{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python <-> Julia  Bridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing Julia interpreter. This may take some time...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<PyCall.jlwrap 0.5.2>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load_ext julia.magic \n",
    "julia_version = %julia VERSION\n",
    "julia_version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%julia\n",
    "@pyimport matplotlib.pyplot as plt\n",
    "@pyimport numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f331c95f390>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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beVlFh2Cw6/uKpaxMNI3q1f0vyJgE1z4GIsoBMBbAAAAdAIwgog4VdvsBwNnM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\nGAwGg6EcRjAYDAaDoRz/B8vF3+W2QN9TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3548b430b8>"
      ]
     },
     "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` and `pi`\n",
    "s = sin(3*t + 4*np.cos(2*t));           # use the numpy cosine and julia sine\n",
    "fig = plt.gcf()                         # **** WATCH THIS VARIABLE ****\n",
    "plt.plot(t, s, color=\"red\", linewidth=2.0, linestyle=\"--\", label=\"sin(3t+4.cos(2t))\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DYTD9YF5iC4YgoQoLOfrV3dTI3U/+f9YTFR2jTSJ//aV3iI+3brA+fXS4XnKy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\noVDU4f8AKJZN5surmxQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3548b430b8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "fig = %julia fig\n",
    "x = np.arange(0, 6, 0.01)\n",
    "fig.axes[0].plot(x, np.sin(x), '--', label='sin')\n",
    "fig.axes[0].set_title('A weird Julia function and Fib')\n",
    "fig.axes[0].legend()\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "\n",
    "# julia fib function\n",
    "jlfib = %julia _fib(n, pyfib) = n <= 2 ? 1 : pyfib(n-1, _fib) + pyfib(n-2, _fib)\n",
    "\n",
    "\n",
    "def pyfib(n, _fib):\n",
    "    \"\"\"\n",
    "    Python fib function\n",
    "    \"\"\"\n",
    "    #print('(P', end='')\n",
    "    if n <= 2:\n",
    "         r = 1\n",
    "    else:\n",
    "        #print('(J', end='')\n",
    "        # here we tell julia (_fib) to recurse using Python\n",
    "        r =  _fib(n-1, pyfib) + _fib(n-2, pyfib)\n",
    "        #print(')',end='')\n",
    "    #print(')',end='')\n",
    "    return r\n",
    "\n",
    "fibonacci = lambda x: pyfib(x, jlfib)\n",
    "\n",
    "fibonacci(10)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
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