",
       "    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",
       "        root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        root.select(\".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",
       "    root.select(\".xlabels\")\n",
       "        .selectAll(\"text\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.data(\"x\", element.asPX(\"x\"));\n",
       "        });\n",
       "\n",
       "    root.select(\".ylabels\")\n",
       "        .selectAll(\"text\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.data(\"y\", element.asPX(\"y\"));\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",
       "    root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
       "    root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
       "    root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n",
       "    root.select(\".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",
       "    root.select(\".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",
       "            }\n",
       "        });\n",
       "\n",
       "    root.select(\".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",
       "            }\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",
       "    // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n",
       "    // will be clipped.\n",
       "    root.select(\".xgridlines\")\n",
       "        .selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                element.attr(\"visibility\", \"visible\");\n",
       "            }\n",
       "        });\n",
       "\n",
       "    root.select(\".ygridlines\")\n",
       "        .selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                element.attr(\"visibility\", \"visible\");\n",
       "            }\n",
       "        });\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\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#fig-b45b194bae2c4c8ea8729f0683468a58\");\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-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-b45b194bae2c4c8ea8729f0683468a58-element-7\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-7\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-8\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-8\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-15\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-15\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-15\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-17\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-17\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-17\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-18\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-18\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-18\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-18\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-18\")\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-b45b194bae2c4c8ea8729f0683468a58-element-19\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-19\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-b45b194bae2c4c8ea8729f0683468a58-element-19\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "    });\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot(...)"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "comm_id": "940a3633-d7e8-48f1-b20d-839a57802fb8",
      "reactive": true
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function demo1(n::Int=20)\n",
    "    @manipulate for v=0.0:0.25:5.0, k=1:2n+1\n",
    "        E = eigfact!(wigner55(n, 1, v))\n",
    "        plot(Coord.Cartesian(ymin=-1, ymax=1),\n",
    "            Guide.ylabel(\"Eigenvector\"), Guide.xlabel(\"Position\"),\n",
    "            layer(x=-n:n, y=E[:vectors][:,k],\n",
    "                Geom.point, Theme(default_color=color(\"black\"))),\n",
    "            layer(x=-n:n, y=map(m->abs(besselj(m-k+(n+1), 2v)), -n:n),\n",
    "                Geom.line, Theme(default_color=color(\"lightgrey\"))),\n",
    "            layer(x=-n:n, y=map(m->-abs(besselj(m-k+(n+1), 2v)), -n:n),\n",
    "                Geom.line, Theme(default_color=color(\"lightgrey\"))),\n",
    "        )\n",
    "    end\n",
    "end\n",
    "demo1()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 2. $N=2n$, $n, v$ finite, 0 diagonals\n",
    "\n",
    "<img src=\"wigner55_pr2.png\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7x7 Symmetric{Int64}:\n",
       "  0   1   1   1   1  -1  -1\n",
       "  1   0  -1   1  -1   1   1\n",
       "  1  -1   0  -1  -1   1  -1\n",
       "  1   1  -1   0   1  -1  -1\n",
       "  1  -1  -1   1   0   1  -1\n",
       " -1   1   1  -1   1   0  -1\n",
       " -1   1  -1  -1  -1  -1   0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function wigner55_2(n::Int, N::Int=n, v::Real=1.0)\n",
    "    T = promote_type(typeof(v*1), typeof(n))\n",
    "    H = zeros(T, 2n+1, 2n+1) #Diagonals are now 0\n",
    "    for m = 1:N, j=1:2n+1-m\n",
    "        H[j,j+m] = v*(2randbool()-1)\n",
    "    end\n",
    "    Symmetric(H, :U)\n",
    "end\n",
    "wigner55_2(3, 6, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 100%|█████████████████████████████████████████| Time: 0:00:04\n"
     ]
    }
   ],
   "source": [
    "t = 1000 #Number of trials\n",
    "n = 30 \n",
    "v = 4 #Magnitude of off-diagonal\n",
    "evals = zeros(2n+1, t)\n",
    "\n",
    "@showprogress for i=1:t\n",
    "    H1 = wigner55_2(n, 2n, v)\n",
    "    evals[:, i] = eigvals(H1)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": [
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       "            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, val);\n",
       "        } else {\n",
       "            return this.node.setAttribute(key, val);\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.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attr(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attr(\"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.attr(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attr(\"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",
       "    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",
       "        root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        root.select(\".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",
       "    root.select(\".xlabels\")\n",
       "        .selectAll(\"text\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.data(\"x\", element.asPX(\"x\"));\n",
       "        });\n",
       "\n",
       "    root.select(\".ylabels\")\n",
       "        .selectAll(\"text\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.data(\"y\", element.asPX(\"y\"));\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",
       "    root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
       "    root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
       "    root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n",
       "    root.select(\".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",
       "    root.select(\".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",
       "            }\n",
       "        });\n",
       "\n",
       "    root.select(\".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",
       "            }\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",
       "    // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n",
       "    // will be clipped.\n",
       "    root.select(\".xgridlines\")\n",
       "        .selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                element.attr(\"visibility\", \"visible\");\n",
       "            }\n",
       "        });\n",
       "\n",
       "    root.select(\".ygridlines\")\n",
       "        .selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                element.attr(\"visibility\", \"visible\");\n",
       "            }\n",
       "        });\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\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#fig-9506259d2ab349b2bc6134a2694b0b3f\");\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-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-9506259d2ab349b2bc6134a2694b0b3f-element-7\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-7\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-8\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-8\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-14\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-14\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-14\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-16\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-16\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-16\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-17\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-17\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-17\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-17\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-17\")\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-9506259d2ab349b2bc6134a2694b0b3f-element-18\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-18\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-9506259d2ab349b2bc6134a2694b0b3f-element-18\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "    });\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot(...)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg = linspace(-v*√8n, v*√8n, 100)\n",
    "plot(Coord.Cartesian(aspect_ratio=φ),\n",
    "    Guide.xlabel(\"Eigenvalue\"), Guide.ylabel(\"Spectral density\"),\n",
    "    layer(x=xg, y=√abs(8n*v^2 - xg.^2)/(4π*n*v^2), Geom.line,\n",
    "        Theme(default_color=color(\"black\"))\n",
    "    ),\n",
    "    layer(x=evals, Geom.histogram(bincount=round(Int,√(t*(2n+1))), density=true))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"wigner55_20.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 3. $N \\rightarrow \\infty$, $v = \\sqrt{qN}$,  nonzero diagonals\n",
    "\n",
    "<img src=\"wigner55_pr3.png\" />\n",
    "\n",
    "<img src=\"wigner55_pr3_2.png\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 100%|█████████████████████████████████████████| Time: 0:00:00\n"
     ]
    }
   ],
   "source": [
    "q = 100\n",
    "n = 50\n",
    "N = 2n\n",
    "t = 10 #Number of trials\n",
    "evals = zeros(2n+1, t)\n",
    "\n",
    "@showprogress for i=1:t\n",
    "    H = wigner55(n, N, √(q*N))\n",
    "    evals[:, i] = eigvals(H)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": [
       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"
      ],
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\"\n",
       "     xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n",
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       "            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, val);\n",
       "        } else {\n",
       "            return this.node.setAttribute(key, val);\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.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attr(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attr(\"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.attr(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attr(\"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",
       "    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",
       "        root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        root.select(\".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",
       "    root.select(\".xlabels\")\n",
       "        .selectAll(\"text\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.data(\"x\", element.asPX(\"x\"));\n",
       "        });\n",
       "\n",
       "    root.select(\".ylabels\")\n",
       "        .selectAll(\"text\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.data(\"y\", element.asPX(\"y\"));\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",
       "    root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
       "    root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
       "    root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n",
       "    root.select(\".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",
       "    root.select(\".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",
       "            }\n",
       "        });\n",
       "\n",
       "    root.select(\".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",
       "            }\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",
       "    // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n",
       "    // will be clipped.\n",
       "    root.select(\".xgridlines\")\n",
       "        .selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                element.attr(\"visibility\", \"visible\");\n",
       "            }\n",
       "        });\n",
       "\n",
       "    root.select(\".ygridlines\")\n",
       "        .selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                element.attr(\"visibility\", \"visible\");\n",
       "            }\n",
       "        });\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\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b\");\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-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-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-7\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-7\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-8\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-8\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-14\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-14\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-14\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-16\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-16\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-16\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-17\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-17\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-17\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-17\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-17\")\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-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-18\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-18\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-e1aea9d5e4bc4beba767f0bd5ba9b37b-element-18\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "    });\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot(...)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v=√(q*N)\n",
    "xg = linspace(-v*√4N, v*√4N, 100)\n",
    "plot(Coord.Cartesian(aspect_ratio=φ),\n",
    "    Guide.xlabel(\"Eigenvalue\"), Guide.ylabel(\"Spectral density\"),\n",
    "    layer(x=xg, y=√abs(4N*v^2 - xg.^2)/(2π*N*v^2), Geom.line,\n",
    "        Theme(default_color=color(\"black\"))\n",
    "    ),\n",
    "    layer(x=evals, Geom.histogram(bincount=round(Int,√(t*(2n+1))), density=true))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Slider{Float64}([Input{Float64}] 0.0,\"logq\",0.0,-5.0:0.1:5.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": [
       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       "f:e,unbind:b(this.node||document,d,e,f||this)}));return this};a[\"un\"+d]=A[\"un\"+d]=function(a){for(var b=this.events||[],e=b.length;e--;)if(b[e].name==d&&(b[e].f==a||!a)){b[e].unbind();b.splice(e,1);!b.length&&delete this.events;break}return this}})(v[y]);A.hover=function(a,b,d,e){return this.mouseover(a,d).mouseout(b,e||d)};A.unhover=function(a,b){return this.unmouseover(a).unmouseout(b)};var r=[];A.drag=function(b,d,f,h,n,p){function u(r,v,w){(r.originalEvent||r).preventDefault();this._drag.x=v;\n",
       "this._drag.y=w;this._drag.id=r.identifier;!q.length&&a.mousemove(e).mouseup(l);q.push({el:this,move_scope:h,start_scope:n,end_scope:p});d&&k.on(\"snap.drag.start.\"+this.id,d);b&&k.on(\"snap.drag.move.\"+this.id,b);f&&k.on(\"snap.drag.end.\"+this.id,f);k(\"snap.drag.start.\"+this.id,n||h||this,v,w,r)}if(!arguments.length){var v;return this.drag(function(a,b){this.attr({transform:v+(v?\"T\":\"t\")+[a,b]})},function(){v=this.transform().local})}this._drag={};r.push({el:this,start:u});this.mousedown(u);return this};\n",
       "A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind(\"snap.drag.*.\"+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\\s*url\\((.+)\\)/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;\"svg\"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z(\"filter\");z(u,{id:k,filterUnits:\"userSpaceOnUse\"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on(\"snap.util.getattr.filter\",\n",
       "function(){k.stop();var d=z(this.node,\"filter\");if(d)return(d=w(d).match(A))&&a.select(d[1])});k.on(\"snap.util.attr.filter\",function(d){if(d instanceof v&&\"filter\"==d.type){k.stop();var f=d.node.id;f||(z(d.node,{id:d.id}),f=d.id);z(this.node,{filter:a.url(f)})}d&&\"none\"!=d||(k.stop(),this.node.removeAttribute(\"filter\"))});a.filter.blur=function(d,f){null==d&&(d=2);return a.format('<feGaussianBlur stdDeviation=\"{def}\"/>',{def:null==f?d:[d,f]})};a.filter.blur.toString=function(){return this()};a.filter.shadow=\n",
       "function(d,f,k,u,p){\"string\"==typeof k&&(p=u=k,k=4);\"string\"!=typeof u&&(p=u,u=\"#000\");null==k&&(k=4);null==p&&(p=1);null==d&&(d=0,f=2);null==f&&(f=d);u=a.color(u||\"#000\");return a.format('<feGaussianBlur in=\"SourceAlpha\" stdDeviation=\"{blur}\"/><feOffset dx=\"{dx}\" dy=\"{dy}\" result=\"offsetblur\"/><feFlood flood-color=\"{color}\"/><feComposite in2=\"offsetblur\" operator=\"in\"/><feComponentTransfer><feFuncA type=\"linear\" slope=\"{opacity}\"/></feComponentTransfer><feMerge><feMergeNode/><feMergeNode in=\"SourceGraphic\"/></feMerge>',\n",
       "{color:u,dx:d,dy:f,blur:k,opacity:p})};a.filter.shadow.toString=function(){return this()};a.filter.grayscale=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {b} {h} 0 0 0 0 0 1 0\"/>',{a:0.2126+0.7874*(1-d),b:0.7152-0.7152*(1-d),c:0.0722-0.0722*(1-d),d:0.2126-0.2126*(1-d),e:0.7152+0.2848*(1-d),f:0.0722-0.0722*(1-d),g:0.2126-0.2126*(1-d),h:0.0722+0.9278*(1-d)})};a.filter.grayscale.toString=function(){return this()};a.filter.sepia=\n",
       "function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {h} {i} 0 0 0 0 0 1 0\"/>',{a:0.393+0.607*(1-d),b:0.769-0.769*(1-d),c:0.189-0.189*(1-d),d:0.349-0.349*(1-d),e:0.686+0.314*(1-d),f:0.168-0.168*(1-d),g:0.272-0.272*(1-d),h:0.534-0.534*(1-d),i:0.131+0.869*(1-d)})};a.filter.sepia.toString=function(){return this()};a.filter.saturate=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"saturate\" values=\"{amount}\"/>',{amount:1-\n",
       "d})};a.filter.saturate.toString=function(){return this()};a.filter.hueRotate=function(d){return a.format('<feColorMatrix type=\"hueRotate\" values=\"{angle}\"/>',{angle:d||0})};a.filter.hueRotate.toString=function(){return this()};a.filter.invert=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncG type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncB type=\"table\" tableValues=\"{amount} {amount2}\"/></feComponentTransfer>',{amount:d,\n",
       "amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
       "{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
       "\n",
       "]]> </script>\n",
       "<script> <![CDATA[\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "    if (typeof define === \"function\" && define.amd) {\n",
       "        // Define as an anonymous module\n",
       "        define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
       "            return factory(Snap);\n",
       "        });\n",
       "    } else {\n",
       "        // Browser globals (glob is window)\n",
       "        // Snap adds itself to window\n",
       "        glob.Gadfly = factory(glob.Snap);\n",
       "    }\n",
       "}(this, function (Snap) {\n",
       "\n",
       "var Gadfly = {};\n",
       "\n",
       "// Get an x/y coordinate value in pixels\n",
       "var xPX = function(fig, x) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
       "};\n",
       "\n",
       "var yPX = function(fig, y) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
       "};\n",
       "\n",
       "\n",
       "Snap.plugin(function (Snap, Element, Paper, global) {\n",
       "    // Traverse upwards from a snap element to find and return the first\n",
       "    // note with the \"plotroot\" class.\n",
       "    Element.prototype.plotroot = function () {\n",
       "        var element = this;\n",
       "        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.svgroot = function () {\n",
       "        var element = this;\n",
       "        while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotbounds = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x0: bbox.x,\n",
       "            x1: bbox.x + bbox.width,\n",
       "            y0: bbox.y,\n",
       "            y1: bbox.y + bbox.height\n",
       "        };\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotcenter = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x: bbox.x + bbox.width / 2,\n",
       "            y: bbox.y + bbox.height / 2\n",
       "        };\n",
       "    };\n",
       "\n",
       "    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
       "    // does everything right.\n",
       "    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
       "    var events = [\"mouseenter\", \"mouseleave\"];\n",
       "\n",
       "    for (i in events) {\n",
       "        (function (event_name) {\n",
       "            var event_name = events[i];\n",
       "            Element.prototype[event_name] = function (fn, scope) {\n",
       "                if (Snap.is(fn, \"function\")) {\n",
       "                    var fn2 = function (event) {\n",
       "                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
       "                            return;\n",
       "                        }\n",
       "\n",
       "                        var reltg = event.relatedTarget ? event.relatedTarget :\n",
       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
       "\n",
       "                        if (reltg != this.node) {\n",
       "                            return fn.apply(this, event);\n",
       "                        }\n",
       "                    };\n",
       "\n",
       "                    if (event_name == \"mouseenter\") {\n",
       "                        this.mouseover(fn2, scope);\n",
       "                    } else {\n",
       "                        this.mouseout(fn2, scope);\n",
       "                    }\n",
       "                }\n",
       "                return this;\n",
       "            };\n",
       "        })(events[i]);\n",
       "    }\n",
       "\n",
       "\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\n",
       "        if (Snap.is(fn, \"function\")) {\n",
       "            var el = this;\n",
       "            var fn2 = function (event) {\n",
       "                fn.apply(el, [event]);\n",
       "            };\n",
       "        }\n",
       "\n",
       "        this.node.addEventListener(\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
       "            fn2);\n",
       "\n",
       "        return this;\n",
       "    };\n",
       "\n",
       "\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
       "    // This is a function to directly update element attributes without going\n",
       "    // through eve.\n",
       "    Element.prototype.attribute = function(key, val) {\n",
       "        if (val === undefined) {\n",
       "            return this.node.getAttribute(key, val);\n",
       "        } else {\n",
       "            return this.node.setAttribute(key, val);\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.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attr(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attr(\"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.attr(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attr(\"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",
       "    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",
       "        root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        root.select(\".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",
       "    root.select(\".xlabels\")\n",
       "        .selectAll(\"text\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.data(\"x\", element.asPX(\"x\"));\n",
       "        });\n",
       "\n",
       "    root.select(\".ylabels\")\n",
       "        .selectAll(\"text\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.data(\"y\", element.asPX(\"y\"));\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",
       "    root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
       "    root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
       "    root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n",
       "    root.select(\".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",
       "    root.select(\".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",
       "            }\n",
       "        });\n",
       "\n",
       "    root.select(\".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",
       "            }\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",
       "    // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n",
       "    // will be clipped.\n",
       "    root.select(\".xgridlines\")\n",
       "        .selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                element.attr(\"visibility\", \"visible\");\n",
       "            }\n",
       "        });\n",
       "\n",
       "    root.select(\".ygridlines\")\n",
       "        .selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                element.attr(\"visibility\", \"visible\");\n",
       "            }\n",
       "        });\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\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92\");\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-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-652d4a207ce9400a9f19e51bd0c2ee92-element-7\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-7\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-8\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-8\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-15\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-15\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-15\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-17\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-17\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-17\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-18\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-18\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-18\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-18\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-18\")\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-652d4a207ce9400a9f19e51bd0c2ee92-element-19\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-19\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-652d4a207ce9400a9f19e51bd0c2ee92-element-19\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "    });\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot(...)"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "comm_id": "e95c3a50-b192-4926-b41c-acd6d98695f4",
      "reactive": true
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@manipulate for logq = -5:0.1:5\n",
    "    \n",
    "    q = 10.0^logq\n",
    "    n = 50\n",
    "    N = 2n\n",
    "    t = 10 #Number of trials\n",
    "    evals = zeros(2n+1, t)\n",
    "\n",
    "    for i=1:t\n",
    "        H = wigner55(n, N, √(q*N))\n",
    "        evals[:, i] = eigvals(H)\n",
    "    end    \n",
    "\n",
    "    v=√(q*N)\n",
    "    xg = linspace(-v*√4N, v*√4N, 100)\n",
    "    plot(Coord.Cartesian(aspect_ratio=φ),\n",
    "        Guide.xlabel(\"Eigenvalue\"), Guide.ylabel(\"Spectral density\"),\n",
    "        layer(x=xg, y=√abs(4N*v^2 - xg.^2)/(2π*N*v^2), Geom.line,\n",
    "    Theme(default_color=color(\"grey\"))\n",
    "        ),\n",
    "    layer(x=[-n; -n:n; n], y=[0; fill(1/(2n+1), 2n+1); 0], Geom.line,\n",
    "    Theme(default_color=color(\"grey\"))\n",
    "        ),\n",
    "        layer(x=evals, Geom.histogram(bincount=round(Int,√(t*(2n+1))), density=true))\n",
    "    )\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"wigner55_pr3_3.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some free probability experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "samplefree (generic function with 1 method)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function samplefree(n, N, t, q)\n",
    "    v=√(q*N)\n",
    "\n",
    "    evals1 = zeros(2n+1, t)\n",
    "    evals2 = zeros(2n+1, t)\n",
    "    \n",
    "    #Sample eigenvalues\n",
    "    @showprogress for i=1:t\n",
    "        𝐤 = Diagonal(1.0*[-n:n;])\n",
    "        𝐯 = wigner55_2(n,2n,v) |> full\n",
    "        Q = full(qrfact!(randn(2n+1, 2n+1))[:Q])\n",
    "        evals1 = eigvals(𝐤 + 𝐯)\n",
    "        evals2 = eigvals(Q*full(𝐤)*Q' + 𝐯)\n",
    "    end\n",
    "    \n",
    "    #Compute normalized histograms\n",
    "    xv, yv1 = hist(evals1, round(Int, √(n*√t)))\n",
    "    yv1 /= sum(yv1)*diff(xv)[1]\n",
    "    xv, yv2 = hist(evals2, xv)\n",
    "    yv2 /= sum(yv2)*diff(xv)[1]\n",
    "\n",
    "    midpoints(xv), yv1, yv2\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "plotdensities (generic function with 1 method)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function plotdensities(xv, yv1, yv2)\n",
    "    cm = map(x->AlphaColorValue(x, 0.5), palette(\"Set1\", 3)[1:2])\n",
    "    plot(Guide.xlabel(\"Eigenvalue\"), Guide.ylabel(\"Density\"),\n",
    "        layer(x=xv, y=yv1, Geom.bar, Theme(default_color=cm[1])),\n",
    "        layer(x=xv, y=yv2, Geom.bar, Theme(default_color=cm[2])),\n",
    "        Guide.manual_color_key(\"\", [\"exact\", \"free\"], cm), \n",
    "    )\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Slider{Float64}([Input{Float64}] 0.0,\"logq\",0.0,-5.0:0.5:5.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 100%|█████████████████████████████████████████| Time: 0:00:01\n"
     ]
    },
    {
     "ename": "LoadError",
     "evalue": "palette not defined\nwhile loading In[13], in expression starting on line 1",
     "output_type": "error",
     "traceback": [
      "palette not defined\nwhile loading In[13], in expression starting on line 1",
      "",
      " in plotdensities at In[12]:2",
      " in anonymous at no file:3",
      " in lift at /Users/jiahao/.julia/v0.3/Reactive/src/Reactive.jl:298"
     ]
    }
   ],
   "source": [
    "@manipulate for logq = -5:0.5:5\n",
    "    xv, yv1, yv2 = samplefree(30, 2n, 100, 10.0^logq)\n",
    "    plotdensities(xv, yv1, yv2)\n",
    "end"
   ]
  }
 ],
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