gene plot

gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "using RDatasets, Gadfly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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       "            return factory(Snap);\n",
       "        });\n",
       "    } else {\n",
       "        // Browser globals (glob is window)\n",
       "        // Snap adds itself to window\n",
       "        glob.Gadfly = factory(glob.Snap);\n",
       "    }\n",
       "}(this, function (Snap) {\n",
       "\n",
       "var Gadfly = {};\n",
       "\n",
       "// Get an x/y coordinate value in pixels\n",
       "var xPX = function(fig, x) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
       "};\n",
       "\n",
       "var yPX = function(fig, y) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
       "};\n",
       "\n",
       "\n",
       "Snap.plugin(function (Snap, Element, Paper, global) {\n",
       "    // Traverse upwards from a snap element to find and return the first\n",
       "    // note with the \"plotroot\" class.\n",
       "    Element.prototype.plotroot = function () {\n",
       "        var element = this;\n",
       "        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.svgroot = function () {\n",
       "        var element = this;\n",
       "        while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotbounds = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x0: bbox.x,\n",
       "            x1: bbox.x + bbox.width,\n",
       "            y0: bbox.y,\n",
       "            y1: bbox.y + bbox.height\n",
       "        };\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotcenter = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x: bbox.x + bbox.width / 2,\n",
       "            y: bbox.y + bbox.height / 2\n",
       "        };\n",
       "    };\n",
       "\n",
       "    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
       "    // does everything right.\n",
       "    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
       "    var events = [\"mouseenter\", \"mouseleave\"];\n",
       "\n",
       "    for (i in events) {\n",
       "        (function (event_name) {\n",
       "            var event_name = events[i];\n",
       "            Element.prototype[event_name] = function (fn, scope) {\n",
       "                if (Snap.is(fn, \"function\")) {\n",
       "                    var fn2 = function (event) {\n",
       "                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
       "                            return;\n",
       "                        }\n",
       "\n",
       "                        var reltg = event.relatedTarget ? event.relatedTarget :\n",
       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
       "\n",
       "                        if (reltg != this.node) {\n",
       "                            return fn.apply(this, event);\n",
       "                        }\n",
       "                    };\n",
       "\n",
       "                    if (event_name == \"mouseenter\") {\n",
       "                        this.mouseover(fn2, scope);\n",
       "                    } else {\n",
       "                        this.mouseout(fn2, scope);\n",
       "                    }\n",
       "                }\n",
       "                return this;\n",
       "            };\n",
       "        })(events[i]);\n",
       "    }\n",
       "\n",
       "\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\n",
       "        if (Snap.is(fn, \"function\")) {\n",
       "            var el = this;\n",
       "            var fn2 = function (event) {\n",
       "                fn.apply(el, [event]);\n",
       "            };\n",
       "        }\n",
       "\n",
       "        this.node.addEventListener(\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
       "            fn2);\n",
       "\n",
       "        return this;\n",
       "    };\n",
       "\n",
       "\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
       "    // This is a function to directly update element attributes without going\n",
       "    // through eve.\n",
       "    Element.prototype.attribute = function(key, val) {\n",
       "        if (val === undefined) {\n",
       "            return this.node.getAttribute(key);\n",
       "        } else {\n",
       "            this.node.setAttribute(key, val);\n",
       "            return this;\n",
       "        }\n",
       "    };\n",
       "});\n",
       "\n",
       "\n",
       "// When the plot is moused over, emphasize the grid lines.\n",
       "Gadfly.plot_mouseover = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    xgridlines.data(\"unfocused_strokedash\",\n",
       "                    xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // reveal zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 1.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "// Unemphasize grid lines on mouse out.\n",
       "Gadfly.plot_mouseout = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    var destcolor = root.data(\"unfocused_xgrid_color\");\n",
       "\n",
       "    xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // hide zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 0.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "var set_geometry_transform = function(root, tx, ty, scale) {\n",
       "    var xscalable = root.hasClass(\"xscalable\"),\n",
       "        yscalable = root.hasClass(\"yscalable\");\n",
       "\n",
       "    var old_scale = root.data(\"scale\");\n",
       "\n",
       "    var xscale = xscalable ? scale : 1.0,\n",
       "        yscale = yscalable ? scale : 1.0;\n",
       "\n",
       "    tx = xscalable ? tx : 0.0;\n",
       "    ty = yscalable ? ty : 0.0;\n",
       "\n",
       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
       "\n",
       "    root.selectAll(\".geometry, image\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.transform(t);\n",
       "        });\n",
       "\n",
       "    bounds = root.plotbounds();\n",
       "\n",
       "    if (yscalable) {\n",
       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
       "        root.selectAll(\".xfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(xfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".ylabels\")\n",
       "            .transform(xfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var y = cy * scale + ty;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (xscalable) {\n",
       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
       "        var xtrans = new Snap.Matrix().translate(tx, 0);\n",
       "        root.selectAll(\".yfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(yfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".xlabels\")\n",
       "            .transform(yfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
       "\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var x = cx * scale + tx;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
       "                    }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
       "    var size_attribs = [\"font-size\"];\n",
       "    var unscaled_selection = \".geometry, .geometry *\";\n",
       "    if (xscalable) {\n",
       "        size_attribs.push(\"rx\");\n",
       "        unscaled_selection += \", .xgridlines\";\n",
       "    }\n",
       "    if (yscalable) {\n",
       "        size_attribs.push(\"ry\");\n",
       "        unscaled_selection += \", .ygridlines\";\n",
       "    }\n",
       "\n",
       "    root.selectAll(unscaled_selection)\n",
       "        .forEach(function (element, i) {\n",
       "            // circle need special help\n",
       "            if (element.node.nodeName == \"circle\") {\n",
       "                var cx = element.attribute(\"cx\"),\n",
       "                    cy = element.attribute(\"cy\");\n",
       "                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
       "                                                        cx, cy);\n",
       "                element.transform(unscale_t);\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            for (i in size_attribs) {\n",
       "                var key = size_attribs[i];\n",
       "                var val = parseFloat(element.attribute(key));\n",
       "                if (val !== undefined && val != 0 && !isNaN(val)) {\n",
       "                    element.attribute(key, val * old_scale / scale);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "};\n",
       "\n",
       "\n",
       "// Find the most appropriate tick scale and update label visibility.\n",
       "var update_tickscale = function(root, scale, axis) {\n",
       "    if (!root.hasClass(axis + \"scalable\")) return;\n",
       "\n",
       "    var tickscales = root.data(axis + \"tickscales\");\n",
       "    var best_tickscale = 1.0;\n",
       "    var best_tickscale_dist = Infinity;\n",
       "    for (tickscale in tickscales) {\n",
       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
       "        if (dist < best_tickscale_dist) {\n",
       "            best_tickscale_dist = dist;\n",
       "            best_tickscale = tickscale;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
       "        root.data(axis + \"tickscale\", best_tickscale);\n",
       "        var mark_inscale_gridlines = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        var mark_inscale_labels = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
       "        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
       "    var old_scale = root.data(\"scale\");\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    // compute the viewport derived from tx, ty, and scale\n",
       "    var x_min = -width * scale - (scale * width - width),\n",
       "        x_max = width * scale,\n",
       "        y_min = -height * scale - (scale * height - height),\n",
       "        y_max = height * scale;\n",
       "\n",
       "    var x0 = bounds.x0 - scale * bounds.x0,\n",
       "        y0 = bounds.y0 - scale * bounds.y0;\n",
       "\n",
       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
       "\n",
       "    tx += x0;\n",
       "    ty += y0;\n",
       "\n",
       "    // when the scale change, we may need to alter which set of\n",
       "    // ticks is being displayed\n",
       "    if (scale != old_scale) {\n",
       "        update_tickscale(root, scale, \"x\");\n",
       "        update_tickscale(root, scale, \"y\");\n",
       "    }\n",
       "\n",
       "    set_geometry_transform(root, tx, ty, scale);\n",
       "\n",
       "    root.data(\"scale\", scale);\n",
       "    root.data(\"tx\", tx);\n",
       "    root.data(\"ty\", ty);\n",
       "};\n",
       "\n",
       "\n",
       "var scale_centered_translation = function(root, scale) {\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var scale0 = root.data(\"scale\");\n",
       "\n",
       "    // how off from center the current view is\n",
       "    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
       "        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
       "\n",
       "    // rescale offsets\n",
       "    xoff = xoff * scale / scale0;\n",
       "    yoff = yoff * scale / scale0;\n",
       "\n",
       "    // adjust for the panel position being scaled\n",
       "    var x_edge_adjust = bounds.x0 * (1 - scale),\n",
       "        y_edge_adjust = bounds.y0 * (1 - scale);\n",
       "\n",
       "    return {\n",
       "        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
       "        y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "// Initialize data for panning zooming if it isn't already.\n",
       "var init_pan_zoom = function(root) {\n",
       "    if (root.data(\"zoompan-ready\")) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    // The non-scaling-stroke trick. Rather than try to correct for the\n",
       "    // stroke-width when zooming, we force it to a fixed value.\n",
       "    var px_per_mm = root.node.getCTM().a;\n",
       "\n",
       "    // Drag events report deltas in pixels, which we'd like to convert to\n",
       "    // millimeters.\n",
       "    root.data(\"px_per_mm\", px_per_mm);\n",
       "\n",
       "    root.selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
       "        if (sw > 0) {\n",
       "            element.attribute(\"stroke-width\", sw);\n",
       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
       "        }\n",
       "    });\n",
       "\n",
       "    // Store ticks labels original tranformation\n",
       "    root.selectAll(\".xlabels > text, .ylabels > text\")\n",
       "        .forEach(function (element, i) {\n",
       "            var lm = element.transform().localMatrix;\n",
       "            element.data(\"static_transform\",\n",
       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
       "        });\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\");\n",
       "    var ygridlines = root.select(\".ygridlines\");\n",
       "    var xlabels = root.select(\".xlabels\");\n",
       "    var ylabels = root.select(\".ylabels\");\n",
       "\n",
       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
       "    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
       "    if (root.data(\"xtickscales\") === undefined) {\n",
       "\n",
       "        // index all the tick scales that are listed\n",
       "        var xtickscales = {};\n",
       "        var ytickscales = {};\n",
       "        var add_x_tick_scales = function (element, i) {\n",
       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "        var add_y_tick_scales = function (element, i) {\n",
       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "\n",
       "        if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
       "\n",
       "        root.data(\"xtickscales\", xtickscales);\n",
       "        root.data(\"ytickscales\", ytickscales);\n",
       "        root.data(\"xtickscale\", 1.0);\n",
       "    }\n",
       "\n",
       "    var min_scale = 1.0, max_scale = 1.0;\n",
       "    for (scale in xtickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    for (scale in ytickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    root.data(\"min_scale\", min_scale);\n",
       "    root.data(\"max_scale\", max_scale);\n",
       "\n",
       "    // store the original positions of labels\n",
       "    if (xlabels) {\n",
       "        xlabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"x\", element.asPX(\"x\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    if (ylabels) {\n",
       "        ylabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"y\", element.asPX(\"y\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    // mark grid lines and ticks as in or out of scale.\n",
       "    var mark_inscale = function (element, i) {\n",
       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
       "    };\n",
       "\n",
       "    if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "    if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "\n",
       "    // figure out the upper ond lower bounds on panning using the maximum\n",
       "    // and minum grid lines\n",
       "    var bounds = root.plotbounds();\n",
       "    var pan_bounds = {\n",
       "        x0: 0.0,\n",
       "        y0: 0.0,\n",
       "        x1: 0.0,\n",
       "        y1: 0.0\n",
       "    };\n",
       "\n",
       "    if (xgridlines) {\n",
       "        xgridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
       "                        pan_bounds.x0 = bounds.x1 - bbox.x;\n",
       "                    }\n",
       "                    if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
       "                        pan_bounds.x1 = bounds.x0 - bbox.x;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (ygridlines) {\n",
       "        ygridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
       "                        pan_bounds.y0 = bounds.y1 - bbox.y;\n",
       "                    }\n",
       "                    if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
       "                        pan_bounds.y1 = bounds.y0 - bbox.y;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // nudge these values a little\n",
       "    pan_bounds.x0 -= 5;\n",
       "    pan_bounds.x1 += 5;\n",
       "    pan_bounds.y0 -= 5;\n",
       "    pan_bounds.y1 += 5;\n",
       "    root.data(\"pan_bounds\", pan_bounds);\n",
       "\n",
       "    root.data(\"zoompan-ready\", true)\n",
       "};\n",
       "\n",
       "\n",
       "// Panning\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var dx0 = root.data(\"dx\"),\n",
       "        dy0 = root.data(\"dy\");\n",
       "\n",
       "    root.data(\"dx\", dx);\n",
       "    root.data(\"dy\", dy);\n",
       "\n",
       "    dx = dx - dx0;\n",
       "    dy = dy - dy0;\n",
       "\n",
       "    var tx = tx0 + dx,\n",
       "        ty = ty0 + dy;\n",
       "\n",
       "    set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    root.data(\"dx\", 0);\n",
       "    root.data(\"dy\", 0);\n",
       "    init_pan_zoom(root);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onend = function(event) {\n",
       "    var root = this.plotroot();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_scroll = function(event) {\n",
       "    if (event.shiftKey) {\n",
       "        var root = this.plotroot();\n",
       "        init_pan_zoom(root);\n",
       "        var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n",
       "        new_scale = Math.max(\n",
       "            root.data(\"min_scale\"),\n",
       "            Math.min(root.data(\"max_scale\"), new_scale))\n",
       "        update_plot_scale(root, new_scale);\n",
       "        event.stopPropagation();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseover = function(event) {\n",
       "    this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseout = function(event) {\n",
       "     this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomout_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "    var min_scale = root.data(\"min_scale\"),\n",
       "        scale = root.data(\"scale\");\n",
       "    Snap.animate(\n",
       "        scale,\n",
       "        Math.max(min_scale, scale / 1.5),\n",
       "        function (new_scale) {\n",
       "            update_plot_scale(root, new_scale);\n",
       "        },\n",
       "        200);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "    var max_scale = root.data(\"max_scale\"),\n",
       "        scale = root.data(\"scale\");\n",
       "\n",
       "    Snap.animate(\n",
       "        scale,\n",
       "        Math.min(max_scale, scale * 1.5),\n",
       "        function (new_scale) {\n",
       "            update_plot_scale(root, new_scale);\n",
       "        },\n",
       "        200);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_track_click = function(event) {\n",
       "    // TODO\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_mousedown = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_mouseup = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "// compute the position in [0, 1] of the zoom slider thumb from the current scale\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
       "    if (scale >= 1.0) {\n",
       "        return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n",
       "    }\n",
       "    else {\n",
       "        return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "var update_plot_scale = function(root, new_scale) {\n",
       "    var trans = scale_centered_translation(root, new_scale);\n",
       "    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
       "\n",
       "    root.selectAll(\".zoomslider_thumb\")\n",
       "        .forEach(function (element, i) {\n",
       "            var min_pos = element.data(\"min_pos\"),\n",
       "                max_pos = element.data(\"max_pos\"),\n",
       "                min_scale = root.data(\"min_scale\"),\n",
       "                max_scale = root.data(\"max_scale\");\n",
       "            var xmid = (min_pos + max_pos) / 2;\n",
       "            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
       "            element.transform(new Snap.Matrix().translate(\n",
       "                Math.max(min_pos, Math.min(\n",
       "                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
       "    });\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y) {\n",
       "    var root = this.plotroot();\n",
       "    var min_pos = this.data(\"min_pos\"),\n",
       "        max_pos = this.data(\"max_pos\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"old_scale\");\n",
       "\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var xmid = (min_pos + max_pos) / 2;\n",
       "    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
       "                   dx / (max_pos - min_pos);\n",
       "\n",
       "    // compute the new scale\n",
       "    var new_scale;\n",
       "    if (xpos >= 0.5) {\n",
       "        new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n",
       "    }\n",
       "    else {\n",
       "        new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n",
       "                        Math.log(min_scale));\n",
       "    }\n",
       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
       "\n",
       "    update_plot_scale(root, new_scale);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragstart = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "\n",
       "    // keep track of what the scale was when we started dragging\n",
       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragend = function(event) {\n",
       "};\n",
       "\n",
       "\n",
       "var toggle_color_class = function(root, color_class, ison) {\n",
       "    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
       "    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
       "    if (ison) {\n",
       "        guides.animate({opacity: 0.5}, 250);\n",
       "        geoms.animate({opacity: 0.0}, 250);\n",
       "    } else {\n",
       "        guides.animate({opacity: 1.0}, 250);\n",
       "        geoms.animate({opacity: 1.0}, 250);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.colorkey_swatch_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var color_class = this.data(\"color_class\");\n",
       "\n",
       "    if (event.shiftKey) {\n",
       "        root.selectAll(\".colorkey text\")\n",
       "            .forEach(function (element) {\n",
       "                var other_color_class = element.data(\"color_class\");\n",
       "                if (other_color_class != color_class) {\n",
       "                    toggle_color_class(root, other_color_class,\n",
       "                                       element.attr(\"opacity\") == 1.0);\n",
       "                }\n",
       "            });\n",
       "    } else {\n",
       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "return Gadfly;\n",
       "\n",
       "}));\n",
       "\n",
       "\n",
       "//@ sourceURL=gadfly.js\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#fig-19f20ece761f40d283e81a69a33cb617\");\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-4\")\n",
       "   .drag(function() {}, function() {}, function() {});\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-6\")\n",
       "   .data(\"color_class\", \"color_setosa\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-7\")\n",
       "   .data(\"color_class\", \"color_versicolor\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-8\")\n",
       "   .data(\"color_class\", \"color_virginica\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-10\")\n",
       "   .data(\"color_class\", \"color_setosa\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-11\")\n",
       "   .data(\"color_class\", \"color_versicolor\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-12\")\n",
       "   .data(\"color_class\", \"color_virginica\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-15\")\n",
       "   .mouseenter(Gadfly.plot_mouseover)\n",
       ".mouseleave(Gadfly.plot_mouseout)\n",
       ".mousewheel(Gadfly.guide_background_scroll)\n",
       ".drag(Gadfly.guide_background_drag_onmove,\n",
       "      Gadfly.guide_background_drag_onstart,\n",
       "      Gadfly.guide_background_drag_onend)\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-19\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-19\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-125\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-125\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-418\")\n",
       "   .data(\"mouseover_color\", \"RGB{U8}(0.804,0.361,0.361)\")\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-418\")\n",
       "   .data(\"mouseout_color\", \"RGB{U8}(0.416,0.416,0.416)\")\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-418\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-422\")\n",
       "   .data(\"max_pos\", 104.83)\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-422\")\n",
       "   .data(\"min_pos\", 87.83)\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-422\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-424\")\n",
       "   .data(\"max_pos\", 104.83)\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-424\")\n",
       "   .data(\"min_pos\", 87.83)\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-424\")\n",
       "   .data(\"mouseover_color\", \"RGB{U8}(0.804,0.361,0.361)\")\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-424\")\n",
       "   .data(\"mouseout_color\", \"RGB{U8}(0.416,0.416,0.416)\")\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-424\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ".mousedown(Gadfly.zoomslider_thumb_mousedown)\n",
       ".mouseup(Gadfly.zoomslider_thumb_mouseup)\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-426\")\n",
       "   .data(\"mouseover_color\", \"RGB{U8}(0.804,0.361,0.361)\")\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-426\")\n",
       "   .data(\"mouseout_color\", \"RGB{U8}(0.416,0.416,0.416)\")\n",
       ";\n",
       "fig.select(\"#fig-19f20ece761f40d283e81a69a33cb617-element-426\")\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(dataset(\"datasets\",\"iris\"), x=\"SepalWidth\", y=\"SepalLength\", color=\"Species\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><tr><th></th><th>cat</th><th>vec</th><th>val</th></tr><tr><th>1</th><td>1</td><td>8</td><td>1.0438097862355737</td></tr><tr><th>2</th><td>2</td><td>4</td><td>0.0850061570596047</td></tr><tr><th>3</th><td>3</td><td>4</td><td>0.07700237792477498</td></tr><tr><th>4</th><td>4</td><td>0</td><td>1.1622753275415851</td></tr><tr><th>5</th><td>5</td><td>0</td><td>1.0247127494489456</td></tr><tr><th>6</th><td>6</td><td>4</td><td>0.09269313130194529</td></tr><tr><th>7</th><td>7</td><td>12</td><td>0.07887103270868287</td></tr><tr><th>8</th><td>8</td><td>4</td><td>0.08718056310114282</td></tr><tr><th>9</th><td>9</td><td>4</td><td>0.08183323639690525</td></tr><tr><th>10</th><td>10</td><td>20</td><td>0.0816226482622277</td></tr></table>"
      ],
      "text/plain": [
       "10x3 DataFrame\n",
       "| Row | cat | vec | val       |\n",
       "|-----|-----|-----|-----------|\n",
       "| 1   | 1   | 8   | 1.04381   |\n",
       "| 2   | 2   | 4   | 0.0850062 |\n",
       "| 3   | 3   | 4   | 0.0770024 |\n",
       "| 4   | 4   | 0   | 1.16228   |\n",
       "| 5   | 5   | 0   | 1.02471   |\n",
       "| 6   | 6   | 4   | 0.0926931 |\n",
       "| 7   | 7   | 12  | 0.078871  |\n",
       "| 8   | 8   | 4   | 0.0871806 |\n",
       "| 9   | 9   | 4   | 0.0818332 |\n",
       "| 10  | 10  | 20  | 0.0816226 |"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rem = readtable(\"remy.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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       "            return factory(Snap);\n",
       "        });\n",
       "    } else {\n",
       "        // Browser globals (glob is window)\n",
       "        // Snap adds itself to window\n",
       "        glob.Gadfly = factory(glob.Snap);\n",
       "    }\n",
       "}(this, function (Snap) {\n",
       "\n",
       "var Gadfly = {};\n",
       "\n",
       "// Get an x/y coordinate value in pixels\n",
       "var xPX = function(fig, x) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
       "};\n",
       "\n",
       "var yPX = function(fig, y) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
       "};\n",
       "\n",
       "\n",
       "Snap.plugin(function (Snap, Element, Paper, global) {\n",
       "    // Traverse upwards from a snap element to find and return the first\n",
       "    // note with the \"plotroot\" class.\n",
       "    Element.prototype.plotroot = function () {\n",
       "        var element = this;\n",
       "        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.svgroot = function () {\n",
       "        var element = this;\n",
       "        while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotbounds = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x0: bbox.x,\n",
       "            x1: bbox.x + bbox.width,\n",
       "            y0: bbox.y,\n",
       "            y1: bbox.y + bbox.height\n",
       "        };\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotcenter = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x: bbox.x + bbox.width / 2,\n",
       "            y: bbox.y + bbox.height / 2\n",
       "        };\n",
       "    };\n",
       "\n",
       "    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
       "    // does everything right.\n",
       "    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
       "    var events = [\"mouseenter\", \"mouseleave\"];\n",
       "\n",
       "    for (i in events) {\n",
       "        (function (event_name) {\n",
       "            var event_name = events[i];\n",
       "            Element.prototype[event_name] = function (fn, scope) {\n",
       "                if (Snap.is(fn, \"function\")) {\n",
       "                    var fn2 = function (event) {\n",
       "                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
       "                            return;\n",
       "                        }\n",
       "\n",
       "                        var reltg = event.relatedTarget ? event.relatedTarget :\n",
       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
       "\n",
       "                        if (reltg != this.node) {\n",
       "                            return fn.apply(this, event);\n",
       "                        }\n",
       "                    };\n",
       "\n",
       "                    if (event_name == \"mouseenter\") {\n",
       "                        this.mouseover(fn2, scope);\n",
       "                    } else {\n",
       "                        this.mouseout(fn2, scope);\n",
       "                    }\n",
       "                }\n",
       "                return this;\n",
       "            };\n",
       "        })(events[i]);\n",
       "    }\n",
       "\n",
       "\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\n",
       "        if (Snap.is(fn, \"function\")) {\n",
       "            var el = this;\n",
       "            var fn2 = function (event) {\n",
       "                fn.apply(el, [event]);\n",
       "            };\n",
       "        }\n",
       "\n",
       "        this.node.addEventListener(\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
       "            fn2);\n",
       "\n",
       "        return this;\n",
       "    };\n",
       "\n",
       "\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
       "    // This is a function to directly update element attributes without going\n",
       "    // through eve.\n",
       "    Element.prototype.attribute = function(key, val) {\n",
       "        if (val === undefined) {\n",
       "            return this.node.getAttribute(key);\n",
       "        } else {\n",
       "            this.node.setAttribute(key, val);\n",
       "            return this;\n",
       "        }\n",
       "    };\n",
       "});\n",
       "\n",
       "\n",
       "// When the plot is moused over, emphasize the grid lines.\n",
       "Gadfly.plot_mouseover = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    xgridlines.data(\"unfocused_strokedash\",\n",
       "                    xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // reveal zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 1.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "// Unemphasize grid lines on mouse out.\n",
       "Gadfly.plot_mouseout = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    var destcolor = root.data(\"unfocused_xgrid_color\");\n",
       "\n",
       "    xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // hide zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 0.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "var set_geometry_transform = function(root, tx, ty, scale) {\n",
       "    var xscalable = root.hasClass(\"xscalable\"),\n",
       "        yscalable = root.hasClass(\"yscalable\");\n",
       "\n",
       "    var old_scale = root.data(\"scale\");\n",
       "\n",
       "    var xscale = xscalable ? scale : 1.0,\n",
       "        yscale = yscalable ? scale : 1.0;\n",
       "\n",
       "    tx = xscalable ? tx : 0.0;\n",
       "    ty = yscalable ? ty : 0.0;\n",
       "\n",
       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
       "\n",
       "    root.selectAll(\".geometry, image\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.transform(t);\n",
       "        });\n",
       "\n",
       "    bounds = root.plotbounds();\n",
       "\n",
       "    if (yscalable) {\n",
       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
       "        root.selectAll(\".xfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(xfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".ylabels\")\n",
       "            .transform(xfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var y = cy * scale + ty;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (xscalable) {\n",
       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
       "        var xtrans = new Snap.Matrix().translate(tx, 0);\n",
       "        root.selectAll(\".yfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(yfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".xlabels\")\n",
       "            .transform(yfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
       "\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var x = cx * scale + tx;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
       "                    }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
       "    var size_attribs = [\"font-size\"];\n",
       "    var unscaled_selection = \".geometry, .geometry *\";\n",
       "    if (xscalable) {\n",
       "        size_attribs.push(\"rx\");\n",
       "        unscaled_selection += \", .xgridlines\";\n",
       "    }\n",
       "    if (yscalable) {\n",
       "        size_attribs.push(\"ry\");\n",
       "        unscaled_selection += \", .ygridlines\";\n",
       "    }\n",
       "\n",
       "    root.selectAll(unscaled_selection)\n",
       "        .forEach(function (element, i) {\n",
       "            // circle need special help\n",
       "            if (element.node.nodeName == \"circle\") {\n",
       "                var cx = element.attribute(\"cx\"),\n",
       "                    cy = element.attribute(\"cy\");\n",
       "                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
       "                                                        cx, cy);\n",
       "                element.transform(unscale_t);\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            for (i in size_attribs) {\n",
       "                var key = size_attribs[i];\n",
       "                var val = parseFloat(element.attribute(key));\n",
       "                if (val !== undefined && val != 0 && !isNaN(val)) {\n",
       "                    element.attribute(key, val * old_scale / scale);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "};\n",
       "\n",
       "\n",
       "// Find the most appropriate tick scale and update label visibility.\n",
       "var update_tickscale = function(root, scale, axis) {\n",
       "    if (!root.hasClass(axis + \"scalable\")) return;\n",
       "\n",
       "    var tickscales = root.data(axis + \"tickscales\");\n",
       "    var best_tickscale = 1.0;\n",
       "    var best_tickscale_dist = Infinity;\n",
       "    for (tickscale in tickscales) {\n",
       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
       "        if (dist < best_tickscale_dist) {\n",
       "            best_tickscale_dist = dist;\n",
       "            best_tickscale = tickscale;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
       "        root.data(axis + \"tickscale\", best_tickscale);\n",
       "        var mark_inscale_gridlines = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        var mark_inscale_labels = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
       "        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
       "    var old_scale = root.data(\"scale\");\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    // compute the viewport derived from tx, ty, and scale\n",
       "    var x_min = -width * scale - (scale * width - width),\n",
       "        x_max = width * scale,\n",
       "        y_min = -height * scale - (scale * height - height),\n",
       "        y_max = height * scale;\n",
       "\n",
       "    var x0 = bounds.x0 - scale * bounds.x0,\n",
       "        y0 = bounds.y0 - scale * bounds.y0;\n",
       "\n",
       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
       "\n",
       "    tx += x0;\n",
       "    ty += y0;\n",
       "\n",
       "    // when the scale change, we may need to alter which set of\n",
       "    // ticks is being displayed\n",
       "    if (scale != old_scale) {\n",
       "        update_tickscale(root, scale, \"x\");\n",
       "        update_tickscale(root, scale, \"y\");\n",
       "    }\n",
       "\n",
       "    set_geometry_transform(root, tx, ty, scale);\n",
       "\n",
       "    root.data(\"scale\", scale);\n",
       "    root.data(\"tx\", tx);\n",
       "    root.data(\"ty\", ty);\n",
       "};\n",
       "\n",
       "\n",
       "var scale_centered_translation = function(root, scale) {\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var scale0 = root.data(\"scale\");\n",
       "\n",
       "    // how off from center the current view is\n",
       "    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
       "        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
       "\n",
       "    // rescale offsets\n",
       "    xoff = xoff * scale / scale0;\n",
       "    yoff = yoff * scale / scale0;\n",
       "\n",
       "    // adjust for the panel position being scaled\n",
       "    var x_edge_adjust = bounds.x0 * (1 - scale),\n",
       "        y_edge_adjust = bounds.y0 * (1 - scale);\n",
       "\n",
       "    return {\n",
       "        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
       "        y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "// Initialize data for panning zooming if it isn't already.\n",
       "var init_pan_zoom = function(root) {\n",
       "    if (root.data(\"zoompan-ready\")) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    // The non-scaling-stroke trick. Rather than try to correct for the\n",
       "    // stroke-width when zooming, we force it to a fixed value.\n",
       "    var px_per_mm = root.node.getCTM().a;\n",
       "\n",
       "    // Drag events report deltas in pixels, which we'd like to convert to\n",
       "    // millimeters.\n",
       "    root.data(\"px_per_mm\", px_per_mm);\n",
       "\n",
       "    root.selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
       "        if (sw > 0) {\n",
       "            element.attribute(\"stroke-width\", sw);\n",
       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
       "        }\n",
       "    });\n",
       "\n",
       "    // Store ticks labels original tranformation\n",
       "    root.selectAll(\".xlabels > text, .ylabels > text\")\n",
       "        .forEach(function (element, i) {\n",
       "            var lm = element.transform().localMatrix;\n",
       "            element.data(\"static_transform\",\n",
       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
       "        });\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\");\n",
       "    var ygridlines = root.select(\".ygridlines\");\n",
       "    var xlabels = root.select(\".xlabels\");\n",
       "    var ylabels = root.select(\".ylabels\");\n",
       "\n",
       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
       "    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
       "    if (root.data(\"xtickscales\") === undefined) {\n",
       "\n",
       "        // index all the tick scales that are listed\n",
       "        var xtickscales = {};\n",
       "        var ytickscales = {};\n",
       "        var add_x_tick_scales = function (element, i) {\n",
       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "        var add_y_tick_scales = function (element, i) {\n",
       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "\n",
       "        if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
       "\n",
       "        root.data(\"xtickscales\", xtickscales);\n",
       "        root.data(\"ytickscales\", ytickscales);\n",
       "        root.data(\"xtickscale\", 1.0);\n",
       "    }\n",
       "\n",
       "    var min_scale = 1.0, max_scale = 1.0;\n",
       "    for (scale in xtickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    for (scale in ytickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    root.data(\"min_scale\", min_scale);\n",
       "    root.data(\"max_scale\", max_scale);\n",
       "\n",
       "    // store the original positions of labels\n",
       "    if (xlabels) {\n",
       "        xlabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"x\", element.asPX(\"x\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    if (ylabels) {\n",
       "        ylabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"y\", element.asPX(\"y\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    // mark grid lines and ticks as in or out of scale.\n",
       "    var mark_inscale = function (element, i) {\n",
       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
       "    };\n",
       "\n",
       "    if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "    if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "\n",
       "    // figure out the upper ond lower bounds on panning using the maximum\n",
       "    // and minum grid lines\n",
       "    var bounds = root.plotbounds();\n",
       "    var pan_bounds = {\n",
       "        x0: 0.0,\n",
       "        y0: 0.0,\n",
       "        x1: 0.0,\n",
       "        y1: 0.0\n",
       "    };\n",
       "\n",
       "    if (xgridlines) {\n",
       "        xgridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
       "                        pan_bounds.x0 = bounds.x1 - bbox.x;\n",
       "                    }\n",
       "                    if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
       "                        pan_bounds.x1 = bounds.x0 - bbox.x;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (ygridlines) {\n",
       "        ygridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
       "                        pan_bounds.y0 = bounds.y1 - bbox.y;\n",
       "                    }\n",
       "                    if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
       "                        pan_bounds.y1 = bounds.y0 - bbox.y;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // nudge these values a little\n",
       "    pan_bounds.x0 -= 5;\n",
       "    pan_bounds.x1 += 5;\n",
       "    pan_bounds.y0 -= 5;\n",
       "    pan_bounds.y1 += 5;\n",
       "    root.data(\"pan_bounds\", pan_bounds);\n",
       "\n",
       "    root.data(\"zoompan-ready\", true)\n",
       "};\n",
       "\n",
       "\n",
       "// Panning\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var dx0 = root.data(\"dx\"),\n",
       "        dy0 = root.data(\"dy\");\n",
       "\n",
       "    root.data(\"dx\", dx);\n",
       "    root.data(\"dy\", dy);\n",
       "\n",
       "    dx = dx - dx0;\n",
       "    dy = dy - dy0;\n",
       "\n",
       "    var tx = tx0 + dx,\n",
       "        ty = ty0 + dy;\n",
       "\n",
       "    set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    root.data(\"dx\", 0);\n",
       "    root.data(\"dy\", 0);\n",
       "    init_pan_zoom(root);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onend = function(event) {\n",
       "    var root = this.plotroot();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_scroll = function(event) {\n",
       "    if (event.shiftKey) {\n",
       "        var root = this.plotroot();\n",
       "        init_pan_zoom(root);\n",
       "        var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n",
       "        new_scale = Math.max(\n",
       "            root.data(\"min_scale\"),\n",
       "            Math.min(root.data(\"max_scale\"), new_scale))\n",
       "        update_plot_scale(root, new_scale);\n",
       "        event.stopPropagation();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseover = function(event) {\n",
       "    this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseout = function(event) {\n",
       "     this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomout_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "    var min_scale = root.data(\"min_scale\"),\n",
       "        scale = root.data(\"scale\");\n",
       "    Snap.animate(\n",
       "        scale,\n",
       "        Math.max(min_scale, scale / 1.5),\n",
       "        function (new_scale) {\n",
       "            update_plot_scale(root, new_scale);\n",
       "        },\n",
       "        200);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "    var max_scale = root.data(\"max_scale\"),\n",
       "        scale = root.data(\"scale\");\n",
       "\n",
       "    Snap.animate(\n",
       "        scale,\n",
       "        Math.min(max_scale, scale * 1.5),\n",
       "        function (new_scale) {\n",
       "            update_plot_scale(root, new_scale);\n",
       "        },\n",
       "        200);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_track_click = function(event) {\n",
       "    // TODO\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_mousedown = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_mouseup = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "// compute the position in [0, 1] of the zoom slider thumb from the current scale\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
       "    if (scale >= 1.0) {\n",
       "        return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n",
       "    }\n",
       "    else {\n",
       "        return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "var update_plot_scale = function(root, new_scale) {\n",
       "    var trans = scale_centered_translation(root, new_scale);\n",
       "    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
       "\n",
       "    root.selectAll(\".zoomslider_thumb\")\n",
       "        .forEach(function (element, i) {\n",
       "            var min_pos = element.data(\"min_pos\"),\n",
       "                max_pos = element.data(\"max_pos\"),\n",
       "                min_scale = root.data(\"min_scale\"),\n",
       "                max_scale = root.data(\"max_scale\");\n",
       "            var xmid = (min_pos + max_pos) / 2;\n",
       "            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
       "            element.transform(new Snap.Matrix().translate(\n",
       "                Math.max(min_pos, Math.min(\n",
       "                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
       "    });\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y) {\n",
       "    var root = this.plotroot();\n",
       "    var min_pos = this.data(\"min_pos\"),\n",
       "        max_pos = this.data(\"max_pos\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"old_scale\");\n",
       "\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var xmid = (min_pos + max_pos) / 2;\n",
       "    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
       "                   dx / (max_pos - min_pos);\n",
       "\n",
       "    // compute the new scale\n",
       "    var new_scale;\n",
       "    if (xpos >= 0.5) {\n",
       "        new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n",
       "    }\n",
       "    else {\n",
       "        new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n",
       "                        Math.log(min_scale));\n",
       "    }\n",
       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
       "\n",
       "    update_plot_scale(root, new_scale);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragstart = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "\n",
       "    // keep track of what the scale was when we started dragging\n",
       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragend = function(event) {\n",
       "};\n",
       "\n",
       "\n",
       "var toggle_color_class = function(root, color_class, ison) {\n",
       "    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
       "    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
       "    if (ison) {\n",
       "        guides.animate({opacity: 0.5}, 250);\n",
       "        geoms.animate({opacity: 0.0}, 250);\n",
       "    } else {\n",
       "        guides.animate({opacity: 1.0}, 250);\n",
       "        geoms.animate({opacity: 1.0}, 250);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.colorkey_swatch_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var color_class = this.data(\"color_class\");\n",
       "\n",
       "    if (event.shiftKey) {\n",
       "        root.selectAll(\".colorkey text\")\n",
       "            .forEach(function (element) {\n",
       "                var other_color_class = element.data(\"color_class\");\n",
       "                if (other_color_class != color_class) {\n",
       "                    toggle_color_class(root, other_color_class,\n",
       "                                       element.attr(\"opacity\") == 1.0);\n",
       "                }\n",
       "            });\n",
       "    } else {\n",
       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "return Gadfly;\n",
       "\n",
       "}));\n",
       "\n",
       "\n",
       "//@ sourceURL=gadfly.js\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#fig-7dfd0114d9e14c1099cd58355a1652c9\");\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-4\")\n",
       "   .drag(function() {}, function() {}, function() {});\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-6\")\n",
       "   .data(\"color_class\", \"color_8\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-7\")\n",
       "   .data(\"color_class\", \"color_4\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-8\")\n",
       "   .data(\"color_class\", \"color_0\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-9\")\n",
       "   .data(\"color_class\", \"color_12\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-10\")\n",
       "   .data(\"color_class\", \"color_20\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-12\")\n",
       "   .data(\"color_class\", \"color_8\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-13\")\n",
       "   .data(\"color_class\", \"color_4\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-14\")\n",
       "   .data(\"color_class\", \"color_0\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-15\")\n",
       "   .data(\"color_class\", \"color_12\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-16\")\n",
       "   .data(\"color_class\", \"color_20\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-19\")\n",
       "   .mouseenter(Gadfly.plot_mouseover)\n",
       ".mouseleave(Gadfly.plot_mouseout)\n",
       ".mousewheel(Gadfly.guide_background_scroll)\n",
       ".drag(Gadfly.guide_background_drag_onmove,\n",
       "      Gadfly.guide_background_drag_onstart,\n",
       "      Gadfly.guide_background_drag_onend)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-23\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-23\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-177\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-177\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-303\")\n",
       "   .data(\"mouseover_color\", \"RGB{U8}(0.804,0.361,0.361)\")\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-303\")\n",
       "   .data(\"mouseout_color\", \"RGB{U8}(0.416,0.416,0.416)\")\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-303\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-307\")\n",
       "   .data(\"max_pos\", 113.53)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-307\")\n",
       "   .data(\"min_pos\", 96.53)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-307\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-309\")\n",
       "   .data(\"max_pos\", 113.53)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-309\")\n",
       "   .data(\"min_pos\", 96.53)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-309\")\n",
       "   .data(\"mouseover_color\", \"RGB{U8}(0.804,0.361,0.361)\")\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-309\")\n",
       "   .data(\"mouseout_color\", \"RGB{U8}(0.416,0.416,0.416)\")\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-309\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ".mousedown(Gadfly.zoomslider_thumb_mousedown)\n",
       ".mouseup(Gadfly.zoomslider_thumb_mouseup)\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-311\")\n",
       "   .data(\"mouseover_color\", \"RGB{U8}(0.804,0.361,0.361)\")\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-311\")\n",
       "   .data(\"mouseout_color\", \"RGB{U8}(0.416,0.416,0.416)\")\n",
       ";\n",
       "fig.select(\"#fig-7dfd0114d9e14c1099cd58355a1652c9-element-311\")\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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(rem, x=\"cat\", y=\"val\", color=\"vec\", Scale.color_discrete())"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Julia 0.3.11",
   "language": "julia",
   "name": "julia-0.3"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.3.11"
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 },
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}