{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Turing]: AD chunk size is set as 40\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ldamodel_vec (generic function with 9 methods)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Distributions\n",
    "using Turing\n",
    "using Stan\n",
    "\n",
    "# Load data; loaded data is a list of dict named `ldastandata`\n",
    "include(Pkg.dir(\"Turing\")*\"/stan-models/lda-stan.data.jl\")\n",
    "topicdata = ldastandata[1]\n",
    "\n",
    "# Load model\n",
    "include(Pkg.dir(\"Turing\")*\"/stan-models/lda.model.jl\")\n",
    "#= NOTE: loaded model is defined as below\n",
    "@model ldamodel(K, V, M, N, w, doc, beta, alpha) = begin\n",
    "  theta = Vector{Vector{Real}}(M)\n",
    "  for m = 1:M\n",
    "    theta[m] ~ Dirichlet(alpha)\n",
    "  end\n",
    "\n",
    "  phi = Vector{Vector{Real}}(K)\n",
    "  for k = 1:K\n",
    "    phi[k] ~ Dirichlet(beta)\n",
    "  end\n",
    "\n",
    "  phi_dot_theta = [log([dot(map(p -> p[i], phi), theta[m]) for i = 1:V]) for m=1:M]\n",
    "  for n = 1:N\n",
    "    Turing.acclogp!(vi, phi_dot_theta[doc[n]][w[n]])\n",
    "  end\n",
    "end\n",
    "=#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Turing]: AD chunk size is set as 100\n"
     ]
    },
    {
     "data": {
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      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setchunksize(100)    # increase AD chunk-size to 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "inputHidden": false,
    "outputHidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Turing]:  Assume - `theta` is a parameter\n",
      "  in @~(::Any, ::Any) at compiler.jl:76\n",
      "[Turing]:  Assume - `phi` is a parameter\n",
      "  in @~(::Any, ::Any) at compiler.jl:76\n",
      "[Turing] looking for good initial eps...\n",
      "[Turing.NUTS] found initial ϵ: 0.5\n",
      "[Turing.WARNING]: Numerical error has been found in gradients.\n",
      "  in verifygrad(::Array{Float64,1}) at ad.jl:87\n",
      "[Turing]:  Adapted ϵ = 4.539410952991489e-76, 200 HMC iterations is used for adaption.\n",
      "  in adapt_step_size(::Turing.WarmUpManager, ::Float64) at adapt.jl:54\n",
      "[NUTS] Finished with\n",
      "  Running time        = 249.17596517799976;\n",
      "  #lf / sample        = 62.761;\n",
      "  #evals / sample     = 62.763;\n",
      "  pre-cond. diag mat  = [0.999999,0.999999,0.999998,0.....\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Object of type \"Turing.Chain\"\n",
       "\n",
       "Iterations = 1:1000\n",
       "Thinning interval = 1\n",
       "Chains = 1\n",
       "Samples per chain = 1000\n",
       "\n",
       "[0.0117366 0.988263 … 0.405007 0.594993; 0.0117366 0.988263 … 0.405007 0.594993; … ; 0.0117366 0.988263 … 0.405007 0.594993; 0.0117366 0.988263 … 0.405007 0.594993]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples = sample(ldamodel(data=topicdata), NUTS(1000, 0.65))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Function for visualization topic models.\n",
       "\n",
       "Usage:\n",
       "\n",
       "```\n",
       "vis_topic_res(samples, K, V, avg_range)\n",
       "```\n",
       "\n",
       "  * `samples` is the chain return by `sample()`\n",
       "  * `K` is the number of topics\n",
       "  * `V` is the size of vocabulary\n",
       "  * `avg_range` is the end point of the running average\n"
      ],
      "text/plain": [
       "Function for visualization topic models.\n",
       "\n",
       "Usage:\n",
       "\n",
       "```\n",
       "vis_topic_res(samples, K, V, avg_range)\n",
       "```\n",
       "\n",
       "  * `samples` is the chain return by `sample()`\n",
       "  * `K` is the number of topics\n",
       "  * `V` is the size of vocabulary\n",
       "  * `avg_range` is the end point of the running average\n"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load visualization script for topic models; visualization function is called `vis_topic_res`\n",
    "include(Pkg.dir(\"Turing\")*\"/stan-models/topic_model_vis_helper.jl\")\n",
    "\n",
    "@doc vis_topic_res  # show the usage of the visualization function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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       "            Element.prototype[event_name] = function (fn, scope) {\n",
       "                if (Snap.is(fn, \"function\")) {\n",
       "                    var fn2 = function (event) {\n",
       "                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
       "                            return;\n",
       "                        }\n",
       "\n",
       "                        var reltg = event.relatedTarget ? event.relatedTarget :\n",
       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
       "\n",
       "                        if (reltg != this.node) {\n",
       "                            return fn.apply(this, event);\n",
       "                        }\n",
       "                    };\n",
       "\n",
       "                    if (event_name == \"mouseenter\") {\n",
       "                        this.mouseover(fn2, scope);\n",
       "                    } else {\n",
       "                        this.mouseout(fn2, scope);\n",
       "                    }\n",
       "                }\n",
       "                return this;\n",
       "            };\n",
       "        })(events[i]);\n",
       "    }\n",
       "\n",
       "\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\n",
       "        if (Snap.is(fn, \"function\")) {\n",
       "            var el = this;\n",
       "            var fn2 = function (event) {\n",
       "                fn.apply(el, [event]);\n",
       "            };\n",
       "        }\n",
       "\n",
       "        this.node.addEventListener(\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
       "            fn2);\n",
       "\n",
       "        return this;\n",
       "    };\n",
       "\n",
       "\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
       "    // This is a function to directly update element attributes without going\n",
       "    // through eve.\n",
       "    Element.prototype.attribute = function(key, val) {\n",
       "        if (val === undefined) {\n",
       "            return this.node.getAttribute(key);\n",
       "        } else {\n",
       "            this.node.setAttribute(key, val);\n",
       "            return this;\n",
       "        }\n",
       "    };\n",
       "\n",
       "    Element.prototype.init_gadfly = function() {\n",
       "        this.mouseenter(Gadfly.plot_mouseover)\n",
       "            .mouseleave(Gadfly.plot_mouseout)\n",
       "            .dblclick(Gadfly.plot_dblclick)\n",
       "            .mousewheel(Gadfly.guide_background_scroll)\n",
       "            .drag(Gadfly.guide_background_drag_onmove,\n",
       "                  Gadfly.guide_background_drag_onstart,\n",
       "                  Gadfly.guide_background_drag_onend);\n",
       "        this.mouseenter(function (event) {\n",
       "            init_pan_zoom(this.plotroot());\n",
       "        });\n",
       "        return this;\n",
       "    };\n",
       "});\n",
       "\n",
       "\n",
       "// When the plot is moused over, emphasize the grid lines.\n",
       "Gadfly.plot_mouseover = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    var keyboard_zoom = function(event) {\n",
       "        if (event.which == 187) { // plus\n",
       "            increase_zoom_by_position(root, 0.1, true);\n",
       "        } else if (event.which == 189) { // minus\n",
       "            increase_zoom_by_position(root, -0.1, true);\n",
       "        }\n",
       "    };\n",
       "    root.data(\"keyboard_zoom\", keyboard_zoom);\n",
       "    window.addEventListener(\"keyup\", keyboard_zoom);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    xgridlines.data(\"unfocused_strokedash\",\n",
       "                    xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // reveal zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 1.0}, 250);\n",
       "};\n",
       "\n",
       "// Reset pan and zoom on double click\n",
       "Gadfly.plot_dblclick = function(event) {\n",
       "  set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
       "};\n",
       "\n",
       "// Unemphasize grid lines on mouse out.\n",
       "Gadfly.plot_mouseout = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
       "    root.data(\"keyboard_zoom\", undefined);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    var destcolor = root.data(\"unfocused_xgrid_color\");\n",
       "\n",
       "    xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // hide zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 0.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "var set_geometry_transform = function(root, tx, ty, scale) {\n",
       "    var xscalable = root.hasClass(\"xscalable\"),\n",
       "        yscalable = root.hasClass(\"yscalable\");\n",
       "\n",
       "    var old_scale = root.data(\"scale\");\n",
       "\n",
       "    var xscale = xscalable ? scale : 1.0,\n",
       "        yscale = yscalable ? scale : 1.0;\n",
       "\n",
       "    tx = xscalable ? tx : 0.0;\n",
       "    ty = yscalable ? ty : 0.0;\n",
       "\n",
       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
       "\n",
       "    root.selectAll(\".geometry, image\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.transform(t);\n",
       "        });\n",
       "\n",
       "    bounds = root.plotbounds();\n",
       "\n",
       "    if (yscalable) {\n",
       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
       "        root.selectAll(\".xfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(xfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".ylabels\")\n",
       "            .transform(xfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var y = cy * scale + ty;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (xscalable) {\n",
       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
       "        var xtrans = new Snap.Matrix().translate(tx, 0);\n",
       "        root.selectAll(\".yfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(yfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".xlabels\")\n",
       "            .transform(yfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
       "\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var x = cx * scale + tx;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
       "                    }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
       "    var size_attribs = [\"font-size\"];\n",
       "    var unscaled_selection = \".geometry, .geometry *\";\n",
       "    if (xscalable) {\n",
       "        size_attribs.push(\"rx\");\n",
       "        unscaled_selection += \", .xgridlines\";\n",
       "    }\n",
       "    if (yscalable) {\n",
       "        size_attribs.push(\"ry\");\n",
       "        unscaled_selection += \", .ygridlines\";\n",
       "    }\n",
       "\n",
       "    root.selectAll(unscaled_selection)\n",
       "        .forEach(function (element, i) {\n",
       "            // circle need special help\n",
       "            if (element.node.nodeName == \"circle\") {\n",
       "                var cx = element.attribute(\"cx\"),\n",
       "                    cy = element.attribute(\"cy\");\n",
       "                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
       "                                                        cx, cy);\n",
       "                element.transform(unscale_t);\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            for (i in size_attribs) {\n",
       "                var key = size_attribs[i];\n",
       "                var val = parseFloat(element.attribute(key));\n",
       "                if (val !== undefined && val != 0 && !isNaN(val)) {\n",
       "                    element.attribute(key, val * old_scale / scale);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "};\n",
       "\n",
       "\n",
       "// Find the most appropriate tick scale and update label visibility.\n",
       "var update_tickscale = function(root, scale, axis) {\n",
       "    if (!root.hasClass(axis + \"scalable\")) return;\n",
       "\n",
       "    var tickscales = root.data(axis + \"tickscales\");\n",
       "    var best_tickscale = 1.0;\n",
       "    var best_tickscale_dist = Infinity;\n",
       "    for (tickscale in tickscales) {\n",
       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
       "        if (dist < best_tickscale_dist) {\n",
       "            best_tickscale_dist = dist;\n",
       "            best_tickscale = tickscale;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
       "        root.data(axis + \"tickscale\", best_tickscale);\n",
       "        var mark_inscale_gridlines = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        var mark_inscale_labels = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
       "        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
       "    var old_scale = root.data(\"scale\");\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    // compute the viewport derived from tx, ty, and scale\n",
       "    var x_min = -width * scale - (scale * width - width),\n",
       "        x_max = width * scale,\n",
       "        y_min = -height * scale - (scale * height - height),\n",
       "        y_max = height * scale;\n",
       "\n",
       "    var x0 = bounds.x0 - scale * bounds.x0,\n",
       "        y0 = bounds.y0 - scale * bounds.y0;\n",
       "\n",
       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
       "\n",
       "    tx += x0;\n",
       "    ty += y0;\n",
       "\n",
       "    // when the scale change, we may need to alter which set of\n",
       "    // ticks is being displayed\n",
       "    if (scale != old_scale) {\n",
       "        update_tickscale(root, scale, \"x\");\n",
       "        update_tickscale(root, scale, \"y\");\n",
       "    }\n",
       "\n",
       "    set_geometry_transform(root, tx, ty, scale);\n",
       "\n",
       "    root.data(\"scale\", scale);\n",
       "    root.data(\"tx\", tx);\n",
       "    root.data(\"ty\", ty);\n",
       "};\n",
       "\n",
       "\n",
       "var scale_centered_translation = function(root, scale) {\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var scale0 = root.data(\"scale\");\n",
       "\n",
       "    // how off from center the current view is\n",
       "    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
       "        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
       "\n",
       "    // rescale offsets\n",
       "    xoff = xoff * scale / scale0;\n",
       "    yoff = yoff * scale / scale0;\n",
       "\n",
       "    // adjust for the panel position being scaled\n",
       "    var x_edge_adjust = bounds.x0 * (1 - scale),\n",
       "        y_edge_adjust = bounds.y0 * (1 - scale);\n",
       "\n",
       "    return {\n",
       "        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
       "        y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "// Initialize data for panning zooming if it isn't already.\n",
       "var init_pan_zoom = function(root) {\n",
       "    if (root.data(\"zoompan-ready\")) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    // The non-scaling-stroke trick. Rather than try to correct for the\n",
       "    // stroke-width when zooming, we force it to a fixed value.\n",
       "    var px_per_mm = root.node.getCTM().a;\n",
       "\n",
       "    // Drag events report deltas in pixels, which we'd like to convert to\n",
       "    // millimeters.\n",
       "    root.data(\"px_per_mm\", px_per_mm);\n",
       "\n",
       "    root.selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
       "        if (sw > 0) {\n",
       "            element.attribute(\"stroke-width\", sw);\n",
       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
       "        }\n",
       "    });\n",
       "\n",
       "    // Store ticks labels original tranformation\n",
       "    root.selectAll(\".xlabels > text, .ylabels > text\")\n",
       "        .forEach(function (element, i) {\n",
       "            var lm = element.transform().localMatrix;\n",
       "            element.data(\"static_transform\",\n",
       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
       "        });\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\");\n",
       "    var ygridlines = root.select(\".ygridlines\");\n",
       "    var xlabels = root.select(\".xlabels\");\n",
       "    var ylabels = root.select(\".ylabels\");\n",
       "\n",
       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
       "    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
       "    if (root.data(\"xtickscales\") === undefined) {\n",
       "\n",
       "        // index all the tick scales that are listed\n",
       "        var xtickscales = {};\n",
       "        var ytickscales = {};\n",
       "        var add_x_tick_scales = function (element, i) {\n",
       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "        var add_y_tick_scales = function (element, i) {\n",
       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "\n",
       "        if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
       "\n",
       "        root.data(\"xtickscales\", xtickscales);\n",
       "        root.data(\"ytickscales\", ytickscales);\n",
       "        root.data(\"xtickscale\", 1.0);\n",
       "    }\n",
       "\n",
       "    var min_scale = 1.0, max_scale = 1.0;\n",
       "    for (scale in xtickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    for (scale in ytickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    root.data(\"min_scale\", min_scale);\n",
       "    root.data(\"max_scale\", max_scale);\n",
       "\n",
       "    // store the original positions of labels\n",
       "    if (xlabels) {\n",
       "        xlabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"x\", element.asPX(\"x\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    if (ylabels) {\n",
       "        ylabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"y\", element.asPX(\"y\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    // mark grid lines and ticks as in or out of scale.\n",
       "    var mark_inscale = function (element, i) {\n",
       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
       "    };\n",
       "\n",
       "    if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "    if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "\n",
       "    // figure out the upper ond lower bounds on panning using the maximum\n",
       "    // and minum grid lines\n",
       "    var bounds = root.plotbounds();\n",
       "    var pan_bounds = {\n",
       "        x0: 0.0,\n",
       "        y0: 0.0,\n",
       "        x1: 0.0,\n",
       "        y1: 0.0\n",
       "    };\n",
       "\n",
       "    if (xgridlines) {\n",
       "        xgridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
       "                        pan_bounds.x0 = bounds.x1 - bbox.x;\n",
       "                    }\n",
       "                    if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
       "                        pan_bounds.x1 = bounds.x0 - bbox.x;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (ygridlines) {\n",
       "        ygridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
       "                        pan_bounds.y0 = bounds.y1 - bbox.y;\n",
       "                    }\n",
       "                    if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
       "                        pan_bounds.y1 = bounds.y0 - bbox.y;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // nudge these values a little\n",
       "    pan_bounds.x0 -= 5;\n",
       "    pan_bounds.x1 += 5;\n",
       "    pan_bounds.y0 -= 5;\n",
       "    pan_bounds.y1 += 5;\n",
       "    root.data(\"pan_bounds\", pan_bounds);\n",
       "\n",
       "    root.data(\"zoompan-ready\", true)\n",
       "};\n",
       "\n",
       "\n",
       "// drag actions, i.e. zooming and panning\n",
       "var pan_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        root.data(\"dx\", 0);\n",
       "        root.data(\"dy\", 0);\n",
       "        root.data(\"tx0\", root.data(\"tx\"));\n",
       "        root.data(\"ty0\", root.data(\"ty\"));\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        dx /= px_per_mm;\n",
       "        dy /= px_per_mm;\n",
       "\n",
       "        var tx0 = root.data(\"tx\"),\n",
       "            ty0 = root.data(\"ty\");\n",
       "\n",
       "        var dx0 = root.data(\"dx\"),\n",
       "            dy0 = root.data(\"dy\");\n",
       "\n",
       "        root.data(\"dx\", dx);\n",
       "        root.data(\"dy\", dy);\n",
       "\n",
       "        dx = dx - dx0;\n",
       "        dy = dy - dy0;\n",
       "\n",
       "        var tx = tx0 + dx,\n",
       "            ty = ty0 + dy;\n",
       "\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
       "    }\n",
       "};\n",
       "\n",
       "var zoom_box;\n",
       "var zoom_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        var bounds = root.plotbounds();\n",
       "        var width = bounds.x1 - bounds.x0,\n",
       "            height = bounds.y1 - bounds.y0;\n",
       "        var ratio = width / height;\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        x = xscalable ? x / px_per_mm : bounds.x0;\n",
       "        y = yscalable ? y / px_per_mm : bounds.y0;\n",
       "        var w = xscalable ? 0 : width;\n",
       "        var h = yscalable ? 0 : height;\n",
       "        zoom_box = root.rect(x, y, w, h).attr({\n",
       "            \"fill\": \"#000\",\n",
       "            \"opacity\": 0.25\n",
       "        });\n",
       "        zoom_box.data(\"ratio\", ratio);\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        var bounds = root.plotbounds();\n",
       "        if (yscalable) {\n",
       "            y /= px_per_mm;\n",
       "            y = Math.max(bounds.y0, y);\n",
       "            y = Math.min(bounds.y1, y);\n",
       "        } else {\n",
       "            y = bounds.y1;\n",
       "        }\n",
       "        if (xscalable) {\n",
       "            x /= px_per_mm;\n",
       "            x = Math.max(bounds.x0, x);\n",
       "            x = Math.min(bounds.x1, x);\n",
       "        } else {\n",
       "            x = bounds.x1;\n",
       "        }\n",
       "\n",
       "        dx = x - zoom_box.attr(\"x\");\n",
       "        dy = y - zoom_box.attr(\"y\");\n",
       "        if (xscalable && yscalable) {\n",
       "            var ratio = zoom_box.data(\"ratio\");\n",
       "            var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
       "            var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
       "            dx = width * dx / Math.abs(dx);\n",
       "            dy = height * dy / Math.abs(dy);\n",
       "        }\n",
       "        var xoffset = 0,\n",
       "            yoffset = 0;\n",
       "        if (dx < 0) {\n",
       "            xoffset = dx;\n",
       "            dx = -1 * dx;\n",
       "        }\n",
       "        if (dy < 0) {\n",
       "            yoffset = dy;\n",
       "            dy = -1 * dy;\n",
       "        }\n",
       "        if (isNaN(dy)) {\n",
       "            dy = 0.0;\n",
       "        }\n",
       "        if (isNaN(dx)) {\n",
       "            dx = 0.0;\n",
       "        }\n",
       "        zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
       "        zoom_box.attr(\"width\", dx);\n",
       "        zoom_box.attr(\"height\", dy);\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var zoom_bounds = zoom_box.getBBox();\n",
       "        if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
       "            return;\n",
       "        }\n",
       "        var plot_bounds = root.plotbounds();\n",
       "        var zoom_factor = 1.0;\n",
       "        if (yscalable) {\n",
       "            zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
       "        } else {\n",
       "            zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
       "        }\n",
       "        var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
       "            ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
       "        zoom_box.remove();\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        zoom_box.remove();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
       "    var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
       "    var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
       "    var drag_action = zoomable ? zoom_action :\n",
       "                      panable  ? pan_action :\n",
       "                                 undefined;\n",
       "    root.data(\"drag_action\", drag_action);\n",
       "    if (drag_action) {\n",
       "        var cancel_drag_action = function(event) {\n",
       "            if (event.which == 27) { // esc key\n",
       "                drag_action.cancel(root);\n",
       "                root.data(\"drag_action\", undefined);\n",
       "            }\n",
       "        };\n",
       "        window.addEventListener(\"keyup\", cancel_drag_action);\n",
       "        root.data(\"cancel_drag_action\", cancel_drag_action);\n",
       "        drag_action.start(root, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.update(root, dx, dy, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onend = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
       "    root.data(\"cancel_drag_action\", undefined);\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.end(root, event);\n",
       "    }\n",
       "    root.data(\"drag_action\", undefined);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_scroll = function(event) {\n",
       "    if (event.shiftKey) {\n",
       "        increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
       "        event.preventDefault();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseover = function(event) {\n",
       "    this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseout = function(event) {\n",
       "     this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomout_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_track_click = function(event) {\n",
       "    // TODO\n",
       "};\n",
       "\n",
       "\n",
       "// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
       "// The constants a, b, and c are solved using the constraint that the function\n",
       "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
       "var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return a * Math.exp(b * position) + c;\n",
       "}\n",
       "\n",
       "// inverse of scale_from_slider_position\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return 1 / b * Math.log((scale - c) / a);\n",
       "}\n",
       "\n",
       "var increase_zoom_by_position = function(root, delta_position, animate) {\n",
       "    var scale = root.data(\"scale\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\");\n",
       "    var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
       "    position += delta_position;\n",
       "    scale = scale_from_slider_position(position, min_scale, max_scale);\n",
       "    set_zoom(root, scale, animate);\n",
       "}\n",
       "\n",
       "var set_zoom = function(root, scale, animate) {\n",
       "    var min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"scale\");\n",
       "    var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
       "    if (animate) {\n",
       "        Snap.animate(\n",
       "            old_scale,\n",
       "            new_scale,\n",
       "            function (new_scale) {\n",
       "                update_plot_scale(root, new_scale);\n",
       "            },\n",
       "            200);\n",
       "    } else {\n",
       "        update_plot_scale(root, new_scale);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "var update_plot_scale = function(root, new_scale) {\n",
       "    var trans = scale_centered_translation(root, new_scale);\n",
       "    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
       "\n",
       "    root.selectAll(\".zoomslider_thumb\")\n",
       "        .forEach(function (element, i) {\n",
       "            var min_pos = element.data(\"min_pos\"),\n",
       "                max_pos = element.data(\"max_pos\"),\n",
       "                min_scale = root.data(\"min_scale\"),\n",
       "                max_scale = root.data(\"max_scale\");\n",
       "            var xmid = (min_pos + max_pos) / 2;\n",
       "            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
       "            element.transform(new Snap.Matrix().translate(\n",
       "                Math.max(min_pos, Math.min(\n",
       "                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
       "    });\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var min_pos = this.data(\"min_pos\"),\n",
       "        max_pos = this.data(\"max_pos\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"old_scale\");\n",
       "\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var xmid = (min_pos + max_pos) / 2;\n",
       "    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
       "                   dx / (max_pos - min_pos);\n",
       "\n",
       "    // compute the new scale\n",
       "    var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
       "\n",
       "    update_plot_scale(root, new_scale);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    // keep track of what the scale was when we started dragging\n",
       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragend = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "var toggle_color_class = function(root, color_class, ison) {\n",
       "    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
       "    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
       "    if (ison) {\n",
       "        guides.animate({opacity: 0.5}, 250);\n",
       "        geoms.animate({opacity: 0.0}, 250);\n",
       "    } else {\n",
       "        guides.animate({opacity: 1.0}, 250);\n",
       "        geoms.animate({opacity: 1.0}, 250);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.colorkey_swatch_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var color_class = this.data(\"color_class\");\n",
       "\n",
       "    if (event.shiftKey) {\n",
       "        root.selectAll(\".colorkey text\")\n",
       "            .forEach(function (element) {\n",
       "                var other_color_class = element.data(\"color_class\");\n",
       "                if (other_color_class != color_class) {\n",
       "                    toggle_color_class(root, other_color_class,\n",
       "                                       element.attr(\"opacity\") == 1.0);\n",
       "                }\n",
       "            });\n",
       "    } else {\n",
       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "return Gadfly;\n",
       "\n",
       "}));\n",
       "\n",
       "\n",
       "//@ sourceURL=gadfly.js\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#img-d0f2c0e7\");\n",
       "fig.select(\"#img-d0f2c0e7-10\")\n",
       "   .init_gadfly();\n",
       "fig.select(\"#img-d0f2c0e7-12\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-12\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-13\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-13\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-17\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-17\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-17\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-19\")\n",
       "   .data(\"max_pos\", 102.76)\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-19\")\n",
       "   .data(\"min_pos\", 85.76)\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-19\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#img-d0f2c0e7-20\")\n",
       "   .data(\"max_pos\", 102.76)\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-20\")\n",
       "   .data(\"min_pos\", 85.76)\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-20\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-20\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-20\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-21\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-21\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-d0f2c0e7-21\")\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis_topic_res(samples, topicdata[\"K\"], topicdata[\"V\"], 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nbmodel (generic function with 10 methods)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data; loaded data is a list of dict named `nbstandata`\n",
    "include(Pkg.dir(\"Turing\")*\"/stan-models/MoC-stan.data.jl\")\n",
    "topicdata2 = nbstandata[1]\n",
    "\n",
    "# Load model\n",
    "include(Pkg.dir(\"Turing\")*\"/stan-models/MoC.model.jl\")\n",
    "#= NOTE: loaded model is defined as below\n",
    "@model nbmodel(K, V, M, N, z, w, doc, alpha, beta) = begin\n",
    "  theta ~ Dirichlet(alpha)\n",
    "\n",
    "  phi = Array{Any}(K)\n",
    "  for k = 1:K\n",
    "    phi[k] ~ Dirichlet(beta)\n",
    "  end\n",
    "\n",
    "  log_theta = log(theta)\n",
    "  Turing.acclogp!(vi, sum(log_theta[z[1:M]]))\n",
    "\n",
    "  log_phi = map(x->log(x), phi)\n",
    "  for n = 1:N\n",
    "    Turing.acclogp!(vi, log_phi[z[doc[n]]][w[n]])\n",
    "  end\n",
    "\n",
    "  phi\n",
    "end\n",
    "\n",
    "=#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Turing]:  Assume - `theta` is a parameter\n",
      "  in @~(::Any, ::Any) at compiler.jl:76\n",
      "[Turing]:  Assume - `phi` is a parameter\n",
      "  in @~(::Any, ::Any) at compiler.jl:76\n",
      "[Turing] looking for good initial eps...\n",
      "[Turing.WARNING]: Numerical error has been found in gradients.\n",
      "  in verifygrad(::Array{Float64,1}) at ad.jl:87\n",
      "[Turing.NUTS] found initial ϵ: 0.605\n",
      "[Turing]:  Adapted ϵ = 1.1348527382479003e-76, 200 HMC iterations is used for adaption.\n",
      "  in adapt_step_size(::Turing.WarmUpManager, ::Float64) at adapt.jl:54\n",
      "[NUTS] Finished with\n",
      "  Running time        = 2.253066046;\n",
      "  #lf / sample        = 1.003;\n",
      "  #evals / sample     = 1.005;\n",
      "  pre-cond. diag mat  = [0.999997,0.999994,0.999994,0.....\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Object of type \"Turing.Chain\"\n",
       "\n",
       "Iterations = 1:1000\n",
       "Thinning interval = 1\n",
       "Chains = 1\n",
       "Samples per chain = 1000\n",
       "\n",
       "[0.0394719 0.67126 … 1.10815e-7 0.0209499; 0.0394719 0.67126 … 1.10815e-7 0.0209499; … ; 0.0394719 0.67126 … 1.10815e-7 0.0209499; 0.0394719 0.67126 … 1.10815e-7 0.0209499]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples2 = sample(nbmodel(data=topicdata2), NUTS(1000, 0.65))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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       "                        this.mouseover(fn2, scope);\n",
       "                    } else {\n",
       "                        this.mouseout(fn2, scope);\n",
       "                    }\n",
       "                }\n",
       "                return this;\n",
       "            };\n",
       "        })(events[i]);\n",
       "    }\n",
       "\n",
       "\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\n",
       "        if (Snap.is(fn, \"function\")) {\n",
       "            var el = this;\n",
       "            var fn2 = function (event) {\n",
       "                fn.apply(el, [event]);\n",
       "            };\n",
       "        }\n",
       "\n",
       "        this.node.addEventListener(\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
       "            fn2);\n",
       "\n",
       "        return this;\n",
       "    };\n",
       "\n",
       "\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
       "    // This is a function to directly update element attributes without going\n",
       "    // through eve.\n",
       "    Element.prototype.attribute = function(key, val) {\n",
       "        if (val === undefined) {\n",
       "            return this.node.getAttribute(key);\n",
       "        } else {\n",
       "            this.node.setAttribute(key, val);\n",
       "            return this;\n",
       "        }\n",
       "    };\n",
       "\n",
       "    Element.prototype.init_gadfly = function() {\n",
       "        this.mouseenter(Gadfly.plot_mouseover)\n",
       "            .mouseleave(Gadfly.plot_mouseout)\n",
       "            .dblclick(Gadfly.plot_dblclick)\n",
       "            .mousewheel(Gadfly.guide_background_scroll)\n",
       "            .drag(Gadfly.guide_background_drag_onmove,\n",
       "                  Gadfly.guide_background_drag_onstart,\n",
       "                  Gadfly.guide_background_drag_onend);\n",
       "        this.mouseenter(function (event) {\n",
       "            init_pan_zoom(this.plotroot());\n",
       "        });\n",
       "        return this;\n",
       "    };\n",
       "});\n",
       "\n",
       "\n",
       "// When the plot is moused over, emphasize the grid lines.\n",
       "Gadfly.plot_mouseover = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    var keyboard_zoom = function(event) {\n",
       "        if (event.which == 187) { // plus\n",
       "            increase_zoom_by_position(root, 0.1, true);\n",
       "        } else if (event.which == 189) { // minus\n",
       "            increase_zoom_by_position(root, -0.1, true);\n",
       "        }\n",
       "    };\n",
       "    root.data(\"keyboard_zoom\", keyboard_zoom);\n",
       "    window.addEventListener(\"keyup\", keyboard_zoom);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    xgridlines.data(\"unfocused_strokedash\",\n",
       "                    xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // reveal zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 1.0}, 250);\n",
       "};\n",
       "\n",
       "// Reset pan and zoom on double click\n",
       "Gadfly.plot_dblclick = function(event) {\n",
       "  set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
       "};\n",
       "\n",
       "// Unemphasize grid lines on mouse out.\n",
       "Gadfly.plot_mouseout = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
       "    root.data(\"keyboard_zoom\", undefined);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    var destcolor = root.data(\"unfocused_xgrid_color\");\n",
       "\n",
       "    xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // hide zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 0.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "var set_geometry_transform = function(root, tx, ty, scale) {\n",
       "    var xscalable = root.hasClass(\"xscalable\"),\n",
       "        yscalable = root.hasClass(\"yscalable\");\n",
       "\n",
       "    var old_scale = root.data(\"scale\");\n",
       "\n",
       "    var xscale = xscalable ? scale : 1.0,\n",
       "        yscale = yscalable ? scale : 1.0;\n",
       "\n",
       "    tx = xscalable ? tx : 0.0;\n",
       "    ty = yscalable ? ty : 0.0;\n",
       "\n",
       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
       "\n",
       "    root.selectAll(\".geometry, image\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.transform(t);\n",
       "        });\n",
       "\n",
       "    bounds = root.plotbounds();\n",
       "\n",
       "    if (yscalable) {\n",
       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
       "        root.selectAll(\".xfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(xfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".ylabels\")\n",
       "            .transform(xfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var y = cy * scale + ty;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (xscalable) {\n",
       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
       "        var xtrans = new Snap.Matrix().translate(tx, 0);\n",
       "        root.selectAll(\".yfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(yfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".xlabels\")\n",
       "            .transform(yfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
       "\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var x = cx * scale + tx;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
       "                    }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
       "    var size_attribs = [\"font-size\"];\n",
       "    var unscaled_selection = \".geometry, .geometry *\";\n",
       "    if (xscalable) {\n",
       "        size_attribs.push(\"rx\");\n",
       "        unscaled_selection += \", .xgridlines\";\n",
       "    }\n",
       "    if (yscalable) {\n",
       "        size_attribs.push(\"ry\");\n",
       "        unscaled_selection += \", .ygridlines\";\n",
       "    }\n",
       "\n",
       "    root.selectAll(unscaled_selection)\n",
       "        .forEach(function (element, i) {\n",
       "            // circle need special help\n",
       "            if (element.node.nodeName == \"circle\") {\n",
       "                var cx = element.attribute(\"cx\"),\n",
       "                    cy = element.attribute(\"cy\");\n",
       "                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
       "                                                        cx, cy);\n",
       "                element.transform(unscale_t);\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            for (i in size_attribs) {\n",
       "                var key = size_attribs[i];\n",
       "                var val = parseFloat(element.attribute(key));\n",
       "                if (val !== undefined && val != 0 && !isNaN(val)) {\n",
       "                    element.attribute(key, val * old_scale / scale);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "};\n",
       "\n",
       "\n",
       "// Find the most appropriate tick scale and update label visibility.\n",
       "var update_tickscale = function(root, scale, axis) {\n",
       "    if (!root.hasClass(axis + \"scalable\")) return;\n",
       "\n",
       "    var tickscales = root.data(axis + \"tickscales\");\n",
       "    var best_tickscale = 1.0;\n",
       "    var best_tickscale_dist = Infinity;\n",
       "    for (tickscale in tickscales) {\n",
       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
       "        if (dist < best_tickscale_dist) {\n",
       "            best_tickscale_dist = dist;\n",
       "            best_tickscale = tickscale;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
       "        root.data(axis + \"tickscale\", best_tickscale);\n",
       "        var mark_inscale_gridlines = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        var mark_inscale_labels = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
       "        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
       "    var old_scale = root.data(\"scale\");\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    // compute the viewport derived from tx, ty, and scale\n",
       "    var x_min = -width * scale - (scale * width - width),\n",
       "        x_max = width * scale,\n",
       "        y_min = -height * scale - (scale * height - height),\n",
       "        y_max = height * scale;\n",
       "\n",
       "    var x0 = bounds.x0 - scale * bounds.x0,\n",
       "        y0 = bounds.y0 - scale * bounds.y0;\n",
       "\n",
       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
       "\n",
       "    tx += x0;\n",
       "    ty += y0;\n",
       "\n",
       "    // when the scale change, we may need to alter which set of\n",
       "    // ticks is being displayed\n",
       "    if (scale != old_scale) {\n",
       "        update_tickscale(root, scale, \"x\");\n",
       "        update_tickscale(root, scale, \"y\");\n",
       "    }\n",
       "\n",
       "    set_geometry_transform(root, tx, ty, scale);\n",
       "\n",
       "    root.data(\"scale\", scale);\n",
       "    root.data(\"tx\", tx);\n",
       "    root.data(\"ty\", ty);\n",
       "};\n",
       "\n",
       "\n",
       "var scale_centered_translation = function(root, scale) {\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var scale0 = root.data(\"scale\");\n",
       "\n",
       "    // how off from center the current view is\n",
       "    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
       "        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
       "\n",
       "    // rescale offsets\n",
       "    xoff = xoff * scale / scale0;\n",
       "    yoff = yoff * scale / scale0;\n",
       "\n",
       "    // adjust for the panel position being scaled\n",
       "    var x_edge_adjust = bounds.x0 * (1 - scale),\n",
       "        y_edge_adjust = bounds.y0 * (1 - scale);\n",
       "\n",
       "    return {\n",
       "        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
       "        y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "// Initialize data for panning zooming if it isn't already.\n",
       "var init_pan_zoom = function(root) {\n",
       "    if (root.data(\"zoompan-ready\")) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    // The non-scaling-stroke trick. Rather than try to correct for the\n",
       "    // stroke-width when zooming, we force it to a fixed value.\n",
       "    var px_per_mm = root.node.getCTM().a;\n",
       "\n",
       "    // Drag events report deltas in pixels, which we'd like to convert to\n",
       "    // millimeters.\n",
       "    root.data(\"px_per_mm\", px_per_mm);\n",
       "\n",
       "    root.selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
       "        if (sw > 0) {\n",
       "            element.attribute(\"stroke-width\", sw);\n",
       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
       "        }\n",
       "    });\n",
       "\n",
       "    // Store ticks labels original tranformation\n",
       "    root.selectAll(\".xlabels > text, .ylabels > text\")\n",
       "        .forEach(function (element, i) {\n",
       "            var lm = element.transform().localMatrix;\n",
       "            element.data(\"static_transform\",\n",
       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
       "        });\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\");\n",
       "    var ygridlines = root.select(\".ygridlines\");\n",
       "    var xlabels = root.select(\".xlabels\");\n",
       "    var ylabels = root.select(\".ylabels\");\n",
       "\n",
       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
       "    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
       "    if (root.data(\"xtickscales\") === undefined) {\n",
       "\n",
       "        // index all the tick scales that are listed\n",
       "        var xtickscales = {};\n",
       "        var ytickscales = {};\n",
       "        var add_x_tick_scales = function (element, i) {\n",
       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "        var add_y_tick_scales = function (element, i) {\n",
       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "\n",
       "        if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
       "\n",
       "        root.data(\"xtickscales\", xtickscales);\n",
       "        root.data(\"ytickscales\", ytickscales);\n",
       "        root.data(\"xtickscale\", 1.0);\n",
       "    }\n",
       "\n",
       "    var min_scale = 1.0, max_scale = 1.0;\n",
       "    for (scale in xtickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    for (scale in ytickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    root.data(\"min_scale\", min_scale);\n",
       "    root.data(\"max_scale\", max_scale);\n",
       "\n",
       "    // store the original positions of labels\n",
       "    if (xlabels) {\n",
       "        xlabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"x\", element.asPX(\"x\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    if (ylabels) {\n",
       "        ylabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"y\", element.asPX(\"y\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    // mark grid lines and ticks as in or out of scale.\n",
       "    var mark_inscale = function (element, i) {\n",
       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
       "    };\n",
       "\n",
       "    if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "    if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "\n",
       "    // figure out the upper ond lower bounds on panning using the maximum\n",
       "    // and minum grid lines\n",
       "    var bounds = root.plotbounds();\n",
       "    var pan_bounds = {\n",
       "        x0: 0.0,\n",
       "        y0: 0.0,\n",
       "        x1: 0.0,\n",
       "        y1: 0.0\n",
       "    };\n",
       "\n",
       "    if (xgridlines) {\n",
       "        xgridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
       "                        pan_bounds.x0 = bounds.x1 - bbox.x;\n",
       "                    }\n",
       "                    if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
       "                        pan_bounds.x1 = bounds.x0 - bbox.x;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (ygridlines) {\n",
       "        ygridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
       "                        pan_bounds.y0 = bounds.y1 - bbox.y;\n",
       "                    }\n",
       "                    if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
       "                        pan_bounds.y1 = bounds.y0 - bbox.y;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // nudge these values a little\n",
       "    pan_bounds.x0 -= 5;\n",
       "    pan_bounds.x1 += 5;\n",
       "    pan_bounds.y0 -= 5;\n",
       "    pan_bounds.y1 += 5;\n",
       "    root.data(\"pan_bounds\", pan_bounds);\n",
       "\n",
       "    root.data(\"zoompan-ready\", true)\n",
       "};\n",
       "\n",
       "\n",
       "// drag actions, i.e. zooming and panning\n",
       "var pan_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        root.data(\"dx\", 0);\n",
       "        root.data(\"dy\", 0);\n",
       "        root.data(\"tx0\", root.data(\"tx\"));\n",
       "        root.data(\"ty0\", root.data(\"ty\"));\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        dx /= px_per_mm;\n",
       "        dy /= px_per_mm;\n",
       "\n",
       "        var tx0 = root.data(\"tx\"),\n",
       "            ty0 = root.data(\"ty\");\n",
       "\n",
       "        var dx0 = root.data(\"dx\"),\n",
       "            dy0 = root.data(\"dy\");\n",
       "\n",
       "        root.data(\"dx\", dx);\n",
       "        root.data(\"dy\", dy);\n",
       "\n",
       "        dx = dx - dx0;\n",
       "        dy = dy - dy0;\n",
       "\n",
       "        var tx = tx0 + dx,\n",
       "            ty = ty0 + dy;\n",
       "\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
       "    }\n",
       "};\n",
       "\n",
       "var zoom_box;\n",
       "var zoom_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        var bounds = root.plotbounds();\n",
       "        var width = bounds.x1 - bounds.x0,\n",
       "            height = bounds.y1 - bounds.y0;\n",
       "        var ratio = width / height;\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        x = xscalable ? x / px_per_mm : bounds.x0;\n",
       "        y = yscalable ? y / px_per_mm : bounds.y0;\n",
       "        var w = xscalable ? 0 : width;\n",
       "        var h = yscalable ? 0 : height;\n",
       "        zoom_box = root.rect(x, y, w, h).attr({\n",
       "            \"fill\": \"#000\",\n",
       "            \"opacity\": 0.25\n",
       "        });\n",
       "        zoom_box.data(\"ratio\", ratio);\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        var bounds = root.plotbounds();\n",
       "        if (yscalable) {\n",
       "            y /= px_per_mm;\n",
       "            y = Math.max(bounds.y0, y);\n",
       "            y = Math.min(bounds.y1, y);\n",
       "        } else {\n",
       "            y = bounds.y1;\n",
       "        }\n",
       "        if (xscalable) {\n",
       "            x /= px_per_mm;\n",
       "            x = Math.max(bounds.x0, x);\n",
       "            x = Math.min(bounds.x1, x);\n",
       "        } else {\n",
       "            x = bounds.x1;\n",
       "        }\n",
       "\n",
       "        dx = x - zoom_box.attr(\"x\");\n",
       "        dy = y - zoom_box.attr(\"y\");\n",
       "        if (xscalable && yscalable) {\n",
       "            var ratio = zoom_box.data(\"ratio\");\n",
       "            var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
       "            var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
       "            dx = width * dx / Math.abs(dx);\n",
       "            dy = height * dy / Math.abs(dy);\n",
       "        }\n",
       "        var xoffset = 0,\n",
       "            yoffset = 0;\n",
       "        if (dx < 0) {\n",
       "            xoffset = dx;\n",
       "            dx = -1 * dx;\n",
       "        }\n",
       "        if (dy < 0) {\n",
       "            yoffset = dy;\n",
       "            dy = -1 * dy;\n",
       "        }\n",
       "        if (isNaN(dy)) {\n",
       "            dy = 0.0;\n",
       "        }\n",
       "        if (isNaN(dx)) {\n",
       "            dx = 0.0;\n",
       "        }\n",
       "        zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
       "        zoom_box.attr(\"width\", dx);\n",
       "        zoom_box.attr(\"height\", dy);\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var zoom_bounds = zoom_box.getBBox();\n",
       "        if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
       "            return;\n",
       "        }\n",
       "        var plot_bounds = root.plotbounds();\n",
       "        var zoom_factor = 1.0;\n",
       "        if (yscalable) {\n",
       "            zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
       "        } else {\n",
       "            zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
       "        }\n",
       "        var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
       "            ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
       "        zoom_box.remove();\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        zoom_box.remove();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
       "    var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
       "    var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
       "    var drag_action = zoomable ? zoom_action :\n",
       "                      panable  ? pan_action :\n",
       "                                 undefined;\n",
       "    root.data(\"drag_action\", drag_action);\n",
       "    if (drag_action) {\n",
       "        var cancel_drag_action = function(event) {\n",
       "            if (event.which == 27) { // esc key\n",
       "                drag_action.cancel(root);\n",
       "                root.data(\"drag_action\", undefined);\n",
       "            }\n",
       "        };\n",
       "        window.addEventListener(\"keyup\", cancel_drag_action);\n",
       "        root.data(\"cancel_drag_action\", cancel_drag_action);\n",
       "        drag_action.start(root, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.update(root, dx, dy, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onend = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
       "    root.data(\"cancel_drag_action\", undefined);\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.end(root, event);\n",
       "    }\n",
       "    root.data(\"drag_action\", undefined);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_scroll = function(event) {\n",
       "    if (event.shiftKey) {\n",
       "        increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
       "        event.preventDefault();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseover = function(event) {\n",
       "    this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseout = function(event) {\n",
       "     this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomout_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_track_click = function(event) {\n",
       "    // TODO\n",
       "};\n",
       "\n",
       "\n",
       "// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
       "// The constants a, b, and c are solved using the constraint that the function\n",
       "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
       "var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return a * Math.exp(b * position) + c;\n",
       "}\n",
       "\n",
       "// inverse of scale_from_slider_position\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return 1 / b * Math.log((scale - c) / a);\n",
       "}\n",
       "\n",
       "var increase_zoom_by_position = function(root, delta_position, animate) {\n",
       "    var scale = root.data(\"scale\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\");\n",
       "    var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
       "    position += delta_position;\n",
       "    scale = scale_from_slider_position(position, min_scale, max_scale);\n",
       "    set_zoom(root, scale, animate);\n",
       "}\n",
       "\n",
       "var set_zoom = function(root, scale, animate) {\n",
       "    var min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"scale\");\n",
       "    var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
       "    if (animate) {\n",
       "        Snap.animate(\n",
       "            old_scale,\n",
       "            new_scale,\n",
       "            function (new_scale) {\n",
       "                update_plot_scale(root, new_scale);\n",
       "            },\n",
       "            200);\n",
       "    } else {\n",
       "        update_plot_scale(root, new_scale);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "var update_plot_scale = function(root, new_scale) {\n",
       "    var trans = scale_centered_translation(root, new_scale);\n",
       "    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
       "\n",
       "    root.selectAll(\".zoomslider_thumb\")\n",
       "        .forEach(function (element, i) {\n",
       "            var min_pos = element.data(\"min_pos\"),\n",
       "                max_pos = element.data(\"max_pos\"),\n",
       "                min_scale = root.data(\"min_scale\"),\n",
       "                max_scale = root.data(\"max_scale\");\n",
       "            var xmid = (min_pos + max_pos) / 2;\n",
       "            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
       "            element.transform(new Snap.Matrix().translate(\n",
       "                Math.max(min_pos, Math.min(\n",
       "                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
       "    });\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var min_pos = this.data(\"min_pos\"),\n",
       "        max_pos = this.data(\"max_pos\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"old_scale\");\n",
       "\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var xmid = (min_pos + max_pos) / 2;\n",
       "    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
       "                   dx / (max_pos - min_pos);\n",
       "\n",
       "    // compute the new scale\n",
       "    var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
       "\n",
       "    update_plot_scale(root, new_scale);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    // keep track of what the scale was when we started dragging\n",
       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragend = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "var toggle_color_class = function(root, color_class, ison) {\n",
       "    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
       "    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
       "    if (ison) {\n",
       "        guides.animate({opacity: 0.5}, 250);\n",
       "        geoms.animate({opacity: 0.0}, 250);\n",
       "    } else {\n",
       "        guides.animate({opacity: 1.0}, 250);\n",
       "        geoms.animate({opacity: 1.0}, 250);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.colorkey_swatch_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var color_class = this.data(\"color_class\");\n",
       "\n",
       "    if (event.shiftKey) {\n",
       "        root.selectAll(\".colorkey text\")\n",
       "            .forEach(function (element) {\n",
       "                var other_color_class = element.data(\"color_class\");\n",
       "                if (other_color_class != color_class) {\n",
       "                    toggle_color_class(root, other_color_class,\n",
       "                                       element.attr(\"opacity\") == 1.0);\n",
       "                }\n",
       "            });\n",
       "    } else {\n",
       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "return Gadfly;\n",
       "\n",
       "}));\n",
       "\n",
       "\n",
       "//@ sourceURL=gadfly.js\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#img-d3d8811a\");\n",
       "fig.select(\"#img-d3d8811a-10\")\n",
       "   .init_gadfly();\n",
       "fig.select(\"#img-d3d8811a-12\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-12\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-13\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-13\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-17\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-17\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-17\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-19\")\n",
       "   .data(\"max_pos\", 102.76)\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-19\")\n",
       "   .data(\"min_pos\", 85.76)\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-19\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#img-d3d8811a-20\")\n",
       "   .data(\"max_pos\", 102.76)\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-20\")\n",
       "   .data(\"min_pos\", 85.76)\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-20\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-20\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-20\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-21\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-21\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-d3d8811a-21\")\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis_topic_res(samples2, topicdata2[\"K\"], topicdata2[\"V\"], 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "using ProgressMeter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x,n = 1,10\n",
    "p = Progress(n)\n",
    "for iter = 1:10\n",
    "    x *= 2\n",
    "    sleep(0.5)\n",
    "    ProgressMeter.next!(p; showvalues = [(:iter,iter), (:x,x)])\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "anaconda-cloud": {},
  "kernel_info": {
   "name": "julia-0.5"
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  "kernelspec": {
   "display_name": "Julia 0.5.2",
   "language": "julia",
   "name": "julia-0.5"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.5.2"
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 },
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 "nbformat_minor": 4
}