Buggy IJulia notebook - Interact doesn't work

Nuclear energy levels.ipynb

{
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  "name": "",
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     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Wigner's original application of random matrix theory was to study the distribution of nuclear energy levels. In this notebook we revisit Wigner's original application using modern data from the [Evaluated Nuclear Structure Data File Database](http://www.nndc.bnl.gov/ensdf/) at the [National Nuclear Data Center](http://www.nndc.bnl.gov).\n",
      "\n",
      "To analyze a given nuclide, look up the ENSDF data file associated with it in the database (usually you will want the *ADOPTED LEVELS, GAMMAS* entry). Some of these have already been looked up and stored in the `datafiles` dictionary."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "using Compat #For use with both Julia v0.3 and v0.4\n",
      "using Gadfly #For plotting\n",
      "using Interact"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "\n",
        "    // RadioButtons\n",
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        "    // Latex\n",
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        "\t    keyboard_manager: IPython.keyboard_manager\n",
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        "\t    // coordingate with Comm and redraw Signals\n",
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        "\t\twidget.sendUpdate = function () {\n",
        "\t\t    // `this` is a widget here.\n",
        "\t\t    // TODO: I have a feeling there's some\n",
        "\t\t    //       IPython bookkeeping to be done here.\n",
        "\t\t    // Widgets.log(\"State changed\", this, this.getState());\n",
        "\t\t    comm.send({value: this.getState()});\n",
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    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we keep track of the URL of the data set for a given nuclide. The `nuclide` variable specifies which nuclide we want to analyze."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nuclide = \"I131\"\n",
      "\n",
      "#Download data\n",
      "datafiles = @compat Dict(\n",
      "    #Radiocarbon dating\n",
      "    \"C14\"  => \"http://www.nndc.bnl.gov/useroutput/AR_0E57A699F47BD7B9AA46AFD8D289942C_13.ens\",\n",
      "\n",
      "    #Medicine\n",
      "    \"Co60\" => \"http://www.nndc.bnl.gov/useroutput/AR_0E57A699F47BD7B9AA46AFD8D289942C_10.ens\",\n",
      "\n",
      "    \"I129\" => \"http://www.nndc.bnl.gov/useroutput/AR_0E57A699F47BD7B9AA46AFD8D289942C_17.ens\",\n",
      "    \"I130\" => \"http://www.nndc.bnl.gov/useroutput/AR_0E57A699F47BD7B9AA46AFD8D289942C_18.ens\",\n",
      "    \"I131\" => \"http://www.nndc.bnl.gov/useroutput/AR_0E57A699F47BD7B9AA46AFD8D289942C_19.ens\",\n",
      "\n",
      "    #Nuclear waste\n",
      "    \"Cs137\" => \"http://www.nndc.bnl.gov/useroutput/AR_0E57A699F47BD7B9AA46AFD8D289942C_14.ens\",\n",
      "\n",
      "    #X-ray source for medical imaging\n",
      "    \"Gd151\"=> \"http://www.nndc.bnl.gov/useroutput/AR_0E57A699F47BD7B9AA46AFD8D289942C_12.ens\",\n",
      "\n",
      "    #Smoke detectors\n",
      "    \"Am241\"=> \"http://www.nndc.bnl.gov/useroutput/AR_0E57A699F47BD7B9AA46AFD8D289942C_11.ens\",\n",
      "\n",
      "    \"Pb206\"=> \"http://www.nndc.bnl.gov/useroutput/AR_0E57A699F47BD7B9AA46AFD8D289942C_3.ens\",\n",
      "    \"Pb207\"=> \"http://www.nndc.bnl.gov/useroutput/AR_0E57A699F47BD7B9AA46AFD8D289942C_2.ens\",\n",
      "    \"Pb208\"=> \"http://www.nndc.bnl.gov/useroutput/AR_0E57A699F47BD7B9AA46AFD8D289942C_1.ens\",\n",
      "    \"Po210\"=> \"http://www.nndc.bnl.gov/useroutput/AR_0E57A699F47BD7B9AA46AFD8D289942C_16.ens\",\n",
      "    \"U235\" => \"http://www.nndc.bnl.gov/useroutput/AR_09CB581BB90DBC20099CC8348ED33DD1_2.ens\",\n",
      "    \"U238\" => \"http://www.nndc.bnl.gov/useroutput/AR_09CB581BB90DBC20099CC8348ED33DD1_3.ens\",\n",
      "    \"Pu240\"=> \"http://www.nndc.bnl.gov/useroutput/AR_0E57A699F47BD7B9AA46AFD8D289942C_4.ens\",\n",
      "    \"Pu241\"=> \"http://www.nndc.bnl.gov/useroutput/AR_0E57A699F47BD7B9AA46AFD8D289942C_5.ens\",\n",
      ")\n",
      "\n",
      "fn = nuclide * \".ens\"\n",
      "isfile(fn) || download(datafiles[nuclide], fn)\n",
      "\n",
      "#Parse ENSDF file format\n",
      "#Ref: http://www.nndc.bnl.gov/nndcscr/documents/ensdf/ensdf-manual.pdf\n",
      "levels = Float64[]\n",
      "open(fn) do f\n",
      "    for line in readlines(f)\n",
      "        length(line) \u2265 19 || continue\n",
      "        line[6:8] == \"  L\" && try\n",
      "            push!(levels, float(line[10:19])) #Most lines have data here\n",
      "        catch ArgumentError \n",
      "            info(\"Skipped line: $line\")\n",
      "        end\n",
      "    end\n",
      "end\n",
      "x\u0304=mean(levels)\n",
      "n=length(levels)\n",
      "println(\"The data for $nuclide contains $n energy levels.\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The data for I131 contains 87 energy levels.\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that we have read in the nuclear energy levels, we now compute their normalized histogram. This histogram is normalized in two ways:\n",
      "\n",
      "1. The data are rescaled so that the mean is exactly 1.\n",
      "2. The counts are normalized so that the result is a probability density."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Normalized histogram\n",
      "function normhist(data::AbstractVector, x\u0304=nothing; nbins = 0)\n",
      "    n = length(data)\n",
      "    nbins == 0 && (nbins = int(\u221a(n)))\n",
      "    x\u0304 == nothing && (x\u0304 = mean(data))\n",
      "    xgrid, counts = hist(data, linspace(minimum(data), maximum(data), nbins))\n",
      "    xgrid/x\u0304, counts*x\u0304/(n*(xgrid[2]-xgrid[1]))\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "normhist (generic function with 2 methods)"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now plot the results against Wigner's surmise,\n",
      "\n",
      "$$W(x) = \\frac \\pi 2 x \\exp\\left(-\\pi \\frac {x^2} 4\\right),$$\n",
      "\n",
      "which is an accurate approximation of the level spacing of the Gaussian unitary ensemble (GUE). The Wigner surmise is accurate for some nuclei (e.g. $^{238}$U) but not others (e.g. $^{208}$Pb)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xgrid2 = linspace(0, maximum(levels)/x\u0304, n)\n",
      "@manipulate for nb=1:2int(\u221an) begin\n",
      "    xgrid, counts = normhist(levels, nbins=nb)\n",
      "    plot(Guide.title(\"Energy levels for $nuclide (N = $n)\"),\n",
      "        Guide.xlabel(\"Normalized energy (mean = 1)\"), Guide.ylabel(\"Probability density\"),\n",
      "        layer(x=xgrid2, y=map(x->\u03c0/2*x*exp(-\u03c0*x^2/4), xgrid2), \n",
      "            Geom.line, Theme(default_color=color(\"red\"))),\n",
      "        layer(x=midpoints(xgrid), y=counts, Geom.bar, Theme(default_color=color(\"black\"))))\n",
      "end end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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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-cdcc4bd1e74c44da9d1af51110f98ece\");\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-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-cdcc4bd1e74c44da9d1af51110f98ece-element-7\")\n",
        "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
        ";\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-element-7\")\n",
        "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
        ";\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-element-8\")\n",
        "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
        ";\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-element-8\")\n",
        "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
        ";\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-element-14\")\n",
        "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
        ";\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-element-14\")\n",
        "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
        ";\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-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-cdcc4bd1e74c44da9d1af51110f98ece-element-16\")\n",
        "   .data(\"max_pos\", 120.42)\n",
        ";\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-element-16\")\n",
        "   .data(\"min_pos\", 103.42)\n",
        ";\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-element-16\")\n",
        "   .click(Gadfly.zoomslider_track_click);\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-element-17\")\n",
        "   .data(\"max_pos\", 120.42)\n",
        ";\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-element-17\")\n",
        "   .data(\"min_pos\", 103.42)\n",
        ";\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-element-17\")\n",
        "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
        ";\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-element-17\")\n",
        "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
        ";\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-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-cdcc4bd1e74c44da9d1af51110f98ece-element-18\")\n",
        "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
        ";\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-element-18\")\n",
        "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
        ";\n",
        "fig.select(\"#fig-cdcc4bd1e74c44da9d1af51110f98ece-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"
       ],
       "metadata": {
        "comm_id": "3871dc8b-95e8-41b3-99f2-7064b3c80e1e",
        "reactive": true
       },
       "output_type": "pyout",
       "png": 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