software-carpentry/data/python-102.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Plotting with Matplotlib and NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "[Matplotlib](http://matplotlib.org/) is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.\n",
    "\n",
    "Before we begin plotting, we must activate the PyLab module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['axes']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "If you want to use matplotlib and numpy from your script and not from the ipython notebook you can:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's create some artifical data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x = linspace(0.01, 20, 20)\n",
    "y = x ** 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Plot is as easy as this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdc065f24e0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6H8/XJekYYHtENOXqqtB7R83MOm7r1uwRv489VnRL2q/dCUXSG8l6D5dGxIuSWt5+Xs7b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\nyuL/A8rvXvNVNLhUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc1c93e2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Line style and labels are controlled in a way similar to Matlab:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdc061e1e48>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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awYPh9tvh4IOLjqTXqnQKvxkY16LscuDBiDgWeAiYBiDpeOBc4DjgdOAGaeed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\nCudkZGZmhXMyMjOzwjkZmZlZ4f4/CD5l50qND1EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc1caa6128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x, y, 'r--o')\n",
    "xlabel('x')\n",
    "ylabel('y')\n",
    "title('My first nice plot with dots')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Make it more fancy, like xkcd plots! http://xkcd.com/1299/\n",
    "Hint: For the xkcd style you need a recent matplotlib version, but it's worth it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:1288: UserWarning: findfont: Font family ['Humor Sans', 'Comic Sans MS'] not found. Falling back to Bitstream Vera Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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KT08XtjGs8/b2RmVlJRQKhdAX3hAQEUpKSlBQUICSkhKUl5ejpKQERUVFKCgoQGlpKZRK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\nCCGio2RECCFEdP8PNIhGeoX+bUQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc1cafa978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with xkcd():\n",
    "    plot(x, y, 'r--o')\n",
    "    xlabel('x')\n",
    "    ylabel('y')\n",
    "    title('My first nice plot with dots.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "You can plot several individual lines at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdc1c960e10>,\n",
       " <matplotlib.lines.Line2D at 0x7fdc1c960f98>,\n",
       " <matplotlib.lines.Line2D at 0x7fdc1c966908>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JCy6AmTMr/r4gcDKwzAnBm59/nuQVK6oUuDWhhxwpwYR2OyC3zJ83Egjycr7b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\n/wFk3r7SWwXgLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc0619be48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x, y, 'r--o', x, y ** 1.1, 'bs', x, y ** 1.2, 'g^-' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "1. Plot the above graph with the y-axis on a log scale.\n",
    "2. Limit the x axis range to between 2 and 6.\n",
    "3. Label the axes with something descriptive and give the plot a fun title."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdc1c4449e8>,\n",
       " <matplotlib.lines.Line2D at 0x7fdc1c444b70>,\n",
       " <matplotlib.lines.Line2D at 0x7fdc1c44b5f8>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JCy6AmTMr/r4gcDKwzAnBm59/nuQVK6oUuDWhhxwpwYR2OyC3zJ83Egjycr7b\neSPffX098JegC1i+LI+FXz9d8o/4M/asCL2NmtZOLNXinMPv/BT6CynyF1FYXPJc8ufS9xas+4bM\nb/8ITT+BgcX8OC6PH3f2ZfvWNxiTMYaCwgIKigooKCwgvzD/0OuCooIjXi/56WroPhmWrIFr90P9\nG+DyeiwprscNH35Ky0Ytadmw5eHnhi3p3LLzEe/dM240K9Ytg1sO95CZ3ZgOuy4meVRy+Q/B74e9\newPPxcWHnrf8uAoW9oKFZX9jcGzJXwnLl0NRUeBRXBx4btYMfvrTI5revGFDhZ/75lWrYPx4+MUv\nyn9xzRp44QUoLAycLCssZHNGXsXtbNgAc+bAkCGBaWcHDsD+/YH9+veHzz47Yvtr2xcyfO2scu0k\n12kD69dXeAzatoVbb4XGjaFxY/zDhpG4ePERmxwxHNGhA4weXXFbZRxzSCKEk3YQucD1uoccKZE9\nEfmT2+CMN1leZyED/mtM4If89NPLb7drN2QFOurLu/qh80pYPRcG7gF/SRsNlzLg3VXltj9CmfaX\n/2RJoJ01Je242+CM0SxP+Jpr/2vM4e0r+jVq927IyjpcT5cVh+txt8IZb7CsYTpXv50Z2H7PbsjK\n5ug7YrpmTaBrN5adtQy6ZsDKhSVt/B7OeoVlCelc/t9v4ABnQGIirktnSu+t6XA459iw8Bu+eGAk\nGWc7OPMryMiEgXvBfgfnPkl6/fX0HDUdv/MHQrcgH/+Wzfhx+I3Dz/XrsfGiIrhgHMzZBlcXQr0b\n4dLGfF13H/WTn6XIHAbUdUY9q0u9RonUTahLvYR6gec69ah7sIic07dDoy+geW6gR9o7GzY/zg9N\nt5K24QQa1W1Eo3qNAs9bt3PCxFQaFxmNioxGxSXPXc/gt3O6sXfzVXBpSqCdpEbwzFjaFr/MqqQc\n8H8PbmMgWPv2hfdHlvvr2pz+AFxZvoe8/uMC4Jnyf7/ffRcY56xTBxISDj233NmDPdtmlNu8ZeK1\nMHRooBdbp07guW5d6NULnj9yPrU7cKD88QB38GDghFhFEhMD4V+/PtSrB/XqccK05+h40Fdu0911\niuC882Du3MAYbYMGgalp9esHvpejHDMo+/WD++6ruJ42bQJDFaVtdO/OvsWLqxy2tWlIoiap9Ma+\nZvYfQIpz7qqSPz8EuKNPRpqZ7uorIhKiUG/sG0xo1wGyCJyI3AR8DQxxzq0Ot0gREQlPpcMjzrli\nM/sjMJ0TpzquAAACoUlEQVTDU/4U2CIiHqi0py0iIjVH+TMZITKzq8xsjZllm1lo0yOkHDPbYGbL\nzCzdzL72up5YY2ZvmNkWM1te5r2WZjbdzLLMbJqZNfeyxlhxjM8y2cw2mtnSksdVXtYYS8ysvZnN\nNLOVZpZpZveVvB/Sz2eVQrvMwpv+wJnAEDOrYLqIhMAP+JxzPZ1zFU6tlON6k8DPY1kPAZ8757oD\nM4G/VntVsamizxLg7865c0sen1XwdalYEfDfzrkzgf8E/lCSlyH9fFa1p31o4Y1zrhAoXXgj4TMi\n8BtQvHLOzQGOnvQ8CPh3yet/Az+v1qJi1DE+Szg82VJC4Jzb7JzLKHm9F1gNtCfEn8+qhkNFC2/a\nVbHNeOeAGWa2yMxu97qYWqKVc24LBP7hAK08rifW/dHMMszsdQ01hcfMOgI9gAVA61B+PtWjq3ku\ncM6dC1xN4NenC70uqBbS2ffwvQx0ds71ADYDf/e4nphjZk2A8cD9JT3ucuv0jrd/VUP7e6DsVcPb\nl7wnYXLObSp53gZ8xDEuGSAh2WJmrQHMrA1wjKWKUhnn3DZ3eMrZv4Dzvawn1phZXQKB/ZZzbnLJ\n2yH9fFY1tBcBp5lZkpnVB34FfFzFNuOWmTUu+V8YM0sErgRWeFtVTDKOHHf9GLi55PXvgMlH7yDH\ndMRnWRIqpa5DP5+hGg2scs69WOa9kH4+qzxPu2TKz4scXnjzVJUajGNm1olA79oRWPj0tj7P0JjZ\nO4APOBHYAiQDk4APgVOBb4FfOud2elVjrDjGZ3kpgbFYP7ABuLN0PFaOz8wuAGYDmQT+jTvgYQKr\nzD8gyJ9PLa4REYkhOhEpIhJDFNoiIjFEoS0iEkMU2iIiMUShLSISQxTaIiIxRKEtIhJDFNoiIjHk\n/wFk3r7SWwXgLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc061b97b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x, y, 'r--o', x, y ** 1.1, 'bs', x, y ** 1.2, 'g^-' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "One more example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tavrWxMMVETcDU2vWza9Znldn3w/WWX962zpoZnVVbgJWz5qrh3cjMEtb0UNh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc1c456da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu, sigma = 100, 15\n",
    "x = mu + sigma * np.random.randn(100000)\n",
    "\n",
    "# the histogram of the data\n",
    "n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)\n",
    "\n",
    "xlabel('Smarts')\n",
    "ylabel('Probability')\n",
    "title('Histogram of IQ')\n",
    "text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
    "axis([40, 160, 0, 0.04])\n",
    "grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "If you feel a bit playful (only in matplotlib > 1.3):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:1288: UserWarning: findfont: Font family ['Humor Sans', 'Comic Sans MS'] not found. Falling back to Bitstream Vera Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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oiNqRmEkCCQkJAJWac3/zpk10DWOyuViSDHRTqVixYkW0qyIIwiXssukd5DsT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\nSK5hpBKQSCSSa5j/D15qOb4SnwzPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc178536a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with xkcd():\n",
    "    x = np.linspace(0, 1)\n",
    "    y = np.sin(4 * np.pi * x) * np.exp(-5 * x)\n",
    "\n",
    "    plt.fill(x, y, 'r')\n",
    "    plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Following example is from [matplotlib - 2D and 3D plotting in Python](http://nbviewer.ipython.org/urls/raw.github.com/jrjohansson/scientific-python-lectures/master/Lecture-4-Matplotlib.ipynb) - great place to start for people interested in matplotlib.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7fdc150ef588>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O/TvYsnsLE8onpOX6CpalS/etFVXAZuLtcw4PwJcuXcbll19JU1ML8NdQ0N19RVpEREQk\nlVJd3jqS9iz3c/X19axatSqhNHHd9zafQ3FxK4HAR+Luc+7sZ9asq2hq+ndgDKnKvCEiIiISSzrK\nW0fSynI/1pdCJJFbKyB6NoxItbW1FBWNBf6PYCrN7BYqERERkf6vs7x15fDKtPWhYLmf6n5Yr3fb\nISL3NieyfSK4hWMLwX3O3wSmA5XAa1x77XXagiEiIiIp11N561TRNox+KpWFSBLZylFRUcGiRbcA\nrwPHAxuAr1FSEmD27Ct63adIpwsuALPMfomISO6LV946VbSy3E/1thBJLL3ZytEZFM+ZM41AoIq2\ntjqWLLldq8qSlEcfzU6/55+fnX5l4Pr2t2+lrm533HYTJgznhhu+nIERieS2dJW3jqRguZ+KlQcZ\nYNWqVQnlPz50K0cNl132LxQWGtOnT4/6/tmzr+Diiy9SjmVJORW8lf6urm43VVXz47arrY3fRmQg\nSFd560jahtGPRG6XmDnzErZs2cCKFYvZsmUDQFd1vsiKfNF038qxDPg4LS0dXHLJPMaMmcjixXdE\nfV9FRQUnnXRS3gXKZrbEzHaY2Uthz11vZm+a2ZrQ17nZHKNIJM1bERmIdh7Yybrt6zi89PC096Vg\nuZ8IL1MdHghXVFRQVVXF2rVru1aJ9+xZTWPjU8yadVWP+5APbuWoAa4k+IuI64E9tLVN4sor58QM\nmPPUXcA5UZ7/kbtPDX39KdODEolD81ZEBpwnNz9JgRWkpbx1JAXL/UD4donIQLgziL744i/R2HgY\nvTnw17mVY9CgC4FhwDjgZuApYB3wHHPmfD2hHM75wN2fAXZFeUlHviRnad6KyECzr3kfyzctZ1TZ\nqIz0p2C5H4iV+SJ8NXn//j8DOwke+INEDvzV19czadJRPPHEowQCu4FaYHy3fgKBqoFQcORqM1tn\nZj83s/JsD0YkQZq3ItIv/bnuz7S1t6X9YF8nHfDrB7pnvjgSeJyWls0ABAJVocN5AP8LnMaQIZPo\n6Hizx4p8kVkwvvCFy7j99iW0tb1OeIaNtra6/l5w5DbgBnd3M/sO8CNgVrSG8+fP73pcXV1NdXV1\nJsYneaympoaampp0XDrheQuau9I7aZy3InE1tTXx8OsPZ2xVGVIULIcOj9xKcKV6ibvfHPH6NOBB\nYFPoqfvc/Tup6FsObpe49NIzaG1tB8bQ0eFs3rwlIn3c8ZSUBLjvvh8wfvx4GhoaqK+vPyRgjlbQ\nZMmS6bz00iruueeXLFwYnhoudsDdH7h7+B6TO4A/xGobHnCIJCIyMF2wYEFKrtubeQuau9I7qZq3\nZjaX4D/iOoCXgc8DQwieKK8k+OvMT7n7nqQGLP3K828+z4HWAxwx5IiM9Zn0NgwzKwB+SvCAyQeA\nmWZ2XJSmT4cdNlGgnGIzZpxJUVEx8FdgIy0tTzN37jwWLryJ0tLpDBs2ldLS6dx55+3s3LmLE088\nI2ZWjGDp6koit3U0NDTwve/dyNatr/PEEz9jy5YNcctn5yEjbK+nmY0Oe+1i4JWMj0gkPs1byStm\nNgb4T2Cqu59AcPFuJjAPWOHuxwJPEiwJKwJAW0cbD2x4gJGDR2a031SsLJ8MbHT3LQBmdi9wIcES\nbuF02CSNOvctH9xyEQxwp06dzJYtG7ryHkMwfVxw1Ti4ZePyy6/sVgZ7zZp17Nu3gVgFTSLLYUNw\nNTrfcyub2a+BauBwM6sjmPpjuplNJrjyUQvMztoARaLQvJU8VggMMbMOoBTYRjA4nhZ6/W6C6Zjm\nZWV0knPWbl/Le43vMXHExIz2m4pgeSywNez7NwkG0JFOM7N1BH8YrnH3V1PQt4T0VLEvPLhdtWpV\nKKheD0wHxtLU1MiPfnQr3/ved6mvr2fu3HnA/NDr44CNLFy4KOH9zT1V+ctl7v6ZKE/flfGBiPSC\n5q3kI3d/y8x+CNQBB4Dl7r7CzEa5+45Qm7fNLHO/a5ec1uEd3L/hfkaUjsh435k64LcamODuB8zs\nPOAB4JhYjXXYpHfq6+tZu3Ytc+bMZtGi7hX7IgPcqqoqmps3Af9B8B/rNwPHcdNNC6mqqmLq1Mmh\nYPrrBLeP1VJWdjlTp06O2Xfk/uZZs6Z3W6lONx02ERHJL2Y2nOBvoSuBPcBvzeyzQGStzh5rdype\nGDjW169n275tVJVX9fkatetqqV1X2+v3pSJY3gZMCPt+XOi5Lu7eEPb4j2Z2m5kd5u7vRbtgfz9s\nksotC0uXLuPSS6/oOthXXNzKtdd+ktmzr4h67YqKCq677hr+679+zsGcycEgd86caaxd+2zECvV2\n2tvfipnxItb2j9ra2owFy+k6JCUiImkzA9jUGQeY2f3A6cCOztXl0N77d3q6SH+PFyTI3XnwtQcZ\nGhiKWd939VZNrqJqclXX9yvvXpnQ+1KRZ3kVMMnMKs0sAHwaeCi8gZmNCnt8MmCxAuX+LlalPTi0\nXHW4aK/V19dz+eVX0tpaROfBvtbWZ7jxxh/2OIbZs69g0KCdRMuZ3NDQwJIlt3U7FNhTxovu2z8g\nkfzNIiIy4NUBp5pZiQWjn48CrxKMHy4LtbmUYCYtGeA27drEaztfy0hp62iSDpbdvR24GlgO/B24\n193Xm9lsM/tCqNknzewVM1tLMMVcXmxo7Sl47ev14lXaixZEL158B+PHH8NHPzq722u1tbUUFo4C\nJhIe9BYUjGPt2rUxx15RUcGiRbcAnTmTITxn8syZl7BlywZWrFgcN+NFZ9q6RINrERERd38B+B2w\nFniRYBKAnxH8ledZZvYawQD6pqwNUnLGIxsfobSoNKlV5WSkZM+yu/8JODbiucVhj/8H+J9U9JUp\n6Ti0FmvLQnilvch9v/fd9wBXXjkHeI7m5u6vVVVV0d6+g+A95uDBvpaWzVx00cwexz579hUAzJlz\nMGfywoU3dVXji5bxIpaZMy9hxowz8z4bhoiIZI67LwAi9829R3CLhggAb+17izXb1zChfEL8xmmi\nctdR9LQCnIxYWxaAmOWq58z5GsGzkAdfKyqa0LUn+M47b6e4uBU4DZhEcfEZmBUkNPbZs6/oypm8\ncOFNzJ07L2bu5XgqKio46aSTFCiLiIhIyvzpjT9RXFBMgWUvZFWwHEXnCnBk8Nq56tpXsbYsTJky\npYcgupJgZr6Dr7W0HMyZPHPmJWzbtonHHrufxx67jYcf/h2lpUcnPPaKigrKysqYM+frKf/HgYiI\niEhf7Tywkz9v+TOjy0bHb5xGmUodl1d6ylmcrFhbFpYsuY1Zs7qnfZsyZQptbduAbxCe83jRomDO\n4/CsGmeffTYQXBXvzdiXLl3G5z//BZqbRxMtwNZKsYiIiGTDk5ufpMAKKCwozOo4tLIcRboPrUXb\nshDtUN3BcdxMWdkYBg2q5fbbF3HxxRfxne/cGPVAYG/G3rndpLn5QeBdlNFCREREcsG+5n0s37Q8\n66vKoJXlmDJ9aC1W7uXIcaxY8SQTJhxDU1ML8NeohUDijb2zr127doUOHO4AWgjuez6S4uJ6liz5\nmVaVRUREJCue3vI0be1tFBcWZ3soCpZ70puMEMmIl3mjcxydK8FNTf8D/JBY2yZ6KnoS3ldz8yba\n29sJVvP7C3Ak8DiFhV9kxowz0/65RURERCI1tTXxyMZHGFU2Kn7jDNA2jBTpa07mnjJv1NfXs3z5\ncpYvX94VAAcPHp4F1BJt20S8oifhfTU1raSjow04nGDgXQF8hkBgYtKHGUVERET64vk3n+dA6wFK\nikqyPRRAwXJK9BSgxhMr88bixXcwduzRnHPOv3LOOV9k3Lj3sWbNutDhve3AbUA1MKlrXzLQY8q7\naH2Vlh7NoEHarywiIiLZ19bRxgMbHmDk4JHZHkoXBctJSjYnc1lZGU1Nm+ieGm4z3/3uD7qVsW5p\neZq5c+dxww3XMWjQNIYOvYmSEue///vyrgOB8VLeRcvz3N7+FosWfV8V+ERERCTr1m5fy66mXZQF\nyrI9lC4KlpPUm5zMkVs1li5dxoknnkFBwQjgVEpLP0hp6XSuu+6aqGWs29uH8q1v/TeBwHhaWjZx\n663f51vfurYrsI1V9KRzlThWpozZs69IuLy1iIiISDp0eAf3b7ifESUjsj2UbhQsJylegNopcqvG\n4sV3hJW43gQ8SkdHHatXP8Ps2VeEylhvDrtuDS0t79DcvJJ9+16iufnPzJ07r9sKdmQwXFIyjWuv\n/Wq3cURLUdf5XlXgExERkWxZX7+ebfu2UV5Snu2hdKNgOUmJ5DXuvlXjTzQ2/oQvfekrFBVVcnDl\nuJpBg46moaEhahnrgoLzgLHEW8HuDIavueaTmBVwyy2/P2QftQJjERERySXuzoOvPcjQwNBsD+UQ\nCpZTINZqbaeDWzXWA8cBP6SlpZ2mpn8Qa0U6vIz1smU3EgiUADtjto90440/VPlqERERyQubdm3i\ntZ2vcXjp4dkeyiGUZzlFesrJXFUVzGkczGdcQ2cZavgwpaXdS1yHX6OiooKzzz6bVatWMWjQUTQ1\nfZ1g2etK4DWuvfa6qH12BufBgiWg8tUiIiKSyx7Z+AilRaWYWbaHcgitLKdYtHzLFRUVXHfdNRzM\nZwzBtG3v44EHlsY9WHdwX/TxwAbga5SUBJg9+4o47ZUOTkRERHLbtr3bWLN9DUcMOSLbQ4kqJcGy\nmZ1rZhvM7HUz+0aMNj82s41mts7MJqei31wTLd9yZ/D8iU/8K6Wl7xEZwE6ZMiXu/uHu+6LPobT0\nP7nzzttjvieRfdQiIiLZZmblZvZbM1tvZn83s1PMbISZLTez18zsMTPLrdNeknKP/eMxiguKKbDc\nXMNNehuGmRUAPwU+CrwFrDKzB919Q1ib84Cj3f19ZnYKcDtwarJ955LwQ3zB7Q8vcemlZ1BUVNxV\nxnrWrH9nyZLY2y56MnPmJcyYceYhZaxjlbaO1V5ERCSHLAIedfd/M7MiYAhwLbDC3b8fWoD7JjAv\nm4OU9Nl5YCfP1D3D2KFjsz2UmFKxZ/lkYKO7bwEws3uBCwnuF+h0IXAPgLs/H/qX5Ch335GC/nPC\nofuEj6S1tZ3W1me6guclS6azevUzNDQ0xA1gowXBkfuily5dxqxZV3UF40uW3NZtK0dP+6hFRESy\nycyGAf/s7pcBuHsbsMfMLgSmhZrdTfCwj4LlfuqJzU9gGIUFhdkeSkypWO8eC2wN+/7N0HM9tdkW\npU1eO3Sf8OPAGCJTvTU0NMTddpFI+exkKweKiIhk2UTgXTO7y8zWmNnPzGww0LWY5u5vA7m5kVWS\ntq95H49vepzRZaOzPZQe5WQ2jPnz53c9rq6uprq6OmtjiSd8BXjJktuYNSu4zaKlZTMdHU5Ly0t0\nZr9I5JBdtO0cs2ZNZ8aMM7sF2Mp4cVBNTQ01NTXZHoaIiPROETAV+KK7/83MFhJcQfaIdpHfd8mn\neEEO9fSWp2lrb6O4sDgj/dWuq6V2XW2v35eKYHkbMCHs+3Gh5yLbjI/Tpkv45M9l0bZBbNmyoSt4\nXrHiya7gOdE9yosX30Fj42FEKz4S/t7uK9mJB+P9UeQNcsGCBX26jpktAT4G7HD3E0LPjQCWEczX\nVwt8yt33JDdikdTRvJU89iaw1d3/Fvr+9wSD5R2dWzXNbDTwTqwL5Eu8IIdqbG3kkY2PMKpsVMb6\nrJpcRdXkqq7vV969MqH3pWIbxipgkplVmlkA+DTwUESbh4DPAZjZqcDufN+vHGsbBNC1zSK8WMnq\n1c8wadJRPW6TqK+v57vf/QGJFB9Rxou0uAs4J+K5eQQPmhwLPEnwoIlILtG8lbwUigO2mtkxoac+\nCvydYMxwWei5S4EHMz86SbdHNz5KY1sjJUUl2R5KXEkHy+7eDlwNLCc4ye919/VmNtvMvhBq8yiw\n2czeABYDVyXbb7YdrMrXc/npiooK3nhjEyeeeEaPe5A7rzlo0FHA/xIsPjIVOI1rr/1q1CA4XuVA\n6R13fwbYFfH0hQQPmBD670UZHZRIHJq3kue+BPzKzNYBHwJuBG4GzjKz1wgG0DdlcXySBm/ufZOH\nX384pzNghEvJnmV3/xNwbMRziyO+vzoVfeWKRLdBJLoHufs1O4uPPE5JyRdjFh8BZbzIgCPCD5qY\nmQ6aSD5Mlw3WAAAfxklEQVTQvJW84O4vAidFeWlGpscimdHe0c4v1v2C0uJSigpy8ujcIfJjlGkU\nK09xPJ3bIML3JC9ceFO3leXa2lp27doV9SDe2rVru9pNmTKlK+iNvOaSJbGLj0hW6KCJpEwGD6fG\nnLeguSu9o0PVkoxntz7Lxp0bqRpele2hJMzce7yHZpyZeabGFC9PcSI6g+01a9Yxd+48AoEqDhzY\niFkBpaVH09y8KZQV42k6V6CLi88AoLW1HRhDIFDPL36xuKvvvgbwEmRmuHufisubWSXwh7CDUuuB\n6rCDJk+5+/FR3pexeTvQWOhvciD88fZ17vZ13obaau7miMsum09V1fy47Wpr5/OLX8Rvl65rRkrm\nnpsMzd38s7tpN/NWzKN8UDmlxaXZHg4Lpi9IaO4O2JXl3myP6Eln22nTzg1d60iCO1JqaGnpDI4/\nTGnpwZRy7e3Q2loEPAOcQEtL9761tSKrLPTVqfOgyc3ooInkLs1bEclp7s5v//5b2jraciJQ7o3c\nLMKdAYke0Ov9tWoJ5lk/eN3S0vfxwANLWbFiMQ8+uIxAYMwhbQoKxvWpb0kdM/s18CxwjJnVmdnn\nCR4s0UETyVmatyKSDza8u4Fn6p5hzNAx2R5Krw3YleVoB/RaWjaza9cu6uvre7Wy2/1aVcDm0OMj\ngcdpadnctS+5vr6e9vYdBBeBDvbd0fHmgMyRnEvc/TMxXtJBE8lZmrcikuua25q5a91djCgdQYHl\n3zpt/o04jvr6elatWhW37HNknuLi4jPo6HA+9alv9pjeLf61zqG4uJXCwtMJBs7X09HhrFjxZFfb\nO++8neLiVuA0YBKBwEeUI1lERET6pcf+8Rjv7H+H4SXDsz2UPulXwfLSpcuorDwubj7jTp15in/7\n25soKiqmpeXpbgVG4gXc0a61YsViXnzxBQKBQcBfgY20tDzd7XozZ17Ctm2beOyx+3nssdt4882N\nypEsOe+CC4KH7TL9JSIi+Wv7vu08sOGBvMmpHE2/2YbR1wN7FRUVjBgxImp6t8gS0/F0HsxbtWpV\nxPWOpKBgJGvXruXss8/uatv5WCQfPPpo9vo+//zs9S0Szbe/fSt1dbvjtpswYTg33PDlDIxIJPd0\neAf3vHgPgcIAxYXF2R5On/WbYLnzkF1fAt5EC4wkqvv11gP/wf79h3PRRTP7lJ5OJJcoU5MI1NXt\nTjglm8hA9cK2F/h7/d+ZOHxitoeSlH6zDaN7gAq9CXgj9y+Xlk7v1R7iyH3SndcrKZkGXA7UABv7\ntL1DREREJN/sa97HL1/8JaPLRmN5vqeu3wTLyQa84XuOt2zZEHf1tzNAXrz4jqj7pGfOvIQHH1zG\nkCHvozfp6RI9oJhoOxEREZFMu2/9fTS1NTG4eHC2h5K0frMNA4IB6owZZ1JbW0tZWRkNDQ1dwWQi\nFfGiFQOJVk2vs/JfUdFY9u17A3gu6j7pKVOm0NGxlUS3dyRaUTAVlQdFRERE0mHjzo08uflJJpRP\nyPZQUqLfrCx3qqio4I03NnHiiWdw1llXMnbs0Ywb976EM2RAz6vG4QcJ9+1bAhxDrJXj3qx2h1+3\np4wcibYTERERybTW9lbuXHsn5SXlFBYUZns4KdGvVpYhMitGMcFcxk93lZ6+/PJpHH74iK4iIZHi\nrRo/8MDSsIOE9UDPK8fhq909rWwnekAxmYOMIiIiIun0+KbHeWvfW0wckd+H+sL1u2D5YDC5HvgC\nMJqDK7/raWpq4eKLv05Hx9au7QudWy3Kysq6Am1oBq4Ie28w/dvu3bsjMmd8AziVoUOPpa2tLurK\ncbTtHZESzciR6swdIiIiIqnwzv53+P2rv2fssPzNqRxNUsGymY0AlgGVQC3wKXffE6VdLbAH6ABa\n3f3kZPrtSVVVFc3Nm4D/AB4EPsHB0tP/AfyV/fsPrhTv3buXuXPnEQhU0dT0BgUFlQSD0PBV4870\nb8P53Oeu4LOfvYSlS6dTXFxJa+sWFi5cxNSpk+Puie5J55aNWbMOXjdW4J1IOxERkVxnZgXA34A3\n3f3jicYVknvcnV+++EuKCooIFAayPZyUSnZleR6wwt2/b2bfAL4Zei5SB1Dt7ruS7C+uiooKrrvu\nGv7rv+4CqoHbgOlACXA44fuLCwvHMGfO12luXhna1lADnE/3VeNTCG7tvh64mebmidx55//xgx98\nh2nT/jmpADlSols2Em0nIiKS4+YArwLDQt8nGldIjln91mpe3PFi3udUjibZA34XAneHHt8NXBSj\nnaWgr4TNnn0FpaXvEQx6LwG+CLwHvEX3PMx1BAITORhAV1NSMopBg6aFDuTdzLx5X2Hw4InAzcBT\nwDrgOb71rf9OS6BaUVHBSSedFPe6ibYTERHJRWY2juAK1c/Dnk40rpAcsr9lP3e/dDdHDDki73Mq\nR5NsAHuEu+8AcPe3gSNitHPgcTNbZWZXJNlnXOFZKMrKPgjcAiwg+HFPAyZRXPzPLFp0C21tWwgP\noM32snbts135lr/ylS/T3r4VGE/4/uXCwiNYu3Ztuj+KiIhIf7UQuIZgjNBpVIJxheSQBzY8wP7m\n/ZQFyrI9lLSIuw3DzB4HRoU/RXBifytK81iFcD/s7tvNrIJg0Lze3Z+J1ef8+fO7HldXV1NdXR1v\nmN3U19czadJRrF79DC+88AJf/OIt7N9/M/AXgnuXf4/71/jIR87o2v9bWDiG1tY6Fi68heOPP77b\n9RYtuoUrr5xDMKh+HriGAwcqVL46R9TU1FBTU5PtYYiISILM7AJgh7uvM7PqHprGiiuA5OMFSd6m\nXZtY/o/leZFTuXZdLbXranv9vrjBsrufFes1M9thZqPcfYeZjQbeiXGN7aH/1pvZ/cDJQELBcm9F\nFuxYuPAm2trqgM7tFsuA62hrG8uUKadz1123s3DhTcyZ83UCgaOZO3cew4YN67YnePbs4GL4VVed\nRkeHA88BJ9DYWMPnP38hkyefcEiALZkTeYNcsGBB9gYjIiKJ+DDwcTM7HygFhprZL4G3E4krOiUT\nL0jy2jrauGvtXQwdNDQvcipXTa6ianJV1/cr716Z0PuS3YbxEHBZ6PGlBNNPdGNmg82sLPR4CHA2\n8EqS/UYVrWDH3Lnz+M53rgdeJ3iA7yqCe49fo7l5JZdffiVz586juXkl+/atobHxKS699IpDipFc\nfPFFFBcXcbAIyTLgEzQ3j2bKlNMTKnYiIiIi4O7XuvsEdz8K+DTwpLv/f8AfiBNXSO6o2VxD3Z46\nRg4eme2hpFWywfLNwFlm9hrwUeAmADM70sweDrUZBTxjZmsJLsn+wd2XJ9lvVJ05liMr6k2b9s/c\nfvsiiov/BRhJ94wYR1BQ0H0/cmtr+yEV8tauXUsgUEkwnVwNkUF3eBW9zgqAqqonIiLSKzcRJa6Q\n3FO/v55lry7jyKFHZnsoaZdU6jh3fw+YEeX57cDHQo83A5OT6SdRPRXsOOmkk/jIR85gypTTaW7u\nfL2G1ta3KCgoDnvP48AYIgNugLa2bQTTyV1I92InB6vorVjxZLdtINrTLCIiEpu7rwRWhh5HjSsk\nt+xv2c+Pn/8xhVZISVFJtoeTdhlL55YJ4VkwgqnfprNw4U3U1tZSX1/P8ccfz1133U5p6XRKSo4C\nzqewcCLt7a0EAh9h2LCplJR8kUCgnu4p5rYwZcqU0LVvZsiQMRwsWHKwTXgFwPBVaa0wi4iISH/Q\n2t7K4tWL2bZvG6PLRmd7OBnR78pdhxfsWLNmXVd1vvBV3smTT2DKlNOB50LFSF6ipGQav/3tTUyZ\nMiW0OnxohbxDr929TUNDQ6jU9qErzsqHLCIiIvnM3fn1K7/mxbdfpGp4VbaHkzH9LlgGugLTadPO\npbHxqa6AeNas6cyYcSYNDQ2UlBxFc/PBoDYQmMiIESMOCYojC49UVFR0FQS5+OKLurWpr6+PuQ1E\nREREJJ89uvFRVvxjBRNHTOyXxUdi6ZfBMhw87Bdtlbenvc2dOoPinkS26dwGEm1VWkRERCRfvbDt\nBZb9fRkTyidQYP1qF29c/TZY7ikgTmdQ29OqtIiIiEi+2bhzI7f/7XaOLDuS4sLibA8n4/ptsBwv\nII4V1NbX1ycd6CayKi35xcxqgT1AB9Dq7idnd0Qi8Wneikiy3m54mx899yPKB5VTWlya7eFkRb8K\nliMD3XirvJFBbWT1v84DgakIoCXvdQDV7r4r2wMR6QXNWxHps73Ne/nhX39IAQWUl5RnezhZ0282\nnSxduuyQqntA12G8eEFutOp/s2ZdxeLFd0S9rgw4Rj/6eZEBQ/NWRPqkua2Znzz/E3Y37qZiyMBe\nKOwXN9FYgW5v8htHq/5XWDiGOXO+rrzJAuDA42a2ysyuyPZgRBKkeSsivdbe0c6da+/kjffeYMzQ\nMdkeTtb1i20YPWW+SHTbRPQDgXUEAkd3SzGnvMkD1ofdfbuZVRAMPta7+zPhDebPn9/1uLq6murq\n6syOUPJOTU0NNTU16ewi7rwFzV3pnQzMW8kid+e+9ffx1zf/ysThAytFXCz9IlhOJBVcPNEOBC5c\neAtz585L6rrSP4RKuOPu9WZ2P3AyEDNYFklEZGC6YMGClF4/kXkLmrvSO+met5JdK7es5KHXHqJy\neKUC5ZB+ESwDXHvtV7nxxuRSwUU7EDhs2DDlTR7gzGwwUODuDWY2BDgb0P8dJKdp3opIb72842Xu\nWnsX44aNo6ig34SIScv7P4nODBZFRWNpb29hzpzz+MpXvpyytG/KmyzAKOB+M3OCPzO/cvflWR6T\nSDyatyKSsLo9dfz4+R9TMaSCQUWDsj2cnJLXwfLBg33fAG4GjuammxZSVVXF7NmpO8uivMkDm7tv\nBiZnexwivaF5KyKJ2nlgJz/86w8pLS6lLFCW7eGklbvzXuN77GvZl/B78jobRm1tLUVFYwkGyk8B\n64DnmDPn68pYISIiIhLH/pb93PrcrTS3NXNY6WHZHk7aNLY2Urenjro9dYwZNoarT7o64fcmtbJs\nZp8E5gPHAye5+5oY7c4FbiUYnC9x95uT6bdT8GDfFmAi4SnfAoEqZayQfu2CC+DRR7M9ChGRvjOz\nccA9BLcMdQB3uPuPzWwEsAyoBGqBT7n7nqwNtB9rbW9l8erFvLXvLcaXj8/2cFKuwzuo319PU1sT\nZYEyLjruIk4Zewqjykb16jrJbsN4GfhXYHGsBmZWAPwU+CjwFrDKzB509w1J9k1FRQWLFt3ClVfO\nITxjRVtbnTJWSL+WrUD5/POz069IMr797Vupq9udUNsJE4Zzww1fTvOIJKQN+Iq7rzOzMmC1mS0H\nPg+scPfvm9k3gG8C87I50P7I3fnVy7/ipR0vUVleme3hpFRDSwM7D+zEzJhy5BSmV03nuJHH9fnQ\nYlLBsru/BmA95xY5Gdjo7ltCbe8FLgSSDpaBrr3Jc+ZMIxCooq2tThkrZMBwz/YIRHJfXd1uqqrm\nJ9S2tjaxdpI8d38beDv0uMHM1gPjCMYI00LN7gZqULCcUm0dbdy3/j6e2PQEE0f0j1zKbR1tvNPw\nDq3eSkVpBZ894bP805h/YnjJ8KSvnYkDfmOBrWHfv0kwgE6Z2bOv4OKLL1LGChERkTxkZlUED6Q+\nB4xy9x0QDKjN7IgsDq3f2d20m8V/W8yr9a9SObySAsvf42vuzp7mPexu2k1xQTGnjT+Nj1R+hKNG\nHJXSzxU3WDazxwnuJ+p6imAJ1evc/Q8pG0mYvlSTUsaKgUvVpERE8ldoC8bvgDmhFebI35nF/B2a\nqk/2zsadG/nJCz+hsbWRquFVebuivL9lPzsbd+LuVA6v5N/e/29MHj2ZIYEhPb6vr/FC3GDZ3c/q\n9VW72wZMCPt+XOi5mFRNSnpD1aRERPKTmRURDJR/6e4Php7eYWaj3H2HmY0G3on1fsULienwDp7Y\n9AS/evlXDC8ZzthhY7M9pF5rbG3k3QPv0uEdHD74cC4+7mKmHDmFMUPHJBz09zVeSOU2jFgjXQVM\nMrNKYDvwaWBmCvsVERGR/HQn8Kq7Lwp77iHgMoJ5YS8FHozyPknQgdYD3L3ubp5981nGDxtPoDCQ\n7SElrKmtiXcPvEu7t1M+qJyPHfMxThxzIuOHjc/oqniyqeMuAn4CjAQeNrN17n6emR1JMAXMx9y9\n3cyuBpZzMHXc+qRHLiIiInnLzD4MfBZ42czWEtxucS3BIPk3ZnY5sAX4VPZGmd+27d3GT1/4KTv2\n7+Co4UflxbaLlvYW6vfX09bRRlmgjLOPPpt/GvNPVA2vytr+6mSzYTwAPBDl+e3Ax8K+/xNwbDJ9\niYiISP/h7n8BCmO8PCOTY+lv3J1Vb63ijtV3ECgMMKF8Qvw3ZVFreyv1B+ppbW+lpKiE6qpqTh57\nMkeNOIrCglhTJHPyuty1iIiIiBzU2t7Kb1/9LX/a+CdGDx3N4OLB2R5SVK3trexs3ElzWzOBogCn\njz+dU8edyqTDJvU5H3K65NZoRERERKRPdh7Yyf/+7X/Z+N5GKodX5sSqbCd350DrAd5rfA/HCRQG\nOHHMiZw27jSOOfyYnN5LrWBZREREJM+tr1/PT1/4KW0dbVSV50ZauA7vYHfTbvY17wOgYkgFHzvm\nY3xw1AepGl6VcyvIseTHKEVERETkEB3ewR83/pHf/P03HD74cCoGZbfmRHNbM+81vkdLewtmxvEj\nj+fUcady7MhjqRhckRNBfG8pWBYRERHJQw0tDfx8zc9Zs30N44eNp7iwOONjcHf2texjd9Nu3J3B\nxYM5ffzpTD1yKkcfdnTO7pnuDQXLIiIiInlm065N3LbqNnY17mLi8IkZXbFtaW9hd9NuDrQewDDG\nDRvHjKNm8IGKDzC+fHxel9CORsGyiIiISB5wdza+t5E/vPYHXtrxEuUl5YwvH5/2fjuD48bWRgAG\nFw/mhCNOYPLoyRwz8hgOKz0s7WPIJgXLIiIiIjmswzt45Z1XuH/9/WzatYkhgSFUDU/fIb7w4NjM\nKC0q5YQjTuCEUScwccRERpWN6nerxz1RsCwiIiKSg1rbW1mzfQ33r7+f7Q3bKS8pT0uQ3NLewp6m\nPcFtFWaUFJXw/474f3xo1IeYOGIio8tGD6jgOJKCZREREZEc0tjayHNvPscDGx5gT/MeRpSMYOKI\niSm5trvT1NbEvpZ9wZVjjEHFg/jgER9UcByDgmURERGRHLCveR8rt6zkkdcfoamtiZGDR1I1vKrP\n13N3Wtpb2Nu8t2vV2N05bPBhfGj0h/jAyA8wccREjhx6pILjHihYFkmRPEwdKSIiOWDngZ08sfkJ\nHv/H47R5G6OGjGJU0aheX6elvYV9zftoaGkIBsY4wwLDeH/F+zl25LFMKJ/AmKFjKAuUpeFT9F8K\nlkXy1PnnZ3sEIqn37W/fSl3d7oTaTpgwnBtu+HKaRySSPm/te4vH3niMp7c8TYEVMLpsdMK5klvb\nW2loaWBfS7A6Hg6lxaVMOmwSx488ngnDg4Fx+aDyvCwEkksULIukiHu2RyCS/+rqdlNVNT+htrW1\nibUTyRVtHW1s27uNzbs3s3r7al7e8TLFBcWMGzaOwoLCQ9p3bqPY37qfA60HaPd2CijAcQKFAY4a\ncRTvH/l+KodXMmboGA4rPUyBcRooWBYRERFJg+a2Zrbu3cqmXZt48e0Xef2912nvaMfdGRIYwoTy\nCRRYAe7OgdYDXV/uToEV0OEdlJeUM6F8AhOHT2TcsHGMHDySiiEVDA0MVWCcIUnt5jazT5rZK2bW\nbmZTe2hXa2YvmtlaM3shmT7ToaamRn32oz7TwczONbMNZva6mX0j2+OBgfX3OZA+aypla96m48+u\ntja110z19Qb6NVMtF++5EH9uH2g9wIZ3N/Dw6w/znae/w1WPXMWNf76Re1+5l9rdtQwNDGVI8RCK\nC4vZ37KfN/e+ydY9W6nbU4dhHDfyOD5x/Cf4z1P+k/nV87ntgtu4qOQivnb61/jE+z/BKeNO4ejD\njmbYoGFpDZR1z+0u2aOPLwP/CqyM064DqHb3Ke5+cpJ9ptxAmRQDpc9UM7MC4KfAOcAHgJlmdlx2\nRzWw/j4H0mdNlWzOWwXLA++aqZSr91w4dG7vbd7LyzteZtkry/jSH7/EF/7wBa5Zfg23Pncrq7ev\npsM7MIIZKNq8jcHFg5l02CTOnHgmn/vQ5/jKaV/hux/9Lj/7l59x81k3c/XJV3PBMRcw9cipjC8f\nT2lx6YC5/+XyPTepbRju/hqAxf/njZF8YC6SLScDG919C4CZ3QtcCGzI6qhEeqZ5K/kqq3O3wzvY\n27yXdw+82+2rfn89T2x6gqYVTbg77d5Oc1szTW1NtHs7gwoHccKoExhdNprRZaM5rPQwhg0aRvmg\ncoYNGkZJUYm2TeSpTO1ZduBxM2sHfubud2SoX5FUGAtsDfv+TYI3c5Fcpnkr+Srhufvs1mdpbW+l\nraON1o7Wbo/bOtpobW895HFLewst7S3BILejHTPDMAqsoOuxmXXtGXZ3HMfdKS4s5ugRRzNy8EhG\nlY2isryS8pJyhhQPUSDcj5nHOcJvZo8D4cn+jGDwe527/yHU5ingq+6+JsY1jnT37WZWATwOXO3u\nz8Roq5wCkjR3T9ldy8w+AZzj7l8Iff/vwMnu/qWwNpq3khKpmruJzNvQ85q7krRM33NDz2vuStIS\nmbtxV5bd/awUDGR76L/1ZnY/wX8hRg2WU/kDJ5Ii24AJYd+PCz3XRfNWclDceQuau5KTNHclp6Ry\nH3HUSWtmg82sLPR4CHA28EoK+xVJt1XAJDOrNLMA8GngoSyPSSQezVvJV5q7klOSTR13kZltBU4F\nHjazP4aeP9LMHg41GwU8Y2ZrgeeAP7j78mT6Fckkd28HrgaWA38H7nX39dkdlUjPNG8lX2nuSq6J\nu2dZRERERGSgymo6t14UNUlpcnIzG2Fmy83sNTN7zMzKY7RLqphKIuM2sx+b2UYzW2dmk3vbR1/6\nNbNpZrbbzNaEvr6VZH9LzGyHmb3UQ5uUfs54fab6M8YZS0aT5yfy552GPseZ2ZNm9ncze9nMvhT/\nXUn3OcjMng/9/L1sZtenu8+wvgtC8yYjv/pN9l6Tgv4TuhcncJ1U36tTPtfTMZfTNVdTPQ/TMc/M\nrNzMfmtm60N/pqek4rpx+sx4wZJM33d1z81In4n/PLh71r6AY4H3AU8CU2O0KQDeACqBYmAdcFyS\n/d4MfD30+BvATTHabQJG9LGPuOMGzgMeCT0+BXguBX+mifQ7DXgohX+PZwCTgZdivJ6Ozxmvz5R+\nxmT+vNPQZ4+fPU19jgYmhx6XAa+l+3OG+hoc+m8hwW1cJ2fo884F/i8TcyjUX5/vNSnqP+69OIFr\npONenfK5nq65nI65mup5mI55BvwC+HzocREwLJXXz8Q8S7DfjN53dc/NSJ8J/zxkdWXZ3V9z943E\nOBwY0pWc3N1bgc7k5Mm4ELg79Phu4KIY7ZIpppLIuC8E7gFw9+eBcjMbRXIS/fNK2SliD6YB3NVD\nk5R/zgT6hBR+xh6kY372KMHPnuo+33b3daHHDcB6grlQ093vgdDDQQT/R5z2fWNmNg44H/h5uvsK\n75Ys/qYvwXtxPCn/WUjHXE/XXE71XE3TPEzpPDOzYcA/u/tdAO7e5u57U3X9GDJ+z4XM33d1z82I\nhH8e8qGqXrTk5MlOmCPcfQcEJyRwRIx2ncVUVpnZFb3sI5FxR7bZFqVNbyX653VaaEvEI2b2/iT7\n7O2YUvE5E5GJz5iO+ZnTzKyK4ArL8xnoq8CCh4PfBh5391Xp7hNYCFxDBv4nESaZe02uyLufhVTO\n5TTM1XTMw1TPs4nAu2Z2V+hX6D8zs9IUXLcneTfPkqV7btok/POQ9gp+lkBRkwz3G23vaqy/oA97\nWDEVM1vvMYqp5JnVwAR3P2Bm5wEPAMdkeUypNhA+Y8ZZMA3k74A5odWOtHL3DmBKaAXrATN7v7u/\nmq7+zOwCYIe7rzOzajLz2wnIwL0mW/fiXJXquZzKuZrGeZjqeVYETAW+6O5/M7NbgXlAxva69ne6\n56ZVwj8PaQ+WPfmiJgklJ+9Nv6FN+qPcfYeZjQbeiXGNhIup9HHc24Dxcdr0ViIFNBrCHv/RzG4z\ns8Pc/b0k++5pTKn+nD3K4Gfs0/zMR2ZWRPCm/Ut3fzCTfbv7XgtWCj0XSNuNG/gw8HEzOx8oBYaa\n2T3u/rk09pnsvSbRPpIuMBVH3vwspHMup2iupmUepmGevQlsdfe/hb7/HcFzQOmUN/MsWbrnpldv\nfh5yaRtGrH9NpCM5+UPAZaHHlwKHTEJLvphKIuN+CPhcqI9Tgd2d20OSELff8P3CZnYywRSCyQaR\nRuy/w3R8zh77TNNnjCZbyfN7+vNOlzuBV919USY6M7ORFspUE/rV7lnAhnT26e7XuvsEdz+K4N/l\nk+m+aafgXpPyIfXxfen6WUjHXE/pXE71XE3HPEzHPAvdx7eaWedv7T5KegMryG7Bkkzfd3XPTZNe\n/zwkcgowXV8ED9ZtBRqB7cAfQ88fCTwc1u5cgidBNwLzUtDvYcCK0DWXA8Mj+yW4F2sdsBZ4uS/9\nRhs3MBv4QlibnxI82fsifTyF3tt+gS+GJsVa4FnglCT7+zXwFtAM1AGfT/fnjNdnqj9jb/+80/kV\n7bNnoM8PA+1hPxNrgHPT3OcHQ/2sA14iuF0grZ8zov9MZVRJ+l6TgjFEvRf34TqpvlenfK6nYy6n\nc66mah6ma54BHyIYwK4D7gPKU/XZMzXPEuwzo/dd3XPT3levfh5UlEREREREJIZc2oYhIiIiIpJT\nFCyLiIiIiMSgYFlEREREJAYFyyIiIiIiMShYFhERERGJQcGyiIiIiEgMCpZFRERERGL4/wGBgw4Y\n2oV1yAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc178103c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = array([0,1,2,3,4,5])\n",
    "xx = np.linspace(-0.75, 1., 100)\n",
    "x = linspace(0, 5, 10)\n",
    "\n",
    "fig, axes = plt.subplots(1, 4, figsize=(12,3))\n",
    "axes[0].scatter(xx, xx + 0.25*randn(len(xx)))\n",
    "axes[1].step(n, n**2, lw=2)\n",
    "axes[2].bar(n, n**2, align='center', width=0.5, alpha=0.5)\n",
    "axes[3].fill_between(x, x**2, x**3, color='green', alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "When you going to plot something more or less complicated in Matplotlib, the first thing you do is open the [Matplotlib example gallery](http://matplotlib.org/gallery.html) and choose example closest to your case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "You can directly load python code (or basically any text file) to the notebook. This time we download code from the Matplotlib example gallery:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# %load http://matplotlib.org/mpl_examples/mplot3d/contour3d_demo2.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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n1fhaifEuFArs2LGj7/O/8IUvsHnzZi6//HIefPDBlsfF8RysS9JtZ6X2G/rV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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc059125c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import axes3d\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "X, Y, Z = axes3d.get_test_data(0.05)\n",
    "cset = ax.contour(X, Y, Z, extend3d=True, cmap=cm.coolwarm)\n",
    "ax.clabel(cset, fontsize=9, inline=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Choose one of the many code snippets on the [matplotlib gallery](http://matplotlib.org/examples/index.html) and load it into this notebook. Generate new data and have the code snippet visualize that data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Exercise 3: Gene Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "After having sequenced a genome and having obtained a high quality resolution of its DNA sequence,\n",
    "one of the initial tasks is to predict and annotate the protein-coding genes.\n",
    "\n",
    "Protein coding regions of a DNA sequence are first transcribed into messenger RNA (mRNA) and\n",
    "then translated into a protein. During this process, a codon of three DNA nucleotides is translated\n",
    "to a single amino acid and joined together to form a polypeptide, i.e. a protein. The mRNA contains\n",
    "signals for the ribosome telling it where to initiate and terminate translation, the sequence between\n",
    "these two signals is the coding sequence (CDS). These CDS can also be called an open reading\n",
    "frame (ORF). Such an open reading frame is a sequence that contains a start and a stop codon and\n",
    "could be read by the ribosome, only if it is located on an mRNA. A single-stranded DNA sequence\n",
    "contains of three reading frames, this means three possibilities of reading codons off the sequence,\n",
    "starting from position one, two, and three. The codons are then just read as a consecutive sequence\n",
    "of three nucleotides.\n",
    "\n",
    "Given a fragment of DNA sequence ``S`` of n nucleotides, let S[i] denote the i-th nucleotide of sequence\n",
    "S, for $$1 ≤ i ≤ n$$ \n",
    "Let also $$ S[i, . . . , j], i ≤ j$$\n",
    "denote the fragment of S containing nucleotides\n",
    "$$S[i], S[i + 1], . . . , S[j]$$\n",
    "and \n",
    "$$S[i, . . . , i] = S[i]$$\n",
    "With this notation, an open reading frame ORF is a fragment S[i, . . . , j], of length l = j − i + 1, such that S[i, . . . , i + 2] is the start codon ATG and S[j − 2, . . . , j] is one of the stop codons {TAA, TAG, TGA}.\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "u_t(x,t) & = f(x,t) \\\\\n",
    "u(x,0) & = u_0\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "The shortest known protein has 8 amino acids and so ORFs with fewer than 3 + 24 + 3 = 30 nucleotides cannot encode for a protein,\n",
    "therefore l ≥ 30. Furthermore, there are no other stop codons in the ORF except for the one at\n",
    "the end.\n",
    "\n",
    "Bacteriophage φ-X174 was the first genome to be sequenced in 1977 by Fred Sanger and his team.\n",
    "It is a small genome, consisting only of 5,386 nucleotides and is contained in a single-stranded\n",
    "circular chromosome. You shall download its genome sequence from NCBI and use it as an example\n",
    "to identify all ORFs and to identify which of these actually encode for real proteins. As this genome\n",
    "is single stranded you only have to look at the 3 reading frames, of course you would have to look at\n",
    "a further 3 reading frames in the reverse complement sequence if the genome were double-stranded,\n",
    "as genes could lie on either strand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Download the genome from NCBI, the accession number is NC 001422.1. How many genes are annotated?\n",
    "\n",
    "Hint: You can download and ``open()`` the FASTA file like a normal text file or reading it directly from the FTP server with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">tr|Q7T2N8|Q7T2N8_DANRE Foxi one OS=Danio rerio GN=foxi1 PE=4 SV=1\n",
      "\n",
      "MSPFTACAPDRLNNPPLKRTRTLSLRNPSETSTLSRSDRRNSMFLEGERIMNAFGQQPSS\n",
      "\n",
      "QQTSPLQQQDILDMTVYCDSNFSMYQQNLHHHHHHHHHQRPPAHPSGYGLGEYSSPSTNP\n",
      "\n",
      "YLWMNSPGITSTPYLSSPNGGSYIQSGFGSNQRQFLPPPTGFGSADLGWLSISSQQELFK\n",
      "\n",
      "MVRPPYSYSALIAMAIQNAQDKKLTLSQIYQYVADNFPFYKKSKAGWQNSIRHNLSLNDC\n",
      "\n",
      "FKKVARDEDDPGKGNYWTLDPNCEKMFDNGNFRRKRKRRADGNAMSVKSEDALKLADTSS\n",
      "\n",
      "LMSASPPSLQNSPTSSDPKSSPSPSAEHSPCFSNFIGNMNSIMSGNAVRSRDGSSAHLGD\n",
      "\n",
      "FTQHGMSGHEISPPSEPGHLNTNRLNYYSASHNNSGLINSISNHFSVNNLIYNRDGSEV\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from urllib import request\n",
    "url = \"http://www.uniprot.org/uniprot/Q7T2N8.fasta\"\n",
    "data = request.urlopen( url )\n",
    "for line in data:\n",
    "    print(line.decode('utf-8'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Write a method that reads the genome sequence in fasta format, calculates all possible\n",
    "ORFs, and returns a fasta file that contains the ORF DNA sequences and the reading frame\n",
    "number, the starting position i, the ending position j and the length l of each in the header.\n",
    "How many ORFs do you find in total and in each reading frame separately?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Why have we asked you to only look in 3 reading frames and not in 6 as is usually done (Hint:\n",
    "please read our introduction)? What would it mean to look in 6 reading frames, how would you\n",
    "define the other 3 reading frames?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Download all protein sequences annotated for Bacteriophage φ-X174 in one fasta file. Which\n",
    "part of the genes correspond to the respective CDS? Can you find untranslated regions (UTRs)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Write a method to translate all ORFs that are currently saved as DNA sequences\n",
    "into protein sequences. Parse the real protein sequences and compare them to your predicted\n",
    "sequences. Your output shall be a table of ORFs that correspond to real proteins giving the\n",
    "protein accession number (NP...) they correspond to, the reading frame they are located on\n",
    "with starting and ending positions (w.r.t. the genome) and length. Do you find all CDS? If not,\n",
    "why is this?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "codon_table = {\n",
    "    'TTT': 'F', 'TTC': 'F', 'TTA': 'L', 'TTG': 'L', 'TCT': 'S',\n",
    "    'TCC': 'S', 'TCA': 'S', 'TCG': 'S', 'TAT': 'Y', 'TAC': 'Y',\n",
    "    'TGT': 'C', 'TGC': 'C', 'TGG': 'W', 'CTT': 'L', 'CTC': 'L',\n",
    "    'CTA': 'L', 'CTG': 'L', 'CCT': 'P', 'CCC': 'P', 'CCA': 'P',\n",
    "    'CCG': 'P', 'CAT': 'H', 'CAC': 'H', 'CAA': 'Q', 'CAG': 'Q',\n",
    "    'CGT': 'R', 'CGC': 'R', 'CGA': 'R', 'CGG': 'R', 'ATT': 'I',\n",
    "    'ATC': 'I', 'ATA': 'I', 'ATG': 'M', 'ACT': 'T', 'ACC': 'T',\n",
    "    'ACA': 'T', 'ACG': 'T', 'AAT': 'N', 'AAC': 'N', 'AAA': 'K',\n",
    "    'AAG': 'K', 'AGT': 'S', 'AGC': 'S', 'AGA': 'R', 'AGG': 'R',\n",
    "    'GTT': 'V', 'GTC': 'V', 'GTA': 'V', 'GTG': 'V', 'GCT': 'A',\n",
    "    'GCC': 'A', 'GCA': 'A', 'GCG': 'A', 'GAT': 'D', 'GAC': 'D',\n",
    "    'GAA': 'E', 'GAG': 'E', 'GGT': 'G', 'GGC': 'G', 'GGA': 'G',\n",
    "    'GGG': 'G'\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Describe how you would extend your algorithm to be able to identify all protein sequences. From\n",
    "this description it should be clear exactly how you can program this feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "If you look at the location of the genes on the chromosome, what do you notice? What does\n",
    "this observation have to do with the size? Name an advantage of reading frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Use your new plotting skills to plot the Codon usage of all predicted genes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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