{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Getting text (harry potter series 1-3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "# define a function to get .txt files in a folder\n",
    "from os import listdir\n",
    "def list_textfiles(directory):\n",
    "    \"Return a list of filenames ending in '.txt' in DIRECTORY.\"\n",
    "    textfiles = []\n",
    "    for filename in listdir(directory):\n",
    "        if filename.endswith(\".txt\"):\n",
    "            textfiles.append(directory + \"/\" + filename)\n",
    "    return textfiles   \n",
    "\n",
    "# define a function to read the text in a .txt file\n",
    "\n",
    "def read_txt(filename):\n",
    "    try:\n",
    "        f = open(filename,'r')\n",
    "        text = f.read()\n",
    "    finally:\n",
    "        if f:\n",
    "            f.close()\n",
    "    return text\n",
    "\n",
    "#import harry potter textfiles\n",
    "filenames = list_textfiles('HP')\n",
    "raw_texts = []\n",
    "for f in filenames:\n",
    "    raw_texts.append(read_txt(f))\n",
    "print len(raw_texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]]\n",
      "[u'anthem' u'anthems' u'anthill' u'anthology' u'anthony' u'anti'\n",
      " u'anticheating' u'anticipated' u'anticipating' u'anticipation']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "vectorizer = CountVectorizer(input='content')\n",
    "# generate document term matrix\n",
    "dtm = vectorizer.fit_transform(raw_texts)\n",
    "\n",
    "# get the words as feature names\n",
    "\n",
    "vocab = np.array(vectorizer.get_feature_names())\n",
    "\n",
    "# fit_transform returns a sparse matrix (which uses less memory)\n",
    "# but we want to work with a normal numpy array.\n",
    "dtm = dtm.toarray()\n",
    "\n",
    "# normalize counts to rates per 1000 words\n",
    "rates = 1000 * dtm / np.sum(dtm, axis=1, keepdims=True)\n",
    "\n",
    "print rates[:, 1000:1010]\n",
    "print vocab[1000:1010]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measuring distinctiveness\n",
    "\n",
    "Here, in my texts, we will identify the unique words used in each harry potter book.\n",
    "\n",
    "sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22987\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "vectorizer = TfidfVectorizer(input='content', min_df = 1, stop_words = 'english')\n",
    "\n",
    "# Let’s execute the model against our corpus:\n",
    "tfidf_matrix =  vectorizer.fit_transform(raw_texts)\n",
    "feature_names = vectorizer.get_feature_names()\n",
    "\n",
    "tfidfArray = tfidf_matrix.toarray()\n",
    "\n",
    "print len(feature_names)\n",
    "\n",
    "#print tfidfArray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we are got more than 10 thousands words.\n",
    "View the top 5 terms of each news article based on the tfidf score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tfidf for article 1 is:\n"
     ]
    },
    {
     "data": {
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58MADefHFF5tRriRpCBoJViuByRFxTESMBS4AeuobRMTRwDLgosz8UfkypdFjT7fqqXfI\nIYewdetWzj77bLZt28Y73vGOkSpRkrSXBg1WmbkdmAs8AKwD7s3MpyJidkTMrpp9HvglYEFEPBYR\nI3PlT6kN7e5WPQM9+uijTJo0ieXLl3PMMcfw2GOPjVSJkqS91NCAjcxckZnvzszjMvPaat7CzFxY\nPf5YZh6WmSdXP4NemVTaV+3uVj0PP/wwN910E/fccw9Lly7lhBNOYN26dcyZM4fHH3+cyZMnN7ly\nSdJgig5elzS4gw46iDvuuKN/esfg9dNPP32nMVcAX/3qV0e0NknSW+NXjCRJkgoxWEmSJBVisJKG\nmfdAlKR9h2OspGHmPRAlad9hj5UkSVIhBitJkqRCDFaSJEmFGKwkSZIKMVhJkiQVYrCSJEkqxGAl\nSZJUiMFKkiSpEIOVJElSIQYrSZKkQgxWkorYsmULl1xyCZdffjl33XVX//wVK1Ywbdo05s+fD0Bf\nXx9XXHEFF198MbNnz25WuZI0LAxWkopYtmwZM2fOZPHixfT09PTPnzZtGp/5zGf6pzs6Oli0aBF3\n3nknr7zyCn19fc0oV5KGhcFKUhG9vb1MnDgRgM7Ozj22Xbt2LZdccgmHHnooHR2+DUkaPXxHk1TE\nhAkT6O3tBRi0F+qEE07gK1/5Cn19fTz77LMjUZ4kjYgxzS5A0ugwY8YM5s6dy/Lly5k+fToXXXQR\nS5Ys4eGHH+amm27ihRde4F3vehenn3461113HX19fYwZM6a/l0uSRgODlaQiDjroIO64447+6e7u\nbgBOP/30ncZcAdx8880jWpskjRRPBUqSJBVisJK0V7b3ZVutV5JGgqcCJe2VMR3B9au3FV/vvFP2\nK75OSRop9lhJ0h40euFTgCuuuIITTzyxGWVKahEGK0nag0YvfAqwaNEijj/++JEuUVILMVhJ0h4M\n5cKn7WgoPXKSBmewkqQ9GMqFT9vRUHrkJA3OYCVJezBjxgyWLl3KlVde2X/hU6D/wqf33HMPS5cu\nBeDqq69m9erVzJ49m61btzaz7IaN1h45e+LULH4rUJL2YCgXPr322mu59tprR7S+t2pHj9zJJ588\nqnrkdvTETZ8+nQ9/+MP9+23atGkceOCBrFmzpskV7p0tW7YwZ84cxo4dy5QpU/q3a8WKFcyfP59p\n06Yxd+7cJle5b7PHSpL2YUPpkWsno7UnbrSdut1dz+KaNWvo7u6mu7ubNWvW8Oqrr3LhhRcyZ84c\nrrnmmiZWPLiGglVETI2IpyNifUTM28Xy90TEwxGxNSL+uHyZkjQy9rULn+7okfvSl75Ed3c3S5Ys\nAd7skXvooYc4//zzm1zl0I3WsXGjLTDuLih+8Ytf5JZbbmHBggXcfPPNrFu3juOPP54FCxbw/PPP\ns2HDhiZWvWeDngqMiE7gFuBMoBdYGRE9mbm2rtn/A/4QOG9YqpSkEeKFT0eHRm8K3m6hcbSduu3t\n7e2/9lt9UNy8eTOHHnooAC+//DKnnHIK9913H1dddRXPPfccGzdubNkbuDcyxuo0YH1mPgMQEXcD\n5wL9wSozfwL8JCLOHpYqJUlvyfa+ZExHtM1636qhjI1rJ6MtMO4uKL797W9n8+bNRATjxo2jo6Oj\n/xTgZZddxrHHHtuskgfVSLA6Cqjvc+sF3j885UiShoM9caPDaAuMuwuKn/jEJ/j4xz8O0D92bM6c\nOWzbto2uri6OOOKIZpa9RyP6rcCImAXMAjj66KNH8qklSaPQvtYTN9rsLii+973v5c4779yp7YIF\nC0a0tr3VSLDaCNSfyJxQzRuyzLwVuBWgq6urNUdySpLaxmjtiTMwtq9GgtVKYHJEHEMtUF0AfHRY\nq5IkaR9mYGyN9e6NQYNVZm6PiLnAA0AncHtmPhURs6vlCyPincAq4BCgLyI+CZyQmS8NY+2SJKmN\njNbAWK+hMVaZuQJYMWDewrrH/07tFKEkSdI+yyuvS5IkFWKwkiRJKsRgJUmSVIjBSpIkqRCDlSRJ\nUiEGK0mSpEIMVpIkSYUYrCRJkgoxWEmSJBVisJIkSSrEYCVJklSIwUqSJKkQg5UkSVIhBitJkqRC\nDFaSJEmFGKwkSZIKMVhJkiQVYrCSJEkqxGAlSZJUiMFKkiSpEIOVJElSIQYrSZKkQgxWkiRJhRis\nJEmSCjFYSZIkFWKwkiRJKsRgJUmSVIjBSpIkqRCDlSRJUiEGK0mSpEIMVpIkSYU0FKwiYmpEPB0R\n6yNi3i6WR0T8VbX8iYg4tXypkiRJrW3QYBURncAtwFnACcBHIuKEAc3OAiZXP7OALxWuU5IkqeU1\n0mN1GrA+M5/JzNeBu4FzB7Q5F7gza34AHBoR7ypcqyRJUktrJFgdBWyom+6t5g21jSRJ0qgWmbnn\nBhEzgamZ+bFq+iLg/Zk5t67NN4DrM/N71fQ3gT/JzFUD1jWL2qlCgOOBp0ttSAGHAz9tdhHDwO1q\nL25Xe3G72ovb1V5abbt+JTPHD9ZoTAMr2ghMrJueUM0bahsy81bg1gaec8RFxKrM7Gp2HaW5Xe3F\n7Wovbld7cbvaS7tuVyOnAlcCkyPimIgYC1wA9Axo0wNcXH078DeAzZn5fOFaJUmSWtqgPVaZuT0i\n5gIPAJ3A7Zn5VETMrpYvBFYA04D1wKvAfx2+kiVJklpTI6cCycwV1MJT/byFdY8T+IOypY24ljxF\nWYDb1V7crvbidrUXt6u9tOV2DTp4XZIkSY3xljaSJEmFGKwkSZIKGbXBKiImRcSaZtfRKiLizyLi\nd3cxf0p1HTIVMpyvvYj4/m7mf7m65txbWfeIHDMRcdsubovV0iLiTyPij9v1OPL9cN8SEZdGxJHN\nrmMwEfFK9e+REfF31eNLI2J+cyt7axoavL6viYgxmbl9d9PtKDM/3+wa3oqICGpjAvuaXUsz7HgN\nZuZvNruWXRnKMbLjYsPtqN2PI+0zLgXWAM81uY6GZOZzwFv6YNhKRm2PVaUzIhZHxFMR8Q8RcUBE\nXB4RKyPi8YhYGhEHQv8n/oUR8c/AjdUn1CUR8U/Akoh4MCJO3rHiiPheRLyvWRtW1XBQRCyvtmVN\nRHw4Ij5fbd+aiLi1CiQ79WhExNSI+GFEPArMaOY27En1KfvpiLiT2pvERRHxZLVtN9S1eyUirq3+\nDj+IiF9uXtX9hvLaO66q+8mIuKbuU9yUiHgoInqAtdW8HcsiIuZXf5//BRwxjHUfFxH3R8QjVT3v\nqWrY1THzlarNsxExIyJurLbr/ojYr/q970REV/X4I0PZpxExvvrbrax+PlBou39BRFwdET+KiO9R\nu1NEQ8dR9Xe4vdrOZyLiD4erxr0REcdGxOqI+HRELKv2zb9GxI11bX5hv0TE70fETdXjT0TEM3Xr\n+6fmbM2uRcT1EfEHddM7ehw/Xb1unoiI/97MGocqIj5XHe/fi4ivVttzcnV8PBER90XEYdXrswu4\nKyIei4gDml37YGI3PaoRcXZEPBwRh4/ksf+WZeao/AEmAduBk6vpe4ELgV+qa3MN8PHq8ZeBbwCd\n1fSfAo8AB1TTlwB/WT1+N7CqBbbxfGBx3fTbgXfUTS8Bptdt30xgf2r3dZwMRPV3+Uazt2UP+7AP\n+A3gSODfgPHUelq/BZxXtcu67bwR+GwL1D2U1943gI9Uj2cDr1SPpwBbgGPqfm/HshnAP1K7ttyR\nwIvAzGGq+5vA5Gre+4Fv1b2mBh4z3wP2A95H7Zp2Z1XL7qvbX9+h9sY/5H0K/A1wRvX4aGDdMO3D\nXwOeBA4EDqF2jb4/buQ4qv4O3wfeRu2WHD8D9muB1+QaagFxdbV/LgWeofa+sT/wLLU7aOxyvwDv\nBFZW6/s7ahePPorae+N1zdy+XWzvKcB366bXVnXeWu2vjuq1+1vNrrXB7fl14LFqP40D/rV6PT4B\n/HbV5s948/+o7wBdza67ge3a8X42CVhTPb4UmA/8HvAQcFg1f0SO/RI/o73H6seZ+Vj1+BFqO++9\n1SfqJ4Fu4Ffr2v9tZr5RN92Tma/tWAacU33qvozaG2yzPQmcGRE3RMQHM3Mz8KGI+Odq+36HnbcP\n4D3U/i7/mrVX6F+PcM1D9Wxm/oDaG8t3MnNT1k453QX8VtXmdWpvkvDmfm62obz2Tqf2+oLam0e9\nf8nMH+9i/b8FfDUz38haN/q3hrHu3wT+NiIeAxYB76prP/CY+fvM3EbttdkJ3F/Nf5Jf3C97s09/\nF5hf1dIDHBIRB+/dpu7RB4H7MvPVzHyJX7zbxGDH0fLM3JqZPwV+ArRCL+p44OtAd2Y+Xs37ZmZu\nzsyfUwsfv8Ju9ktm/jtwcESMoxbA/oba/vogtf8AW0ZmrgaOiNrYnfcBLwAnAv+JWrB8lNo+nNy8\nKofkA8DXM/Pnmfky8D+Bg4BDM/O7VZuv8Obx0+5+B/gT4OzMfKGaN1LH/ls22sdYba17/AZwALVA\ndF5mPh4Rl1LrFdhhy4Df75/OzFcj4h+Bc4H/Qu0TbVNl5o8i4lRqV72/Jmo3v/4Dap9UNkTEn1L7\nhNPOBu6TXdlW/ecGtf3cCq/rob72dqeR7S9pYN2/DLyYmSfvpv3A+rYCZGZfRNTvlz6Gtl92t087\ngN+ogkArG/h3bIXX5GZqPVFnUJ1aZuh1fp/anTWephamLqP2weBTRSst42+p9S6+E7iHWmi8LjMX\nNbUqNeJ/A8dSnR2q5rXLsT/qe6x2ZRzwfNXz1D3E370N+Ctq3eEvDNZ4uEXtWx+vZuZfA18ATq0W\n/bRK8rsaDPhDYFJEHFdNf2T4Ky3iX4Dfrs61d1Kr+7uD/E6r2d1r7wfUTutC7V6cjXgQ+HBEdEbE\nu4APlStzJy8BP46I34f+sV2lxhbuzT79B+DjOyaibtxjYQ8C50VtjNk4YPqA5e14HL1O7fTKxRHx\n0T2029N+eYjaKagHqfX8fAjYWvWWt5p7qB1PM6mFrAeAy3b0ckTEURFRamzicPsnYHpE7F/Vfw61\nDzUvRMQHqzYX8eZ+epna+027epbae+KdEbGjZ3+kjv23rBU+RY20zwH/DGyq/m34xZeZj0TES8Ad\nw1TbUJ0IfCEi+oBtwJXUxkKsAf6d2hiInWTmzyNiFrA8Il6l9kbZ8gdgZj4fEfOAb1MbI7E8M7/e\n5LKGanevvU8Cfx0RV1M7ddbIf1L3UesuX0utF+Lh4tW+qRv4UkR8ltr4qbuBx/f8K4Pby336h8At\nEfEEtfevB6mNSysqMx+NiHuobedPGHAstfFxtCUizqE2Pm/Jbtrsab88RO004IOZ+UZEbKAWMltO\n1u5pOw7YmJnPU/tQ8x+Bh6P2nZ5XqI0h/EkTy2xIZq6M2pdYngD+L7VT65upjRtbGLUvwjzDm/fp\n/XI1/zXg9LohLW0jM38YEd3UhiFMZ4SO/RK8pc0QVD1E3wHek/vo1/5VXvWm+FpmZkRcQG0g+7nN\nrktS64iIgzPzler94kFgVmY+2uy69Iv2xR6rvRIRFwPXAlcZqlTYr1EblBnUvt13WZPrkdR6bo3a\nhXX3B75iqGpd9lhJkiQVsi8OXpckSRoWBitJkqRCDFaSJEmFGKwkSZIKMVhJkiQV8v8BTSdOxl1J\nyWEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e9ec10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tfidf for article 2 is:\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a0444d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tfidf for article 3 is:\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1045dd050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tfidf for article 4 is:\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pn7u9MdDz8HrBx7qB2gzKL1GbUSqtr7+Xa9j9fXgj2+ZN9P/aV//zQ/6E9uFf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e07fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tfidf for article 5 is:\n"
     ]
    },
    {
     "data": {
      "image/png": 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NwMXUdmg/pp+vI2rfHs+NiAepBaEv9/F4vb3H/gvwpqgNpn878AdgfWY+APxn\n4EcRcW9V5/4Ukpl3UTtseQ/wA57dN8ykNgj4XmrjPS7ux2r/J/C2aMHB63XuBZ6pBm1/pNuyD1eD\nh+8FNlP7u7SCnvbrjbqQ2mH2nwO/r5v/ua7B0tWykuNNd1Vm5lrg29Q+u75NLVRALTx9v3qOfgZ0\njVFdAHy0GiTeMoPXM3MDtS9uHwH23EGb3vaNt1ELvbdWvZJr6fuL3KDxkjbq+uZ3C3B4DpOf/au1\nVT1mz1SHOI4DvtzL4WNpSKsGp9+VmSV7ZzVAhtx4APVPRLwbuBQ4z1ClFnIg8O3qUM0mmvuLSKlp\n6r74fr7JpahB9lhJkiQVMlzGWEmSJA04g5UkSVIhBitJkqRCDFaSJEmFGKwkSZIK+X9gbc+jPana\ncQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109f70710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tfidf for article 6 is:\n"
     ]
    },
    {
     "data": {
      "image/png": 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B/rncvtcDR1S073gs+FZKaSfF76YBuKuc3nF/hZ5t0672497U3eu2N44VvWE0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\nNM6aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109f82850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tfidf for article 7 is:\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x109f82910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = vectorizer.get_feature_names()\n",
    "li = [] #li is a list of tfidf (top 10 words) in the book.\n",
    "tfidf = []\n",
    "for l in tfidfArray:\n",
    "\n",
    "    # sort tf-idf /How to explain this : ???\n",
    "    #print [[feature_names[x],l[x]] for x in (l*-1).argsort()][:5]\n",
    "    tfidf.append([(feature_names[x],l[x]) for x in (l*-1).argsort()][:10])\n",
    "\n",
    "\n",
    "# plot the result\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "def plotTfidf (li): # parameter li is a list of tfidf (top 5 words) in the book. \n",
    "                    #e.g. [(u'pens', 0.43280168447929529), (u'the', 0.29211105475772814), (u'bit', 0.21640084223964764), (u'of', 0.2133540325973512), (u'am', 0.18396048627999845)]\n",
    "    \n",
    "    # set figure size\n",
    "    fig = plt.figure(figsize=[10, 5])\n",
    "    # plot tfidf of a given doc\n",
    "    labels,Y = zip(*li) # See notes below\n",
    "    X = np.arange(len(labels))\n",
    "    plt.bar(X,Y,width = 0.4,facecolor = 'lightskyblue',edgecolor = 'white')  \n",
    "    plt.xticks(X, labels)# stick labels on x axis\n",
    "    for x,y in zip(X,Y):\n",
    "        plt.text(x,y+0.005,round(y,2), ha='center', va= 'bottom',fontsize=7) #round(y,2): two decimals\n",
    "    plt.show()    \n",
    "\n",
    "i = 0\n",
    "for doc in tfidf:\n",
    "    print \"tfidf for article \" + str(i+1) +\" is:\"\n",
    "    plotTfidf(doc)\n",
    "    i +=1   \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the result, we can see that there are common words like 'harry' 'said' 'ron'...\n",
    "This is because that the documents number is too small. So we need to set the max-df parameter, and filter out those words which have a really high df. Please see example here:\n",
    "http://localhost:8888/notebooks/Desktop/temp/UofA%20/Learn%20python/machine%20learning/Feature%20selection_finding%20distinctive%20words(sklearn-CountVectorizer%2B%20TfidfTransformer)%20.ipynb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
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 },
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}