{
 "metadata": {
  "name": "",
  "signature": "sha256:1568d7eace109a897f26c20926949194b306e944d6d27966ba05152dc1a87ae6"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Basic Commands"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We started the notebook using 'ipython notebook' and then clicked 'New Notebook'. We did not load matplotlib automatically, but we do it now (this is the recommended procedure)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/lib/python2.7/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
        "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The 'inline' option will show the plots within the notebook, instead of opening a different window."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Making a simple plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(0, 3.* np.pi, 500)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(x, np.sin(x**2))\n",
      "plt.title('A simple chirp');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DvlzP3/RAFpuduqGyfbibvHRq9eheH5V/RQht+SgsWiTvQkKg2ZS/bXmHUOTP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\n22f69OyAsg35Z2X8hLJ9XJB/sj2nvET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G84uJ6EcAIIQYAfBBALcBeBTAOiHE7+ymHRER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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fcfa975e350>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a=10"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load http://matplotlib.org/mpl_examples/showcase/integral_demo.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "IOError",
       "evalue": "[Errno socket error] [Errno -3] Temporary failure in name resolution",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-7-95a961c9ce82>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'load http://matplotlib.org/mpl_examples/showcase/integral_demo.py'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/usr/lib/python2.7/dist-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2203\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2204\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2205\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2207\u001b[0m     \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/lib/python2.7/dist-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2124\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2125\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2126\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2127\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m<decorator-gen-45>\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, arg_s)\u001b[0m\n",
        "\u001b[0;32m/usr/lib/python2.7/dist-packages/IPython/core/magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    191\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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        "\u001b[0;32m/usr/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body)\u001b[0m\n\u001b[1;32m    895\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mmessage_body\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    896\u001b[0m             \u001b[0mmessage_body\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 897\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    898\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmessage_body\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m             \u001b[0;31m#message_body was not a string (i.e. it is a file) and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    857\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msock\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    860\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mNotConnected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    834\u001b[0m         \u001b[0;34m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    835\u001b[0m         self.sock = self._create_connection((self.host,self.port),\n\u001b[0;32m--> 836\u001b[0;31m                                            self.timeout, self.source_address)\n\u001b[0m\u001b[1;32m    837\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    838\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tunnel_host\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/lib/python2.7/socket.pyc\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m    555\u001b[0m     \u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maddress\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m     \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 557\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgetaddrinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSOCK_STREAM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    558\u001b[0m         \u001b[0maf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m         \u001b[0msock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mIOError\u001b[0m: [Errno socket error] [Errno -3] Temporary failure in name resolution"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Plot demonstrating the integral as the area under a curve.\n",
      "\n",
      "Although this is a simple example, it demonstrates some important tweaks:\n",
      "\n",
      "    * A simple line plot with custom color and line width.\n",
      "    * A shaded region created using a Polygon patch.\n",
      "    * A text label with mathtext rendering.\n",
      "    * figtext calls to label the x- and y-axes.\n",
      "    * Use of axis spines to hide the top and right spines.\n",
      "    * Custom tick placement and labels.\n",
      "\"\"\"\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from matplotlib.patches import Polygon\n",
      "\n",
      "\n",
      "def func(x):\n",
      "    return (x - 3) * (x - 5) * (x - 7) + 85\n",
      "\n",
      "\n",
      "a, b = 2, 9  # integral limits\n",
      "x = np.linspace(0, 10)\n",
      "y = func(x)\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "plt.plot(x, y, 'r', linewidth=2)\n",
      "plt.ylim(ymin=0)\n",
      "\n",
      "# Make the shaded region\n",
      "ix = np.linspace(a, b)\n",
      "iy = func(ix)\n",
      "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n",
      "poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\n",
      "ax.add_patch(poly)\n",
      "\n",
      "plt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n",
      "         horizontalalignment='center', fontsize=20)\n",
      "\n",
      "plt.figtext(0.9, 0.05, '$x$')\n",
      "plt.figtext(0.1, 0.9, '$y$')\n",
      "\n",
      "ax.spines['right'].set_visible(False)\n",
      "ax.spines['top'].set_visible(False)\n",
      "ax.xaxis.set_ticks_position('bottom')\n",
      "\n",
      "ax.set_xticks((a, b))\n",
      "ax.set_xticklabels(('$a$', '$b$'))\n",
      "ax.set_yticks([])\n",
      "\n",
      "plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Plot demonstrating the integral as the area under a curve.\n",
      "\n",
      "Although this is a simple example, it demonstrates some important tweaks:\n",
      "\n",
      "    * A simple line plot with custom color and line width.\n",
      "    * A shaded region created using a Polygon patch.\n",
      "    * A text label with mathtext rendering.\n",
      "    * figtext calls to label the x- and y-axes.\n",
      "    * Use of axis spines to hide the top and right spines.\n",
      "    * Custom tick placement and labels.\n",
      "\"\"\"\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from matplotlib.patches import Polygon\n",
      "\n",
      "\n",
      "def func(x):\n",
      "    return (x - 3) * (x - 5) * (x - 7) + 85\n",
      "\n",
      "\n",
      "a, b = 2, 9  # integral limits\n",
      "x = np.linspace(0, 10)\n",
      "y = func(x)\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "plt.plot(x, y, 'r', linewidth=2)\n",
      "plt.ylim(ymin=0)\n",
      "\n",
      "# Make the shaded region\n",
      "ix = np.linspace(a, b)\n",
      "iy = func(ix)\n",
      "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n",
      "poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\n",
      "ax.add_patch(poly)\n",
      "\n",
      "plt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n",
      "         horizontalalignment='center', fontsize=20)\n",
      "\n",
      "plt.figtext(0.9, 0.05, '$x$')\n",
      "plt.figtext(0.1, 0.9, '$y$')\n",
      "\n",
      "ax.spines['right'].set_visible(False)\n",
      "ax.spines['top'].set_visible(False)\n",
      "ax.xaxis.set_ticks_position('bottom')\n",
      "\n",
      "ax.set_xticks((a, b))\n",
      "ax.set_xticklabels(('$a$', '$b$'))\n",
      "ax.set_yticks([])\n",
      "\n",
      "plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Plot demonstrating the integral as the area under a curve.\n",
      "\n",
      "Although this is a simple example, it demonstrates some important tweaks:\n",
      "\n",
      "    * A simple line plot with custom color and line width.\n",
      "    * A shaded region created using a Polygon patch.\n",
      "    * A text label with mathtext rendering.\n",
      "    * figtext calls to label the x- and y-axes.\n",
      "    * Use of axis spines to hide the top and right spines.\n",
      "    * Custom tick placement and labels.\n",
      "\"\"\"\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from matplotlib.patches import Polygon\n",
      "\n",
      "\n",
      "def func(x):\n",
      "    return (x - 3) * (x - 5) * (x - 7) + 85\n",
      "\n",
      "\n",
      "a, b = 2, 9  # integral limits\n",
      "x = np.linspace(0, 10)\n",
      "y = func(x)\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "plt.plot(x, y, 'r', linewidth=2)\n",
      "plt.ylim(ymin=0)\n",
      "\n",
      "# Make the shaded region\n",
      "ix = np.linspace(a, b)\n",
      "iy = func(ix)\n",
      "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n",
      "poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\n",
      "ax.add_patch(poly)\n",
      "\n",
      "plt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n",
      "         horizontalalignment='center', fontsize=20)\n",
      "\n",
      "plt.figtext(0.9, 0.05, '$x$')\n",
      "plt.figtext(0.1, 0.9, '$y$')\n",
      "\n",
      "ax.spines['right'].set_visible(False)\n",
      "ax.spines['top'].set_visible(False)\n",
      "ax.xaxis.set_ticks_position('bottom')\n",
      "\n",
      "ax.set_xticks((a, b))\n",
      "ax.set_xticklabels(('$a$', '$b$'))\n",
      "ax.set_yticks([])\n",
      "\n",
      "plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "How to fit an elephant (code from Piotr Zolnierczuk listed in [John D. Cook's blog](www.johndcook.com/blog/2011/06/21/how-to-fit-an-elephant/))"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Author: Piotr A. Zolnierczuk (zolnierczukp at ornl dot go)\n",
      "\n",
      "Based on a paper by:\n",
      "Drawing an elephant with four complex parameters\n",
      "Jurgen Mayer, Khaled Khairy, and Jonathon Howard,\n",
      "Am. J. Phys. 78, 648 (2010), d.o.i.:10.1119/1.3254017\n",
      "\"\"\"\n",
      "\n",
      "# elephant parameters\n",
      "p1, p2, p3, p4 = (50 - 30j, 18 + 8j, 12 - 10j, -14 - 60j)\n",
      "p5 = 40 + 20j # eyepiece\n",
      "\n",
      "def fourier(t, C):\n",
      "    f = np.zeros(t.shape)\n",
      "    A, B = C.real, C.imag\n",
      "    for k in range(len(C)):\n",
      "        f = f + A[k]*np.cos(k*t)+ B[k]*np.sin(k*t)\n",
      "    return f\n",
      "\n",
      "def elephant(t, p1, p2, p3, p4, p5):\n",
      "    npar = 6\n",
      "    Cx = np.zeros((npar,), dtype='complex')\n",
      "    Cy = np.zeros((npar,), dtype='complex')\n",
      "    \n",
      "    Cx[1] = p1.real*1j\n",
      "    Cx[2] = p2.real*1j\n",
      "    Cx[3] = p3.real\n",
      "    Cx[5] = p4.real\n",
      "    \n",
      "    Cy[1] = p4.imag + p1.imag*1j\n",
      "    Cy[2] = p2.imag*1j\n",
      "    Cy[3] = p3.imag*1j\n",
      "    \n",
      "    x = np.append(fourier(t, Cx), [-p5.imag])\n",
      "    y = np.append(fourier(t, Cy), [p5.imag])\n",
      "    \n",
      "    return x, y\n",
      "\n",
      "x, y = elephant(np.linspace(0, 2.*np.pi, 1000), p1, p2, p3, p4, p5)\n",
      "plt.plot(y, -x, '.')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f4fa5654a90>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vgLnJQ4ADZwNnEVI+dRkaGnrt9uDgIIOT+OWWD/Msv5+FYmv/85+H55+vfHyq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\n/wfhEaeDlVPQlQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4fbc03a050>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Exercise: Make it wiggle and wink! (Hint: Look at this implementation in [R](www.r-bloggers.com/a-winking-pink-elephant) )"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By looking at the example written in R, we see that the function generating the elephant is basically the same, except for the fact that Cx[4] = Re(p5) - (i-1) is non-zero. The effect of the wiggling elephant is achieved by drawing two lines with slightly different parameters: the variable i takes the values 1, 2.\n",
      "\n",
      "The eyepiece is drawn at the position (-Im(p5), Im(p5)), and the winking effect is achieved by alternating symbols between a plain dot and a symbol in R that looks like a rotated S.\n",
      "\n",
      "Finally, the two images are alternated by means of an animation.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Function that generates the elephant as in R: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def relephant(i,t, p1, p2, p3, p4, p5): #slightly changed definition following example in R, takes additional parameter\n",
      "                                        # i = 1,2 and does not plot the eyepiece, which will be drawn separately.\n",
      "    npar = 6\n",
      "    Cx = np.zeros((npar,), dtype='complex')\n",
      "    Cy = np.zeros((npar,), dtype='complex')\n",
      "    \n",
      "    Cx[1] = p1.real*1j\n",
      "    Cx[2] = p2.real*1j\n",
      "    Cx[3] = p3.real\n",
      "    Cx[4] = p5.real - (i-1)\n",
      "    Cx[5] = p4.real\n",
      "    \n",
      "    Cy[1] = p4.imag + p1.imag*1j\n",
      "    Cy[2] = p2.imag*1j\n",
      "    Cy[3] = p3.imag*1j\n",
      "    \n",
      "    x = fourier(t, Cx)\n",
      "    y = fourier(t, Cy)\n",
      "    \n",
      "    return x, y\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Draw the two elephant contours"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let us test it for different values of i"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# slightly changed elephant parameters\n",
      "p1, p2, p3, p4 = (50 - 30j, 18 + 8j, 12 - 10j, -14 - 60j)\n",
      "p5 = 1+20j"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x1, y1 = relephant(1,np.linspace(0,2*np.pi, 1000), p1,p2,p3,p4,p5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x2, y2 = relephant(2,np.linspace(0,2*np.pi, 1000), p1,p2,p3,p4,p5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Let us plot the two elephants"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(y1,-x1,'.')\n",
      "plt.plot(y2,-x2,'.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f4fa5640b90>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xTLdYVNxUh3gunuCn6pIhteGdOqJfLfjprX5m1JGjmrT6dH9sWrwYKmybrzYv\nZtozFWw2fjhuZsNPCaEGhTpPKHWfDqqJ1sBQLfRd23SlTas2bKrcVBvWqbY35DapgfCO98fh3DTe\nw8UXJ996nEQI+0wyxpzuHIcM+0ycOLHmGp/Ph8/nazab6mLvsuk1rTdlP5S6FnF1eGUpXxd7k+4L\no4SHZcGgITZlLefDqEsDZQxC7Aw1yTeJsceGrhcfDYIHA3/lYuwdlYGng/U4RdJCPCHAXmvn1CXb\nExgY2rSqHRgqf66k7Icysttlk9E2Y58DRF1xD2ZT5SaufuNqfq4W+mCbGioityUb3nyMrj/6kia8\nU01RURFFRUU1x5MnT45r8U8HVhGY8P0fsBi40BizMuicmHr+BasKGDl7ZG2DAWYXsKpgREJPBimR\nx++HgcMstpx1grukcY2nn87kGIv+3qgeECorYZNtc+vUxWyocFbdHrAJzroK0usI7b4mlvchPTnt\nc7jv5PvqCTvsRdwbg0vwc+Dd+2FnBukb+vPSM8kl+g0R154/1KR6TqE21fP+Ou/HTPxDh3tyeHbg\nci6/MMm/OUqT8PvhyAEWVWP61dvgXCSNOwdNZvzA+BT9hggeDCor4Xd3W1jtXw3Uyjc0/HRQTaSK\nte2LUPKwLRMW3g92Ftkt+jP5Tg+2DaNGJV94pyHiXvz3aUCMxL+hcE/XgqV8+7k36b0GpfFYFvQa\nZLHl3H7gcQt/xwMy+PSKReRmJP5jYvBgACGeDqrZW9ioLo2Rpn39/KuFfmenwPHuNnTd058H7/GQ\nlQX9+ye/lx8KFf/9ZOF3Cxn2/LDaBg33KCGoCfWc2zewTWC1oyCQ3S6HT8YtSihvv6nUHRAg8PqP\nd9mU7VkM6SEGhg5lsKML+CY3boCoK+7B7G4DVn9aVHmYORM6dQqsU0hVwQ8m3lM945bV5RXuGh9b\nsnl2ok+FX6nBFerxBKUBCzx4yoNcc9w1cZcGGWk8Hjj55Prt+fkeFi8+2TUoAGzaBLffDuvWAatG\nQfcQA0Qwjrjzk4fsbHjwwYC4V1NZCRs2pFY4J1qkpOdvbbM44pG+2KbWk2v/ZgFrFo5IeW9CCbC3\nUE92+xy+unZ50gv//hLqaaEhKiuhrAyOOAJ8PvXmm4KGfZpIyFi/TvIqQVgW9D3eZt3Io6Hj9ykX\n6lESg3DFPy2SxiQChaWFgRWBwbsDLXmUc36jwq8EvNYThtqs806vFX6oCfV8de1yFX4lKUipmL+9\ny+baghvjo4P4AAAZzUlEQVTrxPpzeOyPPn3cVACY94ZN6SkDoMvK2kYn1JMKMX4ldUgpz79wdTFr\nd5S6UzvnLyL/ZP1BK4EJ3jH3FAaEX6jx+jMP9PLJuEUq/EpSkVLiv3h5hbvhnb/x4QLN6VcC4Z7B\n+RbkX13baCCzrZelVy3RUI+SdKSM+FvbLB77z82utj9cn6GpnQoQCPdsOON4Vy5/lwMyVfiVpCUl\nxN/eZTNo1gn8sMddxmHwwXFZXVqJMjXhnjoTvLPOfkqFX0laUkL8i9cW18vw6bL0UXzHa7wn1bFt\nGHKqDafeWNtoAimdvhxfzOxSlOYmJcS/ZjUv1Kzmnfp7zfBRAvvtrmuzIFClMyjcoxO8SrKT9Iu8\n7F02v7q/N1tMqa7mVVzYNhxxvB/rrJ6QvqfmybDgggJGHD4itsYpyj7QRV77YP6KQpfwsyWHKTep\n16/A/IU21qlDa4Xfye7RcI+SCiS1+Nu7bCa8eaOrTVfzKhAo4XDN/YW1dXsMpEsL3h9dqOEeJSVI\navEvXF3Mxp9KdTWv4sK2YeCJNj8MdDsGM/NfSIq6/IrSGMISfxF5UERWisgyEZknIu2C3rtdRFY7\n758avqlNp+6irs7L7tfVvAqFhbAmrdA9ydsqh1G982NtmqJEjXA9/3eAo4wxxwCrgdsBRKQncB5w\nJDAcmCYi0dr0DQiEfJ787nZX2/jLMtTrT3FsG6671YIRV7vapw5/VMM9SkoRlvgbYxYaY6qcw0+B\nLOf1mcBsY8xuY0wpgYEhqiuqClcXU7G71BXy6Z+li7pSnfkLbcpPda/kzfLkkN/TF2vTFCWqRDLm\nPxZY4LzuDqwJem+t0xY1PvisfshHF3WlNrYNEx6uv5J32m/U61dSj32WdBaRd4FuwU0EfOk7jTFv\nOOfcCfxsjHl5f4yYNGlSzWufz4fP59ufj6nB2mTzyPLboV1tm4Z8lPkLbTb2rbOSt72u5FUSg6Ki\nIoqKiiL2eWEv8hKR0cCVwEnGmF1O222AMcY84By/BUw0xhSHuD7ii7wefGUht349zBXyKchfzojT\nVP1TFduGQ4cXsPGUkTXfiw4tMvl6wlKt36MkJDFd5CUipwN/AM6sFn6HAuACEWklIgcDhwGLw7lX\nUyi317iOO34xUUM+KU49rx944gwt3KakLuHu5DUVaAW86yTzfGqMudYYs0JE/gGsAH4Gro3WRr32\nLpvn197haht5nq0hnxSmJtZ/SqkrtVMneZVUJizxN8b02Mt79wH3hfP5+0Ph6mK2Bpdu3tOC03NG\nRdsMJY4oXFTf69fUTiXVSboVvnUXdnVaNJP8wfpon8oUry12LejKaKFev6IklfiHWth17cVZGvJJ\nYWwbnnzO7RBcfcj96vUrKU9Sib8u7FLqMn+hzaZj3A7BgN4ZMbJGUeKHpBL/L76qdB3rwq7UpnZR\nV6lrotfXQx0CRUkq8f/hR7f4j72kjYZ8UphQ6Z060asoAZJG/O1dNn8vdz/et2vTJkbWKLHGtuGG\nh+p7/TrRqygBkkb8Q8X7+2To432qMu8Nm4rj1OtXlIZIGvEvLnGHfDqVaLw/VbFt+N1f3emdHUW9\nfkUJJmnEv7zcfTz8FI33pyqFhbDlJ/cX4s8naXqnogSTNOLf2uP2/L26rislsW0Yf6cfRl5R2yjQ\no7umdypKMEkh/tYmm5ml7sneLh11sjcVKVxkY506FNL31IR8Mtt66d9d538UJZhwC7vFBS+8V8we\nT2nNjz1tWw4XD02uH7u9y2ZuSSH//OAbMlp3Yd22jfTMyuaoo2DjT2Vkt8smo20GA7oPSOnwRvHa\nYvBYtd8FacH7owtTuk8UJRRJIf4bdtRZvp87EW/nxP2xW5tspi0opOT7bzisazY/7dnJ8+tvo5IN\ngRN2Bv73ZilQSu2uVEBH6c6IrKsZenQPsjqn1mBQU8phaG3b7w6dQW5GbuyMUpQ4JSnEf+NPZa7j\nnekbY2TJ/uMvtZkyr5htZg0vrL8N2jpCbwWd1IhtG7ZUreW5srt5rixwfqf07tx0/ATGHXdJ0teu\nL1xUv5TDicccFCNrFCW+SQrx79Iqe6/H8YplwQuv2HxXVciT398AHZxBrC2hhX5fOyJI/es2717L\n3R/cysQP7+CW/rcx+OD+DD14aFI+DYSq3qmlHBQlNEkh/tvtOg0/xfdkr2XBtJk29/5jPpx6M7Rb\nBx1wC3cood+TTuult3PuyYeyfvtGlrybzZZNQGc/HPcUdKiT7xo0GBizh4eK7+WhxdC1dVcezX+U\n/B75STMIhAr5aPVORWmYiIi/iNwC/AXIMMZsdtoeBYYDO4DRxphlkbhXXaxNNk/9171Ze6Udf+Jv\n21BcDP7vba7723wYdiOc64R2Qon+nnRO4HaOO6gXGPjvpg2cffgoRv3RW7N+wf4dFBXBN9/AgZ1v\nonjNYgrerWALIQaDoHtsqNzABfMuILNNJu+PeT8pYuJavVNRmkYkNnDPAmYChwN9jTGbRWQ4cL0x\n5gwRGQBMMcYMbOD6sHZ4DLVZ+6qbl5ObEz8en2XBgJMtyju8AAOm1opytSAH/fU7SBYntp7AxLMv\nJq9H02P0tg2LF8Oa9TYLVy7mxTfWwEm3QrsNtScF3VckjafPnMWonqMS1ksOtTl7l1Y5/OeW5Qn7\nd1KUfRHuBu6REP9XgD8R2LS9WvyfAAqNMXOcc1YCPmPM+hDXhyX+dz5XwJ+/q/3Rj8+YzbTrz9/v\nz4sktg1z37C5/m/zqTztMkj/OfBGHdFPkxbc0mMmg/tk4Tusf0QFy7Jg+iybe18swnQrhhMfgPTd\ntXaYwP8T+Smg4C2bkW/2dsX7Z48s4PxjR8TaNEVpNsIV/7AWeYnImcAaY8yXdd7qDqwJOl7rtEWc\nXVXulb0Hxomj5/fDIcdYjC3uTWX+hQHhr47BGxyvO527jr+HNTd/z4MXXc6Io06OuKfq9cI9/89D\n+XsjuGfY/5H22Pew8B7Yk1Yj/ADrdqzjyMd7MuerOdi76k6ixDe6TaOiNJ19xvxF5F2gW3ATAdm4\nC7gDGBauEZMmTap57fP58Pl8jb7W2u5O87TssgbOjA7V3v7YP8+Hc66DtpvrefqdWmRx8/ETGHvc\nxVFLv/R64a67YOxYL08/fRf/7/F8uLI/pO0JnCBQZfZwwbwLyPb8ik+u+DghUkN1oldJFYqKiigq\nKorY5+132EdEegELgUoC8pZFwMPvTyAMFBz2+QYY0hxhn4vvnc1LP19YMyRd3Go2L9wRm7CPZUG/\n0/xYw4YEMnjAJfwHpnXiseHTOaf38JiLU0kJHHeShen1NAydVFsOAQIx8zaZLLtmadwPAIGQTy/o\nWFbzHSg4dyEjep0ca9MUpVmJWdjHGPOVMSbTGHOIMeZgoBw41hizgUD8/zLHwIHA1lDCHwl+2dWd\n2ePtEv1MH9uGZ16y6XHWbKyzjgoIf3CIB8jy5LDqpi+5vN95MRd+gLw8KF/h5Z7T74InVsCOjNqJ\nZ4GNlevoNa0P/gp/TO3cF29+u8Al/J1bZGtuv6I0gkgWdquJIBtjFgD/FZFvgSeBayN4Hxc/1Yn5\n1z1uburH9ne74vrp0oI5o+aw4vrlcedFV4eCVi3KpeM/vgA70zUAbPmxgiMf70mJVRJTOxvCX2rz\nxH9vcrWdk3VDXAyuihLvREz8nSeAzUHH1xtjDjPG9DHGNJt6fLvRHeNfXRGdmL9tw+zZcMSv/VSc\n06d2wrFa+KvS+MNx91B28/ec1ys+vP2GyM2Frz7xkvHaUtiSUzNwVc8DHDejX1wOAH95uRg864L6\nvCW/P+2iWJulKAlBwpd0Pqyru5RDjy7NX9rBsqBXns2Ff5qNufooaFvhipd70jNYOm4lD/7mrrjz\n9hvC64UvPvSSvWA5/GM27EmvGQCMqeK4Gf3iKhPItuHVN90F/cb/8ilyf5kY/a0osSbhxb9eGYTw\nli3sE78feg2yKMvvDefVCfMQiO1/c+MX5P0q8fLlvV74qsTD7Innw6zFUJXmGgAumHcBvR4/Gmub\ntc/Pam4KF9lsPta9onf4YC3ipiiNJeHFP5phn5ISOPwEP1vOqx/mSZP0uI3tNwWPB84/H5b+Kw9m\nLIGqdNd6gLJt35P3ZN+YDwAflBdqETdFCYOEF//s9tl7PY4E1fH9vr8pgfE964V5Oh6QwcrrVsR9\nbL8p5OXB0vl5ML1+JtD6ynWc8PQJMQsBWZts/rryeleb5vYrStNIePHfvLnO8abIfr7fD4cdFYjv\nM85ZFBUU5slun8NX136RkGUR9kVeXgOZQEDpD6UsWL0gJnZNe60Y41njKlanRdwUpWkkvPhXsLrO\ncWTy0muyefpbbDi7Or5fK/wiacwZNYevrk3sMM++qM4E6jh3qXsAMHDRvIujngVk2zDl70HVSg10\nEM3tV5SmkvDi365dneP24X9mTTbP3QWYcXkh4/ufXbkkqcI8e8PrhU/f9SKzlkJlRkzTQOe+bbF9\nyJWutkdOfSwl/h0UJZIkvPjLtix3ww/hZXz4/XDUACeb56KR4FkfMr6f580L6z6JRm4ufPaeF55e\nVC8NtF+UBgDbhpuefsGVYdU+LZNReb5mv7eiJBsJL/6bdrt3ryrf/u1+f1ZJCRzez2LreX3d3r5D\nt7aZSRvfbwx5ebD0nVyY6U4DrYrSAFC4yGZbj6mutusP+5t6/YqyHyS8+B/Qyn38mXkCf2nTslAs\nC+65B/qe6oerjq5dNQo1Me6cdjmUXB3/hc6am5osoBlLoj4ABOr4lNfOu9hZXHtafrPdT1GSmYQX\n/4tO7lN7IMCBm/h/s4oada1twzPPQFZPi7vfvsdJ43SXYO7cuisFFxSwPMkndptCXh7M/lsePBm9\nASBQx2eCq+2yI27E21m9fkXZH8LeyStsA8Is6Wzvsun8p4P5ueWmGo8wvfj3bPnHX2r2uq13jQ3z\n58P1t9hs6jwXzrrSVde+mm5tMtXbbwDbhqOPhu93lsDV/SCtKmhCPI0lVy6J6LzI2ZOf5TUzJqiO\nTwtWjf9eyzkoKUtMd/KKBzy/8HBG5jWutj39Hmbiw/VXoFanbx58MFx4rZ9N5x8G54ytzd0Pzt9v\nl63Cvxc8Hvj4Y8hMa/4ngLeX+Hlt9zhX23ldJqrwK0oYJLznDzBnWQEXvDbSHaf/dDy+XdPIyICW\nLaF9+4Dwb91tQb9H4NcPQ5pxXyPQtXVXpuZPZXiP2G+4kghYFvTtC+uqmucJwNpk0/3+XGgbXL0z\nnVXjy1T8lZQm5hu4h0skxN/eZfPLv2SzY/dWl/fOt75AXnpVS6jsAm0tOHpeQPTBJfwiaTxz5tOc\n0/McFf0m0pwDwI2PF/DoRvfAPj7zGaZdMzpS5itKQqLi71BildB3Rt/aImR7+8g63n6XNt34aMwH\nKZvCGQn2NgAgwtIrP2vyAPDhMosTnz8GPBtrP2tbFmvvXKETvUrKE/OYv4jcICIrReRLEbk/qP12\nEVntvHdquPfZF3nePF797atBgrOXP0FFyqbnT+c/E1ar8IeJ1wtLlwbPAUjQQGzoO+M4nv382UYX\ng7M22Zz4/IBa4XeY/ptpKvyKEgHC8vxFxAfcAeQbY3aLSIYxpkJEjgReAvoR2Nh9IdAjlIsfKc+/\nmpe+eImLX7u4Yc/fEZLRvUdz++DbVfQjjGXBoEFQ1v5ZOGdMvaes7gdmsfjK4n1OpF/w0HTmbL/W\ndX37tEzW3OrXsJyiEOOwj4jMAZ40xrxXp/02wBhjHnCO3wQmGWOKQ3xGRMUfAiGgyUWTSSedlukt\naZnekow2GWz9aSuHdTyMsceO1SyeZsS24U8P2Dz0Q1/o7BTeCxLxti09fDCmKGQYyN5lc8uLM5hR\ndkvtdc62mEvHrUzITXIUpTmItfh/DrwOnA7sBH5vjFkqIlOBT4wxLznnzQQWGGNeDfEZERd/JfbY\nNhw7wOY/LebBWWNDZladcehZDDgoj3HHBtI4H/loGo8umcpPZlvgvBrhh1eHL+XsQalVT0lR9ka4\n4t+iETd4F+gW3ETgJ3mXc31HY8xAEekHvAIc0lQjJk2aVPPa5/Ph8/ma+hFKnOHxwOfFHubNG82Y\n23rDVUGTwM43aP63rzH/29e4u/Bu98V1Sms8dtwHKvxKylNUVERRUVHEPi9cz38B8IAx5n3neDUw\nELgSwBhzv9P+FjAxWmEfJb4oKXHqJl12ArRzNl0P9leCJuDrtv3f4W9x54WnRcFKRUksYp3t8xpw\nkmNILtDKGLMJKADOF5FWInIwcBiwOMx7KQlKXh6s+jiXTrO/g4JpUEVA3INFP9jbN8CeNB7q+YEK\nv6I0E+GK/zPAISLyJYHsnssAjDErgH8AK4AFwLXq3qc2ublQusrDMxPG027WWii8C3a2rxX76j87\n20HRXbx64hpuOW9wbI1WlCQmaRZ5KYmDbcOCBVDwls3alkXs6FDMll0byNx5CkMPGs74cR68moyl\nKHtFV/gqiqKkILGO+SuKoigJiIq/oihKCqLiryiKkoKo+CuKoqQgKv6KoigpiIq/oihKCqLiryiK\nkoKo+CuKoqQgKv6KoigpiIq/oihKCqLiryiKkoKo+CuKoqQgKv6KoigpiIq/oihKChKW+ItIHxH5\nREQ+F5HFzj6+1e89KiKrRWSZiBwTvqmKoihKpAjX83+QwN68xwITnWNEJB841BjTA7gaeCLM+0SV\nSG6SHEni0S61qXGoTY0nHu2KR5vCJVzxrwLaO687AGud12cCzwE4m7a3F5FuYd4rasTrP3Q82qU2\nNQ61qfHEo13xaFO4tAjz+puBt0XkYQJbcB/vtHcH1gSdt9ZpWx/m/RRFUZQIsE/xF5F3gWCvXQhs\ntX0ncApwozHmNRE5F3gaGNYchiqKoiiRI6w9fEVkqzGmQ91jEXkCKDTGzHHavwGGGGPqef4iohv4\nKoqi7Afh7OEbbthnrYgMMca8LyInA6ud9gLgOmCOiAwEtoYSfgjPeEVRFGX/CFf8rwQeFZF04Efg\nKgBjzAIRyReRb4EdwJgw76MoiqJEkLDCPoqiKEpiErMVvvG6QExEbhCRlSLypYjcH9R+u2PTShE5\nNZo2Ofe/RUSqRKRTUFtM+klEHnT6YZmIzBORdkHvxbqfTheRb0TELyK3Rvv+jg1ZIvKeiHztfI8m\nOO0dReQdEVklIm+LSPt9fVYz2JYmIiUiUuAc54jIp05/vSwi4UYDmmpPexF5xfm+fC0iA2LdTyJy\ns4h8JSLLReRFEWkVi34SkVkisl5Elge1Ndg3TdYDY0xM/gBvA6c6r4cTmCAGyAfmO68HAJ9G0SYf\n8A7QwjnOcP5/JPA5gTBZDvAtzlNTlOzKAt4C/gt0CuqzWPXTKUCa8/p+4D7ndc8Y91Oac89fAS2B\nZcAR0bp/kB2ZwDHO6wOBVcARwAPAH532W4H7Y2DbzcALQIFzPAf4rfN6OnB1lO15FhjjvG5BYN1Q\nzPoJ8ALfAa2C+ufyWPQT8GvgGGB5UFvIvtkfPYhlbZ94XCA2nkBn7nbuX+G0jwRmG2N2G2NKCUxs\n94+STQCPAH+o0zaSGPWTMWahMabKOfyUwOAEgX+7WPZTf2C1MeZ7Y8zPwGwC/RRVjDHrjDHLnNfb\ngZUE+mgk8HfntL8DZ0XTLhHJIuBczQxqPgmYF2TT2VG0px0w2BjzDIDzvfmBGPcTkA60dbz71oAF\nDCXK/WSM+QjYUqe5bt+MDGpvkh7EUvxvBh4SkTICZSFud9obWiAWDXKBE53Hu0IR6Rtrm0TkTGCN\nMebLOm/Fsp+CGQsscF7H2qa69y+P8v3rISI5BLy3T4Fuxsl6M8asA7pG2ZxqJ8I4tnUGtgQN5OUE\nPN9ocTBQISLPOKGop0SkDTHsJ2OMBTwMlBH4/v4AlBDIWIxVPwXTtU7fVAt8k397zRq3iscFYnux\n6S4C/dHRGDPQmYN4BTgkxjbdQQwWzu3t384Y84Zzzp3Az8aYl6NtXyIgIgcCcwl8z7eHWNMStWwL\nETkDWG+MWSYivuC3omVDCFoAecB1xpjPROQR4Dbq90s0+6kDAS/6VwSE/xXg9Gjdfz/Y775pVvE3\nxjQoWiLyvDHmRue8uSJS/Si6Fjgo6NQsakNCzW3TNcCrznlLRGSP4x2tBbKjbZOI9CIQO/9CRMS5\nb4mI9CeG/eTYNppACOGkoOZmtakRNOu/U1NwQgZzgeeNMa87zetFpJsxZr2IZAIbomjSCcCZEii6\n2BrwAFMIhAfSHK822v1VTuCp9jPneB4B8Y9lP50CfGeM2QwgIv8k0HcdYthPwTTUN03+7cUy7LNW\nRIYASP0FYpc57XtdINYMvIYjZiKSS2DSZ5Nj0/nOrP/BwGHA4uY2xhjzlTEm0xhziDHmYAI/lmON\nMRuIYT+JyOkEwgdnGmN2Bb1VAFwQ7X4KYglwmIj8SkRaARc4NsWCp4EVxpgpQW0FwGjn9eXA63Uv\nai6MMXcYY7KNMYcQ6Jf3jDGXAIXAb2Nk03pgjfNbAzgZ+JoY9hOBcM9AETnAcbiqbYpVPwnup7Pg\nvhkdZEfT9aC5Z6z3MpN9PPAZgeyQTwiIWvV7jxHI2vgCyIuiTS2B54EvHduGBL13u2PTSpwspRj0\n2Xc42T4x7qfVwPcEYqElwLR46ScCj+irHBtvi9G/0wnAHgLZRp87fXQ60AlY6Nj3DtAhRvYNoTbb\n52CgGPATyGhpGWVb+hAYtJcReOpuH+t+IlCefiWwnMCkastY9BPwEoHJ5l0EBqUxQMeG+qapeqCL\nvBRFUVIQ3cZRURQlBVHxVxRFSUFU/BVFUVIQFX9FUZQURMVfURQlBVHxVxRFSUFU/BVFUVIQFX9F\nUZQU5P8Df+BYzldTO4kAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4fbc03a0d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that the eyepiece does not appear, since we left it out from the output of the function 'relephant'\n",
      "Also, the difference between i=1 and i=2 is not noticeable. Let's try to exagerate it a bit."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x2, y2 = relephant(5,np.linspace(0,2*np.pi, 1000), p1,p2,p3,p4,p5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(y1,-x1,'.')\n",
      "plt.plot(y2,-x2,'.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f4fa5517790>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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26m9666bYAdtWKC3sy5HfXsYHC45ic50v5tyBbJiFn+7ifwww3Rgz1tmeCphw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\nipKD/H+gqMNkDo/LMQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4fa557c8d0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It looks better now. Let's draw the eyepiece and try different symbols to mimic the winking."
     ]
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Draw the eye"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The initial elephant had a dot at the position of the eye"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(y1,-x1,'.')\n",
      "plt.plot(p5.imag,p5.imag,'.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f4fa5462750>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jMhpR7Bx37C5rkpeZHQuscfdnC17aGVgTe74uKqu6SZNqcZSRDQ6GFvXJJ8Mb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\nESkyA8RWAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4fa557c1d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The simplest way to make it wink is to use another symbol available from the 'plot' function"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(y1,-x1,'.')\n",
      "plt.plot(p5.imag,p5.imag,'>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f4fa53abb10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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25tcx/m/KlFDnFSvC+6tt82b3q65y7+goffy2NvfFi2tz7Hr5+c/z/5/6+pr7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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4fa557c7d0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Overlay the two elephants using animation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I will follow the detailed explanation of matplotlib's example found in this [animation tutorial](https://jakevdp.github.io/blog/2012/08/18/matplotlib-animation-tutorial).\n",
      "\n",
      "Embedding animations in IPython notebooks is far from trivial. I follow this [example](http://jakevdp.github.io/blog/2013/05/12/embedding-matplotlib-animations)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from tempfile import NamedTemporaryFile\n",
      "\n",
      "VIDEO_TAG = \"\"\"<video controls>\n",
      "<source src=\"data:video/x-m4v;base64,{0}\" type=\"video/mp4\">\n",
      "Your browser does not support the video tag.\n",
      "</video> \"\"\"\n",
      "\n",
      "def anim_to_html(anim):\n",
      "    if not hasattr(anim, '_encoded_video'):\n",
      "        with NamedTemporaryFile(suffix='.mp4') as f:\n",
      "            anim.save(f.name,fps=20, extra_args=['-vcodec', 'libx264'])\n",
      "            video = open(f.name, \"rb\").read()\n",
      "        anim._encoded_video = video.encode(\"base64\")\n",
      "    return VIDEO_TAG.format(anim._encoded_video)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "\n",
      "def display_animation(anim):\n",
      "    plt.close(anim._fig)\n",
      "    return HTML(anim_to_html(anim))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import animation"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "ax = plt.axes(xlim=(-80,100), ylim=(-80,100))\n",
      "line, = ax.plot([],[],lw=2)\n",
      "#ax.set_axis_off()\n",
      "\n",
      "\n",
      "def init():\n",
      "    line.set_data([], [])\n",
      "    return line, \n",
      "\n",
      "def animate(i):\n",
      "    x, y = relephant(i/10.,np.linspace(0,2.*np.pi,1000),p1,p2,p3,p4,p5)\n",
      "    line.set_data(y,-x)\n",
      "    return line,\n",
      "        \n",
      "anim = animation.FuncAnimation(fig, animate, frames=100, interval=20, init_func=init,blit=True)\n",
      "\n",
      "display_animation(anim)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "<source src=\"data:video/x-m4v;base64,AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1wNDEAAAAIZnJlZQAAfpptZGF0AAACrQYF//+p\n",
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       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<IPython.core.display.HTML at 0x7f4fa50c7790>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    }
   ],
   "metadata": {}
  }
 ]
}