{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tools - matplotlib**\n",
    "\n",
    "*This notebook demonstrates how to use the matplotlib library to plot beautiful graphs.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": "true"
   },
   "source": [
    "# Table of Contents\n",
    " <p><div class=\"lev1\"><a href=\"#Plotting-your-first-graph-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Plotting your first graph</a></div><div class=\"lev1\"><a href=\"#Line-style-and-color-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Line style and color</a></div><div class=\"lev1\"><a href=\"#Saving-a-figure-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Saving a figure</a></div><div class=\"lev1\"><a href=\"#Subplots-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Subplots</a></div><div class=\"lev1\"><a href=\"#Multiple-figures-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Multiple figures</a></div><div class=\"lev1\"><a href=\"#Pyplot's-state-machine:-implicit-vs-explicit-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Pyplot's state machine: implicit <em>vs</em> explicit</a></div><div class=\"lev1\"><a href=\"#Pylab-vs-Pyplot-vs-Matplotlib-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Pylab <em>vs</em> Pyplot <em>vs</em> Matplotlib</a></div><div class=\"lev1\"><a href=\"#Drawing-text-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Drawing text</a></div><div class=\"lev1\"><a href=\"#Legends-9\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>Legends</a></div><div class=\"lev1\"><a href=\"#Non-linear-scales-10\"><span class=\"toc-item-num\">10&nbsp;&nbsp;</span>Non linear scales</a></div><div class=\"lev1\"><a href=\"#Ticks-and-tickers-11\"><span class=\"toc-item-num\">11&nbsp;&nbsp;</span>Ticks and tickers</a></div><div class=\"lev1\"><a href=\"#Polar-projection-12\"><span class=\"toc-item-num\">12&nbsp;&nbsp;</span>Polar projection</a></div><div class=\"lev1\"><a href=\"#3D-projection-13\"><span class=\"toc-item-num\">13&nbsp;&nbsp;</span>3D projection</a></div><div class=\"lev1\"><a href=\"#Scatter-plot-14\"><span class=\"toc-item-num\">14&nbsp;&nbsp;</span>Scatter plot</a></div><div class=\"lev1\"><a href=\"#Lines-15\"><span class=\"toc-item-num\">15&nbsp;&nbsp;</span>Lines</a></div><div class=\"lev1\"><a href=\"#Histograms-16\"><span class=\"toc-item-num\">16&nbsp;&nbsp;</span>Histograms</a></div><div class=\"lev1\"><a href=\"#Images-17\"><span class=\"toc-item-num\">17&nbsp;&nbsp;</span>Images</a></div><div class=\"lev1\"><a href=\"#Animations-18\"><span class=\"toc-item-num\">18&nbsp;&nbsp;</span>Animations</a></div><div class=\"lev1\"><a href=\"#Saving-animations-to-video-files-19\"><span class=\"toc-item-num\">19&nbsp;&nbsp;</span>Saving animations to video files</a></div><div class=\"lev1\"><a href=\"#What-next?-20\"><span class=\"toc-item-num\">20&nbsp;&nbsp;</span>What next?</a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting your first graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's make sure that this notebook works well in both python 2 and 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division, print_function, unicode_literals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to import the `matplotlib` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib can output graphs using various backend graphics libraries, such as Tk, wxPython, etc.  When running python using the command line, the graphs are typically shown in a separate window. In a Jupyter notebook, we can simply output the graphs within the notebook itself by running the `%matplotlib inline` magic command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# matplotlib.use(\"TKAgg\")  # use this instead in your program if you want to use Tk as your graphics backend."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot our first graph! :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gmJsUGUqRQ0aFiZJpq1bBokVq0lKbQkaNqCWCo0fhwgvDdpfjxsWuRvLCPVzO6/bb4Zvf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112255f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot([1, 2, 4, 9, 5, 3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yep, it's as simple as calling the `plot` function with some data, and then calling the `show` function!\n",
    "\n",
    "If the `plot` function is given one array of data, it will use it as the coordinates on the vertical axis, and it will just use each data point's index in the array as the horizontal coordinate.\n",
    "You can also provide two arrays: one for the horizontal axis `x`, and the second for the vertical axis `y`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11233f690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([-3, -2, 5, 0], [1, 6, 4, 3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The axes automatically match the extent of the data.  We would like to give the graph a bit more room, so let's call the `axis` function to change the extent of each axis `[xmin, xmax, ymin, ymax]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112421a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([-3, -2, 5, 0], [1, 6, 4, 3])\n",
    "plt.axis([-4, 6, 0, 7])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's plot a mathematical function. We use NumPy's `linspace` function to create an array `x` containing 500 floats ranging from -2 to 2, then we create a second array `y` computed as the square of `x` (to learn about NumPy, read the [NumPy tutorial](tools_numpy.ipynb))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QoIFfFfvNN37hi1r0xe/ee32CnzJFCb6YpZLkmwJLKzz+IvFcZUeZ2Wwze8nM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\nIqYkLyJSxJTkRUSKmJK8iEgR+/9JZLyXtkE0oAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1124dfbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.linspace(-2, 2, 500)\n",
    "y = x**2\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a bit dry, let's add a title, and x and y labels, and draw a grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xh1uC2BhTd3PnugEgM2e6eUeFJNBOalXtk6py8F7LSeUQJ361S9avDwMGuPkR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\nBWGMMSYlqyCMMcakZBWEMcaYlP4/bKA7vZdJjDAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1125ecb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "plt.title(\"Square function\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y = x**2\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Line style and color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, matplotlib draws a line between consecutive points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dvR/A3V8ExrVioBK/onbY1SWXVkkS6KOA6cC33X068N+E5Zahq6BaFZWGFKXDri65tNqo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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11277b790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([0, 100, 100, 0, 0, 100, 50, 0, 100], [0, 0, 100, 100, 0, 100, 130, 100, 0])\n",
    "plt.axis([-10, 110, -10, 140])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can pass a 3rd argument to change the line's style and color.\n",
    "For example `\"g--\"` means \"green dashed line\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0dDqSCRGnn+yOG1f2Ao8Kz6Gr6hhVbaaqrYB7gMWqOghIBR707fYAML+ixwgU\n6+USHlSVuevnEj85Hs82j9NxKtVFNS9i85ObuSzmMq6Zeg3DPxnO3qNlnguZEOHKXi4icgPwO1Xt\nIyINgPeBS4HtQH9VPVjKc2wduim2aMsiRi0aRaG3kPE9xhf3JY9Ee4/u5fmvnmf2utm8/8v3ubnV\nzU5HMiHELiwyIWvPkT0MmjeIbQe38dyNz5XalzxSbTu4jZjqMdSr7vilHCaEWEE3IetE4QneXvc2\n97W7jwuqXOB0HGNCnhV0Y1xkyfYlHDx2kF6xvSJ2WiqSWftc47gDeQdYvmu50zFcwateRi8aTfcZ\n3Vm6Y6nTcUwIiYiCbr1cnJObn8uEpROIfTWW+ZscX/DkCje0uKG4D/v9KffT+53erMta53SsiOTG\nXi4hz3q5VL78wnymrJxC7KRYlmcuZ8mQJTx303NOx3KNKlFVGJwwmE1PbKJHyx4MmT+EAm+B07Ei\nTqj1cvH30n9jSnVvyr3sz9tPyoAUOjfp7HQc16oWXY0R145geJfhNp9urKCb4JjSawr1q9e3IlNJ\nyvpzVlX7HUSQiJhyMZUvpkaMFRKHedVL9xndrQ97BLGCbiosIzuDIfOHcPDYGRcCmxAQJVFMvnNy\ncR/2mWtmUugtdDqWCaKIKOjWyyWwdh3exbDUYXSd3pXYBrFUrVLV6UimDCX7sE9ZOYWE1xNYvHWx\n07Fcw5W9XCp0YLuwKOwcyDvAhGUTmLpqKr/q+CtGdhsZtn3JI5Gq8vHmj6kRXcP6w4Sxs11YZB+K\nmnLblL2J7Nxs1jy6hqZ1mzodx5wnEaFXbC+nY5ggsjN0YwxHTxxl79G91oc9DNil/+a8qCq5+blO\nxzCVaOXuldaH3QWsoJtTLNqyiM7TOvPyNy87HcVUop83/znpv05HEOJei2PsF2M5fPyw07HMeYqI\ngm69XM5tZeZKes7qyaMfP8rT1z3NmO5jnI5kKlnDWg2ZePtEVjy8gq0Ht3L5pMvZnbPb6VghzXq5\nOMB6uZTNq14GfjiQ3u/05hdxv2DD4xsYcOUA+5KJCNYypiUz+83k64e+5uI6FzsdJ6RZLxcTUqIk\nivva3ce03tOoVbWW03FMCLmswWVORzDnyU7DDL1ie1kxN+X2xoo3rA97iLKCHiFy83OZv9H6kRv/\n1a1W1/qwhygr6C5X4C0o7ks+Z90c65lt/Daw3cDiPuw9ZvVg8LzBbD2w1elYBj8Kuog0FZHFIrJe\nRNaJyHBy8s+6AAAKVElEQVTf9hgRWSgim0TkMxFx/CvLI7GXi6oyd/1c4ifH897695g3YB7v3/0+\n0VH2sYnx38k+7Juf3EyrmFZMWDbB6UiOcE0vFxFpDDRW1dUiUhtYCfQFhgDZqvqiiIwEYlR1VCnP\ntytFg+jlb15mzro5jL95PD1a9bBWtsa4xNmuFA3Ypf8i8i/gVd/PDaqa5Sv6HlVtW8r+VtCD6FjB\nMapWqWrLD42jvOq1v4MBFvRL/0WkBdAB+BZopKpZAKq6B2gYiGOY81M9urr9QzKO2nFoB7GTYq0P\neyXye0LVN93yATBCVY+IyOmn3WWehicnJxffTkxMJDEx0d84EWXX4V388cs/cs+V93BjyxudjmPM\nKZrVa8abd73JqLRRvPT1S7xw0wv0iu1l03/nyePx4PF4yrWvX1MuIhINfAR8oqoTfdvSgcQSUy5f\nqGpcKc+1KZcKKtmXfGjHoYzqNsr6kpuQpap8lPERYxaPoW61ukzvM522F50xC2vKKZhTLv8ENpws\n5j4LgAd9tx8AHF/87JZeLscKjjFh6QRiX40t7kv+Ys8XrZibkCYi9G7Tm9XDVjPs6mHUq+b4wreA\nCbVeLhWechGR64H7gHUi8l+KplbGABOA90XkIWA70D8QQf3hll4uhd5CMrIzWDJkiZ3hmLBTJaoK\ngxMGOx0joDzbPPzl67+wcNBCp6MAfhR0VV0GVCnj4R4VfV1TtlpVazG973SnYxgTcJuzN1Ovej0a\n1rI1FP6wZRAhan/efqcjGFNp0rakWR/2ALCCHmJO9iUfPM9db02NOZvHOj3GykdWFvdh//u3f+d4\nwXGnY4UdK+ghIiM7g/5z+xf3JZ83YJ7TkYypVC3qt2Bmv5l8PuhzFm1dRNd/dsVWwp2fiGjsEeq9\nXP705Z945btXeOrap5jRd4a1sjURrX2j9qQOTGXPkT0hv2bdNb1c/D6wrUMvtmr3KlrUb2HLD40x\n5xT0S/+Nf666+Cor5saUg1e9/ObT31gf9jJYQa8kBd4C3lz9Jnn5eU5HMSZsedVLy/otrQ97Gayg\nB5mq8sGGD4ifHM+stbPIzst2OpIxYSs6KvqUPuzXTL2GEZ+MYO/RvU5HCwk2hx5EaVvSGL1oNF71\nWl9yY4Jg79G9PP/V81x18VU80OEBp+NUikrph36+KrOgr96zmvkb5zM2cWylHA/g253fMmjeIJ6/\n6Xl+ecUvrZWtMS50ovAEefl51Kteef1pIv5DUSd6uXRp0oX0X6fTP76/FXNjHFDgLQh6H3bPNg93\nz707qMc4H1ZpgkRE7Ps7jXFQSnoKHd7oQOqm1Ii5QMkKuh8O5B1gVNoonvvqOaejGGNOc/cVd/PC\nTS8wZvEYus/oztIdS52OFHRW0CsgNz/3lL7kD3Z40OlIxpjTlOzD/sjVj3B/yv30ertXSPUvDzSb\nEzgPqsq0VdMY9+U4rm16rfUlNyYMnOzDPiB+AHM3zA2pS/UDLSIKeqB6uYgIu4/sJmVACp2bdA5A\nMmNMZakWXY37298f0Ne0Xi4nDxwB69CNMeHhq+1f0aFxh5AqzmWJ+GWLFbHz8E6nIxhjKsnc9XNd\n0YfdCvppTvYl7/bPbtZ3xZgIMemOSaQNSmPR1kW0ebUNM9fMDPoa9mCwKRefzJxMxnnG8WH6h/zu\nut8xvMtw60tuTARaumMpI9NG0r5he/7R6x9OxzlDxF/6fy4p6Sk8nPowQzsOZeT1I7mw5oVORzLG\nOEhVyTmRE5Jz6o4UdBG5Dfg7RdM601V1wmmPh0wvlz1H9lDgLaBp3aaVkscY4w4R0ctFRKKAV4Fb\ngXhgoIg4tmD7XL1cGtdubMXcGHNOPx76kSHzhxT3YY+UXi6dgc2qul1V84F3gb5BOla5KMrc9XNZ\nm7XWyRjGmDBWv3p9mtdrHrJ92IN1YVET4McS93dSVOQdk7YljbQtaXRu0plOl3Qq3v7UdU/RKqbV\nGfv/9eu/svXgmd+GYvvb/rZ/5O5fp1odkhOTycvP48WvXwSgdtXaZzzPKY5eKZqcnFx8OzExkcTE\nxKAcp+ulXflVx1/RrlG7M1rZ1oiuUepzmtdvTrXoamdst/1tf9vf9u/UpBOTbp/E1gNbuf3y20vd\nJ1A8Hg8ej6dc+wblQ1ERuRZIVtXbfPdHAVryg9FQWuVijDHhwokrRZcDrUWkuYhUBe4BFgTpWMYY\nYwjSlIuqForIE8BC/v+yxfRgHMsYY0wRu7DIGGPCiDXnMsaYCGAF3RhjXMIKujHGuIQVdGOMcQkr\n6MYY4xJW0I0xxiUipqCX99LZcGXjC29uHp+bxwahNT4r6C5h4wtvbh6fm8cGoTW+iCnoxhjjdlbQ\njTHGJRy99N+RAxtjTJgLuS+JNsYYE1g25WKMMS5hBd0YY1wiIgq6iNwmIhtFJENERjqdxx8i0lRE\nFovIehFZJyLDfdtjRGShiGwSkc9EpJ7TWf0hIlEiskpEFvjuu2Z8IlJPROaKSLrv99jFZeMb7RvX\nWhGZIyJVw3l8IjJdRLJEZG2JbWWOxzf+zb7f7y2VmdX1BV1EooBXgVuBeGCgiLR1NpVfCoCnVDUe\nuA74tW88o4A0VW0DLAZGO5gxEEYAG0rcd9P4JgL/VtU4IAHYiEvGJyLNgYeBjqranqIv0RlIeI9v\nBkX1o6RSxyMiVwD9gTjgdmCyiJT6AWYwuL6gA52Bzaq6XVXzgXeBvg5nqjBV3aOqq323jwDpQFOK\nxvSWb7e3gLucSeg/EWkK3AFMK7HZFeMTkbpAd1WdAaCqBap6CJeMDzgMnABqiUg0UAPYRRiPT1WX\nAgdO21zWePoA7/p+r9uAzRTVoEoRCQW9CfBjifs7fdvCnoi0ADoA3wKNVDULioo+0NC5ZH77G/AM\nUHIJllvG1xLYJyIzfFNKU0SkJi4Zn6oeAP4K7KCokB9S1TRcMr4SGpYxntPrzS4qsd5EQkF3JRGp\nDXwAjPCdqZ++/jQs16OKyJ1Alu9dyNneqobl+CiagrgKeE1VrwKOUvT23S2/v1bAb4HmwCUUnanf\nh0vGdxYhMZ5IKOi7gGYl7jf1bQtbvreyHwCzVHW+b3OWiDTyPd4Y2OtUPj9dD/QRkS3AO8BNIjIL\n2OOS8e0EflTVFb77H1JU4N3y+7sGWKaq+1W1EJgHdMU94zuprPHsAi4tsV+l1ptIKOjLgdYi0lxE\nqgL3AAsczuSvfwIbVHViiW0LgAd9tx8A5p/+pHCgqmNUtZmqtqLod7VYVQcBqbhjfFnAjyIS69t0\nM7Ael/z+gE3AtSJS3fdh4M0Ufbgd7uMTTn3HWNZ4FgD3+Fb2tARaA99VVkhU1fU/wG0U/UXbDIxy\nOo+fY7keKARWA/8FVvnG1wBI841zIVDf6awBGOsNwALfbdeMj6KVLct9v8MUoJ7LxvcMRf9JraXo\nA8MLwnl8wNtAJnCcos8GhgAxZY2HohUv31O0YOGWysxql/4bY4xLRMKUizHGRAQr6MYY4xJW0I0x\nxiWsoBtjjEtYQTfGGJewgm6MMS5hBd0YY1zCCroxxrjE/wNEDFH+eAaKiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1128dbe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([0, 100, 100, 0, 0, 100, 50, 0, 100], [0, 0, 100, 100, 0, 100, 130, 100, 0], \"g--\")\n",
    "plt.axis([-10, 110, -10, 140])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can plot multiple lines on one graph very simply: just pass `x1, y1, [style1], x2, y2, [style2], ...`\n",
    "\n",
    "For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J07F92Kcsn8LAtgOZFJhE60atvS4rtkRQy0WBLuIDBwoO8PTnTzN91XRu73Q7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112897d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([0, 100, 100, 0, 0], [0, 0, 100, 100, 0], \"r-\", [0, 100, 50, 0, 100], [0, 100, 130, 100, 0], \"g--\")\n",
    "plt.axis([-10, 110, -10, 140])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or simply call `plot` multiple times before calling `show`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J07F92Kcsn8LAtgOZFJhE60atvS4rtkRQy0WBLuIDBwoO8PTnTzN91XRu73Q7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11254bdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([0, 100, 100, 0, 0], [0, 0, 100, 100, 0], \"r-\")\n",
    "plt.plot([0, 100, 50, 0, 100], [0, 100, 130, 100, 0], \"g--\")\n",
    "plt.axis([-10, 110, -10, 140])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also draw simple points instead of lines. Here's an example with green dashes, red dotted line and blue triangles.\n",
    "Check out [the documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot) for the full list of style & color options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7sOHx6yD6mQ3n3Ajn3Grn3McttCnmc9fi8WV17swsLw/gWOBo4O/AyS20Wwrs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\ngv4/zWsyiRSjbi4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1124e98d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-1.4, 1.4, 30)\n",
    "plt.plot(x, x, 'g--', x, x**2, 'r:', x, x**3, 'b^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot function returns a list of `Line2D` objects (one for each line).  You can set extra attributes on these lines, such as the line width, the dash style or the alpha level.  See the full list of attributes in [the documentation](http://matplotlib.org/users/pyplot_tutorial.html#controlling-line-properties)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YtnjduOsYUjeu3bdORUTckpaeu7X2OeC5ZI6RTsGyxfJt5cwonsGkEZMA6Obr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\nHqTgLiLiQQruIiIe9P8Bt68WcL6/RNAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112751510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-1.4, 1.4, 30)\n",
    "line1, line2, line3 = plt.plot(x, x, 'g--', x, x**2, 'r:', x, x**3, 'b^')\n",
    "line1.set_linewidth(3.0)\n",
    "line1.set_dash_capstyle(\"round\")\n",
    "line3.set_alpha(0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving a figure\n",
    "Saving a figure to disk is as simple as calling [`savefig`](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.savefig) with the name of the file (or a file object). The available image formats depend on the graphics backend you use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DD9gMI+9t9sxJJ2mzFUmN+uQlcdq1s92npk2z1nynTvZx//3www+h06Vm3Tp4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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1124d6690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-1.4, 1.4, 30)\n",
    "plt.plot(x, x**2)\n",
    "plt.savefig(\"my_square_function.png\", transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subplots\n",
    "A matplotlib figure may contain multiple subplots. These subplots are organized in a grid. To create a subplot, just call the `subplot` function, and specify the number of rows and columns in the figure, and the index of the subplot you want to draw on (starting from 1, then left to right, and top to bottom). Note that pyplot keeps track of the currently active subplot (which you can get a reference to by calling `plt.gca()`), so when you call the `plot` function, it draws on the *active* subplot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Cv5j3SWRNVNWVwD4Z2ikwXkSmichVBcqMPDb7N6KGCDzxhAsi+/rrsLUpDZ5+GurVgzPP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\nMAzDKGPMCBiGYZQxZgQMwzDKmP8Pl7YRbugR7JEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127514d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-1.4, 1.4, 30)\n",
    "plt.subplot(2, 2, 1)  # 2 rows, 2 columns, 1st subplot = top left\n",
    "plt.plot(x, x)\n",
    "plt.subplot(2, 2, 2)  # 2 rows, 2 columns, 2nd subplot = top right\n",
    "plt.plot(x, x**2)\n",
    "plt.subplot(2, 2, 3)  # 2 rows, 2 columns, 3rd subplot = bottow left\n",
    "plt.plot(x, x**3)\n",
    "plt.subplot(2, 2, 4)  # 2 rows, 2 columns, 4th subplot = bottom right\n",
    "plt.plot(x, x**4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Note that `subplot(223)` is a shorthand for `subplot(2, 2, 3)`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is easy to create subplots that span across multiple grid cells like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TMCmJ9EVEKmntWvjd7+D446FfPxg8GLbcMulcFRZ7qVSuKOhvwE9yTwQb6Nev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\nvCTeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11290f950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 2, 1)  # 2 rows, 2 columns, 1st subplot = top left\n",
    "plt.plot(x, x)\n",
    "plt.subplot(2, 2, 2)  # 2 rows, 2 columns, 2nd subplot = top right\n",
    "plt.plot(x, x**2)\n",
    "plt.subplot(2, 1, 2)  # 2 rows, *1* column, 2nd subplot = bottom\n",
    "plt.plot(x, x**3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you need more complex subplot positionning, you can use `subplot2grid` instead of `subplot`. You specify the number of rows and columns in the grid, then your subplot's position in that grid (top-left = (0,0)), and optionally how many rows and/or columns it spans.  For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Xw/HHw9tv+0JfHKxcCSecYCaaPfeMW5qS0zMwndYH3hWRPVT13eqDgUl1a1Xd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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e72bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot2grid((3,3), (0, 0), rowspan=2, colspan=2)\n",
    "plt.plot(x, x**2)\n",
    "plt.subplot2grid((3,3), (0, 2))\n",
    "plt.plot(x, x**3)\n",
    "plt.subplot2grid((3,3), (1, 2), rowspan=2)\n",
    "plt.plot(x, x**4)\n",
    "plt.subplot2grid((3,3), (2, 0), colspan=2)\n",
    "plt.plot(x, x**5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you need even more flexibility in subplot positioning, check out the [GridSpec documentation](http://matplotlib.org/users/gridspec.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple figures\n",
    "It is also possible to draw multiple figures. Each figure may contain one or more subplots. By default, matplotlib creates `figure(1)` automatically. When you switch figure, pyplot keeps track of the currently active figure (which you can get a reference to by calling `plt.gcf()`), and the active subplot of that figure becomes the current subplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cywiqIEla1T+wmryqfq2qh6rqH1T1EFV9KK4LDhkCy5YFFF1y5OTkhB1CQnn5\n0lcmlw3Sq3w//wwnnwz//W9yXi+pa9eIiMb8ek8/Db17Q9OmiQ3KOeeSZOlSOOkk6NgRnngCataM\n7f+JCFrFjtfUTfLRtmyBWrWCD8g555Jk8mQ47TS45Rb4y1+2r7kmniSfHmvXXHIJTJgQdhTOOVcl\nL7wAp5wCzz8P116bvPZ4SJea/IYN0KAB7JAe70nOOQdQWAg33wzvvAOjRsGBB1btOvHU5AOdDJUw\nDRuWPP/4Y9h9d9h77/Dicc65SmzaBGedBXl58MUXsMsu4cSRflXj+fNh+fLKz3POuZB8+y0ceSTs\nsQeMHx9egod0aa4pTzIHmzrnXAw+/BDOOANuvx2uvjqYFJX5Ha/leeABG0/vnHMp4D//sRE0Q4fC\nNdekRh00sJq8iOwOvAQ0AYqAZ1R1cKlzgq3Jr4tsVL7zzsFd0znntlNeHlx/Pbz/vnWwtm4d7PVT\npSZfAFyvqm2BzsDVInJAgNf/vZ13Lknwy5fD6NEJfTnnnCtt5kw4/HDraJ06NfgEH68glzX4QVVn\nRJ7/AswDWgR1/UqtXQsrVybt5Zxz2a2oCB55BLp3h7/9DV55BXbaKeyofi8hHa8isheQCxwUSfjF\nXw+2uaYiq1dDkybJeS3nXFb54Qe44AKbwjNsGOyzT2JfL6XGyYtIfeAN4NroBF9s4MCB/3uek5OT\nmIWFVq+GXr1scGr19JgK4JxLD2PG2CT8Sy6BO+6AGjWCf43c3Fxyc3MDuVbQG3lXB0YD41T10TK+\nn7yafEGBJ3jnXGA2b4abboKRI230TJcuyXvtVOl4BXgOmFtWgk+64gSvCn372uwE55yrgtmzoUMH\nWLMGZsxIboKPV5BDKI8CPgK+BjTyuFVV/xt1TvJq8tG++grat/e1b5xz20XVlgS+80548EFrhw9j\n7HvmLzUcpPHjoW5d+OMfw43DOZfSZs+Gq66C336zkTOtWoUXSyo116S+atXs4ZxzZfjlF2t779YN\n+vWDzz4LN8HHK/uSfPfutnIQwNatNg+5qCjcmJxzoVOFt96CNm1siGRxTT7d64TZPfxkwwZYsSI1\nFphwzoXm22+hf39YsgReegnSaMvYSmVfTT5a48YwcGBJkv/oI/jpp1BDcs4lz5YtcPfdNnKmSxcb\nOZNJCR6yvSZf2nvv2Q5UYS7+7JxLiokTbSngNm1g2jRo2TLsiBIj+0bXxGrTJpgzBzp3DjsS51yA\nFi+G224UpgJRAAASyUlEQVSDKVNg8GA46aSwI6qcj65JhMWL4e23w47COReQpUttKYJOnaBtW6vD\npUOCj5fX5GM1YoStJ5rolYicc4FauRL+8Q8YPhyuuAJuuCH9tqBIiZq8iDwrIqtFZFZQ10wpxRuU\nOOfSwpo1tpFHu3ZQp45tD33vvemX4OMVZHPN88DxAV4vtVxxRUktfs0a67FJ108lzmWwn36CW26B\nAw6A/HxrlnnoIdhtt7AjC0eQm4ZMBtYHdb2UVqeOLWVcPPTSk71zodu4EQYMgP33h/XrbTjkY49B\ns2ZhRxYu73itih13hBNPLDm+9VZ49dXw4nEuiy1cCNdeC3vvbZ2rU6fC00/DnnuGHVlqSPo4+aRs\nGpJsN9+87dIIH34IHTtC7drhxeRcBisqgrFj4fHHbZHZSy6xvVb32CPsyIKRypuGtARGqerB5Xw/\nfUfXxErVVjV66ilo1CjsaJzLKOvXw/PP2/K/jRrZUgRnnJH59amUGF1THEvkkb1E4LXXShL8ggVw\n2WXhxuRcmvv6a7j8chv7MG2aLf07dSqcf37mJ/h4BTmEchjwKbC/iCwTkQuDunZaa9HCPksWW7bM\nRuc45yr0yy/W1ZWTAz172p/SvHmW4Dt18nUFY+WToZJtyBBrULziCjsuKvIdq5yL+O03GDfOJi79\n979w1FFWWz/llMRsmJ0ufGeodNanD9x4Ixx9dNiROBeK/HyYNMkS+8iR8Ic/WLfWKafArruGHV1q\n8CSfzjZtglq17KEKf/kL3HMP7LRT2JE5lzCFhTYIbfhwWyJq//0tsfft6+PayxJPkvelhsPWoEHJ\n86IiW9i6+GubN1sn7gUXhBKac0H68UdbzXviRBgzBpo3t8T+5ZeZu8xvKvAkn0qqVYNzzy053rjR\ntqoptno1zJoFxx2X/Nic205btsCnn1pSnzABFi2yjTmOO86mlqTzvqnpxJtr0snMmdYrdcstdrxk\niQ1BaNcu3Licw1ob584tSeqTJ8OBB1pS79HDRsTUrBl2lOnJ2+Sz1bhxluivusqOp061IQjt24cb\nl8sKGzfamPWpU+3x2Wf269ejhz2OOSb7VnxMFE/yzrz1lnXgFq+r88orsNdeNg7NuTj89pt9kJwy\npSSpL19u9YkjjrBHx442WcnHrwfPk7wr2/vvQ9OmtoklwE03wZ//XDJcc9Uq28/WP0O7iMJC+O47\nm6i9YIGtwT5tmjXDtG5tybxDB/u3bVuo7r16SZESSV5EegKDsFm0z6rqA2Wc40k+TPPmQePGJRuV\n9+sH11xTkvSfftqWUM6UVZ5cudav3zaRFz//9lv7FWnduuRx6KFWY69bN+yos1foSV5EdgAWAscC\n3wNTgX6qOr/UeZ7kU9kzz9iml02b2nGnTvDii/aXDrY495lnlsxQ+eknW6PHZ+ymDFW7LStX2mPF\nipLn0V8rKNg2kRc/WrXyZJ6KUmGcfAdgkaoujQQ0HOgNzK/wf7nUcuml2x6PHw/16pUcFxVt+/n8\nmGNg1KiShbvPOQcGDSp5ExgyBM46C+rXt+P582G//fwzfgwKCyEvzwZPrV9vj3XrKv53zRpL4nXr\n2jovxY/dd7cmluiv7bqrt51ni6D+2loAy6OOV2CJ36Wz0rNur7122+OZM7c9vvBC21Cl2LJl237/\nkkts3nrxkItmzWx5wV13RRX0j13Y+NK75NVqRF4e7HLjhSy5bjBba+1IQQG0fPR6Fp17F79Vr09B\nAew37C7mnnADv1WrR0EBtPnvw8zLuZL86nVQhTbvP8a8Iy+hoEYdioqgzcdPMe+I8ymoUQeAgz55\nmnkdzqewhi1j2PbTIcw/4ryS40+GMPfw8yioXhtVaPvFc8xtfxb51SLH015i7kGnU1C9NkVF0Hrm\nCObu92c2a20KCmDfbyYwr0kOW7QmBQWwx6opfNPgD/xWWIPNm6Hu+pUsL2jGr5t3IC/P5r7l5dmj\noMCSdb169mFp551//2+rVtt+bbfdLIF7TdxF801DXNyKimDDBljX8ljWzyqpWa5vcQ/rHo2qbe42\nmXUnW+00Lw/yayxl04E1yNtsCa5dtadYcVgDatezRNVra2++uKEW1LSheSeuPZiPBleH2vZhoM+S\nHXm/cAc0crzTol/5oraSX8Nqqc0XrmVmPaWwph3vt2gZi3cqIj9yfODiJSzZuZCtkYWv2iz6hiWN\noo4Xf8OyXQr/d/6hi+ewtmnJccNvp/HL7qdQUNuO91ryPt+07kXNehbvse89yw5HdYYda1K9OvQc\n9Hc+ufZ1aFCDunUhp//xzBsymVpNGlK3Lux9dHN+nj6XOs0aUrMmSNcuMHp0yQzo226zR3EWnzQJ\nunYtWbmrsNAm1Lm0l3KbhohIJ2CgqvaMHN8CaOnOV2+TTz9btvy+Tbd0W++qVZZ3yqptlvXvjjva\n+dGP2rU9P/Hrr/bDKG5HmTHDJroV/2AGD4YrryxJ6r17w4gRNmwW7M1g9WrbgxjguuvgwQdLzl+0\nyJrLvJ0m7aRCx2s1YAHW8boKmAKcqarzSp3nST4FbdkC33zz+9EW33xjE16aNrV23dLtvMXPmzf3\njRtSQn5+SUIvKrLtk665xpJ6fr4NkZk9u+T4oovgpZfsWNXeZIr7T1xKCT3JR4LoCTxKyRDK+8s4\nx5N8iH7+2SqH8+Ztm8xXrLC+09at4YADth1p0bixD57JSFu22Cphp5xix2vXWu9s8VpJxTt2lO6M\nd6FIiSQf04t5kk+aLVtsLbPoGYrffWef/tu02TaZ77OPz4dyWG2+uClnzRr4z3/g1lvtePFiG0L7\n6KPhxZfFPMlnucJCq5VPnVqS1OfMsZp49AzFgw7K7t11XBw2bLCRUH/8ox1PnGjLZrzwgh3n5Vnf\nQXH/gAuUJ/ks9M03Jav9ffCBjXsuXkOkQwfbXceH0rmEyc+3WVfFE+dGjLBfyGeeseNff7UOYG/r\nC4Qn+SywYYMtRTNhgv0t5eWVLOHavXvJ35pzoYlu7rnvPjsubu7ZutXbBOPgST4D5efDF1+UJPXZ\ns20xyR49LLkfdJCPhHMprqCgZHbz+efb4ninnmrH0W8IrlKe5DNEYSF8/LHte/nmmzZM8fjjLakf\ndZQPU3RprLDQHsW1+WOOsXH/Bx0UblxpwpN8GlO1Gvvw4das2aSJLQ55+umw995hR+dcgvz0EzRs\naJ21hYW2ZMbDD3uTTjlSYYEytx1UbdmX4cNtn+5atWxxx/fft6GNzmW84uWuwdomO3YsSfCbNsH0\n6bZkg4ubJ/kkWrrURpwNH2477fTrB++8Awcf7M2TLovVrr3tBvbLl9uaPcVJ3tfkiUtQyxr0BQYC\nBwJHqOpX5ZyXdc01qlZDf+wx29j47LNt9d0OHTyxOxeTf/7Tlmm4+eawIwlN6G3yItIaKAKeBm70\nJG9LCAwdCo8/bpWQ/v0twUcvz+6ci0FBgY27L176euxYa96JbvLJcKG3yavqgkggWV83XbjQ1oV6\n+WXo1g3+/W/o0sVr7c5VWfXq2+5tkJtrbZwuJj4dLQCFhdaE2LOnzfquX98WAnvjDWtW9ATvXIAe\nfNDGF4Otc929u7WLujLFXJM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11263cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YUShS7pYuDUdt6OdBalqzBr7+9dBqYepUbYAQSUrJLhHCtkJ3EQmzV4MGaWZC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\nCfv/ST5WQayPGrYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112481310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-1.4, 1.4, 30)\n",
    "\n",
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "plt.plot(x, x**2)\n",
    "plt.title(\"Square and Cube\")\n",
    "plt.subplot(212)\n",
    "plt.plot(x, x**3)\n",
    "\n",
    "plt.figure(2, figsize=(10, 5))\n",
    "plt.subplot(121)\n",
    "plt.plot(x, x**4)\n",
    "plt.title(\"y = x**4\")\n",
    "plt.subplot(122)\n",
    "plt.plot(x, x**5)\n",
    "plt.title(\"y = x**5\")\n",
    "\n",
    "plt.figure(1)      # back to figure 1, current subplot is 212 (bottom)\n",
    "plt.plot(x, -x**3, \"r:\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pyplot's state machine: implicit *vs* explicit\n",
    "So far we have used Pyplot's state machine which keeps track of the currently active subplot. Every time you call the `plot` function, pyplot just draws on the currently active subplot. It also does some more magic, such as automatically creating a figure and a subplot when you call `plot`, if they don't exist yet. This magic is convenient in an interactive environment (such as Jupyter).\n",
    "\n",
    "But when you are writing a program, *explicit is better than implicit*. Explicit code is usually easier to debug and maintain, and if you don't believe me just read the 2nd rule in the Zen of Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Zen of Python, by Tim Peters\n",
      "\n",
      "Beautiful is better than ugly.\n",
      "Explicit is better than implicit.\n",
      "Simple is better than complex.\n",
      "Complex is better than complicated.\n",
      "Flat is better than nested.\n",
      "Sparse is better than dense.\n",
      "Readability counts.\n",
      "Special cases aren't special enough to break the rules.\n",
      "Although practicality beats purity.\n",
      "Errors should never pass silently.\n",
      "Unless explicitly silenced.\n",
      "In the face of ambiguity, refuse the temptation to guess.\n",
      "There should be one-- and preferably only one --obvious way to do it.\n",
      "Although that way may not be obvious at first unless you're Dutch.\n",
      "Now is better than never.\n",
      "Although never is often better than *right* now.\n",
      "If the implementation is hard to explain, it's a bad idea.\n",
      "If the implementation is easy to explain, it may be a good idea.\n",
      "Namespaces are one honking great idea -- let's do more of those!\n"
     ]
    }
   ],
   "source": [
    "import this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fortunately, Pyplot allows you to ignore the state machine entirely, so you can write beautifully explicit code. Simply call the `subplots` function and use the figure object and the list of axes objects that are returned. No more magic! For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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P3Ox+SZL2SpIUA+BzAIP09psbSTJpTUgidn0qEHEbUYLCS5ZAsW9fuOfPN6ZR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\n4WEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127e8110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+iCYWU4LPZYkk+UbAihKPP48/V1p7M5tnZlPN7MiURCcZtddeftrc5s1w7rl+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\nkhcRyWFK8iIiOUxJXkQkh/1/Rfi+v/OzAxcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112420750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-2, 2, 200)\n",
    "fig1, (ax_top, ax_bottom) = plt.subplots(2, 1, sharex=True)\n",
    "fig1.set_size_inches(10,5)\n",
    "line1, line2 = ax_top.plot(x, np.sin(3*x**2), \"r-\", x, np.cos(5*x**2), \"b-\")\n",
    "line3, = ax_bottom.plot(x, np.sin(3*x), \"r-\")\n",
    "ax_top.grid(True)\n",
    "\n",
    "fig2, ax = plt.subplots(1, 1)\n",
    "ax.plot(x, x**2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For consistency, we will continue to use pyplot's state machine in the rest of this tutorial, but we recommend using the object-oriented interface in your programs.\n",
    "\n",
    "# Pylab *vs* Pyplot *vs* Matplotlib\n",
    "\n",
    "There is some confusion around the relationship between pylab, pyplot and matplotlib. It's simple: matplotlib is the full library, it contains everything including pylab and pyplot.\n",
    "\n",
    "Pyplot provides a number of tools to plot graphs, including the state-machine interface to the underlying object-oriented plotting library.\n",
    "\n",
    "Pylab is a convenience module that imports matplotlib.pyplot and NumPy in a single name space. You will find many examples using pylab, but it is no longer recommended (because *explicit* imports are better than *implicit* ones)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drawing text\n",
    "You can call `text` to add text at any location in the graph. Just specify the horizontal and vertical coordinates and the text, and optionally some extra attributes.  Any text in matplotlib may contain TeX equation expressions, see [the documentation](http://matplotlib.org/users/mathtext.html) for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ORqnQ078/FC8uI1wLA53WIMD07w+VKsGoUU5HolTomTsXli6FMWOcjiQ4aEve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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11263c210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-1.5, 1.5, 30)\n",
    "px = 0.8\n",
    "py = px**2\n",
    "\n",
    "plt.plot(x, x**2, \"b-\", px, py, \"ro\")\n",
    "\n",
    "plt.text(0, 1.5, \"Square function\\n$y = x^2$\", fontsize=20, color='blue', horizontalalignment=\"center\")\n",
    "plt.text(px - 0.08, py, \"Beautiful point\", ha=\"right\", weight=\"heavy\")\n",
    "plt.text(px, py, \"x = %0.2f\\ny = %0.2f\"%(px, py), rotation=50, color='gray')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Note: `ha` is an alias for `horizontalalignment`\n",
    "\n",
    "For more text properties, visit [the documentation](http://matplotlib.org/users/text_props.html#text-properties).\n",
    "\n",
    "It is quite frequent to annotate elements of a graph, such as the beautiful point above. The `annotate` function makes this easy: just indicate the location of the point of interest, and the position of the text, plus optionally some extra attributes for the text and the arrow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tmt+6FfLlszoaIbLv2LFj3FHvDvpH9ie0digAB346wOdDPid6dzSFCxd26vG6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\nEMKL/T8NEj13fbb1AwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112639310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, px, py, \"ro\")\n",
    "plt.annotate(\"Beautiful point\", xy=(px, py), xytext=(px-1.3,py+0.5),\n",
    "                           color=\"green\", weight=\"heavy\", fontsize=14,\n",
    "                           arrowprops={\"facecolor\": \"lightgreen\"})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also add a bounding box around your text by using the `bbox` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L9FzTphHaz8/P7vGmTftM+/n56enTZ2favmvXUbv7DxkyXPv5+elZs77OtH3o0EitlNLF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\nhPBh/w/hvOQ+vYnpVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1124adc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, px, py, \"ro\")\n",
    "\n",
    "bbox_props = dict(boxstyle=\"rarrow,pad=0.3\", ec=\"b\", lw=2, fc=\"lightblue\")\n",
    "plt.text(px-0.2, py, \"Beautiful point\", bbox=bbox_props, ha=\"right\")\n",
    "\n",
    "bbox_props = dict(boxstyle=\"round4,pad=1,rounding_size=0.2\", ec=\"black\", fc=\"#EEEEFF\", lw=5)\n",
    "plt.text(0, 1.5, \"Square function\\n$y = x^2$\", fontsize=20, color='black', ha=\"center\", bbox=bbox_props)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just for fun, if you want an [xkcd](http://xkcd.com)-style plot, just draw within a `with plt.xkcd()` section:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L4VT8LRYLAHoAv/0GfPwxMGEClxalN9XV1XjxxRfx66+/oqysDJWVlTCZTFybxcPTIMFg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\nEhK1EEn8JSQkJGohkvhLSEhI1EL+HyfBQlLVQ40oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1128079d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.xkcd():\n",
    "    plt.plot(x, x**2, px, py, \"ro\")\n",
    "\n",
    "    bbox_props = dict(boxstyle=\"rarrow,pad=0.3\", ec=\"b\", lw=2, fc=\"lightblue\")\n",
    "    plt.text(px-0.2, py, \"Beautiful point\", bbox=bbox_props, ha=\"right\")\n",
    "\n",
    "    bbox_props = dict(boxstyle=\"round4,pad=1,rounding_size=0.2\", ec=\"black\", fc=\"#EEEEFF\", lw=5)\n",
    "    plt.text(0, 1.5, \"Square function\\n$y = x^2$\", fontsize=20, color='black', ha=\"center\", bbox=bbox_props)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Legends\n",
    "The simplest way to add a legend is to set a label on all lines, then just call the `legend` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A4O/vT2hoKC1atAD++fR1t+dla5al1exWPF/zeUJvhqbn6irxOer5rZ+5SjyO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\nJMVdCCEsSIq7EEJYkBR3IYSwoP8Pty27aLnc6mkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112807c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-1.4, 1.4, 50)\n",
    "plt.plot(x, x**2, \"r--\", label=\"Square function\")\n",
    "plt.plot(x, x**3, \"g-\", label=\"Cube function\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Non linear scales\n",
    "Matplotlib supports non linear scales, such as logarithmic or logit scales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4c6XcNOAOM1tCcPXOlTHn+RB3f4Zg5tingGcI/qHNiTVUBjO7E3gM\nOMrM1pjZJOBq4DNm9iLBl1SHl+/FkPHHwD7A/4X/jn4aZ0ZoM2cmJwHlnTZy/jdwuJk9C9wJdHiQ\np5uzRESqSNzlHRERKSMN+iIiVUSDvohIFdGgLyJSRTToi4hUEQ36IiJVRIO+iEgV0aAvIlJF/j99\n91seM1/U/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11257c9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0nCefDC++CLfcAhdfDOvWZXe+OFzPOGT0y4qAMSZjbdu6cYIlS+D44914gYkn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\nCBhjTAGzImCMMQXMioAxxhSw/weDLj/d6TgbZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1128b3310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UrXpX/XgMowJkWVsY15ksrjNZsqqzVy/LBJ8+HY45Bh55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\n6TgSU7M0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1123e8750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MuiVHbnfmnWVzzOwjZjbTzJ5P3teD8s7UlpldlGR72sxuNbMt884E7Z+WbWYf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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112928b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0.1, 15, 500)\n",
    "y = x**3/np.exp(2*x)\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(x, y)\n",
    "plt.yscale('linear')\n",
    "plt.title('linear')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure(2)\n",
    "plt.plot(x, y)\n",
    "plt.yscale('log')\n",
    "plt.title('log')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure(3)\n",
    "plt.plot(x, y)\n",
    "plt.yscale('logit')\n",
    "plt.title('logit')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure(4)\n",
    "plt.plot(x, y - y.mean())\n",
    "plt.yscale('symlog', linthreshy=0.05)\n",
    "plt.title('symlog')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ticks and tickers\n",
    "The axes have little marks called \"ticks\".  To be precise, \"ticks\" are the *locations* of the marks (eg. (-1, 0, 1)), \"tick lines\" are the small lines drawn at those locations, \"tick labels\" are the labels drawn next to the tick lines, and \"tickers\" are objects that are capable of deciding where to place ticks. The default tickers typically do a pretty good job at placing ~5 to 8 ticks at a reasonable distance from one another.\n",
    "\n",
    "But sometimes you need more control (eg. there are too many tick labels on the logit graph above). Fortunately, matplotlib gives you full control over ticks.  You can even activate minor ticks.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M2HZbOPvsFc8pPolI3CZMgG98A157bcVzykkTkbqaOtXf46M4QBMRSYosX0kT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\nE+KuiZem+GRmFwGLnHNXtXpe8UmkRk8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mSXp8MrP9gYXAHRqkSaiefRaOPx5eew3WWivu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\nLrzQTSvs1i2ZAZrvPvsSa79DptwUrjlzXP457jhXhnHBBS7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dhYjIwgYNgvXXh6239h2J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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e72590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-2, 2, 100)\n",
    "\n",
    "plt.figure(1, figsize=(15,10))\n",
    "plt.subplot(131)\n",
    "plt.plot(x, x**3)\n",
    "plt.grid(True)\n",
    "plt.title(\"Default ticks\")\n",
    "\n",
    "ax = plt.subplot(132)\n",
    "plt.plot(x, x**3)\n",
    "ax.xaxis.set_ticks(np.arange(-2, 2, 1))\n",
    "plt.grid(True)\n",
    "plt.title(\"Manual ticks on the x-axis\")\n",
    "\n",
    "ax = plt.subplot(133)\n",
    "plt.plot(x, x**3)\n",
    "plt.minorticks_on()\n",
    "ax.tick_params(axis='x', which='minor', bottom='off')\n",
    "ax.xaxis.set_ticks([-2, 0, 1, 2])\n",
    "ax.yaxis.set_ticks(np.arange(-5, 5, 1))\n",
    "ax.yaxis.set_ticklabels([\"min\", -4, -3, -2, -1, 0, 1, 2, 3, \"max\"])\n",
    "plt.title(\"Manual ticks and tick labels\\n(plus minor ticks) on the y-axis\")\n",
    "\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polar projection\n",
    "Drawing a polar graph is as easy as setting the `projection` attribute to `\"polar\"` when creating the subplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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W9jgAN0tF6pZ4pSKEcBNCfP/jjz/mPMXWghiNxiKJ1Jw+fTpSUlKwbt26p0qy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\nKy6h4sKFC7vy/yOFll8u6LhAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11252f2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "radius = 1\n",
    "theta = np.linspace(0, 2*np.pi*radius, 1000)\n",
    "\n",
    "plt.subplot(111, projection='polar')\n",
    "plt.plot(theta, np.sin(5*theta), \"g-\")\n",
    "plt.plot(theta, 0.5*np.cos(20*theta), \"b-\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3D projection\n",
    "\n",
    "Plotting 3D graphs is quite straightforward. You need to import `Axes3D`, which registers the `\"3d\"` projection. Then create a subplot setting the `projection` to `\"3d\"`. This returns an `Axes3DSubplot` object, which you can use to call `plot_surface`, giving x, y, and z coordinates, plus optional attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G5YUV+oAHmpZupTVTfJ5JnZASkghd+/JdykoT2RLtO4iUD5k0R09OcPy1j9D32CM8/Man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FsBMRL7RRwXPouv0sVmRsd+Al58a4VoLEk6W0av05LNJBkdHSHoc6E29iMVS3jmuRdOPZ+HjfdS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\ndTyeQattLQaQL1Tbp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RifZmtVsMQaGJnE79lBpzeSz+WQSKXIZXVUWgMylYVCVUMhm0Sj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112c58e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "x = np.linspace(-5, 5, 50)\n",
    "y = np.linspace(-5, 5, 50)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "R = np.sqrt(X**2 + Y**2)\n",
    "Z = np.sin(R)\n",
    "\n",
    "figure = plt.figure(1, figsize = (12, 4))\n",
    "subplot3d = plt.subplot(111, projection='3d')\n",
    "surface = subplot3d.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=matplotlib.cm.coolwarm, linewidth=0.1)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to display this same data is *via* a contour plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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63lx/GdE0dF/UHWAjcBGwAXgHcMPsApKeC8wzs8OSjgFeB/xZ6PJz1lekb5As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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127ec8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(X, Y, Z, cmap=matplotlib.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To draw a scatter plot, simply provide the x and y coordinates of the points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PfO7mvXZQESebXQu86O5z7n4G2E/jc7S7CfgjAHf/BjBhZpcShhXjd/en3P1U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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11389e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy.random import rand\n",
    "x, y = rand(2, 100)\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may also optionally provide the scale of each point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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u3Vxuu+1umT9/fpHeptxut3z++ecyadIkiY+PLxZDMnPmTHE6u+Tz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112639f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y, scale = rand(3, 100)\n",
    "scale = 500 * scale ** 5\n",
    "plt.scatter(x, y, s=scale)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And as usual there are a number of other attributes you can set, such as the fill and edge colors and the alpha level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t6WW5qsLV6wUu9kxStI4hGbMoOQfWQg3PHQ7Q0vxwPd+pFHx+8V59J1sEVdFj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YeKJAqYLCUqJR06HZhMEpIkUZY1ZAvaDX9TVXWOOxNiQoqqipBPPg93BisMRe9Q3xXGaF57PSUJ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\nMAzDGMWYETAMwxjF/B+D8ZkX18nx8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11389e3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for color in ['red', 'green', 'blue']:\n",
    "    n = 100\n",
    "    x, y = rand(2, n)\n",
    "    scale = 500.0 * rand(n) ** 5\n",
    "    plt.scatter(x, y, s=scale, c=color, alpha=0.3, edgecolors='blue')\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lines\n",
    "You can draw lines simply using the `plot` function, as we have done so far. However, it is often convenient to create a utility function that plots a (seemingly) infinite line across the graph, given a slope and an intercept. You can also use the `hlines` and `vlines` functions that plot horizontal and vertical line segments.\n",
    "For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+Y+XjZ3qGM+njCwuLDA4GGB+PMTMzRHt7RWrjhlBIJBx209c3SnHxP8fpdOL1\nupmY6EWSWjO26SAHFwyu0tm5yqNHi6iqnfz8dS5ffge93sWf/uk1BgYWWVlJPHs//nE74+NbBbb2\nekf2skP69fr9AXp65rPivd12zGN/8xic5NDhOMc6ruZ9WKe73+w3cGKdzWEd734dSdIWew1f95Js\nAgEBUbRhMCSiLFG0Ew4HkOVE7YubN5270vr2uu6dL6uiRBFFttVVyXRPjUYj16418OjROB7PBDMz\nQzQ1dWA2WwmH5+junkKvN27bQKCnZ5ibN7fS7dJT5UKhEUQxREODisv1D1it5QSDieyUZDR5+XIN\nXV3TuN17v3jp5VpNJgvBoJ9Hj8Z5//0LqYgusXT9c8JhCwaDn/feq2R+voenT5eRJJWzZ9/GbM6h\np6eHM2cCPHgQ2CxOlNgFfmcJ3fZ2ObWYKS8vF71+jbW1IPH4GFeuZN6gd7978qtf/RMWS0XGZz09\nElcUZdeWbI8eDQAQi5UxPb3O+rqN8XEPFks3ilKMLEeoqjqza8FQkkwOLhnxSpJEPK4wPT1NJGIm\nEvGgqrl89VU3N26cwW63c/Pm9oJa5eX7B1wHBYB37tzhxo0b3L8/+lLf28PyUp33SQ4dXsQwJBOH\njfB3fm55eWnb7HdDQ96hOpvnlVWSpHckO5d978d+HZXBYMBsVrdldsTjG5jNcsruO9u1XydrsVh2\nLQVvbEzUypAkedtk3c5820R9lgoWFuYZHZ2nt/drZDlKTU0Djx9PUVubT2PjVnEnrzeh0zscjlQ7\nt6SAVdraivnDP/w+OTk5BAKBVCZIkuTmBkk7ZMqGCIfDxONmZFnm6dMpFEWH37+QWgK/fem6jMfj\n5quv7lFZWYyqxvjww8ssLnrx+0P4fEsoijHlCL1e6O7+nJ/85H1KSkpS59yZLQMmRDFER8feG/Tu\nV39lZGSJq1c/PNBRZTrGygrEYirLy+vo9RWUlMiMj88QDPq4eLEQWdYTDg9nnM9Iv55MEW8stk4w\nCHV1TZujo/eJx2MoyhSjo6NI0sVdz95B78hhAsCT1qoPatORjnUiRznsyU5w6JA81qNHA6ysgNnM\nrgmTnRwn6j7szduZeTE5uY4k1eJw1CAIIgMDA4giJ5oCuBfbl31H6e2d5PHjLi5ePJNa9p3JFgd1\nVLIsc/lyDYFAH/39n6MoYVpa8rl8uW1bRHaUFX87o5Hp6ZltkemHH9Zy9+5Qxrzo3t55qqpu0d4+\nwdDQEgZsawvXAAAgAElEQVRDPk7nWfx+HXNzI5SXe4nFFHp7h/H5lhDFEG+/fTbl1JIO+dKlqpRD\nNhgMGI3GlGa7U/bJtG1ZeueWWAI/idV6FoNBJRpdpKtrkvz8/LRJRxuKojAz40OSaiksrGZ2FkZG\nFrh8uQm/30skkoMs25icXCcSseFyBVlZsfKXf/lPfPLJTSorK1M239jwMja2jiQ5SEbo++2snmzn\nzk0mAJqbr2fcmGLne5XpvprNEAqFCQRi2GxGXK4lBMHL+HgQ+C01NVZsNlvG+Yx0kudKj3h9Pi99\nff9EPB4jP78En89FMOgiJ2eD2tqqPaP5/ZAkCUVZx+fzplJm05/N5N6mBwWJmQKul5FI8dInLE9y\n6LC15kcAXswCoMNG+OmfU1UIBGKYzQI6XeIavV4zLS02hoYyd1yhUIh794awWpuPlN6UiWRHYrXq\n6OmZwWo9iyjqEMWCbRkIOx+ww3RUTqeTa9fOoar9BAJgNm8Vztlrt/id0XVTU9G29iY7hkxFlb78\n8ncZU778fj9eb4ySEhP19QWMjCxtlksdp62tlrW1KOvrT+nvdwEFGI3ljIwYGRpKFF2SJDmjQw4G\nV4nHFSTJjtkMb71VkaqvbbXqGBraAExcvVrPzl1sZFmmqamIx4+7EEUd4bAbQdDR07OOKHbT0VGb\nekaiUQW324vdDpFIBFWN8+TJU/r7H1FZmceFCyUEAqt4vTIuV5BQqACXa5G5OSNTU7/iRz9q4f33\n27DZbLs63J0S0U722mTCYrEc6llPZhvV1joYHh7A49Gn7jfA0NA3zM/7mZub4uzZaxiNIaqrc+jt\n/ZarV9851NqCnc+i1WqjtraKcHiN0dGnmEy1NDbauXChGaNx5cgj7uR+qH5/nMHBz7ftNrQzet8v\n4MwUcB20YfNJ8Upy605i6JCMEi2WJvLyDqdFHUfzPuxoIf1zoZBIPD5MVdXbqdlvnS5CcXHxrqXe\nkHgAvvtuiO5uD3l5C5w7p2K32w/cEmsvtibGNlAUHQaDiixHUrvO+P1+fvObz8nJqdm19dlhooy7\nd58xPW1CFO2srm4QDvfx8ceX990t/uZNG7Ozc/T0uHj61M3Q0Nquh3pnUSVFUfB6dam/JzuTxETZ\nEoODc0xNyTQ3l1FTYyUWk7ly5SyqGsfpNNDWVks0OsHiogWjsRqDwcjycpjvvhvCZDJhsTRhter4\n5S+/o76+hUuXqunqmmVuzkt9fT6CEMDtfoLVWkJOjolg0I8o2gEj0WgUk2l3LW+bzUZbWxGKYqa3\n14XFUk5enh6brYaennFaW0u5c6eTgYFVJiZWqKysorc3gMHQSH5+iKqqc8jyAiZTKYHAEwKBx6ys\nOPH53AiCjYKCGnJyvIyNRbDZxunoqNvMk+fAPPmkTTNtMmGzJd6bQGAaYNfGFEnZamPDy9df96Um\nZYuLVYxGIzZbYrK6vb2CTz55l6+/7sPv1yPLPhoayjAajUSjdlQ1TjCYSDnca1FP+jOc/iw6nQau\nX3+PGzeWePp0FkmyYDSuHHn0ntwPVZJqMZkEGhsvIwjzuyovJv3FXgFnppFqUvdPn2/p6hrm+nXT\noTetPiyvbWL0yyxmk2lV3F6fS97kZObFysr6LoefSSu32ZrJyxsHCnj2bJGmJgGdLvOWWJl68Z1R\ndLIqn9+/QDy+TGvrWRQlSjC4yt27Xr78cpTm5gpaW4vR642pDQMO6qj8fj/PnrkpKvoeer2JSCTI\n4OAXXLmSWCG4127xLpebv/u7TkSxCaMRSktN2ybzYPsLGw5H6e4eY25ujq6uUdraatHrdYhigL6+\nDXS6Wi5dqmBgYJzOzu+or0+k3G1sjG/rjIxGhVAIHA4jkUgQk0lAUXQEAgJ5eabNRSdmRNHOxoaL\n+fkgOl0rNlsZgqAyOvprmprWWFtbRpJ0RKMeJMmCTle+rXNLj8AmJ2e5c+cLlpYc2Gxd/NEfJaLN\nxUWRaDSKwWDg6tWbdHQIdHUN0dc3QEuLleLiPGKxOE+fDvH06TNisTAdHQ4CgXmiUR2qKlFcnIMk\nBdDr8wgEvLhcbgYGhhFFGYNBpaREj063hiRVZ3w2dr43yU0mku+NzWbjxo2tSdGNDS+/+91TAgEw\nGBQCgQCzs2aKir6HosR49Ohz6urKqK5O7Iea3NPy2rVzDAz8Fp8vxNDQIlVVDmKxFbq6xtDpnMTj\nXior11OZKzvZK9XTaDRSWZnI5T/O6D19P9TE/p2Jui0VFZZ9pZdMAedeuj8knq3k7+bnw3z5ZTey\n/HylKHa16bmP8Io4zoTlcfO8d66KO2jPQovFgsVi2ZV5kemz6dHmuXMVPHs2zdraAj6fK5UKdrRV\ne1sPR3pVvnB4kWg0gCCAwVDH+fMFWCzFu5Y2H0bWEgSRZKkFVRUQBBGDIblbvBGbbWu3eLCwvr7O\nt9/2oaqVWCxnGB2d4tmzGYqL3dvqWafPY3R1LWAyFfCjH33MxMQyDx58Q2trIfn5Al98Mb+Zwheh\nubkGn0/mvffqsFgsu9p95UotAwPfsLwcTlsEtEByU12dTkdtbTPx+AZ6feFm9KqmIulgMMzMzAzd\n3f3EYnpyc72cPVvG7GwEm01I7fH44MEwsVgpFouDvr4JcnKuk5+fSyxm4d69LsxmOyMjw4RCZYyM\nrNDRcQ6z2czFiw34/VM4HHDnzhDT0xFcri4aGm5hs+Xhdltpb8/h2bMF5ueDqKqe0tIKVHUFvV5h\nZGSNlpa36e8fo7NzkqWlOS5dKiMeV7l5s2XXEL61tXTf9yb5jiSv/+uv+5ie1iOKdoLBJVyuGYqL\n30KvNxGP+4nHc4jHjayurjE9vc7amodYrJP19Q10uiJmZmYZHw+yuBikrq4AjycKhIDgge/cfqme\nxxm9J8sQxGImzGaJcDiEwWDC41FR1d1plen+IpOGvZfun3y2kqUoJidnnrsURSZeW+f9IvImM3GY\nbJO9nGemByxTOmHyAbDbnTQ1yXi9QT74oG3PeiD7rdrb2b70qnyKonDv3jxWayK3ea+lzfu9GBaL\nhXPnHExNDRMI2FAUD1VViQ5r527x1dUOgsFh7t5dp68vwOrqBhDD6byAosyi17OtnnVyIc/Fi1UE\nAlBSkojKi4rKGR6+SygU4Isv3MzPG7DbCzEYzPT399DYKKc2J9jZ7uQmyA8ejCEIFiQpkf1hMBjo\n6UksOikrc6EoEfz+aUpK/BgMq3g84HLNMTExwfJyM3p9McGgi76+dfr6+nnrLR8dHYnodnDwGT//\n+RN0Oj+x2Ap+v4Hc3FwUxcXqqpvV1UlMpmXef/+PcDrzmJ19zP37/djtOYRCEI97+frrL7Db24lE\nhjGbf8jq6jKNjR0sLPRw7lwun3xylvv3hxkdnScQWOHcOQetrYkSALm5heh0s9jt57Dbr1Jams/U\n1DB37w4Qj6tYLM1YrUZiMSUl3fT0HPze7BxlGY1lDA72YbOtYDC4ARFR9KCqNiYnQRSLycsLotMV\ncPv239PS8ntYrSuIYoTJyfvU1VVw9erFzQqPBtzukYzZVn6/n87O8X1TPY9K+nzM0NAoVVUXWF2d\nZm0tTDTaz8WL7+957P3e750+aPuGzYksmaqqM89diiITr63zhqNPfh5H8z5InjnKYqFMn+3pGd71\nMu23qm7n6OIw8lH6hGB6bnMkEtpzafNeyLLMzZstPHo0ztLSDPPzi0hSBXfvDqX0zocPxwALojiL\nIIDDcYHCwnF0OokHDx4gCDoEYZ62tg5isVV2Fl0SxQAQIH33mMXFVc6fv47d7sZmy2d0tJuamgqC\nwRWamtr2bXtBQQEff+xMaeV9fV50ujVaW0sJhcL8t//2DU1N11BVP3/yJ1fp6Zmjt7eb8fEZBKEG\nna4BtzvA4uI6sdhb5OeXEg67mJoycffuAD09M1gsF3E4zhMIeHn69K/IzV2iqen3KS+XMZmW0Omi\n2GyOzUnNGv77f/85tbUXyMkxU1p6henph7z1VhNer4zfX0kwGCUWixCJRNDpIpSVlfHjH5elyhJs\nZfKs4fV6UBR5UwPWYzbbWF8XuHdvjEjEQCTiobS0DLvdTEFBGLPZzPXrDbtkQEVR+OKLL/je976X\nsqcgiChKHFUNoaoi+flG/P4RRkfHkCSVy5edOBwehodXsNs3aG6uQa83oih6JicncDiuYDCIRCJe\npqeXqKoKZszsgC0n6fXGGByc4+rVGhLbvj2fwwuFQnz33RA2WzPFxTZEMY++vnuUlhbics1SW1vD\nwMAyRqNxW3R/+/Ztbty4se/7vZcPOu5mJkfhtXbecLJ5k5k4SJ45iva+12fNZvOuxRfJaztodHEY\n+Sh9yJeUJfz+OdrbC2htbd+V/72zXnam1XbvvtvMl192U1Hxgx3DwWY++ugSfr9/s2LgGlZrQhLq\n7x8nJydKUZGPjo5rmEwmQqH5VNGl9BoYgUB3apPdZPTidOYjy0vIspEzZwqpqjJgMpWkluAfxM5F\nJU+eDGzehyry8xvx+TYYGRnFbDZx8eIVbLY6ZmclJifnkKR8FMWEIMQ3Ry4K8bjI6qqLUMhMU1MD\nk5PThMMR4vE1Zmbmcbms2O0BLl6sZH5+lrt3u7l8uQlRlKioyKetrRKz2U40GkYUQ6iqSmWlk6Gh\nBVZWuujvX+bMmSUaG/95yvaxWJzu7tlUFFhTY2dkZIpQaJpIxEBNTcemjjtORUU9s7M+DIZW3O5F\nLBYnExO9rK+Xb+5ZuSUDJuWV3t5l9Pr+1JxBWZnMvXtfE4/nAGs4HCoffPDHSJK4mVk1RVWVg5GR\nh0Qi5QwPr1JcbKC0NMba2jp6vQtVDdDQUMCZMza83n58Psu27BS/37+t8JbVqmNqSqanZ5irVy+y\nc5/Qo+Byubl3L5kMMM65cxUUFpbS0FBDNLrOjRv/875yxs4UYFWNb64e3R0cpZP+uxelELz2zvso\nHEfzPsiBHuQ80x3hfp/dqxPab6Y73SEfNpWpujqxkW1Ly0VkmV17IR42pzkWiyHLjozDweQu6clK\neaKYR2FhKefPnyU/fw273YiiLBEKRVJFl3bXwDDQ0VGHLMup6EVRopSW2vn8888IBIIoipEf/Whr\nqXVyyA27rytTx7m4qBCNKpw7d5murnEURcfq6iTl5VYaGy9gNnuoqSliY2OWubknhELLFBc34nDE\nUBQXqmoiP9/E0pKLeDxGXV0ljx7dp6iolKqqCwhCFXNzowQChTQ26pHlGA8efEN1tRlV9dPZOcry\n8iJOZx4FBRIez28wGu1EIo9pbq7A6bRSWVlJf/9iqg75zihwfHyYd99tpqmpiAcPRhkZuYfLFSA/\nH86frwGWcbnWWF2doKDAS0VFMU+eTJGTcyFjhsQHH1zA5/Ny924/t261YLVaqK2VUVUDkYgFv9+K\nJImYTBbi8Tj37s3z6NEsOl0JodA84+MKXV3TXL9ejF7vxmRaxmYzUltbik63SGNjIU+eJLZWu3Pn\nWer5Sq8SCNDaWsWDB9+wsPAUm00+lsNLjnSt1mby8hZIJANM09QkI4phjMa8feWM9Dzv5eV5JidX\nCAZVYrExrlwpPrDkbpIjp0ergAdYBlb2/tipct7HZT/j7+XcAWZmZhkcXNqWjreXoz3s/pKQWYPL\nFLnvV8UuL293tJH++UROcxQIculSNcGgb1t2yF4d0c4Iqq7ORk/PHVpaGjEa43z00WVstu22DIVC\nTE7OYLXWb6uBYTC0IsuJEp1VVVaePPmO3t5lqqpqqaiwMTfn4R//cYK5uRB1dbl0dU3S37+CLBtp\nbk7kLic7G1VV8fkW0emKyclxsrw8z+joFMGgyPz8FI2N7wAq8/NBpqZW2dh4Ql1dMRMTk9TUKLzz\nTi4bGzITE/MMDnaRl1dAbq6V5uYbNDeX8rOf/Q6vV0cwOMnFixcoKjrPs2eDhEIrhMMhzp+/RG5u\nIbOzj5HlKJcufcjf//0dlpaczMysc/nyeerrE3Mger1KUdH7WCwOwuEQPT2/5u231xAEgVBI3DVy\ni8ViqbmN7ft4mpGkEIFAAEGIEgoFURQFKEOWdQSDfnQ6A4GAQCwWxWyO43KtMDo6z9qah3C4k3jc\nyPXrl4hGw3i9G/zsZ0Pcv/8Mq1XG63Wh05VhMjmQpBz6+7+iqekdoIKKihIcjl50ugCSJCOKs9TV\n5dPXN09OzgVkWcf9+49J5syHQkEGBj6nsDCxYEav19HeXkJHR92+m03vx/ZkAJVnzxZZW1vA6w3S\n0ZFIBkjUmdlazbszuk9mvXz66TeIYtPmXM4Nenrmt2nw+y7KUUH2ysjLcsIZr7DlmDP9exWwAAWb\nP3twqpz389Tz3k+e2enck+lVXV0L6PV5NDYWIcuOzXKYzbsc7UE7gqSzd5nN5l2RwH5V7Do7b3Pp\n0q1t0Ub655N1m/3+AI8edSNJjm27ne/stJKLb5LHEIQoPT0zKIoORTFSX2+gpmZLV0+/rlgsRlXV\nGVZXp3G7F0nWwFhZWaWzc4InTxYYG5shEgkRi5m5eLGI2dkwTucl/P5FwMKnn/6KlRUdBkM7sqzw\n7JkPsznR2UxNzfCLX3Th8YgsLf01b71Vgc8Xpbr6AiMj8zx9+gVTU4uIYoyKig+AedbXg/T399LW\nVkR5eS3z8yFyc8tYX+/nz/7sj8nNzcNg0DE+PsXNm838+Z+X4HK56OrKo6cnwvi4C1UtQFUfU1iY\nh92ei6JEMRpVwIHFYkWWTTQ1XSUa9ZCb62B8/FuuXjVjNBowGs0Eg15GR4eZm1vgP//nL6ivr2Vm\nZj41ktk5ypNlObX8//JlkUePhtnYGMfvl5AkG7OzAqHQEmfOeBkYcCEIVkQxRG7uGktLIcbGJB48\n+Ixr1/6MvDwTubmFdHb+lsLCZoxGE8PD09TXt2Gz2fF6Q4yNjfHRR9/nyy8HWVy0s7joQxSfUV+v\nw2ZrRFGKefvtUjY2vAwOLvHkyVpKy1bV+LaceavVRnV1OV5vP8GgI/U8HddxJ5fgJ0sW2+12mpoE\nfD4XH3zQhtFopLrat201749/3L7tXEl/YTabaWo6i8NRhU6nQ5Zk3BMeIr0RZL+Md9zH3BM3osuA\nyRugSHCgX9fv7YwL0/5dDVzZ8ft8IL0P2bWHToI3wnlnQ03v9EnBZKlR0DM/rzI+3sW5c2coKoqm\nanok27lzZeXOmiiZFrIkJYZIRABCFBUFDrWEORaLs1+pzPTPJxZRuJmZmaSt7fcQRd2u3c6TndbC\nwiK9vV46O5fR6+cJBv0MDUVTS8Tj8WUmJ73U1GS2ncFgIDfXRGFhDZKU2Lg4HB6mt3eeyUkrPl89\nwaATlyuCokS4fz9IYeEKHR0xZDmKJOkZGfFiMtVRUFCNooQYHr5NZWU+6+vr/OIXXVit72OzSWxs\nPOO7776ltraMycllHI7zlJdPs75uQVXX8PkEAgETi4t+OjosXLlSR3f3LBZLE2ZzHAhy7940paUB\n9HqBM2dCqWfP4XBw8WINjx9/js/nIhqNkpenZ2zsGZ9/7qa1tZh3322hp2eecDiIKLLpxBQUJUws\nFsVisdDY6GR8vIvx8TUEoQC7vQSn8xZut4umpkr6+u5tez72kts6OurwelXy8ozo9eUoSpyurnV6\nenqIx+cpKKhFrw+Rk7PGO+/8M0ZG1giF7ExP9/PDH17Dbs9JOVS3W0cwuEJHx7uYzebNVaKTjIzM\nAg70ehm9XmBhYZGampJtz9bw8CQWSxMOh46JCYHOzj6uXHmLeHwDiKZy5hOLcBpYWVllcDBAX583\n42Iu2H9eJj0YCoUCBAKdKenv2rWtZIDUal5RRFiPs/j1HOVTCrJbTjjdh8D/ANOSibeHqzBsGNB7\nRCSPSsxYh1wiouarhMQYOXklqPkywfIIw5YFGt8pQyqRMjvjE+K1d95HqQfyMup5JyNPu93CwsIy\nZvNlRFGHotiYnHyAJLVta3v6ysr6+kRNFFWtRK8vQpZNqeL1yWOrqsrk5AyCUMraWoz19RBPn37H\n9etluyLv9OjY4xEBP7//+61MT49TWVlEKDS8q05xet3m0tJVPB6RUCixq0tyt/P0rbYUReHOnUEW\nF/OR5RzicS8Wyzxer2czFzxAc3NNKqtkr861oSGPgYHhzb0w/bS0FNPd7SIeN6IoEbxeHUZjJSZT\nmFBogsnJMerre+joaGFiYpSRkUn0ejODg3+LIESIRhXi8VGqq2WCQRN2u8zY2CxOZyuRyAaBwBqP\nH49w5oyDQKCQtbUpgsEpJKkCp7OahYUV7tx5xM2bVSR3+onFVGZmppmdjTE3pxCPB3E4RmltzWFh\nIZIqoNTQUMuNG408ffoMQbjG6GgvomhjdHSRa9cUGhry6O2dIjfXx+zsL5EkExsbUFLiIx6Pc/Nm\nC6LYj8cDOTl6otEqcnLycbu9OBxOmprOcvlyEbm5ufsGKxaLBaNRQVEkZDnON988JhKJsrhopL39\nfcxmgfLyfJ4+/SUWi40rV/IQhMRzZjQaNiNWiYsXExOLZjPo9bpUoFJSIvDVVw9ZXc3Dbs/nvffa\nWVtz4/N58Xr7U7Xbt3Y/2iAeh4GBEYLBJWpqzFitNtzukW0S4s6J5f1Sc3fOy7S2ltLzdB5L9CzW\ngIn4Upj40iwNzlL0G3qkf5BgGYRFgctTDZg2jMgeiJkgbK9BKBegGCiAW0W3oBDEKyI6I/StjhCw\niKh5Qd66Wo7T6cTv9/P42/m0/WH1rKwEqH4ndGhN/LjB52vtvF9UTe/nieST0Wsw6Ke0tIiVlQVC\noSlkuYKysvLUKq5MKyv7+2dxu3243fPo9QYMhjj5+UEWFhYZHl4jGk1sYeZ0Wunt7SYczsHjWcJg\nKOWv//oO/+bffLirIJHT6eT8eYWHDxN5ztPTgZQss1ed4q00p2pyc/u37Xbu8azy4EGAeNxMMLhK\nIODlyy+XyMkpoKrKjMlUyNLSJBUVIouLfvT6PPr6ZikpWd226i9J+ovo93tRlAhWawnDw2vEYl5E\nUSAejxAOu4EooZCRnBwJm02ltNSD1zvGnTtfc/bsO6yvO+jrewIYaWtrorm5js8++5K5OQ/z8/m4\nXEGKi00Iwjp6vZHV1T7W1kwYjVacTgd2exk+3zBWay4WS4CKijbu3Rtmbs6LThdDVf2sr2/g8xlx\nOKqQJAVVDfKznz3m5s0fkZNjS21PlpNThyjamZ1dxGSqo7y8momJh/yn//Q558+fw2yGP/zDZj77\nbACowm43UVeXx5Mn03R01PH++21IUh9GYw3Dw9N4vW7St0U7TOaFLMtcuVJLX99XPHnSSyxmpaLi\nCmtrAh6PD53OhiCISJJKKOTHZDJRUWFjeHiQ9XUDweAqihLl/n09RmOc+vpcxse3ZDJZFjGZ8tDp\nJMCKy6VQVWWjoUHPhx+2bUt3Tex+tIjZXMXFi3Hq688gCHO8+27ztmXjyRrv23T9DT3hxTCyX0ZZ\nUJj92kOlrxHDhsTqMydmr5FiyY7sBsml8N5GPnGTgOIExWnAbylEbpATkXBSpsiFwekpxKIKpCIT\nwfjWBs6ZvKINGxeV2kMt1DlKZszzFKPLGud9HId51CXyh9G8n7ey39YqwXFUdYHS0jwaG1sxm20o\nyniGFMOtlZVu9wzT00M0N/8B+fnVeL1uxsa+wGIRcTjOpyYdnz79JYWFdaytxXA43kUU3ej1YR4+\nHOODD2zbdqrx+/10d0/jdF5IPVw9PcPEYktIUtG+FQRhex1rUUys0LRYmpDlxIRmMKggCGYWF6PM\nzQ1z5owVq3UDWc7DYBAIBt3MzIzj8UBubn+qqiFs73zTJ0ivXm1EUaIEAp1UVKwzMzPKxsYT/H4b\neXlF5OXVUFd3k4ICHQUFcfx+Ga83xsTEYxRFwGJZp6nJTF5eIT09MdrazjE6Oo3bvcHs7Ldcv97O\n06fz1NS8RSAg4HJN4POtYbM5CQRiOJ0BmppakeV5pqamOXfuErOzftbWoiwtjVFb+z9RVlZNPK6y\nujpLJBLarNnhx2g0UV1dTjg8yvr6PD6fhfr6Knp7xxgcHEGSQkhSFLtdx8bGEi0t58jNPYdOp8Pv\nD/Dw4QJer4LBoNLQkMf09DT5+UEmJ39HdXX5ZqU+DqzMl6SgoIB/8S+u8NOfPkSWDYCL6monIyOD\nuN1molEbly/n4fMN0t3tZ2zsKR9/fI0zZ+L85jcr6HRn0esjlJU5GRlxpZxtKBTi22+HMZnOodP5\nWFiYZ21thLffvso773RsKyHR0JBHV9dT1tZ85OUFaW6qwSk48QyvEwwHsYftSGsSrCQkirb+Yowb\nMfQbErJLRfacQ7CCWqCi5qgUyrlIxXpC1gjePFiujBBtiyIWG5iPjhGxb+AobE0976HQ+C6nLCFx\n1l2YGGWuZ07h2+kv9pr3SowaE2mtR0kFfN7gMyuc93Ed5knX9N6r0ExyxhsOVyBq59L0eHwdRVnZ\nM8UwubKysNDF2bOXWV+P4nZPIMtRysqKiETkVJnOxMROKb29j9nYKKagIJeSEic6nRuv15eqoZAc\nToIl44KHYNCPyZS5pnNSEpFleVsda0g4DaNxa0JTr1cRhClUNUw8HiUcdmGxBLFai6msbOTRo27a\n2n6PUGgVSXJsppGZUvWwd06Qgo5oNIzJZMFozOfy5UJ0Oh1tbR18880cOl0pojhKS0sl0egqk5Mu\nAoEY4XAeVVXv0t//NZHIOENDc0xOTjE09BSjsRxZFmhqMuP3n0WnU1lbWwTKNxctRSgvv8qlS3V8\n9923xGKLCIKZvDwTi4shFhfDrKys0N3dRyRiY2DgNoIgkJtbREWFgXB4ka6uAXS6HOLxDSorI3z0\n0WUuXFjkb/7mAaOjPUiSFVk2YzI1srGRR3FxMaOjX9LSYt68T9DdPUooFGd2FmIxI4ODPfzpn17D\nbrcjSW2Ew2EePBjNKCnsfDbTg6GysjJaWyfp64PJyQUGB0dQVR+FhecoKzORn2/BbDZz6dJ57HYT\n+VWySjgAACAASURBVPmX+OyzX6LXt2A2VzM6OsezZ8OUlIRTE9ahUIiFBS8FBc20XTATXFghPBPm\nXbEZ+2/tsAaBqSBrz3zILhvXPOd5xy1i9lnQr4soxjg+4xkoEHDl+rDXGjGUGRBrRWzn9Qy7JwlY\nBdT8IM03S1H1bEs/bWl5m9zcQp7cnwJM5FzNRVGCCKEIl1trDrWC9CgpfJmCy+0186GlxbZtzcRB\nAenz1md65c77eXqfwyxiSSeZt7mXQXcaMxwO8ejRNC5XBJ0usVoqU87zXm1LX5p+mBTD5GTWmTMl\nqfSlcNhLeq2E5eV55uZWaWpq4quvOjEaC9DpoKLCysBAL+3t7yNJIgMDiQJKly83ZFzwcOvWD3at\n/AoGtySRZE54cmdz8NPeXrltQjMe9xKJuDhzpoy1NR2RiIf6+jPk5iY+Hwz6kCQHqioQi61jNJZs\nK9KTyAQIbDseBNHpDKmOWJZl9PpcGhrq8XplgkEb4+MyfX2zxOOTlJQ4aWio5OHDaeJxM2bzLGZz\nBR5PDI9nEqezgby8SwSDXkZGPue995pRlDD19TVMTMxhMplxu0PU1KzgcFTyr//1h9y//xUu1wQb\nG3qmp2exWNoYHXVjs/0Bev04OTkis7NfUl7eyLlzhf8/eW8aG9eV5Xn+XrwX773YGUEGg2Rw38VN\nIrUvtrykXbk4F3dlZtVMVTVyqtHVQBcaGKCB/tTAYIDGAI0B5kt3TwI16O7M7qyuyspacnHaaTtt\nyzYlS6IWiuK+M7gEyWBEkLEv772YD6EIkRJpy7IqKxt1Pmkh77vx4t5zzz3n/P9/0uk6wmEboAJ5\noDjv5uZm/vAPBX7wgxvkcgah0AJ1dT0EAgl8vjiiaOb48XoWFmbZ2Ihy//44klRNIgEdHU0kEgp3\n7qzwO79zCkmS0HUdw7A+xre9P622/3vb370EBYrAoijV1R7a2p6ns7MRQdgB4mSzZqqrqzh79mXS\nqSTmRCVVBYnk7ShDeitSxErtnIZ+y4yhGLg2Xfy7qe9iTVqxpUXSpip2zd/EuG+wXRelotPGth7D\naHCjDclEbFm2mcDkk0hZTYzPzdDffwmv1/cgOp7m8uVeJEnCho1+relAQfLDDyfKPkIUq7l/f5ie\nnk7q61Pk83vs7AgHCvxH8Qodtk8/zV/A4cHlYVS8MzOzZcDYkwSkXzT4/Ad33l/09Pk8p+dnvdD9\n5OyqauH69TtsbChYLG4WFhapr7fw0kuP8zh/mn2eFkOArq4sk5OzGIYVw4jT3l6Bw+FgcrJYdJyc\nnC0veo+nndHRD/H7rUCEykoHs7MBkkmN5eUwDQ12DMOgt7eBq1ffZ20NKirUMjvbo61+pZRIiVDn\nb/7mHY4dO8fqapJUSmJy8tqDgmexsFhZucG9ewuMjSWRJAfHjnlIpWJkMsVe3b29+2xvB4lEpmhs\nbOTGjbvE47NcuvQNFEU9gKQ0DGtZ2DYanSu3ipUWuKbl6eqq4e/+7hM0TUeWzfh8x7ly5V2gmspK\nBbfbjMXSgqI4MYx5dnfNaJqN4eE/x+VqIBhcIpdzsrQEkUgOQVBobm7FbM7S0uJicLAVTcvj8VgY\nHHwBRbEgCA0sLt5gb69ATU1RCKC/v45AIMwf/VEvXq+XTz7ZoKOjg3y+2Dmxn7PD7/fT3e3m3Xe3\n8HjqCIdnHqSIlnnlFVf5gH/nndt0d59kc9ODqtYxOTlGW5sTsJXHEkURXX8oHpBIxMnlwoyPx3A6\nBx7r5S9xoP/63WHmRiJkV21UrdrpLrhpTHjpXnMj7EhUC14scQU1bjzoprByyvwCWoXANimS1gxx\ni0Ztv4+UM0bubA5zrZmp6UVmoxr31kNshDPU17s4fvwsqdQkLS1FXc2amhJ3u0I2ZOH8+WKB3DAX\ncLsrj6SH3b9vHs2De70+eno6aW9XWFlxlmXi9pNXPSvk9VHB5alTzUf6LXgyycPPG3w+av/gzvtZ\npD6e5IvSNI3/+l//hnPn/uDQF/ooOXt1tYvFxUWOH/9drFYrsiywvh4gnU7icLiOPGA+b+6+NPdH\nr2AOxy4ff7zKnTtOJCnGV77ShddbCXTi9RY3RE1NHSdO9HH6tA+r1cr3v/82dvtpvF4Hq6spAoFV\njh+vYWYmiCSZMJvzDAwUc86lfF7p8CiRVhXBG0XGt3Tawvx8CIejG4ejyIU9NbXFxYvFyvq1a1Gq\nqk7y8sv1rK/vMjNzk/r6Nb75zZeQJDOZzAzd3ZVsb1cjSRVkMjskEjpTU0EKBQuPIikfFbYtcZC0\ntjpZXJwlkdBxOsMoSiWFgsjIyCI+Xxc2mxVNW2N19WcYhkFV1QVCIQvhsEwwmKal5QyRSACHo56/\n/dtrOBy96LqLgYELCMImPt8eiuIkFJpAkvK0tTVTWVlkGKysdCEIXcA0drsZVYVcLo3LpZX73UuH\nSxHufxg9gUE6rSDLTiKRDZLJLRwOO4bRTzweR5ZlZNnDyZM1/OQn11hd3SWbXaWyspp83nyAcjaV\ngsmJd6izVpJeyVKv+Egu52itNrCnQdhRqZq5gP/HVuQYSBGVc9GXyUoaKatIVMoQltLElG2ypm0y\n7h3av9KCUC0wvjPHtcAIxy8N0NLtYG4uwsjIOmazi8HBHjasAsnkOnVnLUg2iRPnWoh/PMFSLgmK\njf7+k1gsDjIZJ/l8DFFMPgaCKTFu5nIzXL8+icnkOJIetrSXRFE81EesrOQOpJC+KHnVo3blyhVO\nnz59qJMGjvRbnycg/SLiNM/kUwqCsAzsAQaQLxQKZ554Al/w9CnZk+SXdN382JWz2DcdfYycPZWa\no6nJg81mRRTNiGKWVCoNmI48YJ42d//o6b67G+WHP/wRAwP/C5IkMjU1zn/4Dzf45jf7HhSLHtJN\nQhKr1UoqlaKxsZZIJEQstktjo8ruboCbNz/Cbq9jaOg0VquLsbFFLl9+nFqzSH+6w8xMHpOpyBaY\nywXJ5RpQlCIXtmGkuH8/jGEsk0hscffuJslkJ263TnOzj1SqDp9PZmJigULBQiwWoKPDz4UL/eTz\neQoFHz/84V38fidudw3R6Cbz86O89trAAXKkqaktTKYmXK6iRNfi4iwXL3axtrbOG2+EsVhOEY9L\npFJ1wBKvvnqJCxcGCQRukUzucutWiGg0gd/fwurqKsFgDKt1m8HBdnZ3a/H53ESjcVZW5jCZorhc\nJmQ5z4kTxVz0Rx9NsLsbwW530tLiIpG4z9mzNdy8+TdYrfWk08YBceJPW7/B4CaLi0liMQNFaaGp\nqYtsdg2XK4Dd3sPIyCLPP9eLJaOj7IlcMBpIJySc6S6aPvFQc1NC+B8CzEuci/WX+4xz5jxGpYju\nMbFl7JG07mHtVsj6dJakVfSTLqQ6KxFxlyvj7yFaG4jFakgkMqyu3qelJcupU25eemkQZ22RMuF4\npomNtya58GInqqpSW1tLc7ODhYUour7D9vb0Y8XSV189Bdxift6KJFkerJMYDodEc7OTn/3scRCM\npmkP8vxpwMxh9LCP7qXSAV56xz09PsbH44fu52eJ9TgquHxUf/XR7/3zBKRPe0t4Vp/SAF4oFArR\np/nlLyqN9qT5pZMnT5DJpCkK0u5iMqUQRfFQcna/30NHh87W1hgmkxOPJ4TDESWZXD4UHPFFcveP\nntTFwp8LWVZZXFzA5TpFLiciCE3AIsnkJBsbOsvLq1RWOvj+99+moaGO1dUgx47VU1HhRder2N3N\nEo1mSCRk5ufjSFKYqqo02Wz2sfx/8bka2WwUWRYwmXJ0dHjZ3Jxie1tHlnXyeQO7vQZVbeDttxcJ\nBDQslhQWi8rq6g6CsMfy8h4eTy+GIZHNSgjCIo2Ng9jtDnZ3I9TVOchm5xgZuQOY8Hh0dnZ2ygK9\nCwuLjIwEsNlkDGOGjo4GTKYiEdD0dIimppOkUiLJZJ6dnVlOnKhFFAUEQaSmxkkolCGfT2IYaXK5\nPE6nDU3LU1/vwWbzoWlL2Gxejh1r4b33fkF1tZ+hofO0tdWysBDCarURj8cYH38PSVLo7XXzve8V\ngSm///vPEYvFyoAcTdPY29sjm81y9mzbA16XLBbVAlHQNjSCbyfom+ukZdeKGFUxhbPUiu1UGb1U\nveHEmqhCSStcshxjT05Raaohbc9ja7Gg+iqIKdskjidYOL6HrcWB5oa4kuKDax/zwguXyeUy3L07\nxfj4DM3NNo4fr+X48XoWF+fKDI3HTlQ+SJm5yGYVKiurEMU1HI7aMpteiZjKYunm6tWZA3lzUZTo\n7LQwO2s9pFjay/nzXaRSd1lYeBuTSeDYMTeDg62MjW2UJe103WBxcbGs6G42V3D8eDNgYLF0lFNN\nwKGUsKUDfH8n1cxM+O+Fra9kpZz3UU76KL/1rALSz7JnNZoAmL7QRJ7i9CltnqtXp3C5jn+q9mPp\nhV65cqss4dTd7WJnp4hdfZSc3WTK8vLLgw+uqims1goGBweOhK1/kdz9o6e7yWRGUfaIx8MYhkw+\nn0eW03g8VcRiMU6erOaTT+YYGnqJ2dkAdvtpotEQPT1NTE4+RN+dOdPGj350Dbv9JA5HkTNkefl9\nRPHEAX5jSFJfb2V2No7F0gIk6e1tQ9OcXL5sYXx8k2zWRDweoafnPBMT6yhKJ62tBbLZTRYX36Si\nIsfJk3ZGRuIYhg+LxUxb2wuEQu+ytzdGOu3BZEpx/HgNKysqvb3HEUWFdHqBycktZFlhdDTAyEiA\nhQUdkylKNGrw/vu/ZGhIobfXgWEo2Gw6TqcDVbVTWbmHJC2zt7eIqhoMDjby0UdxVNWBKGbI5bJY\nrQmSyUUMw4wgyHR0+NndnSKTqaW21szrr5+lurrYIbC5Gea990bZ3vbhcNSQSoXI5XK4K9xICYnd\n2T02PklhChsIoQi5jQzxBbCnbbjzGo6MBUfGgzklo9t0BK9Am7meOkeBqVCAqDnPuhxgx69irvNT\necxFVFqkYTCBoIqIYiv3728gSX5EcZWeHjOatkXLxS5iV6PkVC+qakFLFJGy2WyGqalFRLGRwUEH\n3d0+YJWGhgYaGh52n8Tjcd544xOuXPkLNE2hUAjz2mtfw+/veYyY6qi8+fj4PQTBdmSx1OGopb8/\nxvHj9WWVm/2SdgChUHE/JJOpsvpPifd9v2rUUZSwuq4fAL58Xgf5tPiNz+I2+qKdLE9rz2rEAvCu\nIAg68GeFQuH/e0bjHmmRSJQPPxxnbGyT5eU4fX0Fhob6cDrdRzrNO3fuYLNVcOZMbxl0Mjk5idkM\nLS3VLC0F2N3VMYwFzp4tai++9NKTVa2/SO7+sJP6X/7L5/jVr26wtRVFUWr4ylcGKRSMcgeGJLlQ\nFBlNk3G73USju4+h77LZLC0tDYRCIaLRXSQpT0tLA8lkkj/7sx/T3/8tVlcTJBLw05++T3PzEA5H\nB4JQKIsc1Nd3UF9f5JF2OERMpqI6jsm0g2FInDr1HNvb43R2xrl8uZ+9vWs4HE6czgoKBQOr1cnF\ni92oqoqiKGxubjI3N4rZvIdhZOjo8JHJbHLz5gKq2o7NJqOqacbHt6iu7sBsjiEI8NFH48zObrK+\nbiYSuY/b7aSlReeP//hLOJ3Ocq7RZvPx9a93Eot9yMZGglQqRn+/i4qKDHZ7jEAghL/ORld1htPO\nIWpXvdx/c5iz9gtYg07iizaGsnWIYR1LsgVb0kD8A5GCrYBsV+l0t6B5BNYyYQLRFWR/F2tSlquz\n18jWxmg/34Oz1c1u+iNOnuzg3r0tgsEC9fX9BIN7hEIRQqEpnh/4Cilnkr6+ehKJRUgJNDS4OX5c\nZGJijWBwibq6OGfOtBGLxejpqWZy8uH6+Pa3h7h+fYSRkSUslgZaWx2YzfVks9bHKBgcDgc+n4/f\n+71T5PM57t5dZ3s7TVNTUfNyv3TXrVtX6O09+4ADx1SmQTUMBVFMHljfJlOKqanUgQh5fn4Wv99/\n6H4odhdlGB0N0Nd3vszSd//+ff7wDy9w//5DoecnoYQ9zEEe5aCfJqW5v8/7aYLLZ1U0PXL8ZzTO\nxUKhEBQEwUvRiU8VCoXhR3/oe9/7Hs3NzQBUVFRw4sSJ8su5cuUKwBP9XdM0/vN//itWVnSOH/8j\n4vFNrl17j0Bgkn/yT/5XzOYc169fRxTFA79/69Ytjh//BpWVHm7dKo7X1OSjr8/Bj3/8C3I5EwMD\nPZw58zwTExPl50mSdOR8Ll26RDab5fr166RSKaAo3joxcZXOTh+SdPyJPt+9e/cAneeeO4eiKAwP\nD9PeLlJTU8vqao633vpzGhtt/Omf/hE2m42JiauI4hKK4icej7K4OIyuV9Lf78fj8TA8PIyu67jd\nVXi9tYyNXSOb1RFFB1evTvHhh6PcupXm8uV/isdjJxj8BYXCPSoqqjAMlYmJ96mra0eSTgJw9+5d\nstk4igKRyByTkzcBmUxmGb8/gyw7uHbtEwwjwfT0KMHgJG43fP3rJ3C5XAwPD5ff19BQLdev3yAc\nzpLJnCGTmSSb3aGmJgnYyOUEotF5CoUtKitdBAIFhoc/QhTtdHX9Pk6njYWFv6CpyVZElBbg3b97\nFzEmUhVvRd5z0jmT5Hi2hovK16ies/HJ8s9x4eJV7UtYUiLvSx9gcido9GjMk+Ku/S0stSKyu5E5\ntrnvniGjFlAq4LnXbaBEmZwMc/nyHxCL7fGXf/m3jI4u0Vr1h6yvb7GYncZbyFNlu0wivM29e/dQ\nFCtDQ+f5+c9vcvPmW3i9Znp7B7h/f4tr135JfX0ThpHH59tiZWWRhYU1+vvPk8vtsbJyl1gMbt+e\nRZbrWFu7waVLbbz++usoisKHH37I1NQ4itKGonRw+/YnzM3d4jvfeR5FUQ6sr2w2y+joOJK0gSzX\nsbkZ4+bNaySTi1y69BpWK9y7N8LqaghZVtB1jbW1G3z00R5udzfpdIHZ2Z9z6VJbOQqemLhKY6ML\nh6MPVbUc2E/ZbJaRkRFSqXh5P4yMvIsgQDxeYHx8EZttFavVwZkz59jbE7l+/TpzczFefrm4Xwxj\nlampMZqaJByOojDy8HD00P1T2p/R6C6yXAPYmJ6+QWenj69//evlZgVZbuDChd8hk0nzX/7Ln3Pi\nRAsvv/zykftzdHT0qfzTF/37lStX+MEPfgBQ9peHmVAoFI78z6cxQRD+DyBeKBT+n0f+vfCsnpVM\nJnn33WlWV6243cdIJhOMj9/F7Y5y5kw1Fy92HSnSW+oXfYi+egCJ5fMptMPR/Z9f5Kq0v8J+9eoM\nqtpZztEbxkqZkjUajXLnToBIJM3y8jotLQ243cpjEUXp50r5z1Qqhdt9grt3J1lYcGK3izQ2VjM9\n/R5+fydnzzajaXl0femAOHDJMpkM//2/v8mdOwaGUUGhEOLiRRu/93uvcvXqDLmcl/v3F5iZ2cYw\ngrz2Wi8vvXQCt9td/myxWIwf/ehauUDc0OBmamqEM2deJR7f5T/9x7/CiNTQ6+1BjUtYEus0qhVU\nFhqpyNuoMdmQ93K4cmBPKghRAV0pkK/QMPkK7JpTrGZj7EpgqpHJOnPcDoyiNLSRdbpIqDEU5yT/\n+l//zgMWyNgDTUyJv/7r97lyJY7T2YNhxKmtFejuVrl0qZH33x8jEHABFm7fvs3Vqzex2b5OKqWR\nSESorIzw+uv/lFDoNpnMPf7Fv/iTBzWVe+ztGaiqhKI0cP/+W8hyPalUFLM5g8ORx2otoGkiW1tJ\n6upasFoVrl5dQZJMvPLKlxEEiUTiff7Vv/oaqqqyt7fH97//MVbrWYLBKJlMgXj8Q/7tv331sQ2v\naRrvv3+PqSkNh2OAZDLF9PQN6uqynD7duE+66+Fabmy08tOfju2jQXVhNm8+lnc+aj/tB6okk0lu\n3JhH1+tZXIwwPr6OybTD66+/jMViJ5Mp5rNL6700VjI5+cSUsCVF+EfnW2Lw/PjjwD7+EQiFpnju\nucYn5h/5hzRBECiURGP32ReOvAVBsAKmQqGQEATBBrwK/J9fdNxPM0VRsFrBMGLkcmkkSaK11U5b\nm6nMqXCYfVYh4fM426MLlEVq1tKifdrD4FFyere7ilAoVE4H7Uc+KsqJA/wQ+23/1fKhhqWD3t5W\nlpZuEA4X8PkiXLx4jJWVKRIJUFXjUFm0koDr9rbA0NDvlJkGNzY+IBKJkM/LeDzVWK27nDzZQy63\ngc3m5erwDCfbmlm+uYuwoyJFs7y4fQIvDSgxCXnXxOmgC/k/ghSp4sup/52sqBFTsoRIkLDYyFdk\nyTiyhOxmMq1m4uom3t4kWaeG4G1CcVpJpxMYxgpnzrQReG8Zl6sfRbWSioa5+p/GON5wEbvdiykV\nZX39Y7a3QwQCybKizMBAHefOdbKw8AmGEcNqFenurkVVd1AUpdwhUdxHCSyWDIXCXTQthcMhI8sF\nlpbeo1AY48UXeykUDCRJobnZy8jI++TzHkQxS2trB17vCa5ff5tjx15+kGpowzCWkOUc4XC8OHfF\njSzLLCwsMDBwinDYRiwWK6/vQsHAbrfT3e0mlYoRiXgPDVokSaKnx8edO6PAGpDhG984TSKxRF9f\nLQ5HsT60PwWRzWbp6ck8pEGVJEKhyGN558MU3g/LCefzMoHAHqrawokTDdy9+yFXr37A+fNtnD7d\n+hjuoKQHWaK4/TTTtCJ3jyi2UV19jGw2w9JSgMZGU/nzPEsk9m+LPYu0iQ/4O0EQCg/G+/NCofDO\nMxj3SJMkidOnW0mlxpmaerdcfHzuub4jHTc8zGE9i0LCpxUo9+sxPml+7TDhhP3k9PsX3JPk7x7N\n/ZXygaVFvLg4wauvDjI6eoXW1npstjwvvvj8YwXZ0jjJZIr79zfY3c2wtBjCK+XxmhyYwgKV63U4\n/8JJx7iAvKfx6koj9rQFaVfBllRQE9XkZQ2np4pClUTGkWc9v4bQUSBbV2CnLUHKGiRXkUf31DK5\nvUJOqGBi4m2gHYvFQl2dk+npdxGEPG1tDfT2uqlo9/DGG+uwIRAMrlFXV0MyucDOzjaGYWF+/j2a\nmhoQhBS9vbXo+hLx+A6GEcfns3P37hI+3znm52/T2NhZpuL1++0sLNynUKhjcnKFb397CF3XUdUq\nzp3rIBbbJZ0eJBjMYbM1oesG4fAyDsc2mjZGc3MnGxsJLJYPkGUPKyvrdHa2sbCwRG1tK6FQllhs\nB1m2IklmCgUDt7uaaDSMqu6QTsuoqhVdj6LrDsBGNLqJoiRxOottfSXq2JWVYjeUYcTo7a06NJLU\nNA2Hw0FLi5WVlV0UpYLbt2fI55ceFARD5TU0PDzMpUuXymtFEIr77TCHt70d4vr1GTIZAVXdY2Cg\n68ANq7SOir9TBHo5HCqCkKavr4baWp2zZ9vLDvppi3zZbBZBsGGxGORy6QdNBzrwMHh6mu6PL8L/\n/5uwL+y8C4XCEnDiM3/wGZvb7ea1187z4ouHy159mj2LQsKTKMl8npbBRw+DR8np9yv0fBE1+1Kk\nFIks0t7u5k/+5FWsFitKWkGKSDBDmUS+xE1hCpvRNgqc1ntQYxLf2j1NVtTJOXOk7BpCdTNW2Uqd\nW2RFCTFr3iFfoZC0ZcDbSsS0QCKfwe32cOpUB5JkZnFxC02bZ2UlS6Fg0NqqYjI5aWysoKlCZ3p6\ng2xWoKZmh2AwQipViyDo/PN/PkRPTw+KovDRRxOIooNAIILZPMTGxi6xmMj2doLu7nZyORgdHeP4\n8WPYbGYUJYui2EinU2SzOcbHkywv30bXtzEMNyZTDzZbPfPzKTyeegYGWtD1HHNzq9TW1pbBOE5n\nBZK0iCgmkOUWJMkFmIjHVzh58n/D52sklYoTCPyK7m47Z868iq5rBAIab789TFWVjKLoeDxOcrkV\nGhutRKPbSFKOlpYmJib+mtu3NWy2POHwLMFgFq/Xx+/+7mCZE6YYKReFoIvdUBKnT3c+tsb2dxVN\nT68iSbVAgUAgQHNzO1VVxw4ghmOxOB9+OHEoF/Z+h7e9HeL73/8VwWBx3/n9FgxjmgsX4P79x5kq\nz5xpY3LyI7a3s1gsAm1ttchy6FAa48+7N0t7sabGxfr6JLu7IoaxwJkzz5fH+k10f/ym7X/qTyBJ\n0hNdq0r2LE/Ro07z/fzFcDTh06N22GFQIqf/TMrMR9Xs7wRQM61UxERMYVgd3sLpdZJdy5GZhhPh\nbs7sHcP9I1vRYYcBKweUPoxKg+10kZsi32cwvhVGr0zTft5PTI7z0Y33aGz04PPZOHeuE5PbhAUL\n7Vot9k2B27eXWRrdw1xIIBgam5sJ1taStLQUgU+yrKGqNk6dGsRisROLRbhy5RfMzORxOGowDA1V\nXWd8fAtJGsJkytDb20Y8LmOzFSHju7t5YrE9RkYWkeUQqhqjsrICl6sTq7WamZlFlpcNXC4ZVW0g\nm52jo6OHpaVtamoGefPNWfL5DIYxT0+PRH19K5qWw2RyksnojI4uASqZTFFBqPR9J5MGTucyPT1u\nMpktDCNIXZ3OrVs+NjYKRKPrNDdXkslYyOWKwLA7dyaw2fpQVRM2m4LZvMxXv9rFwsIu9+5ts7Y2\nQnW1itXqweezEA6ncDoddHU14/cnuHChl6WlGAsL2gGn+NJLx490SPtvc4oCgpBkczNAdbWMyWRH\nEOzk83lMJhOhUIpwOIyq1lEo+HG7K9C05gd557oDwZGmaVy/Pksw6KOq6iKaliMUmiaXW8dkMlFZ\nOfRYYOH1evne957nxo0iNbEoBunq8vFFTdM0gsFN4vE4c3NBNC1Pe7uNl19+/jF65M97MBzmL34b\nhF9K9j+18/6HtqNalR51wo8SPh2W5nj0MChxe0iShKqoZUFSJahQd0PFmshhScgIOxr+cA2W/9sC\nO2DaMvHCTj+aXCBt18nY8+iVLvQene180RkLQzK7tiyL1gVOfrkNqUZ6TOkjnUwz/XEEr9dX3CAj\nu8RiUGl1srcXRpI0HA4noig89jnq6+sxmURu336LublNqqraqa/vYHV1iqtX38PplPD7faytzvbM\n7gAAIABJREFU7XLsWJbR0SUmJ1e5c2eFysodWlraaGioYGcngSSdw+nsR9P2CIVmyWSKcm2FQoGZ\nmXl2djppaqqnUHCTzd4kGk1SW5tib2+HsbEpdL2GzU2D9vZjFAoxBgcrMZvNXLkSxecbZGlpnZ0d\nD/Pzv+T1119HknRisTALCzFcrk4KBcjnU9y4Mcc3v/kc9fVW/vqvb5HPO4jHg1y6ZKe+voNbt6Zw\nOGYRBAmTqYrx8VkqK/ew22uIx3fJZk0EAmEkyU59fR97eyqBQAKr1cLg4BCVlWH29nL8+tcf095+\nmsuXXySZ3CMen2VtLcaPfzxGRUUXAwN+ZNl8gE2wZPuL3aUIfb+QRDi8A3Th87WyszNKILBKS4ud\nDz+cIpVaZ2xsjHzeRGOjFUlap7u7EcOwPub0irdEBV1PsbKyAVhJpaI0NUXJ55uPRD16vV6+/GX3\nA/qDrU9VynkSi0Si3Lq1yO3b66hqA319XciyCKx+6nhP64CPutE+S4f+ecb6R+W8/z5yWI8u7MOc\n8H7Cp0wmzZ3bs1w+7ihGvfuER90hNy9uuUgHMmQCGuY9GX2vgBgvIFgF8ILkleh2+wkV4mScBXRv\nFv8rFZiaTVANOUeO//ev3sJS8cIDYE6SROID/viPX2B6JFLmRblxY5g6n5tkRRKbaCObPJpkPpvN\nEIvtMjk5xuzsMLoOvb1n8Hg6DziS0uGVTCZ5++1bLC8bzM9rLC9P094+x3e/+1VGRoYZHHwBl8vD\n9vYIv/rVHRSlir29dhwOF1VVNiyWNKKYR1VbqK72oigyFksLa2tr5PPRB7zXC2xva9y7dw+Ho550\nepa6Oh2LZQOzWWZk5BY7OwIeTwPb2y40bZa6OhMejwfDWCeRKKDrGaqre9nZuU1z83eIRveorJSJ\nxeZJJGI4HM1YLAXa2p5ndvYW4XCYn/98DI/ny1itDlR1iY8/fpNXX82TSCzT29vA+PgvyOV0dH2T\n73znEpqWJxK5x/LyAtPTMm1tJxgfn6SqaodMxko+L7C9nUQQGojF1ohEmhkdvY/H04XbXce9eyEa\nG6uxWFyYzQ1MTq5z6lQH+bzM6uoak5Ob6LqFZHIbWZbQdQsrK8XuI0gyMbGGw6FjMmWw2yUSiR3S\naSuNjSo7O8v88pdvUVHRz+nT32Jqaok7d/6S1tbvoigyY2PFXv9HC3uKomCxGBhGHE0D0DCMLFar\nCYvF+MzC4Gcp5TyJlW4VJlMTglBgfd3OwsIsx45V4/PpR4LjPk+/935/cVSDQn+/dmia6Gns8/ai\n/6Ny3l/EPksduhQZEwLnppMLa50IOwLCjkBkOoMtbUGKgjliQdwbQDhCkLTQXGDJEcJ4zoteWSDj\nyJK2TfH8Kz3l58rI+DTXg/lUHJiPntRp7PAfAOY0NtaSzWYxmVIkEnESiRQTE4vs7fnQtL1idP8I\n1W3pEBoZmXwQ2dQzMHAK8LOyMovHM8D09DpDQ61lRrhSoTYcTvPjH9+lvf33aW2V0XWRSORnpNNx\nJElFUYqR2bFjDdy5c5tIJM7mpook1RMK5fB4zBiGCVnepba2ikhkhVAoiyCMc+bMt7h/fwNZbiOf\nD+DxVJNMxqmqaiYcfo+ODpG5uQXu3Nkkn+9EFGOsr8tEInNcuFCPy+XiwoUu3njjzwmH23A6TVgs\nDiorPXR2+mhpsRGPVyCKBXK5NO3tTSiKlUgkTzQaJZu14XCITE8vkssJpNMK9fUwPp4jne6ltXWA\n2dm7eL0qPT0vkEwmuHXrDdbXN4lEVGAORUmxuysgCDJNTU4SiWrC4RAmUztuN1itTczOXqWurh5d\n36Sz8xjDw2MIgoymbVNXZ8Ewtvjxj7fRtFa2trZJJNKoapyWFj8Ox0tsbgbJ5XQUpR1FUUilREKh\nEV577Zt4PF50vYatrT1E0UZHx3mK4shenE4fsdg0Vms16XSInp4Tj633Uu3k+vUFdnam0PU8g4Me\nGhu9tLdXsLx8tDBBNpt9cBso7qmn5SQp1YgcDjvBYFGtyGKRyOcVlpYmEcWBQ/fws6KwUFULu7sm\nRkYWqKg4/oWVvD5tbkfZPyrn/cRR9z5nrAU1dud22R5PIu1aDleH3pczzrs1ds1JMk4D3Z2jqsdG\n2LNLxKsi+BSS1jRxZe6AM95vmWSGyV8GCYd30RIyUiZHVSH92OI+Kn8niiIWi0F3dzVms0I0GmJ6\nOsjduw6i0RBzc/cIBASsVgfd3ccIBCKAhXPnHqe6LYnYplICDkfzA0WeOgKBWXQ9h6aZSSRimM05\nCoUCn3wyg9XajSzH0bQ2pqeXcbvbiUajaFqacHgMTctw69YaVqtIY6ONxkaJ27fXgAEEwSCXixII\nBGlocPCnf/oCH3xwD4tFxWoN881vPo/T6SQUChAITJNMQiQySj5vxuXKATbi8WqqqjzY7auYzScQ\nxQyaFqRQ2ODcuZeQJIna2lr+zb/5Gv/+3/8CXZfxel309rpR1RgrK7vIchUOh0YwmGVp6S28XpHm\nZpifD5PPbzI1NYMst7OxESIel3jzzUliMQNBsJBIpEkkKoA0N26MUyiYCAYrqK7upb7ez8jIz1HV\nPiwW6OysJ5udI5kcJxyuoaqqghMnatnYmMXtttHRkaGzs42NjQjt7edZWVkjk0kwOXmDlhYnJlMr\nyaQXRWlma2v2AXXxNhcuOAiFNhAECzabi76+KkRRoqEhgcm0QSKRxGzO8dxzvSwtfUw0uo3DUUku\nt0Nrawfnz/eRz2fR9doyP/WjwUtNTQ2XLnViGP4HDniP6ekRHA4XZvPjwgQlOwoa/2lte4cFTg9v\nhinq6uoJhWbIZEJIUi1+v78sN7jfPi+FxX5/sZ8uutT9VSgkD6UMeBpyrE+b21H2j8N5PxIZl53u\nUX9+4Iz1Sp2EkiEiaBhOC0qrg3yDeKQ6tKZpfPQIaGHhgcjv2Nj0gevQUV+uKIqsrKxjt7+E232Q\nj+Sz7ABt6OR7NDbWEggE6e+/hNtdyfx8AatVpbvbjcfTysrKBIZhoVAwk8mkH5BHmYhEImVxW5vN\nRqGQ5N69ZZaWtgkERHw+C+n0NLlcGF2vpb3dw5Ur41y7VhQn9nicxOMRBEGlqiqHw5FBUdL4fNU0\nNHTsg0WPUlVlA0wYxjY7O/PU1CTo6ani937vDDU1NXR3d7O+vsHCgpdgUGZ6+jZvvTWCJL3M0pJK\nNtuCqi4xMNDC5qaZfF7FMFQqK/0kEtsoigmHY5uurkoqKirK78rnq+GrXx1kbGwLwwBdv05FRQMr\nK1Gs1ir6+7/E9PQM169vYRgiXV29QAMNDZusrNwgElnCbJYYGjrJysokS0uTVFb6yOdVRLGAIAjo\neiWTk2MPipRmTCYL1dVdeL1ufL49AoE4iYRBW1sVmraOy+XHblfo6zsGBHj55RPs7Ozwox+NYrNV\n09Cg0tDQjaZtIYoisqyTTudxuyvQ9RwmkwGYiEa3UVXhAcOfCVVtQNPyeL3WAwXwWCyO32/nypWf\no+syPl+evr4G4vFFCoUkZ8+2IUlFuuSRkQX293K73e6yRF4yaWJ6evaAuMJ+YYKSaVoxxdDXd56F\nhSC7uzlGR0f5Z//sxSP3w6d1Th2UG3TT3X0Cm811QG5wvz1tv/f+jp2pqXdobvbj8Vg4e7aNsbGN\nZ9I//jRz+5/TeT+lM75iu8ILTS8cSFOUBUn3py+qIFPI8N57o5jNrczMpLHZashmZxka6iUaPVwd\n+qjT02q1lpFen1WI0HX9AB+JIKSpra2iqOh9dA/7/mtXa6uF6uo4odAturra8Xp9JBJxNM2M2ezG\nZCowO/sJFRVWVlZGEUU/qgper4mVldkHcw+WEaOCAKKYp67OzvLybbJZhVOnWunvP0FVVRVXr85g\nsXQRjwcQhA4ikTgtLe3Mz39IJLKJqhaoqXGzu6vR1uZiYMBDoaCzsZFicnKdnp5+ZNmPpulEIh/R\n329hYWGXublcGRXq9Z5CksyMjSUoFGoIh4PoeppIZJXKSpHV1Slqahzcv7+OKFaSTqdIpQJomkQ+\nv40kdXL79jKnT5vKKih+/yX8foEPP/wpAwMNnD/fzfDwFAsLdpzOagRhBp+vCP9eWbGxvn6dM2c8\nPP+8makpFV1vYGJiE8OwYDZXousykUgUk0lga2uGjz/OkUpt09hYi8fTzfb2DLncFJlMmLU1K6pa\nhclkcPLkIIODs5jNMUTRiaqmGRrqQlVVampqGBqqJZkssL6eZ3k5Tj6/Rnu7l/b2NpaWxgiFIng8\ngQfpkATZ7DD19c1IkoauRwgGx7BaC2UwTGm93Lq1SHX1Kb7znVNkMklkeZtkcg0oRpRjYxs0NOzy\ns589RFo2Ntq4enWGL33pRLlgH4lEAMr1lKMi0NL+KKXNZFkhkzEfGV0elUooyeg5HI5H5AbjaFr4\nyODo8/Z7X7lyhUuXLpXn0NJiwes9RiIxwcWLXQ+ARdIzYQ98ml703w7n/ZTO+LCc8VHOGAW4Arzw\n2dOJRKJcuzbD6OgubneYXC6DonhJJjV2d3eOPBE/7fT8rDal/Z0CbreC11tLMhlnenqTtbU4N27M\nHxDvffR3I5EImYzpQJ94IlEFJNne3mJxMcL8fBGW/KUvnWZ29iaRiE5raw+qaiaVSvLrX3/CN7/5\nu9TU1B1QDFHVKo4dq2JycoH29n5isSk6O6uoqalhe3ub3d0MZvMeqVSera1htrc3cblEXC4bFy6c\nxufrJBy+x/DwKFtbLhwOM83NXhTFwGQS8PudhEIhdF0AthHFjnJBa3c3wtTUMLW1ZvL5PIpSgaLY\n0LQsXV2vUlOzgq7Po2mbCEKcwcFaFhY22dm5SzrtxWbTqK4eoqKiHrO5jWvXpjl3rljwE4QMMzMB\nNjezFArbdHZ6OXOmjdnZa6yv6+h6EU25tRVjb88gFosxPT3Ct799lsnJO3i95xHFLDU1x8hmd2lp\naWB9PYDX20RFRS+NjUPk84v4/Tampt6hqamGb3yjmoWFCNvbEhZLivr6kwQCcRobq7h0qfGxdlJJ\nkhgcbOQHP3gI/e7puUwmM4ssh7hwwcPY2CiCYEVR8rS11XD5ch9Op5NkMsXoaIBU6nFaimBwk+Hh\nOSKRECDS0mKjvr7A4uIWzz//VUp88T/5yRvIcgc+3zHC4SDvvHObykoTgjDKhQtFUI7H40FVg58Z\nNSqKgsmU4v79WRyOASwWgURCYnJy69AUy2HB0Pp6mvfeG0UUH2IfPk1u8FH7vP3eh+Ev0mlXOS3z\nLPvHP+9Yv1nn/e94ts74c9pn5bxLnRK3bi3icPRSWbkI1JBMjjE//ysymRi6vsB3v3v6wIvdn5Mr\nnZ67u0Wq1TNn2o78Eh5FL+4nnp+bm+XevXWs1mbOnu19rKujZPuvdMVcYiXV1XUPNpJBT08TP/rR\nMKLYRnu7lVyununpCV55pZNEwkRn5wUAYrEI6fQ2LlfxcNivGGIypRgfn0fX/QSDSdLpan74w49p\nappgdTXH8nKQbDZPMukgFnMgy348HplYLM7m5iZWq4Asm2hquogsK8TjcOfOB3zjGwOMji4xPz+K\nySRQXW3m9OlWKirqynlEu92JIJiIx3dxOCowjASVlQWCwSip1DqqGubUqfPs7Fyhu7sGv/8kgvAR\n0egrgB9ZzmKzuVleXsdmWyKV2kXXp8jn80xORrBYumhpqWZ+/i4/+ckEp0838q1vDTA1tUU6vcfC\nQhJJsgMt6HoUVT3D3l4lJ0++wPZ2AFW1oihp/H43ZnMGQcgSDm8Rj8/g9zdSV1eJ02mioaGZ3l47\n3d1eNjdnkeUCmpZBUVQSiV10vSgBd9imtVqt9PR0PgZVP3u2DgCHQ0KSmssR7eTkIhcvurl/fwOb\nrYfKyscLYOPj64TD4HSeRZKsBAKzpFL3OH78fPndFxVwXJjNGul0nGBwG8Now+XSsNubuXNnsbwe\nnyRq3A/TN5k2kaQ8AwPNZLPrh+aJHw2GEok4i4sBTpx4Hre76rEazbMG6ZVI8D4rnfEsQH9PM9Zv\n1nmneKbO+FlayQnG4xrT00HOnu2gu7uRiYlFlpZm6e7up7+/BZvNWiaVlyTp0Jx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MEHzz9/gL6+n1Ne7qSmJk48HuLv/u4feeGFOsrLy/noo8cYDAH8fh+wA7ACUSCyrEz5zmOxn5WV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112582ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy.random import randn\n",
    "\n",
    "def plot_line(axis, slope, intercept, **kargs):\n",
    "    xmin, xmax = axis.get_xlim()\n",
    "    plt.plot([xmin, xmax], [xmin*slope+intercept, xmax*slope+intercept], **kargs)\n",
    "\n",
    "x = randn(1000)\n",
    "y = 0.5*x + 5 + randn(1000)*2\n",
    "plt.axis([-2.5, 2.5, -5, 15])\n",
    "plt.scatter(x, y, alpha=0.2)\n",
    "plt.plot(1, 0, \"ro\")\n",
    "plt.vlines(1, -5, 0, color=\"red\")\n",
    "plt.hlines(0, -2.5, 1, color=\"red\")\n",
    "plot_line(axis=plt.gca(), slope=0.5, intercept=5, color=\"magenta\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112359190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [1, 1.1, 1.8, 2, 2.1, 3.2, 3, 3, 3, 3]\n",
    "plt.subplot(211)\n",
    "plt.hist(data, bins = 10, rwidth=0.8)\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.hist(data, bins = [1, 1.5, 2, 2.5, 3], rwidth=0.95)\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kiEg28A1gtceMMefaN1Hw3iaxY8cOBp4fuu62vvr26suKl1ZUWOfi++R0ph/8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\nB4YALnwRyheRZFXNF5HuQESPAXXhyPE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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1125b1050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1 = np.random.randn(400)\n",
    "data2 = np.random.randn(500) + 3\n",
    "data3 = np.random.randn(450) + 6\n",
    "data4a = np.random.randn(200) + 9\n",
    "data4b = np.random.randn(100) + 10\n",
    "\n",
    "plt.hist(data1, bins=5, color='g', alpha=0.75, label='bar hist') # default histtype='bar'\n",
    "plt.hist(data2, color='b', alpha=0.65, histtype='stepfilled', label='stepfilled hist')\n",
    "plt.hist(data3, color='r', histtype='step', label='step hist')\n",
    "plt.hist((data4a, data4b), color=('r','m'), alpha=0.55, histtype='barstacked', label=('barstacked a', 'barstacked b'))\n",
    "\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Images\n",
    "Reading, generating and plotting images in matplotlib is quite straightforward.\n",
    "\n",
    "To read an image, just import the `matplotlib.image` module, and call its `imread` function, passing it the file name (or file object). This returns the image data, as a NumPy array. Let's try this with the `my_square_function.png` image we saved earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(288, 432, 4) float32\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "img = mpimg.imread('my_square_function.png')\n",
    "print(img.shape, img.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have loaded a 288x432 image. Each pixel is represented by a 4-element array: red, green, blue, and alpha levels, stored as 32-bit floats between 0 and 1.  Now all we need to do is to call `imshow`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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woEwNYK9KSkp4++0jTJ3qSu/ePUzHMc7X15chQ4YwbNgwuna17rlrrugculKq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\nRiqHmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112724890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tadaaa! You may want to hide the axes when you are displaying an image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q7+b4qWY21cwGm1nDGBmL5DnGzEZlMlcpcqyhmX2aeWyj3VO0uMfMzH6ayfio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\nP7kre5pDEhmNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11449f850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's just as easy to generate your own image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    1    2 ...,   97   98   99]\n",
      " [ 100  101  102 ...,  197  198  199]\n",
      " [ 200  201  202 ...,  297  298  299]\n",
      " ..., \n",
      " [9700 9701 9702 ..., 9797 9798 9799]\n",
      " [9800 9801 9802 ..., 9897 9898 9899]\n",
      " [9900 9901 9902 ..., 9997 9998 9999]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112359810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = np.arange(100*100).reshape(100, 100)\n",
    "print(img)\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we did not provide RGB levels, the `imshow` function automatically maps values to a color gradient. By default, the color gradient goes from blue (for low values) to red (for high values), but you can select another color map.  For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148f0d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img, cmap=\"hot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also generate an RGB image directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAD7CAYAAAChScXIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148e1650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = np.empty((20,30,3))\n",
    "img[:, :10] = [0, 0, 0.6]\n",
    "img[:, 10:20] = [1, 1, 1]\n",
    "img[:, 20:] = [0.6, 0, 0]\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the `img` array is just quite small (20x30), when the `imshow` function displays it, it grows the image to the figure's size. By default it uses [bilinear interpolation](https://en.wikipedia.org/wiki/Bilinear_interpolation) to fill the added pixels. This is why the edges look blurry.\n",
    "You can select another interpolation algorithm, such as copying the color of the nearest pixel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAD7CAYAAAChScXIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0QYYRLQBBhFtgEH+DzauLdnV5hW0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1144a6a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img, interpolation=\"nearest\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Animations\n",
    "Although matplotlib is mostly used to generate images, it is also capable of displaying animations, depending on the Backend you use. In a Jupyter notebook, we need to use the `nbagg` backend to use interactive matplotlib features, including animations. We also need to import `matplotlib.animation`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib nbagg\n",
    "import matplotlib.animation as animation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we start by creating data points, then we create an empty plot, we define the update function that will be called at every iteration of the animation, and finally we add an animation to the plot by creating a `FuncAnimation` instance.\n",
    "\n",
    "The `FuncAnimation` constructor takes a figure, an update function and optional arguments. We specify that we want a 100-frame long animation, with 20ms between each frame. At each iteration, `FuncAnimation` calls our update function and passes it the frame number `num` (from 0 to 99 in our case) followed by the extra arguments that we specified with `fargs`.\n",
    "\n",
    "Our update function simply sets the line data to be the first `num` data points (so the data gets drawn gradually), and just for fun we also add a small random number to each data point so that the line appears to wiggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,[encoded data, 68472 characters]\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-1, 1, 100)\n",
    "y = np.sin(x**2*25)\n",
    "data = np.array([x, y])\n",
    "\n",
    "fig = plt.figure()\n",
    "line, = plt.plot([], [], \"r-\") # start with an empty plot\n",
    "plt.axis([-1.1, 1.1, -1.1, 1.1])\n",
    "plt.plot([-0.5, 0.5], [0, 0], \"b-\", [0, 0], [-0.5, 0.5], \"b-\", 0, 0, \"ro\")\n",
    "plt.grid(True)\n",
    "plt.title(\"Marvelous animation\")\n",
    "\n",
    "# this function will be called at every iteration\n",
    "def update_line(num, data, line):\n",
    "    line.set_data(data[..., :num] + np.random.rand(2, num) / 25)  # we only plot the first `num` data points.\n",
    "    return line,\n",
    "\n",
    "line_ani = animation.FuncAnimation(fig, update_line, frames=100, fargs=(data, line), interval=67)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving animations to video files\n",
    "Matplotlib relies on 3rd-party libraries to write videos such as [FFMPEG](https://www.ffmpeg.org/) or `mencoder`. In this example we will be using FFMPEG so be sure to install it first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Writer = animation.writers['ffmpeg']\n",
    "writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)\n",
    "line_ani.save('my_wiggly_animation.mp4', writer=writer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What next?\n",
    "Now you know all the basics of matplotlib, but there are many more options available. The best way to learn more, is to visit the [gallery](http://matplotlib.org/gallery.html), look at the images, choose a plot that you are interested in, then just copy the code in a Jupyter notebook and play around with it."
   ]
  }
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