welcome-to-julia.ipynb

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   "source": [
    "Welcome to Julia\n",
    "================\n",
    "\n",
    "<img src=\"https://camo.githubusercontent.com/e1ae5c7f6fe275a50134d5889a68f0acdd09ada8/687474703a2f2f6a756c69616c616e672e6f72672f696d616765732f6c6f676f5f68697265732e706e67\" alt=\"Julia logo\" style=\"width: 300px;\"/>\n",
    "\n",
    "*Scott Thomas, Institute of Astronomy*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Welcome to Julia. This short presentation covers the following topics:\n",
    "\n",
    "* What is Julia?\n",
    "* How can you get started using Julia at the IoA?\n",
    "* What is Julia good at?\n",
    "* What is Julia not good at?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# What is Julia?\n",
    "\n",
    "<img src=\"images/what-is-julia.png\" alt=\"Juli-what?\" style=\"width: 300px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Julia is a new scientific language designed for **\"greedy, unreasonable, demanding\"** programmers who want speed, flexibility, and usability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "It's a young language - in open development for about three years - but it shows a lot of promise. In particular, Julia promises to help break the \"two-language problem\". In science, one often wants to try out things on a small scale, develop code interactively, and tinker, before settling down to make it run fast on large amounts of data. Many scientists and programmers find that they need to move between two languages to do this: a dynamic language like Python that is easy to code in, and a fast language like C that gives numerical power. For many of us who aren't the best programmers in the world (myself definitely included), doing this incurs a significant overhead as we struggle with two different languages. Julia aims to remove the need for this division."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "subslide"
    }
   },
   "source": [
    "## Speed with flexibility\n",
    "\n",
    "<img src=\"images/benchmarks.png\" alt=\"Julia benchmarks\" style=\"width: auto;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Here are benchmark results from a number of algorithms implemented in various languages. It's important to note that aren't based on writing optimal code in every situation. Rather, they're intended to show what sort of raw speed you can get by writing the same algorithm (which may be relatively naïve) in different languages. In general, Julia can provide speeds to within a factor of 2 of C, and orders of magnitude faster than dynamic languages like Python or Matlab."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Familiarity\n",
    "\n",
    "Users of Matlab, Python or IDL will be right at home."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mathematical operations\n",
    "[1,2,3] + [4,5,6]\n",
    "# => [5, 7, 9]\n",
    "\n",
    "# nice short functions\n",
    "f(x) = 7x\n",
    "f(3)\n",
    "# => 21\n",
    "\n",
    "# arrays\n",
    "onetwothree = [1,2,3]\n",
    "notseven = [1:6; 8:10]\n",
    "square = [1 2 3;\n",
    "          4 5 6;\n",
    "          7 8 9]\n",
    "\n",
    "# indexing\n",
    "square[2,3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    " ## Easily call Python..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Julia has a very good \"foreign function interface\" - it's easy to work with stuff written in other languages. This is important for a young language, as it makes it possible to draw on vast libraries of Python code which don't yet have equivalents in Julia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PyObject chararray(['  2.7387550E-15', '  1.9348280E-15'], \n",
       "      dtype='|S15')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using PyCall\n",
    "@pyimport astropy.io.fits as fits\n",
    "hdulist = fits.open(\"data/fits-spectrum.fits\")\n",
    "hdrdata = hdulist[2][:data]\n",
    "\n",
    "hdrdata[\"DATAMAX\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## ... and use Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Loading help data...\n"
     ]
    },
    {
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",
      "text/plain": [
       "Figure(PyObject <matplotlib.figure.Figure object at 0x7f26e6e73750>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <mpl_toolkits.mplot3d.art3d.Poly3DCollection object at 0x7f26e687f050>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using PyPlot\n",
    "f(x, y) = (1 - x^2) + (y - x^2)^2\n",
    "xs = linspace(-2, 2)\n",
    "ys = linspace(-1, 3)\n",
    "zs = [f(x,y) for x in xs, y in ys];\n",
    "\n",
    "surf(xs, ys, zs, cmap=\"Spectral_r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Or use Julia's own (pretty) plotting libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, Julia has its own plotting libraries like Winston and Gadfly (below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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       "f:e,unbind:b(this.node||document,d,e,f||this)}));return this};a[\"un\"+d]=A[\"un\"+d]=function(a){for(var b=this.events||[],e=b.length;e--;)if(b[e].name==d&&(b[e].f==a||!a)){b[e].unbind();b.splice(e,1);!b.length&&delete this.events;break}return this}})(v[y]);A.hover=function(a,b,d,e){return this.mouseover(a,d).mouseout(b,e||d)};A.unhover=function(a,b){return this.unmouseover(a).unmouseout(b)};var r=[];A.drag=function(b,d,f,h,n,p){function u(r,v,w){(r.originalEvent||r).preventDefault();this._drag.x=v;\n",
       "this._drag.y=w;this._drag.id=r.identifier;!q.length&&a.mousemove(e).mouseup(l);q.push({el:this,move_scope:h,start_scope:n,end_scope:p});d&&k.on(\"snap.drag.start.\"+this.id,d);b&&k.on(\"snap.drag.move.\"+this.id,b);f&&k.on(\"snap.drag.end.\"+this.id,f);k(\"snap.drag.start.\"+this.id,n||h||this,v,w,r)}if(!arguments.length){var v;return this.drag(function(a,b){this.attr({transform:v+(v?\"T\":\"t\")+[a,b]})},function(){v=this.transform().local})}this._drag={};r.push({el:this,start:u});this.mousedown(u);return this};\n",
       "A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind(\"snap.drag.*.\"+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\\s*url\\((.+)\\)/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;\"svg\"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z(\"filter\");z(u,{id:k,filterUnits:\"userSpaceOnUse\"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on(\"snap.util.getattr.filter\",\n",
       "function(){k.stop();var d=z(this.node,\"filter\");if(d)return(d=w(d).match(A))&&a.select(d[1])});k.on(\"snap.util.attr.filter\",function(d){if(d instanceof v&&\"filter\"==d.type){k.stop();var f=d.node.id;f||(z(d.node,{id:d.id}),f=d.id);z(this.node,{filter:a.url(f)})}d&&\"none\"!=d||(k.stop(),this.node.removeAttribute(\"filter\"))});a.filter.blur=function(d,f){null==d&&(d=2);return a.format('<feGaussianBlur stdDeviation=\"{def}\"/>',{def:null==f?d:[d,f]})};a.filter.blur.toString=function(){return this()};a.filter.shadow=\n",
       "function(d,f,k,u,p){\"string\"==typeof k&&(p=u=k,k=4);\"string\"!=typeof u&&(p=u,u=\"#000\");null==k&&(k=4);null==p&&(p=1);null==d&&(d=0,f=2);null==f&&(f=d);u=a.color(u||\"#000\");return a.format('<feGaussianBlur in=\"SourceAlpha\" stdDeviation=\"{blur}\"/><feOffset dx=\"{dx}\" dy=\"{dy}\" result=\"offsetblur\"/><feFlood flood-color=\"{color}\"/><feComposite in2=\"offsetblur\" operator=\"in\"/><feComponentTransfer><feFuncA type=\"linear\" slope=\"{opacity}\"/></feComponentTransfer><feMerge><feMergeNode/><feMergeNode in=\"SourceGraphic\"/></feMerge>',\n",
       "{color:u,dx:d,dy:f,blur:k,opacity:p})};a.filter.shadow.toString=function(){return this()};a.filter.grayscale=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {b} {h} 0 0 0 0 0 1 0\"/>',{a:0.2126+0.7874*(1-d),b:0.7152-0.7152*(1-d),c:0.0722-0.0722*(1-d),d:0.2126-0.2126*(1-d),e:0.7152+0.2848*(1-d),f:0.0722-0.0722*(1-d),g:0.2126-0.2126*(1-d),h:0.0722+0.9278*(1-d)})};a.filter.grayscale.toString=function(){return this()};a.filter.sepia=\n",
       "function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {h} {i} 0 0 0 0 0 1 0\"/>',{a:0.393+0.607*(1-d),b:0.769-0.769*(1-d),c:0.189-0.189*(1-d),d:0.349-0.349*(1-d),e:0.686+0.314*(1-d),f:0.168-0.168*(1-d),g:0.272-0.272*(1-d),h:0.534-0.534*(1-d),i:0.131+0.869*(1-d)})};a.filter.sepia.toString=function(){return this()};a.filter.saturate=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"saturate\" values=\"{amount}\"/>',{amount:1-\n",
       "d})};a.filter.saturate.toString=function(){return this()};a.filter.hueRotate=function(d){return a.format('<feColorMatrix type=\"hueRotate\" values=\"{angle}\"/>',{angle:d||0})};a.filter.hueRotate.toString=function(){return this()};a.filter.invert=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncG type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncB type=\"table\" tableValues=\"{amount} {amount2}\"/></feComponentTransfer>',{amount:d,\n",
       "amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
       "{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
       "\n",
       "]]> </script>\n",
       "<script> <![CDATA[\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "    if (typeof define === \"function\" && define.amd) {\n",
       "        // Define as an anonymous module\n",
       "        define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
       "            return factory(Snap);\n",
       "        });\n",
       "    } else {\n",
       "        // Browser globals (glob is window)\n",
       "        // Snap adds itself to window\n",
       "        glob.Gadfly = factory(glob.Snap);\n",
       "    }\n",
       "}(this, function (Snap) {\n",
       "\n",
       "var Gadfly = {};\n",
       "\n",
       "// Get an x/y coordinate value in pixels\n",
       "var xPX = function(fig, x) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
       "};\n",
       "\n",
       "var yPX = function(fig, y) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
       "};\n",
       "\n",
       "\n",
       "Snap.plugin(function (Snap, Element, Paper, global) {\n",
       "    // Traverse upwards from a snap element to find and return the first\n",
       "    // note with the \"plotroot\" class.\n",
       "    Element.prototype.plotroot = function () {\n",
       "        var element = this;\n",
       "        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.svgroot = function () {\n",
       "        var element = this;\n",
       "        while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotbounds = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x0: bbox.x,\n",
       "            x1: bbox.x + bbox.width,\n",
       "            y0: bbox.y,\n",
       "            y1: bbox.y + bbox.height\n",
       "        };\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotcenter = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x: bbox.x + bbox.width / 2,\n",
       "            y: bbox.y + bbox.height / 2\n",
       "        };\n",
       "    };\n",
       "\n",
       "    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
       "    // does everything right.\n",
       "    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
       "    var events = [\"mouseenter\", \"mouseleave\"];\n",
       "\n",
       "    for (i in events) {\n",
       "        (function (event_name) {\n",
       "            var event_name = events[i];\n",
       "            Element.prototype[event_name] = function (fn, scope) {\n",
       "                if (Snap.is(fn, \"function\")) {\n",
       "                    var fn2 = function (event) {\n",
       "                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
       "                            return;\n",
       "                        }\n",
       "\n",
       "                        var reltg = event.relatedTarget ? event.relatedTarget :\n",
       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
       "\n",
       "                        if (reltg != this.node) {\n",
       "                            return fn.apply(this, event);\n",
       "                        }\n",
       "                    };\n",
       "\n",
       "                    if (event_name == \"mouseenter\") {\n",
       "                        this.mouseover(fn2, scope);\n",
       "                    } else {\n",
       "                        this.mouseout(fn2, scope);\n",
       "                    }\n",
       "                }\n",
       "                return this;\n",
       "            };\n",
       "        })(events[i]);\n",
       "    }\n",
       "\n",
       "\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\n",
       "        if (Snap.is(fn, \"function\")) {\n",
       "            var el = this;\n",
       "            var fn2 = function (event) {\n",
       "                fn.apply(el, [event]);\n",
       "            };\n",
       "        }\n",
       "\n",
       "        this.node.addEventListener(\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
       "            fn2);\n",
       "\n",
       "        return this;\n",
       "    };\n",
       "\n",
       "\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
       "    // This is a function to directly update element attributes without going\n",
       "    // through eve.\n",
       "    Element.prototype.attribute = function(key, val) {\n",
       "        if (val === undefined) {\n",
       "            return this.node.getAttribute(key, val);\n",
       "        } else {\n",
       "            return this.node.setAttribute(key, val);\n",
       "        }\n",
       "    };\n",
       "});\n",
       "\n",
       "\n",
       "// When the plot is moused over, emphasize the grid lines.\n",
       "Gadfly.plot_mouseover = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    xgridlines.data(\"unfocused_strokedash\",\n",
       "                    xgridlines.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attr(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attr(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // reveal zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 1.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "// Unemphasize grid lines on mouse out.\n",
       "Gadfly.plot_mouseout = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    var destcolor = root.data(\"unfocused_xgrid_color\");\n",
       "\n",
       "    xgridlines.attr(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attr(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // hide zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 0.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "var set_geometry_transform = function(root, tx, ty, scale) {\n",
       "    var xscalable = root.hasClass(\"xscalable\"),\n",
       "        yscalable = root.hasClass(\"yscalable\");\n",
       "\n",
       "    var old_scale = root.data(\"scale\");\n",
       "\n",
       "    var xscale = xscalable ? scale : 1.0,\n",
       "        yscale = yscalable ? scale : 1.0;\n",
       "\n",
       "    tx = xscalable ? tx : 0.0;\n",
       "    ty = yscalable ? ty : 0.0;\n",
       "\n",
       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
       "\n",
       "    root.selectAll(\".geometry, image\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.transform(t);\n",
       "        });\n",
       "\n",
       "    bounds = root.plotbounds();\n",
       "\n",
       "    if (yscalable) {\n",
       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
       "        root.selectAll(\".xfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(xfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".ylabels\")\n",
       "            .transform(xfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var y = cy * scale + ty;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (xscalable) {\n",
       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
       "        var xtrans = new Snap.Matrix().translate(tx, 0);\n",
       "        root.selectAll(\".yfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(yfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".xlabels\")\n",
       "            .transform(yfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
       "\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var x = cx * scale + tx;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
       "                    }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
       "    var size_attribs = [\"font-size\"];\n",
       "    var unscaled_selection = \".geometry, .geometry *\";\n",
       "    if (xscalable) {\n",
       "        size_attribs.push(\"rx\");\n",
       "        unscaled_selection += \", .xgridlines\";\n",
       "    }\n",
       "    if (yscalable) {\n",
       "        size_attribs.push(\"ry\");\n",
       "        unscaled_selection += \", .ygridlines\";\n",
       "    }\n",
       "\n",
       "    root.selectAll(unscaled_selection)\n",
       "        .forEach(function (element, i) {\n",
       "            // circle need special help\n",
       "            if (element.node.nodeName == \"circle\") {\n",
       "                var cx = element.attribute(\"cx\"),\n",
       "                    cy = element.attribute(\"cy\");\n",
       "                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
       "                                                        cx, cy);\n",
       "                element.transform(unscale_t);\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            for (i in size_attribs) {\n",
       "                var key = size_attribs[i];\n",
       "                var val = parseFloat(element.attribute(key));\n",
       "                if (val !== undefined && val != 0 && !isNaN(val)) {\n",
       "                    element.attribute(key, val * old_scale / scale);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "};\n",
       "\n",
       "\n",
       "// Find the most appropriate tick scale and update label visibility.\n",
       "var update_tickscale = function(root, scale, axis) {\n",
       "    if (!root.hasClass(axis + \"scalable\")) return;\n",
       "\n",
       "    var tickscales = root.data(axis + \"tickscales\");\n",
       "    var best_tickscale = 1.0;\n",
       "    var best_tickscale_dist = Infinity;\n",
       "    for (tickscale in tickscales) {\n",
       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
       "        if (dist < best_tickscale_dist) {\n",
       "            best_tickscale_dist = dist;\n",
       "            best_tickscale = tickscale;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
       "        root.data(axis + \"tickscale\", best_tickscale);\n",
       "        var mark_inscale_gridlines = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        var mark_inscale_labels = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
       "        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
       "    var old_scale = root.data(\"scale\");\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    // compute the viewport derived from tx, ty, and scale\n",
       "    var x_min = -width * scale - (scale * width - width),\n",
       "        x_max = width * scale,\n",
       "        y_min = -height * scale - (scale * height - height),\n",
       "        y_max = height * scale;\n",
       "\n",
       "    var x0 = bounds.x0 - scale * bounds.x0,\n",
       "        y0 = bounds.y0 - scale * bounds.y0;\n",
       "\n",
       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
       "\n",
       "    tx += x0;\n",
       "    ty += y0;\n",
       "\n",
       "    // when the scale change, we may need to alter which set of\n",
       "    // ticks is being displayed\n",
       "    if (scale != old_scale) {\n",
       "        update_tickscale(root, scale, \"x\");\n",
       "        update_tickscale(root, scale, \"y\");\n",
       "    }\n",
       "\n",
       "    set_geometry_transform(root, tx, ty, scale);\n",
       "\n",
       "    root.data(\"scale\", scale);\n",
       "    root.data(\"tx\", tx);\n",
       "    root.data(\"ty\", ty);\n",
       "};\n",
       "\n",
       "\n",
       "var scale_centered_translation = function(root, scale) {\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var scale0 = root.data(\"scale\");\n",
       "\n",
       "    // how off from center the current view is\n",
       "    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
       "        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
       "\n",
       "    // rescale offsets\n",
       "    xoff = xoff * scale / scale0;\n",
       "    yoff = yoff * scale / scale0;\n",
       "\n",
       "    // adjust for the panel position being scaled\n",
       "    var x_edge_adjust = bounds.x0 * (1 - scale),\n",
       "        y_edge_adjust = bounds.y0 * (1 - scale);\n",
       "\n",
       "    return {\n",
       "        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
       "        y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "// Initialize data for panning zooming if it isn't already.\n",
       "var init_pan_zoom = function(root) {\n",
       "    if (root.data(\"zoompan-ready\")) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    // The non-scaling-stroke trick. Rather than try to correct for the\n",
       "    // stroke-width when zooming, we force it to a fixed value.\n",
       "    var px_per_mm = root.node.getCTM().a;\n",
       "\n",
       "    // Drag events report deltas in pixels, which we'd like to convert to\n",
       "    // millimeters.\n",
       "    root.data(\"px_per_mm\", px_per_mm);\n",
       "\n",
       "    root.selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
       "        if (sw > 0) {\n",
       "            element.attribute(\"stroke-width\", sw);\n",
       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
       "        }\n",
       "    });\n",
       "\n",
       "    // Store ticks labels original tranformation\n",
       "    root.selectAll(\".xlabels > text, .ylabels > text\")\n",
       "        .forEach(function (element, i) {\n",
       "            var lm = element.transform().localMatrix;\n",
       "            element.data(\"static_transform\",\n",
       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
       "        });\n",
       "\n",
       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
       "    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
       "    if (root.data(\"xtickscales\") === undefined) {\n",
       "\n",
       "        // index all the tick scales that are listed\n",
       "        var xtickscales = {};\n",
       "        var ytickscales = {};\n",
       "        var add_x_tick_scales = function (element, i) {\n",
       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "        var add_y_tick_scales = function (element, i) {\n",
       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "\n",
       "        root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        root.select(\".ylabels\").selectAll(\"text\").forEach(add_y_tick_scales);\n",
       "\n",
       "        root.data(\"xtickscales\", xtickscales);\n",
       "        root.data(\"ytickscales\", ytickscales);\n",
       "        root.data(\"xtickscale\", 1.0);\n",
       "    }\n",
       "\n",
       "    var min_scale = 1.0, max_scale = 1.0;\n",
       "    for (scale in xtickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    for (scale in ytickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    root.data(\"min_scale\", min_scale);\n",
       "    root.data(\"max_scale\", max_scale);\n",
       "\n",
       "    // store the original positions of labels\n",
       "    root.select(\".xlabels\")\n",
       "        .selectAll(\"text\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.data(\"x\", element.asPX(\"x\"));\n",
       "        });\n",
       "\n",
       "    root.select(\".ylabels\")\n",
       "        .selectAll(\"text\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.data(\"y\", element.asPX(\"y\"));\n",
       "        });\n",
       "\n",
       "    // mark grid lines and ticks as in or out of scale.\n",
       "    var mark_inscale = function (element, i) {\n",
       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
       "    };\n",
       "\n",
       "    root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
       "    root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
       "    root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n",
       "    root.select(\".ylabels\").selectAll(\"text\").forEach(mark_inscale);\n",
       "\n",
       "    // figure out the upper ond lower bounds on panning using the maximum\n",
       "    // and minum grid lines\n",
       "    var bounds = root.plotbounds();\n",
       "    var pan_bounds = {\n",
       "        x0: 0.0,\n",
       "        y0: 0.0,\n",
       "        x1: 0.0,\n",
       "        y1: 0.0\n",
       "    };\n",
       "\n",
       "    root.select(\".xgridlines\")\n",
       "        .selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                var bbox = element.node.getBBox();\n",
       "                if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
       "                    pan_bounds.x0 = bounds.x1 - bbox.x;\n",
       "                }\n",
       "                if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
       "                    pan_bounds.x1 = bounds.x0 - bbox.x;\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "\n",
       "    root.select(\".ygridlines\")\n",
       "        .selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                var bbox = element.node.getBBox();\n",
       "                if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
       "                    pan_bounds.y0 = bounds.y1 - bbox.y;\n",
       "                }\n",
       "                if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
       "                    pan_bounds.y1 = bounds.y0 - bbox.y;\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "\n",
       "    // nudge these values a little\n",
       "    pan_bounds.x0 -= 5;\n",
       "    pan_bounds.x1 += 5;\n",
       "    pan_bounds.y0 -= 5;\n",
       "    pan_bounds.y1 += 5;\n",
       "    root.data(\"pan_bounds\", pan_bounds);\n",
       "\n",
       "    // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n",
       "    // will be clipped.\n",
       "    root.select(\".xgridlines\")\n",
       "        .selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                element.attr(\"visibility\", \"visible\");\n",
       "            }\n",
       "        });\n",
       "\n",
       "    root.select(\".ygridlines\")\n",
       "        .selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                element.attr(\"visibility\", \"visible\");\n",
       "            }\n",
       "        });\n",
       "\n",
       "    root.data(\"zoompan-ready\", true)\n",
       "};\n",
       "\n",
       "\n",
       "// Panning\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var dx0 = root.data(\"dx\"),\n",
       "        dy0 = root.data(\"dy\");\n",
       "\n",
       "    root.data(\"dx\", dx);\n",
       "    root.data(\"dy\", dy);\n",
       "\n",
       "    dx = dx - dx0;\n",
       "    dy = dy - dy0;\n",
       "\n",
       "    var tx = tx0 + dx,\n",
       "        ty = ty0 + dy;\n",
       "\n",
       "    set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    root.data(\"dx\", 0);\n",
       "    root.data(\"dy\", 0);\n",
       "    init_pan_zoom(root);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onend = function(event) {\n",
       "    var root = this.plotroot();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_scroll = function(event) {\n",
       "    if (event.shiftKey) {\n",
       "        var root = this.plotroot();\n",
       "        init_pan_zoom(root);\n",
       "        var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n",
       "        new_scale = Math.max(\n",
       "            root.data(\"min_scale\"),\n",
       "            Math.min(root.data(\"max_scale\"), new_scale))\n",
       "        update_plot_scale(root, new_scale);\n",
       "        event.stopPropagation();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseover = function(event) {\n",
       "    this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseout = function(event) {\n",
       "     this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomout_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "    var min_scale = root.data(\"min_scale\"),\n",
       "        scale = root.data(\"scale\");\n",
       "    Snap.animate(\n",
       "        scale,\n",
       "        Math.max(min_scale, scale / 1.5),\n",
       "        function (new_scale) {\n",
       "            update_plot_scale(root, new_scale);\n",
       "        },\n",
       "        200);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "    var max_scale = root.data(\"max_scale\"),\n",
       "        scale = root.data(\"scale\");\n",
       "\n",
       "    Snap.animate(\n",
       "        scale,\n",
       "        Math.min(max_scale, scale * 1.5),\n",
       "        function (new_scale) {\n",
       "            update_plot_scale(root, new_scale);\n",
       "        },\n",
       "        200);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_track_click = function(event) {\n",
       "    // TODO\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_mousedown = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_mouseup = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "// compute the position in [0, 1] of the zoom slider thumb from the current scale\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
       "    if (scale >= 1.0) {\n",
       "        return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n",
       "    }\n",
       "    else {\n",
       "        return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "var update_plot_scale = function(root, new_scale) {\n",
       "    var trans = scale_centered_translation(root, new_scale);\n",
       "    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
       "\n",
       "    root.selectAll(\".zoomslider_thumb\")\n",
       "        .forEach(function (element, i) {\n",
       "            var min_pos = element.data(\"min_pos\"),\n",
       "                max_pos = element.data(\"max_pos\"),\n",
       "                min_scale = root.data(\"min_scale\"),\n",
       "                max_scale = root.data(\"max_scale\");\n",
       "            var xmid = (min_pos + max_pos) / 2;\n",
       "            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
       "            element.transform(new Snap.Matrix().translate(\n",
       "                Math.max(min_pos, Math.min(\n",
       "                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
       "    });\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y) {\n",
       "    var root = this.plotroot();\n",
       "    var min_pos = this.data(\"min_pos\"),\n",
       "        max_pos = this.data(\"max_pos\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"old_scale\");\n",
       "\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var xmid = (min_pos + max_pos) / 2;\n",
       "    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
       "                   dx / (max_pos - min_pos);\n",
       "\n",
       "    // compute the new scale\n",
       "    var new_scale;\n",
       "    if (xpos >= 0.5) {\n",
       "        new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n",
       "    }\n",
       "    else {\n",
       "        new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n",
       "                        Math.log(min_scale));\n",
       "    }\n",
       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
       "\n",
       "    update_plot_scale(root, new_scale);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragstart = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    init_pan_zoom(root);\n",
       "\n",
       "    // keep track of what the scale was when we started dragging\n",
       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragend = function(event) {\n",
       "};\n",
       "\n",
       "\n",
       "var toggle_color_class = function(root, color_class, ison) {\n",
       "    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
       "    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
       "    if (ison) {\n",
       "        guides.animate({opacity: 0.5}, 250);\n",
       "        geoms.animate({opacity: 0.0}, 250);\n",
       "    } else {\n",
       "        guides.animate({opacity: 1.0}, 250);\n",
       "        geoms.animate({opacity: 1.0}, 250);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.colorkey_swatch_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var color_class = this.data(\"color_class\");\n",
       "\n",
       "    if (event.shiftKey) {\n",
       "        root.selectAll(\".colorkey text\")\n",
       "            .forEach(function (element) {\n",
       "                var other_color_class = element.data(\"color_class\");\n",
       "                if (other_color_class != color_class) {\n",
       "                    toggle_color_class(root, other_color_class,\n",
       "                                       element.attr(\"opacity\") == 1.0);\n",
       "                }\n",
       "            });\n",
       "    } else {\n",
       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "return Gadfly;\n",
       "\n",
       "}));\n",
       "\n",
       "\n",
       "//@ sourceURL=gadfly.js\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054\");\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-4\")\n",
       "   .drag(function() {}, function() {}, function() {});\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-6\")\n",
       "   .data(\"color_class\", \"color_f1\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-7\")\n",
       "   .data(\"color_class\", \"color_f2\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-8\")\n",
       "   .data(\"color_class\", \"color_f3\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-10\")\n",
       "   .data(\"color_class\", \"color_f1\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-11\")\n",
       "   .data(\"color_class\", \"color_f2\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-12\")\n",
       "   .data(\"color_class\", \"color_f3\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-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-c5c84f6c7bb94eeea9fb4589442cc054-element-19\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-19\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-132\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-132\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-256\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-256\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-256\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-260\")\n",
       "   .data(\"max_pos\", 111.58)\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-260\")\n",
       "   .data(\"min_pos\", 94.58)\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-260\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-262\")\n",
       "   .data(\"max_pos\", 111.58)\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-262\")\n",
       "   .data(\"min_pos\", 94.58)\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-262\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-262\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-262\")\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-c5c84f6c7bb94eeea9fb4589442cc054-element-264\")\n",
       "   .data(\"mouseover_color\", \"#cd5c5c\")\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-264\")\n",
       "   .data(\"mouseout_color\", \"#6a6a6a\")\n",
       ";\n",
       "fig.select(\"#fig-c5c84f6c7bb94eeea9fb4589442cc054-element-264\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "    });\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot(...)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import Gadfly\n",
    "\n",
    "funcs = [sin, cos, besselj1]\n",
    "Gadfly.plot(funcs, 0, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# How to get started\n",
    "## Three ways to run Julia\n",
    "\n",
    "<img src=\"images/ways-to-use-julia.png\" alt=\"Ways to use Julia\" style=\"width: auto;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## On your own computer\n",
    "\n",
    "<img src=\"images/way-1-laptop.png\" alt=\"On your own computer\" style=\"width: auto;\"/>\n",
    "\n",
    "... from the [Julia website](http://julialang.org/downloads/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "You can download Julia and install it on your own computer. As of the time of writing, version 0.3.6 is the stable version."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "You can also try [Juno](http://junolab.org) which is a nice editor bundled with Julia.\n",
    "\n",
    "<img src=\"http://junolab.org/images/error.png\" alt=\"Juno demonstration\" style=\"width: 800px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"images/way-1-laptop.png\" alt=\"On your own computer\" style=\"width: auto;\"/>\n",
    "\n",
    "Advantages:\n",
    "\n",
    "* roll your own installation for maximum customization\n",
    "* you can use the beta 0.4 version for more features at the risk of things breaking occasionally\n",
    "\n",
    "Disadvantages:\n",
    "\n",
    "* have to install stuff\n",
    "* uses space (not too much)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## On the IoA servers\n",
    "\n",
    "<img src=\"images/way-2-ioa.png\" alt=\"On the IoA servers\" style=\"width: auto;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Thanks to Roderick, we have our own installation of Julia on the IoA cluster. See the [tutorial](julia-tutorial.ipynb) for a little more information on setting this up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Activate it by doing \n",
    "\n",
    "* `module load git python/2.7`\n",
    "* `module load julia`\n",
    "\n",
    "Following this, typing `julia` will start the julia command line. It looks like this:\n",
    "\n",
    "<img src=\"images/julia-terminal.png\" alt=\"Julia command line\" style=\"width: auto;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"images/way-2-ioa.png\" alt=\"On the IoA servers\" style=\"width: auto;\"/>\n",
    "\n",
    "Advantages:\n",
    "\n",
    "* no need to install anything\n",
    "* we have pre-built important packages for you\n",
    "* can still add your own packages to your home directory\n",
    "\n",
    "Disadvantages:\n",
    "\n",
    "* system packages don't automatically show up at the moment\n",
    "* your own packages will use a bit of space in your home directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## JuliaBox online notebook\n",
    "\n",
    "<img src=\"images/way-3-cloud.png\" alt=\"In the cloud\" style=\"width: auto;\"/>\n",
    "\n",
    "With a Google account (Gmail, Google+, Youtube, etc) you can use [JuliaBox](http://www.juliabox.org), sync your notebooks to Google Drive or connect a Github account. JuliaBox has a large number of common packages already installed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"images/way-3-cloud.png\" alt=\"In the cloud\" style=\"width: auto;\"/>\n",
    "\n",
    "Advantages:\n",
    "\n",
    "* use anywhere with a web browser\n",
    "* it's real pretty\n",
    "* packages are precompiled so they go fast\n",
    "\n",
    "Disadvantages:\n",
    "\n",
    "* tied to Google account\n",
    "* 6 hour time limit per session\n",
    "* might disappear\n",
    "* little control over host machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# What is Julia good at?\n",
    "\n",
    "<img src=\"images/good-julia.png\" alt=\"Julia is good at...\" style=\"width: auto;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Fast numerical computing without the fuss\n",
    "\n",
    "<img src=\"images/just-in-time.png\" alt=\"JIT compilation\" style=\"width: auto;\"/>\n",
    "\n",
    "* Julia is *dynamically typed* (Python, IDL) rather than *statically typed* (C, Fortran), but aims to take the best features from both.\n",
    "* Clever type inference and just-in-time compilation means that once you've used a function, a speedy version of it has been compiled and stored for later use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Much of the secret to Julia's speed comes from its clever system of types. Many more restrictive languages feature strong type systems as well, but Julia is nice in that it doesn't make you think about them unless you want to. If you don't specify a type, Julia will try and work it out for you, and will do its best to make your code fast regardless."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Interfaces nicely with Python, C, Fortran\n",
    "\n",
    "* A really nice \"foreign function interface\" makes calling other code easy. Put in the library, the function, the return and argument types and you're done!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76500000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccall((:clock, \"libc\"), Int32, ())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Even easier: use pre-made packages which wrap the GSL.\n",
    "* As we saw before, calling Python is easy with PyCall. You can use any Python library you have installed. Calling Fortran routines is also possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Effective, strong type system\n",
    "\n",
    "<img src=\"images/type-hierarchy.png\" alt=\"Type hierarchy\" style=\"width: auto;\"/>\n",
    "\n",
    "* Julia is built on a strong foundation of \"types\". If you write a function and tell it to work on a *Number*, it will work on any *Real* or *Complex* number, *Integer* or *Floating Point*.\n",
    "* You never have to worry about it if you don't need to, but this gives you a really powerful way to structure your own code by writing your own types."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "The diagram above shows how part of the type system is laid out. All numbers belong to an appropriate type, and Julia knows how to perform the most efficient operations on any particular type. When you write *your* functions, you have the option of specifying the types of data you're going to provide. This has a couple of advantages:\n",
    "\n",
    "* the function won't work on the wrong type, letting you catch errors early\n",
    "* you can write different versions of functions to work on different types (more on that in a moment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"images/my-types.png\" alt=\"My type hierarchy\" style=\"width: auto;\"/>\n",
    "\n",
    "* For example, some of my equation of state calculations are done using this type of structure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "I am doing work involving the equations of state (EOS) of planetary interiors. Some equations of state are simple: they just need a function evaluation, or an interpolation. Some need numerical inversion, so they need additional information to evaluate. And some are piecewise, consisting of many equations of state stacked together. By laying my code out in a type hierarchy, I can choose whether the functions I write will apply to all of these, or just some, as is appropriate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Multiple dispatch\n",
    "\n",
    "is best shown with an example.\n",
    "\n",
    "<img src=\"images/multi-dispatch-example.png\" alt=\"Julia is not yet good at...\" style=\"width: auto;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Let's say we want to classify things according to whether they're `Living` or `Dead`. Let's further say that we want to make two types of living things: `Animal`s, for which we know a species name and a noise, and `Plant`s, for which we only know a species name. Here's a hierarchy of types for that.\n",
    "\n",
    "Let's further imagine that we want to define two functions here: one called `isalive` that tells us if a particular thing is alive, and another called `say` that tells us what noise a particular thing makes. We want `say` to work on every `Living` thing, even `Plant`s, but we don't want it to work for dead things.\n",
    "\n",
    "How would that look?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "abstract Living\n",
    "abstract Dead\n",
    "type Skeleton <: Dead end\n",
    "type Plant <: Living\n",
    "    species\n",
    "end\n",
    "type Animal <: Living\n",
    "    species\n",
    "    noise\n",
    "end;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "We just defined the types, making `Plant`s and `Animal`s `Living`. We also made a `Dead` `Skeleton` type, because why not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "randy = Animal(\"squirrel\", \"scritch scritch\")\n",
    "palmy = Plant(\"palm tree\")\n",
    "skelly = Skeleton();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Then we make some instances of those types. Randy the squirrel, who goes \"scritch scritch\". Palmy the palm tree, who doesn't have a noise. And Skelly the skeleton."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "isalive(l) = isa(l, Living)\n",
    "say(a::Animal) = println(\"The $(a.species) goes '$(a.noise)'.\")\n",
    "say(l::Living) = println(\"I don't know what a $(l.species) sounds like.\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "We write our functions. The `isalive` function just checks if something is `Living`. But `say` is more interesting: we wrote it twice, once for `Animal`s in particular and again as a fallback `Living` case. Now everything should behave as expected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true\n",
      "false\n",
      "The squirrel goes 'scritch scritch'.\n",
      "I don't know what a palm tree sounds like.\n"
     ]
    }
   ],
   "source": [
    "println(isalive(palmy))\n",
    "println(isalive(skelly))\n",
    "say(randy)\n",
    "say(palmy);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "And finally, we should get an error when we try to call the function `say` on something it's not defined to handle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "`say` has no method matching say(::Skeleton)\nwhile loading In[7], in expression starting on line 1",
     "output_type": "error",
     "traceback": [
      "`say` has no method matching say(::Skeleton)\nwhile loading In[7], in expression starting on line 1",
      ""
     ]
    }
   ],
   "source": [
    "say(skelly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Parallelism\n",
    "\n",
    "I haven't tried this much, but there is parallel computing support. You can open Julia on multiple cores and share work among them. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Here we roll a 6-sided die one million times and sum the result. You can see that making the for-loop into a parallel for-loop is a simple matter of adding an `@parallel` tag, and we also have the option of adding a reduction function at the end. In this case we choose to sum all the values `(+)`. If we started Julia on multiple cores, it would be distributed automatically among them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.502831"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = 1_000_000\n",
    "\n",
    "roll_a_die() = rand(1:6)\n",
    "total = @parallel (+) for i=1:N\n",
    "    roll_a_die()\n",
    "end\n",
    "\n",
    "average = total / N"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# What is Julia not good at (yet)?\n",
    "\n",
    "<img src=\"images/bad-julia.png\" alt=\"Julia is not yet good at...\" style=\"width: auto;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Language stability\n",
    "\n",
    "<img src=\"images/constantly-changing.png\" alt=\"Constantly changing\" style=\"width: auto;\"/>\n",
    "\n",
    "* Things are constantly changing\n",
    "* Release branches like 0.3.6 (current version) are stable but the language will evolve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "If you want to never have to rewrite your code ever again, Julia is not for you. Things will change - though at this stage, the language is stable enough that they will only be minor. Far more significant will be how packages change over time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Package support\n",
    "\n",
    "<img src=\"images/few-astro-libraries.png\" alt=\"Few astro libraries\" style=\"width: auto;\"/>\n",
    "\n",
    "* Little in the way of astronomy packages\n",
    "* Packages may be sparsely documented and sometimes buggy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Can't make arbitrary code fast\n",
    "\n",
    "* Despite function specialization, it's still very possible to write slow code in Julia\n",
    "* Functions in libraries like Scipy use C anyway, so they can be pretty good in many situations\n",
    "* If you can already code in C/Fortran/whatever, you are not going to gain any speed here\n",
    "* Julia's advantage is being able to write numerically heavy code using simple syntax - ideal for fixing the \"two-language problem\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Tinkering may be required\n",
    "\n",
    "<img src=\"images/tinkering-required.png\" alt=\"Tinkering may be required\" style=\"width: auto;\"/>\n",
    "\n",
    "* But the community are quick to fix problems, and you can ask me if you're just getting going."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Summary\n",
    "\n",
    "<img src=\"images/julia-summary.png\" alt=\"Graphical summary of Julia\" style=\"width: auto;\"/>\n",
    "\n",
    "* New language\n",
    "* Pretty speedy\n",
    "* Nice interoperability\n",
    "* Clean and powerful"
   ]
  }
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