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  {
   "cells": [
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "In this notebook, we describe how to retrieve our browsing history from Safari (Part 1) and produce some simple plots (Part 2) in order to see what websites we visit most often.\n",
      "\n",
      "(Note: this was run on Mac OS X 10.10.2)."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Part 1: Retrieving our Safari Browsing History"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To access our browsing history, we go to ~/Library/Safari and look for the database *History.db*. We make a copy of it in a folder in our workspace, e.g. to *~/Workspace/web_browsing/hs.db*."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "cp ~/Library/Safari/History.db ~/Workspace/web_browsing/hs.db"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now let us fire up sqlite3 and see what tables are inside the database."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%script sqlite3 hs.db\n",
      ".tables"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "history_items       history_tombstones  history_visits      metadata          \n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Among the four (4) tables above, we'll be using 'history_items' and 'history_visits'. Let's take a look at their schemata."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%script sqlite3 hs.db\n",
      ".schema history_items"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CREATE TABLE history_items (id INTEGER PRIMARY KEY AUTOINCREMENT,url TEXT NOT NULL UNIQUE,domain_expansion TEXT NULL,visit_count INTEGER NOT NULL,daily_visit_counts BLOB NOT NULL,weekly_visit_counts BLOB NULL,autocomplete_triggers BLOB NULL,should_recompute_derived_visit_counts INTEGER NOT NULL);\n",
        "CREATE INDEX history_items__domain_expansion ON history_items (domain_expansion);\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%script sqlite3 hs.db\n",
      ".schema history_visits "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CREATE TABLE history_visits (id INTEGER PRIMARY KEY AUTOINCREMENT,history_item INTEGER NOT NULL REFERENCES history_items(id) ON DELETE CASCADE,visit_time REAL NOT NULL,title TEXT NULL,load_successful BOOLEAN NOT NULL DEFAULT 1,http_non_get BOOLEAN NOT NULL DEFAULT 0,synthesized BOOLEAN NOT NULL DEFAULT 0,redirect_source INTEGER NULL UNIQUE REFERENCES history_visits(id) ON DELETE CASCADE,redirect_destination INTEGER NULL UNIQUE REFERENCES history_visits(id) ON DELETE CASCADE,origin INTEGER NOT NULL DEFAULT 0,generation INTEGER NOT NULL DEFAULT 0);\n",
        "CREATE INDEX history_visits__last_visit ON history_visits (history_item, visit_time DESC, synthesized ASC);\n",
        "CREATE INDEX history_visits__origin ON history_visits (origin, generation);\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The first thing to note is that each *history visit* table is associated with one *history item*, as we can tell from 'history_item' column in 'history_visits' table.\n",
      "\n",
      "`history_item INTEGER NOT NULL REFERENCES history_items(id) ON DELETE CASCADE`\n",
      "\n",
      "Moreover, as we can tell from the columns of the two tables, 'history_visits' contains information about individual visits to websites, like the *time* of the visit and the *title* of the website, while *history_items* contains general and aggregate information about the websites we visit, such as their url and number of visits.\n",
      "\n",
      "Let's take a look now at the addresses that we visit most often (and despair later)..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%script sqlite3 hs.db\n",
      "SELECT id, url, visit_count  FROM history_items ORDER BY visit_count DESC LIMIT 5;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "491|https://twitter.com/|4048\n",
        "9252|https://www.facebook.com/|2380\n",
        "17706|http://international.nytimes.com/|1225\n",
        "18|http://www.theverge.com/|1211\n",
        "23141|https://mail.google.com/mail/u/0/#inbox|1134\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "...As well as our most recent visits."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%script sqlite3 hs.db\n",
      "SELECT V.title, I.url, V.visit_time\n",
      "FROM history_items as I,\n",
      "(SELECT history_item, title, visit_time FROM history_visits ORDER BY visit_time DESC LIMIT 5) as V\n",
      "WHERE I.id = V.history_item;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Sanja Scepanovic and 2 others - Messages|https://www.facebook.com/messages/conversation-1680195825528863|462218817.510182\n",
        "Facebook|https://www.facebook.com/login.php?login_attempt=1&lwv=111|462218817.412889\n",
        "Espoo Cine 2015|http://boxoffice.espoocine.fi/ewt/?lang=fin&tctpage=cart|462218724.79521\n",
        "Espoo Cine 2015: bikes vs cars|http://www.espoocine.fi/2015/fi/ohjelmisto/elokuvat/bikes-vs-cars|462218707.2001\n",
        "Espoo Cine 2015: bikes vs cars|http://www.espoocine.fi/2015/en/programme/movies/bikes-vs-cars|462218641.31604\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that, from what I can tell at least, time is expressed as the number of seconds since the beginning of 2001-01-01."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally, let's dump to text files ('visits.csv') only the information that interests us: for now, that's only the web address visited (url) and the associated timestamp."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash --out visits\n",
      "sqlite3 hs.db \\\n",
      "\"SELECT I.url, V.visit_time \\\n",
      "FROM history_items as I, \\\n",
      "(SELECT history_item, title, visit_time FROM history_visits) as V \\\n",
      "WHERE I.id = V.history_item;\" \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open('visits.txt', 'w')\n",
      "f.write(visits)\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "That was it, our browsing history is in file 'visits.txt'."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "head -5 visits.txt\n",
      "echo \"... ... ...\"\n",
      "tail -5 visits.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "http://www.apple.com/iphone/|431973567.3\n",
        "http://live.arstechnica.com/apples-september-9-2014-event/|431975282.8\n",
        "http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDAQFjAA&url=http%3A%2F%2Fwww.stevens.edu%2F&ei=NofoU56VCMeP0AW6rYCIBg&usg=AFQjCNFN7sE7yPBglg-N1stqD034Ifa_pQ&bvm=bv.72676100,d.d2k|429440704.7\n",
        "http://www.theverge.com/2014/9/9/6122731/iphone-6-apple-4-7-inch-display-announced|431982335.9\n",
        "http://www.apple.com/live/2014-sept-event/|431919219.233333\n",
        "... ... ...\n",
        "http://www.espoocine.fi/2015/en/programme/movies/bikes-vs-cars|462218641.31604\n",
        "http://www.espoocine.fi/2015/fi/ohjelmisto/elokuvat/bikes-vs-cars|462218707.2001\n",
        "http://boxoffice.espoocine.fi/ewt/?lang=fin&tctpage=cart|462218724.79521\n",
        "https://www.facebook.com/login.php?login_attempt=1&lwv=111|462218817.412889\n",
        "https://www.facebook.com/messages/conversation-1680195825528863|462218817.510182\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Part 2: Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "import matplotlib.pyplot as plt\n",
      "import time\n",
      "import datetime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let us parse the text file we created and extract the timestamps of our visits."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open('visits.txt')\n",
      "tt = [] # stores timestamps of our visits\n",
      "for line in f:\n",
      "    try:\n",
      "        tokens = line.rstrip().split('|')\n",
      "        time_sec = float(tokens[-1])\n",
      "        tt.append(time_sec)\n",
      "    except:\n",
      "        sys.stderr.write(line)\n",
      "        sys.stderr.write('\\n')\n",
      "f.close()\n",
      "# sort the timestamps\n",
      "tt.sort()\n",
      "\n",
      "# turn into standard POSIX by adding\n",
      "# the first second of 2001-1-1 UTC\n",
      "zero_sec = time.mktime(datetime.datetime(2001, 1, 1).timetuple())\n",
      "for i in range(len(tt)): tt[i] += zero_sec"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let us plot the daily number of pages we've visited for that period of time."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bin_size = 24 * 3600 # have one bin per 24 hours\n",
      "num_of_bins = (tt[-1] - tt[0]) / bin_size\n",
      "# figure out intervals to accomodate number of labels on x-axis\n",
      "num_of_xlabels = 10\n",
      "label_bin = (tt[-1] - tt[0]) / (num_of_xlabels - 1)\n",
      "locs = [tt[0] + label_bin * (i) for i in range(num_of_xlabels)]\n",
      "labels = map(lambda x: str(datetime.datetime.fromtimestamp(x)).split()[0], locs)\n",
      "# plot the histogram\n",
      "plt.figure(figsize=(15, 5))\n",
      "plt.hist(tt, num_of_bins, range = (tt[0], tt[-1]))\n",
      "plt.xticks(locs, labels)\n",
      "plt.ylabel(\"Daily Visits\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.text.Text at 0x108166b50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OLt8k0yxz8vebTxxOXubU6Vvfqa0yb1//qG3jREwnj5sUkzaM+/xR28A0ZZ72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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107080390>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What are websites we've visited most often?\n",
      "We've already had an idea about this, but now let us do more elaborate processing."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import re\n",
      "import numpy as np\n",
      "website_pattern = re.compile('http(s)?://(\\w+\\.)?(\\w+?)\\.\\w+?/')\n",
      "\n",
      "verbose = False\n",
      "\n",
      "f = open('visits.txt')\n",
      "website_counts = {} # stores counts for each website\n",
      "addresses = {} # stores the different addresses (base-urls) for each website\n",
      "for line in f:\n",
      "    try:\n",
      "        # keep only the url, ignore the timestamp\n",
      "        tokens = line.rstrip().split('|')\n",
      "        m = website_pattern.search(\"\".join(tokens[:-1]))\n",
      "        try:\n",
      "            # parse the url to extract the website name\n",
      "            # for example, if the url is http://www.facebook.com/ab238ub\n",
      "            # the website name is 'facebook'\n",
      "            website = m.group(3)\n",
      "            # update count\n",
      "            website_counts[website] = website_counts.get(website, 0) + 1\n",
      "            # keep track of the different base urls that match this website\n",
      "            # for example, if the url is http://www.facebook.com/ab238ub\n",
      "            # the base url is http://www.facebook.com/\n",
      "            if website not in addresses:\n",
      "                addresses[website] = {}\n",
      "            addresses[website][m.group(0)] = addresses[website].get(m.group(0), 0) + 1\n",
      "        except Exception, e:\n",
      "            if verbose:\n",
      "                sys.stderr.write(str(e))\n",
      "                sys.stderr.write('\\n')\n",
      "    except:\n",
      "        sys.stderr.write(line)\n",
      "        sys.stderr.write('\\n')\n",
      "f.close()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the number of visits to the websites we visit most frequently"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "k = 5 # The number of top websites to present\n",
      "\n",
      "# Keep the top-k websites\n",
      "k = min(k, len(website_counts))\n",
      "top_websites = sorted(website_counts.items(),\n",
      "                key = lambda x: x[1], reverse = True)[:k]\n",
      "# The number of visits to websites that are not among the top-k\n",
      "other_visits = sum(x[1] for x in top_websites[k:])\n",
      "\n",
      "# Let's make a histogram for the top-k websites\n",
      "plt.figure(figsize = (10, 5)) # create new figure\n",
      "bar_width = 0.5\n",
      "xpos = np.arange(len(top_websites)) + bar_width # bar positions on the x-axis\n",
      "plt.bar(xpos, [w[1] for w in top_websites], bar_width) # places the bars\n",
      "plt.xticks(xpos+bar_width/2, [w[0] for w in top_websites]) # ticks on x-axis\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "([<matplotlib.axis.XTick at 0x1077d3c90>,\n",
        "  <matplotlib.axis.XTick at 0x1077aff10>,\n",
        "  <matplotlib.axis.XTick at 0x10811ee50>,\n",
        "  <matplotlib.axis.XTick at 0x10812d6d0>,\n",
        "  <matplotlib.axis.XTick at 0x10812de10>],\n",
        " <a list of 5 Text xticklabel objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SbyV5S6sdU1XbW3s7cExrPxPYOrLuVuC4OerbWl2SJOmgsmIf139ZVX0vya8AG5PcM/pg\nVVWS2sfnkCRJOijsU/Cqqu+1r3+X5LPAS4DtSZ5RVQ+104jfb4tvA44fWf1ZDDNd21p7tL5t7mfc\nMNKeajdJkqTxmp6eZnp6esHlUrW4CakkTwYOqarHkjwF+DJwMfAq4OGquiTJBcDKqrqgXVz/SYZw\ndhzwFeC5bVbsVuA8YBPwBeAjVXXjrOer4czmpAmL3UcHsuFzF5O33e7vSTOZ+1uaBEmoqtnXwe/T\njNcxwGfbBxNXAH9ZVV9O8i3g2iTrgS3A6wGq6u4k1wJ3AzuAc2vnT5xzgSuAw4EbZocuSZKkg8Gi\nZ7x6c8ZrsjgDMlnc35IONvPNePmX6yVJkjoxeEmSJHVi8JIkSerE4CVJktSJwUuSJKkTg5ckSVIn\nBi9JkqRODF6SJEmdGLwkSZI6MXhJkiR1YvCSJEnqxOAlSZLUicFLkiSpE4OXJElSJwYvSZKkTgxe\nkiRJnRi8JEmSOjF4SZIkdWLwkiRJ6sTgJUmS1InBS5IkqZMV4x6AJGmyJBn3EMamqsY9BI2ZwUuS\nNAaTGEAmN3BqJ4OXJEnabyZ5hnMuBi9JkrSfOcM5w4vrJUmSOjF4SZIkdWLwkiRJ6sTgJUmS1InB\nS5IkqRODlyRJUicGL0mSpE4MXpIkSZ0YvCRJkjoxeEmSJHVi8JIkSerE4CVJktSJwUuSJKkTg5ck\nSVInBi9JkqRODF6SJEmdGLwkSZI6MXhJkiR1YvCSJEnqxOAlSZLUicFLkiSpE4OXJElSJ8smeCVZ\nl+SeJPcm+XfjHo8kSdJSWxbBK8khwH8C1gFrgDckef54RzVqetwDUFfT4x6Aupoe9wDU1fS4B6Cu\npsc9gF0si+AFvAS4r6q2VNXjwP8ATh/zmEZMj3sA6mp63ANQV9PjHoC6mh73ANTV9LgHsIvlEryO\nAx4Yub+11SRJkg4ayyV41bgHIEmStL+lavyZJ8lLgQ1Vta7dvxD4RVVdMrLM+AcqSZK0h6oqs2vL\nJXitAP4WOAV4ENgEvKGqvjPWgUmSJC2hFeMeAEBV7UjyDuBLwCHA5YYuSZJ0sFkWM16SJEmTYLlc\nXH/ASjKV5Ppxj0ODJEcnedteLP+N9vWEJG8Yqf96klfvjzFqcZKcl+TuJFcvUX/TSdYuQT8bkrxr\nKcakxUnymt394e0kJybZvJd9/nK/Jrk4ySn7Ok7ttNjjJsmWJKv2R9972v++MnjpYPNU4Nw9Xbiq\nXtaazwH+9chDJwG/uTdP3K5V1P7zNuBVVXX2EvW3VNP9njYYs6q6fvTDWEvV7Uj/F1XVTUvc/6Rb\n7HGzJ+vtyzG534/niQheSd7T/h3RzUk+meRdSV6U5JYktyf5qyQr27Lz1V+c5I4ktyX54Fy/PSV5\nSpKPJ7k1yd8kOa33tor3A/+g7aePJ3kNQJLPJrm8td+c5D+09o9H1nt5W+8PgYuBM9v9fzXfvk3y\npiTXJbkJ2Nh7YydFkv8C/CpwY5I/TPLNth++keR5bZlDkvxpks3t+H1Hq69ts1vfSnJjkmeMdH12\n28ebk7y4Lb8qyedaH/8nyQt3V2+qLfOWJDckeVKP12UStNmqe5J8IsnfJvnLJKe2ff/d9rP5TUk+\n2pY/ph3v3263l7auDknyF0nuTPKlmX3U9tmmtuynkxw+xxiuSPIvW/v9Se5q3wcf7PZCHASS/Pu2\nD28G/mGrfTVt5jnJ05Pc39qzj+e3z+rr8CRfTLJ+nr5njsnpJep/lxzR6nNmht2qqoP6BrwYuA14\nInAE8F3gXcDtwMvbMhcDH2rtO+ap3wmc3Np/AtzR2lPA9a39PuB3Wnslwyc1nzzu12CSbsAJwObW\nPhP4QGtvAr7Z2p8A/nlrP9a+vmJmP7b75wAfGbk/574F3sTwx39XjnvbD/YbcD+wCjgSOKTVXgV8\nurXfBlwLPKHdfypwKPBN4Gkj3xOXt/Y08F9b++Uj3zcfBd7T2q8EblugflH7mfIO4LPAoeN+rQ6m\nG3Ai8DjwAiDAt0b24WntNT8H+GirfQo4r7WfABw10sevjSwzczyvGnmu9wLvGN2vrf0J4LeBpwH3\njCx/1LhfnwPlBqxleH99UjuG723HzVeBf9yWeTpwf2vvcjy3r/cz/JzfCLxxN33/fntsKfqfK0fM\n9D9nZtjdbRJOjbwM+FxV/Qz4WYbrsZ7C8EZ5c1vmSuB/JjkKOHqO+tHAEVV1a6t/EvgXczzXqcBr\nkvxBu38YcDzDm7T6GP2bKV8Hfi/D//28C1jZZjteyvAmOd96M/dHa3Pt22cz/Fa1saoeWaLxa2Er\ngauSPJfh9Z/5OXYKcFlV/QKgqn6Y5B8xvGF/JQkMn5p+sC1fwDVt2ZuTHNWO9ZcxvMlSVV9N8rQk\nR+6mHuDfMATw06vq5/t38yfS/VV1F0CSu4CvtPqdDKFq1CuBNwK074UfZbhm5/6quqMt89cj670w\nwwz40QxvqjfuZhyPAD/NMHv+v9pNe+blwF9V1U8ZXsPrFlh+l+O51QN8Hrikqq5ZZN972/9cOYL5\nMsNCTzwJwavY9U11tvke39s6wG9X1b0Ljkr7XVVta9O+64CvMcyWnAn8uKp+stDqc9R22bdJTgYW\n6ktLJwyzEjdV1WuTnMjwG+3o47OXv6uq/tke9j+z3/fm2C9gM/DrDL9obdnD59Ke+/uR9i+An420\n53ofm2s/jfbxc4bZEYArgNOqanOScxjOYswY/TmQqvp5kpcwvGm/juEXOC+63zPzvRfvYPiFCHbu\nkxnzHW9fB15N+8Vpjr5H2zvYeVnV/uh/of52MQnXeH2DYabisCRHMMxU/QT4YZLfaMucDUxX1Y/m\nqT8KPNYOOICz5nmuLwHnzdxJctISb4sW9hjDVPOMW4DfA/43cDPwBwwhbKH1Zt+fb9/u0YGmJXUU\nO2et3jRS3wi8NckhAEmeCtwD/MrMdT5JDk2ypi0fhiBOO+YfaT8DbgZ+p9WngL+rqsd2Uw/DaYh/\nC1yX5Nil32TthZsYTiPNXMdz1BzLjM5oHwE8lORQhpmymmMZWn8zZ0u+CPw+Q9jWnvkacEaSJ7WZ\n4te0+haGU4UwhNkZcx3PM/6I4b36P8/T9+gZqS3AP9nH/mfniN8CmC8zLPRCHPTBq6q+BVzHcB72\nBobfTB9huCbgg0luB34N+OO2ynz19cB/S3Ibw7U9j44+Tfv6XuDQDBfh38lwvlcdVdXDwDfaBZMf\nYHizPKSq/i/Dm+NTW+2Xq7SvtwM/bxfYns8wi7Im7eJ65t+3RYdPwQjY+Vp/APiTJH/D8JvyzOv/\nMeD/AXck+TbDf794nOGH7SWtdhvwT0f6+2nr51KGYxxgA7C2/Qx4H8PPhN3VC6iq+gZDsP9C9vPH\n0SfQ7GNsrmNupnY+8MokdzBcD/b8OdYZPW7fA9zKMMvxnXmWmbl/JHB9+x64GXjn3m3G5Kqq2xiu\nrbud4b14E8Nr+qfA29px+DR2czzP6u984PAk75+n7xlL0f9cOWImA8yXGeY1EX9ANclTquonSZ7M\nMPPxlqr69mL6aO0LgGOqyoNOkqSD3FLkiBmTcI0XwF+00wtPAq5Y5Iv1Wxn+efcKhqnLNy3d8CRJ\n0jK2FDkCmJAZL0mSpOXgoL/GS5IkabkweEmSJHVi8JIkSerE4CVJktSJwUuSJKkTg5ckSVIn/x9m\nRStyf39RXgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107eb6450>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## For each of the top-k websites, show the most popular\n",
      "## addresses (base urls)\n",
      "\n",
      "m = 3       # Show at most m addresses...\n",
      "pct = 0.90  # ... or stop at 90% of visits\n",
      "\n",
      "# For each of the top-k websites...\n",
      "for website_name, website_visits in top_websites:\n",
      "\n",
      "    # ... store the most frequently visited base urls ...\n",
      "    website_addresses = sorted(addresses[website_name].items(),\n",
      "                               key = lambda x: x[1], reverse = True)\n",
      "    top_addresses = []; visits = 0.\n",
      "    for address, address_count in website_addresses:\n",
      "        visits += address_count\n",
      "        top_addresses.append((address, address_count))\n",
      "        if len(top_addresses) > m or visits / website_visits >= pct:\n",
      "            break\n",
      "        \n",
      "    # ... and the number of visits to addresses that are not among the top\n",
      "    other_num = website_visits - visits\n",
      "\n",
      "    # make a pie-chart for this website\n",
      "    # if it is associated with many addresses\n",
      "    if len(top_addresses) >= m: # TODO change this condition if you want\n",
      "        plt.figure(figsize = (7,7))\n",
      "        labels = [x[0] for x in top_addresses] + [\"other\"]\n",
      "        sizes = [x[1] for x in top_addresses] + [other_num] \n",
      "        # arbitrary choice of colors -- I like blue\n",
      "        n = float(len(sizes))\n",
      "        colors = [( p / (n + 1.), 0., (1. - p / (n + 1.)))\\\n",
      "                                  for p in xrange(len(sizes))]\n",
      "        colors[-1] = 'grey'\n",
      "        plt.pie(sizes, labels=labels, colors=colors,\n",
      "                labeldistance = 1.1, autopct='%1.1f%%', startangle = 90)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ajuhgDjzpJRRiZDeVfh4uUoIRkcDFYjGGDRvWiq5t91oKMdJpZrYfuIEwLehS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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106a78350>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Resources"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For more information on the tools we used in this session, check the following websites.\n",
      "1. Tutorial on the [sqlite3 command line tool](http://zetcode.com/db/sqlite/tool/)\n",
      "2. Python's documentation for the [sqlite3 module](https://docs.python.org/2/library/sqlite3.html)\n",
      "3. [Running scripts in iPython]( http://nbviewer.ipython.org/github/ipython/ipython/blob/d835d46dcc50043971b4a9915398bad1b5d63648/docs/examples/notebooks/Script%20Magics.ipynb)\n",
      "4. [Markdown syntax](http://daringfireball.net/projects/markdown/syntax)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": null
    }
   ],
   "metadata": {}
  }
 ]
}