20141202_Euler61.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We first define the numbers we will use in this notebook:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def triangular(n):\n",
      "    return n * (n + 1) / 2\n",
      "\n",
      "def square(n):\n",
      "    return n * n\n",
      "\n",
      "def pentagonal(n):\n",
      "    return n * (3 * n - 1) / 2\n",
      "\n",
      "def hexagonal(n):\n",
      "    return n * (2 * n - 1)\n",
      "\n",
      "def heptagonal(n):\n",
      "    return n * (5 * n - 3) / 2\n",
      "\n",
      "def octagonal(n):\n",
      "    return n * (3 * n - 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html.widgets import interact"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funcs = [triangular, square, pentagonal, hexagonal, heptagonal, octagonal]\n",
      "labels = list(map(lambda f: f.__name__, funcs))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "labels"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "['triangular', 'square', 'pentagonal', 'hexagonal', 'heptagonal', 'octagonal']"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def eval_func(func_name, n):\n",
      "    func = funcs[labels.index(func_name)]\n",
      "    print(func(n))\n",
      "\n",
      "interact(eval_func, \n",
      "         func_name=labels,\n",
      "         n=(1, 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "15\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<function __main__.eval_func>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also represent the various numbers as geometric figures using the roots of unity."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pylab import linspace, exp, pi, plot, append, axis, xlim, ylim, figure"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def compute_unity_roots(n, r=1.0):\n",
      "    thetas = linspace(0, 2 * pi, n, endpoint=False) - (pi / 2 - pi / n)\n",
      "    roots = r * exp(1j * thetas)\n",
      "    return roots"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "compute_unity_roots(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "array([  8.66025404e-01-0.5j,   2.83276945e-16+1.j ,  -8.66025404e-01-0.5j])"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = compute_unity_roots(3)\n",
      "z = append(z, z[0])\n",
      "plot(z.real, z.imag, '-o')\n",
      "axis('equal')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(-1.0, 1.0, -0.60000000000000009, 1.2)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yl6isWuW2dfv0U23rFjTa9jD4NFtG4ubaa6FRI7j/ft9JpDzuugt++AGmTPGdRMpDxV3i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\nauyfAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x44f5c18>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_unity_roots(n):\n",
      "    z = compute_unity_roots(n)\n",
      "    z = append(z, z[0])\n",
      "    plot(z.real, z.imag, '-o')\n",
      "    xlim(-1.1, 1.1)\n",
      "    ylim(-1.1, 1.1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interact(plot_unity_roots,\n",
      "         n=(1, 20))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<function __main__.plot_unity_roots>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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r+POfYeJEaN8exoyxcSBul3QlkWj6gTRxxBHw179ac83770PXrjZ3zYYNoSMT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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa563b38>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can now move on to a superposition of polygons:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_unity_roots_until(n, order):\n",
      "    for o in range(1, order + 1):\n",
      "        z = compute_unity_roots(n, r=o)\n",
      "        if o == 1:\n",
      "            reference_z = z[-1]\n",
      "        else:\n",
      "            z -= (z[-1] - reference_z)\n",
      "        z = append(z, z[0])\n",
      "        \n",
      "        plot(z.real, z.imag, '-o')\n",
      "        axis('equal')\n",
      "        axis('off')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_unity_roots_until(4, 3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEACAYAAAB4ayemAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACF9JREFUeJzt2jFvVFcax+H/WWxjFJItQkKRJcUGKlxt4SifANtFumWb\nFClMF/gKho8QoEvrLsVqKWDKtKGmW5ICK0XWNEmIALPobOElwmCkleK5d9+Z55FGss8079E9/vn6\njlvvPQDU8IexBwDgfyfaAIWINkAhog1QiGgDFCLaAIWINkAhog1QiGgDFCLaAIWINkAhog1QiGgD\nFCLaAIWINkAhog1QiGgDFCLaAIWINkAhog1QiGgDFCLaAIWINkAhog1QiGgDFCLaAIWINkAhog1Q\nyMLYA8yTldY2ziRX3k6Wf0me7CTX7/V+e+y5YFqc+aMn2gNZaW3jk+TLr5KzL9YuJR+ttBaHmFnk\nzE+HxyMDOZNcefnwJslXydkPk8tjzQTT5MxPhzvtgbydLB+2vpqspbU+9DwwbR+/Yf1kcmLQQWaM\naA/kl+TJYet3k0l6Xx96Hpi2b1ubJLnw6vqj5PEI48wMj0cGspNcv5Tcf3ltM/nuQXJjrJlgmpz5\n6Wi9+8t8KCutbXyYXF5N1u4mkwfJDR/IMMuc+aMn2mNoraf3NvYYMBhn/sh4PAJQiGgDFCLaAIWI\nNkAhog1QiGgDFCLaAIWINkAhog1QiGgDFCLaAIWINkAhog1QiGgDFCLaAIWINkAhog1QiGgDFCLa\nAIWINkAhog1QiGgDFCLaAIWINkAhog1QiGgDFCLaAIWINkAhog1QiGgDFCLaAIWINkAhog1QiGgD\nFCLaAIWINkAhog1QiGgDFCLaAIWINkAhog1QiGgDFCLaAIWINkAhog1QiGgDFCLaAIWINkAhog1Q\niGgDFCLaAIWINkAhog1QiGgDFCLaAIWI9oDa6bbRzrVJkrRzbdJOt42xZ2L6Xlz3dr5947rzey2M\nPcC8aKfbRv6UL/NpzuZqks9yIbfyUTvd0n/st8eej+k4cN1fcN35HdxpD+WdXDnwg5skn+Zs3snl\nkSZiCK47R8yd9lCWsnzo+gdZa9daH3gahvLBG9aXcmLQOZgZoj2UvTw5dP2HTPpWXx94GgbSttsk\nyYXX3tjL4+GnYRZ4PDKUn3M9t3L/wNqtfJefc2OkiRiC684Ra737y3wo7XTbyDu53O9nrZ3NJD/n\nhg+jZt+L654PspYf5vS6t9bText7jFkg2mNwgOdSu9Z635rT6+7MHxmPRwAKEW2AQkQboBDRBihE\ntAEKEW2AQkQboBDRBihEtAEKEW2AQkQboBDRBihEtAEKEW2AQkQboBDRBihEtAEKEW2AQkQboBDR\nBihEtAEKEW2AQkQboBDRBihEtAEKEW2AQkQboBDRBihEtAEKEW2AQkQboBDRBihEtAEKEW2AQkQb\noBDRBihEtAEKEW2AQkQboBDRBihEtAEKEW2AQkQboBDRBihEtAEKEW2AQkQboBDRBihEtAEKEW2A\nQkQboBDRBihEtAEKEW2AQkQboBDRBihEtAfU2spGa+uT/a/XJ62tbIw901Be7L21i9/M297hKC2M\nPcC82I/UJ18mX51NWpI7F5JLH7W2kt7v3R57vmk6uPcX5mPvcNTcaQ/mzJWD0Ur2v//w8jjzDGme\n9w5Hy532YN5ePnx9da219GFnGdrHb1g/eWLQMWAGiPZgfnly+PrdSe9ZH3aWYbX27STJhdffefR4\n8GGgOI9HBrNzPbl0/+Da5nfJgxvjzDOked47HC132gPp/d7t1laSbFxOspZsTJIHN+bhg7iDe19d\nS+7Ozd7hqLXeZ/xx6v+j1np6b2OPMYbW0nvPfO79Wut9az6v+zyf+aPm8QhAIaINUIhoAxQi2gCF\niDZAIaINUIhoAxQi2gCFiDZAIaINUIhoAxQi2gCFiDZAIaINUIhoAxQi2gCFiDZAIaINUIhoAxQi\n2gCFiDZAIaINUIhoAxQi2gCFiDZAIaINUIhoAxQi2gCFiDZAIaINUIhoAxQi2gCFiDZAIaINUIho\nAxQi2gCFiDZAIaINUIhoAxQi2gCFiDZAIaINUIhoAxQi2gCFiDZAIaINUIhoAxQi2gCFiDZAIaIN\nUIhoAxQi2gCFiDZAIaINUIhoAxQi2gM6dfzE9urywtOrSVaXF56eOn5ie+yZhtLaylZr67vJ52lt\nfbe1la2xZxpKe79ttXNtN39P2rm2295vc7P3lda21lvb/TzJemu7K21+9j41vXevAV7vLi1vX1xs\nvSe/vS4utv7u0vL22LNN+5Wc30o2917aet///vzW2LNNfe/vZSt/yV6upv/2+kv28l5mfu/nk63N\nZO/lM7+Z7J3P7O99mq/Wex/798ZcWF1eeHr36fOl19aPH9u7++Tfx8eYaSj7d9h3Tr3+zvrD3u+8\nN/xEw2nn2m4+y+t7387D/s8+03tfb233Tl7f+3ry8E6f7b1P08LYA8yLt5Jjh61vPH2+lNZm+jfn\n/ubaYW+dmou9Xz30rZnf++k3rJ9MFgcdZMaI9kB+TZ7nkHDfPn5s76o77Zk1z3faP7a2m0PutB8l\nz0YYZ2b4IHIg3/fFr/+2ePBu8+Jiy/d98euRRhrQzs3k0is/qJvP9tdn3E+5mVuvROofeZafMvN7\n30luXnol0JvJs53M/t6nyTPtAZ06fmL7z+3ZX99Kjv2aPP++L3798Onjz8aeawj7/y1y5ovk5GLy\n6Fmyc7P3e9fGnmsI7f22lT/miyxlMXv7we7/6nOx95XWts4kX5xMFh/9N9j3+nzsfVpEG6AQj0cA\nChFtgEJEG6AQ0QYoRLQBChFtgEJEG6AQ0QYoRLQBChFtgEJEG6AQ0QYoRLQBChFtgEJEG6AQ0QYo\nRLQBChFtgEJEG6AQ0QYoRLQBChFtgEJEG6AQ0QYoRLQBChFtgEJEG6AQ0QYoRLQBCvkPzH6jD0QD\nUDUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa7fa8d0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interact(plot_unity_roots_until,\n",
      "         n=(1, 20),\n",
      "         order=(1, 20))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yoPCSSF0/yb3X91DTLJcBsxGZgvdzMj2ZdMjqz3cOsDPtLBSNMiESaVhsqLqa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\nQyxoG2NMDvl/XYiTEn2znPkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc48b048>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pentagonal(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "12"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Solving the problem: we want 4 digits numbers. So let's compute the 4 digit numbers in each sets of numbers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from collections import defaultdict\n",
      "four_digits = defaultdict(list)\n",
      "\n",
      "for func, label in zip(funcs, labels):\n",
      "    too_big = False\n",
      "    n = 1\n",
      "    value = func(n)\n",
      "    while not too_big:\n",
      "        n += 1\n",
      "        value = func(n)\n",
      "        if value > 999 and value < 10000:\n",
      "            four_digits[label].append(str(value))\n",
      "        if value > 10000:\n",
      "            too_big = True"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "four_digits"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "defaultdict(<type 'list'>, {'square': ['1024', '1089', '1156', '1225', '1296', '1369', '1444', '1521', '1600', '1681', '1764', '1849', '1936', '2025', '2116', '2209', '2304', '2401', '2500', '2601', '2704', '2809', '2916', '3025', '3136', '3249', '3364', '3481', '3600', '3721', '3844', '3969', '4096', '4225', '4356', '4489', '4624', '4761', '4900', '5041', '5184', '5329', '5476', '5625', '5776', '5929', '6084', '6241', '6400', '6561', '6724', '6889', '7056', '7225', '7396', '7569', '7744', '7921', '8100', '8281', '8464', '8649', '8836', '9025', '9216', '9409', '9604', '9801'], 'octagonal': ['1045', '1160', '1281', '1408', '1541', '1680', '1825', '1976', '2133', '2296', '2465', '2640', '2821', '3008', '3201', '3400', '3605', '3816', '4033', '4256', '4485', '4720', '4961', '5208', '5461', '5720', '5985', '6256', '6533', '6816', '7105', '7400', '7701', '8008', '8321', '8640', '8965', '9296', '9633', '9976'], 'triangular': ['1035', '1081', '1128', '1176', '1225', '1275', '1326', '1378', '1431', '1485', '1540', '1596', '1653', '1711', '1770', '1830', '1891', '1953', '2016', '2080', '2145', '2211', '2278', '2346', '2415', '2485', '2556', '2628', '2701', '2775', '2850', '2926', '3003', '3081', '3160', '3240', '3321', '3403', '3486', '3570', '3655', '3741', '3828', '3916', '4005', '4095', '4186', '4278', '4371', '4465', '4560', '4656', '4753', '4851', '4950', '5050', '5151', '5253', '5356', '5460', '5565', '5671', '5778', '5886', '5995', '6105', '6216', '6328', '6441', '6555', '6670', '6786', '6903', '7021', '7140', '7260', '7381', '7503', '7626', '7750', '7875', '8001', '8128', '8256', '8385', '8515', '8646', '8778', '8911', '9045', '9180', '9316', '9453', '9591', '9730', '9870'], 'pentagonal': ['1001', '1080', '1162', '1247', '1335', '1426', '1520', '1617', '1717', '1820', '1926', '2035', '2147', '2262', '2380', '2501', '2625', '2752', '2882', '3015', '3151', '3290', '3432', '3577', '3725', '3876', '4030', '4187', '4347', '4510', '4676', '4845', '5017', '5192', '5370', '5551', '5735', '5922', '6112', '6305', '6501', '6700', '6902', '7107', '7315', '7526', '7740', '7957', '8177', '8400', '8626', '8855', '9087', '9322', '9560', '9801'], 'hexagonal': ['1035', '1128', '1225', '1326', '1431', '1540', '1653', '1770', '1891', '2016', '2145', '2278', '2415', '2556', '2701', '2850', '3003', '3160', '3321', '3486', '3655', '3828', '4005', '4186', '4371', '4560', '4753', '4950', '5151', '5356', '5565', '5778', '5995', '6216', '6441', '6670', '6903', '7140', '7381', '7626', '7875', '8128', '8385', '8646', '8911', '9180', '9453', '9730'], 'heptagonal': ['1071', '1177', '1288', '1404', '1525', '1651', '1782', '1918', '2059', '2205', '2356', '2512', '2673', '2839', '3010', '3186', '3367', '3553', '3744', '3940', '4141', '4347', '4558', '4774', '4995', '5221', '5452', '5688', '5929', '6175', '6426', '6682', '6943', '7209', '7480', '7756', '8037', '8323', '8614', '8910', '9211', '9517', '9828']})"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[(label, len(four_digits[label])) for label in labels]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "[('triangular', 96),\n",
        " ('square', 68),\n",
        " ('pentagonal', 56),\n",
        " ('hexagonal', 48),\n",
        " ('heptagonal', 43),\n",
        " ('octagonal', 40)]"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "On cherche \u00e0 \u00e9tender la cha\u00eene \u00e0 droite et \u00e0 gauche."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def filter_can_extend_left(four_digit, candidates):\n",
      "    left_most = four_digit[:2]\n",
      "    return [candidate for candidate in candidates if candidate[2:4] == left_most]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for label in labels:\n",
      "    print filter_can_extend_left('4995', four_digits[label])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[]\n",
        "['1849', '3249', '8649']\n",
        "[]\n",
        "[]\n",
        "[]\n",
        "[]\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def filter_can_extend_right(four_digit, candidates):\n",
      "    right_most = four_digit[2:4]\n",
      "    return [candidate for candidate in candidates if candidate[0:2] == right_most]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for label in labels:\n",
      "    print filter_can_extend_right('4995', four_digits[label])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['9591']\n",
        "[]\n",
        "['9560']\n",
        "[]\n",
        "['9517']\n",
        "[]\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import networkx as nx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colors = ['blue', 'green', 'red', 'yellow', 'brown', 'orange']\n",
      "g = nx.Graph()\n",
      "for label, color in zip(labels, colors):\n",
      "    for node_value in four_digits[label]:\n",
      "        g.add_node(node_value, color=color)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_values = reduce(list.__add__, [four_digits[label] for label in labels])\n",
      "for label in labels:\n",
      "    for node_value in four_digits[label]:\n",
      "        to_right = filter_can_extend_right(node_value, all_values)\n",
      "        for other_node in to_right:\n",
      "            g.add_edge(node_value, other_node)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(10, 10))\n",
      "nx.draw_networkx(g)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6FSEhIWjevDl25ruhwG471J2Pdj8DiHF6XF/fR1cAuw8cQJs2bfDuu+/e8k0FQog7REki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mBk8nN4+CbysXm/z+/PEeNmRfG//8nHZzFRkOCnb1mjacS8Hb5SAonEwFcSEWr4GDcRE2QNDKHf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Hz4MPLy8mC1WhEbG4vt27erut5iqm7Bo+xjbS2AbLI+sr4F0I2jHnTVR62hiIyPHz9+0yXS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\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g5s2bqFmzJnr06IE6KlWFwq3lEdiFa26xWEQC+2+//Qa9Xu/W47NZs2ZlwOC9e/cg5TjM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\nq9WbD5KWxtjYWIaZzTX23oUYDMzJyeGVK1eYlJTE9u3bs16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bt25UKxTVFix2fDZDZPoS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F0Ww2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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc38a2b0>"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Je crois que cette approche ne fonctionne pas. Utilisons plut\u00f4t l'approche r\u00e9cursive."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def recursive_chain(current_node, available_labels):\n",
      "    \"\"\"\n",
      "    returns a list of lists\n",
      "    \"\"\"\n",
      "    if len(available_labels) == 0:\n",
      "        return [[current_node]]\n",
      "    else:\n",
      "        chains = []\n",
      "        for label in available_labels:\n",
      "            next_nodes = filter_can_extend_right(current_node, four_digits[label])\n",
      "            for next_node in next_nodes:\n",
      "                chains += recursive_chain(next_node, [other_label for other_label in available_labels if other_label != label])\n",
      "        output = []\n",
      "        for chain in chains:\n",
      "            output.append([current_node] + chain)\n",
      "        return output"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "recursive_chain('1849', labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "[['1849', '4950', '5041', '4186', '8614', '1426', '2640'],\n",
        " ['1849', '4950', '5041', '4186', '8640', '4030', '3010'],\n",
        " ['1849', '4950', '5041', '4141', '4186', '8626', '2640'],\n",
        " ['1849', '4950', '5041', '4141', '4186', '8640', '4030'],\n",
        " ['1849', '4950', '5017', '1764', '6426', '2640', '4005'],\n",
        " ['1849', '4950', '5017', '1782', '8281', '8128', '2821'],\n",
        " ['1849', '4950', '5050', '5017', '1764', '6426', '2640'],\n",
        " ['1849', '4950', '5041', '4186', '8614', '1426', '2640'],\n",
        " ['1849', '4950', '5041', '4186', '8640', '4030', '3010'],\n",
        " ['1849', '4950', '5041', '4141', '4186', '8626', '2640'],\n",
        " ['1849', '4950', '5041', '4141', '4186', '8640', '4030'],\n",
        " ['1849', '4950', '5017', '1711', '1177', '7744', '4485'],\n",
        " ['1849', '4950', '5017', '1764', '6426', '2628', '2821'],\n",
        " ['1849', '4950', '5017', '1764', '6426', '2640', '4005'],\n",
        " ['1849', '4950', '5017', '1764', '6426', '2640', '4095'],\n",
        " ['1849', '4950', '5017', '1782', '8281', '8128', '2821'],\n",
        " ['1849', '4961', '6112', '1225', '2556', '5625', '2512'],\n",
        " ['1849', '4961', '6112', '1225', '2556', '5688', '8836'],\n",
        " ['1849', '4961', '6112', '1225', '2512', '1225', '2556'],\n",
        " ['1849', '4961', '6112', '1225', '2512', '1225', '2500'],\n",
        " ['1849', '4961', '6112', '1275', '7569', '6943', '4371'],\n",
        " ['1849', '4961', '6112', '1225', '2512', '1225', '2556'],\n",
        " ['1849', '4961', '6112', '1225', '2512', '1225', '2556'],\n",
        " ['1849', '4961', '6112', '1225', '2556', '5625', '2512'],\n",
        " ['1849', '4961', '6112', '1225', '2556', '5688', '8836'],\n",
        " ['1849', '4961', '6112', '1225', '2512', '1225', '2500'],\n",
        " ['1849', '4961', '6112', '1225', '2512', '1275', '7569'],\n",
        " ['1849', '4961', '6112', '1225', '2512', '1225', '2556'],\n",
        " ['1849', '4961', '6112', '1288', '8836', '3655', '5565'],\n",
        " ['1849', '4961', '6112', '1288', '8836', '3655', '5565'],\n",
        " ['1849', '4961', '6175', '7526', '2628', '2850', '5041']]"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "On choisit un point de d\u00e9part : par exemple les nombres triangulaires, on g\u00e9n\u00e8re toutes les cha\u00eenes et on retient seulement celle en accord avec l'\u00e9nonc\u00e9."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "labels"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "['triangular', 'square', 'pentagonal', 'hexagonal', 'heptagonal', 'octagonal']"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "On peut maintenant r\u00e9soudre le probl\u00e8me de l'\u00e9nonc\u00e9 :"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for four_digit in four_digits['triangular']:\n",
      "    chains = recursive_chain(four_digit, ['square', 'pentagonal'])\n",
      "    for chain in chains:\n",
      "        if chain[-1][2:4] == chain[0][0:2]:\n",
      "            print chain"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['8128', '2882', '8281']\n"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for four_digit in four_digits['triangular']:\n",
      "    chains = recursive_chain(four_digit, ['square', 'pentagonal', 'hexagonal', 'heptagonal', 'octagonal'])\n",
      "    for chain in chains:\n",
      "        if chain[-1][2:4] == chain[0][0:2]:\n",
      "            print chain"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['8256', '5625', '2512', '1281', '8128', '2882']\n"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sum(map(int, ['8256', '5625', '2512', '1281', '8128', '2882']))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "28684"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Challenge de R\u00e9mi sur les fonctions qui comptent des choses"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from collections import Counter\n",
      "def CountOccurencesInText(word,text):\n",
      "    \"\"\"Number of occurences of word (case insensitive) in text\"\"\"\n",
      "    \n",
      "    c = Counter(text.upper().replace(',', ' ')\\\n",
      "                            .replace('.', ' ')\\\n",
      "                            .replace('!', ' ')\\\n",
      "                            .replace('?', ' ')\\\n",
      "                            .replace('\\n', ' ')\\\n",
      "                            .split(sep=' '))\n",
      "    \n",
      "    return c[word.upper()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "CountOccurencesInText(\"python\",text)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "split() takes no keyword arguments",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-21-b29722adc291>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mCountOccurencesInText\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"python\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m<ipython-input-17-3e8b14420310>\u001b[0m in \u001b[0;36mCountOccurencesInText\u001b[1;34m(word, text)\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[1;34m\"\"\"Number of occurences of word (case insensitive) in text\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCounter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m','\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m                            \u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m                            \u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'!'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m                            \u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'?'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m                            \u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'\\n'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m                            \u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mTypeError\u001b[0m: split() takes no keyword arguments"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "text=\"\"\"Georges is my name and I like python. Oh ! your name is georges? And you like Python!\n",
      "Yes is is true, I like PYTHON\n",
      "and my name is GEORGES\"\"\"\n",
      "# test with a little text.\n",
      "assert( 3 == CountOccurencesInText(\"Georges\",text) )\n",
      "assert( 3 == CountOccurencesInText(\"GEORGES\",text) )\n",
      "assert( 3 == CountOccurencesInText(\"georges\",text) )\n",
      "assert( 0 == CountOccurencesInText(\"george\",text) )\n",
      "assert( 3 == CountOccurencesInText(\"python\",text) )\n",
      "assert( 3 == CountOccurencesInText(\"PYTHON\",text) )\n",
      "assert( 2 == CountOccurencesInText(\"I\",text) )\n",
      "assert( 0 == CountOccurencesInText(\"n\",text) )\n",
      "assert( 1 == CountOccurencesInText(\"true\",text) )    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "split() takes no keyword arguments",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-20-2ded7e7be7bc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m and my name is GEORGES\"\"\"\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# test with a little text.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[1;32massert\u001b[0m\u001b[1;33m(\u001b[0m \u001b[1;36m3\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mCountOccurencesInText\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Georges\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;32massert\u001b[0m\u001b[1;33m(\u001b[0m \u001b[1;36m3\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mCountOccurencesInText\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"GEORGES\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32massert\u001b[0m\u001b[1;33m(\u001b[0m \u001b[1;36m3\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mCountOccurencesInText\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"georges\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m<ipython-input-17-3e8b14420310>\u001b[0m in \u001b[0;36mCountOccurencesInText\u001b[1;34m(word, text)\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[1;34m\"\"\"Number of occurences of word (case insensitive) in text\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCounter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m','\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m                            \u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m                            \u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'!'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m                            \u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'?'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m                            \u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'\\n'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m                            \u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mTypeError\u001b[0m: split() takes no keyword arguments"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# regard ' as text:\n",
      "assert ( 0 == CountOccurencesInText ( \"maley\", \"John O'maley is my friend\" ) )\n",
      "# Test it but with a BIG length file. (we once had a memory error with this...)\n",
      "text = \"\"\"The quick brown fox jump over the lazy dog.The quick brown fox jump over the lazy dog.\"\"\" * 500\n",
      "text += \"\"\"The quick brown fox jump over the lazy dog.The quick brown Georges jump over the lazy dog.\"\"\"\n",
      "text += \"\"\"esrf sqfdg sfdglkj sdflgh sdflgjdsqrgl \"\"\" * 4000\n",
      "text += \"\"\"The quick brown fox jump over the lazy dog.The quick brown fox jump over the lazy python.\"\"\" \n",
      "text += \"\"\"The quick brown fox jump over the lazy dog.The quick brown fox jump over the lazy dog.\"\"\" * 500\n",
      "text += \"\"\"The quick brown fox jump over the lazy dog.The quick brown Georges jump over the lazy dog.\"\"\"\n",
      "text += \"\"\"esrf sqfdg sfdglkj sdflgh sdflgjdsqrgl \"\"\" * 4000\n",
      "text += \"\"\"The quick brown fox jump over the lazy dog.The quick brown fox jump over the lazy python.\"\"\" \n",
      "text += \"\"\"The quick brown fox jump over the lazy dog.The quick brown fox jump over the lazy dog.\"\"\" * 500\n",
      "text += \"\"\"The quick brown fox jump over the lazy dog.The quick brown Georges jump over the lazy dog.\"\"\"\n",
      "text += \"\"\"esrf sqfdg sfdglkj sdflgh sdflgjdsqrgl \"\"\" * 4000\n",
      "text += \"\"\"The quick brown fox jump over the lazy dog.The quick brown fox jump over the lazy python.\"\"\" \n",
      "text += \"\"\"The quick brown fox jump over the true lazy dog.The quick brown fox jump over the lazy dog.\"\"\"\n",
      "text += \"\"\"The quick brown fox jump over the lazy dog.The quick brown fox jump over the lazy dog.\"\"\" * 500\n",
      "text += \"\"\" I vsfgsdfg sfdg sdfg sdgh sgh I sfdgsdf\"\"\"\n",
      "text += \"\"\"The quick brown fox jump over the lazy dog.The quick brown fox jump over the lazy dog.\"\"\" * 500\n",
      "assert( 3 == CountOccurencesInText(\"Georges\",text) )\n",
      "assert( 3 == CountOccurencesInText(\"GEORGES\",text) )\n",
      "assert( 3 == CountOccurencesInText(\"georges\",text) )\n",
      "assert( 0 == CountOccurencesInText(\"george\",text) )\n",
      "assert( 3 == CountOccurencesInText(\"python\",text) )\n",
      "assert( 3 == CountOccurencesInText(\"PYTHON\",text) )\n",
      "assert( 2 == CountOccurencesInText(\"I\",text) )\n",
      "assert( 0 == CountOccurencesInText(\"n\",text) )\n",
      "assert( 1 == CountOccurencesInText(\"true\",text) )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 195
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Troisi\u00e8me batch"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assert( 0 == CountOccurencesInText(\"reflexion mirror\",\n",
      " \"I am a senior citizen and I live in the Fun-Plex 'Reflexion Mirror' in Sopchoppy, Florida\") )\n",
      "assert( 1 == CountOccurencesInText(\"'reflexion mirror'\",\n",
      " \"I am a senior citizen and I live in the Fun-Plex 'Reflexion Mirror' in Sopchoppy, Florida\") )\n",
      "assert( 1 == CountOccurencesInText(\"reflexion mirror\",\n",
      " \"I am a senior citizen and I live in the Fun-Plex (Reflexion Mirror) in Sopchoppy, Florida\") )\n",
      "assert( 1 == CountOccurencesInText(\"reflexion mirror\",\n",
      " \"Reflexion Mirror\\\" in Sopchoppy, Florida\") )\n",
      "assert( 1 == CountOccurencesInText(\"reflexion mirror\",\n",
      " u\"I am a senior citizen and I live in the Fun-Plex \u00abReflexion Mirror\u00bb in Sopchoppy, Florida\") )\n",
      "assert( 1 == CountOccurencesInText(\"reflexion mirror\",\n",
      " u\"I am a senior citizen and I live in the Fun-Plex \\u201cReflexion Mirror\\u201d in Sopchoppy, Florida\") )\n",
      "assert( 1 == CountOccurencesInText(\"legitimate\",\n",
      " u\"who is approved by OILS is completely legitimate: their employees are of legal working age\") )\n",
      "assert( 0 == CountOccurencesInText(\"legitimate their\",\n",
      " u\"who is approved by OILS is completely legitimate: their employees are of legal working age\") )\n",
      "assert( 1 == CountOccurencesInText(\"get back to me\",\n",
      " u\"I hope you will consider this proposal, and get back to me as soon as possible\") )\n",
      "assert( 1 == CountOccurencesInText(\"skin-care\",\n",
      " u\"enable Delavigne and its subsidiaries to create a skin-care monopoly\") )\n",
      "assert( 1 == CountOccurencesInText(\"skin-care monopoly\",\n",
      " u\"enable Delavigne and its subsidiaries to create a skin-care monopoly\") )\n",
      "assert( 0 == CountOccurencesInText(\"skin-care monopoly in the US\",\n",
      " u\"enable Delavigne and its subsidiaries to create a skin-care monopoly\") )\n",
      "assert( 1 == CountOccurencesInText(\"get back to me\",\n",
      " u\"When you know:get back to me\") )\n",
      "assert( 1 == CountOccurencesInText(\"don't be left\" , \"\"\"emergency alarm warning.\n",
      "Don't be left unprotected. Order your SSSS3000 today!\"\"\" ) )\n",
      "assert( 1 == CountOccurencesInText(\"don\" , \"\"\"emergency alarm warning.\n",
      "Don't be left unprotected. Order your don SSSS3000 today!\"\"\" ) )\n",
      "assert( 1 == CountOccurencesInText(\"take that as a 'yes'\",\n",
      " \"Do I have to take that as a 'yes'?\") )\n",
      "assert( 1 == CountOccurencesInText(\"don't take that as a 'yes'\",\n",
      " \"I don't take that as a 'yes'?\") )        \n",
      "assert( 1 == CountOccurencesInText(\"take that as a 'yes'\",\n",
      " \"I don't take that as a 'yes'?\") )\n",
      "assert( 1 == CountOccurencesInText(\"don't\",\n",
      " \"I don't take that as a 'yes'?\") )\n",
      "assert( 1 == CountOccurencesInText(\"attaching my c.v. to this e-mail\",\n",
      " \"I am attaching my c.v. to this e-mail.\" ))\n",
      "assert ( 1 == CountOccurencesInText ( \"Linguist\", \"'''Linguist Specialist Found Dead on Laboratory Floor'''\" ))\n",
      "assert ( 1 == CountOccurencesInText ( \"Linguist Specialist\", \"'''Linguist Specialist Found Dead on Laboratory Floor'''\" ))\n",
      "assert ( 1 == CountOccurencesInText ( \"Laboratory Floor\", \"'''Linguist Specialist Found Dead on Laboratory Floor'''\" ))\n",
      "assert ( 1 == CountOccurencesInText ( \"Floor\", \"'''Linguist Specialist Found Dead on Laboratory Floor'''\" ))\n",
      "assert ( 1 == CountOccurencesInText ( \"Floor\", \"''Linguist Specialist Found Dead on Laboratory Floor''\" ))        \n",
      "assert ( 1 == CountOccurencesInText ( \"Floor\", \"__Linguist Specialist Found Dead on Laboratory Floor__\" ))\n",
      "assert ( 1 == CountOccurencesInText ( \"Floor\", \"'''''Linguist Specialist Found Dead on Laboratory Floor'''''\" ))\n",
      "assert ( 1 == CountOccurencesInText ( \"Linguist\", \"'''Linguist Specialist Found Dead on Laboratory Floor'''\" ))\n",
      "assert ( 1 == CountOccurencesInText ( \"Linguist\", \"''Linguist Specialist Found Dead on Laboratory Floor''\" ))        \n",
      "assert ( 1 == CountOccurencesInText ( \"Linguist\", \"__Linguist Specialist Found Dead on Laboratory Floor__\" ))\n",
      "assert ( 1 == CountOccurencesInText ( \"Linguist\", \"'''''Linguist Specialist Found Dead on Laboratory Floor'''''\" ))\n",
      "assert ( 1 == CountOccurencesInText ( \"Floor\", \"\"\"Look: ''Linguist Specialist Found Dead on Laboratory Floor'' is the headline today.\"\"\"))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "AssertionError",
       "evalue": "",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-199-9b1d26a5a20d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m  \"I am a senior citizen and I live in the Fun-Plex 'Reflexion Mirror' in Sopchoppy, Florida\") )\n\u001b[1;32m      3\u001b[0m assert( 1 == CountOccurencesInText(\"'reflexion mirror'\",\n\u001b[0;32m----> 4\u001b[0;31m  \"I am a senior citizen and I live in the Fun-Plex 'Reflexion Mirror' in Sopchoppy, Florida\") )\n\u001b[0m\u001b[1;32m      5\u001b[0m assert( 1 == CountOccurencesInText(\"reflexion mirror\",\n\u001b[1;32m      6\u001b[0m  \"I am a senior citizen and I live in the Fun-Plex (Reflexion Mirror) in Sopchoppy, Florida\") )\n",
        "\u001b[0;31mAssertionError\u001b[0m: "
       ]
      }
     ],
     "prompt_number": 199
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "CountOccurencesInText(\"'reflexion mirror'\", \n",
      "                      \"I am a senior citizen and I live in the Fun-Plex 'Reflexion Mirror' in Sopchoppy, Florida\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 200,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 200
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"I am a senior citizen and I live in the Fun-Plex 'Reflexion Mirror' in Sopchoppy, Florida\".upper().replace(',', ' ')\\\n",
      "                            .replace('.', ' ')\\\n",
      "                            .replace('!', ' ')\\\n",
      "                            .replace('?', ' ')\\\n",
      "                            .replace('\\n', ' ')\\\n",
      "                            .split(sep=' ')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 201,
       "text": [
        "['I',\n",
        " 'AM',\n",
        " 'A',\n",
        " 'SENIOR',\n",
        " 'CITIZEN',\n",
        " 'AND',\n",
        " 'I',\n",
        " 'LIVE',\n",
        " 'IN',\n",
        " 'THE',\n",
        " 'FUN-PLEX',\n",
        " \"'REFLEXION\",\n",
        " \"MIRROR'\",\n",
        " 'IN',\n",
        " 'SOPCHOPPY',\n",
        " '',\n",
        " 'FLORIDA']"
       ]
      }
     ],
     "prompt_number": 201
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}