{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Waking up Julia takes up to 40 seconds the first time\n",
    "import julia\n",
    "j = julia.Julia()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.656854249492381\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# evaluation using Julia interpreter\n",
    "print(j.eval(\"sqrt(1 +31)\"))\n",
    "print(j.sind(90))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing Julia interpreter. This may take some time...\n"
     ]
    }
   ],
   "source": [
    "# Warning: loading '%load_ext julia.magic' before 'import julia' will change behaviour of julia object\n",
    "# So here, we do it after\n",
    "%load_ext julia.magic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%julia @pyimport matplotlib.pyplot as plt\n",
    "%julia @pyimport numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xd08d470>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "AABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4508c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%julia\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=\"--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6765"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fibonacci with each layer being Python than Julia\n",
    "jfib = %julia jfib(n, fib) = n < 2 ? n : fib(n-1, jfib) + fib(n-2, jfib)\n",
    "    \n",
    "def pyfib(n, fib):\n",
    "    if n < 2:\n",
    "         return n\n",
    "    return fib(n-1, pyfib) + fib(n-2, pyfib)\n",
    "\n",
    "pyfib(20, jfib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# original examples from Fernando Perez\n",
    "# http://nbviewer.ipython.org/github/fperez/talk-1312-nips/blob/master/IPython-Interactive-Computing.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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