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 },
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Investigating Good Articles for C\u00f4te d'Ivoire and Uganda"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "For <a href=\"https://fr.wikipedia.org/wiki/Projet:Kumusha_Takes_Wiki\">Kumusha Takes Wiki</a> - by <a href=\"http://notconfusing.com/\">Max Klein</a>\n",
      "<br />\n",
      "10 January 2014"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Our principal question in this investigation is what makes a good article about a country in the hope of improving articles related to C\u00f4te d'Ivoire and Uganda\n",
      "\n",
      "We will explore several aspects of the quality of the articles and their relationship to different language Wikipedias. First we look at articles literally describing the countries of Ivory Coast and Uganda and compare them to other country articles in English, French and Swahili Wikipedias. This is done to both sanity check our method, and also to determine which subsequent articles to explore. Here we utilise the research [Tell Me More: An Actionable Quality Model for Wikipedia](http://www-users.cs.umn.edu/~morten/publications/wikisym2013-tellmemore.pdf) by _Warncke-Wang, Cosley, and Reidl_ which is meant to measure points of quality that can be more easily addressed. Secondly, we move to sets of articles _about a subject_ of that nation. These articles are chosen to be most important by how often they occur in a specific Wikipedia. Next we attempt to apply the lessons from [Predicting Quality Flaws in User-generated Content: The Case of Wikipedia](http://www.uni-weimar.de/medien/webis/publications/papers/stein_2012i.pdf) by _Anderka, Stein, Lipka_ which are about finding user-specified quality flaws.\n",
      "\n",
      "This report is an _IPython Notebook_ so it contains all the code necessary to reproduce these results. Much of the the code is at the end, to maintain non-technical readability."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Getting the Wikidata item for every country"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We want to compare literal country articles worldwide. Since we will be analysing English, French, and Swahili Wikipedias, we find the _Wikidata items_ related to these countries (here denoted by their identifiers beginning with \"Q\"). Each Wikidata items points to all the different Wikipedia language versions available. In an attempt to not include language bias, this list was first queried from the Wikidata property [\"instance of\"](https://www.wikidata.org/wiki/Property:P31) with target [country](https://www.wikidata.org/wiki/Q6256) (through the [offline Wikidata parser](https://github.com/mkroetzsch/wda)  ). However this only yielded 133 items. Even though it is Anglocentric our list is scraped from English Wikipedia [List of Countries by Popultation](https://en.wikipedia.org/wiki/List_of_countries_by_population), yielding:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "country_qids = json.load(open('country_qids.json'))\n",
      "ugandaqid, cdiqid= 'Q1036', 'Q1008'\n",
      "print \"We are comparing: \", len(country_qids), 'countries\".'\n",
      "print \"Some of their Wikidata QIDs are: \", map(lambda qid: 'http://wikidata.org/wiki/'+qid, country_qids[:5])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "We are comparing:  242 countries\".\n",
        "Some of their Wikidata QIDs are:  [u'http://wikidata.org/wiki/Q16', u'http://wikidata.org/wiki/Q33788', u'http://wikidata.org/wiki/Q25230', u'http://wikidata.org/wiki/Q874', u'http://wikidata.org/wiki/Q37']\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Let's inspect each metric"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We look at the 5 statistics that are mentioned in the literature as \"Actionable Metrics\". The metrics are:\n",
      "\n",
      "1. __Completeness__ - This counts the number of intra-wiki links, to other articles.\n",
      "1. __Informativeness__ - This is a combination of the ratio of (_readable text_ to _wikitext markup_) plus _number of images_. \n",
      "1. __Numeber of Headings__ - A count of the sections and subsections.\n",
      "1. __Article Length__ - The lenght of the _readable_ article in characters.\n",
      "1. __Reference Rate__ - The number of references per article length.\n",
      "\n",
      "In _Tell me more_ weights are used, and we use the same weights, but for expermientation they could be changed."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def report_actionable_metrics(wikicode, completeness_weight=0.8, infonoise_weight=0.6, images_weight=0.3):\n",
      "    completeness = completeness_weight * num_reg_links(wikicode)\n",
      "    informativeness = (infonoise_weight * infonoise(wikicode) ) + (images_weight * num_file_links(wikicode) )\n",
      "    numheadings = len(section_headings(wikicode))\n",
      "    articlelength = readable_text_length(wikicode)\n",
      "    referencerate = article_refs(wikicode) / readable_text_length(wikicode)\n",
      "    \n",
      "    return {'completeness': completeness, 'informativeness': informativeness, 'numheadings': numheadings, 'articlelength': articlelength, 'referencerate': referencerate} "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 140
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will now look at each metric one by one, on the set of our Wikidata Q-IDs to get a feel for how these metrics will be used later on.\n",
      "\n",
      "For each metric the Uganda and C\u00f4te d'Ivoire article's position amongst other countries are shown by the _z-score_ which is a measure of how many standard deviations they are above or below the average. Additionally we display a graph of the distribution of how the Country articles score, separated by Wikipedia language. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "metric_analyse_density('informativeness', 12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Uganda (Q1036), C\u00f4te d'Ivoire (Q1008) informativeness z-scores.\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>language</th>\n",
        "      <th>en</th>\n",
        "      <th>fr</th>\n",
        "      <th>sw</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Q1036</th>\n",
        "      <td> 0.381577</td>\n",
        "      <td>-0.643210</td>\n",
        "      <td>-0.272597</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Q1008</th>\n",
        "      <td>-0.431621</td>\n",
        "      <td> 3.311523</td>\n",
        "      <td>-0.384729</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 499,
       "text": [
        "language        en        fr        sw\n",
        "Q1036     0.381577 -0.643210 -0.272597\n",
        "Q1008    -0.431621  3.311523 -0.384729"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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kee3DDz90fz9q1ChGjRrVjr2pTwZbH3LoYDrvOyxwCrliV+q4jKHJZ8ddL2D/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\nJjTpIUBtAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x23099090>"
       ]
      }
     ],
     "prompt_number": 499
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Most notable here, C\u00f4te d'Ivoire has a very high score in French Wikipedia - 3.31 standard deviations above average.\n",
      "\n",
      "In general these distributions are quite standard with a relatively long tail, also known as positive Skew, which we would expect from user generated content. Also English and French outpace a spikier Swahili, which fits in line with standard conceptions about the popularities of these languages."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "metric_analyse_density('completeness', 1500)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Uganda (Q1036), C\u00f4te d'Ivoire (Q1008) completeness z-scores.\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>language</th>\n",
        "      <th>en</th>\n",
        "      <th>fr</th>\n",
        "      <th>sw</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Q1036</th>\n",
        "      <td>-0.443975</td>\n",
        "      <td>-0.176491</td>\n",
        "      <td> 1.736298</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Q1008</th>\n",
        "      <td>-0.973200</td>\n",
        "      <td> 1.767785</td>\n",
        "      <td>-0.505017</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 500,
       "text": [
        "language        en        fr        sw\n",
        "Q1036    -0.443975 -0.176491  1.736298\n",
        "Q1008    -0.973200  1.767785 -0.505017"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HXpp3vYbzOwlBnjzJcZ0qLi7G+tMyovWOZHuqXMYvwlTwVfOc7fUc/3rue2vi\nX49ztp6sU00jeApS/qfOMcaY1+Jk2w7oUnlFheA5W8a8HSfbdlBvZEvgXWSMeTlOtu2AAAhxObly\n5S/GGCfbdkDkOm0gAMicbRnzapxs2wEBLotsBQC7m/rCGPMMnGzbQd3ZWSF4ZMuYt+Nk2w6I4JJx\nBXjOljFv51HJdtq0aVi7di2sVqu7u3JNiMh16RcBdl6MwJhX86hkO3LkSLzyyisICQnB448/jp07\nd7q7S61SbxTLtzNnzOt5VLJ99tlncfDgQaSlpSEgIAAzZsxAbGwsXn75ZRw/ftzd3Wsxuc6mBsHJ\nljGv51HJ1qlfv35444038Omnn8LX1xevvPIKBg0ahLFjxyoVuzyZ3NCcLSdbxryaxyXbrKwsLFy4\nEDfeeCMeffRR/PKXv8SJEydw/vx5TJo0yaVKl6dy3lnXSYB3kDHm7Twq2Q4dOhQjRoxAUVERVq5c\niaysLCxYsACRkZHw9fXFs88+69GV2J3qdlEIvkDGmLfzqHq2zz33HKZOnQofH59G25w8ebLjOtRK\nRK6FwyUANk62jHk1jxrZGo1GnDhxwuVYdnY2tmzZ4qYetc6Vt8QBAEkAdl5oy5hX86hk+8QTT9S7\np5der8cTTzzhph61Tt28KoEvkDHm7Twq2V68eBFhYWEux0JDQ5Vb0lwv5DpDWyFxbQTGvJ1HJdvo\n6Ghs3brV5di2bdsQHR3tph61Tt2bParA0wiMeTuPukD28ssv46677sIjjzyCmJgY5OXlYfny5Vi+\nfLm7u3ZV6hWiAa9GYMzbedTIdurUqUhNTUV5eTk2bNiAyspKpKamXhdra68ky3UukEmCky1jXs6j\nRrYAkJSUhKSkJHd345rUnTHgHWSMMY9KtjU1Nfj4449x6NAhVFRUAHBW0BL45JNP3Ny7lpPrVP3i\nEouMMY9Ktg8++CAOHz6MO+64AyEhIcpx4VKv0PPVXWcr+O66jHk9j0q2KSkpOHHiBEwmk7u7ck3s\ndXaQ8QUyxphHXSCLiopCTU2Nu7tx7eQGRrZu6wxjzBN41Mh25syZmDZtGubNm4fu3bu7PHbrrbe6\nqVdXzwbHFl0nvkDGGPOoZLtkyRIIIbBgwYJ6j9WtmeDJGiqxyMmWMe/mUcn2eqjo1RJ119kKISDz\nFTLGvJpHzdkCgNVqRVpaGlatWgUAKC8vV5aBXS/qbtcFnLfG4YTLmLfyqGT7448/olevXpgzZw4e\neeQRAMCHVlOwAAAgAElEQVT27dvx8MMPu7lnV6fuagQAEMTLvxjzZh6VbH/zm9/g5ZdfRlZWFjQa\nDQBg9OjRSEtLc3PPrg6Ro9KXyzHBGxsYa09/+MMf0K9fPyQkJOD3v/89ZFlWiliVlpZCpVIhPT0d\ngONO3h19E1mPmrM9evQoHnjgAZdjfn5+qKqqclOPWkduYGQLHtky1m6Kioqwbt06ZGVlAQDMZjMk\nSULv3r1x9OhR/PTTTxgyZAh27NiBxMREFBQUICYmpkP76FHJNioqCvv27UNiYqJybO/evYiNjW1x\njLy8PKSkpECWZQwePBjJycn12mzatAm5ubnQaDSYNm0aQkNDAQBffvklcnJy4O/vj7lz5yrtq6qq\nsHr1apSVlSEwMBD33HMPdDpdo32ou4MMuDRn2+JXwVjnkHe+FD9Xtb6ac1dfFXqGBDbbLjAwEDqd\nDo888ggmT56MyZMnAwBuueUW7NixAydOnMDzzz+PZcuWYdSoUS45pqN4VLL9y1/+gsmTJ+Oxxx5D\nbW0tXnvtNXzwwQdYtmxZi86XZRkbN27EzJkzYTAYsGzZMsTFxSE4OFhpk5ubi+LiYsybNw8FBQXY\nsGEDZs+eDQAYOHAgkpKSsHbtWpe46enpiImJwYgRI5Ceno709HSMHTu28X40MLIlHtkyL/RzlR1v\nZ7Y+2T7TD+jZgnYqlQp79uzB1q1bsWbNGrzzzjvYunUrRo4ciffeew+FhYV45ZVX8Le//Q3btm3D\nyJEjW92n1vKoOdvJkycjJSUFFy9exKhRo3Dq1CmsXbsW48ePb9H5Z86cQVBQEAIDA6FSqRAfH4/s\n7GyXNtnZ2UhISAAAREREoLq6GuXl5QAcI2tfX996ca88Z+DAgcpHFYvFgrNnz7p8WSwWyARIdeo5\nCEG8i4yxdlJRUYHS0lJMnDgRb731FjIyMgAAiYmJ2LlzJ1QqFbRaLRISEvDPf/7TLcnWo0a2ADBo\n0CC8//77rTrXYrHAaDQqPxuNRhQUFLi0MZvNCAgIcGljNpuh1+sbjVteXq487u/vryTn/fv3Y/v2\n7S5tR40aBd/om6BTA77qy3/LdLIVAQF+0Ptc+9+39q4dwfHdE7szxHcXi8WCqVOnorq6GkSEt99+\nGwCg1WoRGRmJYcOGAXBcGFu1ahX69+/f4X30qGT7wgsvNFrh65VXXmmz56E6n+evpqqYEEJpP2TI\nEPTq1cvlcYPBgG9OV8CuBuyqy3Grq2QUl9bCqrm2CmYmkwklJSXXFIPje15sT4x/PSXm7t27Y/fu\n3Q0+tmPHDuX7GTNmYMaMGR3VLRcelWxPnz7tkvgKCwuxY8cO3HnnnS063zlKdSorK3MZ6TbUxmw2\n17ujb116vV4Z3VosFvj7+wNwJNaGzhWoqb8aAYAsN7TdgbHOq6uvCs/0u7bzOwuPSrYff/xxvWMp\nKSlYuXJli84PCwtDUVERSktLYTAYkJmZibvuusulTVxcHPbs2YP4+HgUFBRAp9M1OYXgPOfQoUNI\nTk5GRkYGevfu3WT7hlYjAHyHXeZ9eoYEtugClzcQVPcztYex2+0wmUwuo9Gm5ObmIiUlBUSEQYMG\n4ZZbbsG+ffsAAEOHDgUAbNy4EXl5efWWfq1Zswb5+fmorKyEv78/xowZg0GDBl310q/Pj/6MAA2g\nuaL019kKGUODJXTVXducrad91PSm+Ndz31sT/3qaRrgeeNTI9qeffnL5ubKyEv/5z38QGRnZ4hix\nsbH11uU6k6zTpEmTGjz37rvvbvC4r68vZs6c2eI+yA0tqBVcQJwxb+ZRybZnT9cPHH5+fhg4cCD+\n/e9/u6lHrVdvUwM42TLmzTwq2coNDgmvPw2+CuKatox5M4/a1NBZyORaPBxwFKbhZMuY9/KokW2P\nHj0ghHBZB9vQz6dOnXJH91qswZxKgL1zDNwZY63gUcl23rx5+Pe//42nnnoKkZGROHXqFJYsWYKZ\nM2fWu8jlyaiB2giSAGyevfCDMdaOPCrZfvzxx9i8eTMiIiKUYxMnTsSECRPw//7f/3Njz65OY8mW\nL5Ax5r08as62sLCw3gYDvV6PM2fOuKlHrUOovwXYMbJ1T38YY+7nUcl2ypQpmDp1KlJTU3Hs2DFs\n3rwZ06ZNw5QpU9zdtRZr7D5jEgAbbyFjzGt5VLJ9//33MXz4cDz++OMYPHgwHn/8cQwbNgwffPCB\nu7t2zSQJsLm7E4wxt/GoOVtfX1+88cYbeOONN9zdlVZr8JY4AFTg1QiMeTOPGtkCQGpqKh5++GHl\nthb79u3Dt99+6+ZetRyR4+aOdUkCsHKyZcxreVSyXbJkCR5//HHExsYqNSh1Oh0WLlzo5p61XGNF\nFCUhuOoXY17Mo5Lt22+/jW+++QbPP/88VCpHHcs+ffoot6G5HpDyP64kwTvIGPNmHpVsy8vL0aNH\nD5djtbW10Gq1burR1aOGFtniUiEankZgzGt5VLK95ZZb6l0cW7JkCcaMGeOmHl29xka2fCtzxryb\nR61GWLJkCe644w4sW7YM5eXl6NWrFwwGA77++mt3d63FiBx30q07vOWRLWPezWOSrSzLyMrKQnp6\nOg4fPoz8/HxERkYiKSkJkuRRA/AmERouRCMJ0eiGB8ZY5+cxyVaSJEyZMgXl5eW46aabcNNNN7m7\nS63SZD4Vjh1m0lXczZcx1jl41JBx5MiR2LVrl7u7cU2amikQXECcMa/lMSNbAIiKisLEiRMxbdo0\nREREKMVchBB45ZVX3Ny7FmoimRIv/2LMa7l9ZPvOO+8o35vNZkybNg0AcObMGRQUFOD06dM4ffq0\nu7p31WxEDW5qAOC4NU5HdoYx5jHcPrL94x//iN/+9rcAgPXr18Nisbi5R9em6WtgxCNbxryU25Pt\njTfeiPnz56Nv376w2Wz417/+1WC7hx9+uIN71jpyUyNbnkZgzGu5PdmuWrUKf/3rX/HZZ5/BarXi\n008/bbDd9ZNsG66N4GSnxqonMMY6M7cn27i4OHz00UcAgFtvvfW6qvDVEHsTuVQAsPGkLWNeye0X\nyK50vSdaoOlpAklwsmXMW3lUsu0MqIk5W0Fc05Yxb8XJto01dQddSeKbPjLmrTjZtjEZjgpfDREC\nqOHlCIx5JU62bUyWG19roBFALd+ugTGvxMm2jVET+3VVfB8yxrwWJ9s2ZpMbv0CmEkANj2wZ80pu\nX2fb1vLy8pCSkgJZljF48GAkJyfXa7Np0ybk5uZCo9Fg2rRpCA0NbfLcbdu24cCBA/Dz8wMAjB07\nFj179mzw+e3U+JytWhKotLXBi2SMXXc6VbKVZRkbN27EzJkzYTAYsGzZMsTFxSE4OFhpk5ubi+Li\nYsybNw8FBQXYsGEDZs+e3ey5w4cPx/Dhw5vtg02mRj8uSKLp1QqMsc6rU00jnDlzBkFBQQgMDIRK\npUJ8fDyys7Nd2mRnZyMhIQEAEBERgerqapSXlzd7LrXwLgsyOZJqQyQBWDnZMuaVOtXI1mKxwGg0\nKj8bjUYUFBS4tDGbzQgICHBpYzabmz13z549yMjIQFhYGMaPHw+dTgeLxVKvSll5jW+jc7YSABvP\n2TLmlTpVsm2plo5SnRITEzFq1CgAwHfffYfNmzdj6tSp2L9/P7Zv3+7SNvzm2xHdqy/0OlWDsXSy\nFcYAf6gaG/62gMlkavW5HN9zY3eG+KxxnSrZOkepTmVlZS6j1YbamM1mGI1GyLLc6Ln+/v7K8cGD\nB2PlypUAgCFDhqBXr14u8Y9W6lBVVQG1reEZmuoqwsXiWmhVrUu2JpMJJSUlrTqX43tubE+Mz4m5\nbXWqOduwsDAUFRWhtLQUdrsdmZmZiIuLc2kTFxeHjIwMAEBBQQF0Oh30en2T5145VXDs2DGEhIQA\nAAwGA8LCwly+fLS+Tb6pJIiL0TDmhTrVyFaSJEyaNAmffvopiAiDBg1CcHAw9u3bBwAYOnQoYmNj\nkZubi8WLFytLv5o6FwC++eYbnDt3DoDjr/3kyZMb7YOtiQtkgOOvW61M8Oeatox5FUFXO4HJmrQl\nvwpyTQV0jUwTnK2UkRAkIdSvdR8qPO2jpjfFv5773pr4PI3QtjrVNIInaPK2OHDsIqvmjQ2MeR1O\ntm1MlqnJaQS1ACp4ZwNjXoeTbRtrbhmtjwqosHZIVxhjHoSTbRujZm74qJUEKnljA2Neh5NtG5Ob\nKEQDAGqJK38x5o042bYxGU2PbCUhYJcdBWsYY96Dk20bszdRz/YyQi1vbGDMq3CybQeiqXkEOKYZ\nKvnOj4x5FU62bqCWgApea8uYV+Fk6wZaCSit4ZEtY96Ek20bC/FruLTilXQqwMxrbRnzKpxs25if\npvm31EcleBqBMS/DydYNVELAxsu/GPMqnGzdRRBvbmDMi3CydRO1ACx890fGvAYnWzdRC8DMyZYx\nr8HJ1k181UBJrbt7wRjrKJxs3USnEijn5V+MeY1OdQ+y64laAFV2x50dpCu295JsBdWWADUXQTYL\nyF4DIWkg1HpA2xVk0Lmx14yx1uJk6yZCCIBk1NgBnWSFbM6GXHoAqC4EIAGSxvEl1CCyA7INoFpY\nzmtgtakh/GMhBQ6A0IVACAEiQmVlJex2O2RZhiRJ0Gg00Gq1kCT+AMOYu3GydSMfqoTl7F6oK/cD\nQg34hkIE9IcQjSdHH70eNWUXYSs/geLj21B4oQy5PxtwpswAm1z/1ymEQEBAAEJDQ9GrVy/06NED\n/v7+7fmyGGMN4GTrDrIVOvMehBXtRYWvCUFBfSGklv0qzOYKpP+QhZzjJ6GSCF0DfRATVIwBoRdR\nIUWhTPSBTTJcfipZRnV1NYqKirBx40bU1tbCaDQiISEB8fHxCAgIaK9XyRi7AifbDqauLoD+4loA\nhBr/3vhZFYhIydzseRZLJXbuzcDZsxcQYNAiPjYcPj6Xf30VJEMrFyEcKaiWQ1AixaNWCoIkSfDz\n84Ofnx9CQ0MdbSsqcOjQIaSlpcFkMmHIkCHo3bs39Hp9e71sxrweJ9uOQjb4FaVCV34IVl1PyJoA\n+JBAka3pwjWyLGP/wWM4kHEM3YONGJoQA2ttA2vGhIQaEYwa6goNlaK7/C1q5SCUSPGoEcEu9+rx\n9/dHTEwMAMBisWDnzp3YunUrIiIi8Itf/AKBgYHQ6fhCHGNtiZNtB1DVFMJwfhVIqFCjHwgIR4JV\nC4KNAKssQSPVv3XD+YvFSN26EyTbkNAnEj4+aqhUEppcMSYErMIEK0xQy2Z0k7+HDXqUSvGoEiFA\nnflgg8EAg8EAIkJpaSnWr1+PiooKxMTEICEhAWFhYdBoNG34bjDmnTjZtieyw7d0O/xKd6HWNway\nxlSviYpsqKqTbG02O9J2HUBW1k+IiQ5BV5OxVU9vk4ywwQiVXIGu8m7YoUWZ1A+VIgwkXH/1QgiY\nTCb06NEDZrMZFy5cwBdffAFJktCnTx/Ex8cjJCQEKlXzJSQZY/Vxsm0nkrUIhgurIcnVqDYMdKw2\naICPsKHMqoFR7ai5eKbwIlK/2QmdToUhA26EWn3tyc0u+aMS/pDkapjkQzAhA2UiDhXSDZCFT/2+\nSxK6d++O7t27w2634/Tp0/jxxx8hSRIiIyMRFxeHHj16wGg0NnsLIMaYAyfbtkYErXkv/Iu/gVUb\nCZsuusnm/iorimp9EaKyIW3XAeQdP4me0aEwGdt+eZYs6VCFCAjZCiNyYLIdQbkUCYuIhVUYG7wH\nu0qlQnh4OMLDw2Gz2VBSUoJvvvkGtbW1kCQJQUFByuNBQUHQ6/Xw8/ODWs3/tBi7Ev8X0daEgE9l\nLmr8+zs2JTTDT7Ih43wF9v3wNQL8fTC4/41QtfMmBJI0qEYYABk6+WfoRT5k6FAmYiDkPo2ep1ar\nERwcjODgYACOi3eVlZUoKChAdnY2rFYr7HZH3UitVgu9Xo+goCB069YNQUFBCAoKglarBRHxiJh5\nHU627cCmi4Bkr2q2XVVVNfYfyMSp2gAk3hCBLn4dvNNLSKgR3VADQJJrYEQufKtyYLBpUCEiUCEi\nYBUB9S6qOUmSBL1e3+CSMavVipqaGlgsFhQWFqKmpgZWqxVarRY1NTXQ6/Xo1q0bevTooYyK/fz8\n2vkFM+Y+nGzdQJZlHP+pAEd+PIouXQMQG2kCkQ8gF7mvT5IW1QgHNDrU1pbBjwpgFLkACdSKQFSK\n7qgW3WAThgbneevSaDTQaDRKUnXS6/Uwm82ora2FxWLBvn37kJaWBrvdDo1Ggy5duiAyMhIREREI\nDg6GwWDgi3KsU+Bk24GICIXnLuLQwaMgyIiLi4KP1gdWVKJE1QVd5CJ4wodrWdKiBiGocfwEFVXB\nQCcQKI4BBMhCBSsMqBVdUCsCYYUeduELGT71Vjk0RJIk6HQ66HQ6ZUoCAGw2GyoqKpCTk4OMjAzY\nbI6LhoGBgQgLC0N4eDhCQkIQEBAAX1/f9nnxjLUTTrYdhIiwfccemMvM6BEZAoPh8gUwDWohC6Bc\n6GGgcjf2sgFCgl34w45L/RUAyA4V1cCXzsNfnIKADQIyBAGyUMMO7aUvX9iEP2zCDzK0sEMHtd0G\nNdVAhgoENQiSMk2hVqsREBDgsoWYiFBVVYXz58/j+PHjqK2tVUbBAQEB6Nq1K0JCQhAUFASTyQSN\nRgObzcYX6JjH6XT/IvPy8pCSkgJZljF48GAkJyfXa7Np0ybk5uZCo9Fg2rRpyjbWxs6tqqrC6tWr\nUVZWhsDAQNxzzz1XvcNKCIHu3U2IiOja4ONGuQTn1GHwt+ZCgoffwUGoYBd+sKPOHOulRCzBDkE2\nqFEBHyqDgBVCyBCQoa1Ww2ivBYgunQCQUIGgggyN8mUXPpcStAZGnRbBOg3kEH/ICIAMH9jshBqr\nHZaScygsOIHqWjusNhlarRZVVVVQq9Xw8/NT5pSNRiMMBgN0Oh18fX2h0+mg1Wqh1WqhUqmg0Wig\nVqu5QhprN50q2cqyjI0bN2LmzJkwGAxYtmwZ4uLiXD6q5ubmori4GPPmzUNBQQE2bNiA2bNnN3lu\neno6YmJiMGLECKSnpyM9PR1jx4696v75+mphtTZ8D3Mt1aBSWHFBFYLu9nOO1wMJEi5vdiAA9lbU\ne7dCA03T+87ajlBBhgoQPvWTMQBZo0O1rRou8yXkSMSOL0ei1lAFJJgB56hZyADoUhuABAE+BBEE\nIOhSnheAj48vqmttsMkqWG0Eq41QbZFRXUyw2AGb3bFjzy5LsNkBu6yCXRYgoYJNBlQqDSSVDzQ+\nWqjUGqg1Ovj6+kOr1aFb9+4gAvz89dBpddD5+sHX1w8qlQYaH43jHJXm0hI6iVdcMBedKtmeOXMG\nQUFBCAwMBADEx8cjOzvbJdlmZ2cjISEBABAREYHq6mqUl5ejpKSk0XOzs7Mxa9YsAMDAgQPx8ccf\ntyrZNidALkaRqjvKJQOswvER2yib0cX+M6qFFhdV3SAkHXzVxQixnW82gVYJHc6pQmEVPoi1ZnvE\nfHCDhAS6cjx/NR11SdoEu0qLGlRCSDIkH4LWh6DzleH4MyXD8SfM8bMjcdsuxagFQJBlGSTLl+oC\nE+yyDLvNDlu1DZVmFaqrq/GzXYZMBCICyeQ4jxwdkSRApZKgVqmgUTtGzEKlhlqlhlCpoJJUgKR2\n/D8EIFQgCEBI0Gr9UF1rBUEFCA1kqAGhUWobSyo1IPlAUvlApfKBn78B0Tf2BCQ1hFA7pmOEBDin\nZoTzOQQAAbJpQcoqGXH5zRNXfK+0Z22tUyVbi8UCo/Hy1laj0YiCggKXNmaz2WVO0Gg0wmw2N3lu\neXm5srzJ398f5eXlyvNZLBaX+NW1vhCygNpeXa9/gmyQbU0vCQuy/QSb8IGKHCO6clUgTqi6Q012\n6K2F8IeMItkf2eoboL7U5opnuJSwhGMMKCTo7cUItpthlyubfF4nW60Mm61+39tKu8e3Eqz2hm7u\nprr01QICLv9lCAAaADqtDuqapvvuTNA2mwyr3Y4qmw22WjvIbnckaNkGmawAHMka5Pj9CQFofXxg\nra3G5T8IMiRBANkhQCDIAJHjt0yELl0CEanpDeByHDj/ZBEp39Olxyy+vrBWV8Nllkpc8YPzW2eu\nTV7csveLtUinSrYtRdT8nGhjC++FEMrx/fv3Y/v27S6PR0VF4a677oLBYKh3blIr++tksViwf/9+\nTBgypMH418oZ/xaO36Gxr4w/5DqNz5rXqa4GOEepTmVlZS6j1YbamM1mGI3GBo87/1Hq9XqX0azz\nTgdDhgzBnDlzlK9p06YhPz+/3mi3rVgsFmzfvp3juyH+9dz3jojPmtepkm1YWBiKiopQWloKu92O\nzMxMxMXFubSJi4tDRkYGAKCgoAA6nQ56vb7Jc+Pi4nDo0CEAQEZGBnr37g3AUZ4wLCxM+bpybpgx\nxq7UqaYRJEnCpEmT8Omnn4KIMGjQIAQHB2Pfvn0AgKFDhyI2Nha5ublYvHixsvSrqXMBIDk5GatX\nr8bBgweVpV+MMXY1OlWyBYDY2FjExsa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       "text": [
        "<matplotlib.figure.Figure at 0x20660050>"
       ]
      }
     ],
     "prompt_number": 500
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Reassuringly Uganda performs highly here in Swahili, meaning that it is quite intralinked to other articles."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "metric_analyse_density('numheadings', 75)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Uganda (Q1036), C\u00f4te d'Ivoire (Q1008) numheadings z-scores.\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>language</th>\n",
        "      <th>en</th>\n",
        "      <th>fr</th>\n",
        "      <th>sw</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Q1036</th>\n",
        "      <td>-1.695575</td>\n",
        "      <td> 0.028298</td>\n",
        "      <td> 0.023235</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Q1008</th>\n",
        "      <td> 0.660221</td>\n",
        "      <td>-0.411489</td>\n",
        "      <td>-0.427908</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 501,
       "text": [
        "language        en        fr        sw\n",
        "Q1036    -1.695575  0.028298  0.023235\n",
        "Q1008     0.660221 -0.411489 -0.427908"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7MqgKdfKRXUkKCTLZdiFZH0GSQkdQJNv58+ezdu1a3O7rlNEKQqqiIARociRH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\nohYDlegUJwIddcThUJNwKr3QlPpCPDU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ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6067a10>"
       ]
      }
     ],
     "prompt_number": 501
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here English appears to have almost a [bimodal distribution](https://www.wikidata.org/wiki/Q255598#sitelinks-wikipedia), for which we are accepting your explanations of why that should be so."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "metric_analyse_density('articlelength',150000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Uganda (Q1036), C\u00f4te d'Ivoire (Q1008) articlelength z-scores.\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>language</th>\n",
        "      <th>en</th>\n",
        "      <th>fr</th>\n",
        "      <th>sw</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Q1036</th>\n",
        "      <td> 0.105196</td>\n",
        "      <td>-0.498248</td>\n",
        "      <td> 0.190613</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Q1008</th>\n",
        "      <td> 0.335907</td>\n",
        "      <td> 2.764950</td>\n",
        "      <td>-0.244492</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 505,
       "text": [
        "language        en        fr        sw\n",
        "Q1036     0.105196 -0.498248  0.190613\n",
        "Q1008     0.335907  2.764950 -0.244492"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yL//CwoULufrqq4NyrWCXxvEzDIN3332X0aNHExcXd95KDV6lqKiCkhJXo9pv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\n87asrMz8WunnNCJxK50wb+BX5YQ4Oy6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       "text": [
        "<matplotlib.figure.Figure at 0x5fb0a50>"
       ]
      }
     ],
     "prompt_number": 505
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Again C\u00f4te d'Ivoire scores a very high, 2.77, in French Wikipedia. It must be quite a long article."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "metric_analyse_density('referencerate', 0.01)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Uganda (Q1036), C\u00f4te d'Ivoire (Q1008) referencerate z-scores.\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>language</th>\n",
        "      <th>en</th>\n",
        "      <th>fr</th>\n",
        "      <th>sw</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Q1036</th>\n",
        "      <td> 0.014790</td>\n",
        "      <td>-0.608334</td>\n",
        "      <td> 3.129056</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Q1008</th>\n",
        "      <td>-1.183966</td>\n",
        "      <td> 1.663328</td>\n",
        "      <td>-0.265412</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 503,
       "text": [
        "language        en        fr        sw\n",
        "Q1036     0.014790 -0.608334  3.129056\n",
        "Q1008    -1.183966  1.663328 -0.265412"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uDwrpshVCgB+HbdeuXVmzZo3XsnXr1tG1a1cfVdQEmrRshRB1/PYE2fPPP8+d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\nE4EtVUtubi4Wi4UOHTpcsIbWqKe4uJj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       "text": [
        "<matplotlib.figure.Figure at 0x21110a10>"
       ]
      }
     ],
     "prompt_number": 503
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These results offer credibility to this techqnique because the Wikipedias of the native languages of those countries show the highest quality. For the Uganda articles performance is slightly below average in English and French but strong in Swahili. For the C\u00f4te d'Ivoire articles, the real stand out is French Wikipedia, at times 3 standard deviations above average, but again below average in other languages. So we can somewhat confidently apply this technique to other sets of articles so see what improvements could be made. Remember a weakness in any one of the 5 metrics is an indicator for a specific type of task that could be done on those articles. Next we will inspect how to determine other sets of articles to which we can apply this method."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Highest Occuring Sections"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we will find the hightest occuring section names in Country Articles by language. The assumption we use here is that if a section name is highly occurring it is an important subject in that language."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top_n_sections('fr',20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "242 total articles inspected in fr.\n"
       ]
      },
      {
       "html": [
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>lang</th>\n",
        "      <th>secname</th>\n",
        "      <th>freq</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>      Liens externes</td>\n",
        "      <td> 0.904959</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>            Histoire</td>\n",
        "      <td> 0.884298</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>          G\u00e9ographie</td>\n",
        "      <td> 0.826446</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td> Notes et r\u00e9f\u00e9rences</td>\n",
        "      <td> 0.809917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>            \u00c9conomie</td>\n",
        "      <td> 0.785124</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>         D\u00e9mographie</td>\n",
        "      <td> 0.752066</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>           Politique</td>\n",
        "      <td> 0.685950</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>          Voir aussi</td>\n",
        "      <td> 0.685950</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>   Articles connexes</td>\n",
        "      <td> 0.648760</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>               Codes</td>\n",
        "      <td> 0.644628</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>             Culture</td>\n",
        "      <td> 0.640496</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>       Bibliographie</td>\n",
        "      <td> 0.491736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>              Climat</td>\n",
        "      <td> 0.301653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>        Subdivisions</td>\n",
        "      <td> 0.301653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>             Langues</td>\n",
        "      <td> 0.289256</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>               Sport</td>\n",
        "      <td> 0.243802</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>           \u00c9ducation</td>\n",
        "      <td> 0.239669</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>           Religions</td>\n",
        "      <td> 0.231405</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>            Religion</td>\n",
        "      <td> 0.219008</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> fr</td>\n",
        "      <td>               Sant\u00e9</td>\n",
        "      <td> 0.198347</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "<IPython.core.display.HTML at 0xb0077d0>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top_n_sections('en',20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "242 total articles inspected in en.\n"
       ]
      },
      {
       "html": [
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>lang</th>\n",
        "      <th>secname</th>\n",
        "      <th>freq</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>                  History</td>\n",
        "      <td> 0.954545</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>                  Economy</td>\n",
        "      <td> 0.888430</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>             Demographics</td>\n",
        "      <td> 0.876033</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>           External links</td>\n",
        "      <td> 0.867769</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>                 See also</td>\n",
        "      <td> 0.842975</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>                Geography</td>\n",
        "      <td> 0.822314</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>               References</td>\n",
        "      <td> 0.805785</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>                  Culture</td>\n",
        "      <td> 0.797521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>                 Religion</td>\n",
        "      <td> 0.657025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>                Education</td>\n",
        "      <td> 0.648760</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>                  Climate</td>\n",
        "      <td> 0.545455</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>                 Military</td>\n",
        "      <td> 0.462810</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td> Administrative divisions</td>\n",
        "      <td> 0.438017</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>                 Politics</td>\n",
        "      <td> 0.438017</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>                   Sports</td>\n",
        "      <td> 0.396694</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>        Foreign relations</td>\n",
        "      <td> 0.384298</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>                Languages</td>\n",
        "      <td> 0.367769</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>                  Cuisine</td>\n",
        "      <td> 0.363636</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>                   Health</td>\n",
        "      <td> 0.359504</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> en</td>\n",
        "      <td>          Further reading</td>\n",
        "      <td> 0.342975</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "<IPython.core.display.HTML at 0xb007490>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top_n_sections('sw',7)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "233 total articles inspected in sw.\n"
       ]
      },
      {
       "html": [
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>lang</th>\n",
        "      <th>secname</th>\n",
        "      <th>freq</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td> sw</td>\n",
        "      <td>          Historia</td>\n",
        "      <td> 0.412017</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> sw</td>\n",
        "      <td>         Jiografia</td>\n",
        "      <td> 0.334764</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> sw</td>\n",
        "      <td> {{Infobox Country</td>\n",
        "      <td> 0.334764</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> sw</td>\n",
        "      <td>    Viungo vya nje</td>\n",
        "      <td> 0.330472</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> sw</td>\n",
        "      <td>        Tazama pia</td>\n",
        "      <td> 0.184549</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> sw</td>\n",
        "      <td> {{Infobox country</td>\n",
        "      <td> 0.175966</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> sw</td>\n",
        "      <td>    Viungo vya Nje</td>\n",
        "      <td> 0.154506</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "<IPython.core.display.HTML at 0xb0072d0>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looking at the most frequent sections in each what patterns emerge? First let us acknowledge that some of the sections here are not conent - like \"Notes\" and \"References\". The 'leader' section - the section before any named sections - usually startts with a code to include the Infobox Country in Swahili, which is where that cryptic result comes from. Ignoring non-content sections though there is a clear Winner in all three languages - _History_. \n",
      "\n",
      "In all three languages History is the one section you can most rely on being there, occurring in 88% of French, 95% of English and 41% of Swahili country articles. In Swahili and French, the second most popular sections are Geography, but in English it's Economy. So this informs us on which Wikipedia Categories to apply our metrics on next the: History, Economy, and Geography categories."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Subject and Nation Analysis"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Given that we now have the top sections for each language, we devise a method to look at representative articles. We find a Category in each Wikipedia for each subject in our highest occurring section headings and for each major nation associated with our languages.\n",
      "We create a script to present every article in every category and all its recursive subcategories to a human judge. The full list of categories for the second part of this analysis comes from is:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "categoryjson = json.load(open('ethnosets-categories-capitalized.json','r'))\n",
      "for subject, nationdict in categoryjson.iteritems():\n",
      "    print subject\n",
      "    for nation, langdict in nationdict.iteritems():\n",
      "        print \" |---\"  + nation\n",
      "        for lang in ['fr','en','sw']:\n",
      "            try:\n",
      "                print\"    |---\" + langdict[lang]\n",
      "            except:\n",
      "                pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Geography\n",
        " |---C\u00f4te d'Ivoire\n",
        "    |---Cat\u00e9gorie:G\u00e9ographie_de_la_C\u00f4te_d'Ivoire\n",
        "    |---Geography_of_Ivory_Coast\n",
        "    |---Jamii:Jiografia_ya_Cote_d'Ivoire\n",
        " |---USA\n",
        "    |---Cat\u00e9gorie:G\u00e9ographie_des_\u00c9tats-Unis\n",
        "    |---Category:Geography_of_the_United_States\n",
        "    |---Jamii:Jiografia_ya_Marekani\n",
        " |---Uganda\n",
        "    |---Cat\u00e9gorie:G\u00e9ographie_de_l'Ouganda\n",
        "    |---Category:Geography_of_Uganda\n",
        "    |---Jamii:Jiografia_ya_Uganda\n",
        " |---France\n",
        "    |---Cat\u00e9gorie:G\u00e9ographie_de_la_France\n",
        "    |---Category:Geography_of_France\n",
        "History\n",
        " |---C\u00f4te d'Ivoire\n",
        "    |---Cat\u00e9gorie:Histoire_de_la_C\u00f4te_d'Ivoire\n",
        "    |---Category:History_of_Ivory_Coast\n",
        " |---Uganda\n",
        "    |---Cat\u00e9gorie:Histoire_de_l'Ouganda\n",
        "    |---Category:History_of_Uganda\n",
        "    |---Jamii:Historia_ya_Uganda\n",
        " |---USA\n",
        "    |---Cat\u00e9gorie:Histoire_des_\u00c9tats-Unis\n",
        "    |---Category:History_of_the_United_States\n",
        "    |---Jamii:Historia_ya_Marekani\n",
        " |---France\n",
        "    |---Cat\u00e9gorie:Histoire_de_France\n",
        "    |---Category:History_of_France\n",
        "    |---Jamii:Historia_ya_Ufaransa\n",
        "Economy\n",
        " |---C\u00f4te d'Ivoire\n",
        "    |---Cat\u00e9gorie:\u00c9conomie_de_la_C\u00f4te_d'Ivoire\n",
        "    |---Category:Economy_of_Ivory_Coast\n",
        "    |---Jamii:Uchumi_wa_Cote_d'Ivoire\n",
        " |---USA\n",
        "    |---Cat\u00e9gorie:\u00c9conomie_des_\u00c9tats-Unis\n",
        "    |---Category:Economy_of_the_United_States\n",
        "    |---Jamii:Uchumi_wa_Marekani\n",
        " |---Uganda\n",
        "    |---Cat\u00e9gorie:\u00c9conomie_de_l'Ouganda\n",
        "    |---Category:Economy_of_Uganda\n",
        "    |---Jamii:Uchumi_wa_Uganda\n",
        " |---France\n",
        "    |---Cat\u00e9gorie:\u00c9conomie_de_la_France\n",
        "    |---Category:Economy_of_France\n",
        "    |---Jamii:Uchumi wa Ufaransa\n"
       ]
      }
     ],
     "prompt_number": 266
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looking at every title of every article in every subcategory of these categories, a human (your author) accepted or rejected each article. The purpose of this filtering process was to remove noise, such as articles about economists out of Econom, or highschools out of Geography, while being able to select articles from many diverse categories. In the end we accepted a total of:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ethnosets = !ls ethnosets/\n",
      "reduce(lambda a, b: a+b, map(lambda l: len(l), map(lambda f: json.load(open('ethnosets/'+f)), ethnosets)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 271,
       "text": [
        "24630"
       ]
      }
     ],
     "prompt_number": 271
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "24630 Articles which were fetched as live data off Wikipedia. Live so that we could compare the results after any editing efforts. We used Wikimedia labs for the [data pull script](https://github.com/notconfusing/kumusha_takes_wiki/blob/master/big_data_pull_by_subject.py) if you are interested. Then for each of those articles we ask Wikidata to give us the page if possible in all our desired languages. And then on all those pages we call the Actionable Metrics as described earlier. \n",
      "\n",
      "If you are keeping track that means we have four active variables: \n",
      "\n",
      "1. the Nation, \n",
      "2. the Wikipedia language, \n",
      "3. the subject, and \n",
      "4. the metric type.\n",
      "\n",
      "\n",
      "Below is a matrix of heatmaps displaying the mean average - not the full distribution as we previously graphed - of the specified metrics versus specified subjects. In the inner dimensions we graph across all pages in a specified language versus a specified nation. That is If you look at any individual heatmap you can see it compares a Wikipedia language and a nation. Looking down the rows of the greater matrix we have varying metric types, and looking down the columns of the greater matrix we have the subject categories."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "make_heat_map()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7L/XWh+xf35OtpaWFuLg4DBo0CM2bN8fo0aPRtGlT5Ofn4/r169izZw/27t0L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\nc+fOYcaMGaiurkZaWho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       "text": [
        "<matplotlib.figure.Figure at 0xb8b55d0>"
       ]
      }
     ],
     "prompt_number": 146
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Analysis\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This heatmap can act as a guide to where one could find good examples of articles. Say for istance you wanted to improve History articles of Uganda. First look at the vertical History column and within it, the vertical Uganda column. Then moving your eyes up and down you can see that while English Wikipedia has the best reference rate, French Wikipedia has a slightly higher code & images score, which would be differently useful depending on how you wanted to improve these articles.\n",
      "\n",
      "For Economy, we can see that the clearest horizontal band across each metric is English. This is a promising result because we knew that English unlike French or Swahili placed more of an emphasis on Economy in their country articles. However if we take a by-nation approach we see that the bluest square is almost always in the France column, not in the USA column. Yet, wouldn't we expect that if English values Economy, that Economy of the USA articles would perform most strongly? One exlpanation is that ther are so many English USA company articles that infact the _average_ quality is brought down. Some data that would give creedence to this idea is that English articles about France's economy outperform in the _code and images_ metric. Often bot opertions will leave a lot of templated code, on many relatively obscure articles for which this could be a trace. For instance in Economy articles then, it would be recommended to look at French articles of France for written quality, and English for templating ideas.\n",
      "\n",
      "Looking in the History column, in each of the metric-rows there is strong horizontal band of blue in the French sub-row. This indicates that French history articles are good across most nations. Except in the bottom two metrics, Article length and references per article, English seems to outperform, particularly for Uganda and the USA. Swahili here performs better for the USA than for Uganda. In fact in the Swahili band, many datapoints do not register because there are 25 or less articles in question (see caveat below). However within the Uganda column, English and French do register, we can interpret these as translation opportunities to get more Swahili content. As for C\u00f4te d'Ivoire, we also find that in all but referencing French does better than English. This would suggest that for C\u00f4te d'Ivoire's history, they unfortunately have no better direct examples to follow (but they do have French history to look up to).\n",
      "\n",
      "Moving to Geography, a notable pattern is the domination of French Wikipedia about France in - again - all but the referencing metric. We encounter some movement from Swahili Wikipedia for the first time. You can see two fainter, but still noticeable vertical strips in C\u00f4te d'Ivoire and Uganda for all Wikipedia languages. So actually this means that Geogrpahy is the relative strong point for these African countries. To improve their relative content coverage Swahili editors would do better to focus on Economy and History if they wanted to work on the more-needed areas of their Wikipedia. And within all the things they could do, we see the bluest points within code and images, which is likely from bot-created Africa place articles. The needed places to improve those articles are section count and article length which for English and French about France, and English about USA could best be a guide."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Caveats"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since we are using the mean average, sometimes if there are not a lot of pages in a language for a category, if one of the pages is particularly impressive then the average can appear high without it meaning that language is a useful information source. This occurred for instance Swahili Economy of France in Wikilinks. We set a threshold minimum number of contributing articles in a heatmap to 25 to reduce this noise, so a white square will appear if there were less than 25 articles. All subject-nations not meeting this threshold can be found in the code appendix. In fact these are the total counts of how many articles are being considered in each group."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "display_article_counts()display_article_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>subj</th>\n",
        "      <th>nation</th>\n",
        "      <th>lang</th>\n",
        "      <th>count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> usa</td>\n",
        "      <td> en</td>\n",
        "      <td> 7376</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> fra</td>\n",
        "      <td> fr</td>\n",
        "      <td> 4201</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> usa</td>\n",
        "      <td> en</td>\n",
        "      <td> 3303</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> fra</td>\n",
        "      <td> fr</td>\n",
        "      <td> 3135</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> fra</td>\n",
        "      <td> fr</td>\n",
        "      <td> 1962</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> usa</td>\n",
        "      <td> en</td>\n",
        "      <td> 1756</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> fra</td>\n",
        "      <td> en</td>\n",
        "      <td> 1322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> cdi</td>\n",
        "      <td> fr</td>\n",
        "      <td> 1249</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> cdi</td>\n",
        "      <td> en</td>\n",
        "      <td> 1139</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> usa</td>\n",
        "      <td> fr</td>\n",
        "      <td> 1135</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> fra</td>\n",
        "      <td> en</td>\n",
        "      <td> 1103</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> fra</td>\n",
        "      <td> en</td>\n",
        "      <td>  971</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> uga</td>\n",
        "      <td> en</td>\n",
        "      <td>  602</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> usa</td>\n",
        "      <td> fr</td>\n",
        "      <td>  473</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> usa</td>\n",
        "      <td> fr</td>\n",
        "      <td>  397</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> uga</td>\n",
        "      <td> en</td>\n",
        "      <td>  320</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> usa</td>\n",
        "      <td> sw</td>\n",
        "      <td>  288</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> uga</td>\n",
        "      <td> fr</td>\n",
        "      <td>  219</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> uga</td>\n",
        "      <td> sw</td>\n",
        "      <td>  183</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> cdi</td>\n",
        "      <td> en</td>\n",
        "      <td>  115</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> cdi</td>\n",
        "      <td> sw</td>\n",
        "      <td>  111</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> uga</td>\n",
        "      <td> fr</td>\n",
        "      <td>  110</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> cdi</td>\n",
        "      <td> fr</td>\n",
        "      <td>  107</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> cdi</td>\n",
        "      <td> fr</td>\n",
        "      <td>   84</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> uga</td>\n",
        "      <td> en</td>\n",
        "      <td>   78</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> usa</td>\n",
        "      <td> sw</td>\n",
        "      <td>   39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> cdi</td>\n",
        "      <td> en</td>\n",
        "      <td>   39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> uga</td>\n",
        "      <td> sw</td>\n",
        "      <td>   11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> usa</td>\n",
        "      <td> sw</td>\n",
        "      <td>   10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> uga</td>\n",
        "      <td> fr</td>\n",
        "      <td>   10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> fra</td>\n",
        "      <td> sw</td>\n",
        "      <td>    8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> fra</td>\n",
        "      <td> sw</td>\n",
        "      <td>    6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> fra</td>\n",
        "      <td> sw</td>\n",
        "      <td>    5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> uga</td>\n",
        "      <td> sw</td>\n",
        "      <td>    3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> cdi</td>\n",
        "      <td> sw</td>\n",
        "      <td>    1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "<IPython.core.display.HTML at 0x559f050>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Top Templates by Subject and Nation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The previous approach of looking at the top section heading, was not very useful here because within a broad category like \"Economy\" or \"Geography\" there are too many sub-subjects which have different expected flavours of section headings. The code appendix shows the results of this experimentation. Instead we look at the top templates, to see if sepecific quality flaws could be identified using the techniques of [Stein's Quality Flaws](http://www.uni-weimar.de/medien/webis/publications/papers/stein_2012i.pdf). Regretably this study only disucsses English Wikipedia, so here we would need a French or Kiswhaili Wikipedian to identify the top Clean-up tags in French Wikipedia. While \"Citaiton neeed\" does appear for some categories neither \"Citation n\u00e9cessaire\" nor \"Ukweli\" crops up, so this indicates maybe a direct translation is not possible.\n",
      "\n",
      "In fact all of the most common clean-up tags make no apperance in our top 20 of each subject-nation files, except \"Citation needed\". These subject-nation combinations have the highest incidence of \"Citation needed\":\n",
      "\n",
      "    Subject-nation| Frequency of \"citation needed\" (per article)\n",
      "\n",
      "    history-usa   |  0.501708\n",
      "    economy-fra   |  0.282183\n",
      "    economy-usa   |  0.270058\n",
      "    history-fra   |  0.226929\n",
      "    history-cdi   |  0.173913\n",
      "    geography-usa |  0.154013\n",
      "    economy-cdi   |  0.128205\n",
      "\n",
      "This probably  highlights lack of tagging with clean-up templates more than anything. It seems counter-intuitive that the more highly scoring subject-nations would also need more citing that those which scored less is all other areas. Our sample of Wikipedia articles here is too small for, or is otherwise somehow not the right application for this cleanup tag analysis.\n",
      "\n",
      "Still, looking over the data one can see that other citation templates feature highly. Therefore we can investigate the relative types of Citations used.  We do this only in English Wikipedia for lack of understanding of French and Swahili citation philosophies.\n",
      "\n",
      "First we retrive the statistics for _all occurences_ of English Wikipeda Citation templates from Wikimedia labs, and then convert them into a proportion.\n",
      "\n",
      "    MariaDB [enwiki_p]> select tl_title, count(*) from templatelinks where tl_title like 'Cite_web' or tl_title like 'Cite_news' or tl_title like 'Cite_journal' or tl_title like 'Cite_book' group by tl_title;\n",
      "    +--------------+----------+\n",
      "    | tl_title     | count(*) |\n",
      "    +--------------+----------+\n",
      "    | Cite_book    |   536258 |\n",
      "    | Cite_journal |   328129 |\n",
      "    | Cite_news    |   444447 |\n",
      "    | Cite_web     |  1560207 |\n",
      "    +--------------+----------+\n",
      "    4 rows in set (11 min 36.68 sec)\n",
      "    \n",
      "This global statistic, serves as a benchmark for all the compositions we can infer from our Top template work if we consider all the occurences of _Cite book, Cite Journal, Cite News, and Cite web_ from our subject-nation files. See below the graphical reperesentation."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "make_cite_plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8Q2JiIomJiTz77LOcPXvWd/+SJUto37498fHxPPDAAxw6dKjGx//ggw9ISkpi\n69atl//FcSopzxUpHSCnRUoHOK5F1sRXKaVs8uGHH3L69GmGDBnid5uqf9Z327ZtwPkJc0lJCd27\nd2f9+vVkZWWxbt06ioqK6N27NyNHjgy4X6/Xy/79+/nLX/7CrFmz2LRpEwC//OUv2bFjB7t27WLX\nrl3s2LGDzMxMADZv3sy0adNYs2YNhw4dok2bNowYMeKix87OzmbUqFH86U9/8k3UlVLKyWRNfB22\nziQgKS1SOkBOi5SOMHPkyBESEhKIiPD/bbXqsoaaljgsXLiQtLQ0OnToQEREBGlpaezcuZP8/Hy/\nj5menk6jRo3o3Lkzjz/+OKtXrwbg9ddf5xe/+AUJCQkkJCSQnp7OypUrffc9+eSTdOvWjQYNGpCV\nlcWHH35Y7dXlN954gwkTJpCdnU1KSkrQXw8RpDxXpHSAnBYpHeC4FlkTX6WUskl8fDxFRUVX9Mtq\neXl5TJ48mdjYWGJjY4mPjwegoKDA7+e0bt3a935SUpJvyULlK7lV7yssLKzxviZNmhAfH19tP/Pm\nzWP48OF06tTpsnuUUircyJr4OmydSUBSWqR0gJwWKR1hJjU1lYYNG7Ju3Tq/21Rd6lD1/UpJSUks\nXryY4uJi39vJkyfp0aOH38es+irtgQMHcLvdALjdbnJzc6vdl5iYWON9J0+e5MiRI777AdasWcO6\ndeuYN2+e/2jppDxXpHSAnBYpHeC4FlkTX6WUsklMTAwzZ85k4sSJrF+/nlOnTlFWVsbGjRuZOnUq\nUH15Q7NmzYiIiCAnJ8f3sQkTJvDiiy+yZ88e4Pz63zVr1gTcb2ZmJt9++y27d+9m+fLlDB8+HICR\nI0eSmZlJUVERRUVFzJw5k9GjR/vuW7ZsGbt27eLMmTNMmzaNHj16kJSU5Htct9vNpk2b+M1vfsPC\nhQut+SIppZTNIu0egKUcts4kICktUjpATouUjjA0ZcoUWrRoQWZmJo888ghRUVGkpKQwffp0AN+1\ngQEaN27M9OnT6dWrF2VlZbz77rsMHjyY0tJSRowYQV5eHjExMfTv35+HHnqoxv25XC769u1Lu3bt\nqKio4LnnnuPOO+8EYMaMGZw4cYIuXboA8PDDDzNjxgwA7rjjDl544QWGDRtGcXExvXr14ve//321\nx4Xzyyg2bdqEx+OhQYMGPPHEE6H5woUrKc8VKR0gp0VKBziuxWXq6CKSLpcr6OtVulwu2LIlRCP6\nTr9+Ib+dw16FAAAgAElEQVSOppQOkNMipQPktNS644KxREfHBXWt3WBFRcVy4sTRkD2+qu5yflbU\n9nGvqufJlZLSIqUD5LTU4c/GmvYja6mDw9aZBCSlRUoHyGmR0vGdEyeOYowJ2ZtOeq9iUp4rUjpA\nTouUDnBci6yJr1JKKaWUUn7Imvg6bJ1JQFJapHSAnBYpHUqFmpTnipQOkNMipQMc1yJr4quUUkop\npZQfsia+DltnEpCUFikdIKdFSodSoSbluSKlA+S0SOkAx7XImvgqpZRSSinlh6yJr8PWmQQkpUVK\nB8hpkdKhVKhJea5I6QA5LVI6wHEtsia+SimllFJK+SFr4uuwdSYBSWmR0gFyWqR0OFBWVhbjxo2z\n5LEOHDhAVFRUnVwIPlgRERF8+eWXdg/jykl5rkjpADktUjrAcS2yJr5KqatOdNNo358CDsVbdNPo\noMazatUqUlJSiIqKwu12M3DgQLZv3w5AWloaS5YsASA3N5eIiAgqKiouqzspKYmSkhLfnxdWSil1\naZF2D8BSDltnEpCUFikdIKdFSsd3So6XQEYIHz+jpNbbzp07l1mzZrFo0SLuvvtuGjRoQHZ2Nhs2\nbKBXr141fk64vWJbXl5OZKSsHw2XTcpzRUoHyGmR0gGOa9FXfJVSygLHjx8nPT2dBQsWMHjwYBo1\nakS9evW49957mTVrFgAZGRk8+uijAPTp0weApk2bEhUVxccffwzA0qVL6dSpE3FxcQwYMIADBw7U\nuL8LXzEuLCxk0KBBxMfH0759e373u9/5th07diw///nPfbe9Xi+tW7f23U5OTubll1+mS5cuREVF\nkZOTQ0REBCtWrKBNmzY0a9aMF1980bf9jh07SE1NJTY2FrfbzY9+9CPKysqs+DIqpVRIyZr4Omyd\nSUBSWqR0gJwWKR1h5sMPP+T06dMMGTLE7zZVlyVs27YNOD9hLikpoXv37qxfv56srCzWrVtHUVER\nvXv3ZuTIkbXa/4gRI0hKSuLQoUOsXbuWadOmsWXLFt9+L7Uk4ve//z0bN27k2LFj1KtXD4Dt27ez\nd+9eNm3axMyZM/niiy8AiIyM5De/+Q1Hjhzhww8/ZNOmTSxYsKBW43QUKc8VKR0gp0VKBziuRdbE\nVymlbHLkyBESEhKIiPD/bbXqsoaaljgsXLiQtLQ0OnToQEREBGlpaezcuZP8/PyA+87Pz+evf/0r\ns2bNokGDBnTt2pWnnnqKFStWBNxfJZfLxY9//GMSExNp2LCh7+Pp6ek0bNiQLl260LVrV3Z+9wPu\n+9//PrfeeisRERG0adOGp59+mvfffz/gGJVSKhzImvg6bJ1JQFJapHSAnBYpHWEmPj6eoqKiy/5l\nNYC8vDwmT55MbGwssbGxxMfHA1BQUBDw8woLC4mLi6NJkya+jyUlJV3y86qquvShUosWLXzvN27c\nmJMnTwKwd+9e7rvvPlq2bElMTAzTp0/nyJEjtd6XY0h5rkjpADktUjrAcS2yJr5KKWWT1NRUGjZs\nyLp16/xuU3W5QU1LD5KSkli8eDHFxcW+t5MnT9KjR4+A+3a73Rw9epTS0lLfxw4cOECrVq0AaNKk\nCadOnfLd99VXXwUc26X88Ic/pFOnTuzfv5/jx4/zy1/+8oom/EopVVdkTXwdts4kICktUjpATouU\njjATExPDzJkzmThxIuvXr+fUqVOUlZWxceNGpk6dClRfbtCsWTMiIiLIycnxfWzChAm8+OKL7Nmz\nBzi//nfNmjWX3Hfr1q3p2bMnaWlpnDlzhk8//ZSlS5cyevRoALp168Y777xDcXExX331Fb/+9a+v\nqLW0tJSoqCgaN27Mv//9b1555ZUrerywJeW5IqUD5LRI6QDHtcia+CqllI2mTJnC3LlzyczMpHnz\n5iQlJbFgwQLfL7xV/SWzxo0bM336dHr16kVsbCw7duxg8ODBTJ06lREjRhATE8PNN9/Mu+++W6t9\nr169mtzcXNxuN0OHDmXmzJncfvvtADz66KN07dqV5ORkBgwYwIgRIy75Cm+g+2fPns2qVauIjo7m\n6aefvujx9NrCSqlw5TJ1dBFJl8sV9PUqXS4XfPdbySHTr1/Ir6MppQPktEjpADktte64YCzRTaPP\nX8s3RKJiojhx7ETIHv9yffnll3To0EHcZcQu52dFbR/3qnqeXCkpLVI6QE5LHf5srGk/epVypZSj\nheOktC589tlnJCcn2z0MpZRyFFlLHRy2ziQgKS1SOkBOi5SOq9jcuXMZP348L730kt1DkU3Kc0VK\nB8hpkdIBjmuRNfFVSqmrwJQpUzh06BDDhg2zeyhKKeUosia+DruWXEBSWqR0gJwWKR1KhZqU54qU\nDpDTIqUDHNcia+KrlFJKKaWUH7Imvg5bZxKQlBYpHSCnRUqHUqEm5bkipQPktEjpAMe1yJr4KqWU\nUkop5Yesia/D1pkEJKVFSgfIaZHSoVSoSXmuSOkAOS1SOsBxLbImvkopFcaysrIYN26c3cNQSqmr\nlqyJr8PWmQQkpUVKB8hpkdLxnbjoaN+fAg7FW1x0dFDjWbVqFSkpKURFReF2uxk4cCDbt28HIC0t\njSVLlgCQm5tLREQEFRUVln9NlEWkPFekdICcFikd4LgWWRNfpdRVp7ikBAMheysuqf2fQ547dy7P\nPvssM2bM4JtvviE/P5+JEyeyYcMGv59TR381XimlFNImvg5bZxKQlBYpHSCnRUpHmDl+/Djp6eks\nWLCAwYMH06hRI+rVq8e9997LrFmzAMjIyODRRx8FoE+fPgA0bdqUqKgoPv74YwCWLl1Kp06diIuL\nY8CAARw4cKDG/VW+YrxixQratGlDs2bNePHFF333G2N46aWXaNeuHQkJCQwfPpzi4mIAxowZw9y5\ncwEoKCggIiKCBQsWAJCTk0N8fDwARUVF3HfffcTGxhIfH0+fPn2urom6lOeKlA6Q0yKlAxzXImvi\nq5RSNvnwww85ffo0Q4YM8buNy+Xyvb9t2zbg/IS5pKSE7t27s379erKysli3bh1FRUX07t2bkSNH\nBtzv9u3b2bt3L5s2bWLmzJl88cUXAMybN48NGzawdetWDh06RGxsLBMnTgTA4/Hg9XoBeP/997nh\nhhvYunWr73blpHzOnDm0bt2aoqIivvnmG7Kysqo1KKWU08ia+DpsnUlAUlqkdICcFikdYebIkSMk\nJCQQEeH/22rVV0treuV04cKFpKWl0aFDByIiIkhLS2Pnzp3k5+f7fcz09HQaNmxIly5d6Nq1K7t2\n7fI9VmZmJm63m/r165Oens7atWupqKigT58+fPDBBxhj2LZtGz/72c9865Dff/99+vbtC0CDBg04\ndOgQubm51KtXj169el3W18axpDxXpHSAnBYpHeC4FlkTX6WUskl8fDxFRUVX9MtqeXl5TJ48mdjY\nWN/yAji/HMGfFi1a+N5v3LgxpaWlvscaMmSI77E6depEZGQkX3/9NW3btqVJkybs3LmTbdu2cd99\n9+F2u9m7dy9bt271TXyfe+452rVrR//+/Wnbtq1vyYZSSjmVrImvw9aZBCSlRUoHyGmR0hFmUlNT\nadiwIevWrfO7TdVlAjUtGUhKSmLx4sUUFxf73k6ePEmPHj2CHk9SUhLZ2dnVHuvUqVO0bNkSgL59\n+7JmzRrKyspwu9307duX5cuXU1xcTLfvzpFrr72W2bNnk5OTw4YNG5g7dy6bN28OeiyOJeW5IqUD\n5LRI6QDHtcia+CqllE1iYmKYOXMmEydOZP369Zw6dYqysjI2btzI1KlTgerLG5o1a0ZERAQ5OTm+\nj02YMIEXX3yRPXv2AOfX/65Zs+ayxjNhwgSmTZvm++W4w4cPV7u6RN++fZk/f75vPa/H42H+/Pn0\n7t3bNyl/++232b9/P8YYoqOjqVevHvXq1bus8SilVDiQNfF12DqTgKS0SOkAOS1SOsLQlClTmDt3\nLpmZmTRv3pykpCQWLFjg+4W3ymsDw/llCdOnT6dXr17ExsayY8cOBg8ezNSpUxkxYgQxMTHcfPPN\nvPvuu373F+gXzSZPnsygQYPo378/0dHRpKamsmPHDt/9ffr0obS01Dfx7dWrF99++63vNsC+ffu4\n6667iIqKomfPnkycONG3DOKqIOW5IqUD5LRI6QDHtUTaPQCllLoSsVFRuIK41u7lPH4wRo0axahR\no2q8Lz09vdrt559/nueff77ax0aPHs3o0aMvuZ/k5GTOnTtX7WNbtmzxve9yuXj22Wd59tlna/z8\nDh06VFuPHBMTQ1lZWbVtnnnmGZ555plLjkUppZxC1sTXYetMApLSIqUD5LRI6fjO0RMn7B6CkkrK\nc0VKB8hpkdIBjmuRtdRBKaWUUkopP2RNfB22ziQgKS1SOkBOi5QOpUJNynNFSgfIaZHSAY5rkTXx\nVUoppZRSyg9ZE1+HrTMJSEqLlA6Q0yKlQ6lQk/JckdIBclqkdIDjWmRNfJVSSimllPLjkhPf7Oxs\nOnbsSPv27Wv8c5VFRUUMGDCAbt260blzZ5YvXx6KcdaOw9aZBCSlRUoHyGmR0qFUqEl5rkjpADkt\nUjrAcS0BJ77nzp1j0qRJZGdns2fPHlavXs3nn39ebZv58+fzve99j507d+L1evnJT35CeXl5SAet\nlFJKKaVUsAJOfHfs2EG7du1ITk6mfv36jBgxgvXr11fbpmXLlpz47jqaJ06cID4+nshImy4P7LB1\nJgFJaZHSAXJapHQ4UFZWFuPGjbN7GNUkJyezadMmu4cRnqQ8V6R0gJwWKR3guJaAE9+CggJat27t\nu92qVSsKCgqqbTNu3Dh2796N2+2ma9eu/OY3vwnNSJVSqgbRsbG+PwUcirfo2NigxrNq1SpSUlKI\niorC7XYzcOBAtm/fDkBaWhpLliwBIDc3l4iIiGp/Pc0OVf+MslJKSRfwpdnafDN88cUX6datG16v\nl5ycHO666y527dpFVA1/5nPs2LEkJycD0LRpU7p164bH4wHA6/UCXHTbp3INSeW/LGq6vX8/PPhg\n7bevwt/+rbpd6/FU3l67Ftq1q/323+0zVOO/rONRqVu3oPtDfjwq91nbr6+k8+vCY1ObzyfMzq8q\nSo4dgyp/qtdqJf361XrbuXPnMmvWLBYtWsTdd99NgwYNyM7OZsOGDfTq1avGzzHGWDVUMWw/vy73\n+1eIx19nP0/qqMcnxN+/9OdJkD0Ona9Uvp+bm0sgLhPgu+5HH31ERkYG2dnZwPn/pouIiGDq1Km+\nbQYOHMj06dN939TvuOMOZs2aRUpKSvUduVxBf4N3uVzB/UCreuLVVr9+If/BE3QHBN9SBx2gx0SP\nSejUuuOCsVzWsQxGLduPHz9Oq1atWL58OcOGDatxm4yMDHJycli5ciVJSUkcPHiQJk2aAPDee+/R\nvXt3li5dyuzZs/nqq6+49dZbWbx4MUlJSRc91pgxY+jatStTpkzx/e/c/Pnz+X//7/+Rk5PDrbfe\nypEjRwB46623mDFjBnl5eXTq1ImFCxdy8803A3D99dczfvx4Vq5cyaFDhxg8eDCvvPIKDRs2vNyv\n2BW5nJ8VtX1cCc/5OukAOS1SOkBOSx3+bKxpPwGXOqSkpLBv3z5yc3M5e/Ysb7zxBoMGDaq2TceO\nHXnvvfcA+Prrr/niiy+44YYbLBx6EBy2ziQgKS1SOkBOi5SOMPPhhx9y+vRphgwZ4nebqv+Ltm3b\nNuD8hLmkpITu3buzfv16srKyWLduHUVFRfTu3ZuRI0fW+Fgej8f3Ssf777/PDTfcwNatW323+/Tp\nA8A///lPnnzySZYsWcLRo0cZP348gwYNoqysDDj/ivOqVav4y1/+Qk5ODnv37iUzM/OKvx4iSHmu\nSOkAOS1SOsBxLQEnvpGRkcyfP5+7776bTp06MXz4cG688UYWLVrEokWLAJg2bRp///vf6dq1K3fe\neScvv/wycXFxdTJ4pZQKF0eOHCEhIYGICP/fVqu++lDTKxELFy4kLS2NDh06EBERQVpaGjt37iQ/\nP/+ibfv06cMHH3yAMYZt27bxs5/9zLeW+P3336dv374ALF68mPHjx3PLLbfgcrl47LHHaNiwIR99\n9BFwfjI+adIkEhMTiY2NZfr06axevfqKvhZKKRWuLnkd33vuuYcvvviC/fv3k5aWBsD48eMZP348\nAAkJCbz55pvs2rWLf/3rX4waNSq0Iw7EYdeSC0hKi5QOkNMipSPMxMfHU1RUdEW/rJaXl8fkyZOJ\njY0lNjaW+Ph4gIt+qRigbdu2NGnShJ07d7Jt2zbuu+8+3G43e/fuZevWrb6Jb15eHnPmzPE9Zmxs\nLAcPHqSwsND3WFV/iTkpKanafVc1Kc8VKR0gp0VKBziuRf9ym1JKWSA1NZWGDRuybt06v9tUXepQ\n0y8PJyUlsXjxYoqLi31vJ0+epEePHjU+Xt++fVmzZg1lZWW43W769u3L8uXLKS4uptt3//2YlJTE\n9OnTqz1maWkpw4cP9z3OgQMHqr3vdruD7ldKKSeQNfF12DqTgKS0SOkAOS1SOsJMTEwMM2fOZOLE\niaxfv55Tp05RVlbGxo0bfb8QXHV5Q7NmzYiIiCAnJ8f3sQkTJvDiiy+yZ88e4Pz63zVr1vjdZ9++\nfZk/f75vPa/H42H+/Pn07t3bN7EeN24cCxcuZMeOHRhjOHnyJG+//TalpaW+Mf32t7+loKCAo0eP\n8stf/pIRI0ZY+8VxKinPFSkdIKdFSgc4rsWmvzShlFLyTJkyhRYtWpCZmckjjzxCVFQUKSkpTJ8+\nHah+zdzGjRv7rohTVlbGu+++y+DBgyktLWXEiBHk5eURExND//79eeihh2rcX58+fSgtLfVNfHv1\n6sW3337ruw3wgx/8gCVLljBp0iT27dtHo0aN6N27t+9SQC6Xi0ceeYT+/ftTWFjI4MGDmTFjRgi/\nSkopZZ+AlzOzdEd6ObPgPimMLw+ix6SW9JgE5XIvZxYdG3v+Wr4hEtW0KSeKi0P2+Ko6vZxZYHrp\nLD0moSTlmID/7yX6iq9SytF0UqqUUqq2dI1vuJLSIqUD5LRI6VAq1KQ8V6R0gJwWKR3guBZZE1+l\nlFJKKaX8kDXxddi15AKS0iKlA+S0SOlQKtSkPFekdICcFikd4LgWXeOrlHKUyOjoGq+Bq2SIjY21\newhKKcFkTXwdts4kICktUjpATovDO8rXr7+yBwjX34wOlpQOqLPf8g6aw58rPlI6QE6LlA5wXIus\npQ5KKaWUUkr5IWvi67B1JgFJaZHSAXJapHSAnBYpHaAt4UhKB8hpkdIBjmuRNfFVSimllFLKD1kT\nX4etMwlISouUDpDTIqUD5LRI6QBtCUdSOkBOi5QOcFyLrImvUkoppZRSfsia+DpsnUlAUlqkdICc\nFikdIKdFSgdoSziS0gFyWqR0gONaZE18lVJKKaWU8kPWxNdh60wCktIipQPktEjpADktUjpAW8KR\nlA6Q0yKlAxzXImviq5RSSimllB+yJr4OW2cSkJQWKR0gp0VKB8hpkdIB2hKOpHSAnBYpHeC4FlkT\nX6WUUkoppfyQNfF12DqTgKS0SOkAOS1SOkBOi5QO0JZwJKUD5LRI6QDHtcia+CqllFJKKeWHrImv\nw9aZBCSlRUoHyGmR0gFyWqR0gLaEIykdIKdFSgc4rkXWxFcppZRSSik/ZE18HbbOJCApLVI6QE6L\nlA6Q0yKlA7QlHEnpADktUjrAcS2yJr5KKaWUUkr5IWvi67B1JgFJaZHSAXJapHSAnBYpHaAt4UhK\nB8hpkdIBjmuRNfFVSimllFLKD1kTX4etMwlISouUDpDTIqUD5LRI6QBtCUdSOkBOi5QOcFyLrImv\nUkoppZRSfsia+DpsnUlAUlqkdICcFikdIKdFSgdoSziS0gFyWqR0gONaZE18lVJKKaWU8kPWxNdh\n60wCktIipQPktEjpADktUjpAW8KRlA6Q0yKlAxzXImviq5RSSimllB+yJr4OW2cSkJQWKR0gp0VK\nB8hpkdIB2hKOpHSAnBYpHeC4FlkTX6WUUkoppfyQNfF12DqTgKS0SOkAOS1SOkBOi5QO0JZwJKUD\n5LRI6QDHtcia+CqllFJKKeWHrImvw9aZBCSlRUoHyGmR0gFyWqR0gLaEIykdIKdFSgc4rkXWxFcp\npZRSSik/ZE18HbbOJCApLVI6QE6LlA6Q0yKlA7QlHEnpADktUjrAcS2yJr5KKaWUUkr5IWvi67B1\nJgFJaZHSAXJapHSAnBYpHaAt4UhKB8hpkdIBjmuRNfFVSimllFLKD1kTX4etMwlISouUDpDTIqUD\n5LRI6QBtCUdSOkBOi5QOcFyLrImvUkoppZRSfsia+DpsnUlAUlqkdICcFikdIKdFSgdoSziS0gFy\nWqR0gONaZE18lVJKKaWU8kPWxNdh60wCktIipQPktEjpADktUjpAW8KRlA6Q0yKlAxzXImviq5RS\nSimllB+yJr4OW2cSkJQWKR0gp0VKB8hpkdIB2hKOpHSAnBYpHeC4FlkTX6WUUkoppfyItHsAlnLY\nOpOApLRI6QA5LVI6QE6LlA7QllCLjIR+/epmP+EoHI/J5ZDSAY5rCdMzWymllFIXKS/H1MFuXOXl\ndbAXpeqerKUODltnEpCUFikdIKdFSgfIaZHSAdoShrx2D8BKQo6JmA5wXIusia9SSimllFJ+yJr4\nOmydSUBSWqR0gJwWKR0gp0VKB2hLGPLYPQArCTkmYjrAcS2yJr5KKaWUUkr5IWvi67B1JgFJaZHS\nAXJapHSAnBYpHaAtYchr9wCsJOSYiOkAx7XImvgqpZRSSinlh6yJr8PWmQQkpUVKB8hpkdIBclqk\ndIC2hCGP3QOwkpBjIqYDHNcia+KrlFJKKaWUH7Imvg5bZxKQlBYpHSCnRUoHyGmR0gHaEoa8dg/A\nSkKOiZgOcFyLrImvUkoppZRSfsia+DpsnUlAUlqkdICcFikdIKdFSgdoSxjy2D0AKwk5JmI6wHEt\nsia+SimllFJK+SFr4uuwdSYBSWmR0gFyWqR0gJwWKR2gLWHIa/cArCTkmIjpAMe1yJr4KqWUUkop\n5Yesia/D1pkEJKVFSgfIaZHSAXJapHSAtoQhj90DsJKQYyKmAxzXImviq5RSSimllB+Rdg/AUjt3\nOu5fHn5JaZHSAXJapHSAnBYpHaAtYchLmL7qGxkJ/fqFfh/hSMi5BTiuRV/xVUoppVTdKy8HTJBv\nW4Lbvry87nqUI8ia+DroXxyXJKVFSgfIaZHSAXJapHSAtoQhj90DsJTH7gFYQ8i5BTiuRdbEVyml\nlFJKKT9kTXwddi25gKS0SOkAOS1SOkBOi5QO0JYw5LV7AJby2j0Aawg5twDHtcia+CqllFJKKeVH\nmP6642Vy2DqTgKS0SOkAOS1SOkBOi5QO0JYw5LF7AJby2D0Aa4TruXUVXGlD1sRXKaWUUkpdnvJy\nTIh34bL5Shuyljo4bJ1JQFJapHSAnBYpHSCnRUoHaEsY8to9AEt57R6ANYScW+C8IyJr4quUUkop\npZQfl5z4Zmdn07FjR9q3b8+sWbNq3Mbr9fK9732Pzp074/F4rB5j7YXrmpnLIaVFSgfIaZHSAXJa\npHSAtoQhj90DsJTH7gFYQ8i5Bc47IgHX+J47d45Jkybx3nvvkZiYyC233MKgQYO48cYbfdscO3aM\niRMn8u6779KqVSuKiopCPmillFJKKaWCFfAV3x07dtCuXTuSk5OpX78+I0aMYP369dW2WbVqFcOG\nDaNVq1YAJCQkhG60lyJozYyYFikdIKdFSgfIaZHSAdoShrx2D8BSXrsHYA0h5xY474gEnPgWFBTQ\nunVr3+1WrVpRUFBQbZt9+/Zx9OhR+vXrR0pKCitXrgzNSJVSSimllLoCAZc6uFyuSz5AWVkZn3zy\nCZs2beLUqVOkpqbSo0cP2rdvf9G2Y8eOJTk5GYCmTZvSrVs335pgr9cLcNFtn8p/HVWui/F3+zK3\n97d/q27XejxV1/3s3BnU9l6vN2Tjv+zjcZm3Q348KvcZ7Piqfm4Q24fV+dWt22Wdj3p+hfD8Cnb7\nOjq/qo2tNuO52s+vKi2hGL/v9nf78VTu7xK3Kz9W2+0rb/s+N9THI6giT5Dbh+HzPcx/nni/209l\n3aVuV34sqO1D8HyvfD83N5dAXMYYv5ds++ijj8jIyCA7OxuArKwsIiIimDp1qm+bWbNm8e2335KR\nkQHAU089xYABA3jwwQer78jlIsCuah6cywVbtgT1OUHr1y/ocQVLSgfIaZHSAXJa6qQD5LRI6QA5\nLXXUEfrvKuCCujkmob9qrIxzC8ScX3VxboH/eWfApQ4pKSns27eP3Nxczp49yxtvvMGgQYOqbfPA\nAw/wwQcfcO7cOU6dOsXHH39Mp06drB19bQlaMyOmRUoHyGmR0gFyWqR0gLaEIa/dA7CU1+4BWEPI\nuQXOOyIBlzpERkYyf/587r77bs6dO8eTTz7JjTfeyKJFiwAYP348HTt2ZMCAAXTp0oWIiAjGjRtn\n38RXKaWUUkopPy75J4vvuece7rnnnmofGz9+fLXbP/3pT/npT39q7cguh6Dr4olpkdIBclqkdICc\nFikdoC1hyGP3ACzlsXsA1hByboHzjoj+5TallFJKKXVVkDXxFbRmRkyLlA6Q0yKlA+S0SOkAbQlD\nXrsHYCmv3QOwhpBzC5x3RGRNfJVSSimllPJD1sRX0JoZMS1SOkBOi5QOkNMipQO0JQx57B6ApTx2\nD8AaQs4tcN4RkTXxVUoppZRSyg9ZE19Ba2bEtEjpADktUjpATouUDtCWMOS1ewCW8to9AGsIObfA\neUdE1sRXKaWUUkopP2RNfAWtmRHTIqUD5LRI6QA5LVI6QFvCkMfuAVjKY/cArCHk3ALnHRFZE1+l\nlFJKKaX8kDXxFbRmRkyLlA6Q0yKlA+S0SOkAbQlDXrsHYCmv3QOwhpBzC5x3RGRNfJVSSimllPJD\n1ovS4y4AACAASURBVMRX0JoZMS1SOkBOi5QOkNMipQO0JQx57B6ApTx2D8AaQs4tcN4RkTXxVUop\npZRSyg9ZE19Ba2bEtEjpADktUjpATouUDtCWMOS1ewCW8to9AGsIObfAeUdE1sRXKaWUUkopP2RN\nfAWtmRHTIqUD5LRI6QA5LVI6QFvCkMfuAVjKY/cArCHk3ALnHRFZE1+llFJKKaX8kDXxFbRmRkyL\nlA6Q0yKlA+S0SOkAbQlDXrsHYCmv3QOwhpBzC5x3RGRNfJVSSimllPJD1sRX0JoZMS1SOkBOi5QO\nkNMipQO0JQx57B6ApTx2D8AaQs4tcN4RkTXxVUoppZRSyg9ZE19Ba2bEtEjpADktUjpATouUDtCW\nMOS1ewCW8to9AGsIObfAeUdE1sRXKaWUUkopP2RNfAWtmRHTIqUD5LRI6QA5LVI6QFvCkMfuAVjK\nY/cArCHk3ALnHRFZE1+llFJKKaX8kDXxFbRmRkyLlA6Q0yKlA+S0SOkAbQlDXrsHYCmv3QOwhpBz\nC5x3RGRNfJVSSimllPJD1sRX0JoZMS1SOkBOi5QOkNMipQO0JQx57B6ApTx2D8AaQs4tcN4RkTXx\nVUoppZRSyg9ZE19Ba2bEtEjpADktUjpATouUDtCWMOS1ewCW8to9AGsIObfAeUdE1sRXKaWUUkop\nP2RNfAWtmRHTIqUD5LRI6QA5LVI6QFvCkMfuAVjKY/cArCHk3ALnHRFZE1+llFJKKaX8kDXxFbRm\nRkyLlA6Q0yKlA+S0SOkAbQlDXrsHYCmv3QOwhpBzC5x3RGRNfJVSSimllPJD1sRX0JoZMS1SOkBO\ni5QOkNMipQO0JQx57B6ApTx2D8AaQs4tcN4RkTXxVUoppZRSyg9ZE19Ba2bEtEjpADktUjpATouU\nDtCWMOS1ewCW8to9AGsIObfAeUck0u4BKKWUUko5VmQk9OtXN/tRV0zWV1HQmhkxLVI6QE6LlA6Q\n0yKlA7QlDHnsHoClPHYP4GLl5Zg62I2rvLwO9hI8j90DCJKspQ5KKaWUUkr5IWviK2jNjJgWKR0g\np0VKB8hpkdIB2hKGvHYPwFJeuwdgCa/dA7CQ1+4BBEnWxFcppZRSSik/ZE18hazHAuS0SOkAOS1S\nOkBOi5QO0JYw5LF7AJby2D0AS3jsHoCFPHYPIEiyJr5KKaWUUkr5IWviK2Q9FiCnRUoHyGmR0gFy\nWqR0gLaEIa/dA7CU1+4BWMJr9wAs5LV7AEGSNfFVSimllFLKD72Ob12oi4tbh+uFrcP1mFwOKS1S\nOkBOi5QO0JYw5LF7AJby2D0AS3jsHoCFPHYPIEhhOlsSpg4ubh2uF7ZWSimllAoXspY6CFmPBc5b\nM+OXoGMipkVKB8hpkdIB2hKGvHYPwFJeuwdgCa/dA7CQ1+4BBEnWxFcppZRSSik/ZE18hazHAuet\nmfFL0DER0yKlA+S0SOkAbQlDHrsHYCmP3QOwhMfuAVjIY/cAgiRr4quUUkoppZQfsia+QtZjgfPW\nzPgl6JiIaZHSAXJapHSAtoQhr90DsJTX7gFYwmv3ACzktXsAQZI18VVKKaWUUsoPWZczE7IeC5y3\nZsavcD0mem1lGaS0SOkAbQlDHrsHYCmP3QOwhMfuAVjIY/cAghSmP5mVCjG9trJSSil11ZG11EHI\neixw3poZv/SYhB9Bx0RMi5QO0JYw5LV7AJby2j0AS3jtHoCFvHYPIEiyJr5KKaWUUkr5Ed5LHa7i\ndZgeuwdgFSFr5ECPSViS0iKlA7QlDHnsHoClPHYPwBIeuwdgIY/dAwhSeM76Kuk6TKWUUkopZRFR\nSx28dg/AQl67B2AVIWvkQI9JWJLSIqUDtCUMee0egKW8dg/AEl67B2Ahr90DCJKoia9SSimllFL+\niJr4euwegIU8dg/AKkLWyIEek7AkpUVKB2hLGPLYPQBLeewegCU8dg/AQh67BxAkURNfpZRSSiml\n/BE18fXaPQALee0egFWErJEDPSZhSUqLlA7QljDktXsAlvLaPQBLeO0egIW8dg8gSKImvkoppZRS\nSvkjauLrsXsAFvLYPQCrCFkjB3pMwpKUFikdoC1hyGP3ACzlsXsAlvDYPQALeeweQJBETXyVUkop\npZTyR9TE12v3ACzktXsAVhGyRg70mIQlKS1SOkBbwpDX7gFYymv3ACzhtXsAFvLaPYAgiZr4KqWU\nUkop5Y+oia/H7gFYyGP3AKwiZI0c6DEJS1JapHSAtoQhj90DsJTH7gFYwmP3ACzksXsAQRI18VVK\nKaWUUsofURNfr90DsJDX7gFYRcgaOdBjEpaktEjpAG0JQ167B2Apr90DsITX7gH4UR9whfitfp3V\n1CzS5v0rpZRSSqkwUAaQEeQn5QLJQewj2Me3mKhXfD12D8BCHrsHYBUha+RAj0lYktIipQO0JQx5\n7B6ApTx2D8ASHrsHYKVkuwcQHFETX6WUUkoppfwRNfH12j0AC3ntHoBVhKyRAz0mYUlKi5QO0JYw\n5LV7AJby2j0AS3jtHoCVcu0eQHBETXyVUkoppZTyR9TE12P3ACzksXsAVhGyRg70mIQlKS1SOkBb\nwpDH7gFYymP3ACzhsXsAVkq2ewDBueTENzs7m44dO9K+fXtmzZrld7u//e1vREZG8qc//cnSASql\nlFJKKWWFgBPfc+fOMWnSJLKzs9mzZw+rV6/m888/r3G7qVOnMmDAAIwxIRvspXht27P1vHYPwCpC\n1siBHpOwJKVFSgdoSxjy2j0AS3ntHoAlvHYPwEq5dg8gOAEnvjt27KBdu3YkJydTv359RowYwfr1\n6y/a7n/+53948MEHadasWcgGqpRSSiml1JUI+AcsCgoKaN26te92q1at+Pjjjy/aZv369WzevJm/\n/e1vuFyu0Iy0Fjy27dl6HrsHYBUha+QgjI9JZCT06xf6fYQjKeeXlA7QljDksXsAlvLYPQBLeOwe\ngJWS7R5AcAL+NKvNJPaZZ57hpZdewuVyYYwJuNRh7NixJCcnA9C0aVO6deuGx+MBwOv1Alx0u1Ll\nLY/Ft32P72f/lt0O0fgrb1fuM2Tjv+B4+P4LsPIHg8W3Q3084PzX0FPlfUJw27evUPaUl7MlROOv\nvO0qL9fzK4jbvn2GaPwX/he8049H5T4df35VaQnF+Ovq50nl7UohPx4hLtKfJ8Hd9i1dSCY0twnN\n873y/dzcXAJxmQAz1Y8++oiMjAyys7MByMrKIiIigqlTp/q2ueGGG3yT3aKiIho3bsySJUsYNGhQ\n9R19NzEOhsvlIpjP8BL8v6JcEPJ1ycF2QPAtddEB3/1jaMuWS29YqeoP/9rq10+PSTD7EfQ8Cerc\ngrA+v6Q8T67aYwLBtwj53gV1+Jyvg+/EekyC2IfLFfI/WUxGHf5srGE/AV/xTUlJYd++feTm5uJ2\nu3njjTdYvXp1tW2+/PJL3/uPP/44999//0WTXqWUUkoppewWcOIbGRnJ/Pnzufvuuzl37hxPPvkk\nN954I4sWLYL/396dh0dVZO8DfxuCAiqgiCJuAVFBSAgJOwI3oiyO8HUQXACXqCxuuCsoI8R1GEVB\nHRVQUASVRWVT0RGoUdZABEFQULEFQWUHQRCS3N8fme5fAonmdio5qcP7eR6esZNO6q2pXk6qz70X\nQL9+/UolZFF50gEs8qQD2KKkRw7QsyaedACbyurji33XOiiZiycdwCpPOoAVnnQAm+KlAwTzl6+c\nnTt3RufOnfN9rbCCd9y4cXZSERG5LCsLwT/CDTqG3IHERESuKicdwCYjHcAiIx3AFiXnwQT0rImR\nDmCTmseXkQ5gj5o1gZq5GOkAVhnpAFYY6QA2haUDBKOq8CUiIiIiKoyqwteTDmCRJx3AFiU9coCe\nNfGkA9ik5vHlSQewR82aQM1cPOkAVnnSAazwpAPYFC8dIBhVhS8RERERUWFUFb5GOoBFRjqALUp6\n5AA9a2KkA9ik5vFlpAPYo2ZNoGYuRjqAVUY6gBVGOoBNYekAwagqfImIiIiICqOq8PWkA1jkSQew\nRUmPHKBnTTzpADapeXx50gHsUbMmUDMXTzqAVZ50ACs86QA2xUsHCEZV4UtEREREVBhVha+RDmCR\nkQ5gi5IeOUDPmhjpADapeXwZ6QD2qFkTqJmLkQ5glZEOYIWRDmBTWDpAMKoKXyIiIiKiwqgqfD3p\nABZ50gFsUdIjB+hZE086gE1qHl+edAB71KwJ1MzFkw5glScdwApPOoBN8dIBglFV+BIRERERFUZV\n4WukA1hkpAPYoqRHDtCzJkY6gE1qHl9GOoA9atYEauZipANYZaQDWGGkA9gUlg4QjKrCl4iIiIio\nMKoKX086gEWedABblPTIAXrWxJMOYJOax5cnHcAeNWsCNXPxpANY5UkHsMKTDmBTvHSAYFQVvkRE\nREREhVFV+BrpABYZ6QC2KOmRA/SsiZEOYJOax5eRDmCPmjWBmrkY6QBWGekAVhjpADaFpQMEo6rw\nJSIiIiIqTJx0AJs86QAWedIBbFHSIwfoWRNPOoBNah5fnnQAe9SsCdTMxZMOYJUnHcAKTzpAYcoB\nGFoKYwhSVfgSERERUYxyAMAv4TFCJfv7/4KqwtegDP8VFZBBGZ1LXByQmlryY5RBBmV0TQIy0DEP\nALk9mCp25QzUrIqaNYGauRioeXRBy2wMNMwiwsCl2bDHl4LJyoIPFPnfvAD3jfxDVlapTYeIiIiO\nHiHf90t4T/t/A4VCCDpUKBQq6Q13hIDAuQKPoWQegJ65aJkHoGcuoVAImDevRMcAAKSmls5cSmFV\nuCZFVypzKaV5lMabdqk955U8T7gmgUYpvffGAsbhji8RERERHRVUFb5GOoBFRjqAJUY6gEVGOoAl\nRjqATUrOs6pqVdSsCdTMxUgHsMpIB7DCSAewykgHCERV4UtEREREVBj2+IL9pIHGUTIXLfMA9MxF\nXT+pgj45dWvCHt+ijwP2kxZ5BK5J0FHY40tEREREVNJUFb5GOoBFRjqAJUY6gEVGOoAlRjqATUp6\nMFWtipo1gZq5GOkAVhnpAFYY6QBWGekAgagqfImIiIiICsMeX7CfNNA4SuaiZR6Anrmo6ydV0Cen\nbk3Y41v0ccB+0iKPwDUJOgp7fImIiIiISpqqwtdIB7DISAewxEgHsMhIB7DESAewSUkPpqpVUbMm\nUDMXIx3AKiMdwAojHcAqIx0gEFWFLxERERFRYdjjC/aTBhpHyVy0zAPQMxd1/aQK+uTUrQl7fIs+\nDthPWuQRuCZBR2GPLxERERFRSVNV+BrpABYZ6QCWGOkAFhnpAJYY6QA2KenBVLUqatYEauZipANY\nZaQDWGGkA1hlpAMEoqrwJSIiIiIqDHt8wX7SQOMomYuWeQB65qKun1RBn5y6NWGPb9HHAftJizwC\n1yToKKI9vnElPnIxVEDuQpf0GERERESkX5kufA8BwNAAPxAGEB9wjCC/vxQZAJ5wBhsMdMwD0DMX\nAx3zAJDbg5mUJJ3CAgM1q6JmTaBmLgZqHl3QMhsDDbOIMHBpNmW68CUiIrIiLg5ITS35MYioTNP1\nLI2XDmCPJx3AEk86gEWedABLPOkANinYjcvlSQewp6yuSVZWyXcuZmWV8Aix8aQDWOVJB7DCkw5g\nlScdIBCe1YGIiIiIjgq6Ct+wdAB7jHQAS4x0AIuMdABLjHQAm5ScZ1XVqqhZEz2rYqQDWGWkA1hh\npANYZaQDBKKr8CUiIiIiKoSuwjdeOoA9nnQASzzpABZ50gEs8aQD2FRW+0kD86QD2KNmTfSsiicd\nwCpPOoAVnnQAqzzpAIHoKnyJiIiIiAqhq/ANSwewx0gHsMRIB7DISAewxEgHsElNP6mRDmCPmjXR\nsypGOoBVRjqAFUY6gFVGOkAgugpfIiIiIqJC6Cp846UD2ONJB7DEkw5gkScdwBJPOoBNavpJPekA\n9qhZEz2r4kkHsMqTDmCFJx3AKk86QCC6Cl8iIiIiokLoKnzD0gHsMdIBLDHSASwy0gEsMdIBbFLT\nT2qkA9ijZk30rIqRDmCVkQ5ghZEOYJWRDhCIrsKXiIiIiKgQugrfeOkA9njSASzxpANY5EkHsMST\nDmCTmn5STzqAPWrWRM+qeNIBrPKkA1jhSQewypMOEIiuwpeIiIiIqBC6Ct+wdAB7jHQAS4x0AIuM\ndABLjHQAm9T0kxrpAPaoWRM9q2KkA1hlpANYYaQDWGWkAwSiq/AlIiIiIiqErsI3XjqAPZ50AEs8\n6QAWedIBLPGkA9ikpp/Ukw5gj5o10bMqnnQAqzzpAFZ40gGs8qQDBKKr8CUiIiIiKoSuwjcsHcAe\nIx3AEiMdwCIjHcASIx3AJjX9pEY6gD1q1kTPqhjpAFYZ6QBWGOkAVhnpAIHoKnyJiIiIiAqhq/CN\nlw5gjycdwBJPOoBFnnQASzzpADap6Sf1pAPYo2ZN9KyKJx3AKk86gBWedACrPOkAgegqfImIiIiI\nCqGr8A1LB7DHSAewxEgHsMhIB7DESAewSU0/qZEOYI+aNdGzKkY6gFVGOoAVRjqAVUY6QCC6Cl8i\nIiIiokLoKnzjpQPY40kHsMSTDmCRJx3AEk86gE1q+kk96QD2qFkTPaviSQewypMOYIUnHcAqTzpA\nILoKXyIiIiKiQugqfMPSAewx0gEsMdIBLDLSASwx0gFsUtNPaqQD2KNmTfSsipEOYJWRDmCFkQ5g\nlZEOEIiuwpeIiIiIqBC6Ct946QD2eNIBLPGkA1jkSQewxJMOYJOaflJPOoA9atZEz6p40gGs8qQD\nWOFJB7DKkw4QiK7Cl4iIiIioELoK37B0AHuMdABLjHQAi4x0AEuMdACb1PSTGukA9qhZEz2rYqQD\nWGWkA1hhpANYZaQDBKKr8CUiIiIiKoSuwjdeOoA9nnQASzzpABZ50gEs8aQD2KSmn9STDmCPmjXR\nsyqedACrPOkAVnjSAazypAMEoqvwJSIiIiIqhK7CNywdwB4jHcASIx3AIiMdwBIjHcAmNf2kRjqA\nPWrWRM+qGOkAVhnpAFYY6QBWGekAgegqfImIiIiICqGr8I2XDmCPJx3AEk86gEWedABLPOkANqnp\nJ/WkA9ijZk30rIonHcAqTzqAFZ50AKs86QCB6Cp8iYiIiIgKUaTCd/bs2ahXrx7OPfdcDBs27Ijv\nT5w4EY0aNUJiYiJat26NlStXWg9aJGGZYUuCkQ5giZEOYJGRDmCJkQ5gk5p+UiMdwB41a6JnVYx0\nAKuMdAArjHQAq4x0gEDi/uoO2dnZuP322/Hpp5/i9NNPR9OmTdG1a1fUr18/ep86dergs88+Q9Wq\nVTF79mz07dsXixcvLtHgRERERERB/OWOb0ZGBurWrYv4+HhUqFABV199NaZPn57vPi1btkTVqlUB\nAM2bN8dPP/1UMmn/SrzMsCXBkw5giScdwCJPOoAlnnQAm9T0k3rSAexRsyZ6VsWTDmCVJx3ACk86\ngFWedIBA/rLw3bRpE84888zo7TPOOAObNm0q9P6vvfYaLr30UjvpiIiIiIgs+ctWh1AoVORfNm/e\nPIwdOxYLFiwo8Ps33HAD4uPjAQDVqlVDUlISPM8DABhjAOCI21Hh//1v/J/c/gVAiwD3z6Ow8a3d\n/t84XmS8v7g9AkBSgPtHxiyx/IetR1HnE/laUe8fvV3C6xEZs8h5AKwAcFeA++dVlh5febMV5f55\n51Baj69on2hk97Cw25GvFfX+/7tdGo+vYI+woM/4yG2USP6Y1yPvWhT1/nnm4PrrV/T3l6HnO1Dc\nR1cpPL5K+BWM7yfBbpdGxVISz/fIf4fDYfyZkO/7/p/dYfHixRg6dChmz54NAHjqqadQrlw5PPjg\ng/nut3LlSnTr1g2zZ89G3bp1jxwoFMJfDFXgz2BogB8II3i7w1AEzhVUKBRC0BEM8hcdfzkGSn4e\nQPC5GASbB1A6c+GaBBwDpbMmmDcv2A+tWBH8o/XU1NKZSymsCtek6LQ850tjHkApPudLYVW4JgHG\nULImQOF1Z7m/+sEmTZrg22+/RTgcxsGDBzFp0iR07do13302bNiAbt26YcKECQUWvaUmXm5o2zzp\nAJZ40gEs8qQDWOJJB7BJTT+pJx3AHjVromdVPOkAVnnSAazwpANY5UkHCOQvWx3i4uLw4osvomPH\njsjOzsZNN92E+vXrY9SoUQCAfv364dFHH8XOnTtxyy23AAAqVKiAjIyMkk1ORERERBTAX7Y6WBuI\nrQ6BGJS9j9gAXR+rc00CjAG2OgTBVoeyuSYanvP8WN2grH2szjUxKGtrAhSj1YGIiIiISIOyveNb\nPgTklFCgiHKAn132/hoMPAbK5u5iTGOgbO6aBB4DXJNAY8SyuxiLMrnjG9MoXJMAND1PSuNNu+zu\nLgYehWsSZAwlawIUXnf+ZY+vqBygxBcgp+inayMiIiIidylrdTDSAawx0gEsMdIBLDLSASwx0gFs\nynvOWKcZ6QD2lNE1qYDcHbOS/Feh1GYTjJEOYJWRDmCFkQ5glZEOEEjZ3vFVIvKCW9JjEBFRwQ4B\nwQ6WBgIfMH0o6O8nolKnrPD1pAMUKKYX3KBjlPDvj5UnHcAiTzqAJZ50AJvUnDPWkw5gj5o1gZpz\nw3vSAazypANY4UkHsMqTDhCIslYHIiIiIqKCKSt8jXQAe8LSAeww0gEsMtIBLDHSAWwqo/2kwRnp\nAPaoWRPwdbhMMtIBrDDSAawy0gECUVb4EhEREREVTFnh60kHsCdeOoAdnnQAizzpAJZ40gFsUtNP\n6kkHsEfNmoCvw2WSJx3ACk86gFWedIBAlBW+REREREQFU1b4GukA9oSlA9hhpANYZKQDWGKkA9ik\npp/USAewR82agK/DZZKRDmCFkQ5glZEOEIiywpeIiIiIqGDKCl9POoA98dIB7PCkA1jkSQewxJMO\nYJOaflJPOoA9atYEfB0ukzzpAFZ40gGs8qQDBKKs8CUiIiIiKpiywtdIB7AnLB3ADiMdwCIjHcAS\nIx3AJjX9pEY6gD1q1gR8HS6TjHQAK4x0AKuMdIBAlBW+REREREQFC/m+75fKQKEQgg4VCoUAlHS8\n4LkCjxAKAUNLdAhgKEp8HsD/1rGkx0DJz0XLPAA9cwmFQsC8eSU6BgAgNbV05qLltUvLmpQPATkl\nOgRQDvCz3X/tAkrxOa/kecI1CTRK6b03FjBOXImPTEREJC0HKPE39JxQyf5+Iio2Za0ORjqAPWHp\nAHYY6QAWGekAlhjpADap6Sc10gHsUbMmgJZ1MdIBrDLSAaww0gGsMtIBAlFW+BIRERERFUxZ4etJ\nB7AnXjqAHZ50AIs86QCWeNIBbFJzzlhPOoA9atYE0LIunnQAqzzpAFZ40gGs8qQDBKKs8CUiIiIi\nKpiywtdIB7AnLB3ADiMdwCIjHcASIx3AJjX9pEY6gD1q1gTQsi5GOoBVRjqAFUY6gFVGOkAgygpf\nIiIiIqKCKSt8PekA9sRLB7DDkw5gkScdwBJPOoBNavpJPekA9qhZE0DLunjSAazypANY4UkHsMqT\nDhCIssKXiIiIiKhgygpfIx3AnrB0ADuMdACLjHQAS4x0AJvU9JMa6QD2qFkTQMu6GOkAVhnpAFYY\n6QBWGekAgSgrfImIiIiICqas8PWkA9gTLx3ADk86gEWedABLPOkANqnpJ/WkA9ijZk0ALeviSQew\nypMOYIUnHcAqTzpAIMoKXyIiIiKigikrfI10AHvC0gHsMNIBLDLSASwx0gFsUtNPaqQD2KNmTQAt\n62KkA1hlpANYYaQDWGWkAwSirPAlIiIiIiqYssLXkw5gT7x0ADs86QAWedIBLPGkA9ikpp/Ukw5g\nj5o1AbSsiycdwCpPOoAVnnQAqzzpAIEoK3yJiIiIiAqmrPA10gHsCUsHsMNIB7DISAewxEgHsElN\nP6mRDmCPmjUBtKyLkQ5glZEOYIWRDmCVkQ4QiLLCl4iIiIioYMoKX086gD3x0gHs8KQDWORJB7DE\nkw5gk5p+Uk86gD1q1gTQsi6edACrPOkAVnjSAazypAMEoqzwJSIiIiIqmLLC10gHsCcsHcAOIx3A\nIiMdwBIjHcAmNf2kRjqAPWrWBNCyLkY6gFVGOoAVRjqAVUY6QCBx0gHILRUAhEphDCIiIiLblBW+\nnnQAe+KlAxTsEADAL+ExSrq0jo0nHcASTzqATWr6ST3pAPaoWRNAy7p40gGs8qQDWOFJB7DKkw4Q\niLJWByIiIiKigikrfI10AHvC0gFsMdIBrDHSASwx0gFsUtNPaqQD2KNmTQAt62KkA1hlpANYYaQD\nWGWkAwSirPAlIiIiIiqYssLXkw5gT7x0AFs86QDWeNIBLPGkA9ikpp/Ukw5gj5o1AbSsiycdwCpP\nOoAVnnQAqzzpAIEoK3yJiIiIiAqmrPA10gHsCUsHsMVIB7DGSAewxEgHsElNP6mRDmCPmjUBtKyL\nkQ5glZEOYIWRDmCVkQ4QiLLCl4iIiIioYCHf90v2pKyRgUIhBB0qFAqhpM8ZCwTPFXiE8iEgp0SH\nAMoBfnbJL6WWNTkmFPrfOYlLTgUAB0vh6RUKhUphRVDyz5NQCJg3r0THAACkppbOXBQ8T7gmgUcp\nlXmUxpt2qT3nuSZFHwdck0CjFFJ3KruARRmVA5T4AymnbF70oaw6mi/EQUTuKo2rZ0bGIdJIWauD\nkQ5gkZEOYImRDmCRkQ5ghZEOYJOaflIjHcAeNWsClMV1+f9/tAf5Ny/wz5T0J2KxM9IBrDDSAawy\n0gEC4Y4vkeNKYweIuz9ERAXjLrxb2ONbWn1yCuYB6JmLlnkA/5vL0BIeZCh7fIPQ8vjimgQeRck8\nAD1z0TIPQM9cZHt8lbU6EBEREREVTFnha6QDWGSkA1hipANYZKQD2BGWDmCRmn5SIx3AHjVrAuhZ\nFyMdwCIjHcASIx3AIiMdIBBlhS8RERERUcGUFb6edACLPOkAlnjSASzypAPYES8dwKKkJOkEKX3Q\n9gAAIABJREFUlnjSAexRsyaAnnXxpANY5EkHsMSTDmCRJx0gEGWFLxERERFRwZQVvkY6gEVGOoAl\nRjqARUY6gB1h6QAWqeknNdIB7FGzJoCedTHSASwy0gEsMdIBLDLSAQJRVvgSERERERVMWeHrSQew\nyJMOYIknHcAiTzqAHfHSASxS00/qSQewR82aAHrWxZMOYJEnHcASTzqARZ50gECUFb5ERERERAVT\nVvga6QAWGekAlhjpABYZ6QB2hKUDWKSmn9RIB7BHzZoAetbFSAewyEgHsMRIB7DISAcIJE46ABEV\nUzmU/CWLlf2JTERER6eQXxoXTEbh10z+q5/RcM1oLfMA9MxFyzwAPXMJhULAvHklOgYAIDWVj6+i\njsA1CTqKknkAeuaiZR6AnrmU3ntjQeNwH4eIiIiIjgrKCl8jHcAiIx3AEiMdwCIjHcASIx3AHjX9\npEY6gD1q1gTQsy5GOoBFRjqAJUY6gEVGOkAgygpfIiIiIqKCsceXPTPBRlEyFy3zAPTMhf2kgUfh\nmgSgak3YTxpkFCXzAPTMhT2+REREREQlTlnha6QDWGSkA1hipANYZKQDWGKkA9ijpp/USAewR82a\nAHrWxUgHsMhIB7DESAewyEgHCERZ4UtEREREVDD2+LJnJtgoSuaiZR6AnrmwnzTwKFyTAFStCftJ\ng4yiZB6Anrmwx5eIiIiIqMQpK3yNdACLjHQAS4x0AIuMdABLjHQAe9T0kxrpAPaoWRNAz7oY6QAW\nGekAlhjpABYZ6QCBKCt8iYiIiIgKxh5f9swEG0XJXLTMA9AzF/aTBh6FaxKAqjVhP2mQUZTMA9Az\nF/b4EhERERGVOGWFr5EOYJGRDmCJkQ5gkZEOYImRDmCPmn5SIx3AHjVrAuhZFyMdwCIjHcASIx3A\nIiMdIBBlha+mF1wtc9EyD0DPXLTMA8B330knsIRrUjZpWRct8wD0zEXLPADX5vKXhe/s2bNRr149\nnHvuuRg2bFiB9xkwYADOPfdcNGrUCMuXL7cesuh2CY5tm5a5aJkHoGcuWuYBYO9e6QSWcE3KJi3r\nomUegJ65aJkH4Npc/rTwzc7Oxu23347Zs2djzZo1ePvtt/H111/nu8+HH36I7777Dt9++y1Gjx6N\nW265pUQDExERERHF4k8L34yMDNStWxfx8fGoUKECrr76akyfPj3ffWbMmIHrr78eANC8eXPs2rUL\nv/76a8kl/lNhoXFLQlg6gCVh6QAWhaUDWBKWDmDPL79IJ7AkLB3AHjVrAuhZl7B0AIvC0gEsCUsH\nsCgsHSCQuD/75qZNm3DmmWdGb59xxhlYsmTJX97np59+wqmnnnrE78s9TUZQQX/mjeAjxJQr8Cgx\n/EywuZTOPACuSYARuCbBpKYG/5mPPw78I2Xz8cU1KXlanvMlPw9Az1y0zAPQM5fSe2880p8WvkUN\ndvh50gr6uVI6XTARERERUYH+tNXh9NNPx8aNG6O3N27ciDPOOONP7/PTTz/h9NNPtxyTiIiIiKh4\n/rTwbdKkCb799luEw2EcPHgQkyZNQteuXfPdp2vXrhg/fjwAYPHixahWrVqBbQ5ERERERJL+tNUh\nLi4OL774Ijp27Ijs7GzcdNNNqF+/PkaNGgUA6NevHy699FJ8+OGHqFu3Lo477jiMGzeuVIITERER\nEQUR8h1svh0+fHih3wuFQrjnnntKMQ0RUWy2bNmCAwcORG+fddZZgmmKR9NcXHXnnXdi5MiR6NKl\nyxHfC4VCmDFjhkAq0mbLli3417/+hdWrV0ef86FQCHPnzhVOVjR/uuNbVv3222+FHkAneaRgLHr0\n6IEpU6YgISHhiO+FQiGsXLlSIFXsFi1ahAEDBmDNmjU4ePAgsrOzcfzxx2PPnj3S0QLRMo9169bh\noYceOuIFav369cLJgmndujUWLFiA448//ojneCgUcm5dZsyYgXvvvRebN2/GKaecgh9//BH169fH\n6tWrpaMFpmkurj/vr732WgDAvffeK5zEHi2vYVrmAQC9evXCVVddhVmzZmHUqFF4/fXXUaNGDelY\nReeTqE2bNvm+7/s//PBDgf9ck5yc7K9bt85PSkrys7Ky/LFjx/oPPvigdKzAtMyjVatW/n/+8x8/\nISHBD4fD/pAhQ/zBgwdLxzrqJSQk+Fu3bvWTkpJ83/f9uXPn+mlpacKpYqNpLlqe95poeQ3TMg/f\n9/3GjRv7vp/73I9ISUmRihOYk60OEfv378drr72GNWvWYP/+/dGdoLFjxwonO3qlpKQgMzMTiYmJ\n0d3qpKQkrFjh1rW8tcwjOTkZX3zxBRISErBq1ap8X3PJjh07/vT7J510UiklsSPy+GrUqBG++OIL\nlC9fPt9jzSUa5+Lq876gTw4jXPwEEdDzGqZlHgDQokULLF68GB06dMCAAQNQq1Yt9OjRA99//710\ntCJxstUh4tprr0X9+vUxe/ZsDBkyBBMmTED9+vWlYwVS0Ee3ES5+hHvcccfhjz/+QKNGjfDAAw+g\nZs2aTp7DWcs8KlasiOzsbNStWxcvvvgiatWqhX379knHCiw5ORmhUAi+72PDhg048cQTAQA7d+7E\n2WefjR9++EE4YTAnnngifvvtN7Rp0wa9evXCKaecguOPP146Vkw0zcX15/3MmTMBAC+99BKA3PdI\n3/cxceJEyVjFouU1TMs8AODhhx/Grl27MHz4cNxxxx3Ys2cPnnvuOelYReb0jm/kL/HIX+eHDh3C\nhRdeeMTV5VwwePBg1KpVC7179wYATJw4EZs3b8Zjjz0mnCyYH3/8EaeccgoOHjyI5557Dnv27MGt\nt96KunXrSkcLJBwO49RTT3V+HhkZGahfvz527dqFf/zjH9izZw8eeOABtGjRQjpaTPr06YO///3v\nuPTSSwEAH330Ed5//32MHj1aOFkw+/btQ8WKFZGTk4OJEydiz5496NWrF6pXry4dLbC9e/eiUqVK\nKuai5Xlf0C5148aNsXz5cqFEsVu6dCnq1avn/GuYttdip4k1WVjQtGlT3/d9/8ILL/RXrlzpb9my\nxa9du7Zwqtjk7ZX5s6+VZYcOHfJ79uwpHYMKkZWV5e/atUs6RrE0aNCgSF8ryw4dOuR7nicdg/7C\n9u3b/RUrVkjHiEliYqL/+eefR2/Pnz/fb9SokWCi2GRlZfn33nuvdAw6zH333efv3r3bP3jwoH/R\nRRf51atX98ePHy8dq8icbnXo06cPduzYgccffxxdu3bF3r17ndshjTjuuOMwYcIEXHPNNQCAd955\nx7mPC+Pi4vDjjz/ijz/+wLHHHisdp1gSEhKiH60DuW0nVapUQdOmTTF48GBndrOuueYajBo1CuXL\nl0fTpk2xe/du3HnnnXjggQeko8WkVq1aePzxx9G7d2/4vo+33nrLuStFxsXFoVy5cti1axeqVasm\nHafYTjjhhCO+VrVqVTRt2hTDhw9HnTp1BFLFpl27dpg5cyaysrKQkpKCGjVqoHXr1k59jAvkHueS\nlpaG3bt3AwCqVavm5Dn2y5cvj/nz5zt5xqbDdenSpdD3lH79+qFixYrCCYvuk08+wdNPP433338f\n8fHxeO+999CmTZvoWUXKOqdbHTT54YcfcOedd2LhwoUAck/fNHLkSMTHx8sGC+jaa6/FN998g65d\nu6Jy5coA3Dy38v3334+4uDj07NkTvu/jnXfewe+//46aNWtiwYIF0V66sq5Ro0b48ssvMXHiRHzx\nxRf45z//ieTk5OjBFa7Zvn070tPT8fnnnwMA2rZtiyFDhjh3cFvXrl2xfPlydOjQId/z5PnnnxdO\nFtzgwYNx5pln5vuj/fvvv0fjxo3xyiuvwBgjGzCASIvAq6++io0bNyI9PT3fwUiu2bVrFwA4/QdW\n//79sXnzZvTo0SPfc6Vbt27CyYIZMGAAtm3bhmuuuQa+72PSpEmoUqUKypUrhz179uDNN9+Ujlhk\nDRo0wOrVq3HTTTehe/fu6Ny5c/S9xgVO7/hu27YN6enpmD9/PkKhENq0aYNHHnnEmd24vGrXrq3i\n5OJ169bFOeecg5ycHOzdu1c6Tsw+/fTTfP1wiYmJ0R65PztyuqzJysrCoUOHMG3aNNx2222oUKGC\n0zsn1atXd7I4PNwVV1wRfeOO7AK5ui4zZszId7aAvn37IikpCcOGDcNTTz0lmCy47Oxs/Pzzz5g8\neTIef/xxAHByXQYNGoQHH3wwWvDu3LkTw4cPj87JJQcOHED16tWPuDiCa4XvwoULsWzZsujtrl27\nokmTJli2bBkaNGggmCy4Ll26oF69eqhYsSJefvllbNmyxakda6d7fNu3b+8/+uij/vr16/3vv//e\nf+yxx/z27dtLx4rJtdde6+/cuTN6e8eOHU6dC7N3796+7/v+c889J5zEjoSEBH/x4sXR20uWLPET\nExN93/ej5yt1wciRI/1atWr5nTp18rOzs/0ffvjBv/DCC6Vjxax9+/b5nifbt2/3O3ToIJgomIsu\nusj3fd+///77hZPY07x5c/+dd97xs7Oz/ezsbH/SpEl+8+bNfd/3nesrnTx5sp+QkOD379/f933f\n/+677/xu3boJpwquoP/fXXrd8n3ff+CBB3zf9/1JkyYJJ7GjXr16fjgcjt4Oh8N+vXr1fN93b218\nP/e1Nysry/d939+7d6//888/CycqOqdbHRo2bIivvvoq39dc/ViqoKNwXTp/5AUXXIBPP/0UnTp1\nKvCjTdc+il66dCnS0tKiu9YnnHACXnvtNTRo0AAffPABrrzySuGEsfF9H9nZ2YiLc/PDHg3Pk1df\nfRU33ngj3nrrrSO+n5ycLJCqeL7//nvceeedWLx4MYDcc3yOGDECp59+OjIzM3HhhRcKJzz6JCYm\nIiMjI7oLt3//fjRp0sSpq+k1bNgQq1atQnJyspNnozjchx9+iP79+0d73tevX4+XXnoJqampGDNm\nDO666y7hhEX3xhtvRD8J8fN8WnXddddJxioyN9/9/qdDhw54++23cdVVVwEApkyZgg4dOginio3v\n+9ixY0e0QNyxYweys7OFUxVd//790b59e6xfvx4pKSn5vufiZRmbNm2Kr776KnpwSNWqVaPfc6no\nTU9PP+KACgB45JFHJGPFrHz58vjxxx9x9tlnA8g9/VS5cuWEUxVdeno6Hn30UWzatKnAy8rOmzdP\nIFXxnHPOOZg1a1aB33Ot6E1LS8v3fAFynzOuXRSpV69eaN++PW688Ub4vo9x48Y5U5REdO7cGSee\neCL27t17xAGULp7j/tJLL8W6devwzTffIBQK4fzzz4/+YeJS0QvkbgxF3ksOHDiAOXPmIDk52ZnH\nmJM7vnkv+rBv377oG19OTg6OO+44/Pbbb5LxYjJ+/Hg88cQTuPLKK+H7PqZMmYKHH37YmQdSRP/+\n/fHKK69Ixyi2vAVj3h4/1wrGZ555Jpp///79mDVrFi644ALn3sgjZs+ejb59+6Jdu3bwfR+fffYZ\nRo8ejU6dOklHC+TRRx917rFUmLS0tHy3Xb6C5tSpU/M9X95//33UqlULL7zwgnCy4D766CPMmTMH\nAHDJJZegY8eOwoli07VrVxXHv0R2SQ/fhHDtPb4gu3btwlVXXYWPP/5YOkqROFn4arV69WrMnTsX\noVAIF110ES644ALpSMUyevRo9O3bVzpGTLQVjBF//PEHOnTogP/+97/SUWK2detWLF68GKFQCM2b\nN0eNGjWkIxXL0KFDMXToUOkYMdNULB4uJycHrVu3xqJFi6SjxGzmzJno0qWLdAwrZs2ahcsuu0w6\nRkxuv/32AndJp06dKpys+A4ePIiGDRti3bp10lGKxPnCd/r06fjss88QCoXQrl07FU/wUaNGoV+/\nftIxis3VKwUVREPBCOS20DRr1gzfffeddJRiGzJkCNLT06VjFJum5wmgo1iM+Oabb3DZZZc5/XzR\n9PjSNBfXdknzyltn5eTkYM2aNbjyyisxbNgwwVRF53SP78CBA7F06VL06tULvu/j+eefx8KFC507\nhc7hXnnlFRWFryb79u3Dpk2bpGMElvfUazk5OdiyZYuaj9hnzJihovB1fO/hCOvWrcPWrVulY8Qk\nbxtdKBTCqaee6sybObmlcuXK+OGHH6RjxCTv8QkVKlTAWWedhTPPPFMwUTBOF74ffPABVqxYgfLl\nywMAbrjhBiQlJTlf+Lps+/bt0fMou9yXpaVgzHuhjQoVKuCUU05BhQoVBBPZ43LBuGrVquhjLDMz\nUzhN8WgqFl0+93he2dnZ0fdF14+50DKXwnZJXeR5XvS/Z86cidatW8uFiYHTrQ6JiYmYN29etNDa\nvn07UlNT851M3RV5C8aNGzc69ddTXueeey6SkpKQlpaGzp07O3nydyD3bAERWgpGl3uuI/IWjHnf\nEF1z4YUX4o8//kBaWhp69eqV76whVDa43Htdp04dXHHFFUhLS3P+WBEtc8l7mk8Xd0kL42L7iTvn\nASrAoEGDkJycjOuvvx7XX389UlJS8NBDD0nHikmLFi3Qo0cPfPjhhzjjjDOk48Rs7dq16NOnD8aP\nH4+6deti0KBBzjS85xUfHx/998EHHzhf9ALAyy+/LB2h2G655RY0bdoUL730ktO7c/Pnz8fEiROx\nYcMGJCcn45prrsEnn3wiHavYXC0UCzJ9+nTpCDFbsWIFzj33XNx8881o3rw5Ro0a5dzpvyK0zMXz\nvOi/HTt2qCh6nVWKF8soEZs2bfKnTZvmT58+3akrhxwuOzvb//jjj/2rrrrKr1Onjj9w4EB/7dq1\n0rGKZc6cOf5pp53mV6lSxW/btq2/YMEC6UgxcfGqOgXRMo+1a9f6Dz74oF+nTh3/6quv9j/++GPp\nSDE7dOiQP2XKFP+0007z69Wr55933nn+1KlTpWPFTMtjzPfdu+pcYebNm+fXqlXLr1Spkn/dddf5\n3377rXSkmGmZi+vPk8gV23zfz3eFU1c42eqQmZmZ7yN0/7Dz4rl49aO85s6di969e2Pfvn3RnuVW\nrVpJxyqSbdu2YeLEiRg/fjxOPfVU3HzzzejSpQu+/PJLdO/ePV8LgStc/CgnQksLzeGysrIwbdo0\nDBgwAFWrVkVOTg6efPJJXHHFFdLRiuTLL7/E66+/jlmzZuGSSy7BzTffjOTkZGzevBktWrTAhg0b\npCPGxKWr6BVESytNVlYWPvjgA4wbNw7hcBjXXXcdevbsifnz5+Ohhx5y6lM4TXOJcPk9BVDQfiJc\neMekXbt2vud5hf5z0datW/0RI0b4ycnJfufOnf13333XP3jwoL906VL/7LPPlo5XZOeee66fnp7u\nb9iw4YjvPfXUUwKJYrNt27bofxc0F1fUrVvX7969u//BBx/4OTk50nGKbcWKFf5dd93l161b17/l\nllv8zMxM3/dzP/k588wzhdMVXdu2bf033njD37dv3xHfe+ONNwQSxW7lypXR/867E+Si1q1b+02a\nNPH//e9/+7t27ZKOE7PatWv7aWlpBX7Kdvvttwskip2Wubi+S5rX7t27/VGjRvktW7b0mzVr5r/y\nyiv+7t27pWMVmZOFr0ZaCsbs7GzpCFZoKRi1tdBoKhi10FIsRrjeSpOVleWnp6dLx7BC01xq167t\n33ffff7q1aulo1jlYvuJk60Ou3fvxq+//orzzjsPADB58mQcOHAAANCxY0eceuqpkvFikpOTE730\nssuWLl2KJ598EuFwGFlZWQByW1BcO9NGTk4OPv30U4wdOxZLly7FlVdeibS0tOhjzkUut9BoM3/+\nfKSnpx/xPFm/fr1wstisW7cOY8eOxZQpU9CsWTOkpaWhQ4cO0rFi5norTdOmTbF06VLpGFZomcue\nPXvwzjvv4PXXX0d2djZuvPFGXHPNNahSpYp0tMBcbz9xsvDt06cPWrVqFb1GfN26ddG5c2fs378f\ncXFxTp7rT0vBeN555+GZZ55Bw4YN8xXy8fHxcqGKyeWCUVvPtZaC8fzzz8eIESOQnJycr4/05JNP\nFkxVPK4Xi4Ce3uu7774bhw4dwlVXXYXjjjsu+nUXj3/RNJcIYwx69eqFnTt3okePHvjHP/6BunXr\nSscqsjp16sDzPNx8881HvBfecccdZf5y5U4WvklJSfjiiy+ihVXeRvHWrVtjwYIFkvFioqVgdPX/\n/8NpKRjPO+889O7dG2lpaUcc2PbPf/4TAwcOFEoWGy0FY/PmzbFkyRLpGFZoKRYBoF27drjpppvQ\nvXt3VK5cOd/3xo8fj+uuu04oWTCe5xV4DvV58+YJpCkeLXNxfZc0Ijs7G0888YSTF3SKcLLwbdiw\nIb766qvo7bxH4jZo0ACrV6+WihYzLQXjJ598gkmTJuHiiy/GMcccAyB3R65bt27CyYLRUjBqaaGJ\ncL1gjFylbcqUKcjOzka3bt1w7LHHRr/v4i6WlmKRqCS5vkual+vtJ04Wvo0aNcLs2bNx2mmn5fv6\npk2b0LlzZ+faAwA9BWOvXr2wdu1aNGjQIF/BNW7cOMFUwWkpGLW00GgpGPPuXvm+f8ROlmu7WNpo\naaXZtWsX0tPT8dlnnwHIfdw98sgjTl4hUMNcNOyS5uV6+4mThe+ECRMwYsQIDB8+PPp/dGZmJu67\n7z4MGDDAyR0GLQXj+eefj2+++cbZSxVHaCkYtbTQsGAsu7QUi4CeVppu3bohISEB119/PXzfx5tv\nvomVK1fivffek44WmJa5uL5Lmpfr7SdOFr4AMHv2bDzxxBNYs2YNgNwWh0GDBqFz587CyWKjpWBM\nS0vDfffdhwYNGkhHKRYtBaOWFhpttm3bhvT0dMyfPx+hUAht2rTBI488Er3YiEu0FIuA+600EY0a\nNcKXX375l19zgZa5uL5LqkmcdIBYderUCZ06dZKOYU2rVq2wZs0a5wvGRYsWISkpCbVr145+FO3i\nTmmNGjXQtWtX6RjFNmTIENx0003Ot9BEaCkYr776arRr1w7vvfcefN/HW2+9hauuugqffvqpdLTA\nqlWr5uyGQ0SklSY1NRX333+/s600EZUqVcLnn3+ONm3aAMjdlT+8/9oVWuayfPlyhEKhI9odXNkl\nzcv19hNnd3y1qVevHr7//nvnC8bI2Q7yfiwNuLdTyp7rsuniiy9Gu3bt0Lt372jBaIxxrmA8/ABd\nAEhISMCqVauEEgWnpe8a0NdKs2LFClx33XXYvXs3AODEE0/EG2+8gUaNGgknC07TXLRwvf2EhW8Z\noaVgBHJfqD7//PPojpyLL1BaCkYtLTQRGgpGALjnnnvQtGlTXHXVVQByi8eMjAwMHz5cOFnRaSsW\nNVm/fj3q1KkTLRarVq0a/ZprtMzF9V3SvFxvP2HhW4ZoKBhHjhyJMWPGoFu3bvB9H9OmTUOfPn0w\nYMAA6WiBaCkYtfRcR2goGAHg+OOPx++//x79oyonJyfa9xcKhbBnzx7JeEctLa00ec9tH5GSkhLd\npXeJlrm4vkuaV4sWLfD000/naz+5//77sWjRIuFkReNk4bt48WL069cP3333HRITE/Haa6/hggsu\nkI5VLFoKxoSEBCxevDj6Jr5v3z60aNHCuR05LQWjlhaaCBaMZY+WYhFwv5Xm66+/xpo1a3D//ffj\nmWeeie7E79mzB08//bRT57jXNBfA/V3SvFxvP3Hy4LbbbrsNzzzzDNq0aYOZM2fi7rvvxscffywd\nq1heffVVLFmyJPomPnDgQLRo0cK5whdAvtYAV8+Fq+UgvdmzZwM4soXGVXv37pWOYM3OnTvx7bff\n4sCBA9GvtW3bVjBRbDQdqPfLL7/gH//4R/T24MGDMWnSJMFEwaxbtw4zZ87E7t27MXPmzOjXTzjh\nBIwZM0YwWXCa5gLoOUgPAKpUqYKVK1ce0X7iCid3fA//6KOgj0Jck5CQgIyMDFSqVAkAsH//fjRr\n1sy5ndJnn30Wr7/+er6d6xtuuAF33323dLRA2HNddmkoGMeMGYPnn38eGzduROPGjbF48WK0bNkS\nc+fOlY4WmJa+a0BPK82iRYvQsmVL6RhWaJmL67ukebnefuLkju/u3bujuwuH33bxyHsg96P15s2b\n5ysYb7zxRulYgd1zzz1o165d9GPP119/HY0bN5aOFVh8fLyKgvHwFprevXs72UIToaVgHDlyJJYu\nXYqWLVti3rx5+OabbzBo0CDpWDHp0KED3n777XzFYocOHYRTxWb06NEYMWIErr32WgD/v5Vm9OjR\nTrXSvPfee2jQoAEqVaqETp064csvv8Rzzz0XnZdLtMzF9V1S4P+3nxxec+3ZsyffRkRZ5+SO7w03\n3JDvoKPDjyh27cj7iMzMzHx9ci4WjEDu5Rl/+eUXZGVlRdflrLPOEk4VDHuuy6aGDRtGC8YVK1ZE\nC8b3339fOlogTZo0wbJly5CUlITFixejYsWKuOCCC6IX5HEJ+67Lnkjv6Pvvv49Zs2bh2WefRZs2\nbZxr1QL0zMX1XVIAmD59Ot5//33MnDkz33nuTzjhBFx99dVo1aqVYLqic3LHd9y4cc4fbV+QpKQk\n1KxZM1owbtiwwbmC8YUXXkB6ejpOOeWUfFdxcq3QYs912VSxYsVoO9CBAwdQr149rF27VjhVcGec\ncQZ27tyJyy+/HJdccglOPPFEJ9toAF1914COVprIpaNnzZqF7t27o2rVqs6+Z7o+Fy27pADwf//3\nf/i///s/59tPnCx8Tz75ZDRv3hytW7dGq1at0Lx5c2ebxCO0FIwjRozA2rVrnTyi+3AaCkYtLTQR\nWgrGadOmAQCGDh0Kz/OwZ88ep69EqaFYBPS00nTp0gX16tVDxYoV8fLLL2PLli2oWLGidKyYuD4X\nbQfpAe63nzjZ6rB7924sXrwYCxcuxMKFC/HFF18gPj4eF154IVq1ahXtNXPJOeecg4yMDOcLxtTU\nVHzyySeoUKGCdJRi0XKQHqCnheZwxphowRi5up4LsrKy0LBhQ3zzzTfSUazQUiwCelo4/Yk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ClT8n3voYceKu04REREROQAJwvft99+O/rfTz75ZL7vffTRR6Udh4iIiIgc4GThS0REREQUFAtf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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbaaf790>"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Immediately _Geography_ seems to be less diversely cited for all nations. We can also see signs that _Economy_ on the whole has more news citations, and that _History_ on average utilises more book citations. However the more major fact seems to be that Web citations are almost always more than half. Notably, when they are not, we have 2 out of 3 being heavy news citations in History of C\u00f4te d'Ivoire and History of Uganda. \n",
      "\n",
      "All of this should be quite reassuring. A main disadvantage facing African content, previously postulated, was the lack of citable sources. That problem is exacerbated if we consider only printed historical works as useful to expanding content. However since across the board citations are not heavily reliant on books or journals - even in the Encyclopedia's strong suites like the Economy of the USA - citing should be less of an impediment to Kumusha Takes Wiki editors."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Coordinate Frequncies"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As a curiosity, we investigate the frequnency of occurence of the [geo-coordinate template](http://www.wikidata.org/wiki/Q6294369#sitelinks-wikipedia) in our subject-nation sets."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "coordinate_frequencies()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>subj</th>\n",
        "      <th>nation</th>\n",
        "      <th>lang</th>\n",
        "      <th>coord_count</th>\n",
        "      <th>freq</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> usa</td>\n",
        "      <td> en</td>\n",
        "      <td> 6859</td>\n",
        "      <td>   0.9299078</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> uga</td>\n",
        "      <td> en</td>\n",
        "      <td>  550</td>\n",
        "      <td>   0.9136213</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> fra</td>\n",
        "      <td> en</td>\n",
        "      <td>  781</td>\n",
        "      <td>   0.7080689</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> uga</td>\n",
        "      <td> en</td>\n",
        "      <td>   42</td>\n",
        "      <td>   0.5384615</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> cdi</td>\n",
        "      <td> en</td>\n",
        "      <td>  490</td>\n",
        "      <td>   0.4302019</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> cdi</td>\n",
        "      <td> fr</td>\n",
        "      <td>  428</td>\n",
        "      <td>   0.3426741</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> usa</td>\n",
        "      <td> fr</td>\n",
        "      <td>  360</td>\n",
        "      <td>   0.3171806</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> usa</td>\n",
        "      <td> en</td>\n",
        "      <td>  510</td>\n",
        "      <td>   0.2904328</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> fra</td>\n",
        "      <td> fr</td>\n",
        "      <td>  792</td>\n",
        "      <td>   0.1885265</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> fra</td>\n",
        "      <td> sw</td>\n",
        "      <td>    1</td>\n",
        "      <td>   0.1666667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> cdi</td>\n",
        "      <td> en</td>\n",
        "      <td>    6</td>\n",
        "      <td>   0.1538462</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> fra</td>\n",
        "      <td> en</td>\n",
        "      <td>  202</td>\n",
        "      <td>   0.1527988</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> usa</td>\n",
        "      <td> fr</td>\n",
        "      <td>   43</td>\n",
        "      <td>   0.1083123</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> uga</td>\n",
        "      <td> fr</td>\n",
        "      <td>   23</td>\n",
        "      <td>   0.1050228</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> usa</td>\n",
        "      <td> en</td>\n",
        "      <td>  345</td>\n",
        "      <td>   0.1044505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> fra</td>\n",
        "      <td> en</td>\n",
        "      <td>   91</td>\n",
        "      <td>  0.09371782</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> usa</td>\n",
        "      <td> fr</td>\n",
        "      <td>   42</td>\n",
        "      <td>  0.08879493</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> uga</td>\n",
        "      <td> en</td>\n",
        "      <td>   16</td>\n",
        "      <td>        0.05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> uga</td>\n",
        "      <td> sw</td>\n",
        "      <td>    6</td>\n",
        "      <td>  0.03278689</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> fra</td>\n",
        "      <td> fr</td>\n",
        "      <td>   62</td>\n",
        "      <td>  0.03160041</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> fra</td>\n",
        "      <td> fr</td>\n",
        "      <td>   94</td>\n",
        "      <td>  0.02998405</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   economy</td>\n",
        "      <td> cdi</td>\n",
        "      <td> fr</td>\n",
        "      <td>    2</td>\n",
        "      <td>  0.01869159</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>   history</td>\n",
        "      <td> cdi</td>\n",
        "      <td> en</td>\n",
        "      <td>    1</td>\n",
        "      <td> 0.008695652</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td> geography</td>\n",
        "      <td> usa</td>\n",
        "      <td> sw</td>\n",
        "      <td>    1</td>\n",
        "      <td> 0.003472222</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<IPython.core.display.HTML at 0xa442ed0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are some sets that show to be almost entirely tagged with the coordinate template. In English the nations with the closets cultural ties have higher frequencies of coordinate tagging, which is what those familiar with English Wikipedia would expect. But French Wikipedia does not seem to follow the same trends. The perencatage of coordinate-tagged geography of France in French (18%), seems low  compared to the Geography of USA in English (93%). This seems especially odd as both C\u00f4te d'Ivoire and USA, have higher coordinate template frequencies than France herself. Unlike English Wikipedia as the coordinate tagging is inversely proportial to cultural ties - a proxy for domain expertise. This could be related to French-wikipedia categorization techniques. Recall we are looking at all the subcategories of \"[insert subject] of [insert snation]\", so it could be that there are types of Geography articles that exist in French Wikipedia, that are not tied to a coordinate. Those would be sort of \"non-obvious\", non-coordinate articles and would more likely to be written about France in the French Wikipedia. This would be supported by the fact that that the total articles with coordinates about France is almost the same in our sets, in both English and French, but the French set is simply larger, containing more non-coordinate tagged articles."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Conclusions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Our main task was to understand what makes a good article with respect to a nation. Despite the question's subjectivity, there are facts we have shown about the current state of Wikipedia, and if you consider any of them \"useful\" then you can try to emulate them. Our main findings are that when it comes to a encyclopedically desrcibing a nation, all of English, French and Swahili Wikipedias most often write about the nation's history. French and Swahili then consider Geography next most important, while English thinks it Economy. We have created a graphical guide involving our 3 languages, 3 subjects, 4 nations, and 5 metrics. One can consult this guide to know where work needs to be done, and which areas of our Wikipedias to look for as an example. Lastly we determined that we do not have much information about where users have left requests for improvement to the countries in question, but if English Wikipedia is a model, then Web citations are usually sufficient."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Further Directons"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The way in which the swaths of articles were chosen for the subject-nation sets could be unsatisfactory for several reasons. Firstly the category system may not accurately represnt a Wikipedia's available items on a subject. Secondly, since the process involved a human judge, error is certainly introduced. A better way of determining these subject-nation sets would be useful. Also, no by-hand human-reading investigation was done on those sets, instead we opted for algorithmic methods. If a sound methodology for the human analysis of pages is available, that would be a good technique to compare to the algorithmic ones presented here."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Start of Supporting Code"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Code also available by git on github https://github.com/notconfusing/kumusha_takes_wiki"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Infonoise metric of Stvilia (2005) in concept, although the implementation may differ since we are not stopping and stemming words, because of the multiple languages we need to handle\n",
      "\n",
      "def readable_text_length(wikicode):\n",
      "    #could also use wikicode.filter_text()\n",
      "    return float(len(wikicode.strip_code()))\n",
      "\n",
      "def infonoise(wikicode):\n",
      "    wikicode.strip_code()\n",
      "    ratio = readable_text_length(wikicode) / float(len(wikicode))\n",
      "    return ratio\n",
      "\n",
      "#Helper function to mine for section headings, of course if there is a lead it doesn't quite make sense.\n",
      "\n",
      "def section_headings(wikicode):\n",
      "    sections = wikicode.get_sections()\n",
      "    sec_headings = map( lambda s: filter( lambda l: l != '=', s), map(lambda a: a.split(sep='\\n', maxsplit=1)[0], sections))\n",
      "    return sec_headings\n",
      "\n",
      "#i don't know why mwparserfromhell's .fitler_tags() isn't working at the moment. going to hack it for now\n",
      "import re\n",
      "def num_refs(wikicode):\n",
      "    text = str(wikicode)\n",
      "    reftags = re.findall('<(\\ )*?ref', text)\n",
      "    return len(reftags)\n",
      "\n",
      "def article_refs(wikicode):\n",
      "    sections = wikicode.get_sections()\n",
      "    return float(reduce( lambda a,b: a+b ,map(num_refs, sections)))\n",
      "\n",
      "#Predicate for links and files in English French and Swahili\n",
      "\n",
      "def link_a_file(linkstr):\n",
      "    fnames = [u'File:', u'Fichier:', u'Image:', u'Picha:']\n",
      "    bracknames = map(lambda a: '[[' + a, fnames)\n",
      "    return any(map(lambda b: linkstr.startswith(b), bracknames))\n",
      "\n",
      "def link_a_cat(linkstr):\n",
      "    cnames =[u'Category:', u'Cat\u00e9gorie:', u'Jamii:']\n",
      "    bracknames = map(lambda a: '[[' + a, cnames)\n",
      "    return any(map(lambda b: linkstr.startswith(b), bracknames))\n",
      "\n",
      "def num_reg_links(wikicode):\n",
      "    reg_links = filter(lambda a: not link_a_file(a) and not link_a_cat(a), wikicode.filter_wikilinks())\n",
      "    return float(len(reg_links))\n",
      "\n",
      "def num_file_links(wikicode):\n",
      "    file_links = filter(lambda a: link_a_file(a), wikicode.filter_wikilinks())\n",
      "    return float(len(file_links))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pywikibot\n",
      "import mwparserfromhell as pfh\n",
      "import os\n",
      "import datetime\n",
      "import pandas as pd\n",
      "import json\n",
      "from collections import defaultdict\n",
      "from ggplot import *\n",
      "import operator\n",
      "from IPython.display import HTML\n",
      "\n",
      "%pylab inline\n",
      "\n",
      "\n",
      "langs = ['en','fr','sw']\n",
      "nations = ['usa', 'fra', 'cdi', 'uga']\n",
      "\n",
      "wikipedias = {lang: pywikibot.Site(lang, 'wikipedia') for lang in langs}\n",
      "wikidata = wikipedias['fr'].data_repository()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "VERBOSE:pywiki:Starting 1 threads...\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['xlim', 'mpl', 'colors', 'ylim']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "this will be our data structure dict of dicts, until we have some numbers to put into pandas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def enfrsw():\n",
      "    return {lang: None for lang in langs}\n",
      "def article_attributes():\n",
      "    return {attrib: enfrsw() for attrib in ['sitelinks', 'wikitext', 'wikicode', 'metrics']}\n",
      "\n",
      "def do_sitelinks(langs, qids, data):\n",
      "    for qid in qids:\n",
      "        page = pywikibot.ItemPage(wikidata, qid)\n",
      "        wditem = page.get()\n",
      "        for lang in langs:\n",
      "            try:\n",
      "                data[qid]['sitelinks'][lang] = wditem['sitelinks'][lang+'wiki']\n",
      "            except KeyError:\n",
      "                pass\n",
      "    return data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 143
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Functions to get the page texts in all our desired languages"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_wikitext(lang, title):\n",
      "    page = pywikibot.Page(wikipedias[lang],title)\n",
      "    def get_page(page):\n",
      "        try:\n",
      "            pagetext = page.get()\n",
      "            return pagetext\n",
      "        except pywikibot.exceptions.IsRedirectPage:\n",
      "            redir = page.getRedirectTarget()\n",
      "            get_page(redir)\n",
      "        except pywikibot.exceptions.NoPage:\n",
      "            raise pywikibot.exceptions.NoPage\n",
      "            print 're raising'\n",
      "    return get_page(page)\n",
      "\n",
      "def do_wikitext(langs, data):\n",
      "    for qid, attribs in data.iteritems():\n",
      "        for lang, sl in attribs['sitelinks'].iteritems():\n",
      "            if sl:\n",
      "                try:\n",
      "                    if randint(0,100) == 99:\n",
      "                        print sl\n",
      "                    data[qid]['wikitext'][lang] = get_wikitext(lang, sl)\n",
      "                except:\n",
      "                    print 'bad sitelink', sl\n",
      "                    continue\n",
      "    return data\n",
      "\n",
      "def do_wikicode(langs, data):\n",
      "    for qid, attribs in data.iteritems():\n",
      "        for lang, pagetext in attribs['wikitext'].iteritems():\n",
      "            if pagetext:\n",
      "                data[qid]['wikicode'][lang] = pfh.parse(pagetext)\n",
      "    return data\n",
      "\n",
      "def do_metrics(data):\n",
      "    for qid, attribs in data.iteritems():\n",
      "        for lang, wikicode in attribs['wikicode'].iteritems():\n",
      "            if wikicode:\n",
      "                data[qid]['metrics'][lang] = report_actionable_metrics(wikicode)\n",
      "    return data\n",
      "\n",
      "# this will take a lot of network time since we are going to load about 300 pages, but we'll save the data off so we don't have to do it uneccesarrily\n",
      "\n",
      "def make_data(langs, qids, savename):\n",
      "    print 'getting these qids: ', qids \n",
      "    data = defaultdict(article_attributes)\n",
      "    print 'getting sitelinks'\n",
      "    data = do_sitelinks(langs, qids, data)\n",
      "    print 'getting wikitext'\n",
      "    data = do_wikitext(langs, data)\n",
      "    print 'converting to wikicode'\n",
      "    data = do_wikicode(langs, data)\n",
      "    print 'computing metrics'\n",
      "    data = do_metrics(data)\n",
      "\n",
      "        \n",
      "    hashable_data = {qid: \n",
      "                        {'wikitext': attribdict['wikitext'],\n",
      "                         'metrics': attribdict['metrics'],\n",
      "                         'sitelinks': attribdict['sitelinks']}\n",
      "                             for qid, attribdict in data.iteritems()}\n",
      "    print 'saving now'\n",
      "    #save the results\n",
      "    safefilename = savename+str(datetime.datetime.now())+'.json'\n",
      "    with open(safefilename,'w') as f3:\n",
      "        json.dump(hashable_data,f3)\n",
      "    with open(savename+'latest.json','w') as f4:\n",
      "        json.dump(hashable_data, f4)\n",
      "    return data\n",
      "\n",
      "#i don't call this unless i have time to uncomment it\n",
      "#arts = make_data(langs, country_qids, 'countrydata')\n",
      "\n",
      "#time to get into pandas, lets throw everything into a data frame \n",
      "\n",
      "df = pd.DataFrame(columns=['Country','language','metric','val'])\n",
      "\n",
      "arts = json.load(open('countrydata-latest.json','r'))\n",
      "\n",
      "for qid, attribdict in arts.iteritems():\n",
      "    for attribname, langdict in attribdict.iteritems():\n",
      "        if attribname == 'metrics':\n",
      "            for lang, metrics in langdict.iteritems(): \n",
      "                try:\n",
      "                #someteimes there wasn't an article in that language and thus no corresponding len\n",
      "                    for metric_name, metric_val in metrics.iteritems():\n",
      "                        df = df.append({'Country': qid, 'language':lang, 'metric':metric_name, 'val':float(metric_val)}, ignore_index=True)\n",
      "                except:\n",
      "                    pass\n",
      "df = df.convert_objects(convert_numeric=True)\n",
      "\n",
      "metric_list = ['completeness','informativeness','numheadings','articlelength','referencerate']\n",
      "\n",
      "langs_df_dict = {lang: df[df['language'] == lang] for lang in langs}\n",
      "metric_df_dict = {metric: df[df['metric'] == metric] for metric in metric_list}\n",
      "\n",
      "#for later calculation\n",
      "uganda_zscores = defaultdict(list)\n",
      "cdi_zscores = defaultdict(list)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 494
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def metric_analyse_density(ametric, xlimit):\n",
      "    inf_df = metric_df_dict[ametric]\n",
      "    \n",
      "    zscore = lambda x: (x - x.mean()) / x.std()\n",
      "    \n",
      "    inf_piv = inf_df.pivot(index='Country', columns='language', values='val')\n",
      "    \n",
      "    inf_piv_z = inf_piv.apply(zscore)\n",
      "    metric_analyse_density_plot(ametric, xlimit, inf_df)\n",
      "    print 'Uganda ('+ugandaqid+\"), C\u00f4te d'Ivoire (\"+cdiqid+\") \" +ametric+ \" z-scores.\" \n",
      "    return inf_piv_z.ix[[ugandaqid,cdiqid]]\n",
      "\n",
      "    \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 495
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def metric_analyse_density_plot(ametric, xlimit, inf_df):\n",
      "    p = ggplot(aes(x='val', colour='language', fill=True, alpha = 0.3), data=inf_df) + geom_density() + labs(\"score\", \"frequency\") + \\\n",
      "    scale_x_continuous(limits=(0,xlimit)) + ggtitle(ametric + '\\nall country articles\\n                                                                        ')\n",
      "    p.rcParams[\"figure.figsize\"] = \"4, 3\"\n",
      "    p.draw()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 496
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "def defaultint():\n",
      "    return defaultdict(int)\n",
      "\n",
      "section_count = defaultdict(defaultint)\n",
      "sorted_secs = defaultdict(list)\n",
      "total_articles = defaultdict(int)\n",
      "\n",
      "articles = json.load(open('countrydata-latest.json','r'))\n",
      "\n",
      "for qid, attribdict in articles.iteritems():\n",
      "    for attribname, langdict in attribdict.iteritems():\n",
      "        if attribname == 'wikitext':\n",
      "            for lang, wikitext in langdict.iteritems(): \n",
      "                if wikitext:\n",
      "                    total_articles[lang] += 1\n",
      "                    wikicode = pfh.parse(wikitext)\n",
      "                    secs = section_headings(wikicode)\n",
      "                    for sec in secs:\n",
      "                        sec = sec.strip()\n",
      "                        section_count[lang][sec] += 1\n",
      "                        \n",
      "section_df = pd.DataFrame(columns=['lang','secname','freq'])\n",
      "for lang, sec_dict in section_count.iteritems():\n",
      "    for secname, seccount in sec_dict.iteritems():\n",
      "        freq = seccount/float(total_articles[lang])\n",
      "        section_df = section_df.append({'lang':lang, 'secname':secname, 'freq':freq}, ignore_index=True)  \n",
      "        \n",
      "#section_df = section_df.convert_objects(convert_numeric=True)\n",
      "section_df.head()\n",
      "top_secs = section_df[section_df.freq > 0.1]\n",
      "\n",
      "sort_secs= top_secs.sort(columns='freq', ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def top_n_sections(lang,n):\n",
      "    top = sort_secs[sort_secs.lang==lang].iloc[:n].convert_objects(convert_numeric=True)\n",
      "    print str(total_articles[lang]) + ' total articles inspected in ' + lang + '.' \n",
      "    return HTML(top.to_html(index=False, columns=['lang','secname','freq']))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def top_sections_ethnoset(ethnoset_filename):\n",
      "    \n",
      "    def defaultint():\n",
      "        return defaultdict(int)\n",
      "\n",
      "    section_count = defaultdict(defaultint)\n",
      "    sorted_secs = defaultdict(list)\n",
      "    total_articles = defaultdict(int)\n",
      "    \n",
      "    articles = json.load(open(ethnoset_filename,'r'))\n",
      "    \n",
      "    for qid, attribdict in articles.iteritems():\n",
      "        for attribname, langdict in attribdict.iteritems():\n",
      "            if attribname == 'wikitext':\n",
      "                for lang, wikitext in langdict.iteritems(): \n",
      "                    if wikitext:\n",
      "                        total_articles[lang] += 1\n",
      "                        wikicode = pfh.parse(wikitext)\n",
      "                        secs = section_headings(wikicode)\n",
      "                        for sec in secs:\n",
      "                            sec = sec.strip()\n",
      "                            section_count[lang][sec] += 1\n",
      "                        \n",
      "    section_df = pd.DataFrame(columns=['lang','secname','freq'])\n",
      "    for lang, sec_dict in section_count.iteritems():\n",
      "        for secname, seccount in sec_dict.iteritems():\n",
      "            freq = seccount/float(total_articles[lang])\n",
      "            section_df = section_df.append({'lang':lang, 'secname':secname, 'freq':freq}, ignore_index=True)\n",
      "            \n",
      "    #section_df = section_df.convert_objects(convert_numeric=True)\n",
      "    section_df.head()\n",
      "    top_secs = section_df[section_df.freq > 0.1]\n",
      "    \n",
      "    sort_secs= top_secs.sort(columns='freq', ascending=False)\n",
      "\n",
      "    return sort_secs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls ethnosa!ls ethnosave/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "economy-cdi.jsonlatest.json    geography-uga.jsonlatest.json\r\n",
        "economy-fra.jsonlatest.json    geography-usa.jsonlatest.json\r\n",
        "economy-uga.jsonlatest.json    history-cdi.jsonlatest.json\r\n",
        "economy-usa.jsonlatest.json    history-fra.jsonlatest.json\r\n",
        "geography-cdi.jsonlatest.json  history-uga.jsonlatest.json\r\n",
        "geography-fra.jsonlatest.json  history-usa.jsonlatest.json\r\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def display_article_counts():\n",
      "    filenames = !ls ethnosave\n",
      "    \n",
      "    art_counts = pd.DataFrame(columns=['subj','nation', 'lang','count'])\n",
      "    for filename in filenames:\n",
      "        spl = filename.split('-')\n",
      "        subj, nation = spl[0], spl[1].split('.')[0]\n",
      "        \n",
      "        fileaddr = 'ethnosave/' + filename\n",
      "        articles = json.load(open(fileaddr,'r'))\n",
      "        total_articles = defaultdict(int)\n",
      "        for qid, attribdict in articles.iteritems():\n",
      "            for attribname, langdict in attribdict.iteritems():\n",
      "                if attribname == 'wikitext':\n",
      "                    for lang, wikitext in langdict.iteritems(): \n",
      "                        if wikitext:\n",
      "                            total_articles[lang] += 1\n",
      "        for lang, count in total_articles.iteritems():\n",
      "            art_counts = art_counts.append({'subj': subj, 'nation': nation, 'lang': lang, 'count': count} ,ignore_index = True)\n",
      "    return HTML(art_counts.sort(columns='count', ascending=False).to_html(index=False))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def make_heat_map():\n",
      "    \n",
      "\n",
      "    subj_list = ['economy','history','geography']\n",
      "    metric_list = ['completeness','informativeness','numheadings','articlelength','referencerate']\n",
      "    #pivtables = {metric: {subj: None for subj in subj_list} for metric in metric_list}\n",
      "    \n",
      "    fig, axes = plt.subplots(nrows = len(metric_list), ncols = len(subj_list), sharex='col', sharey='row' )\n",
      "    '''\n",
      "    for metric, subjdict in pivtables.iteritems():\n",
      "        for subj, pivtab in subjdict.iteritems():\n",
      "            natlangdf = means_df[(means_df.metric == metric) & (means_df.subj == subj)]\n",
      "            natlangpiv = pd.pivot_table(natlangdf, values='means', rows='lang', cols='nation')\n",
      "            pivtables[metric][subj] = natlangpiv\n",
      "            \n",
      "    '''\n",
      "    \n",
      "    for axarr, metric in zip(axes, metric_list):\n",
      "        for ax, subj in zip(axarr, subj_list):\n",
      "            \n",
      "            natlangdf = means_df[(means_df.metric == metric) & (means_df.subj == subj)]\n",
      "            natlangpiv = pd.pivot_table(natlangdf, values='means', rows='lang', cols='nation')\n",
      "            heatmap = ax.pcolor(natlangpiv, cmap='Blues')\n",
      "            ax.set_yticks(np.arange(0.5, len(natlangpiv.index), 1))\n",
      "            ax.set_yticklabels(natlangpiv.index)\n",
      "            ax.set_xticks(np.arange(0.5, len(natlangpiv.columns), 1))\n",
      "            ax.set_xticklabels(natlangpiv.columns)\n",
      "            cbar = plt.colorbar(mappable=heatmap, ax=ax)\n",
      "        \n",
      "    fig.suptitle('Heatmap of Actionable Metrics by Country versus Wikipedia Language, \\n by Subject Category', fontsize=18)\n",
      "    fig.set_size_inches(12,12,dpi=600)\n",
      "    #fig.tight_layout()\n",
      "    \n",
      "    subj_titles = ['Economy','History','Geography']\n",
      "    metric_titles =['Wikilinks','Code & Images to Text Ratio','Section Count','Article Length', 'References per Article Length'] \n",
      "    for i in range(len(subj_titles)):\n",
      "        axes[0][i].set_title(subj_titles[i])\n",
      "    for j in range(len(metric_titles)):\n",
      "        axes[j][0].set_ylabel(metric_titles[j])\n",
      "        \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "means_df[(means_df.metric == 'referencerate') & (means_df.subj == 'geography')]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>subj</th>\n",
        "      <th>nation</th>\n",
        "      <th>lang</th>\n",
        "      <th>metric</th>\n",
        "      <th>means</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td> geography</td>\n",
        "      <td> usa</td>\n",
        "      <td> en</td>\n",
        "      <td> referencerate</td>\n",
        "      <td> 0.003102</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td> geography</td>\n",
        "      <td> usa</td>\n",
        "      <td> fr</td>\n",
        "      <td> referencerate</td>\n",
        "      <td> 0.001050</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td> geography</td>\n",
        "      <td> usa</td>\n",
        "      <td> sw</td>\n",
        "      <td> referencerate</td>\n",
        "      <td> 0.000012</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td> geography</td>\n",
        "      <td> fra</td>\n",
        "      <td> en</td>\n",
        "      <td> referencerate</td>\n",
        "      <td> 0.000573</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52</th>\n",
        "      <td> geography</td>\n",
        "      <td> fra</td>\n",
        "      <td> fr</td>\n",
        "      <td> referencerate</td>\n",
        "      <td> 0.001929</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53</th>\n",
        "      <td> geography</td>\n",
        "      <td> fra</td>\n",
        "      <td> sw</td>\n",
        "      <td> referencerate</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54</th>\n",
        "      <td> geography</td>\n",
        "      <td> cdi</td>\n",
        "      <td> en</td>\n",
        "      <td> referencerate</td>\n",
        "      <td> 0.004123</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>55</th>\n",
        "      <td> geography</td>\n",
        "      <td> cdi</td>\n",
        "      <td> fr</td>\n",
        "      <td> referencerate</td>\n",
        "      <td> 0.002138</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>56</th>\n",
        "      <td> geography</td>\n",
        "      <td> cdi</td>\n",
        "      <td> sw</td>\n",
        "      <td> referencerate</td>\n",
        "      <td> 0.006302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>57</th>\n",
        "      <td> geography</td>\n",
        "      <td> uga</td>\n",
        "      <td> en</td>\n",
        "      <td> referencerate</td>\n",
        "      <td> 0.001590</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>58</th>\n",
        "      <td> geography</td>\n",
        "      <td> uga</td>\n",
        "      <td> fr</td>\n",
        "      <td> referencerate</td>\n",
        "      <td> 0.000560</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>59</th>\n",
        "      <td> geography</td>\n",
        "      <td> uga</td>\n",
        "      <td> sw</td>\n",
        "      <td> referencerate</td>\n",
        "      <td> 0.000114</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "         subj nation lang         metric     means\n",
        "48  geography    usa   en  referencerate  0.003102\n",
        "49  geography    usa   fr  referencerate  0.001050\n",
        "50  geography    usa   sw  referencerate  0.000012\n",
        "51  geography    fra   en  referencerate  0.000573\n",
        "52  geography    fra   fr  referencerate  0.001929\n",
        "53  geography    fra   sw  referencerate  0.000000\n",
        "54  geography    cdi   en  referencerate  0.004123\n",
        "55  geography    cdi   fr  referencerate  0.002138\n",
        "56  geography    cdi   sw  referencerate  0.006302\n",
        "57  geography    uga   en  referencerate  0.001590\n",
        "58  geography    uga   fr  referencerate  0.000560\n",
        "59  geography    uga   sw  referencerate  0.000114"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def load_ethnosaves():\n",
      "    ethnosaves = !ls ethnosave\n",
      "    \n",
      "    subj_df_dict = {subj: pd.DataFrame(columns=['qid','subj','nation','lang','metric','val']) for subj in ethnosaves}\n",
      "    \n",
      "    for ethnosavefile in ethnosaves:\n",
      "        nameparts = ethnosavefile.split('-')\n",
      "        subj = nameparts[0]\n",
      "        dotparts = nameparts[1].split('.')\n",
      "        nation = dotparts[0]\n",
      "        arts = json.load(open('ethnosave/'+ethnosavefile,'r'))\n",
      "        print subj, nation\n",
      "        sdf = subj_df_dict[ethnosavefile]\n",
      "        for qid, attribdict in arts.iteritems():\n",
      "            for attribname, langdict in attribdict.iteritems():\n",
      "                if attribname == 'metrics':\n",
      "                    for lang, metrics in langdict.iteritems(): \n",
      "                        try:\n",
      "                        #someteimes there wasn't an article in that language and thus no corresponding len\n",
      "                            for metric_name, metric_val in metrics.iteritems():\n",
      "                                sdf = sdf.append({'qid': qid, 'subj':subj, 'nation':nation, 'lang':lang, 'metric':metric_name, 'val':float(metric_val)}, ignore_index=True)\n",
      "                        except:\n",
      "                            pass\n",
      "        subj_df_dict[ethnosavefile] = sdf\n",
      "        lens = map(lambda d: len(d), subj_df_dict.itervalues())\n",
      "        print lens\n",
      "    return subj_df_dict\n",
      "\n",
      "subj_df_dict = load_ethnosaves()\n",
      "subj_df = pd.concat(subj_df_dict)\n",
      "assert(len(subj_df) == reduce(lambda a, b: a+b, map(lambda df: len(df), subj_df_dict.itervalues())))\n",
      "\n",
      "subj_df = subj_df.convert_objects()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "economy cdi\n",
        "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 735]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "economy"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " fra\n",
        "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14690, 735]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "economy uga\n",
        "[0, 0, 0, 495, 0, 0, 0, 0, 0, 0, 14690, 735]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "economy"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " usa\n",
        "[0, 0, 0, 495, 0, 0, 0, 0, 0, 18550, 14690, 735]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "geography"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " cdi\n",
        "[12495, 0, 0, 495, 0, 0, 0, 0, 0, 18550, 14690, 735]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "geography"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " fra\n",
        "[12495, 0, 0, 495, 0, 26550, 0, 0, 0, 18550, 14690, 735]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "geography"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " uga\n",
        "[12495, 0, 0, 495, 0, 26550, 0, 0, 5020, 18550, 14690, 735]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "geography"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " usa\n",
        "[12495, 0, 43992, 495, 0, 26550, 0, 0, 5020, 18550, 14690, 735]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "history"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " cdi\n",
        "[12495, 0, 43992, 495, 0, 26550, 995, 0, 5020, 18550, 14690, 735]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "history"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " fra\n",
        "[12495, 0, 43992, 495, 0, 26550, 995, 22325, 5020, 18550, 14690, 735]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "history"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " uga\n",
        "[12495, 0, 43992, 495, 2165, 26550, 995, 22325, 5020, 18550, 14690, 735]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "history"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " usa\n",
        "[12495, 11340, 43992, 495, 2165, 26550, 995, 22325, 5020, 18550, 14690, 735]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "means_df = pd.DataFrame(columns=['subj','nation','lang','metric','means'])\n",
      "\n",
      "for subj in ['geography','history','economy']:\n",
      "    for metric in ['completeness','informativeness','numheadings','articlelength','referencerate']:\n",
      "        for nation in ['usa','fra','cdi','uga']:\n",
      "            for lang in ['en','fr','sw']:\n",
      "                spec_df = subj_df[(subj_df.subj == subj) & (subj_df.nation == nation) & (subj_df.metric == metric) & (subj_df.lang == lang)]['val']\n",
      "                mean = spec_df.mean()\n",
      "                if (not str(mean)[0] in '0123456789'):\n",
      "                    mean = 0.0\n",
      "                if len(spec_df) <= 25:\n",
      "                    print len(spec_df), subj, metric, nation, lang\n",
      "                    mean = 0.0\n",
      "                means_df = means_df.append({'subj':subj, 'nation':nation, 'lang':lang, 'metric':metric, 'means':mean}, ignore_index=True)\n",
      "                \n",
      "means_df = means_df.convert_objects(convert_numeric=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "6 geography completeness fra sw\n",
        "6"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " geography informativeness fra sw\n",
        "6"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " geography numheadings fra sw\n",
        "6"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " geography articlelength fra sw\n",
        "6"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " geography referencerate fra sw\n",
        "8"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " history completeness fra sw\n",
        "0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " history completeness cdi sw\n",
        "3"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " history completeness uga sw\n",
        "8"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " history informativeness fra sw\n",
        "0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " history informativeness cdi sw\n",
        "3"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " history informativeness uga sw\n",
        "8"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " history numheadings fra sw\n",
        "0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " history numheadings cdi sw\n",
        "3"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " history numheadings uga sw\n",
        "8"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " history articlelength fra sw\n",
        "0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " history articlelength cdi sw\n",
        "3"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " history articlelength uga sw\n",
        "8"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " history referencerate fra sw\n",
        "0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " history referencerate cdi sw\n",
        "3"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " history referencerate uga sw\n",
        "10"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy completeness usa sw\n",
        "5"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy completeness fra sw\n",
        "1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy completeness cdi sw\n",
        "10"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy completeness uga fr\n",
        "11"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy completeness uga sw\n",
        "10"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy informativeness usa sw\n",
        "5"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy informativeness fra sw\n",
        "1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy informativeness cdi sw\n",
        "10"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy informativeness uga fr\n",
        "11"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy informativeness uga sw\n",
        "10"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy numheadings usa sw\n",
        "5"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy numheadings fra sw\n",
        "1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy numheadings cdi sw\n",
        "10"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy numheadings uga fr\n",
        "11"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy numheadings uga sw\n",
        "10"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy articlelength usa sw\n",
        "5"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy articlelength fra sw\n",
        "1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy articlelength cdi sw\n",
        "10"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy articlelength uga fr\n",
        "11"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy articlelength uga sw\n",
        "10"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy referencerate usa sw\n",
        "5"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy referencerate fra sw\n",
        "1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy referencerate cdi sw\n",
        "10"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy referencerate uga fr\n",
        "11"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " economy referencerate uga sw\n"
       ]
      }
     ],
     "prompt_number": 145
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def top_sections_ethnoset(ethnoset_filename):\n",
      "    print ethnoset_filename\n",
      "    \n",
      "    def defaultint():\n",
      "        return defaultdict(int)\n",
      "\n",
      "    section_count = defaultdict(defaultint)\n",
      "    sorted_secs = defaultdict(list)\n",
      "    total_articles = defaultdict(int)\n",
      "    \n",
      "    articles = json.load(open(ethnoset_filename,'r'))\n",
      "    \n",
      "    for qid, attribdict in articles.iteritems():\n",
      "        for attribname, langdict in attribdict.iteritems():\n",
      "            if attribname == 'wikitext':\n",
      "                for lang, wikitext in langdict.iteritems(): \n",
      "                    if wikitext:\n",
      "                        total_articles[lang] += 1\n",
      "                        wikicode = pfh.parse(wikitext)\n",
      "                        secs = section_headings(wikicode)\n",
      "                        for sec in secs:\n",
      "                            sec = sec.strip()\n",
      "                            section_count[lang][sec] += 1\n",
      "                        \n",
      "    section_df = pd.DataFrame(columns=['lang','secname','freq'])\n",
      "    for lang, sec_dict in section_count.iteritems():\n",
      "        for secname, seccount in sec_dict.iteritems():\n",
      "            freq = seccount/float(total_articles[lang])\n",
      "            section_df = section_df.append({'lang':lang, 'secname':secname, 'freq':freq}, ignore_index=True)\n",
      "            \n",
      "    #section_df = section_df.convert_objects(convert_numeric=True)\n",
      "    section_df.head()\n",
      "    top_secs = section_df[section_df.freq > 0.1]\n",
      "    \n",
      "    sort_secs= top_secs.sort(columns='freq', ascending=False)\n",
      "    return sort_secs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def top_templates_ethnoset(ethnoset_filename):\n",
      "    \n",
      "    def defaultint():\n",
      "        return defaultdict(int)\n",
      "\n",
      "    template_count = defaultdict(defaultint)\n",
      "    sorted_templates = defaultdict(list)\n",
      "    total_articles = defaultdict(int)\n",
      "    \n",
      "    articles = json.load(open(ethnoset_filename,'r'))\n",
      "    \n",
      "    for qid, attribdict in articles.iteritems():\n",
      "        for attribname, langdict in attribdict.iteritems():\n",
      "            if attribname == 'wikitext':\n",
      "                for lang, wikitext in langdict.iteritems(): \n",
      "                    if wikitext:\n",
      "                        total_articles[lang] += 1\n",
      "                        wikicode = pfh.parse(wikitext)\n",
      "                        temps = wikicode.filter_templates()\n",
      "                        for temp in temps:\n",
      "                            tempname = temp.name\n",
      "                            tempname = tempname.strip().lower()\n",
      "                            template_count[lang][tempname] += 1\n",
      "                        \n",
      "    temp_df = pd.DataFrame(columns=['lang','tempname','freq'])\n",
      "    for lang, temp_dict in template_count.iteritems():\n",
      "        for tempname, tempcount in temp_dict.iteritems():\n",
      "            freq = tempcount/float(total_articles[lang])\n",
      "            temp_df = temp_df.append({'lang':lang, 'tempname':tempname, 'freq':freq}, ignore_index=True)\n",
      "            \n",
      "    #section_df = section_df.convert_objects(convert_numeric=True)\n",
      "    top_templates = temp_df[temp_df.freq > 0.1]\n",
      "    \n",
      "    sort_temps= top_templates.sort(columns='freq', ascending=False)\n",
      "    temps_dict = dict()\n",
      "    for lang in template_count.iterkeys():\n",
      "        try:\n",
      "            temps_dict[lang] = sort_temps[sort_temps.lang==lang].iloc[:20].convert_objects(convert_numeric=True)\n",
      "        except:\n",
      "            temps_dict[lang] = sort_temps[sort_temps.lang==lang].convert_objects(convert_numeric=True)\n",
      "\n",
      "    return temps_dict"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ethnosaves = !ls ethnosave\n",
      "filenames = map(lambda name: 'ethnosave/'+name, ethnosaves)ethnosaves = !ls ethnosave\n",
      "filenames = map(lambda name: 'ethnosave/'+name, ethnosaves)\n",
      "sort_dfs = map(top_sections_ethnoset, filenames)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ethnosave/economy-cdi.jsonlatest.json\n",
        "ethnosave/economy-fra.jsonlatest.json"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ethnosave/economy-uga.jsonlatest.json"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ethnosave/economy-usa.jsonlatest.json"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ethnosave/geography-cdi.jsonlatest.json"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ethnosave/geography-fra.jsonlatest.json"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ethnosave/geography-uga.jsonlatest.json"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ethnosave/geography-usa.jsonlatest.json"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ethnosave/history-cdi.jsonlatest.json"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ethnosave/history-fra.jsonlatest.json"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ethnosave/history-uga.jsonlatest.json"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ethnosave/history-usa.jsonlatest.json"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 384
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def make_cite_plot(): \n",
      "    citedf = pd.DataFrame(columns=['setname','cite','freq'])\n",
      "    for i in range(len(filenames)):\n",
      "        for lang, df in temp_dfs[i].iteritems():\n",
      "            if lang == 'en':\n",
      "                df = df[(df.tempname == 'cite web') | (df.tempname == 'cite book') | (df.tempname == 'cite news') | (df.tempname == 'cite journal')]\n",
      "                setname = filenames[i][10:-16]\n",
      "                tot = 0\n",
      "                for row in df.iterrows():\n",
      "                    cols = row[1]\n",
      "                    tot += cols['freq']\n",
      "                for row in df.iterrows():\n",
      "                    cols = row[1]\n",
      "                    citedf = citedf.append({'setname':setname, 'cite': cols['tempname'], 'freq':cols['freq']/float(tot)}, ignore_index=True)\n",
      "    \n",
      "    \n",
      "    cite_dict = {\"cite book\":536258, \"cite journal\":328129, \"cite news\":444447, \"cite web\":1560207}\n",
      "    globaltot = reduce(lambda a,b: a+b, cite_dict.itervalues())\n",
      "    globaltotfloat = float(globaltot)\n",
      "    globciteratio = map(lambda cd: (cd[0], cd[1]/globaltotfloat), cite_dict.iteritems() )\n",
      "    \n",
      "    for cite in globciteratio:\n",
      "        citetype, freq = cite[0], cite[1]\n",
      "        citedf = citedf.append({'setname':'English WP Global', 'cite': citetype, 'freq':freq}, ignore_index=True)       \n",
      "    \n",
      "    citedf = citedf.convert_objects(convert_numeric=True)\n",
      "    citepiv = citedf.pivot(index = 'setname', columns = 'cite')\n",
      "    citeplot = citepiv.plot(kind='bar', stacked=True)\n",
      "    citeplot.legend(('Citation type', 'Cite book', 'Cite journal', 'Cite news', 'Cite web'), loc=9)\n",
      "    citeplot.figure.set_size_inches(12,8)\n",
      "    citeplot.set_xlabel('subject-nation')\n",
      "    citeplot.set_title('Composition of Citation Type, by Subject-Nation')\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 124
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is where I look at the template occurences."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ethnosaves = !ls ethnosave\n",
      "filenames = map(lambda name: 'ethnosave/'+name, ethnosaves)\n",
      "temp_dfs = map(top_templates_ethnoset, filenames)\n",
      "for i in range(len(filenames)):\n",
      "    for lang, df in temp_dfs[i].iteritems():\n",
      "        #print ''\n",
      "        #print filenames[i]\n",
      "        #print df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 383
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def coordinate_frequencies():\n",
      "    def coord_templates_ethnoset(ethnosavefile, coord_df):\n",
      "        \n",
      "        nameparts = ethnosavefile.split('-')\n",
      "        subj = nameparts[0]\n",
      "        dotparts = nameparts[1].split('.')\n",
      "        nation = dotparts[0]\n",
      "      \n",
      "        total_articles = defaultdict(int)\n",
      "        coord_articles = defaultdict(int)\n",
      "        \n",
      "        articles = json.load(open('ethnosave/'+ethnosavefile,'r'))    \n",
      "    \n",
      "        for qid, attribdict in articles.iteritems():\n",
      "            for attribname, langdict in attribdict.iteritems():\n",
      "                if attribname == 'wikitext':\n",
      "                    for lang, wikitext in langdict.iteritems(): \n",
      "                        if wikitext:\n",
      "                            total_articles[lang] += 1\n",
      "                            wikicode = pfh.parse(wikitext)\n",
      "                            temps = wikicode.filter_templates()\n",
      "                            for temp in temps:\n",
      "                                tempname = temp.name\n",
      "                                tempname = tempname.strip().lower()\n",
      "                                if tempname == 'coord':\n",
      "                                    coord_articles[lang] += 1\n",
      "        \n",
      "        for lang, coord_count in coord_articles.iteritems():\n",
      "            freq = coord_count / float(total_articles[lang])\n",
      "            coord_df = coord_df.append({'subj':subj, 'nation':nation, 'lang':lang, 'coord_count':coord_count, 'freq':freq}, ignore_index=True)       \n",
      "        return coord_df\n",
      "    \n",
      "    coord_df = pd.DataFrame(columns=['subj','nation','lang','coord_count','freq'])\n",
      "    \n",
      "    ethnosaves = !ls ethnosave\n",
      "    for ethnosave in ethnosaves:\n",
      "        coord_df = coord_templates_ethnoset(ethnosave, coord_df)\n",
      "    \n",
      "    coord_df_sort = coord_df.sort(columns='freq', ascending=False) \n",
      "    \n",
      "    return HTML(coord_df_sort.to_html(index=False, columns=['subj','nation','lang','coord_count','freq']))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}