{
 "metadata": {
  "celltoolbar": "Slideshow",
  "name": "",
  "signature": "sha256:ff0ce22b890865bb1af95d39587d5c0642e2ab199c57784ff2dc96c601ea15e3"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# IPython is not just for Python...\n",
      "\n",
      "Example: Julia Language! \n",
      "\n",
      "<img src=\"images/julia.jpeg\", width=\"30%\">\n",
      "\n",
      "<small>(thanks to Fernando Perez for providing the examples below)</small>\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "%run talktools"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "html": [
        "<style>\n",
        "/*\n",
        "   Simple CSS styling for using IPython Notebooks for talks.\n",
        "\n",
        "   Author: Fernando Perez <fernando.perez@berkeley.edu>\n",
        "*/\n",
        "\n",
        "talk-title\n",
        "{\n",
        "  color: #111133;\n",
        "  font-size: 220%;\n",
        "  font-weight:bold;\n",
        "  line-height: 1.2;\n",
        "  margin: 10px 50px 10px;\n",
        "  }\n",
        "\n",
        "talk-subtitle\n",
        "{\n",
        "  color: #111155;\n",
        "  font-size: 150%;\n",
        "  font-weight:bold;\n",
        "  line-height: 1.2;\n",
        "  margin: 20px 50px 20px;\n",
        "  text-align: center;\n",
        "  }\n",
        "\n",
        ".slide-header, p.slide-header\n",
        "{\n",
        "  color: #498AF3;\n",
        "  font-size: 180%;\n",
        "  font-weight:bold;\n",
        "  margin: 0px 20px 10px;\n",
        "  page-break-before: always;\n",
        "  text-align: center;\n",
        "  }\n",
        "\n",
        ".rendered_html\n",
        "{\n",
        "  color: #2C5494;\n",
        "  font-family: Ubuntu;\n",
        "  font-size: 170%;\n",
        "  line-height: 1.1;\n",
        "  margin: 0.5em 0;\n",
        "  }\n",
        "\n",
        ".rendered_html h1\n",
        "{\n",
        "  color: #1111AA;\n",
        "  line-height: 1.2;\n",
        "  margin: 0.15em 0em 0.5em;\n",
        "  page-break-before: always;\n",
        "  text-align: center;\n",
        "  }\n",
        "\n",
        "\n",
        ".rendered_html h2\n",
        "{\n",
        "  color: #111133;\n",
        "  line-height: 1.2;\n",
        "  margin: 1.1em 0em 0.5em;\n",
        "  text-align: center;\n",
        "  }\n",
        "\n",
        ".rendered_html h3\n",
        "{\n",
        "  font-size: 100%;\n",
        "  line-height: 1.2;\n",
        "  margin: 1.1em 0em 0.5em;\n",
        "  }\n",
        "\n",
        ".rendered_html h5\n",
        "{\n",
        "  font-size: 50%;\n",
        "  line-height: 1;\n",
        "  margin: 0.3em 0em 0.3em;\n",
        "  font-weight: bold;\n",
        "  text-align: center;\n",
        "  }\n",
        "\n",
        ".rendered_html h6\n",
        "{\n",
        "  font-size: 50%;\n",
        "  line-height: 1;\n",
        "  margin: 0.3em 0em 0.3em;\n",
        "  font-weight: normal;\n",
        "  text-align: center;\n",
        "  }\n",
        "\n",
        ".rendered_html li\n",
        "{\n",
        "  line-height: 1.8;\n",
        "  }\n",
        "\n",
        ".input_prompt, .CodeMirror-lines, .output_area\n",
        "{\n",
        "  font-family: Consolas;\n",
        "  font-size: 120%;\n",
        "  }\n",
        "\n",
        ".gap-above\n",
        "{\n",
        "  padding-top: 200px;\n",
        "  }\n",
        "\n",
        ".gap01\n",
        "{\n",
        "  padding-top: 10px;\n",
        "  }\n",
        "\n",
        ".gap05\n",
        "{\n",
        "  padding-top: 50px;\n",
        "  }\n",
        "\n",
        ".gap1\n",
        "{\n",
        "  padding-top: 100px;\n",
        "  }\n",
        "\n",
        ".gap2\n",
        "{\n",
        "  padding-top: 200px;\n",
        "  }\n",
        "\n",
        ".gap3\n",
        "{\n",
        "  padding-top: 300px;\n",
        "  }\n",
        "\n",
        ".emph\n",
        "{\n",
        "  color: #386BBC;\n",
        "  }\n",
        "\n",
        ".strike\n",
        "{\n",
        "  text-decoration:line-through;\n",
        "  color: #94B8F3;\n",
        "  }\n",
        "\n",
        ".warn\n",
        "{\n",
        "  color: red;\n",
        "  }\n",
        "\n",
        ".center\n",
        "{\n",
        "  text-align: center;\n",
        "  }\n",
        "\n",
        ".nb_link\n",
        "{\n",
        "    padding-bottom: 0.5em;\n",
        "}\n",
        "\n",
        "table.mytable\n",
        "{\n",
        "    border: 0px;\n",
        "}\n",
        "\n",
        ".mytable tbody\n",
        "{\n",
        "    border: 0px;\n",
        "}\n",
        "\n",
        ".mytable td\n",
        "{\n",
        "    border: 0px;\n",
        "}\n",
        "\n",
        ".mytable tr\n",
        "{\n",
        "    border: 0px;\n",
        "}\n",
        "\n",
        "</style>\n"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML object>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Using the Julia Magic"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext julia.magic\n",
      "%julia @pyimport matplotlib.pyplot as plt\n",
      "%julia @pyimport numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Initializing Julia interpreter. This may take some time..."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Running src: @pyimport matplotlib.pyplot as plt\n",
        "Ans:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4363833856\n",
        "Pyans (s, r): None ||| None\n",
        "Running src: @pyimport numpy as np\n",
        "Ans:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4363833856\n",
        "Pyans (s, r): None ||| None\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%julia\n",
      "\n",
      "# Note how we mix numpy and julia:\n",
      "t = linspace(0, 2*pi, 1000); # use the julia linspace\n",
      "s = sin(3 * t + 4 * np.cos(2 * t)); # use the numpy cosine and julia sine\n",
      "\n",
      "fig = plt.gcf()  # **** WATCH THIS VARIABLE ****\n",
      "plt.plot(t, s, color=\"red\", linewidth=2.0, linestyle=\"--\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running src: \n",
        "# Note how we mix numpy and julia:\n",
        "t = linspace(0, 2*pi, 1000); # use the julia linspace\n",
        "s = sin(3 * t + 4 * np.cos(2 * t)); # use the numpy cosine and julia sine\n",
        "\n",
        "fig = plt.gcf()  # **** WATCH THIS VARIABLE ****\n",
        "plt.plot(t, s, color=\"red\", linewidth=2.0, linestyle=\"--\")\n",
        "Ans:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4453709952\n",
        "Pyans (s, r): [<matplotlib.lines.Line2D object at 0x114e79ed0>] ||| [<matplotlib.lines.Line2D object at 0x114e79ed0>]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "[<matplotlib.lines.Line2D at 0x114e79ed0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CohcYB0+hZ2FwVhL6Z56hUsUPPWT2TMyFhezWqWNcbf7UVGDECG3VUf/xDwrK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\nRs81a+XI4HkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1061addd0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Julia Variables can be accessed in Python!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = %julia fig\n",
      "fig"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running src: fig\n",
        "Ans: 4450582896\n",
        "Pyans (s, r): Figure(480x320) ||| <matplotlib.figure.Figure object at 0x1061addd0>\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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CohcYB0+hZ2FwVhL6Z56hUsUPPWT2TMyFhezWqWNcbf7UVGDECG3VUf/xDwrK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\nG+dSYBuROTkkLIxmzSisU27NFbuTlAT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       "prompt_number": 4,
       "text": [
        "<matplotlib.figure.Figure at 0x1061addd0>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Getting Crazy: two-stage recursion in Python and Julia"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# define a recursive julia fib function which calls Python\n",
      "fib = %julia fib(n, fib2) = n < 2 ? n : fib2(n-1, fib) + fib2(n-2, fib)\n",
      "\n",
      "    \n",
      "# define a recursive python fib function which calls Julia\n",
      "def fib2(n, fib):\n",
      "    print 'Into Python, n=',n,\n",
      "    if n < 2:\n",
      "         return n\n",
      "    print 'Out to Julia'\n",
      "    return fib(n-1, fib2) + fib(n-2, fib2)\n",
      "\n",
      "\n",
      "fib2(6, fib)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running src: fib(n, fib2) = n < 2 ? n : fib2(n-1, fib) + fib2(n-2, fib)\n",
        "Ans: 4564745280\n",
        "Pyans (s, r):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " <PyCall.jlwrap fib> ||| <PyCall.jlwrap fib>\n",
        "Into Python, n= 6 Out to Julia\n",
        "Into Python, n="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4 Out to Julia\n",
        "Into Python, n= 2 Out to Julia\n",
        "Into Python, n= 1 Into Python, n= 1 Into Python, n= 0 Into Python, n= 3 Out to Julia\n",
        "Into Python, n= 1 Into Python, n= 0 Into Python, n= 3 Out to Julia\n",
        "Into Python, n= 1 Into Python, n= 0 Into Python, n= 2 Out to Julia\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "8"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## The Possibilities are Endless\n",
      "\n",
      "Other language magics and kernels available as well: Julia, R, Ruby, Haskell, etc.\n",
      "\n",
      "Look for the I<font color=\"#AAAAAA\">*[your language here]*</font> notebook!\n",
      "\n",
      "1. [IPython](http://ipython.org) \"Official\" version.\n",
      "1. [IJulia](http://nbviewer.ipython.org/url/jdj.mit.edu/~stevenj/IJulia%20Preview.ipynb)\n",
      "1. [IHaskell](http://nbviewer.ipython.org/github/gibiansky/IHaskell/blob/master/demo/IHaskell.ipynb)\n",
      "1. [IFSharp](http://nbviewer.ipython.org/github/BayardRock/IfSharp/blob/master/Feature%20Notebook.ipynb)\n",
      "1. [IRuby](http://nbviewer.ipython.org/github/minad/iruby/blob/master/IRuby-Example.ipynb)\n",
      "1. [IGo](https://github.com/takluyver/igo)\n",
      "1. [IScala](https://github.com/mattpap/IScala)\n",
      "1. [IMathics](http://nbviewer.ipython.org/gist/sn6uv/8381447)\n",
      "1. [IAldor](https://github.com/mattpap/IAldor)\n",
      "1. [IRKernel](https://github.com/takluyver/IRkernel): [Leaflet routing in R](http://nbviewer.ipython.org/gist/anonymous/9998388/rmaps_leaflet_routing.ipynb)\n",
      "1. [IErlang](https://github.com/robbielynch/ierlang)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}