{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Web browsers use the network too sparingly\n",
    "\n",
    "Author: alcidesv@shimmercat.com\n",
    "\n",
    "## Abstract\n",
    "\n",
    "The typical HTTP request/response model makes difficult for browsers to use their available bandwidth to fetch the website faster. Here we analyze how fast a web page can be fetched."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is in the dataset\n",
    "\n",
    "The dataset contains page-load times, with individual resources, for each of the 1300 resource page loads.The 1300 sites were submitted by performance-conscious site operators that were evaluating their site's performance. The important bits of the dataset,  anonymized, [are available](https://github.com/shimmercat/art_timings/raw/master/data/clean_dataset.json.xz) for you to make your own measurements. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook initialization and loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: GTK3Agg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n",
      "WARNING: pylab import has clobbered these variables: ['rc']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "# Import Bokeh modules for interactive plotting\n",
    "import bokeh.io\n",
    "import bokeh.mpl\n",
    "import bokeh.plotting\n",
    "\n",
    "\n",
    "\n",
    "# Seaborn, useful for graphics\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib\n",
    "#matplotlib.style.use('ggplot')\n",
    "rc = {'lines.linewidth': 2, \n",
    "      'axes.labelsize': 14, \n",
    "      'axes.titlesize': 14, \n",
    "      'axes.facecolor': 'DFDFE5',\n",
    "      'patch.facecolor': 'F37626'\n",
    "     }\n",
    "sns.set_context('notebook', rc=rc)\n",
    "sns.set_style('darkgrid', rc=rc)\n",
    "\n",
    "%pylab\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_formats = {'png', 'retina'}\n",
    "# %config InlineBackend.figure_formats = {'svg',}\n",
    "\n",
    "# Set up Bokeh for inline viewing\n",
    "#bokeh.io.output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from glob import glob\n",
    "import os.path\n",
    "import json\n",
    "import lzma\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    " with lzma.open(\"data/clean_dataset.json.xz\", 'rb') as fin:\n",
    "    dataset_s = fin.read().decode('ascii')\n",
    "    dataset = json.loads(dataset_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parsing the date-times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how the starting times of a request look in the jar file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'startedDateTime': '2015-12-01T08:37:30.598Z',\n",
       " 'timings': {'blocked': 0.382999889552593,\n",
       "  'connect': 248.054999858141,\n",
       "  'dns': 410.19000019878143,\n",
       "  'receive': 33.59492402523745,\n",
       "  'send': 0.13100076466798782,\n",
       "  'ssl': 215.08000046014797,\n",
       "  'wait': 35.99599935114395},\n",
       " 'transferSize': 20469}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtsample = dataset[0]['entries'][0]\n",
    "dtsample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's parse startedDateTime by hand..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rgx_ = re.compile( 'T([0-9]{2}):([0-9]{2}):([0-9]{2}\\.[0-9]*)Z' )\n",
    "ms_ = 1000\n",
    "def dt2milliseconds(dtval):\n",
    "    mo = re.search(rgx_, dtval)\n",
    "    milliseconds = \\\n",
    "       int( mo.group(1) )*3600*ms_ + \\\n",
    "       int( mo.group(2) )*60*ms_ + \\\n",
    "       float( mo.group(3) )*ms_\n",
    "    return milliseconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decorating the entries with the relative start-end time of the request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def decorate_relative_timings(timings, starts_dtval, first_request_start):\n",
    "    # Uses the timings entry in each fetch and its starting time \n",
    "    # to deduce a set of \"unhabited\" intervals and a set of \n",
    "    # \"populated\" intervals. \n",
    "    mseconds_start = dt2milliseconds(starts_dtval) - first_request_start\n",
    "    connect = timings.get('connect',0)\n",
    "    dns = timings.get('dns', 0)\n",
    "    blocked = timings.get('blocked', 0)\n",
    "    # Chrome uses '-1' to signal that the timing doesn't apply\n",
    "    if connect < 0:\n",
    "        connect = 0\n",
    "    if dns < 0: \n",
    "        dns = 0\n",
    "    ssl = timings.get('ssl', 0)\n",
    "    if ssl < 0:\n",
    "        ssl = 0\n",
    "    send = timings.get('send')\n",
    "    wait = timings.get('wait')\n",
    "    receive = timings.get('receive')\n",
    "    \n",
    "    starts_receiving = mseconds_start + connect + dns + ssl + send + wait\n",
    "    ends_receiving = starts_receiving + receive\n",
    "    timings['rel_start'] = mseconds_start\n",
    "    timings['starts_receiving'] = starts_receiving\n",
    "    timings['ends_receiving'] = ends_receiving\n",
    "    \n",
    "def decorate_all_timing_entries(fetch_timings):\n",
    "    first_fetch = fetch_timings['entries'][0]\n",
    "    first_request_start = dt2milliseconds(first_fetch['startedDateTime'])\n",
    "    for entry in fetch_timings['entries']:\n",
    "        decorate_relative_timings(entry['timings'], entry['startedDateTime'], first_request_start)\n",
    "        \n",
    "for fetch_timings in dataset:\n",
    "    decorate_all_timing_entries(fetch_timings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An entry looks like this now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'startedDateTime': '2015-12-01T08:37:30.598Z',\n",
       " 'timings': {'blocked': 0.382999889552593,\n",
       "  'connect': 248.054999858141,\n",
       "  'dns': 410.19000019878143,\n",
       "  'ends_receiving': 943.0469246581198,\n",
       "  'receive': 33.59492402523745,\n",
       "  'rel_start': 0.0,\n",
       "  'send': 0.13100076466798782,\n",
       "  'ssl': 215.08000046014797,\n",
       "  'starts_receiving': 909.4520006328823,\n",
       "  'wait': 35.99599935114395},\n",
       " 'transferSize': 20469}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0]['entries'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sets of intervals\n",
    "\n",
    "Sets of intervals are cool! We can do all sort of interesting things with them!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def merge_sets_step(s1):\n",
    "\n",
    "    # Take the first interval\n",
    "    fi = s1[0]\n",
    "    fi_start, fi_end = fi \n",
    "    r_start = fi_start\n",
    "    r_end = fi_end\n",
    "    # Take the other intervals, and see if we can merge them\n",
    "    for (_i,ai) in enumerate(s1[1:]):\n",
    "        ai_start, ai_end = ai \n",
    "        if ai_start <= r_end:\n",
    "            # A merge is possible \n",
    "            r_end = ai_end \n",
    "        else:\n",
    "            # A merge is not possible. Since the intervals \n",
    "            # are sorted by their starting point, the next \n",
    "            # interval will start at a more distant place. \n",
    "            # Finish by returning the new merged big interval\n",
    "            # and the rest of the sorted set.... \n",
    "            return ((r_start, r_end), s1[_i+1:])\n",
    "    # If I arrive here, it is just a big one\n",
    "    return ((r_start, r_end), [])\n",
    "\n",
    "def merge_interval_sets(iterable_of_intervals):\n",
    "    # Sort the intervals ... \n",
    "    s1 = sorted(iterable_of_intervals, key=(lambda i: i[0]))\n",
    "    si = s1\n",
    "    disjoint_intervals = []\n",
    "    disjoint_voids = []\n",
    "    mi_end_prev = None\n",
    "    while len(si) > 0:\n",
    "        (merged_interval, si_next) = merge_sets_step(si)\n",
    "        mi_start, mi_end = merged_interval\n",
    "        if mi_end_prev:\n",
    "            disjoint_voids.append((mi_end_prev, mi_start))\n",
    "        mi_end_prev = mi_end\n",
    "        disjoint_intervals.append(merged_interval)\n",
    "        si = si_next\n",
    "    return (disjoint_intervals, disjoint_voids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's just test a little bit the merge intervals function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((0, 1), [(2, 3)])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_sets_step([\n",
    "        (0,1),\n",
    "        (2,3)\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((0, 1.5), [(2, 3)])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_sets_step([\n",
    "        (0,1),\n",
    "        (0.5,1.5),\n",
    "        (2,3)\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((0, 3), [])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_sets_step([\n",
    "        (0,1),\n",
    "        (0.5,2.5),\n",
    "        (2,3)\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([(0, 3)], [])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_interval_sets([\n",
    "        (0,1),\n",
    "        (0.5,2.5),\n",
    "        (2,3)\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([(0, 1.5), (2, 4), (4.01, 6)], [(1.5, 2), (4, 4.01)])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_interval_sets([\n",
    "        (0,1),\n",
    "        (0.5,1.5),\n",
    "        (2,3),\n",
    "        (3,4),\n",
    "        (4.01, 6)\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def adv_state(state, token):\n",
    "    if (state,token) == (\"S0\", \"B1\"):\n",
    "        return \"S1\"\n",
    "    if (state,token) == (\"S0\", \"B2\"):\n",
    "        return \"S3\"\n",
    "    if (state,token) == (\"S1\", \"E1\"):\n",
    "        return \"S0\"\n",
    "    if (state,token) == (\"S1\", \"B2\"):\n",
    "        return \"S2\"\n",
    "    if (state, token) == (\"S2\", \"E2\"):\n",
    "        return \"S1\"\n",
    "    if (state, token) == ('S2', 'E1'):\n",
    "        return \"S3\"\n",
    "    if (state, token) == ('S3', 'B1'):\n",
    "        return \"S2\"\n",
    "    if (state, token) == ('S3', 'E2'):\n",
    "        return \"S0\"\n",
    "    raise AssertionError(\"Must-not-happen: \" + str((state, token)))\n",
    "    return None\n",
    "\n",
    "def diff_interval_sets(a_set, b_set):\n",
    "    \"\"\"Computes a \\ b\"\"\"\n",
    "    all_intervals = []\n",
    "    for (a,b) in a_set:\n",
    "        all_intervals.append((\"B1\",a))\n",
    "        all_intervals.append((\"E1\",b))\n",
    "        \n",
    "    for (a,b) in b_set:\n",
    "        all_intervals.append((\"B2\",a))\n",
    "        all_intervals.append((\"E2\",b))\n",
    "        \n",
    "    # Now sort \n",
    "    all_intervals.sort(key=(lambda x: x[1]))\n",
    "    \n",
    "    state = 'S0'\n",
    "    prev_position = None\n",
    "    for (change, position) in all_intervals:\n",
    "        #print((state,change, position, prev_position))\n",
    "        new_state = adv_state(state, change)\n",
    "        if state == 'S1' and new_state != 'S1':\n",
    "            # This most be true for the most part  ... \n",
    "            yield (prev_position, position)\n",
    "            \n",
    "        state = new_state \n",
    "        prev_position = position \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0.2), (0.5, 1), (3.0, 3.4)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(diff_interval_sets(\n",
    "        [\n",
    "            (0,1),\n",
    "            (1.2, 3.4)\n",
    "        ],\n",
    "        [\n",
    "            (0.2, 0.5),\n",
    "            (1.1, 3.0)\n",
    "        ]\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-1, 2), (3.5, 3.6), (3.7, 8.4)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(diff_interval_sets(\n",
    "        [\n",
    "            (-1,2),\n",
    "            (2.4, 8.4)\n",
    "        ],\n",
    "        [\n",
    "            (2.2, 3.5),\n",
    "            (3.6, 3.7)\n",
    "        ]\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good, let's continue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of data points\n",
    "------------------------\n",
    "\n",
    "That is, how many files there are in our little DB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1261"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of requests per page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "counts = pd.DataFrame( list( len(d['entries']) for d in dataset ), columns=['asset_count'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asset_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   asset_count\n",
       "0          135\n",
       "1          157\n",
       "2           81\n",
       "3          425\n",
       "4           18"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1203.000000\n",
       "mean       86.600998\n",
       "std        97.236518\n",
       "min         6.000000\n",
       "25%        26.000000\n",
       "50%        55.000000\n",
       "75%       109.000000\n",
       "max       921.000000\n",
       "Name: asset_count, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_clean = counts.query('asset_count > 5')\n",
    "counts_clean['asset_count'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7208318390>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JlTGQSJIkSaqMgUSSJElSZQwkkiRJkipjIJEkSZJUGQOJJEmSpMoYSCRJkiRV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7208a91780>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 258,
       "width": 402
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts_clean.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f7201070710>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HxNMoI589jzLi1QbAtyPiNZn5tTEO08/SE9zWSZib76ePZuYxUzhnmyYbP5RO8DtT/r5+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\n/wHCHnikh+EV3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7204904c18>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 287,
       "width": 407
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=pyplot.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "(a0,a1,patches)=ax.hist(counts_clean.as_matrix(), bins=np.arange(0,400,25), normed=True)\n",
    "a1.sum()\n",
    "ax.set_xlabel(\"Number of requests\")\n",
    "ax.set_ylabel(\"Normalized density\")\n",
    "ax.set_title(\"Distribution of requests per page\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number above should be used as our total of data-points: \n",
    "1233"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When the first request can actually start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_stoppers(entries_list):\n",
    "    first_entry = entries_list[0]['timings']\n",
    "    connect_time = first_entry['connect']\n",
    "    dns_time = first_entry['dns']\n",
    "    ssl = first_entry.get('ssl', 0)\n",
    "    wait = first_entry.get('wait')\n",
    "    return {\n",
    "         'connect': connect_time,\n",
    "         'dns': dns_time,\n",
    "         'ssl': ssl,\n",
    "         'wait': wait\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>connect</th>\n",
       "      <th>dns</th>\n",
       "      <th>ssl</th>\n",
       "      <th>wait</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>248.055</td>\n",
       "      <td>410.190000</td>\n",
       "      <td>215.08</td>\n",
       "      <td>35.995999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.974</td>\n",
       "      <td>192.953000</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>275.657000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>166.749</td>\n",
       "      <td>182.938000</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>166.886000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.034</td>\n",
       "      <td>246.746000</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>123.576000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.032</td>\n",
       "      <td>1.269001</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>30.242000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   connect         dns     ssl        wait\n",
       "0  248.055  410.190000  215.08   35.995999\n",
       "1   44.974  192.953000   -1.00  275.657000\n",
       "2  166.749  182.938000   -1.00  166.886000\n",
       "3    8.034  246.746000   -1.00  123.576000\n",
       "4   24.032    1.269001   -1.00   30.242000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stoppers = list( [extract_stoppers(d['entries']) for d in dataset] )\n",
    "stoppers_df = pd.DataFrame(stoppers)\n",
    "stoppers_df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>connect</th>\n",
       "      <th>dns</th>\n",
       "      <th>ssl</th>\n",
       "      <th>wait</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>248.055</td>\n",
       "      <td>410.190000</td>\n",
       "      <td>215.08</td>\n",
       "      <td>35.995999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.974</td>\n",
       "      <td>192.953000</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>275.657000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>166.749</td>\n",
       "      <td>182.938000</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>166.886000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.034</td>\n",
       "      <td>246.746000</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>123.576000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.032</td>\n",
       "      <td>1.269001</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>30.242000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   connect         dns     ssl        wait\n",
       "0  248.055  410.190000  215.08   35.995999\n",
       "1   44.974  192.953000   -1.00  275.657000\n",
       "2  166.749  182.938000   -1.00  166.886000\n",
       "3    8.034  246.746000   -1.00  123.576000\n",
       "4   24.032    1.269001   -1.00   30.242000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stoppers_df_clean = stoppers_df.query('connect > 0')\n",
    "stoppers_df_clean[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How the bare connection times are distributed\n",
    "\n",
    "This does not include DNS or SSL times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f720104bba8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Yc+TZ84/5ba1dWFVvSnJ0VZ2e5PQke2cIKNcmeeGk2g8AAKvJVANJVT02yWuTHDh2+gtJ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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72049189e8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 258,
       "width": 402
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stoppers_df_clean['connect'].plot(kind='hist', bins=np.arange(0,1000,25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f7200ef5390>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wJaHZHTiQsvzzSB0PvBE4skpWLge2oCQkVwJfrMVeB/wVeG6t7ADgfOCiiPgy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\nARRE8ER61vEDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7200f717b8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 287,
       "width": 410
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_data = stoppers_df_clean['connect'].as_matrix()\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.hist(_data, normed=True, bins=np.arange(0,1000,25))\n",
    "ax.set_title(\"Connection time\")\n",
    "ax.set_xlabel(\"Milliseconds\")\n",
    "ax.set_ylabel(\"Normalized density\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>connect</th>\n",
       "      <th>dns</th>\n",
       "      <th>ssl</th>\n",
       "      <th>wait</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1151.000000</td>\n",
       "      <td>1151.000000</td>\n",
       "      <td>1151.000000</td>\n",
       "      <td>1151.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>182.709516</td>\n",
       "      <td>176.627490</td>\n",
       "      <td>71.454324</td>\n",
       "      <td>395.762308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>245.427672</td>\n",
       "      <td>274.765574</td>\n",
       "      <td>199.979281</td>\n",
       "      <td>620.154483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.576000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.402000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.528500</td>\n",
       "      <td>17.222500</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>106.582000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>157.720000</td>\n",
       "      <td>76.878000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>206.507999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>201.945000</td>\n",
       "      <td>210.063000</td>\n",
       "      <td>32.626000</td>\n",
       "      <td>417.026000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4436.960001</td>\n",
       "      <td>2661.007000</td>\n",
       "      <td>3979.796000</td>\n",
       "      <td>6569.305000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           connect          dns          ssl         wait\n",
       "count  1151.000000  1151.000000  1151.000000  1151.000000\n",
       "mean    182.709516   176.627490    71.454324   395.762308\n",
       "std     245.427672   274.765574   199.979281   620.154483\n",
       "min       0.576000     0.000000    -1.000000     1.402000\n",
       "25%      32.528500    17.222500    -1.000000   106.582000\n",
       "50%     157.720000    76.878000    -1.000000   206.507999\n",
       "75%     201.945000   210.063000    32.626000   417.026000\n",
       "max    4436.960001  2661.007000  3979.796000  6569.305000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stoppers_df_clean.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "≽ There seems to be two modes. My guess for those are some sites are in EE.UU. (same place where the browser that made the measurements is located) and som are in Europe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How the total times are distributed for connections without SSL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>connect</th>\n",
       "      <th>dns</th>\n",
       "      <th>ssl</th>\n",
       "      <th>wait</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.974</td>\n",
       "      <td>192.953000</td>\n",
       "      <td>-1</td>\n",
       "      <td>275.657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>166.749</td>\n",
       "      <td>182.938000</td>\n",
       "      <td>-1</td>\n",
       "      <td>166.886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.034</td>\n",
       "      <td>246.746000</td>\n",
       "      <td>-1</td>\n",
       "      <td>123.576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.032</td>\n",
       "      <td>1.269001</td>\n",
       "      <td>-1</td>\n",
       "      <td>30.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>175.758</td>\n",
       "      <td>455.540000</td>\n",
       "      <td>-1</td>\n",
       "      <td>232.651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   connect         dns  ssl     wait\n",
       "1   44.974  192.953000   -1  275.657\n",
       "2  166.749  182.938000   -1  166.886\n",
       "3    8.034  246.746000   -1  123.576\n",
       "4   24.032    1.269001   -1   30.242\n",
       "8  175.758  455.540000   -1  232.651"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stoppers_nossl_clean = stoppers_df_clean.query('ssl == -1')\n",
    "stoppers_nossl_clean[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    237.927\n",
       "2    349.687\n",
       "3    254.780\n",
       "4     25.301\n",
       "8    631.298\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_nossl_stoppers = (\n",
    "    stoppers_nossl_clean['connect']+stoppers_nossl_clean['dns']\n",
    ")\n",
    "full_nossl_stoppers[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     763.000000\n",
       "mean      308.526966\n",
       "std       317.086266\n",
       "min         1.459000\n",
       "5%         17.092600\n",
       "10%        24.941600\n",
       "25%        90.488500\n",
       "50%       216.578000\n",
       "75%       405.002000\n",
       "80%       481.743000\n",
       "90%       677.265800\n",
       "95%       875.118801\n",
       "max      2595.431000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_nossl_stoppers.describe(percentiles=[0.05, 0.10, 0.25, 0.50, 0.75, 0.80, 0.90, 0.95])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f7200df1390>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pg1q+8f2ZNZvaSIYZ/Dpr8zqaU/2uhc7rX7aRuWw0K1GCh+bg9Tv6lIUypmlu6NR9u4jY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\nGgt/vO3WriZdgIkz8UK3E1BAPa9Ft+2YiBMmTKjkJyImAwFc3vq8TeU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1SNJ4YPIjSWp2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\nfgAAgIQNk6EtLMA67AkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7200e87fd0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 287,
       "width": 415
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_data = full_nossl_stoppers.as_matrix()\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.hist(_data, normed=True, bins=np.arange(0,1000,25))\n",
    "ax.set_title(\"Time for first byte of requests, no SSL\")\n",
    "ax.set_xlabel(\"Milliseconds\")\n",
    "ax.set_ylabel(\"Normalized density\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How the total times are distributed for connections with SSL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>connect</th>\n",
       "      <th>dns</th>\n",
       "      <th>ssl</th>\n",
       "      <th>wait</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>248.055000</td>\n",
       "      <td>410.190</td>\n",
       "      <td>215.080</td>\n",
       "      <td>35.995999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>338.042000</td>\n",
       "      <td>0.783</td>\n",
       "      <td>171.889</td>\n",
       "      <td>583.673000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>364.349999</td>\n",
       "      <td>547.143</td>\n",
       "      <td>192.664</td>\n",
       "      <td>172.649000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>74.965999</td>\n",
       "      <td>33.080</td>\n",
       "      <td>66.908</td>\n",
       "      <td>51.791000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>26.235000</td>\n",
       "      <td>1.444</td>\n",
       "      <td>19.094</td>\n",
       "      <td>31.299000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      connect      dns      ssl        wait\n",
       "0  248.055000  410.190  215.080   35.995999\n",
       "5  338.042000    0.783  171.889  583.673000\n",
       "6  364.349999  547.143  192.664  172.649000\n",
       "7   74.965999   33.080   66.908   51.791000\n",
       "9   26.235000    1.444   19.094   31.299000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stoppers_ssl_clean = stoppers_df_clean.query('ssl > 0')\n",
    "stoppers_ssl_clean[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SSL connection time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7200d6dbe0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BLMjMXpPAJUmSJHXRqlukMvOKiDgfeFNELAT+AxgEjgfWAm/paLsoIi4Cjo+IbYHFddsT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\nG8EAAAA2ggEAAGwEAwAA2ASb6MfpV+hkPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7200db10b8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 258,
       "width": 396
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stoppers_ssl_clean['ssl'].plot(kind='hist', bins=np.arange(0,1000,25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     388.000000\n",
       "mean      213.935379\n",
       "std       296.875515\n",
       "min         9.387000\n",
       "25%        32.193000\n",
       "50%       157.536500\n",
       "75%       313.120250\n",
       "max      3979.796000\n",
       "Name: ssl, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stoppers_ssl_clean['ssl'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All the time that clients communicating via SSL need to wait before issuing the first request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     873.325001\n",
       "5     510.714000\n",
       "6    1104.157000\n",
       "7     174.954000\n",
       "9      46.773000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_stoppers = (\n",
    "    stoppers_ssl_clean['ssl']+stoppers_ssl_clean['connect']+stoppers_ssl_clean['dns']\n",
    ")\n",
    "full_stoppers[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     388.000000\n",
       "mean      673.190067\n",
       "std       765.692759\n",
       "min        36.078000\n",
       "5%         49.747650\n",
       "10%        68.972600\n",
       "25%       113.160000\n",
       "50%       513.419000\n",
       "75%       969.809500\n",
       "80%      1110.823000\n",
       "90%      1476.443700\n",
       "95%      1829.102000\n",
       "max      8416.813000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_stoppers.describe(percentiles=[0.05, 0.10, 0.25, 0.50, 0.75, 0.80, 0.90, 0.95])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is, the mean time is well over half a second.The difference of mean with the connections that don't use SSL is 673 - 459 = 214, very close to the mean SSL connection time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7200c23e48>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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svQ94Z/0jSZIkaRPN5GMwJEmSJBVmwJAkSZJUjAFDkiRJUjEGDEmSJEnFGDAk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7200c2ada0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 258,
       "width": 396
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_stoppers.plot(kind='hist', bins=np.arange(0,1500,25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f7200b04f98>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vD5wBzIiIr9TjblDr9iBzB3aSJEmSRjHpWn5qCuntgOOBnYHTKa0+XwW2zswn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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7200b4ca58>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 287,
       "width": 415
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_data = full_stoppers.as_matrix()\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.hist(_data, normed=True, bins=np.arange(0,1000,25))\n",
    "ax.set_title(\"Time for first byte of requests, SSL\")\n",
    "ax.set_xlabel(\"Milliseconds\")\n",
    "ax.set_ylabel(\"Normalized density\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How much time the browser waits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "first_entries_file = dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3605.469998903569"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum( e['timings']['wait'] for e in first_entries_file['entries'][20:] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How much waiting time is in the first three seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def total_waiting_for_entries(entries, up_to):\n",
    "    \"\"\"\n",
    "        @param up_to: If the relative starting time of the request is greater than this, \n",
    "                    forget about that\n",
    "    \"\"\"\n",
    "    result = 0\n",
    "    for e in entries:\n",
    "        if e['timings']['rel_start'] < up_to:\n",
    "            result += e['timings']['wait']\n",
    "        else:\n",
    "            # print(e['timings'] )\n",
    "            pass\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The '3000' below is given in milliseconds. That is, how much the \"wait\" time totals for requests that are done in the first three seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "waitings = pd.Series([ total_waiting_for_entries(entries['entries'], 3000) for entries in dataset])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1261.000000\n",
       "mean       5893.788092\n",
       "std       16466.981401\n",
       "min           0.624000\n",
       "25%        1225.002997\n",
       "50%        3462.645000\n",
       "75%        6512.701999\n",
       "max      322341.219996\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waitings.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember, this waiting time is counted in parallel: if the browser issues 20 requests in the first three seconds, and each request has a waiting timing of half a second, the result will be 20\\*500 = 10000 milliseconds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about average waiting time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def average_waiting_for_entries(entries, up_to):\n",
    "    \"\"\"\n",
    "        @param up_to: If the relative starting time of the request is greater than this, \n",
    "                    forget about that\n",
    "    \"\"\"\n",
    "    total = 0\n",
    "    c = 0\n",
    "    for e in entries:\n",
    "        if e['timings']['rel_start'] < up_to:\n",
    "            total += e['timings']['wait']\n",
    "            c += 1\n",
    "    return total/float(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "avg_waitings = pd.Series([ average_waiting_for_entries(entries['entries'], 3000) for entries in dataset])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1261.000000\n",
       "mean      221.834544\n",
       "std       595.062127\n",
       "min         0.624000\n",
       "25%        32.966692\n",
       "50%        94.100165\n",
       "75%       179.861375\n",
       "max      6569.305000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_waitings.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number above is the average waiting time for request. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How much waiting time is until the \"load\" event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'onContentLoad': 1311.8300437927246, 'onLoad': 1910.5799198150635}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0]['pageTimings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def average_waiting_until_load(dts):\n",
    "    total = 0\n",
    "    c = 0\n",
    "    up_to = dts['pageTimings']['onLoad']\n",
    "    if up_to is None:\n",
    "        return None\n",
    "    for e in dts['entries']:\n",
    "        if e['timings']['ends_receiving'] < up_to:\n",
    "            total += e['timings']['wait']\n",
    "            c += 1\n",
    "    if c == 0:\n",
    "        # Happens some times, if the onLoad event for some reason is triggered \n",
    "        # before the page is finished fetching. Can happen for special responses,\n",
    "        # e.g. redirects and the link \n",
    "        return None\n",
    "    return total/float(c)\n",
    "\n",
    "def total_waiting_time_until_load(dts):\n",
    "    total = 0\n",
    "    up_to = dts['pageTimings']['onLoad']\n",
    "    if up_to is None:\n",
    "        return None\n",
    "    for e in dts['entries']:\n",
    "        if e['timings']['ends_receiving'] < up_to:\n",
    "            total += e['timings']['wait']\n",
    "    return total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "avg_wait_till_load = pd.Series([average_waiting_until_load(dts) for dts in dataset if average_waiting_until_load(dts) is not None])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1133.000000\n",
       "mean      119.194050\n",
       "std       188.260546\n",
       "min         0.000000\n",
       "25%        26.485387\n",
       "50%        77.511536\n",
       "75%       156.177000\n",
       "max      2964.742528\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_wait_till_load.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f72009f8e10>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n8BZgRWBvYO+IuA44CTgtM+9t6v6SJEmS+s+ETAXLzFnAj4AfRcTilKCmFdxs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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7200a62fd0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 287,
       "width": 410
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_data = avg_wait_till_load.as_matrix()\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.hist(_data, normed=True, bins=np.arange(0,1000,25))\n",
    "ax.set_title(\"Average time waiting\")\n",
    "ax.set_xlabel(\"Milliseconds\")\n",
    "ax.set_ylabel(\"Normalized density\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tot_wait_till_load = pd.Series([total_waiting_time_until_load(dts) for dts in dataset])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1145.000000\n",
       "mean       6926.033591\n",
       "std       16068.357333\n",
       "min           0.000000\n",
       "25%         718.904999\n",
       "50%        3222.731997\n",
       "75%        7814.526000\n",
       "max      304247.696998\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot_wait_till_load.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How much time the browser is exclusively waiting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def wait_intervals_until_load(dts):\n",
    "    total = 0\n",
    "    up_to = dts['pageTimings']['onLoad']\n",
    "    if up_to is None:\n",
    "        return None\n",
    "    for e in dts['entries']:\n",
    "        timings = e['timings']\n",
    "        if timings['ends_receiving'] < up_to:\n",
    "            connect = timings.get('connect',0)\n",
    "            dns = timings.get('dns', 0)\n",
    "            blocked = timings.get('blocked', 0)\n",
    "            rel_start = timings.get('rel_start')\n",
    "            # Chrome uses '-1' to signal that the timing doesn't apply\n",
    "            if connect < 0:\n",
    "                connect = 0\n",
    "            if dns < 0: \n",
    "                dns = 0\n",
    "            ssl = timings.get('ssl', 0)\n",
    "            if ssl < 0:\n",
    "                ssl = 0\n",
    "            send = timings.get('send')\n",
    "            wait = timings.get('wait')\n",
    "\n",
    "\n",
    "            starts_waiting = rel_start + connect + dns + ssl + send\n",
    "            ends_waiting = starts_waiting + wait\n",
    "            \n",
    "            yield (starts_waiting, ends_waiting)\n",
    "            \n",
    "def disjoint_wait_intervals_until_load(dts):\n",
    "    intervals = list(wait_intervals_until_load(dts))\n",
    "    merged_intervals = merge_interval_sets(intervals)\n",
    "    # The second part of the answer contains the voids, here we are interested \n",
    "    # only in the parts where there was a waiting...\n",
    "    return merged_intervals[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about the intervals where data-traffic happens?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def deduce_interval_optimistic(timings, starts_dtval):\n",
    "    \"\"\"For a given request/response pair, returns the interval \n",
    "       where data is being transferred to the browser \"\"\"\n",
    "    mseconds_start = timings['rel_start']\n",
    "    connect = timings.get('connect',0)\n",
    "    dns = timings.get('dns', 0)\n",
    "    blocked = timings.get('blocked', 0)\n",
    "    # Chrome uses '-1' to signal that the timing doesn't apply\n",
    "    if connect < 0:\n",
    "        connect = 0\n",
    "    if dns < 0: \n",
    "        dns = 0\n",
    "    ssl = timings.get('ssl', 0)\n",
    "    if ssl < 0:\n",
    "        ssl = 0\n",
    "    send = timings.get('send')\n",
    "    wait = timings.get('wait')\n",
    "    receive = timings.get('receive')\n",
    "    \n",
    "    starts_receiving = mseconds_start + connect + dns + ssl + send + wait\n",
    "    ends_receiving = starts_receiving + receive\n",
    "    return (starts_receiving, ends_receiving)\n",
    "\n",
    "def summarize_intervals_from_entries(dts):\n",
    "    \"\"\"\n",
    "    Returns a set of merged intervals where there is \n",
    "    data transfer from the server to the browser.\n",
    "    \"\"\"\n",
    "    entries = dts['entries']\n",
    "    if len(entries) <= 2:\n",
    "        return None\n",
    "    # returns a data frame with the void times, their lengths, \n",
    "    # the void_end and so so...\n",
    "    all_file_intervals = [\n",
    "        deduce_interval_optimistic(entry['timings'], entry['startedDateTime'])\n",
    "        for entry in entries\n",
    "    ]\n",
    "    # Two sets of complimentary, non overlapping intervals. \n",
    "    # The first set (non overlapping) intervals represents when data is being received \n",
    "    # from the server. The second set  represents when no data is being received.\n",
    "    data_traffic_intervals, void_intervals = merge_interval_sets(all_file_intervals)\n",
    "    return data_traffic_intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compute the intervals for the first set just to see that the calculations make sense:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(755.135999172926, 806.6849999576802),\n",
       " (855.605000577867, 947.1300000548364),\n",
       " (979.33999979496, 1042.2140003219251),\n",
       " (1045.553001128137, 1239.5879996344447)]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wait_intervals = disjoint_wait_intervals_until_load(dataset[0])\n",
    "wait_intervals[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(781.1789994761343, 782.8819367736577),\n",
       " (788.6800003424287, 789.0669974684715),\n",
       " (795.882000371814, 796.6599555388093),\n",
       " (802.104999423027, 804.4668221399189)]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_transfer_intervals = summarize_intervals_from_entries(dataset[0])\n",
    "data_transfer_intervals[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(755.135999172926, 781.1789994761343),\n",
       " (782.8819367736577, 788.6800003424287),\n",
       " (789.0669974684715, 795.882000371814),\n",
       " (796.6599555388093, 802.104999423027)]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list( diff_interval_sets(wait_intervals, data_transfer_intervals) )[:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exclusively waiting total (before the load event):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def exclusively_waiting_total(dts):\n",
    "    wait_intervals = disjoint_wait_intervals_until_load(dts)\n",
    "    data_transfer_intervals = summarize_intervals_from_entries(dataset[0])\n",
    "    exclusively_waiting_intervals = diff_interval_sets(wait_intervals, data_transfer_intervals)\n",
    "    \n",
    "    s = 0.0\n",
    "    for (a,b) in exclusively_waiting_intervals:\n",
    "        s += (b - a)\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "652.7602814957474"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exclusively_waiting_total(dataset[5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can check a distribution for everybody:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "waiting_series = pd.Series([exclusively_waiting_total(dts) for dts in dataset])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1261.000000\n",
       "mean     1187.966479\n",
       "std      1319.171288\n",
       "min         0.000000\n",
       "25%       195.649000\n",
       "50%       780.243429\n",
       "75%      1657.775244\n",
       "max      7862.061900\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waiting_series.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How much is that waiting time compared to the load event?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2978779370419944"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def proportion_just_waiting(dts):\n",
    "    exclusively_waiting = exclusively_waiting_total(dts)\n",
    "    load_time = dts['pageTimings']['onLoad']\n",
    "    if load_time is None:\n",
    "        return float('nan')\n",
    "    return exclusively_waiting / load_time\n",
    "\n",
    "proportion_just_waiting(dataset[12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1145.000000\n",
       "mean        0.392320\n",
       "std         0.188519\n",
       "min         0.000000\n",
       "25%         0.246101\n",
       "50%         0.390646\n",
       "75%         0.528817\n",
       "max         0.884145\n",
       "dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waiting_proportion = pd.Series([proportion_just_waiting(dts) for dts in dataset])\n",
    "waiting_proportion.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f72007c3c88>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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exfMY6gb1q5YyjXEWOzKUID9uNiiGPottF/6LiJ0pcTeMdVao5gHWUzWT1IVN379+mDJH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\nSZIkqTETC0mSJEmN/X+Lsz2ccoAbCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72007a3390>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 287,
       "width": 395
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_data = waiting_proportion.as_matrix()\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.hist(_data, normed=True, bins=np.arange(0,1,0.05))\n",
    "ax.set_title(\"Time exclusively waiting\")\n",
    "ax.set_xlabel(\"Proportion of load time\")\n",
    "ax.set_ylabel(\"Normalized density\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How much remains unused\n",
    "\n",
    "This section explores which part of the loading time of web pages goes empty, meaning that no data is being received by the browser."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deducing the intervals from the timings\n",
    "\n",
    "Using the 'timings' key in the dataset entries, and the started time, let's deduce an interval as a tuple with the start and end time in milliseconds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below takes all the intervals, merges and summarizes them. However, instead of looking to the time when data is being received, we look to the part when data is **not** being received. We call those intervals when the browser is not receiving data from the network, *voids*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how it looks for a single entry in the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(781.1789994761343, 782.8819367736577),\n",
       " (788.6800003424287, 789.0669974684715),\n",
       " (795.882000371814, 796.6599555388093),\n",
       " (802.104999423027, 804.4668221399189),\n",
       " (805.353000551462, 807.748070381582)]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_entries = dataset[0]\n",
    "\n",
    "summary = summarize_intervals_from_entries(file_entries)\n",
    "summary[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the list above, the numbers represent milliseconds since the start of the page fetch. Each tuple is a separate interval where the browser is receiving data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shimmercat data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clean_entry(src_entry):\n",
    "    trg_entry = {}\n",
    "    trg_entry['timings'] = src_entry['timings']\n",
    "    trg_entry['startedDateTime'] = src_entry['startedDateTime']\n",
    "    trg_entry['transferSize'] = src_entry['response']['_transferSize']\n",
    "    \n",
    "    return trg_entry\n",
    "\n",
    "def clean_har_record(har_record):\n",
    "    entries = har_record['entries']\n",
    "    clean_entries = []\n",
    "    is_first_entry = True \n",
    "    for e in entries:\n",
    "        ee = clean_entry(e)\n",
    "        if is_first_entry:\n",
    "            timing_ref = e['pageref']\n",
    "            for page in har_record['pages']:\n",
    "                if page['id'] == timing_ref:\n",
    "                    timing_data = page['pageTimings']\n",
    "            is_first_entry = False\n",
    "        clean_entries.append( ee )\n",
    "    obj = {\n",
    "        'entries': clean_entries,\n",
    "        'pageTimings': timing_data\n",
    "    }\n",
    "    return obj\n",
    "\n",
    "def fetch_and_standardize_entries(filename):\n",
    "    data = json.load(open(filename))['log']\n",
    "    decorate_all_timing_entries(data)\n",
    "    o_data = clean_har_record(data)\n",
    "    return o_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.827609755070423"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shimmercat_data = fetch_and_standardize_entries( 'data/www.shimmercat.com.har' ) \n",
    "\n",
    "average_waiting_until_load(shimmercat_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09932061822044232"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportion_just_waiting(shimmercat_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data for some other sites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47653730750799056"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wikipedia_data = fetch_and_standardize_entries( 'data/en.wikipedia.org.har' )\n",
    "\n",
    "proportion_just_waiting(wikipedia_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2824579220730195"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "so_data = fetch_and_standardize_entries('data/stackoverflow.com.har')\n",
    "\n",
    "proportion_just_waiting(so_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11885837530498636"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mozilla_data = fetch_and_standardize_entries('data/developer.mozilla.org.har')\n",
    "proportion_just_waiting(mozilla_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1141.3018913536798"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exclusively_waiting_total(mozilla_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "175.49685073934975"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exclusively_waiting_total(shimmercat_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1472.8008534681883"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exclusively_waiting_total(so_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "518.7080000001589"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exclusively_waiting_total(wikipedia_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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