mhawksey/FL Data Processing.ipynb

## FL Data Processing.ipynb
{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FutureLearn Stats Recipes\n",
    "\n",
    "*Tony Hirst, The Open University / @psychemedia / OUseful.info blog*\n",
    "\n",
    "*This work has been independently produced as a recreational data activity. The author is not affiliated with FutureLearn Ltd or any Open University learning analytics projects.*\n",
    "\n",
    "*The notebook is licensed as a Creative Commons CC-BY work and code contained within it is licensed under an (attribution requiring) `MIT License`.*\n",
    "\n",
    "This notebook contains a few recipes, sketches and doodles around FutureLearn course stats made available to partners. It has been tested using the `psychemedia/ou-tm351-pystack` Docker container on Docker Hub.\n",
    "\n",
    "Note that the notebooks may not be rendered or not work properly in Internet Explorer: try Chrome, Firefox or Safari instead.\n",
    "\n",
    "This notebook will analyse data from four data files made available by FutureLearn (and updated on a daily basis) for each course:\n",
    "\n",
    "- *COURSE_enrolments.csv*: data about role, enrolments, unenrolments and full participation dates;\n",
    "- *COURSE_step-activity.csv*: record of when each step was first visited by each user and if/when it was completed;\n",
    "- *COURSE_comments.csv*: record of each comment along with the time it was posted, who it was posted by, how many likes it has, and whether it was part of a thread;\n",
    "- *COURSE_question-response.csv*: each question attempt by each learner is recorded, along with the answer submitted and whether it was the correct answer.\n",
    "\n",
    "To analyse the data, click in this area and then press the play button in the toolbar (or hit *shift-Return*) to run each cell in turn.\n",
    "\n",
    "You can tell when a code cell has been run because the `In[]` indicator will be populated by a number showing the order in which the cells were run.\n",
    "\n",
    "A `[*]` indicator shows the current cell (or preceding cells if multiple cells are run from the `Cell` menu) is currently being executed.\n",
    "\n",
    "As well as interactively executable code cells, Jupyter notebooks also support a range of interactive widgets that can be used to create interactive dashboard like displays.\n",
    "\n",
    "Once you have run the notebook, you can save an HTML version of it from the `File` menu."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialisation\n",
    "\n",
    "We need to load in some libraries and utility functions that we will use to analyse and manipulate the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
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   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as tkr\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.lines as mlines\n",
    "from matplotlib.patches import Rectangle\n",
    "import random\n",
    "from ipywidgets import widgets, interact\n",
    "from datetime import date, timedelta\n",
    "import datetime as dt\n",
    "from IPython.display import HTML, display\n",
    "\n",
    "def offsetDays(date_str,offset):\n",
    "    ''' Return a date a specified number of days before a particular date'''\n",
    "    if offset<0:\n",
    "        return (pd.to_datetime(date_str)-timedelta(days=abs(offset))).date().strftime('%Y-%m-%d')\n",
    "    return (pd.to_datetime(date_str)+timedelta(days=offset)).date().strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## USER SETTINGS\n",
    "\n",
    "The notebook is self-contained and should be able to generate reports from standard FutureLearn data files.\n",
    "\n",
    "There are however a few settings that are required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Data\n",
    "\n",
    "Download and unzip the data files into a folder contained in the same directory as this notebook file. Also make note of the course name stub used as part of each filename.\n",
    "\n",
    "FutureLearn provide a CSV each day, generated at midnight, so you may want to grab the data frequently during the course, or just analyse it all at the end of the course.\n",
    "\n",
    "Note that it make take some time to load the data in. Also note that the data is held in memory so a low powered machine may struggle for large datasets.\n",
    "\n",
    "For example, if your filenames are of the form `learn-to-code-1_enrolments.csv` in the folder `learn-to-code-1` use the settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#CHANGE THESE SETTINGS FOR YOUR OWN DATA FILE\n",
    "#The settings point to files in a location reataive to the notebook: PATH/STUB_comments.csv etc\n",
    "#For example: learn-to-code-1/learn-to-code-1_comments.csv\n",
    "#PATH='blended-learning-getting-started-1'\n",
    "#STUB='blended-learning-getting-started-1'\n",
    "PATH='blended-learning-digital-skills-2'\n",
    "STUB='blended-learning-digital-skills-2'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notable Dates and Steps\n",
    "\n",
    "Some reports can be highlighted around notable dates (such as the course registration opening date, or start date) or steps (for example, steps where exercises or activities are defined or social steps with particular calls for comment).\n",
    "\n",
    "Identify any notable dates or steps here in order for these to be highlighted automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
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   "outputs": [],
   "source": [
    "#CHANGE THESE SETTINGS FOR YOUR OWN DATA FILE\n",
    "#To help generate useful reports, identify key dates for your course in the form: YYYY-MM-DD\n",
    "COURSE_OPEN_REGISTRATION='2018-02-01'\n",
    "COURSE_START_DATE='2018-04-16'\n",
    "COURSE_END_DATE='2018-04-30'\n",
    "\n",
    "#COURSE_OPEN_REGISTRATION='2016-01-26'\n",
    "#COURSE_START_DATE='2016-02-01'\n",
    "#COURSE_END_DATE='2016-02-21'\n",
    "\n",
    "#We can also define relative timestamps, for example, five days prior to the start date\n",
    "COURSE_PRE_START_MAILINGDATE=offsetDays(COURSE_START_DATE,5)\n",
    "\n",
    "#Set a course name to refer to in graphs/tables\n",
    "COURSE_SHORTNAME='BLE3r2'\n",
    "\n",
    "\n",
    "\n",
    "#Add notable dates to the SPECIAL_DATES list. If there none, set: SPECIAL_DATES=[]\n",
    "#SPECIAL_DATES=[COURSE_OPEN_REGISTRATION,\n",
    "#               COURSE_START_DATE,\n",
    "#               COURSE_END_DATE,\n",
    "#               COURSE_PRE_START_MAILINGDATE ]\n",
    "SPECIAL_DATES=[COURSE_START_DATE,\n",
    "               COURSE_END_DATE]\n",
    "\n",
    "NUMBER_OF_STUDY_WEEKS=2\n",
    "\n",
    "#The SESSION_GAP specifies the minumim time period that distinguishes between two study sessions\n",
    "SESSION_GAP=65\n",
    "\n",
    "THEME_COL = 'deeppink'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#CHANGE THESE SETTINGS FOR YOUR OWN DATA FILE\n",
    "#If you have any exercise or activity steps, they can be highlighted in some step reports\n",
    "#The steps are defined in the form WSS, where W is the week number and SS the step number (with leading 0)\n",
    "#For example, week 1 step 2 is written: 102; week 3 step 12 is written: 312\n",
    "#To highlight those steps, set: EXERCISE_STEPS=[102, 312]\n",
    "#If you do not want to distinguish any steps, use an empty list: EXERCISE_STEPS=[]\n",
    "#EXERCISE_STEPS=[108,207,212,306,315,407,413]\n",
    "EXERCISE_STEPS=[217]\n",
    "\n",
    "#SOCIAL_STEPS may be used to define steps where a social activity is prompted\n",
    "#If you do not want to define any steps, use an empty list: SOCIAL_STEPS=[]\n",
    "#The steps can then be used to highlight particular steps in comment reports\n",
    "#SOCIAL_STEPS=[101,105,108,207,212,217,306,322,405,407,413]\n",
    "SOCIAL_STEPS=[]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the code should \"just work\" without further modification, although you can edit and add code cells, as well as markdown/text commentary cells to the notebook, as you see fit.\n",
    "\n",
    "YOU SHOULD NOT NEED TO MAKE ANY FURTHER CHANGES TO THE NOTEBOOK BELOW THIS LINE (UNLESS YOU WANT TO!)\n",
    "\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now automatically generate some more dates of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "STUDY_WEEK_DATES=[]\n",
    "for i in range(NUMBER_OF_STUDY_WEEKS):\n",
    "    STUDY_WEEK_DATES.append(offsetDays(COURSE_START_DATE,7*i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load in the Data\n",
    "\n",
    "Use the specified data file locations to load the data in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>learner_id</th>\n",
       "      <th>enrolled_at</th>\n",
       "      <th>unenrolled_at</th>\n",
       "      <th>role</th>\n",
       "      <th>fully_participated_at</th>\n",
       "      <th>purchased_statement_at</th>\n",
       "      <th>gender</th>\n",
       "      <th>country</th>\n",
       "      <th>age_range</th>\n",
       "      <th>highest_education_level</th>\n",
       "      <th>employment_status</th>\n",
       "      <th>employment_area</th>\n",
       "      <th>detected_country</th>\n",
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       "  </thead>\n",
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       "      <td>NaT</td>\n",
       "      <td>learner</td>\n",
       "      <td>2018-06-19 05:25:29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>2018-05-31 22:45:19</td>\n",
       "      <td>NaT</td>\n",
       "      <td>learner</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-06-21 11:03:07 UTC</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>GB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3df65169-d469-40bf-9c6a-347128e771ac</td>\n",
       "      <td>2018-05-31 15:41:09</td>\n",
       "      <td>NaT</td>\n",
       "      <td>learner</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             learner_id         enrolled_at unenrolled_at  \\\n",
       "0  55a55027-d633-4d60-8e1c-3c87e464ece0 2018-06-04 06:46:08           NaT   \n",
       "1  1ebfe044-5815-41fa-addb-84fc954b172b 2018-05-31 22:45:19           NaT   \n",
       "2  3df65169-d469-40bf-9c6a-347128e771ac 2018-05-31 15:41:09           NaT   \n",
       "\n",
       "      role fully_participated_at   purchased_statement_at   gender  country  \\\n",
       "0  learner   2018-06-19 05:25:29                      NaN  Unknown  Unknown   \n",
       "1  learner                   NaT  2018-06-21 11:03:07 UTC  Unknown  Unknown   \n",
       "2  learner                   NaT                      NaN  Unknown  Unknown   \n",
       "\n",
       "  age_range highest_education_level employment_status employment_area  \\\n",
       "0   Unknown                 Unknown           Unknown         Unknown   \n",
       "1   Unknown                 Unknown           Unknown         Unknown   \n",
       "2   Unknown                 Unknown           Unknown         Unknown   \n",
       "\n",
       "  detected_country  \n",
       "0               OM  \n",
       "1               GB  \n",
       "2               NL  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load in the data - enrolments file\n",
    "enrolments=pd.read_csv('{}/{}_enrolments.csv'.format(PATH.strip('/'),STUB),parse_dates=[1,2,4])\n",
    "\n",
    "#Preview the data\n",
    "enrolments.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>learner_id</th>\n",
       "      <th>step</th>\n",
       "      <th>week_number</th>\n",
       "      <th>step_number</th>\n",
       "      <th>first_visited_at</th>\n",
       "      <th>last_completed_at</th>\n",
       "      <th>flstep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0a978cc7-a522-4ab3-97a6-015bed98bb9c</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-02-20 15:02:52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c6b13ae2-a106-45a6-9590-1b95455e44a9</td>\n",
       "      <td>1.1</td>\n",
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       "      <td>1</td>\n",
       "      <td>2018-03-20 11:08:33</td>\n",
       "      <td>NaT</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c6e1e640-ec13-4478-92d2-80deb71ce373</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-21 15:14:30</td>\n",
       "      <td>2018-04-16 21:12:37</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                             learner_id step  week_number  step_number  \\\n",
       "0  0a978cc7-a522-4ab3-97a6-015bed98bb9c  1.1            1            1   \n",
       "1  c6b13ae2-a106-45a6-9590-1b95455e44a9  1.1            1            1   \n",
       "2  c6e1e640-ec13-4478-92d2-80deb71ce373  1.1            1            1   \n",
       "\n",
       "     first_visited_at   last_completed_at  flstep  \n",
       "0 2018-02-20 15:02:52                 NaT     101  \n",
       "1 2018-03-20 11:08:33                 NaT     101  \n",
       "2 2018-03-21 15:14:30 2018-04-16 21:12:37     101  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load in the data - steps file - THIS MAY TAKE SOME TIME\n",
    "steps=pd.read_csv('{}/{}_step-activity.csv'.format(PATH.strip('/'),STUB),\n",
    "                  parse_dates=[4,5],\n",
    "                  dtype={'step':object})\n",
    "\n",
    "#Let's generate an ordered numerical key for the week/step combinations that lets us easily distinguish weeks\n",
    "steps['flstep']=steps['week_number']*100+steps['step_number']\n",
    "\n",
    "#Preview the data\n",
    "steps.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
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   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
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       "      <th>parent_id</th>\n",
       "      <th>step</th>\n",
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       "      <th>text</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>likes</th>\n",
       "      <th>first_reported_at</th>\n",
       "      <th>first_reported_reason</th>\n",
       "      <th>moderation_state</th>\n",
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       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
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       "      <td>I'm in a very different digital landscape at t...</td>\n",
       "      <td>2018-04-16 03:51:31</td>\n",
       "      <td>6</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hi, I'm Marco.  I am a course designer and a t...</td>\n",
       "      <td>2018-04-16 05:43:09</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "         id                             author_id  parent_id step  \\\n",
       "0  26989963  82431c12-a923-40ae-8425-2e044cb70793        NaN  1.2   \n",
       "1  26990297  24d31d8e-bfc5-4879-ab09-b0560bad827b        NaN  1.2   \n",
       "2  26991271  89c9df8c-571b-4a61-a263-f072e4b6c52a        NaN  1.1   \n",
       "\n",
       "   week_number  step_number  \\\n",
       "0            1            2   \n",
       "1            1            2   \n",
       "2            1            1   \n",
       "\n",
       "                                                text           timestamp  \\\n",
       "0  On first reading I am not able to identify ind... 2018-04-16 03:09:15   \n",
       "1  I'm in a very different digital landscape at t... 2018-04-16 03:51:31   \n",
       "2  Hi, I'm Marco.  I am a course designer and a t... 2018-04-16 05:43:09   \n",
       "\n",
       "   likes  first_reported_at  first_reported_reason  moderation_state  \\\n",
       "0      2                NaN                    NaN               NaN   \n",
       "1      6                NaN                    NaN               NaN   \n",
       "2      6                NaN                    NaN               NaN   \n",
       "\n",
       "   moderated  flstep  \n",
       "0        NaN     102  \n",
       "1        NaN     102  \n",
       "2        NaN     101  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load in the data - comments file\n",
    "comments=pd.read_csv('{}/{}_comments.csv'.format(PATH.strip('/'),STUB),parse_dates=[7],dtype={'step': object})\n",
    "\n",
    "#The step as defined is not brilliant for sorting - create a new step indexing scheme\n",
    "comments['flstep']=comments['week_number']*100+comments['step_number']\n",
    "\n",
    "#Preview the data\n",
    "comments.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": 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>learner_id</th>\n",
       "      <th>quiz_question</th>\n",
       "      <th>question_type</th>\n",
       "      <th>week_number</th>\n",
       "      <th>step_number</th>\n",
       "      <th>question_number</th>\n",
       "      <th>response</th>\n",
       "      <th>cloze_response</th>\n",
       "      <th>submitted_at</th>\n",
       "      <th>correct</th>\n",
       "      <th>qid</th>\n",
       "      <th>qidr</th>\n",
       "      <th>flstep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>325a784d-e4b0-4709-8f95-389b1789c4e1</td>\n",
       "      <td>2.17.1</td>\n",
       "      <td>MultipleChoice</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-16 14:30:34 UTC</td>\n",
       "      <td>False</td>\n",
       "      <td>217.01</td>\n",
       "      <td>2.17.1.3</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>325a784d-e4b0-4709-8f95-389b1789c4e1</td>\n",
       "      <td>2.17.1</td>\n",
       "      <td>MultipleChoice</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-16 14:30:38 UTC</td>\n",
       "      <td>False</td>\n",
       "      <td>217.01</td>\n",
       "      <td>2.17.1.2</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>325a784d-e4b0-4709-8f95-389b1789c4e1</td>\n",
       "      <td>2.17.1</td>\n",
       "      <td>MultipleChoice</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-16 14:30:41 UTC</td>\n",
       "      <td>True</td>\n",
       "      <td>217.01</td>\n",
       "      <td>2.17.1.4</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             learner_id quiz_question   question_type  \\\n",
       "0  325a784d-e4b0-4709-8f95-389b1789c4e1        2.17.1  MultipleChoice   \n",
       "1  325a784d-e4b0-4709-8f95-389b1789c4e1        2.17.1  MultipleChoice   \n",
       "2  325a784d-e4b0-4709-8f95-389b1789c4e1        2.17.1  MultipleChoice   \n",
       "\n",
       "   week_number  step_number  question_number response cloze_response  \\\n",
       "0            2           17                1        3            NaN   \n",
       "1            2           17                1        2            NaN   \n",
       "2            2           17                1        4            NaN   \n",
       "\n",
       "              submitted_at correct     qid      qidr  flstep  \n",
       "0  2018-04-16 14:30:34 UTC   False  217.01  2.17.1.3     217  \n",
       "1  2018-04-16 14:30:38 UTC   False  217.01  2.17.1.2     217  \n",
       "2  2018-04-16 14:30:41 UTC    True  217.01  2.17.1.4     217  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load in the quiz response data - THIS MAY TAKE SOME TIME\n",
    "qnresp=pd.read_csv('{}/{}_question-response.csv'.format(PATH.strip('/'),STUB),parse_dates=[6])\n",
    "\n",
    "#Create a simple sortable numeric id for each quiz question number\n",
    "qnresp['qid']=(100*qnresp['week_number'])+qnresp['step_number']+qnresp['question_number']/100\n",
    "\n",
    "#Create a key for a particular response\n",
    "qnresp['qidr']=qnresp['quiz_question']+'.'+qnresp['response'].apply(str)\n",
    "\n",
    "#Create the week/step lookup\n",
    "qnresp['flstep']=qnresp['week_number']*100+qnresp['step_number']\n",
    "\n",
    "#Preview the data\n",
    "qnresp.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Reporting\n",
    "\n",
    "Let's start with some simple reports that describe the overall participation state of the course. It will be convenient to create several lists of user IDs that represent different populatations, such as learners who have fully participated, learners who have completed at least one step, or learners who have commented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BLE3r2 Assigned roles: learner, organisation_admin.\n"
     ]
    }
   ],
   "source": [
    "#Roles available\n",
    "print(\"{} Assigned roles: {}.\".format(COURSE_SHORTNAME, ', '.join(([r for r in enrolments['role'].unique()]))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple function will pull out the unique identifiers of people with a particular role:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def usersByRole(role):\n",
    "    return set(enrolments[enrolments['role']==role]['learner_id'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also create some canned groups that may be useful later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Create a range of lists of user IDs for users with different states of enrolment\n",
    "enrolled_learners=usersByRole('learner')\n",
    "\n",
    "unenrolled_learners=set(enrolments[(enrolments['unenrolled_at'].notnull()) &\n",
    "                               (enrolments['role']=='learner')]['learner_id'])\n",
    "\n",
    "fullypart_learners=set(enrolments[(enrolments['fully_participated_at'].notnull()) &\n",
    "                              (enrolments['role']=='learner')]['learner_id'])\n",
    "\n",
    "statement_learners=set(enrolments[(enrolments['purchased_statement_at'].notnull()) &\n",
    "                              (enrolments['role']=='learner')]['learner_id'])\n",
    "\n",
    "#Create a list of learners who have visited at least one step\n",
    "stepstart_learners=set(steps[steps['first_visited_at'].notnull()]['learner_id'])\n",
    "\n",
    "#Create a list of learners who have completed at least one step\n",
    "stepcomplete_learners=set(steps[steps['last_completed_at'].notnull()]['learner_id'])\n",
    "\n",
    "#Create a list of learners who have commented at least once\n",
    "commenting_learners=set(comments[comments['author_id'].isin(enrolled_learners)]['author_id'])\n",
    "\n",
    "#Create a list of learners who have completed at least one quiz element\n",
    "quiz_learners=set(qnresp[qnresp['learner_id'].isin(enrolled_learners)]['learner_id'])\n",
    "\n",
    "#Define a simple dict to record the groups and a way of retrieving the members of each group by name\n",
    "#These groups can be automatically referenced from some interactive chart controls\n",
    "grouplist=['enrolled_learners','unenrolled_learners','fullypart_learners',\n",
    "           'stepstart_learners','stepcomplete_learners','commenting_learners','quiz_learners']\n",
    "groupLookup=lambda x: globals()[x]\n",
    "\n",
    "#Display the enrolment/unenrollment/fully participating counts\n",
    "#If dates are specified, use the form YYYY-MM-DD\n",
    "def date_limiter(df, start=None, end=None, index=None):\n",
    "    if index is not None:\n",
    "        df=df.set_index(index)\n",
    "    df.sort_index(inplace=True)\n",
    "    if start is not None and end is not None:\n",
    "        df=df[start:end]\n",
    "    elif start is not None:\n",
    "        df=df[start:]\n",
    "    elif end is not None:\n",
    "        df=df[:end]\n",
    "    return df\n",
    "\n",
    "#http://stackoverflow.com/a/10567298/454773\n",
    "def timeOfDay_limiter(df, start=None, end=None, index=None):\n",
    "    if index is not None:\n",
    "        df=df.set_index(index)\n",
    "        df.sort_index(inplace=True)\n",
    "    hour = df.index.hour\n",
    "    if start is not None and end is not None:\n",
    "        selector = ((start <= hour) & (hour <= end))\n",
    "        df = df[selector]\n",
    "    elif start is not None:\n",
    "        selector = ((start <= hour) & (hour <= 23))\n",
    "        df = df[selector]\n",
    "    elif end is not None:\n",
    "        selector = ((0 <= hour) & (hour <= end))\n",
    "        df = df[selector]\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now do some simple counting around those sets of individuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enrolled learners: 1,582\n",
      "Fully participating learners: 186\n",
      "Unenrolled learners: 102\n",
      "Enrolled users who completed at least one step: 516\n",
      "Enrolled users who visited at least one step: 949\n",
      "Enrolled users who did not visit at least one step: 633\n",
      "Unenrolled users who completed at least one step: 23\n",
      "Unenrolled users who visited at least one step: 54\n",
      "Unenrolled users who did not visit at least one step: 48\n",
      "Enrolled learners who commented: 189\n",
      "Enrolled learners who did not comment: 1,393\n",
      "Fully participating learners who commented: 73\n",
      "Fully participating learners who did not comment: 116\n",
      "Unenrolled learners who commented: 8\n",
      "Unenrolled learners who did not comment: 181\n"
     ]
    }
   ],
   "source": [
    "print('Enrolled learners: {:,}'.format(len(enrolled_learners)))\n",
    "print('Fully participating learners: {:,}'.format(len(fullypart_learners)))\n",
    "print('Unenrolled learners: {:,}'.format(len(unenrolled_learners)))\n",
    "\n",
    "print('Enrolled users who completed at least one step: {:,}'.format(len(enrolled_learners.intersection(stepcomplete_learners))))\n",
    "print('Enrolled users who visited at least one step: {:,}'.format(len(enrolled_learners.intersection(stepstart_learners))))\n",
    "print('Enrolled users who did not visit at least one step: {:,}'.format(len(enrolled_learners.difference(stepstart_learners))))\n",
    "\n",
    "print('Unenrolled users who completed at least one step: {:,}'.format(len(unenrolled_learners.intersection(stepcomplete_learners))))\n",
    "print('Unenrolled users who visited at least one step: {:,}'.format(len(unenrolled_learners.intersection(stepstart_learners))))\n",
    "print('Unenrolled users who did not visit at least one step: {:,}'.format(len(unenrolled_learners.difference(stepstart_learners))))\n",
    "\n",
    "print('Enrolled learners who commented: {:,}'.format(len(commenting_learners)))\n",
    "print('Enrolled learners who did not comment: {:,}'.format(len(enrolled_learners.difference(commenting_learners))))\n",
    "print('Fully participating learners who commented: {:,}'.format(len(commenting_learners.intersection(fullypart_learners))))\n",
    "print('Fully participating learners who did not comment: {:,}'.format(len(commenting_learners.difference(fullypart_learners))))\n",
    "print('Unenrolled learners who commented: {:,}'.format(len(commenting_learners.intersection(unenrolled_learners))))\n",
    "print('Unenrolled learners who did not comment: {:,}'.format(len(commenting_learners.difference(unenrolled_learners))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Course Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    In BLE3r2 of the 1,582 registered participants, 949 (60%) visited at least one step (active participants). 516 (54%) of active participants \n",
       "    completed at least one step, 189 (20%) commented at least once, 137 (14%) completed at least one quiz, 186 (20%) fully participated \n",
       "    and of these 22 (12%) purchased a statement.\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def summary_report():\n",
    "    total = len(enrolled_learners.intersection(stepstart_learners))\n",
    "    report='''\n",
    "    In {} of the {:,} registered participants, {:,} ({:.0f}%) visited at least one step (active participants). {:,} ({:.0f}%) of active participants \n",
    "    completed at least one step, {:,} ({:.0f}%) commented at least once, {:,} ({:.0f}%) completed at least one quiz, {:,} ({:.0f}%) fully participated \n",
    "    and of these {:,} ({:.0f}%) purchased a statement.\n",
    "    '''.format(COURSE_SHORTNAME,\n",
    "              len(enrolled_learners),\n",
    "              len(enrolled_learners.intersection(stepstart_learners)),\n",
    "              100*len(enrolled_learners.intersection(stepstart_learners))/len(enrolled_learners), \n",
    "              len(enrolled_learners.intersection(stepcomplete_learners)),\n",
    "              100*len(enrolled_learners.intersection(stepcomplete_learners))/total,\n",
    "              len(commenting_learners),\n",
    "              100*len(commenting_learners)/total,\n",
    "              len(quiz_learners),\n",
    "              100*len(quiz_learners)/total,\n",
    "              len(fullypart_learners),\n",
    "              100*len(fullypart_learners)/total,\n",
    "              len(statement_learners),\n",
    "              100*len(statement_learners)/len(fullypart_learners))\n",
    "    return report\n",
    "\n",
    "HTML(summary_report())\n",
    "#HTML(summary_report(len(enrolled_learners.intersection(stepcomplete_learners))))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Looking at these figures as a graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ro0d55ZVXjK/79+/Pl19+edtr7jZXf0VkZCTTpk17oGOIiIiI/BM8ko+7NBgMrFq1Cjc3\nNwDy8/NZv349QUFBeHp6Ur169fvuOyAggICAgDu2S0lJue8x7kV+fj4Gg+GBj3PlyhVyc3ONr8PD\nw+94zd3m6q9ISUlBX6UgIiIi96WkFWx5GTJzoMVK023G1oehnpCfDwYDpGZCzQVQ2hpm+UNTF8jJ\nh+gTELQT8oHmVQquq+4A59IgZC+sO/lQb+1+PJIz9vn5+YWKPYPBQEBAAPb29pw4cQKA1NRURo4c\nSYMGDWjRogVhYWHG9mlpaQwfPhxvb29eeOEFQkND8ff3B2DNmjV07doVgPPnz9OzZ0/q1atH69at\nmTp1KgAfffQRhw4dYvLkyUyePBmAAwcO8OKLL+Lj40O3bt344YcfjOO5u7szceJE6tWrR1hYGHl5\necYxGzZsyJgxY0hL++Ob1xYvXkzjxo3x8/NjyZIlt8xDVFQUvXv3ZtiwYXh4eBAQEEBMTIzx/IYN\nG+jSpQu+vr74+voyfvx44zl/f3/GjRuHn58fEyZMYMCAAaSkpODp6UlqaiqBgYFERkYCcOHCBQYN\nGoSXlxdNmzZl8eLFxvFv5Co0NJQRI0bQs2dPPDw86NatG8eOHTOO98UXXxAQEIC3tzeNGjUiNDS0\nUH4iIiLw9/fH19eX//znP+Tk5LB582bmzZvHtm3bePnll425ad68OX5+fgQGBvLzzz/fMj8iIiLy\nD1a3PKztDE873r5dvUpwOAFeWgddv4LXNxQcH+IBbarBxBiY//+gZ2145Vmws4FFbSE9G/pvggvp\nMLc1VH5wXyz1d3kkC/s/y87O5osvviAzM5O6desCMHLkSKysrNi+fTsRERFER0cTFRUFwMSJE7l2\n7Ro7d+7ks88+Izo62jgrbjAYjL9/8sknPPPMM+zfv5+IiAi+/vprYmJiGD16NF5eXgQFBREUFMS5\nc+cYNGgQgwcPZt++ffTp04cBAwZw5coVY4xZWVnExMTw2muv8fnnn7Nt2zaWL1/Oli1byMjIIDg4\nGIAdO3YQFhbG559/zs6dOzl16tRt7z0mJgZPT08OHjxInz59GDp0KCkpKcTHxzN27FgmTpzIvn37\nWLZsGevXr2fv3r3Ga8+fP8+uXbsYOXIk4eHhODo6Ehsbi729faExhg0bRoUKFYiJiSEiIoIFCxaw\nZ88eY75u2LRpE927d+fgwYM0adKEwYMHk5OTw8GDBwkLC2Pu3LkcPHiQmTNnMmfOHOLi4ozX7t27\nlw0bNrBixQp2797N5s2bad26NYMGDaJFixasXLmS33//nVmzZrF8+XL27t2Lr68vISEh9/aPRURE\nRP4ZNr8EZ69C4rVbt7GyKHgD4GoPKwJgctOCGXkACwNk5UJMPBy4UDCjn5lb8CnAtP0wZjdsOQMb\nfwMrA1Qu81Bu6694ZAv7V155hXr16lG3bl28vLzYv38/S5YsoUKFCiQmJrJ7927ee+89SpQogbOz\nM3379mXlypVkZ2fzzTffMGLECEqXLo2Liwt9+vQxOUaJEiU4cOAAmzZtolSpUmzfvp369esXabd+\n/Xr8/Pzw9/fHwsKCNm3aULNmTb755htjm/bt22NpaUmpUqVYvXo1Q4YMoUKFCpQqVYoRI0awbt06\nsrKy2LhxIx07duTpp5+mRIkSvPvuu7fNg6urK7169cLS0pLOnTvj4uLCjh07qFChAuvXr6d27dpc\nvnyZlJQU7O3tC234bd26NTY2NpQuXfqW/Z89e5Yff/yR//znP9jY2FClShWWLFnCs88+W6Rt/fr1\nef7557G0tOSNN94gPT2d2NhY6tSpw+rVq3FxcSEpKYns7GxsbW0LxdKrVy9KliyJq6srHh4enD59\nukj/VlZW5OTksHz5co4dO8aQIUOIiIi4bX5ERETkH6rFCuizCa7n3rqNc2n4JRmWH4UeGwqW4nz+\nPJSygk8PwelU+L4HrOkEu8/CmuOQcA1C/wc/XILypWBAXUjJhMOXHt693adHco09wIoVK3BzcyM+\nPp6hQ4fi6OhInTp1ADh37hz5+fm0atXKuEY9Ly8PBwcHUlNTyczMpGLFisa+nJ2dTY4xZswYZs+e\nzSeffMI777xDkyZNmDRpEk5OToXa3Zj5rlevHlCwVCgnJwcfHx9jmyeffLJQ+6CgICwtLY3tbWxs\nOH/+PImJiYWK5goVKhjbmVKlSpVCrytWrMilS5ewtLRkxYoVrF69mtKlS1OrVi1ycnIKLWEqV67c\nLfu9ISkpiVKlShUq/m/sbbhdLBYWFpQvX57ExEQMBgNz5sxh8+bNPPnkk9SuXdt43zc4Ov7xMZmV\nlRV5eXlF+nd2diY8PJyFCxeyZMkSHBwcGDZsGF26dLnjfYiIiMg/zE+Jd27z+1VoddPa+0U/woeN\noE45aOAM7mVhyBYoYwMhTeAtL5h1qKCt6xOwsiNUKgO9vobrOfcUnpNTGcqVe7jLdx7Zwv5GUfjU\nU08xZ84cOnXqROXKlRk4cCDly5fHysqKPXv2YGVVcAtXr14lPT0dJycnSpQowfnz541LTi5cuGBy\njF9++YX+/fszcuRI4uLiGD16NJ9++ikTJkwo1K5cuXK0b9++0LKQs2fPFipWb16yUr58eT788EN8\nfX2BgkdnxsXF4eLiQvny5YmPjze2TUpKKrSp9c8SEhIKvY6Pj6d9+/Zs2LCBTZs2sW7dOuMbkZYt\nWxZqezebcitUqMC1a9dIS0ujTJmCj5g2bNjAE088cdtYcnNzSUhIoGLFiixatIgTJ06wbds2Spcu\nTU5ODhs2bLjj2H+WnJxMqVKlCA8PJysri02bNhEUFETjxo3v6k2KiIiISCE1HOAFN/jqVzh9pWBp\nDhQswelUE85cgVXHC46NrActqhQU9jUdC2bxS1pBj/WwM+7WY9xCcnIaly5dLXL8QRb7j+xSnJs5\nOzszatQoZs+ezfHjx6lYsSLe3t5MmTKFzMxMLl++zNChQ/nkk0+wsLCgQ4cOzJw5k7S0NOLj442b\nQf/ss88+Y+rUqWRlZeHk5ISVlZWxWLexsSE9PR0oWGazfft248bVQ4cO0aFDB3788UeT/Xbs2JHQ\n0FAuXbpEdnY2M2bMoH///gB06NCBtWvX8uOPP5KZmXnHRz0eO3aMtWvXkpuby5dffsmlS5do1qwZ\naWlpWFlZYWVlRVZWFuHh4cTHx5OdnW2yHxsbGzIzM8nJKfxu80YuZ8yYQVZWFqdPnyYkJMT4hulm\nu3btIiYmhpycHEJDQ3F0dMTDw4O0tDSsra2xsrIiPT2dkJAQcnJyiox1q7hubCw+d+4cvXv35siR\nI9jY2ODg4ICtrS0lS5a8Yz8iIiIiAFSxg8aVC4pygwH+Uw+mNCso8PvUgROX4X8J8NMlqGYP/f5d\nsJH2yZIQmwC2lhD5QsHrOf+DvPyC/pxsi/vO7uiRLOxNzTR37twZX19fRo8eTX5+PtOnTycpKQl/\nf3+ef/55KlWqxLhx4wCM68UbN27MoEGD8PHxwdraukifEyZMICEhgUaNGuHv70/FihUZOHAgUPCo\nx/nz5zNu3DiqVq3KzJkzmTZtGl5eXowaNYrRo0fj5+dnMt6BAwfi7e1Nt27daNCgAT/99BPz58/H\nwsKC+vXrM3LkSN58800aN25MxYoVsbGxuWUu3Nzc2LFjB35+fqxcuZIFCxZgZ2dH586dqVGjBs2b\nN8ff358jR47QqlUrfvvtN5MxPfPMM9SoUQNfX1/i4uIKnZ8+fToJCQk0btyYPn368Oabb5rca1C3\nbl0WLFiAr68vsbGxhIWFYTAY6N27N5aWlsY1+NnZ2Xh6enLy5EmTsdz8ulmzZhw/fpy2bdtSu3Zt\n3n33Xd588008PDyYMmUKM2fONH6SICIiImLSzU/OftkdVnUs2DD7awoM2lzwRJs5LSE+7Y+n4oza\nBVG/FszUD/WEZUdg8j7oUANc/m9W/T/14MsOBT8+FYsM+6gx5JvhQ8QPHjxInTp1KFGiBADLly9n\n3bp1LF++vJgjuzdRUVFERkb+LV/M9VeFhoby66+/MmvWrOIOpYjjTuOoaVm+uMMQERGRR4GFAb7t\nBi+uhcTrxRLC8dwEkr/uhJvb00XO/eOX4tyrefPmMXfuXPLz80lISGDFihU0adKkuMMSERERkQet\n09Mw40CxFfXF6ZHdPPtXTJgwgfHjx1OvXj1sbGwICAigX79+xR2WiIiIiDxoa44XdwTFxiyX4sg/\ni5biiIiIyKNES3FEREREROS+qbAXERERETEDKuxFRERERMyAWW6elX+WU7lJxR2CiIiIiNGp3CTs\ni2FcbZ6Vx97x48dJTk4r7jAeOU5OZZQXE5SXopQT05QX05QX05SXov7pOXF1rY6lpWWR4w9y86wK\nezELly5dLe4QHjnlytkpLyYoL0UpJ6YpL6YpL6YpL0UpJ6bpqTgiIiIiInJbKuxFRERERMyACnsR\nERERETOgwl5ERERExAyosBcRERERMQMq7EVEREREzIAKexERERERM6DCXkRERETEDKiwFxEREREx\nAyrsRURERETMgAp7EREREREzoMJeRERERMQMqLAXERERETEDKuxFRERERMyACnsRERERETOgwl5E\nRERExAyosBcRERERMQMq7EVEREREzIAKexERERERM6DCXkRERETEDFgVdwAif9Xx48dJTk4r7jAe\nOSkpZZQXE5SXopQT08wtL66u1bG0tCzuMETkAVJhL4+9Uz6zqGZZtrjDeCQ5FXcAjyjlpSjlxDRz\nycup3CROf9MXN7enizsUEXmAVNjLY6+aZVlqWpYv7jBERB5pycUdgIg8cFpjLyIiIiJiBlTYi4iI\niIiYARX2IiIiIiJmwCwL+9TUVNLSHu6TDHJzc7l48eJDHfNmZ8+eLbax7+RRjk1ERETEXBRrYd+r\nVy+Cg4NNnmvRogVfffUV8+fPJygo6Lb9nD9/Hk9PTzIyMgBo06YNFy5cuK+Y/Pz8OHDgwD1fN3z4\ncLZu3Wry3KhRo5gyZcp9xXM3vv32W4YPH/7A+v8rjh49yiuvvFLcYYiIiIiYvWJ9Ks7LL79McHAw\no0aNKvRs3T179pCWlka7du2wsbG5Yz+VKlUiNjbW+Pry5csPJN7bSUlJeehj3nD58mXy8/OLbfzb\nuXLlCrm5ucUdhojIo224F/SqA9YWEH0Sxn0Hmf/3v50WBljbGepVggpzCo61doWx9cG5DOw/D+/u\ngHjzeea+iNyfYp2xb9WqFQaDgR07dhQ6vnr1ajp37oyNjQ2hoaEMGzYMKJj97datG/Xq1aNt27Ys\nWrQIgPj4eNzd3bl+/TpdunQB4KWXXmLbtm0ALFu2jDZt2uDn58ebb75JYmKicazo6GhatmyJt7c3\n06ZNu228X3zxBQEBAXh7e9OoUSNCQ0MB+Oijjzh06BCTJ09m8uTJt+0jLy+P0NBQ/P39adiwIWPG\njDEuG8rPz2fmzJm0bdsWT09PmjdvzooVK4zXTp06lUaNGtGwYUP69etHXFwcP/74IxMmTODIkSM0\natTI5Jjr16+nffv2+Pj40L17d3744Qdj3nx8fAgPDzf2+/HHHxuvS01NZeTIkTRo0IAWLVoQFhZ2\ny/tavHgxzZs3x8/Pj8DAQH7++WeSk5MZMGAAKSkpeHp6kpqaSmZmJsHBwTRp0oQmTZowefJkcnJy\nAAgNDWXEiBH07NkTDw8PunXrxrFjx26bTxGRx17HGvCeH0T9CiH7ILAWjG/wx/kxfgVF/Y0JHOcy\nEN4GzqXBW9+Ce1lY+kLxxC4ij5RiLeytra3p2LEjX331lfHYlStX2Lp1a6HlGwaDAYDg4GDatm3L\n/v37mT17NnPnzuXMmTOF2qxZswaAVatW0aJFCzZu3MiCBQuYO3cuu3btonLlysZlK8eOHWPs2LGE\nhISwd+9eDAYDqampJmM9ePAgYWFhzJ07l4MHDzJz5kzmzJlDXFwco0ePxsvLi6CgoDsuG/r888/Z\ntm0by5cvZ8uWLWRkZBiXI61bt46tW7cSGRlJbGwsI0aM4KOPPuL69evExMSwceNGvv76a3bv3k2l\nSpWYM2cOderU4YMPPqBWrVp89913RcbbvXs348ePZ+LEiezbt48XX3yRvn37kpSUBMDVq1eJj49n\n+/btzJ0MFK83AAAgAElEQVQ7l2XLlnH48GEARo4ciZWVFdu3byciIoLo6GiioqKKjPH7778za9Ys\nli9fzt69e/H19SUkJAQnJyfCw8NxdHQkNjYWe3t7QkJCOHXqFOvXr2ft2rX8/PPPzJs3z9jXpk2b\n6N69OwcPHqRJkyYMHjzYWPiLiJgl3/8r2mcfgi9+hoMXoOP/fZGUfxUY+BwcTfqjvUd5sLWCyCOw\n/iT89yg86wRPOxZP/CLyyCj2zbPdunVj165dxoI6OjqaunXr4urqWqRtiRIl2L59Ozt27KBy5coc\nOHCAqlWrmuz3xtKU1atX07NnT9zc3LCxsWH48OEcPnyYM2fOsHnzZpo0aYK3tzdWVlYMGzYMW1tb\nk/3VqVOH1atX4+LiQlJSEtnZ2dja2t7zhtnVq1czZMgQKlSoQKlSpRgxYgTr1q0jKyuLli1bsmTJ\nEpycnLh48SI2NjZkZWWRmpqKjY0NKSkprFixgjNnzjBx4kRCQkLuOF50dDSdO3fGy8sLCwsLunbt\nipubW6H9AAMGDMDa2pq6detSvXp1Tp8+TWJiIrt37+a9996jRIkSODs707dv30KfINxgZWVFTk4O\ny5cv59ixYwwZMoSIiAiT8URFRTFy5EieeOIJHB0dGTp0aKE+69evz/PPP4+lpSVvvPEG6enphZZZ\niYiYnbirBX+2coWqT4CbA5S1hQqlIbQlTN0PP1wq2r5pFXCyBZ9KBa8r2z3UsEXk0VPs3zxbtWpV\nPDw8WL9+PT169GD16tX079/fZNvp06czc+ZMPvjgA5KSkmjfvj3jxo27bf/nz583zq5DQcFvaWnJ\nuXPnSExMpHz5P76x1NrautDrmxkMBubMmcPmzZt58sknqV27trG/e3H+/HmCgoKMewry8/OxsbHh\n/Pnz2NvbM3HiRPbu3YuzszPu7u5AwfIdLy8vPv74YyIjI5k1axaVK1dm1KhRNG3a9LbjJSUl8eyz\nzxY65uzsbNxcbDAYcHT8Y5bHysqK/Px8zp07R35+Pq1atSI/Px+DwUBeXh4ODg5FxnB2diY8PJyF\nCxeyZMkSHBwcGDZsmHFZ1A3JyclkZGQQGBho/IQlLy+P3NxcsrKyAKhSpYqxvYWFBeXLly+0dEpE\nxOws+hHaVIOZ/gXr6i+mQ9mSsKQtHE+B+f8PpjcvaGtjUVDkf/b/YFBd6PFsQRv4Y6mOiPxjFXth\nDwWbaBcvXoy3tzcJCQm0atXKZLvjx48zevRoPvjgA44fP87w4cNZunQp7dq1u2Xf5cqVo2/fvoWK\nzJMnT1KlShUOHTrEkSNHjMdzcnKMS1T+bNGiRZw4cYJt27ZRunRpcnJy2LBhwz3fa7ly5QgODsbX\n1xcoeExmXFwcLi4uTJgwAYDvvvsOa2trzp8/b1ymdOHCBVxdXYmIiOD69essXbqUt99++46z2c7O\nzsTHxxc6dvbsWby8vG57Xfny5bGysmLPnj1YWRX8M7l69Srp6elF2iYnJ1OqVCnCw8PJyspi06ZN\nBAUF0bhx40LtHBwcsLGxISoqisqVKwOQkZHBpUuXjJukExISjO1zc3NJSEigYsWKt41VROSxlpEL\nPdbDU2Xg4rWC9fMlrOC5/5toOjOooGg3GOD3QVBxLgTHwBc/FbwRePEZGOULZ67cdhgnpzKUK/f3\nzOr/Xf2YG+WlKOXk4XokCvtWrVoRHBzM/Pnz6dKli7GQ/LPg4GBat27N4MGDefLJJ7GwsDDONt88\nc25tbW3ckNqpUycWLlyIt7c3lStXJjIykk8//ZRvv/2Wdu3asXDhQnbt2kWDBg2YM2eOycIVIC0t\nDWtra6ysrEhPT+eTTz4hJyfHuP7bxsbmltferFOnToSGhlK9enUcHByYOXMmmzdv5ptvviEtLY0S\nJUpgYWFBSkqKcalNTk4OR48e5eOPPyYiIgIXFxfs7Oywt7fHYDDcduyOHTvyxhtv0K5dO+rWrUtU\nVBQnTpygZcuW5OTk3PITh4oVK+Lt7c2UKVN45513uH79Om+99RYVK1YsskH43Llz9O7dm4iICGrV\nqoWDgwO2traULFkSGxsbMjMzycnJwcrKioCAAKZNm8aHH36IpaUl48aN48KFCyxduhSAXbt2ERMT\ng4+PD3PmzMHR0REPD4875lVE5LHlVQG+fhFWHoN956HBU7DgB1j76x9t3q0HLarA86sKnpxztC+c\nvAxzYuG1WvD/EuCU6T1iNyQnp3Hp0tW/HG65cnZ/Sz/mRnkpSjkx7UG+2Sn2NfZQUIh36tSJTZs2\n0a1bt1u2mzFjhnFz5gsvvEDDhg3p2rUr8MfmWYAuXbrQu3dvvvrqKzp16sRLL71E//79qVevHtHR\n0YSFhWFnZ0f16tWZPn06kyZNol69eiQmJhZaCnKz3r17Y2lpaVwDnp2djaenJydPngQgICCA+fPn\n33Fp0MCBA/H29qZbt240aNCAn376ifnz52NhYcGwYcM4c+YMPj4+dOnSBVdXV6pUqcLJkydp06YN\nXbt25dVXX8XT05NVq1bx6aefAuDj40N+fj716tUzLmm5wdvbmwkTJjB27Fh8fHxYuXIlCxYsoEKF\nCkXy9ufXM2bMICkpCX9/f55//nkqVapk8v5q167Nu+++y5tvvomHhwdTpkxh5syZlClThmeeeYYa\nNWrg6+tr3Gjs6OhI+/btad68OdeuXWPmzJnGvurWrcuCBQvw9fUlNjaWsLCwIjGKiJiVQxcL1tG3\ndIUg34KiPjgG/pfwx0/SdcinoIDPzoOhW8HRtmCJzk+J8PrXxX0XIvIIMOQ/qg9Al3+c0NBQfv31\nV2bNmnVP1x13GkdNS9N7I0REBI7nJpD8dSfc3J7+y31pFtY05aUo5cQ0s5+xFxERERGRv0aFvYiI\niIiIGXgkNs+KAAwdOrS4QxARERF5bGnGXkRERETEDKiwFxERERExAyrsRURERETMgNbYy2PvVK7p\nbwsWEZECp3KTsC/uIETkgVNhL4+9agfeIjk5rbjDeOQ4OZVRXkxQXopSTkwzp7zYA66u1Ys7DBF5\nwFTYy2OvZs2a+gIME/TFIKYpL0UpJ6YpLyLyuNEaexERERERM6DCXkRERETEDKiwFxERERExAyrs\nRURERETMgAp7EREREREzoMJeRERERMQMqLAXERERETEDKuxFRERERMyACnsRERERETOgwl5ERERE\nxAyosBcRERERMQMq7EVEREREzIAKexERERERM6DCXkRERETEDKiwFxERERExAyrsRURERETMgAp7\nEREREREzoMJeRERERMQMqLAXERERETEDKuxFRERERMyAVXEHIPJXHT9+nOTktOIO45GTklJGeTHh\nUc+Lq2t1LC0tizsMERF5DKmwl8feKZ9ZVLMsW9xhPJKcijuAR9SjmpdTuUmc/qYvbm5PF3coIiLy\nGFJhL4+9apZlqWlZvrjDEPlbJBd3ACIi8tjSGnsRERERETOgwl5ERERExAyosBcRERERMQMq7EVE\nREREzIBZFvapqamkpT3cx9nl5uZy8eLFhzrmzc6ePVtsY9/JoxybiIiIiLm4q8J+165d9OrVC19f\nX3x9fenXrx8//fTTg46tkNDQUIYNG3ZXbdu0acOFCxfuaxw/Pz8OHDhwz9cNHz6crVu3mjw3atQo\npkyZcl/x3I1vv/2W4cOHP7D+/4qjR4/yyiuvFHcYIn+/sfXh4hC4MLjgz+P9/jjX8KmCY/3//ccx\n+xLwWSs41hdiX4cuNR9+zCIiYtbuWNivXLmS0aNH07t3b/bs2cPu3btp2LAhPXv25OTJkw8jRiOD\nwXBX7S5fvvyAIykqJSXloY95w+XLl8nPzy+28W/nypUr5ObmFncYIn+/epXgcAK8tA66fgWvbwBL\nAwT+CyLaw5//mwxvA00qw3u74Jdk+LQFlLUtnthFRMQs3bawz8jIYPLkyUyaNImmTZtiaWmJjY0N\nvXv3pkePHsbCPikpiXfeeQc/Pz+aN2/O1KlTyc7OBgpmq6dOnUr37t3x8PAgMDCQH374gVdeeQVP\nT0/69OlDeno6AIGBgUydOpU2bdrg5eXFsGHDuHLlisnYli1bRps2bfDz8+PNN98kKSkJgC5dugDw\n0ksvsW3bNpNtExMTjf1ER0fTsmVLvL29mTZt2m2T9cUXXxAQEIC3tzeNGjUiNDQUgI8++ohDhw4x\nefJkJk+efNs+8vLyCA0Nxd/fn4YNGzJmzBjjsqH8/HxmzpxJ27Zt8fT0pHnz5qxYscJ47dSpU2nU\nqBENGzakX79+xMXF8eOPPzJhwgSOHDlCo0aNTI65fv162rdvj4+PD927d+eHH34AID4+Hh8fH8LD\nw439fvzxx8brUlNTGTlyJA0aNKBFixaEhYXd8r4WL15M8+bN8fPzIzAwkJ9//pnk5GQGDBhASkoK\nnp6epKamkpmZSXBwME2aNKFJkyZMnjyZnJwcoOBTmREjRtCzZ088PDzo1q0bx44du20+RYqFlQXU\nLQ+u9rAiACY3LTjetSZMbAjr/zTp8WRJaOoC/z0GX/0Kg7dCo0hIyXz4sYuIiNm6bWEfGxtLXl4e\njRs3LnJuxIgRtG7dGoAhQ4ZgYWHB9u3bWbFiBfv372f27NnGtlFRUUyaNIk9e/aQmJjIkCFDCAkJ\nYefOnZw7d461a9ca265du5bQ0FB2795NVlYWH3zwQZGxN27cyIIFC5g7dy67du2icuXKvP322wCs\nWbMGgFWrVtGiRQuTbW8sWzl27Bhjx44lJCSEvXv3YjAYSE1NNZmLgwcPEhYWxty5czl48CAzZ85k\nzpw5xMXFMXr0aLy8vAgKCiIoKOi2Cf/888/Ztm0by5cvZ8uWLWRkZBAcHAzAunXr2Lp1K5GRkcTG\nxjJixAg++ugjrl+/TkxMDBs3buTrr79m9+7dVKpUiTlz5lCnTh0++OADatWqxXfffVdkvN27dzN+\n/HgmTpzIvn37ePHFF+nbt6/xjdDVq1eJj49n+/btzJ07l2XLlnH48GEARo4ciZWVFdu3byciIoLo\n6GiioqKKjPH7778za9Ysli9fzt69e/H19SUkJAQnJyfCw8NxdHQkNjYWe3t7QkJCOHXqFOvXr2ft\n2rX8/PPPzJs3z9jXpk2b6N69OwcPHqRJkyYMHjzYWPiLPDKcSxfMui8/Cj02gMEAC5+H7+LhuSXw\n36MFx26o+kTBn3XKwc99YN9r0PFpyHs0P2kTEZHH020L+5SUFJ544gksLG7dLC4ujsOHD/P+++9T\nsmRJypcvz1tvvWUssAGaNWtG9erVKVmyJHXq1KFZs2a4urpiZ2dH3bp1OXfunLFtYGAgTz/9NKVK\nleLtt99my5Ytxtn/G1avXk3Pnj1xc3PDxsaG4cOHc/jwYc6cOWNsc2Npyu3abt68mSZNmuDt7Y2V\nlRXDhg3D1tb0R+N16tRh9erVuLi4kJSURHZ2Nra2tve8YXb16tUMGTKEChUqUKpUKUaMGMG6devI\nysqiZcuWLFmyBCcnJy5evIiNjQ1ZWVmkpqZiY2NDSkoKK1as4MyZM0ycOJGQkJA7jhcdHU3nzp3x\n8vLCwsKCrl274ubmVmg/wIABA7C2tqZu3bpUr16d06dPk5iYyO7du3nvvfcoUaIEzs7O9O3bt9An\nCDdYWVmRk5PD8uXLOXbsGEOGDCEiIsJkPFFRUYwcOZInnngCR0dHhg4dWqjP+vXr8/zzz2Npackb\nb7xBeno6sbGx95RjkQfu96vQaiWM/x62/w6LfoSyJcHFDlJNzMLfKPKfKgNvbIbNp2G0HzR66qGG\nLSIi5s3qdieffPJJUlNTyc3NxdLSstC5K1euULp0aZKSkihZsiT29vbGc87OziQlJRnXVt98ztLS\nEjs7O+NrCwsL8vLyjK+rVKli/L1ChQpkZ2cXmUU/f/68ccYcCop4S0tLzp07R9WqVe+6bWJiIuXL\nlze2tba2LvT6ZgaDgTlz5rB582aefPJJateubezvXpw/f56goCBjPvPz87GxseH8+fPY29szceJE\n9u7di7OzM+7u7kDB8h0vLy8+/vhjIiMjmTVrFpUrV2bUqFE0bdr0tuMlJSXx7LPPFjrm7Oxs3Fxs\nMBhwdHQ0nrOysiI/P59z586Rn59Pq1atyM/Px2AwkJeXh4ODQ5ExnJ2dCQ8PZ+HChSxZsgQHBweG\nDRtmXBZ1Q3JyMhkZGQQGBhr3S+Tl5ZGbm0tWVhZQ+O/fwsKC8uXLF1o6JfJIqOEAL7gVLKs5faVg\naQ5A1i32k8RdLfhz+++w6yykZ8NLz8C/niyY5b+Jk1MZypWzM9HJg1UcYz4OlBfTlBfTlJeilJOH\n67aFvYeHB9bW1uzatYvmzZsXOjd69Gjs7Ox4++23uXbtGqmpqcYCPi4uDnt7e2PxerebXgESEhKM\nv8fHx2Nra1ukmCxXrhx9+/YtVDiePHmySFF/u7ZVqlTh0KFDHDlyxHg8JyfHuETlzxYtWsSJEyfY\ntm0bpUuXJicnhw0bNtz1fd0cT3BwML6+vkDBYzLj4uJwcXFhwoQJAHz33XdYW1tz/vx5vvrqKwAu\nXLiAq6srERERXL9+naVLl/L222/fcTbb2dmZ+PjChcPZs2fx8vK67XXly5fHysqKPXv2YGVV8M/k\n6tWrxv0QN0tOTqZUqVKEh4eTlZXFpk2bCAoKKrKEy8HBARsbG6KioqhcuTJQsI/j0qVL2NjYAIX/\n/nNzc0lISKBixYq3jVXkoTMY4D/1oMFT8MXP0KcOnLgM/0sw3f5iOuw7Bx1qwIEL0Nq1YHNtbNFP\n/JKT07h06eqDjf9PypWze+hjPg6UF9OUF9OUl6KUE9Me5Jud2y7FubF0ZezYsezcuZPc3FzS09MJ\nDQ1l79699OvXjwoVKtCgQQM++ugjrl27xsWLF5k9ezYdOnS4r4AiIyOJi4vj6tWrzJo1i/bt2xsL\nyxs6derEokWL+P3338nLyyMiIoJXXnmF69evAwUz7zc2pN6qbUZGBu3atSMmJoZdu3aRk5PDnDlz\nTBauAGlpaVhbW2NlZUV6ejohISHk5OQY13/b2Njc8to/xx4aGsqlS5fIzs5mxowZ9O/f3zhGiRIl\nsLCwICUlxbjUJicnh8OHDzNo0CDi4uIoWbIkdnZ22NvbYzAYbjt2x44dWbt2LbGxseTm5rJq1SpO\nnDhBy5YtgVt/4lCxYkW8vb2ZMmUKmZmZXL58maFDh/LJJ58UaXvu3Dl69+7NkSNHsLGxwcHBAVtb\nW0qWLImNjQ2ZmZnk5ORgYWFBQEAA06ZN4+rVq1y7do3333+fUaNGGfvatWsXMTEx5OTkEBoaiqOj\nIx4eHnfMq8hD9WsKDNoMle1gTkuITyt4Ks7N/vzfVp9NcPACTGtW8ESdkTsLinwREZG/yW1n7AFe\nffVV7O3tCQ0NZeTIkVhYWFC3bl2WLl2Km5sbANOmTSM4OJgWLVpgMBjo2LHjfT9X/bnnnmPw4MFc\nvHiRVq1aMWbMmCJtOnXqxJUrV+jfvz9JSUlUr16dsLAw4xKfLl260Lt3b8aPH3/btnZ2dkyfPp1J\nkyZx6dIl2rdvX2gpyM169+7Nu+++S/369SldujT+/v54enpy8uRJ6tevT0BAAB9++CFnz55l4sSJ\nt7y/gQMHkpOTQ7du3bh69Sq1atVi/vz5WFhYMGzYMIKCgvDx8cHe3p4OHTpQpUoVTp48SZs2bTh+\n/Divvvoq6enpVK9enU8//RQAHx8f8vPzqVevHt99951x9hvA29ubCRMmMHbsWM6fP0+NGjVYsGAB\nFSpUID4+vsinKTe/njFjBpMmTcLf35/c3FyaNWvG2LFji9xT7dq1effdd3nzzTdJTk7mqaeeYubM\nmZQpU4ZnnnmGGjVq4Ovry1dffcXo0aOZNm0a7du3JzMzEy8vL2bOnGnsq27duixYsIChQ4dSu3Zt\nwsLC7ukTH5GHZt3Jgh9T9pyDinMLH0u8XlDci4iIPCCG/EfoAeiBgYE8//zz9OjRo7hDkWIQGhrK\nr7/+yqxZs+7puuNO46hpaXpvhMjj5HhuAslfd8LN7emHOq4+LjdNeTFNeTFNeSlKOTGt2JbiiIiI\niIjI4+GRKuy15EJERERE5P7ccY39w/TFF18UdwhSjIYOHVrcIYiIiIg8th6pGXsREREREbk/KuxF\nRERERMyACnsRERERETPwSK2xF7kfp3JNf1uwyOPmVG4S9sUdhIiIPLZU2Mtjr9qBt0hOTivuMB45\nTk5llBcTHuW82AOurtWLOwwREXlMqbCXx17NmjX1BRgm6ItBTFNeRETEXGmNvYiIiIiIGVBhLyIi\nIiJiBlTYi4iIiIiYARX2IiIiIiJmQIW9iIiIiIgZUGEvIiIiImIGVNiLiIiIiJgBFfYiIiIiImZA\nhb2IiIiIiBlQYS8iIiIiYgZU2IuIiIiImAEV9iIiIiIiZkCFvYiIiIiIGVBhLyIiIiJiBlTYi4iI\niIiYARX2IiIiIiJmQIW9iIiIiIgZUGEvIiIiImIGVNiLiIiIiJgBFfYiIiIiImbAqrgDEPmrjh8/\nTnJyWnGHUexcXatjaWlZ3GGIiIhIMVFhL4+9Uz6zqGZZtrjDKFancpM4/U1f3NyeLu5QREREpJio\nsJfHXjXLstS0LF/cYRS75OIOQERERIqV1tiLiIiIiJgBFfYiIiIiImZAhb2IiIiIiBlQYX8PLl68\nSF5e3kMdMyMjg6SkpIc65s3Onj1bbGOLiIiIyN17oIX9rl276NWrF76+vvj6+tKvXz9++umnBznk\nA5OUlMTzzz9PZmbmPV977do13N3dOXfu3D1f26NHj1vmLDAwkMjIyHvu825FRkYybdq0B9a/iIiI\niPx9HthTcVauXMmnn37KpEmTaNSoEbm5uURGRtKzZ09WrlyJm5vbgxr6gbh+/ToZGRnk5+ff87X5\n+fkYDIb7GjclJeW+rvs7pKSk3Nf9SvHKysoiLGw2trYl6NdvKACZmZmsXfslJ0+ewMLCglq16tC+\nfUcsLCz45ZcjbNmykStXUqlSxZUXXuiMg4NjMd+FiIiI3KsHMmOfkZHB5MmTmTRpEk2bNsXS0hIb\nGxt69+5Njx49OHnyJFAwC/7OO+/g5+dH8+bNmTp1KtnZ2QCMGjWKqVOn0r17dzw8PAgMDOSHH37g\nlVdewdPTkz59+pCeng4UzFzPnz+fDh064OHhwdChQ4mNjaVDhw54eXnxzjvvGAvU1NRURo4cSYMG\nDWjRogVhYWHGuEeNGkVwcDA9evTAw8ODrl27cvToUQC6du1Kfn4+jRo14tixY+Tl5REaGoq/vz8N\nGzZkzJgxpKX98SVJixcvpnHjxvj5+bFkyZJb5io/P5+ZM2fStm1bPD09ad68OStXrgRg6NChnD9/\nnrfeeoulS5feNueZmZkEBwfTpEkTmjRpwuTJk8nJyTGemzBhAq1bt8bDw4M2bdqwdetWALKzsxk9\nejR+fn40adKEYcOGcfnyZTZv3sy8efPYtm0bL7/8sskxlyxZQsuWLfH19aVv376cOnUKgP3799Oh\nQwdCQkLw9fWlWbNmLFiwwHjdr7/+SmBgIJ6enrRq1Yro6GjjuWXLltGmTRv8/Px48803SUxMvO19\nS2Hx8WdZtGg+iYmXCh3//vud/PLLUVq1akv9+o04dGgfhw8fIjX1Ml9+uQx7ewc6dXqJhIQLLFt2\n63+vIiIi8uh6IIV9bGwseXl5NG7cuMi5ESNG0Lp1awCGDBmChYUF27dvZ8WKFezfv5/Zs2cb20ZF\nRTFp0iT27NlDYmIiQ4YMISQkhJ07d3Lu3DnWrl1bqO3ChQvZtm0bBw4cYPz48SxcuJANGzbw/fff\ns2vXLgBGjhyJlZUV27dvJyIigujoaKKiooz9REdHM378ePbt20fVqlWZPn06AGvWrMFgMLBnzx7c\n3d35/PPP2bZtG8uXL2fLli1kZGQQHBwMwI4dOwgLC+Pzzz9n586dxoLXlHXr1rF161YiIyOJjY1l\nxIgRTJo0ievXrxMaGkqlSpWYNWsWr7322m1zHhISwqlTp1i/fj1r167l559/Zt68eQAsXLiQU6dO\nERUVRWxsLF26dGHSpEkArF27lt9++42dO3ca7yMiIoLWrVszaNAgWrRoYXyjcbMVK1awaNEiPvvs\nM77//ns8PDzo378/WVlZQMG3wTo6OhITE8P777/PjBkzuHjxItnZ2QwcOJAGDRqwf/9+ZsyYwfjx\n4zl16hQbN25kwYIFzJ07l127dlG5cmWGDx9+2/uWwsLDQ3FwcKB06dKFjufn52NpaUnVqtVwcakK\ngKWlFfHxZ8nJycHT04daterw3HPeJCRc4NKli8URvoiIiPwFD6SwT0lJ4YknnsDC4tbdx8XFcfjw\nYd5//31KlixJ+fLleeutt1izZo2xTbNmzahevTolS5akTp06NGvWDFdXV+zs7Khbt26hNesBAQGU\nK1cOJycnatSo8f/bu/OAKKv9f+DvYYZRQAQRcAMTUCN3FlkUF3AXQRAN1Gt6U9Pcycy03DI1XIoQ\nLbf6una9SpjgGqaiQiKiUpYrSgo4yiKL7MP5/cHPuRIjKAbI8H79U/Os53mD+nnOc54zcHd3h4mJ\nCZo3b462bdsiOTkZqampOHPmDD7++GM0aNAALVu2xMSJE7F3717Vcdzc3NC+fXvI5XIMHToUiYmJ\nAKDq8X/635CQEEyfPh3NmjWDrq4uPvjgAxw8eBCFhYU4cuQIhg8fjnbt2qFBgwb48MMPn5tD//79\nsX37dhgZGUGhUEAul6OwsBCZmZkvlXloaCjmzZuHxo0bo0mTJpgxY4bquv71r38hKCgIOjo6SE5O\nhp6eHhSK0sKtQYMGuHv3LkJCQpCeno5NmzZh5syZlZ7v4MGDGD9+PNq1aweZTIbp06ejsLAQMTEx\nAACZTIZJkyZBS0sL/fv3h66uLu7du4e4uDjk5eXh/fffh0wmQ+fOnbFnzx6YmJggJCQE48ePh5WV\nFbqzCMAAACAASURBVORyOfz9/XHlyhXVz4AqN2XKLPj6joNMpl1mea9erjAyaooNG77E9u1bYGFh\nhS5dbGBoaAgAuH37Jp48eYJ790qzzsx8XONtJyIioldTLWPsjY2NkZmZCaVSCalUWmZdVlYW9PT0\nkJaWBh0dHRgYGKjWtWzZEmlpaVAqlQBQZp1UKoW+vr7qs5aWVpkZap4WKE/XPbutRCJBSUkJkpOT\nIYTAgAEDVOPeS0pKyuzbpMn/xhbLZDLVOf4+Rj4lJQXz589XXZ8QAnK5HCkpKUhNTcVbb72l2rZZ\ns2blcniqqKgIy5cvR3R0NFq2bAlra2sAeKnZd9LT05Gfn49x48ap2imEQHFxMQoLC5GVlYVly5Yh\nPj4erVu3hpmZmeoGxcPDA0+ePEFISAhWrFiBN998E0uXLkWXLl0qPGdaWhpatWql+iyRSNCiRQs8\nePAArVu3hr6+fplrlslkEEIgLS0NJiYmZY719JpTUlIQGBiIDRs2qK5BKpUiOTkZb7zxxgvnUZ+1\naNFS7fLo6LN4+FABb++3UVBQgMOHf0Jk5En07u0KZ2cXREefRVzcBZiYPP0G36q9E0JERES1p1oK\nexsbG2hrayMyMhKurq5l1i1cuBD6+vqYM2cOcnNzkZmZqSrg7927BwMDA1VBWNUXTp/H1NQUMpkM\nUVFRkMlKLz07O1s1Vv9FPG2Tqakpli9fDkdHRwCAUqnEvXv3YG5uDlNTUyQlJan2efZm5e++/PJL\nCCFw9uxZaGtrIyUlpczQoBdhaGgIuVyO0NBQmJmZASh9z+HRo0eQy+VYsmQJ2rZti82bN0MikSA2\nNhZHjx4FACQmJsLR0RF+fn7IzMxEcHAwPv74Yxw+fLjCc7Zs2bLMExMhBJKTk2FsbFzhfqampnj0\nqOz47z179qBTp04wNTXFxIkTMWLECNW627dvs6h/QUZGjWBiUnpDK5WWPi17+vnatd9gbGyM/v37\nAAAiI08gMfEWTEw8MXr02xg4sB9kMhliYmIQFhYGKytz1b6aSJOvraqYiXrMRT3moh5zKY+Z1Kxq\nKeyfDqNYtGiRalac/Px8fP/99/j111+xd+9eNGvWDD169MDKlSuxZMkSZGdnY/369fD09KyOJgEA\nmjdvDnt7e6xevRpz585FXl4eZs+ejebNmyMgIKDSawJKbwQaNmyI4cOHIzg4GJaWljA0NERgYCCO\nHz+OY8eOwdPTE7Nnz4anpyfat29f4ZSROTk5aNCgAbS0tJCRkYEvvvgCEolE9eKrXC4v81KuOlpa\nWvDw8MDatWuxfPlySKVSLF68GA8ePMCuXbuQk5ODhg0bQiKRICUlBV9//TWA0puREydOIDw8HFu2\nbEGTJk2gq6ureoJR0bm9vLwQGBiInj17ok2bNti0aRMkEgmcnJwQHx//3LZ27doVjRs3xubNmzFx\n4kRcvXoVX3/9NX744QcMHz4cW7duhb29PczMzLB7924EBQXhl19+KfMEhtRLT8/Bo0fZAAClsvSJ\nz9PPxsbN8PvvVxAWdhTFxcXIyclB587dkJKSgTVrlqNpU2P07NkHZ86cRcuWZpBIdFT7ahoTE32N\nvbaqYibqMRf1mIt6zKU8ZqJedd7sVNt0l2PGjIGBgQGCg4Mxb948aGlpoWvXrti1a5dqqsu1a9fi\n888/R79+/SCRSDB8+PAqvSz59579ij5/+eWXWLFiBdzc3KBUKtG3b18sWrSo0nOYmJigd+/eGDhw\nIDZt2oSpU6eiqKgIvr6+yM7ORocOHbBp0yZoaWnB2dkZ8+bNw8yZM5Gbm4uxY8eqbgz+btasWZg/\nfz66d+8OAwMDeHp6onXr1rh9+zZat24Nb29vfPrpp7h37x6mTp363OtauHAh1q5dC3d3dxQUFMDO\nzg5fffUVgNLZfhYtWoSdO3eiadOm8PPzw9WrV3H79m2MHz8e9+7dg4eHBwoKCtCxY0esWrUKQOk7\nDjt37sSQIUNw5MiRMuf29PRERkYGpk2bhvT0dHTu3Bnff/89GjZsqPY6n7ZVW1sb3377LZYtW4Yt\nW7agadOmWLFiBSwtLWFpaYmsrCxMnjwZaWlpsLS0xObNm1nU/wPc3YcDAE6dioBEIoGNjT1cXQdC\nJpPB2/ttHD0ajoMHf4SFhSXc3b1rubVERERUFRLBicqpjrthtBjtpaaVb6jBbigfIv2wF6ys2qmW\nsadEPeZSHjNRj7mox1zUYy7lMRP1qrPHvlq/eZaIiIiIiGoGC3siIiIiIg3Awp6IiIiISAOwsCci\nIiIi0gAs7ImIiIiINAALeyIiIiIiDVBt89gT1ZQ7yrTabkKtu6NMg0FtN4KIiIhqFQt7qvMsLsxG\nenrF386r6QwAtGljWdvNICIiolrEwp7qvPbt2/MLMIiIiKje4xh7IiIiIiINwMKeiIiIiEgDsLAn\nIiIiItIALOyJiIiIiDQAC3siIiIiIg3Awp6IiIiISAOwsCciIiIi0gAs7ImIiIiINAALeyIiIiIi\nDcDCnoiIiIhIA7CwJyIiIiLSACzsiYiIiIg0AAt7IiIiIiINwMKeiIiIiEgDsLAnIiIiItIALOyJ\niIiIiDQAC3siIiIiIg3Awp6IiIiISAOwsCciIiIi0gAs7ImIiIiINICsthtA9Kpu3LiB9PSc2m5G\nrWrTxhJSqbS2m0FERES1iIU91Xl3un8NC2nT2m5GrbmjTMPdYxNhZdWutptCREREtYiFPdV5FtKm\naC81re1m1Kr02m4AERER1TqOsSciIiIi0gAs7ImIiIiINAALeyIiIiIiDcDCnoiIiIhIA7CwrycU\nCgVKSkpq5dxJSUm1cl4iIiKi+oSF/XO4ubmha9eusLW1ha2tLWxsbGBra4uff/75hfY9ffp0uf9/\nUQsWLECnTp1ga2sLOzs72Nrawt3dHXv37q3StaSlpWHw4MEoKCgAACxZsgSBgYEV7hMbG4t+/fpV\n6XzPCggIwO7du6u874IFC165DfVJYWEhgoPX4dtvv1YtKygowH//uwurVi1FQMBnCAsLVd3k5eXl\nYv/+HxAQsAxffrkK8fGXaqvpRERE9Io43WUFgoKC0KdPn1o59zvvvIOPPvpI9fnSpUuYMGECzMzM\n0LNnz5c6Vl5eHvLz8yGEAAAsW7as0n3s7e1x4sSJl2u0Go8fP0aTJk1e+ThUuaSk+wgPD0Vq6iM0\nb95CtfzcudO4fv1PDBniidzcJ/jll+MwMzODjU137Nu3BwpFCoYOHY4rV+Jw4MA+WFm1g55eo1q8\nEiIiIqoK9thXQVJSEqytrZGXl6da5uPjgwMHDjx3nwMHDmDQoEFlls2aNQvbtm17oXPa2NigXbt2\nuHHjBgAgOjoao0ePhrOzM+zt7TF79mxVj/y4ceOwYMECuLi4YOrUqfDx8YEQAi4uLrh27RoWLFiA\n1atXAwCys7Mxb948dO/eHT179sSaNWsAADExMXBycgIAhIaG4t///jdmzZoFGxsbeHh4IDo6WtW2\nQ4cOYcSIEXB0dISjoyOWLl0KAPi///s/hIWFYefOnZgzZw4A4Pr16xg3bhy6d+8OT0/PMk8zkpKS\nMGHCBNja2mLMmDF48ODBC2VDpbZsCYahoSH09PTKLBdCQCqV4o03LGBu/gYAQCqVIScnBwkJt9Ct\nmz06d+6GESN8MWPGXOjo6NZG84mIiOgVsbCvIolE8lLbDxgwAA8fPsS1a9cAADk5OYiMjMSwYcMq\n3be4uBgnT57ErVu34ODggLy8PMycORNTpkxBdHQ0Dh06hN9++w3h4eGqff744w8cO3YMa9euxY8/\n/giJRIKoqChYW1uXOfbixYuRk5ODkydPIiwsDJGRkdi3b1+5a4yOjoatrS1iY2Px7rvvYsaMGcjI\nyEBSUhIWLVqEzz77DOfPn8eePXsQFhaGX3/9FRMmTICHhwfGjRuHwMBAPHnyBBMnToS7uztiYmKw\naNEifPTRR0hMTAQAzJ49G+3atUNMTAzmzZv30kOY6rspU2bB13ccZDLtMst79XKFkVFTbNjwJbZv\n3wILCyt06WKDjIw0AEBKShJWr16OoKA1+P33eGhp8a8FIiKiuohDcSrg7+8PmUwGIQQkEgn69euH\nVatWVelYenp6cHV1xeHDh2FtbY3jx4+jS5cuaNasmdrtd+3ahf3796s+m5ub47PPPkPHjh1RUlKC\n0NBQmJubIycnBwqFAk2aNIFCoVBt7+rqquq5zczMBADVUJynCgsLERERgZCQEDRqVDr0YuPGjZDL\n5api+6k2bdpgwoQJAABvb29s374dp06dgoeHB8LDw9GyZUs8fvwYGRkZMDAwKNOWp06fPg1jY2P4\n+fkBALp37w43Nzf8+OOPGDlyJK5evYodO3ZAJpPBxsYG7u7uKC4ufpmY67UWLVqqXR4dfRYPHyrg\n7f02CgoKcPjwT4iMPAkLC0sAQGbmY/j4+OHKlTj88ssxmJmZw9KybU02nYiIiP4BLOwr8NVXX/2j\nY+w9PT2xYsUKfPDBBzh06BA8PDyeu+2//vWvMmPsn6WlpYUTJ05gx44dAABra2vk5+eXmfXGxMSk\n0vZkZWWhqKiozM2Fubk5AJQr7Fu3bl3mc/PmzfHo0SNIpVLs3bsXISEh0NPTQ4cOHVBcXFzuJgIA\nkpOTVU8dgNIbDaVSiUGDBiE1NRW6urrQ1f3fMJBWrVqVawepZ2TUCCYm+gAAqVQLMpkUAGBioo9r\n136DsbEx+vcv/V2OjDyBxMRb6NevNwCgc+dOcHKyRbNmTRAffwk5OemqY2kqTb++qmAm6jEX9ZiL\nesylPGZSs1jYV4FUWlo0FRUVQUdHB0DpS6KVcXFxwZMnTxAdHY24uLhKZ6Z5nkuXLmHjxo0ICQlR\nFeLjx49/6eMYGRlBW1sbCoUCBgYGAICzZ8/i8ePHMDU1LbPtw4cPy3xOSkqCu7s7Dh06hKNHj+Lg\nwYMwMjICAPTv31/t+UxMTGBjY4OdO3eqlikUCjRs2BA5OTnIzc1FVlYWGjdurFpHLyY9PQePHmUD\nAJTKEhQXKwEAjx5lw9i4GX7//QrCwo6iuLgYOTk56Ny5G4qKpGjdug1iYy/C2LgFrl//EwBgaGiq\nOpYmMjHR1+jrqwpmoh5zUY+5qMdcymMm6lXnzQ4H01ZB06ZNoa+vj4iICAClL5cmJydXup9MJsPg\nwYMREBAAFxcX6OtX7Qebk5MDqVQKuVwOpVKJAwcOIDY29rnDVuRyuWq/Z2lpaWHo0KEICgpCTk4O\nHj16hICAAOTn55c7xrVr1/DTTz9BqVRi3759ePToEfr27YucnBzIZDLIZDIUFhZiy5YtSEpKQlFR\nEQBAW1tbdd6+ffsiISEBhw4dQklJCW7fvo1Ro0YhIiICrVq1gp2dHQICAlBYWIj4+HiEhYVVKR8C\ngP+9H+HuPhydOnXFqVMROHfuNGxs7OHqOhAA8Pbb/4K5eWuEhYXi3r1EDBvmjdat29RSm4mIiOhV\nsMf+OSp6OVZbWxtLlixBUFAQVq1ahQEDBqBv375q9/37cTw8PLBnzx5Mnz69ym1zcXHB4MGD4eHh\nAalUik6dOmHEiBFISEhQe04TExP07t0bAwcOxKZNm8qsW7RoEVasWIFBgwZBIpHAz88PI0eORExM\nTJntrKyscOrUKXz++edo06YNtm7dCn19fXh7eyM6Ohqurq7Q0dFB9+7dMWDAAFVbhgwZgjlz5iA5\nORlbt27F1q1bsWLFCixduhR6enoYO3YsfHx8AACBgYFYuHAhnJyc0Lp1awwYMKDKGdVn/v4fl/nc\nsKEOfHz81G7bqFEj+PqOq4lmERERUTWTCHWDoanaKBQKeHh44OzZs6qe9NddaGgodu/eXeZl3tfJ\nDaPFaC81rXxDDXVD+RDph71gZdWuzHI+AlWPuZTHTNRjLuoxF/WYS3nMRL3qHIrDHvsaIoTAzZs3\n8f3338PT07POFPVEREREVDewsK8hEokE48ePR4sWLbB169babg4RERERaRgW9jXo2W9rrUu8vb3h\n7e1d280gIiIiogpwVhwiIiIiIg3Awp6IiIiISAOwsCciIiIi0gAcY0913h1lWm03oVbdUabBoLYb\nQURERLWOhT3VeRYXZiM9PafyDTWUAYA2bSxruxlERERUy1jYU53Xvn17fgEGERER1XscY09ERERE\npAFY2BMRERERaQAW9kREREREGoCFPRERERGRBmBhT0RERESkAVjYExERERFpABb2REREREQagIU9\nEREREZEGYGFPRERERKQBWNgTEREREWkAFvZERERERBqAhT0RERERkQZgYU9EREREpAFY2BMRERER\naQAW9kREREREGoCFPRERERGRBmBhT0RERESkAVjYExERERFpABb2REREREQagIU9EREREZEGkNV2\nA4he1Y0bN5CenvPc9W3aWEIqldZgi4iIiIhqHgt7qvPudP8aFtKm6tcp03D32ERYWbWr4VYRERER\n1SwW9lTnWUibor3U9Lnr02uwLURERES1hWPsiYiIiIg0AAt7IiIiIiINwMKeiIiIiEgDsLDXcAqF\nAiUlJf/4tjVBqVRCoVDUdjOIiIiI6oQ6WdhbW1vDxsYGtra2sLOzg729PSZNmoSbN2/+o+eJiYmB\nk5PTP3rMlzVr1iwEBwerXZeamop58+bB2dkZtra2GDp0KLZs2aJan5aWhsGDB6OgoKDS87zMtjXF\n398fERERtd0MIiIiojqhTs6KI5FIsH//flhZWQEo7dldu3YtJk+ejJMnT0Iikfyj53pdzZkzB+3a\ntUNERAT09PRw7do1TJ8+Hdra2pgwYQLy8vKQn58PIUSlx3qZbWtKRkbGP3asqKhInD8fhdzcJ2je\nvCXc3b3QuHFjhIX9iISE25BKtfDWW50wdOhwznlPREREdVKd7LEXQpQpQKVSKXx8fKBQKJCZmYnQ\n0FD4+Pio1ufm5sLa2hrJyclISkqCvb09FixYAAcHB4SFhSE7Oxvz5s1D9+7d0bNnT6xZs0a1b0lJ\nCb788kv07t0bPXr0wHfffadaFx0djdGjR8PZ2Rn29vaYM2eOqsc7KioKnp6ecHBwgKenJw4ePKja\n78KFCxg5ciS6d+8OX19fxMfHq9b98ccfGDVqFGxsbDB16lRkZWU9N4fff/8dgwcPhp6eHoDSJxkL\nFy6EtrY2AMDHxwdCCLi4uODatWt4/Pgx5s6dCzc3N3Tr1g3Dhw/HpUuX1G5bUlKC4OBguLm5oWfP\nnvjkk0/w5MkTAEBoaCimTp2KBQsWwNbWFoMGDUJsbCw+/PBD2NrawsPDA9evX1e1c8+ePRg0aBCc\nnJwwc+ZMpKamAih9IuLp6YkvvvgCjo6O6Nu3L7Zt2wYAWLlyJS5evIiAgAAEBAS80O/F8yQn38fx\n44fRtm17jBjhi8ePM7B3704cPnwQN29ex9ChnnB0dMHFizE4d+70K52LiIiIqLbUycL+7zIzM7Fj\nxw60b98ehoaGAMr3tD/7OScnB2ZmZoiKisKAAQOwePFi5OTk4OTJkwgLC0NkZCT27dunOnajRo1w\n+vRpfPHFF1i9ejUUCgXy8vIwc+ZMTJkyBdHR0Th06BDi4+MRHh4OAFi4cCFmzpyJmJgYLFy4EMuW\nLcOTJ0+QnJyMqVOnYtq0aTh//jzeffddvPfee8jKykJhYSGmTZuGIUOGIDY2FqNGjUJMTMxzr3vI\nkCGYO3cu1qxZg8jISGRnZ6Nfv34YO3YsAODHH3+ERCJBVFQUrK2tsWbNGkilUhw9ehSxsbGwtbXF\nunXr1G773Xff4cSJE/jhhx/w888/Iz8/H8uXL1ed+9SpU+jbty/i4uLQtWtXTJgwAYMHD0ZMTAys\nra3xzTffAACOHDmCrVu3YuPGjYiMjISZmRn8/f1Vx7lx4waaNGmC6OhofPrpp1i3bh0UCgUWLlwI\nOzs7zJ8/H/Pnz6/y7wYAyOUN4Oo6AP37D8Zbb3VCq1ZmyMx8DAsLKwwZ4omuXW3h6NgDAJCenvZK\n5yIiIiKqLXVyKA4A+Pn5QUur9L5ELpejS5cuCAoKeu72fx9i4uHhAZlMhpKSEkRERCAkJASNGjUC\nAGzcuBFyuRyJiYmQy+WYNGkSJBIJevfuDT09PSQlJcHU1BShoaEwNzdHTk4OFAoFmjRponrZs0GD\nBggLC4O+vj7s7Oxw8eJFAMDu3bvh5OQENzc3AMCgQYOwe/duHDt2DObm5igsLMS7774LAOjXr1+F\nY/xXrlyJAwcO4NChQ9izZw8KCwvRs2dPLFmyBK1atSp37R988AEaNGgALS0tJCUloXHjxqr2Pt3m\n6X9DQkIwd+5cNGvWTLXvgAED8NlnnwEAzMzMMGjQIACAg4MDrly5gv79+wMAnJycsHfvXtVxxo8f\nrxo25e/vD3t7eyQmJgIAZDIZJk2aBC0tLfTv3x+6urq4d++e6rz/BGNjE9U3zyYm3sHNm9dhYWEF\nOzsH1TYREUcAgN9QS0REVAGlUom7dxNeaNuMjEZIT8+p8rnatLHk8NiXVGcL+71796qKxZclkUhg\nbGwMAMjKykJxcXGZQtLc3BwAkJiYCD09PdUNBABoa2tDqVRCIpHgxIkT2LFjB4DSYTD5+fmqWWW2\nbduGoKAgzJ07F3l5efD19cXcuXORkpKCyMhIODiUFpVCCBQXF8Pe3h46Ojqqdj31bIGu7jq8vb3h\n7e2NkpIS/PbbbwgKCsK0adPw008/ldteoVBgxYoVuH37NiwtLdG4cWNVe//+hCMlJQXz589X/YES\nQkAulyMlJQUAYGBgoNpWKpVCX19f9VlLS0t13JSUFAQGBmLDhg2q40ilUiQnJ6v2e/YPrUwmq7Zx\n/jdu/Il9+/ZAV1cXw4Z5AygdanXwYAguX76ITp26onPnbtVybiIiIk1w924CMgdtg4W06Qttb1TF\n89xRpuHusYmvbYebUqlEenoaTExMa7spZdTZwr6i4k9LSwtFRUWqzxkZGc8dmmNkZARtbW0oFApV\nsXr27Fk8fvwYpqbP/2FdunQJGzduREhIiOpGYPz48QCAoqIi/PXXX1i9ejUA4PLly5g+fTo6d+4M\nExMTuLu744svvlAd6/79+2jSpAmuXr0KhUIBIYSqfQqFQm3v9eXLlzFp0iScOXMGOjo60NLSQteu\nXfHxxx/Dy8urTD5Pj+Xv74/Ro0dj9+7dAIADBw6Um0no6bampqZYvnw5HB0dAZT+Av/1119o3bo1\n4uLiXvilYhMTE0ycOBEjRoxQLbt9+zbeeOMNxMXFvdAxXpWRUSMkJl7Hf/6zEyYmJpg+fTqaNm2K\nkpISbNmyBfHx8ejVqxd8fX1f65elq8LERL/yjeoh5lIeM1GPuajHXNSrD7lkZDSCkbQp2ktroKA1\navTaZjpr1iw4Ojqqhj+/LupsYV8RCwsL3L17FwkJCWjVqhU2b95cpmB7tujV0tLC0KFDERQUhC++\n+AJ5eXkICAhQFenPk5OTA6lUCrlcDqVSibCwMMTGxsLGxgZAaRH94YcfYtSoUTAxMYFEIkGTJk3Q\nsWNHvP3224iOjoazszMuXryIyZMnY+PGjbC3t4eBgQGCg4Px/vvv49y5czh37hy6dOlS7vydOnWC\nqakpPv30U8ydOxctW7bEgwcPsGXLFvTu3RsSiQRyuRwAkJ2djYYNG+LJkyfQ0dEBUFpcb9u2DcXF\nxQBQbtvhw4cjODgYlpaWMDQ0RGBgII4fP45jx4691M/Cy8sL27Ztg729PczMzLB7924EBQXhl19+\nqXRfuVyuemH3Vdy6lYgjRw5CKpWiVy833Lr1F27d+gs3b15DfHw8WrduAwuLNxETcxl6enpo1qzF\nK5/zdWBioo9Hj7JruxmvHeZSHjNRj7mox1zUqy+5pKfnVLkXvirnel0zffgwFTk5BVVqX3XerNTJ\nwr6yXtUuXbpg7NixeOeddyCRSDBx4sQyQ0f+vv+nn36KFStWYNCgQZBIJPDz88PIkSPVvrj6dF8X\nFxcMGjQIHh4ekEql6NSpE0aMGIGEhARoa2sjODgYq1atwqpVq9CoUSO88847cHZ2BgAEBgZi7dq1\nuHv3Lpo2bYqFCxeqxtJv2rQJn3zyCb777jt06tQJrq6uaq9RJpNh+/btCAwMhJ+fH7Kzs6Gvr4+B\nAwdi8eLFAEp7y3v37o2BAwdi06ZNWL58OVauXIk1a9agWbNm8PHxwVdffYXMzMxy206dOhVFRUXw\n9fVFdnY2OnTogE2bNpUZlvQivLy8kJWVhcmTJyMtLQ2WlpbYvHlzmaE76vIFSt+DWL58Oe7fv68a\n218Vf/55FSUlJSgpKcH+/T+olstkpb/+f/11Fzt2bAUAWFt3gJ/fO1U+FxEREdWOS5cuYuPGr3H3\n7l20aNECM2f6o1Onrti4MQiRkb8AkKBHDxfMnOkPXV09fPfdZiQk3Mbnn5fOvpeQcBvjx/vhzJkL\nuHTpIgID18Le3gFHjoSjYcOGGDnSD2PGjENQ0DrEx1/G1au/IyUlGdOnz67dC3+GRLxOE5cTVcEN\no8XPfSR4Q/kQ6Ye9XtsxetWpvvQevSzmUh4zUY+5qMdc1Ksvudy+fRNGQw9U+1Ccl/33OyMjA35+\nXpgzZx4GD3ZHVNRZLF36Cbp1s0FRURE++2wVtLSk+OyzRdDV1cXSpSvw3XebcedOApYvLx0enZBw\nGxMmjEZkZAwuXbqIWbOm4r33pmHs2PE4ezYSixbNR0hIOIyNTTBz5hS4uvbHiBGjXvraqrPHXiOm\nuyQiIiKi+is6+ixatTLHkCHDIJFI0LNnL6xZE4gLF85j2rRZaNzYAI0aNcKMGXNw8mQECgsLKz2m\nVCrFmDHvQEtLC71794WOjg6SkpJq4GqqjoU9EREREdVp6elp5SY9MTdvjZKSEjRv/r9355o3bwEh\nBB49eljpMRs1UjdzX8k/1+hqwMKeiIiIiOo0ExNTPHr0qMyy8PCfIJFI8OBBimpZcnKSakITPvQO\nfgAAC9FJREFULS0tFBf/bxbFzMzHNdbe6lInX54lIiIiotpxR1n939J+R5kGg8o3U3F27omgoHU4\nfvwo+vcfiKios/jvf/dg8GB3fPPNeixdugISiRY2bgxCjx69oKurB3PzNxAauh+pqanQ0WmI//73\nh8pP9P9pa/8zM/f901jYExEREdELadPGEnePTUT6C2xrZFT1b541+P/nelGNGxtg9eqvERS0Dl9+\nGYCWLVti1ap1aNfuTWzY8DXeeccPRUVF6NWrD2bN+gAA0KePK86fj8KECX7Q0dHDO+/8G+fORVZw\nlv/N3DdgwCAEBq7BgwfJmDdvYZWusTpwVhyq8zgrjnr1ZYaGl8VcymMm6jEX9ZiLesylPGaiHuex\nJ6pARY8EX/ZRHhEREVFdxcKe6jyLC7Of+6jvZR/lEREREdVVLOypzmvfvj0f9REREVG9x+kuiYiI\niIg0AAt7IiIiIiINwMKeiIiIiEgDsLAnIiIiItIALOyJiIiIiDQAC3siIiIiIg3Awp6IiIiISAOw\nsCciIiIi0gAs7ImIiIiINAALeyIiIiIiDSARQojabgQREREREb0a9tgTEREREWkAFvZERERERBqA\nhT0RERERkQZgYU9EREREpAFY2BMRERERaQAW9kREREREGoCFPdVZf/zxB0aNGgUbGxt4e3vjypUr\ntd2kGhEbG4u3334b9vb2GDhwIPbu3QsAyMrKwowZM2Bvbw83Nzfs37+/zH7r1q2Ds7MzHB0dsXLl\nSmjqTLepqano0aMHTp8+DYC5KBQKTJ06FXZ2dujbty927twJgLnExcXBx8cHdnZ2GDJkCMLDwwEw\nFyKq4wRRHVRQUCB69+4t/vOf/4ji4mKxf/9+4ezsLHJzc2u7adUqMzNTODg4iEOHDgkhhLh69apw\ncHAQUVFRYubMmeKjjz4ShYWF4sqVK8LBwUFcuXJFCCHEzp07haenp0hNTRWpqalixIgRYuvWrbV5\nKdXmvffeEx06dBCnTp0SQoh6n8uIESPEmjVrhFKpFLdu3RIODg7i0qVL9ToXpVIpnJ2dxfHjx4UQ\nQly4cEF07NhRJCUl1btcrly5IlxcXJ67PiwsTPTr109069ZNTJkyRaSmpqrWXb16VYwcOVJ069ZN\neHl5icuXL6vWZWZmiunTpws7Ozvh6uoq9u3bV63X8U+rLJe9e/eKgQMHCjs7OzFy5Ehx4cIF1br6\nnMtTUVFRwtrausy/yZqaS2WZXLhwQXh7e4tu3boJDw8PER0drVpXHZmwsKc66fTp08LV1bXMsmHD\nhokjR47UUotqxp9//ik++uijMstmzpwpgoODRceOHcX9+/dVy5cvXy6WLVsmhBBi1KhRIiQkRLXu\n2LFjYujQoTXT6Br0ww8/CH9/f+Hm5iZOnTolnjx5Ijp06FBvc7l8+bLo1auXKCkpUS27c+eOSEpK\nqte5ZGRkCGtra9UNcmxsrOjWrZtISUmpV7ns27dP2NvbCycnJ7Xr//zzT2FnZyfi4+NFQUGB+OST\nT8TkyZOFEJV3rlR0g/S6qyyXX3/9VTg5OYlr164JIYQIDQ0V9vb24vHjx/U6l6cyMzOFq6trmcJe\nU3OpLBOFQiG6d+8ufv75ZyGEEOHh4aJ79+6ioKCg2jLhUByqkxISEmBlZVVmmYWFBRISEmqpRTXD\n2toaAQEBqs+ZmZmIjY0FAMhkMrRq1Uq17tk8EhIS0LZt2zLr7t69WzONriF37tzB999/j6VLl6qG\nRyQmJkJbW7ve5nL16lW0bdsWq1evhouLCwYPHozLly8jMzOzXudiaGiI0aNH44MPPkDHjh0xbtw4\nLF68GBkZGfUml2+//Ra7du3C+++//9xtwsPD0b9/f3Tu3BlyuRwffvghzpw5g/T0dERHR0MqlcLX\n1xdSqRQ+Pj5o2rQpTp8+jdzcXJw4cQKzZs2CtrY2unTpAg8PDxw4cKAGr7BqXiSXBw8eYNKkSXjz\nzTcBAF5eXtDS0sLNmzfx66+/1ttcnlq6dCnc3d3LLNPEXF4kkwMHDqBnz57o378/AMDd3R3bt2+H\nRCKptkxY2FOdlJeXBx0dnTLLdHR0kJ+fX0stqnnZ2dl4//330blzZzg6OqJBgwZl1jds2FCVR15e\nHho2bFhmXUlJCQoLC2u0zdVFqVRi/vz5WLRoERo3bqxanpubW69zyczMxPnz52FkZIRTp05h1apV\n+Pzzz/HkyZN6nYsQAg0bNsT69etx5coVfPPNN1ixYgVycnLqTS4jR47EgQMH0KlTp+du8/cOFEND\nQxgaGiIhIQF37tx5budKZTfUr7MXyWX48OGYOHGi6vPFixeRm5uLtm3bVtjppOm5AMDBgweRnZ0N\nPz+/Mu+faGIuL5LJH3/8AVNTU8yYMQOOjo7w8/NDUVERtLW1qy0TFvZUJ6kr4vPy8qCrq1tLLapZ\n9+7dw+jRo9GkSROsX78eurq65YqL/Px8VR7PFidP10mlUsjl8hptd3XZsGED3nrrLbi4uJRZrqOj\nU69zkcvlMDQ0xOTJkyGTyWBjY4MBAwZg/fr19TqX48eP47fffsOAAQMgk8nQp08f9O3bt17lYmxs\nXOk26jpQnmZQUedKZTfUr7MXyeVZt27dwuzZszF79mwYGhrW61ySk5Oxfv16rFq1CgAgkUhU6zQx\nlxfJJDMzE/v27cPYsWMRFRUFT09PTJkyBdnZ2dWWCQt7qpMsLS1x586dMsvu3LlT5jG5prp69Sp8\nfX3Rq1cvbNiwAXK5HG+88QaKiorw4MED1XbP9qhZWVmVyUtdT0FdduTIERw+fBgODg5wcHBASkoK\n/P39cerUqXqdi4WFBYqLi8v0nJWUlKBDhw71OpeUlJRyBbxMJkPHjh3rdS5/p66QeNqBUlHnSmU3\n1Jri7NmzGDNmDMaNG4dJkyYBqLjTSZNzEULg448/hr+/P4yNjcvNFlVfc5HL5ejTpw+cnZ0hlUox\nZswY6OrqIi4urtoyYWFPdZKTkxMKCwuxe/duFBcXY//+/UhPTy/XY6tpUlNTMXnyZLz77ruYP3++\narmenh7c3Nywbt065OfnIz4+HuHh4fD09AQAeHp6Ytu2bVAoFEhNTcXmzZvh5eVVW5fxjzty5Agu\nXLiAmJgYxMTEoEWLFvjqq68wbdq0ep1Lz549oaOjg+DgYCiVSsTFxSEiIgJDhgyp17n06NEDf/75\nJ0JDQwEAMTExiIiIwLBhw+p1Ln/39xuZ9PR0ZGVlwcrKqsLOlco6GjRBSEgI5syZg6VLl2LKlCmq\n5fU1lwcPHiA+Ph5Lly6Fg4MDvLy8IIRAnz59EBcXB0tLy3LDSOpDLhYWFuUK9JKSEgghqi+TV38n\nmKh2XL9+Xfj6+gpbW1vh7e1dJ96gf1XffvutsLa2FjY2NqJbt26iW7duwsbGRnz11VciMzNTzJ49\nWzg4OAhXV1fx448/qvZTKpUiMDBQuLi4CEdHR7Fy5coyM6Vomqez4gghxOPHj+t1Ln/99ZeYOHGi\ncHBwEG5ubiI0NFQIwVxOnjwphg8fLuzs7MSwYcNERESEEKL+5XL+/PkKZ8Wxt7cXFy9eFPn5+eKT\nTz4RU6ZMEUL8b5aTXbt2iaKiIrFv3z7Ro0cPkZeXJ4QondHjww8/FHl5eeLKlSvC0dGxTv0dXVEu\nUVFRokuXLiI2Nrbcuvqcy7Pu378v3nzzTdV1a3IuFWXyxx9/iC5duohTp06JkpISsWPHDtGjRw+R\nm5tbbZmwsCciIqqn/l6ULF68WCxZskT1+ciRI6r52qdMmSLS0tJU6yrqXKnoBqkuqCiXd999V3To\n0EHY2NioOllsbGzEmTNnhBD1N5dn3b9/v9w89pqaS2WZnDt3Tnh5eQlbW1sxYsQIER8fr1pXHZlI\nhODX5hEREVHpcJvg4GAsXry4tpvyWmEu6jGX8mo7E46xJyIiIgDA0aNH8fbbb9d2M147zEU95lJe\nbWfCHnsiIiIiIg3AHnsiIiIiIg3Awp6IiIiISAOwsCciIiIi0gAs7ImIiIiINAALeyIiIiIiDcDC\nnoiIiIhIA/w/ZKV9+Zwjd7sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x277f04bb160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Enrollment summary graph\n",
    "cat = ['Registered participants', \n",
    "       'Visited at least one step',\n",
    "       'Completed at least one step',\n",
    "       'Commented at least once',\n",
    "       'Fully Participated',\n",
    "       'Purchased Statement']\n",
    "count = [len(enrolled_learners),\n",
    "         len(enrolled_learners.intersection(stepstart_learners)),\n",
    "         len(enrolled_learners.intersection(stepcomplete_learners)),\n",
    "         len(commenting_learners),\n",
    "         len(fullypart_learners),\n",
    "         len(statement_learners)\n",
    "         ]\n",
    "data = {'cat': cat,\n",
    "        'count': count}\n",
    "\n",
    "plt.rc(\"figure\", figsize=(10, 5))\n",
    "summary = pd.DataFrame(data, columns=['cat', 'count'])\n",
    "ax = summary.plot(kind='barh', color=THEME_COL, title='{} enrollment summary'.format(COURSE_SHORTNAME))\n",
    "ax.invert_yaxis()\n",
    "ax.set_yticklabels(summary.cat)\n",
    "ax.legend(loc=4, prop={'size':13})\n",
    "ax.get_xaxis().set_major_formatter(\n",
    "    tkr.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "\n",
    "for item in ([ax.xaxis.label, ax.yaxis.label] +\n",
    "             ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(13)\n",
    "ax.title.set_fontsize(14)\n",
    "\n",
    "rect_labels = []    \n",
    "\n",
    "# http://matplotlib.org/1.2.1/examples/pylab_examples/barchart_demo2.html\n",
    "for rect, label in zip(ax.patches, count):\n",
    "    width = rect.get_width()\n",
    "    \n",
    "    if (width < 200): # The bars aren't wide enough to print the ranking inside\n",
    "        xloc = width + 1 # Shift the text to the right side of the right edge\n",
    "        clr = 'dimgrey' # Black against white background\n",
    "        align = 'left'\n",
    "    else:\n",
    "        xloc = 0.98*width # Shift the text to the left side of the right edge\n",
    "        clr = 'white' # White on magenta\n",
    "        align = 'right'\n",
    "    \n",
    "    yloc = rect.get_y() + rect.get_height()/2.0\n",
    "    label = ax.text(xloc, yloc, '{:,.0f}'.format(width), horizontalalignment=align,\n",
    "                     verticalalignment='center', color=clr, weight='bold', fontsize=12,\n",
    "                     clip_on=True)\n",
    "    \n",
    "    rect_labels.append(label)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining the cumulative growth of course erolments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lscRVbInsnDlzWLhwIdHR0ZfcZuXKldx99938+9//BqB9+/a89957GAwGNm/e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\nQibgEpGhTcsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x277f09d8ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<p>The top 3 days for enrollments where:</p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Date</th>\n",
       "      <th>Enrollments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2018-04-16</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-17</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-20</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_cumulativeCount(df, group, groupset, index, title, start, end):\n",
    "    plt.rc(\"figure\", figsize=(15, 10))\n",
    "    df=df[df[group].isin(groupset)]\n",
    "    df=df.reset_index().set_index(index)\n",
    "    df.sort_index(inplace=True)\n",
    "    df=date_limiter(df, start, end)\n",
    "    df['Total enrollments']=range(len(df))\n",
    "     \n",
    "    ax=df['Total enrollments'].plot(title=title, color=THEME_COL)\n",
    "    \n",
    "    ax2 = ax.twinx()\n",
    "    tmp = df.groupby(df.index.date).size()\n",
    "    ax2 = tmp.plot()\n",
    "    top = tmp.sort_values(ascending=False)\n",
    "    \n",
    "    ax.axvspan(pd.to_datetime(COURSE_START_DATE), pd.to_datetime(COURSE_END_DATE), color='grey', alpha=0.4, lw=0)\n",
    "    \n",
    "    grey_patch = mpatches.Patch(color='grey', alpha=0.4, label='Course running')\n",
    "    acum_line = mlines.Line2D([], [], color=THEME_COL, label='Total enrollments')\n",
    "    day_line = mlines.Line2D([], [], label='Enrollments by day')\n",
    "    \n",
    "    ax2.legend(loc=2, prop={'size':13}, handles=[grey_patch, acum_line, day_line], bbox_to_anchor=(.07, .93), borderaxespad=0.)\n",
    "    \n",
    "    ax.set_xlabel('Date')\n",
    "    ax.set_ylabel('Accumulated Enrollments')\n",
    "    ax2.set_ylabel('Daily Enrollments')\n",
    "    \n",
    "    ax.get_yaxis().set_major_formatter(\n",
    "    tkr.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "    ax2.get_yaxis().set_major_formatter(\n",
    "    tkr.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "    \n",
    "    for item in ([ax.xaxis.label, ax.yaxis.label, ax2.yaxis.label] +\n",
    "             ax.get_xticklabels() + ax.get_yticklabels() + ax2.get_yticklabels()):\n",
    "        item.set_fontsize(13)\n",
    "    ax.title.set_fontsize(15)\n",
    "    ax2.grid(None)\n",
    "    align_axis(ax, ax2)\n",
    "    plt.show()\n",
    "    return top\n",
    "\n",
    "    \n",
    "# http://stackoverflow.com/a/28527815/1027723        \n",
    "def align_axis(ax1, ax2, step=1):\n",
    "    \"\"\" Sets both axes to have the same number of gridlines\n",
    "        ax1: left axis\n",
    "        ax2: right axis\n",
    "        step: defaults to 1 and is used in generating a range of values to check new boundary \n",
    "              as in np.arange([start,] stop[, step])\n",
    "    \"\"\"\n",
    "    ax1.set_aspect('auto')\n",
    "    ax2.set_aspect('auto')\n",
    "\n",
    "    grid_l = len(ax1.get_ygridlines())  # N of gridlines for left axis\n",
    "    grid_r = len(ax2.get_ygridlines())  # N of gridlines for right axis\n",
    "    grid_m = max(grid_l, grid_r)  # Target N of gridlines\n",
    "\n",
    "    #  Choose the axis with smaller N of gridlines \n",
    "    if grid_l < grid_r:\n",
    "        y_min, y_max = ax1.get_ybound()  # Get current boundaries \n",
    "        parts = (y_max - y_min) / (grid_l - 1)  # Get current number of partitions\n",
    "        left = True \n",
    "    elif grid_l > grid_r:\n",
    "        y_min, y_max = ax2.get_ybound()\n",
    "        parts = (y_max - y_min) / (grid_r - 1)\n",
    "        left = False\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "    # Calculate the new boundary for axis:\n",
    "    yrange = np.arange(y_max + 1, y_max * 2 + 1, step)  # Make a range of potential y boundaries\n",
    "    parts_new = (yrange - y_min) / parts  # Calculate how many partitions new boundary has\n",
    "    y_new = yrange[np.isclose(parts_new, grid_m - 1)]  # Find the boundary matching target\n",
    "\n",
    "    # Set new boundary\n",
    "    if left:\n",
    "        return ax1.set_ylim(top=y_new, emit=True, auto=True)\n",
    "    else:\n",
    "        return ax2.set_ylim(top=y_new, emit=True, auto=True)\n",
    "\n",
    "    \n",
    "tbl_data = plot_cumulativeCount(enrolments,\n",
    "                     'learner_id',\n",
    "                     enrolled_learners,\n",
    "                     'enrolled_at',\n",
    "                     '{} enrolment accumulation'.format(COURSE_SHORTNAME),\n",
    "                     COURSE_OPEN_REGISTRATION,\n",
    "                     offsetDays(COURSE_END_DATE,7)\n",
    "                    )\n",
    "tbl_data = tbl_data.head(3).to_frame().reset_index()\n",
    "tbl_data.columns =['Date','Enrollments']\n",
    "HTML('<p>The top 3 days for enrollments where:</p>'+tbl_data.to_html(index=False))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Course Step Participation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Y9e/f3+p4HicjI0MTJkxQs2bNNHXq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\nJRAAAAAADEIJBAAAAACD/H+3/fQ4hio2hwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x277f1168a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Registrations (n.)</th>\n",
       "      <th>Fully participated (n.)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country Code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>563</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>--</th>\n",
       "      <td>185</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AU</th>\n",
       "      <td>52</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>42</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>29</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>28</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CN</th>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>23</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Registrations (n.)  Fully participated (n.)\n",
       "Country Code                                             \n",
       "GB                           563                     85.0\n",
       "--                           185                     26.0\n",
       "AU                            52                      3.0\n",
       "IN                            47                      5.0\n",
       "US                            42                      6.0\n",
       "SA                            29                      3.0\n",
       "RU                            28                      5.0\n",
       "ES                            27                      1.0\n",
       "CN                            25                      NaN\n",
       "NG                            23                      2.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1 = enrolments[enrolments['role']=='learner']['detected_country'].value_counts()\n",
    "d2 = enrolments[(enrolments['fully_participated_at'].notnull()) &\n",
    "                              (enrolments['role']=='learner')]['detected_country'].value_counts()\n",
    "\n",
    "\n",
    "tmp = pd.concat([d1, d2], axis=1)\n",
    "tmp.columns = ['Registrations (n.)', 'Fully participated (n.)']\n",
    "tmp.index.names = ['Country Code']\n",
    "tmp = tmp.sort_values('Registrations (n.)',ascending=False).iloc[0:10]\n",
    "\n",
    "\n",
    "ax = tmp.plot(kind='bar', figsize=(15,10), color=[THEME_COL,'black'],\n",
    "            title='{} top 10 participant\\'s detected country'.format(COURSE_SHORTNAME))\n",
    "\n",
    "ax.set_ylabel('count')\n",
    "ax.set_xlabel('Country')\n",
    "\n",
    "# ax.get_yaxis().set_major_formatter(\n",
    "#     tkr.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "\n",
    "for item in ([ax.xaxis.label, ax.yaxis.label] +\n",
    "             ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(13)\n",
    "    ax.title.set_fontsize(15)\n",
    "\n",
    "ax.grid(axis='x')\n",
    "\n",
    "    \n",
    "ax.legend(('Registered', 'Fully participated'), prop={'size':13})\n",
    "\n",
    "plt.show()\n",
    "tmp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3zpx3o0qVKtZrs6TUD8UHDx7Uiy++mOl2tx7FiYyM1KVLl/Tss89q9+7d+v333xUSEqI3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\ny7a5Z5Xo6CvZNu65c5ezZWw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MTIwSExN5rKgBJk+erNjYWF1//fWSpAEDBni+\nd1auXKnly5crMzPTyhIbjemdPlBRUaE2bdpIkjp27KjWrVvrjDPOkCS1aNFC1dXVVpZne/zB4n0V\nFRWezyhO34ABA2q9Pnz4sGbPnq3Nmzdr4cKFio2NtagyeyorK1OnTp0kSR9//LFCQ0PVvXt3SdKZ\nZ57pmWKD37Z161ZFRkYqMjJSvXv3troco40ePVqpqalWl2ErlZWVatmypaSffte7du3q+W53OBxq\n1ox1Bhvj4MGDSkxM1J49e5Samqphw4ZZXZLtfPrpp3r88cc9r08eGxsxYoTmzZtnRVlewW+VD/xy\nupfT6az1msHV36ekpMTqEozz9NNPq1+/flaXYWs5OTm67rrrdOTIEa1evZrA1wgdOnTQd999J0nK\nzc1VeHi459jOnTvVuXNnq0qznZycHA0YMEBPPvmkIiMjlZqaqqqqKts+i9KUtWzZku/1Buratas+\n/fRTSdKGDRs0ZMgQz7H333/fc7MHp2/NmjVyuVxq1aqVVq9eTeBrpOrqas/AjaRao3o/36iwK0b6\nfKCqqkqvvPKK50ugsrKy1ofmxIkTVpVmBL5cG2fMmDF1/uBzu9365ptv1KZNG6YmNVJFRYXmz5+v\n1atXa+bMmbrpppusLsm2Ro4cqbvvvluDBw9Wdna2nnvuOUnyjJyOHDnS4grto02bNoqLi1NcXJw+\n/fRTZWVlqaqqSlOmTJHL5VJMTIxnVBW/T15enrp27Wp1GbYyadIkTZkyRT179tSuXbt0//33S5Ie\nf/xxvfLKK0pKSrK4QvsoKyvTnDlztGnTJt1///2eaYlonLPPPlv5+fm66KKLJEm9evXyHNu9e7fO\nO+88q0r73XimzwdO548+FnJpvOTkZM2ZM8fqMmxnxYoVddqCgoLUuXNnde/eXa+//rqmTJliQWX2\n9cknn+i+++5Tu3bttHDhQlaW/J3cbreefPJJ7dq1S9HR0Ro9erQkqX///hoxYoTmzJmj5s25V9lY\nx44dU3Z2tpYtW6Y9e/bos88+s7ok26jvGZ6fb5qtWLFCSUlJcrlcFlRmX3l5edq1a5eGDRumHj16\nSJJuv/12XXvttRo1apTF1dnH0KFDdejQIUVHR59yO6tZs2b5uSr7euKJJ7R9+3YtXbpUQUFBnvaf\nb5oNHjxYt956q4UVNh6hz4fKysrUtm1brVy50jM6FRISoqFDh1pcGfCT3bt3Kz09XWvXrlWHDh30\n/vvvW12SrVx88cWqrq5W7969TxlGsrKy/FyVeaqrq3nGpxHWr1+vqqoqxcTE6PDhw0pMTNTnn3+u\nqKgoxcbGshdaA9R3M/fnm2YxMTFsLwDL/OMf//jNadsPPfSQn6qxv+PHjys+Pl5lZWVyuVzq3Lmz\nDh48qOzsbLVv317p6em2vflI6POBH374QZMnT1ZERIRmzJihyy+/XBdddJFqamr0xRdfaPny5br4\n4outLhMB6sSJE1q3bp0yMjK0Y8cORUdHa8yYMRo0aBDP+zRQfaOnv/TzaBV+28aNG+u0/bwXGs/4\nNExWVpYeeeQR/f3vf9fYsWN1zz33qLCwUH/729+Unp6uPn36aPr06VaXaYyamhr+/WyA++67TwsW\nLPC8fu+992o9gxYdHc0WQg2wd+9e7d27VyNGjJD00yh0UlKSJk+e7JmmiNNXVVWlV199VRs2bNDB\ngwcVGhqqqKgoxcfH1xr9sxtCnw8kJyd7nvNxOp3q37+/Z4uGhx56SMXFxVq0aJHFVSLQHDp0SK++\n+qqWL1+ukJAQjR8/Xk888YTWrFnDiqiNdDrLNt94441+qMQMUVFRddrcbreKi4vVu3dvPfvsswoJ\nCbGgMvsZNWqU7r//fvXv31/l5eUaMGCAnn32WQ0cOFBff/21/vrXv2rDhg1Wl2krBQUFys/P1+WX\nX15rM/Yvv/xSiYmJjOo3wBVXXFFrS4aTl8WX2JqpIXbt2qWJEydqwoQJmjlzpqSfZpolJiYqLy9P\nGRkZrOALSSzk4hM5OTnKyMios2qnJN18882Ki4uzoCoEumHDhik6OlppaWm6/PLLJUlPPfWUxVXZ\n27p16371uMPhIPQ1wLvvvltv+7FjxzR//nw9/PDDWrhwoZ+rsqeioiL1799f0k9LkDscDs8KvRdc\ncAGrIDdQVlaWZs+erXbt2un48eN66aWXdOmll2rp0qVasmSJwsLCrC7RVn453vDL14yanr7Fixfr\nzjvv1KRJkzxtbdu2VVpamtLS0vTYY49p6dKlFlZoL/XNOPmlyMhIP1TifYQ+H/jhhx907rnnel6f\nHPLOPfdcHTlyxIqyEOCio6OVk5Oj8vJyjR071rb/aDUlLMjkH61atdLMmTNZKKMBnE6nKisrFRQU\npC1btqhPnz6eaUklJSUKDg62uEJ7ee6557RgwQK5XC69/PLLWrp0qTp06KC33npL8+bNYxp3A/0y\n1BHyGm/nzp2nvIE7adIkDR8+3M8V2duvLRT47bffSpI+//xzf5XjVYQ+H2jdurWOHDmiM888U5I8\nw+2SVFxcrLZt21pVGgLYI488orKyMr3xxht69NFHlZKSoiNHjqioqIjpnWjyQkJCVF5ebnUZtnHl\nlVfq+eef18iRI7Vq1apaC5E888wznlFAnJ6DBw96tgyJi4vTokWL1KNHD73xxhvq0qWLxdUh0NU3\ns0z6aV+56upqP1djb/XNODlw4IASExNVWVnJ5uyobcCAAcrIyKj32LJlyzRo0CA/VwT8pG3btrr5\n5pu1evVqpaamKjo6WhMnTtSoUaP0z3/+0+rygFMqLCzkeb4GmDVrlrKysnT11VerS5cuGj9+vCTp\nqquuUnZ2tmbMmGFxhfbicDg8o1EtWrSQw+FQWloaga+RampqtHfvXuXn5ys/P1/V1dW1XrPcxOn7\nwx/+oNzc3HqP5ebm6oILLvBzRWZZs2aNYmNj1bp1a9tves9CLj7wxRdfKC4uTrfccovGjh2rzp07\n69ChQ8rKytLLL7+s119/nY1c0WQcPXpUb7zxhv71r39p5cqVVpeDAJafn1+n7cSJE/rmm2+Ulpam\nP/3pT6w42QA1NTUqLS2tFZbXrl2rQYMGqX379hZWZj+/tfAIGqZ3795yOBynDHcOh8O2U+j8LScn\nR4mJiXrwwQc1ZMgQNWvWTG63Wzk5OXrggQeUkJCg6667zuoybaesrEzJycnKzc1VUlKSEZveE/p8\n5JNPPtHcuXO1e/duz93Bnj17at68eerTp4/F1QFA01PfH4ItWrTw7IU2ffp02+6PBHvr27evsrKy\nPJ/N8ePHa9myZbU+qyyND6ssX75cCxYsUHV1tdq2bavDhw+rRYsWmjFjhiZMmGB1ebaTm5urhIQE\n9ezZU/Pnzz/lpvd2Q+jzsW+//VYHDx5USEiIzj//fKvLAQAADcTIFJq6iooKbdu2TaWlperYsaP6\n9u1r6z3lrJKSkqLly5dr7Nixuummm+pdZMiuN3gIfQAAAAAC3sl7GtZ3o8fON3gIfQAAAABgMFbv\nBAAAAACDEfoAAAAAwGCEPgAA/KSiokLFxcVWlwEACDCEPgBAQMrJydHEiRMVFhamsLAw3Xrrrdq1\na5dP3/PGG2/0+XsAAPBLhD4AQMBZvny5EhMTNWnSJH3wwQfatGmTBg8erFtuuUV79+712fuWlpb6\nrG8AAE6F1TsBAAGloqJCgwcPVmpqqiIjI2sdS01N1SWXXKJ+/fpp/vz5+s9//qPg4GDFxMTo7rvv\nVosWLZSQkKAOHTpo1qxZkqT3339fc+fO1bvvvqsVK1ZozZo1CgkJ0YYNGxQaGqpp06YpNjZW06ZN\n04YNG3TGGWfo3nvvVevWrZWVlaWqqioVFRUpPj5eH3/8sZ5//nlPPTfccIOmTJmia6+91q/XCABg\nFkb6AAABZdu2baqurtaQIUPqHJs5c6ZGjBihO++8U82aNdN7772nZcuWacuWLVqyZMkp+zx5A9/c\n3FwNGTJEW7duVXx8vObOnavKykqlpaXpnHPO0eOPP674+HhJ0vbt23XPPfdo/fr1crlc2rJli2c0\nsKCgQPv371dUVJSXrwAAINAQ+gAAAaW0tFRt27ZVs2b1fwUWFRVpx44dSkpKUnBwsM466yzddddd\n+ve//31a/Xfp0kUul0vNmjXTqFGjdPToUZWUlNR7bqdOnRQWFqY2bdqoa9euuuSSS/T2229Lktau\nXaurr75aQUFBjftBAQD4f4Q+AEBA6dixow4fPiy3213nWFlZmQ4cOKDg4GC1a9fO096lSxcVFxfX\n+//8UkhIiOe/mzdvLkmqrq4+ZS0nc7lcWrt2rSQpOztbsbGxv/0DAQDwGwh9AICA0rdvX7Vo0UI5\nOTl1jiUmJuqpp55SeXm5Dh8+7GkvKipSu3bt5HQ61axZM1VVVXmO/Z7FWU6eFipJ0dHR+uSTT5SX\nl6cff/xR4eHhje4bAICfEfoAAAElKChIM2bM0AMPPKCNGzfK7Xbrxx9/VFpamvLy8pSYmKiBAwdq\n/vz5OnbsmA4cOKAlS5Z4Rt26deumDz/8UEePHlVxcbGWLVvWoPc+evToKY+HhIQoPDxcDz/8sK67\n7ro6oRAAgMYg9AEAAs6ECROUkJCgtLQ0DRw4UMOHD9fOnTuVkZGhiy66SIsWLVJVVZWGDx+u0aNH\nq3///rr33nslSePHj1fXrl0VFRWl+Ph4xcTE/Op7nRzcRo8eraSkJD3zzDOnPN/lcumLL76Qy+Xy\nzg8LAAh4bNkAAEATsm3bNiUlJXme7QMA4PdqbnUBAABAOn78uAoLC/XUU0/pL3/5i9XlAAAMwvRO\nAACagLKyMsXFxam6uloTJkywuhwAgEGY3gkAAAAABmOkDwAAAAAMRugDAAAAAIMR+gAAAADAYIQ+\nAAAAADAYoQ8AAAAADEboAwAAAACD/R/c3mt41/m20QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x277f11689b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Registrations (%)</th>\n",
       "      <th>Fully participated (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>0.355879</td>\n",
       "      <td>0.456989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>--</th>\n",
       "      <td>0.116941</td>\n",
       "      <td>0.139785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AU</th>\n",
       "      <td>0.032870</td>\n",
       "      <td>0.016129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>0.029709</td>\n",
       "      <td>0.026882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>0.026549</td>\n",
       "      <td>0.032258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>0.018331</td>\n",
       "      <td>0.016129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>0.017699</td>\n",
       "      <td>0.026882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>0.017067</td>\n",
       "      <td>0.005376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CN</th>\n",
       "      <td>0.015803</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>0.014539</td>\n",
       "      <td>0.010753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Registrations (%)  Fully participated (%)\n",
       "GB           0.355879                0.456989\n",
       "--           0.116941                0.139785\n",
       "AU           0.032870                0.016129\n",
       "IN           0.029709                0.026882\n",
       "US           0.026549                0.032258\n",
       "SA           0.018331                0.016129\n",
       "RU           0.017699                0.026882\n",
       "ES           0.017067                0.005376\n",
       "CN           0.015803                     NaN\n",
       "NG           0.014539                0.010753"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1 = enrolments[enrolments['role']=='learner']['detected_country'].value_counts(normalize=True)\n",
    "d2 = enrolments[(enrolments['fully_participated_at'].notnull()) &\n",
    "                              (enrolments['role']=='learner')]['detected_country'].value_counts(normalize=True)\n",
    "\n",
    "\n",
    "tmp = pd.concat([d1, d2], axis=1)\n",
    "tmp.columns = ['Registrations (%)', 'Fully participated (%)']\n",
    "tmp = tmp.sort_values('Registrations (%)',ascending=False).iloc[0:10]\n",
    "\n",
    "ax = tmp.plot(kind='bar', figsize=(15,10), color=[THEME_COL,'black'],\n",
    "            title='{} top 10 participant\\'s detected country'.format(COURSE_SHORTNAME))\n",
    "\n",
    "ax.set_ylabel('%')\n",
    "ax.set_xlabel('Country')\n",
    "\n",
    "# ax.get_yaxis().set_major_formatter(\n",
    "#     tkr.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "\n",
    "for item in ([ax.xaxis.label, ax.yaxis.label] +\n",
    "             ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(13)\n",
    "    ax.title.set_fontsize(15)\n",
    "\n",
    "ax.grid(axis='x')\n",
    "\n",
    "    \n",
    "ax.legend(('% Registered', '% Fully participated'), prop={'size':13})\n",
    "\n",
    "plt.show()\n",
    "tmp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UqTP1wANlNXnyDPXs+XSeM9za54ED+xQRsVjt23fSqFEhqlq1mrZs+VI+PqXz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\nAADwKaaVwLVr12rTpk2aNm2aY1lRUZF27typmTNnqlGjRoq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lbVXHjGPeMMw77j19zBuG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\nWAglEAAAAAAs5P8BCFU6T7rVdQQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2225829bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d1 = steps[(steps['first_visited_at'].notnull()) & (steps['learner_id'].isin(enrolled_learners))]['flstep'].value_counts().sort_index()\n",
    "d2 = steps[(steps['last_completed_at'].notnull()) & (steps['learner_id'].isin(enrolled_learners))]['flstep'].value_counts().sort_index()\n",
    "\n",
    "tmp = pd.concat([d1, d2], axis=1)\n",
    "\n",
    "ax = tmp.plot(kind='bar', figsize=(15,10), color=[THEME_COL,'black'],\n",
    "            title='{} count of steps visited and completed'.format(COURSE_SHORTNAME))\n",
    "\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_xlabel('Step')\n",
    "\n",
    "ax.get_yaxis().set_major_formatter(\n",
    "    tkr.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "\n",
    "for item in ([ax.xaxis.label, ax.yaxis.label] +\n",
    "             ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(13)\n",
    "    ax.title.set_fontsize(15)\n",
    "\n",
    "ax.grid(axis='x')\n",
    "    \n",
    "ax.legend(('Count of First Visited', 'Count of Last Completed'), prop={'size':13})\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hw4bpxIkTunTpkp5//nm1aNFCLVu21KhRo5SZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22258113080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_delta = d2 / d1\n",
    "ax = d_delta.plot(kind='bar',x='flstep',y=0, color=THEME_COL, figsize=(15,7),\n",
    "            title='{} proportion of steps visited which were completed'.format(COURSE_SHORTNAME))\n",
    "ax.set_ylabel('Ratio of completed to visited steps')\n",
    "ax.set_xlabel('Step')\n",
    "\n",
    "\n",
    "for item in ([ax.xaxis.label, ax.yaxis.label] +\n",
    "             ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(13)\n",
    "    ax.title.set_fontsize(15)\n",
    "\n",
    "ax.grid(axis='x')\n",
    "\n",
    "plot_margin =0.25\n",
    "x0, x1, y0, y1 = plt.axis()\n",
    "plt.axis((x0 - plot_margin,\n",
    "          x1 + plot_margin,\n",
    "          y0 - plot_margin,\n",
    "          y1 + plot_margin))\n",
    "ax.set_ylim(ymin=0)\n",
    "ax.set_ylim(ymax=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at which steps were last completed step by participants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GjFFwcLBy5sypgIAARUZGWv4jS5kyZTR58mRNmjRJ06dPl4eHh/r27Wu75u1B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\nAACQRSyFvri4OPXq1Uu7du2Sq6urrl2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x22259cbc4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<p>Porortion of top final last completed steps:</p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Step</th>\n",
       "      <th>Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>1.112412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>218</td>\n",
       "      <td>0.801418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>217</td>\n",
       "      <td>0.158333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>0.156010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>0.108911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>0.101587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>0.094660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>0.087613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>0.077206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>0.067460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample=steps[steps['learner_id'].isin(enrolled_learners)]\n",
    "\n",
    "step_type ='last_completed_at'\n",
    "\n",
    "#Sort on the basis of first_visited time, with most recent (largest) time first\n",
    "tmp=sample.sort_values(step_type,ascending=False).groupby('learner_id')\n",
    "#Now take the first (most recent) record for each learner\n",
    "tmp=tmp[[step_type,'flstep']].first().reset_index()\n",
    "#The date_limiter() function also sets the index\n",
    "tmp=date_limiter(tmp, index=step_type)\n",
    "tmp.groupby('flstep').size().sort_values(ascending=False).head(10)\n",
    "tmp = tmp.groupby('flstep').size().sort_index()\n",
    "\n",
    "tmp = tmp / (steps[(steps['last_completed_at'].notnull()) & (steps['learner_id'].isin(enrolled_learners))]['flstep'].value_counts().sort_index())\n",
    "\n",
    "#tmp = tmp.drop(tmp.tail(1).index) \n",
    "\n",
    "ax = tmp.plot(kind='bar',x='flstep',y=0,\n",
    "              legend=False,figsize=(15,7), color=THEME_COL,\n",
    "              title='{} proportion of final completed steps per sum of completed steps'.format(COURSE_SHORTNAME))\n",
    "\n",
    "ax.set_ylabel('Final completed step / count of completed steps')\n",
    "ax.set_xlabel('Step')\n",
    "\n",
    "for item in ([ax.xaxis.label, ax.yaxis.label] +\n",
    "             ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(13)\n",
    "    ax.title.set_fontsize(15)\n",
    "\n",
    "ax.grid(axis='x')\n",
    "\n",
    "x0, x1, y0, y1 = plt.axis()\n",
    "plt.axis((x0 - plot_margin,\n",
    "          x1 + plot_margin,\n",
    "          y0 - plot_margin,\n",
    "          y1 + plot_margin))\n",
    "ax.set_ylim(ymin=0)\n",
    "ax.set_ylim(ymax=1.2)\n",
    "plt.show()\n",
    "\n",
    "tbl_data = tmp.sort_values(ascending=False).head(10).to_frame().reset_index()\n",
    "tbl_data.columns =['Step','Ratio']\n",
    "HTML('<p>Porortion of top final last completed steps:</p>'+tbl_data.to_html(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tsDict={'comments': 'timestamp',\n",
    "        'steps_first': 'first_visited_at',\n",
    "        'steps_last':'last_completed_at',\n",
    "        'questions': 'submitted_at',\n",
    "        'enrolments': 'enrolled_at',\n",
    "        'unenrolments': 'unenrolled_at'\n",
    "        }\n",
    "\n",
    "tsData={'comments': comments,\n",
    "        'steps_first': steps,\n",
    "        'steps_last':steps,\n",
    "        'questions': qnresp,\n",
    "        'enrolments': enrolments,\n",
    "        'unenrolments': enrolments\n",
    "        }\n",
    "\n",
    "def activityHeatmap(df,typ='comments',title='',label=True):\n",
    "    tmp=df.reset_index().set_index(tsDict[typ])\n",
    "    tmp['dow']=tmp.index.dayofweek\n",
    "    tmp['hod']=tmp.index.hour\n",
    "    val='author_id' if typ=='comments' else 'learner_id'\n",
    "    tmp=tmp.pivot_table(index=['dow'], columns='hod',values=val,aggfunc='count')\n",
    "    \n",
    "    plt.rc(\"figure\", figsize=(15, 10))\n",
    "    cmap=sns.light_palette(THEME_COL, reverse=False,as_cmap=True)\n",
    "    ax=sns.heatmap(tmp, annot=label, linewidths=.5,cmap=cmap,fmt='g')\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_xlabel('Hour of Day')\n",
    "    ax.set_yticklabels(['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'][::-1])\n",
    "    ax.set_title(title)\n",
    "    for item in ([ax.xaxis.label, ax.yaxis.label] +\n",
    "             ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "        item.set_fontsize(13)\n",
    "        \n",
    "    ax.get_xaxis().set_major_formatter(\n",
    "        tkr.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "    ax.title.set_fontsize(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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YmMj09PTYsWPH2KdPn5hQKOTT/+OPP9jkyZNZt27dGGOMBQcHMw0NDZaQkMAYK9ro6Nix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\na8OqGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22258504748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "typ = 'steps_last'\n",
    "activityHeatmap( date_limiter(steps[steps[tsDict[typ]].notnull()]),typ=typ,\n",
    "                title='{} Heatmap showing time step is completed by hour of day and day of week'.format(COURSE_SHORTNAME),\n",
    "                label=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ULUpERERERJxCfQpERERERMQp1C1KRERERCQl5Hjwpr0udf4jl4cwtXJx/vy1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22258f58b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "activityHeatmap(comments, title='{} Heatmap showing comments by hour of day and day of week'.format(COURSE_SHORTNAME),\n",
    "                label=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total comments 889"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WqVOnAHDbbR2YMWMqKSlH8Hg8vPPOWzz77D8xDAMAqzU36Rs4cAg//PAd69at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\nINNtVvcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22258644e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def studyWeekDatePlot(ax):\n",
    "    for sd in STUDY_WEEK_DATES:\n",
    "        ax.axvline(pd.to_datetime(sd), color='grey', linestyle='--', lw=2)\n",
    "        \n",
    "def accessionPlotByGroup(df,group,attr,title='',enhancements=True):\n",
    "    #Plot the growth in comments for each week\n",
    "    fig, ax = plt.subplots()  \n",
    "    grouped = df.groupby(group)\n",
    "    for key, group in grouped:\n",
    "        group.plot(ax=ax, y=attr,label='Week {}'.format(key),title=title)\n",
    "        ax.get_yaxis().set_major_formatter(\n",
    "            tkr.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "        ax.annotate('{:,.0f}'.format(group.tail(1)[attr][0]),\n",
    "                    xy=(group.tail(1).index,group.tail(1)[attr]), \n",
    "                    xytext=(-40, 5), textcoords='offset points',\n",
    "                    fontsize=13 )\n",
    "        for item in ([ax.xaxis.label, ax.yaxis.label] +\n",
    "                 ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "            item.set_fontsize(13)\n",
    "            ax.title.set_fontsize(15)\n",
    "        ax.legend(loc=2,prop={'size':13})\n",
    "    if not enhancements: return\n",
    "    \n",
    "    ax=studyWeekDatePlot(ax)\n",
    "    \n",
    "if comments.index.name!='timestamp': comments=comments.set_index('timestamp').sort_index()\n",
    "comments['week_accnum']=''\n",
    "comments['week_accnum']=comments.groupby('week_number')['week_accnum'].cumcount()\n",
    "\n",
    "accessionPlotByGroup(comments,'week_number','week_accnum',title='{} Accession plot of comments by week'.format(COURSE_SHORTNAME))\n",
    "HTML('Total comments {:,.0f}'.format(len(comments)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: 'nan'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-25-1e67a1025b2e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m quizHeatmap(qnresp_pivot, percent=False,\n\u001b[1;32m---> 47\u001b[1;33m             title='{} Heatmap showing count of answer option selection by question (correct answer highlighted)'.format(COURSE_SHORTNAME))\n\u001b[0m",
      "\u001b[1;32m<ipython-input-25-1e67a1025b2e>\u001b[0m in \u001b[0;36mquizHeatmap\u001b[1;34m(df, percent, title)\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[1;31m#http://stackoverflow.com/a/31291200/454773\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mans\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcorrect_ans\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_patch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRectangle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcalcCoords\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0medgecolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'black'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m     for item in ([ax.xaxis.label, ax.yaxis.label] +\n\u001b[0;32m     28\u001b[0m              ax.get_xticklabels() + ax.get_yticklabels()):\n",
      "\u001b[1;32m<ipython-input-25-1e67a1025b2e>\u001b[0m in \u001b[0;36mcalcCoords\u001b[1;34m(ans)\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m#Define a function to find the co-rodinates in the grid of the correct answer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcalcCoords\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mans\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcorrect_ans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mcorrect_ans\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: 'nan'"
     ]
    },
    {
     "data": {
      "image/png": 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l7xBSVKiQp71DEBERkceRg/X+0BgNERERERFJd0o0REREREQk3anrlIiIiIiI\nI3CwrlNKNEREREREHIFj5RlKNEREREREHIISDRERERERSXfqOiUiIiIiIunOsfIM3XVKRERERETS\nn1o0REREREQcgbpOiYiIiIhIunOsPEOJhoiIiIiIQ1CLhoiIiIiIpDvHyjM0GFxERERERNKfWjRE\nRERERByBg7Vo2CTRWLp0aYrvtW/f3hZVioiIiIhkcY6Vadgk0QgJCeHHH3+kVatWtiheREREROTx\n41h5hm0SjYCAAEJCQqhXrx5VqlSxRRUiIiIiIo8XJRrxJk+ezO3bt21VvIiIiIjI48XBbm9rs7tO\n5cuXj3/9618AHD161FbViIiIiIg8HiyP+GcnGXJ720mTJmVENSIiIiIiWZhjZRoZcntbY0xGVJOs\ngPfHUKZ4SV5v+1qi6X3GDOWJAoUIfHuwnSITEcm8hk0bTdkSpXi97WtEREYw/INxnPnjd4wxtG7U\njDdf7WLvEOUx8+2Pq/l0xSKcLBZyZM/BiJ4DqVSqHAAXwy7TfkgPvpv5BXk8cts50sxt2LZ5lM1b\nlNcrNSUi6g7Dd3zCmRsXMRhal6rDm5WbA3DzXiRjdy/k9I1Q7sVG81bVlrQu6W3n6MXRxmhkSIuG\nv79/RlSTyOk/ztJ12Nus2bYxyXv/+WoBvxw9mOExiYhkdqfPnaXru2+zdtsm67QPFwRTpGBhVgUv\n5qsZ81n8f99w4PhhO0Ypj5szoeeYNn8On475iBUfLaDXq93oO2EYACs3/cBrw3oRdv1PO0eZuZ2+\ncYGuayax9uxP1mkf/vINRXLlY5XfBL5q+R6Lj2/kQNhpAN7dNo8n3fOzovVYPms8lAl7vuDy7ev2\nCl8SOFaDhm1aNAYNGsTw4cPJnz8/AI0bN7ZFNalatOor2r7ciicLFUk0ffeBn9mxbw8dmrfhVvit\nDI9LRCQzW7TqK9o2TnzuDPz3IOLi4gC48mcY0TExuOd0t1eI8hjK5urKuL4B5M+TD4BKpcpx9cY1\nLl29wqY92/jPex/Qok8nO0eZuS06voG2pevxpHt+67TAmv7Emf8d27dvEB0Xi0e2nNy8F8nui0f4\nqH5vAArnyseyFu+RJ1suu8Qu93GwweA2STT2799Pjx498Pf3p02bNljssFFGvj0EgF3791qnXf4z\njInBH/Df8TNY8v3yDI9JRCSzG9n7f+fOX/Ymmu7k5MSQKUGs276JRrXr83TRYvYITx5TXoWK4HVf\n8jvxvx98U9S+AAAgAElEQVTx0gt1eaJAIWYETATs203bEYysGd/dcdeFI4mmO1mcGLJ1LuvO/kyj\nYtUo4fkEh66eoYBbHj49soat5w8QHRfLGxWbUOzpwvYIXe7nWHmGbbpOeXl58fnnn3Ps2DFatWpF\ncHAwx44dIyIiwhbVpUlMbAyDJgUy/K2BFMib/+ELiIhIIlOHjmb3svXcuHWT2V9+Yu9w5DF05+5d\n3pk0nPOXLjCu73B7h5NlTK3Xi92dZnPjXgSzf11JTFws58PD8MyWk8XNRzL9xX8zYe8ijv551t6h\nioOxSYuGxWLB09OTwMBArl27xpo1a5gzZw5nz55l1apVtqjyoQ6fPEbo5YtMmvchBsPVa38SZwz3\noqMY208nKxGRlGzft5syxUtRKH8B3HLkoEWDl1m3fbO9w5LHzIUrl/j3uCGUeqoECybMIZurq71D\ncnjbQw9RJm9RCuXMg5tLdlqUqMm633/Gt1QdLBbwLVUHgKc8C1OtcBkOhoVQIX9x+wb9uHOwFg2b\nJBoFChSw/p8vXz46depEp0727Tv5TPnK/LjgO+vrWV/8hxu3buquUyIiD7F66wbW79jM6HeGERUV\nxeqtG/F+7gV7hyWPkZsRt/AP+DdtfVrSu8Mb9g4ny1h9Zi/rf9/H6NrdiIqNZvXZvdTxqsy/PApS\nIX9xVv62ndfKN+LqnZv8euU36x2pxJ4cK9OwSaIxffp0WxT7aBxs0IyISKZw37lzWM/+BM2YRMu3\nOmKxWPDxrk9Xvw52DE4eN4t/WM7lP6+wYddm1u/aDMT/3Jo/fha53T3jX+v7Pm3u20zDanQkaOd8\nWq4YjsVioVGxanSp8DIAsxv2471dn7P4+EYM0OdZXyoVKGGfmOUvDvYxt5jMPHrqzE17R5C6EvH3\n6g4LC7dzIKkrWNADyPxxFirkaf3/ypXMe0cwR4mzYEGPTL/PwTHidJRjKCFOzmbyc2dxxzl3ZvYY\nwbHi5KQD3B61TN5Mvz2tx/qkPfYN5GGGxbd8Osz2dASVPnu05Q6/nr5xpJFNWjQ6d+5MdHR0omnG\nGCwWC0uWLLFFlSIiIiIiWZuDtWjYJNEYPHgwgYGBzJ49G2dnZ1tUISIiIiLyeFGiAVWrVqV169ac\nOHECHx8fW1QhIiIiIvKYcaxMwyaJBkCPHj1sVbSIiIiIyOPHsfIM2zyw70HBwcEZUY2IiIiISNZl\necQ/O8mQRGPHjh0ZUY2IiIiISNZlsTzan51kSKKRcAfdqKiojKhORERERCTrUYsGbNq0iQYNGuDj\n48MPP/zAvHnzAI3bEBERERF5XNhkMPjcuXNZuXIlcXFx9OvXj3v37uHn50dmfjagiIiIiEimZoNu\nUDExMQwfPpzQ0FCio6Pp1asXRYoU4a233qJ48eIAdOzYkaZNm7Js2TKWLl2Kq6srvXr1on79+qmW\nbZNEw9XVldy545/8OmfOHLp27UqRIkWw2LGPmIiIiIiIJPbdd9+RN29epkyZws2bN/H19aV37968\n8cYbdOvWzTrf1atXWbhwIStWrODu3bt07NgRb29vXF1dUyzbJl2nvLy8mDhxIrdv38bd3Z1Zs2Yx\nZswYQkJCbFGdiIiIiEjWZ4MxGk2bNqVfv34AxMXF4eLiwpEjR/jxxx/x9/cnMDCQyMhIDh48SLVq\n1XBxccHd3Z3ixYtz4sSJVMu2SaIxYcIEypYta23BKFKkCAsWLKBp06a2qE5EREREJOuzwV2n3Nzc\nyJkzJxEREfTr14/+/ftTpUoV3n33Xb744guKFi3KrFmziIiIwMPDw7pczpw5CQ8PT7VsmyQaLi4u\ntGnTBjc3N+u0AgUKMGLECFtUJyIiIiKS9dnorlMXL16ka9eu+Pn50bx5cxo1akSFChUAaNSoEceP\nH8fDw4OIiAjrMpGRkXh6eqZabobc3lZERERERP4hGyQaV69epXv37gwZMgQ/Pz8AunfvzqFDhwDY\ntWsXFStWpHLlyuzbt4+oqCjCw8MJCQmhdOnSqZZtk8HgIiIiIiKSzmxwY6Xg4GBu3brFnDlzmD17\nNhaLhYCAACZMmICrqysFCxZkzJgx5MqVi86dO9OpUyeMMQwcOJBs2bKlWrYSDRERERGRx9SIESOS\nHd6wePHiJNPatWtHu3bt0ly2Eg0REREREUfgYI+KUKIhIiIiIuIIHCvPUKIhIiIiIuIQHCzR0F2n\nREREREQk3alFQ0RERETEETjYGA21aIiIiIiISLpTi4aIiIiIiCNwsBYNizHG2DsIsS2Lg30oHYkO\nHxEREckw9ZI+2yJNtnZM3zjSKHO3aJy9ae8IUlc8NwBhYeF2DkTsJTPv+4IFPTJ1fAkcIc6CBT2A\nzL2/4a84+f2WfQN5mGKegGNsz8weIzhWnEzeY+8wHu7dFzL99rQe6+czd5z8y8HOnY7Awa4dZ+5E\nQ0RERERE4jlYLxUlGo+ZK1cy75XOQoU8rf9n5jgd5eqhiIiIZDGOlWforlMiIiIiIpL+1KIhIiIi\nIuII1HVKRERERETSnWPlGUo0REREREQcgoO1aGiMhoiIiIiIpDu1aIiIiIiIOALHatBQi4aIiIiI\niKQ/tWiIiIiIiDgCBxujoURDRERERMQROFaeoURDRERERMQhKNEQEREREZF0p65TIiIiIiKS7hwr\nz1CiISIiIiLiGBwr07DJ7W2vXbvGpEmT+OCDD7h+/bp1+qxZs2xRnYiIiIhI1md5xD87sUmiMXTo\nUEqUKEGhQoXw9/cnNDQUgL1799qiOhERERGRrM/BEg2bdJ2Kioqiffv2AJQvX563336bhQsXYoyx\nRXUiIiIiIlmfgw0Gt0mLRmxsLCdOnADgueee46233uLf//43ERERtqhORERERCTrc7AWDZskGoGB\ngYwbN46rV68C0KxZM1599VUuXLhgi+pERERERCSTsUnXqfLly7Nw4ULr66NHj9K6dWtatmxpi+pE\nRERERLI+x+o5ZZsWjQdNmjQpvjKnDKlORERERCQLcqy+UxnyHA17DgIfNm00ZUuU4vW2rxERGcHw\nD8Zx5o/fMcbQulEz3ny1i91iExHJrIZNfY+yJUrz+iuvcS/qHqNnTuHQyaNgDFXKVSKoz1CyZctm\n7zDlMTFs6zzK5ivK65WaEhF1h+HbP+HMzYvx3+Wl6vBmleYA/HhuP8O2zeNJ9wLWZb9sFkhO1+z2\nCj3TW7n+e+Z//SWW//0YvRUZweWrV9i65Afy5clr5+gkCQdr0ciQRMPf3z8jqknk9LmzjJk9hYPH\nj1C2RCkAPlwQTJGChZkROIk7d+/SvGd7alR5jqrlKmV4fCIimdHpc2cZM2vy/86dpQH4eNGnxMXF\nsip4McYYBk8aSfCS+fTt0tPO0UpWd/rGBcbsWsDBsNOUzVcUgA9/+YYiufIxo2Ff7sTco/nyAGo8\nUY6qhUqy/8pvdK/UjJ5V1VU7rXx9muPrE5+oxcTG4N+/J706vq4kI7NysLtO2STRGDRoEMOHDyd/\n/vwANG7c2BbVpGrRqq9o27gVTxYqYp0W+O9BxMXFAXDlzzCiY2Jwz+me4bGJiGRW1nNn4b/OnTWq\nPIdX4ScBsFgslC9ZltPnQuwVojxGFh3bQNsy9XjSPb91WmBNf+LM/77Lb98gOi4Wj+w5Adh/5RSu\nTi6sOfsTOV2z0/+5V6j+RFm7xO6I5i2eT/68+WjX3NfeoUhKHCvPsE2isX//fnr06IG/vz9t2rTB\nYofsa2TvIQDs+iXxQwKdnJwYMiWIdds30ah2fZ4uWizDYxMRyays5879f507az/3gvX/0MsX+XzF\nYsYNGJHhscnjZ2St+O7Nuy4cSTTdyeLEkC1zWXf2ZxoVq0YJzycAyJvDg9alvHnpqefYd/kkb2/4\nkO/8xlM4p67OP8z1mzeY//UiVgYvsncokhoHSzRsMjrby8uLzz//nGPHjtGqVSuCg4M5duxYpnmO\nxtSho9m9bD03bt1k9pef2DscERGHcPjkMfwH9aSzb3terOFt73DkMTf1xV7sfm02N+5FMPvXlQDM\naNiXl556DoBqhcvwbKHS7Aw9bM8wHcay71fwkveLPFn4CXuHIqlyrMHgNkk0LBYLnp6eBAYG8vnn\nn+Ph4cGcOXPo2LGjLapLs+37dnPlz/hne7jlyEGLBi9z5NQJu8YkIuIIvv9xHd2H92VIj770bN/V\n3uHIY2x76CGu3L4BgJtLdlo8XZMjf54lIuoOwQdWPTC3wcXJOeODdEA/bF5P28at7B2GPIxj5Rm2\nSTQKFPjrbg/58uWjU6dOzJw5k1WrHjwBZKzVWzdYWzCioqJYvXUjNZ+pbteYREQyuzVbNzL+4/f5\ndOIsmtV/2d7hyGNu9Zm91haMqNhoVp/ZS60iFcnpmp0vj21g/dmfATj651kOXT1D3X9VsWe4DuFW\nRDjnQv/g2YraVpmegyUaNhmjMX36dFsU+2juGx8yrGd/gmZMouVbHbFYLPh416erXwc7Bicikknd\nd+784LM5AAR+MA5jDBaLhecqVrWO5xDJSMNqdCRox3xarhiOBQuNilWjS8X4BPjjRgMYu3sBM/Yv\nx8XJmQ8b9CZPdt305WF+D/2DQvkL4uys1p9Mz8HuOmUx9nzIxcOcvWnvCFJXPDcAYWHhdg4kdYUK\neVr/v3Lllh0jSZ2jxFmwoEem3+egONNTwYIeQOY/1hPi5PfMe/wAUCz+WHeE7ZnZYwTHipPJe+wd\nxsO9+0Km357WY/185o6TfznYudMRtP/u0ZZbap9ucTZp0ejcuTPR0dGJpiVcBVuyZIktqhQRERER\nkb8pJiaG4cOHExoaSnR0NL169aJUqVIMGzYMJycnSpcuTVBQEADLli1j6dKluLq60qtXL+rXr59q\n2TZJNAYPHkxgYCCzZ89WM5yIiIiISHqwQdep7777jrx58zJlyhRu3bpF69atKVeuHAMHDqR69eoE\nBQWxYcMGnnnmGRYuXMiKFSu4e/cuHTt2xNvbG1dX1xTLtkmiUbVqVVq3bs2JEyfw8fGxRRUiIiIi\nIvIPNW3alCZNmgAQGxuLs7MzR48epXr1+Bsm1atXjx07duDk5ES1atVwcXHB3d2d4sWLc+LECSpV\nqpRi2Ta56xRAjx49lGSIiIiIiKQXG9x1ys3NjZw5cxIREUG/fv0YMGAA9w/hzpUrFxEREURGRuLh\n8dd4lpw5cxIenvr4G5slGvcLDg7OiGpERERERLIui+XR/h7i4sWLdO3aFT8/P5o3b46T018pQmRk\nJJ6enri7uyd6+HbC9NRkSKKxY8eOjKhGRERERCTrskGLxtWrV+nevTtDhgzBz88PgPLly/PTTz8B\nsHXrVqpVq0blypXZt28fUVFRhIeHExISQunSpVMt2yZjNB6U0PwSFRVFtmzZMqJKEREREZGsxQaP\n0QgODubWrVvMmTOH2bNnY7FYGDFiBOPGjSM6OpqSJUvSpEkTLBYLnTt3plOnThhjGDhw4EN/19sk\n0di0aRNjx47FxcWFAQMGMG/ePCB+3MaCBQtsUaWIiIiISBaX/pnGiBEjGDFiRJLpCxcuTDKtXbt2\ntGvXLs1l2yTRmDt3LitXriQuLo5+/fpx7949/Pz8yMzPBhQRERERydQc68Hgtkk0XF1dyZ07/qnZ\nc+bMoWvXrhQpUgSLgz02XUREREQk03Cw39I2GQzu5eXFxIkTuX37Nu7u7syaNYsxY8YQEhJii+pE\nRERERLI+GwwGtyWbJBoTJkygbNmy1haMIkWKsGDBApo2bWqL6kREREREsj4HSzRs0nXKxcWFNm3a\nJJpWoECBZAeaiIiIiIhI1pMht7cVEREREZF/SGM0RERERETkcacWDRERERERR+BYDRpKNERERERE\nHIKDdZ1SoiEiIiIi4ggcK89QoiEiIiIi4hDUoiEiIiIiIunOsfIM3XVKRERERETSn1o0REREREQc\ngbpOSWZWqJCnvUMQERERkUfhWHkGFmOMsXcQYlsWB8t+AfSxFBEREXnAv9c/2nIf+6RvHGmUqVs0\nwsLC7R1CqgoW9AAyf5yOKDNv04IFPTJ1fAkUZ/pxlGNdcaYvR/hsguJMb44QpyMdQ/H/zLJvIA8T\n1sfeEWRZmTrRkPSR0DrgCCekzB6jiIiIiN04WCcV3XVKRERERETSnVo0REREREQcgYONu1WiISIi\nIiLiCBwrz1CiISIiIiLiEJRoiIiIiIhIulPXKRERERERSXeOlWekPdE4deoUN2/eTPQgteeff94m\nQYmIiIiIyIMcK9NIU6IxevRofvzxR4oWLWqdZrFYWLBggc0CExERERGR+zhWnpG2RGPHjh2sWbOG\nHDly2DoeERERERFJTlZMNIoWLZqoy5SIiIiIiGSwrDgYPHfu3DRv3pxnn32WbNmyWadPnDjRZoGJ\niIiIiIjjSlOiUbduXerWrWvrWEREREREJCWO1aCBU1pm8vPzo2LFikRGRnLz5k3KlSuHn5+frWMT\nEREREREHlaZEY+XKlbz99tucP3+eCxcu0KdPH77++usU54+Li2PDhg3s2bOHmzdvMmzYMIYPH87V\nq1fTLXARERERkceKxfJof3aSpq5Tn332GV999RV58+YFoFevXnTp0oVXXnkl2flHjBgBQFhYGDdu\n3KB9+/bkypWLwMBA5s6dm06hi4iIiIg8Rhys61SaEo24uDhrkgGQL18+LKlkR7///juLFi0iKiqK\nli1b0q5dOwCWLl36D8MVEREREXlMOdhdp9LUdaps2bKMHz+eEydOcOLECcaPH0+5cuVSXWbfvn1k\ny5aNzz77DIhPPqKiov55xCIiIiIijyPLI/7ZSZoSjXHjxuHq6srw4cMJCAjAxcWFoKCgFOcfM2YM\nn376KcYYnnzySQAmTZrEu+++mz5Ri4iIiIg8bhws0UhT16kcOXIwdOjQNBdaqlQpZs+ebX199OhR\nPv74478fnYiIiIiI/E8W6jqVcAvbcuXKUb58eetfwuu0mjRp0j+LUkRERETkcZeVWjRWrFgBwPHj\nx5O893fGWxhj/mZYIiIiIiKSiGM1aKRtjEb79u0TvY6Li6Nt27ZprsTf3//vRSUiIiIij59XysCP\n7WHjq/B/baBKQcidHea9DDtfg/WvwhuV4+ctnRc2/W/eja/C5g5wuTc0LWHfdbAlGz5H48CBA3Tu\n3BmAY8eOUa9ePbp06UKXLl1YvXo1AMuWLaNt27Z06NCBzZs3P7TMVFs0unTpwt69e4H47lMWiwVj\nDM7Ozrz00kspLjdo0CCGDx9O/vz5AWjcuHGaVlBEREREHlNP54FRtaHhUrh6Bxo+BfObwo5QiIiG\n2l+CixMsaAbnbsGG3+PnTfCeNxy5CqvP2G8dHNQnn3zCt99+S65cuQA4fPgwb7zxBt26dbPOc/Xq\nVRYuXMiKFSu4e/cuHTt2xNvbG1dX1xTLTbVFY8GCBRw/fhx/f3+OHz/OsWPHOH78OEeOHGHGjBkp\nLrd//3569OjBN998o25TIiIiIvJwUbEwYFN8kgFw4AoUygVVC8FXJ+KnxcTB+rPQsmTiZWsWgRYl\nYeiWDA05w9lojEaxYsUS3cjpyJEjbN68GX9/fwIDA4mMjOTgwYNUq1YNFxcX3N3dKV68OCdOnEi1\n3DR1nerduzc7d+4EIDg4mHfeeYfTp0+nOL+Xlxeff/45x44do1WrVgQHB3Ps2DEiIiLSUp2IiIiI\nPG7Oh8PGc3+9HlMH1oTAz5fg1bLgbIFcrvEJReFciZcN8obxuyAyOmNjzmg26jrl4+ODs7Oz9XXV\nqlUZOnQoX3zxBUWLFmXWrFlERETg4eFhnSdnzpyEh4enWm6aEo3BgwcTEhLCzp07WbNmDQ0bNmTU\nqFEpzm+xWPD09CQwMJDPP/8cDw8P5syZQ8eOHdNSnYiIiIg8rtxc4L9NoHhu6L8JgnaAATZ1gM+a\nwuY/4ls/Ejz/BOTLAStO2S3krKZRo0ZUqFDB+v/x48fx8PBI1GgQGRmJp6dnquWkKdG4efMm/v7+\nbNy4ET8/P3x9fblz506K8xcoUMD6f758+ejUqRMzZ85k1apVaalORERERB5HXu7wQ9v4RMJ3RfzY\nDI9s8N4OeHExvPodGANnbv61TOtSsDTpHVKzpAy6vW337t05dOgQALt27aJixYpUrlyZffv2ERUV\nRXh4OCEhIZQuXTrVctL0wL64uDgOHz7Mhg0b+OKLLzh27BixsbEpzj99+vS/sSoiIiIi8tjLnR2+\nbQOLjsL0n/+a3q1SfLIRsBUKukHnivDm2r/er+0F72bxsRkJ0ngHqX/qvffeY+zYsbi6ulKwYEHG\njBlDrly56Ny5M506dcIYw8CBA8mWLVuq5aQp0RgyZAhTpkzhjTfeoGjRorz66qsEBASky4qIiIiI\niPB6JXgyFzR/On4cBsS3XnT+ASbUhS3/64I/aQ8cDPtruRK54VzqYwWyDBvmGV5eXixZsgSAChUq\nsHjx4iTztGvXjnbt2qW5zDQlGrVq1aJKlSr88ccfGGOYP38+OXPmTHH+zp07Ex2deDCOMQaLxWJd\nARERERERqw/3xf8lp9vqlJcrMc828WRGDvbAvjQlGrt27WLUqFHExsayZMkSWrduzdSpU6lTp06y\n8w8ePJjAwEBmz56daAS7iIiIiIg8KsfKNNI0GHz69OksWrQIT09PChUqxMKFC5kyZUqK81etWpXW\nrVtz4sQJvLy8Ev2JiIiIiMgjyKDB4OklzYPBCxYsaH1dqlSphy7To0ePR49KREREREQSy6DB4Okl\nTS0aTzzxBD/++CMWi4Vbt27x8ccf8+STT6a5kuDg4EcOUEREREREcLgWjTQlGmPGjGHVqlVcvHiR\nRo0acezYMcaMGZPmSnbs2PHIAYqIiIiIiONJU9ep/Pnz/6NnYxhjAIiKinro/XZFRERERCQZjtVz\nKm2JRsOGDbEk0yds48aNyc6/adMmxo4di4uLCwMGDGDevPjbjvXo0YMFCxb8g3BFRERERB5TDjZG\nI02JxsKFC63/x8TEsH79eqKiolKcf+7cuaxcuZK4uDj69evHvXv38PPzs7ZsiIiIiIhI1pamROPB\n29L26NGDNm3a8Pbbbyc7v6urK7lz5wZgzpw5dO3alSJFiiTbKiIiIiIiImngYD+l05Ro/PTTT9b/\njTGcOnWKe/fupTi/l5cXEydOpF+/fri7uzNr1iy6d+/OrVu3/nnEIiIiIiKPIwe7aJ+mRGPGjBnW\n1giLxULevHmZOHFiivNPmDCB7777zrpMkSJFWLBggW5zKyIiIiLyqBwrz3j47W337duHq6srR44c\n4dChQ8TFxdGhQwfWrl3Lli1bkl3GxcWFNm3a4ObmZp1WoEABRowYkX6Ri4iIiIg8TiyWR/uzk1QT\njT179jBgwAB8fHxYunQpCxcupEmTJgwePJj9+/fz4osvZlScIiIiIiLiQFLtOjVr1iyCg4MpX768\ndVqlSpVYtWqVBnaLiIiIiGQkB/v5nWqLRnh4eKIkA+DatWv4+PhoYLeIiIiISEbKSl2n7t69S2xs\nbKJp+fLlo2vXrqk+R0NERERERNKZ5RH/7CTVRKN+/fpMnDgxUbIRGxvL5MmTqVevns2DExERERER\nx5TqGI1+/frRu3dvfHx8rF2ojh07RokSJZgzZ06GBCgiIiIiImSt52i4ubnx6aefsm/fPg4dOgTA\n66+/TvXq1TMkOBERERER+R/HyjPS9sC+atWqUa1aNVvHIiIiIiIiWUSaEg0REREREbGzrNR1SkRE\nREREMgnHyjOwGGOMvYMQEREREZGHmLHv0ZZ7xz5DINSiISIiIiLiCNR1Kh2FRtg7gtR5uQMQFhZu\n50BSV7CgB+AYcWb2GEFxpjdHiNORjiEAfr9l30Aeppgn4BjbM7PHCI4VJ5P32DuMh3v3hUy/Pa3H\n+vnMHSf/crBzpyNwrDwjkycaIiIiIiLyP46VaSjREBERERFxBI6VZyjREBERERFxCEo0REREREQk\n3TnYYHAnewcgIiIiIiJZj1o0REREREQcgWM1aKhFQ0RERERE0p9aNEREREREHIGDjdFQoiEiIiIi\n4ggcK89QoiEiIiIi4hDUoiEiIiIiIunOsfIMJRoiIiIiIg5BiYaIiIiIiKQ/x8o0lGiIiIiIiDgC\nx8ozMuY5GhMnTsyIakREREREsi7LI/6lwYEDB+jcuTMA586do1OnTvj7+zN69GjrPMuWLaNt27Z0\n6NCBzZs3P7RMm7RodOjQwfq/MYbTp09z4MABAJYsWWKLKkVEREREsjYb3XXqk08+4dtvvyVXrlxA\nfCPBwIEDqV69OkFBQWzYsIFnnnmGhQsXsmLFCu7evUvHjh3x9vbG1dU1xXJt0qLx2muvkSNHDsaO\nHcv7779PyZIlef/993n//fdtUZ2IiIiIiDyiYsWKMXv2bOvrI0eOUL16dQDq1avHzp07OXjwINWq\nVcPFxQV3d3eKFy/OiRMnUi3XJolGy5YtGTp0KFOnTiUqKors2bPj5eWFl5eXLaoTEREREcn6bNR1\nysfHB2dnZ+trY4z1/1y5chEREUFkZCQeHh7W6Tlz5iQ8PDzVcm02RqNChQpMmTKF999/n+vXr9uq\nGhERERGRx4QNB2ncx8nprxQhMjIST09P3N3diYiISDI91XL+ds1/Q548eZg5cybjx4+3ZTUiIiIi\nIllfxuQZVKhQgZ9++gmArVu3Uq1aNSpXrsy+ffuIiooiPDyckJAQSpcunWo5Nr+9rZOTE9OmTWPB\nggW2rkpEREREJOvKoNvbvvvuu4wcOZLo6GhKlixJkyZNsFgsdO7cmU6dOmGMYeDAgWTLli3VcjLk\nORr39/MSEREREZFHYKO7TgF4eXlZ7w5bvHhxFi5cmGSedu3a0a5duzSXmSGJhr+/f0ZUk6ITIacY\nN2sqERERODu7MHpAABXLlLdrTCIimdmwqe9RtkRpXn/lNe5F3WP0zCkcOnkUjKFKuUoE9Rn60CtZ\nIiAKbDcAAB+OSURBVOll2NZ5lM1XlNcrNSUi6g7Dt3/CmZsXMcbQulQd3qzSHIAfz+1n2LZ5POle\nwLrsl80Cyema3V6hZ3or13/P/K+/xPK/S+W3IiO4fPUKW5f8QL48ee0cnSThYA/ss0miMWjQIIYP\nH07+/PkBaNy4sS2qSZO79+7S/d0+TBwaRN3na7Np51aGTBzJD599bbeYREQyq9PnzjJm1mQOHj9C\n2RLxfW8/XvQpcXGxrApejDGGwZNGErxkPn279LRztJLVnb5xgTG7FnAw7DRl8xUF4MNfvqFIrnzM\naNiXOzH3aL48gBpPlKNqoZLsv/Ib3Ss1o2fVlnaO3HH4+jTH1yc+UYuJjcG/f096dXxdSUZmpUQD\n9u/fT48ePfD396dNmzZYbNjM8zDbf95NsSeLUvf52gA0rF2PfxV50m7xiIhkZotWfUXbxq14snAR\n67QaVZ7Dq3D8edNisVC+ZFlOnwuxV4jyGFl0bANty9TjSff81mmBNf2JM3EAXLl9g+i4WDyy5wRg\n/5VTuDq5sObsT+R0zU7/516h+hNl7RK7I5q3eD758+ajXXNfe4ciKXKsTMMmiYaXlxezZ89mxowZ\ntGrVihYtWlCvXj2KFi2Ku7u7LapM0dk/zpE/bz5GTBvD8dOnyO3uweCe72RoDCIijmJk7yEA7Nq/\n1zqt9nMvWP8PvXyRz1csZtyAERkemzx+RtbqAsCuC0cSTXeyODFky9z/b+/O46qu8j+Ovy4iLiga\nIoo4qWNu05STlpmaywSTSyKLGyhaaf5sGjMnNReUwlFwUrMGUMgpRVNzw5zUyq0hrUZrynHcm9zC\nBTU1EQWu3N8fDKSh4NC9fL9feD8fDx7ABe95czzfe+/nnnO+Xz46+gUBjdrSxKs+AHdVrUmfezry\n2N1t+PLMIX6/eS7rQqZTr7renS/JhUsXWbhqKWuTlhodRYpjrTrDNae3tdlseHl5ERUVxaJFi6hZ\nsyaJiYmEh4e7orli2a/b+WTnpwzs3ZfV8xYzKHgAIyY+T649t8yziIhY2b8P7WfwiyOIDB5Al3Yd\njY4jFdyrXUby+aAELmZnkvD1WgDe+O0oHru7DQBt6zXnAd9mfJr+byNjWsaK9ak81rELDerVNzqK\nFMdmK92HQVxSaPj4/LgJy9vbm4iICP7yl7/wt7/9zRXNFcu3jg9N7m7MfS1+BcBjHbtwPS+PEyfT\nyzyLiIhVrd/2EcMmjWLc8FGMGDDU6DhSgW1P30NG1kUAqrlX4Ylftmfv+aNk5lwlafdPX2c4cHer\nVPROpIgNH28i7PEgo2NIScroOhrO4pJCY86cOa6421Lp3K4j6adPsu/wAQB27f4nbjY3Gvr5G5xM\nRMQaPkjbwvR5s3krNp6eXX9ndByp4DYe2Vk4g5FzPZeNR3byiN+9VK9chXf2b2bT0S8A2Hf+KHvO\nHeHRhvcbGdcSfsi8zPH0Ezxwr/pKnKtMTm9rJB/vOiRMm83Lc2O5eu0qHh5ViI+ZhUflykZHExEx\nrxum2l97OxGAqNf+hMPhwGaz0ebe1oX7OUTK0oR24UTvWEjv1EnYsBHQqC1D7s0vgOcFjGHa5ym8\n8dUa3N0qMbfbc9SuUrZ7Q63oWPoJfOvUpVIlzf6YnsX2aNgcLriaXmRkJLm5N++BKHhyKrgQyB1J\nz3RyMifzz3/wOnv2ssFBile3bk3AGjnNnhGU09mskNNKxxAAx34wNkhJGnkB1uhPs2cEa+Vk5j+M\njlGylx42fX8WHuvfmTsnDS322GkFKw+W7t/1M+bsay6Z0Rg7dixRUVEkJCSoOhYRERERqYBcUmi0\nbt2aPn36cPDgQQIDA13RhIiIiIhIxWKxpVMu26MxfPhwV921iIiIiEjFY+CpakvDJWed+qmkpKSy\naEZEREREpPzS6W2L2rFjR1k0IyIiIiJSfumCfUUVnNgqJyenLJoTERERERGDuaTQ2Lp1K926dSMw\nMJANGzaQnJwMaN+GiIiIiEipWWzplEs2g8+fP5+1a9eSl5fH6NGjyc7OJiQkBBdcskNEREREpGKw\n2GZwlxQalStXplatWgAkJiYydOhQ/Pz8sFmsc0RERERETMNiL6VdsnTK39+f2NhYsrKyqFGjBvHx\n8cTExPDtt9+6ojkRERERETEZlxQaM2bMoEWLFoUzGH5+fqSkpNCjRw9XNCciIiIiUv5pjwa4u7sT\nGhp6020+Pj5MnjzZFc2JiIiIiJR/FtuGUCantxURERERkYrFJTMaIiIiIiLiZBab0VChISIiIiJi\nBdaqM1RoiIiIiIhYggoNERERERFxOostndJmcBERERERcTrNaIiIiIiIWIHFZjRUaIiIiIiIWIG1\n6gwVGiIiIiIilmCxQkN7NERERERExOlsDofDYXQIEREREREpweZjpft3AY2cm+MOaemUiIiIiIgV\nWGzplLkLjZOZRicoXoMa+Z+P/2BsjpLc7ZX/+bvLxuYoScOa5u9LyO9Ps49NyB+fyukcBce6cjpH\nQc5jJj/eG3lBusn7EsC/hnUeO62Sc9Yuo1MUb+xD+Z+tcAwBZ8+a+/VH3bo1jY5Qbpm70BARERER\nkXw6va2IiIiIiDidteoMFRoiIiIiIpagGQ0REREREXE6a9UZKjRERERERCxBhYaIiIiIiDifayqN\n0NBQatTIPyNgw4YNGTlyJBMmTMDNzY1mzZoRHR1dqvtVoSEiIiIiYgUuqDNycnIASElJKbzt2Wef\n5Y9//CMPPvgg0dHRbN68mYCAgP/5vt2cllJERERERFzHVsqPYhw4cICsrCyGDRvGk08+ye7du9m3\nbx8PPvggAJ07d+azzz4rVVzNaIiIiIiIVFBVq1Zl2LBh9OvXj6NHj/LMM8/gcDgKf+7p6cnly6W7\n6KIKDRERERERK3DB6W0bN25Mo0aNCr+uXbs2+/btK/z5lStX8PLyKtV9a+mUiIiIiIgVuGDp1OrV\nq4mLiwPgzJkzZGZm0rFjR3bu3AlAWloabdu2LVVczWiIiIiIiFiC82c0+vbty8SJE4mIiMDNzY24\nuDhq165NVFQUubm5NG3alO7du5fqvlVoiIiIiIhYgQvOOlW5cmVmzZpV5PbFixf/7PtWoSEiIiIi\nYgUWu2CfS/ZobNy4EYCsrCxmzpzJU089xaxZs7hy5YormhMRERERKf9sttJ9GMQlhcayZcsAmD59\nOrVq1SIqKor69eszdepUVzQnIiIiIlL+uWAzuCu5dOnUsWPHmD59OgBNmzblo48+cmVzIiIiIiLl\nl5ZOwdGjR1m4cCHu7u6F5+Hds2cPubm5rmhORERERKQCsNaUhksKjaSkJDw9PWncuDEHDx7k8uXL\nTJs2TUunRERERERKy1p1hmuWTrVq1YpWrVrRr18/APbt28eKFStc0ZSIiIiISMVg4Mbu0iiTK4MX\nXG1QREREREQqhjK5jobD4SiLZkq0efs2XoqL5sv304yOUsTEV1+heZN7eKrvoMLbTmWcZsDoYaxL\nWkptr1oGpvvR2k3rWbjqHWz/nYf74UomZ85lkLZ8A9617zI4Xb4b+zIvL4/Y+a+x/YvPycvL46m+\ngxj4RKjREYsw89i8kdlzLkl9l+XrVmNzs3F3g4ZMGxuFdy1zjMsbxSXO4cO0LYXHdZNfNGbOlBkG\npyrK7DknvPoyLZo046m+g8jOyeaVv/yZPYf2gcPB/S1/TfQfxuPh4WF0zEIHvz3Mn+JfJTMzk0qV\n3HllzETubd7K6FiFrPI8ZObH+AkfJ9HC+xc8dX9PMnOymJS2gCMXT+IA+jTrxDOtnwDg2KXTTEp7\nk4vXMvH0qEpcl//jl7UbGJPZYsdRhWetCY2yKTQGDx5cFs0U6+h3x/nz/NcxSc1T6D/HjxLzl5n8\n68Bemje5p/D2tZvW80ZKMme/P2dguqKCA3sRHNgLAPt1O4NfGMHI8KdMUWTcqi+Xvb+a4ye/Y8Nf\nV3D5SiYDnn+ae5u15L4WvzI47Y/MOjZ/yuw59x7az9sr32HdX5fjWa06M+fP5fW35vHKmElGRyvi\n6317eG1qHL/51X1GRymWWXP+5/hRYuLzj/UWTZoBMG/pW+TlXedvSctwOByMjZtC0vKFjBoywuC0\n+a5lX2PYS38gdnw0jz7Uga2fpjEudgob3l5ldDTLPA+Z+TH+PxdPErNjIf/K+A8tvH8BwNwvVuHn\n6c0bAc9z1Z5Nr5UTaOfXkta+9zB22zyeuq8HPZu2J+3Ebp7f/Abv9y3b1R9WPI4EyxUaLlk69eKL\nL3L+/PnC7x9//HFXNHPHrl67yvgZU5j43B8NzXErS9etJKx7EN27BBTelnH+HFs/S+PN6a8bmKxk\nycsWUucub/r1CjY6CnDrvtyy4++EPt4bm82GV42a9Or2O9Zt2WhgypuZeWzeyAo5723eio8Wp+JZ\nrTrZOdlknM2gtldto2MVkZOby77DB3nr3cX0GR7O89HjOZVx2uhYRZg559K/rSTs8ZuP9Xb3t+HZ\niGEA2Gw2WjVtwcmMU0ZFLGL7F5/TqMEvePShDgD8tkNn5k41x7JiqzwPmfkxfuneTYS16EL3Xz5c\neFtUhyG81D4CgIwrF8jNs1PDozpnrlzgyKVT9GzaHoDOv2hNVm42+88fK9vMFjyOBKy2G9wlhcZX\nX33F8OHDWb16tSmWTUXPiSU8qC/N/1uxm8mUP4wj6LEe3Pg2sW8dH96YOpOmdzc2Rf/dyoVLF1m4\naimTnxtrdJRCt+rLU2fP4Fe3XuH39Xx8OXMuw4h4t2TmsXkjq+SsVKkSm7d/TJf+Pfliz9eEde9t\ndKQiMs6f5ZE2D/HiiFG8t2AZrX/1a34fZb4Czsw5pzxX9Fjv0OZhGvnnv5OcfuYUi1KX0b1zwO3u\noswdPXGcOnd5M3lWDGHPRvL0uN9jt9uNjgVY53nIzI/xUzoOJeiejsDNfeVmc2PctnkErZ5EO79W\n/LKWH6evnMe3+s1vgtT3vIvTmd+XYWJrHkeC1eoM1xQa/v7+LFq0iP379xMUFERSUhL79+8nMzPT\nFc0V6521K3B3dyeke28cmOPBsjxYsT6Vxzp2oUG9+kZHKVaeI6/IbW5uZXIOhBJZZWxaJWeBgE5d\n+XztFv4w9BmeHv+c0XGKaFi/AUmxrxc+mQ8bMITjJ78j/bS53jW0Ss6f+veh/Qx+cQSRwQPo0q6j\n0XEK2a/b+WTnpwzs3ZfV8xYzKHgAIyY+T65d15f6Ocz8GF/g1W7P8vmQeVzMvkzCP1PJu03hZqbc\nZj2OBBUakD/d5uXlRVRUFIsWLaJmzZokJiYSHh7uiuaKtfbD99lzcC8hIyL4vwmjuXbtGiEjIkyz\n5tSqNny8ibDHg4yOUaIGvvXJuOH/+sy5DOr7+BqY6EdWGZtWyXk8/QRf7vm68PuwHn04efo0ly7/\nYGCqog5+e5j3Nm246TaHA9zdy2TL3B2zSs4brd/2EcMmjWLc8FGMGDDU6Dg38a3jQ5O7GxfuHXis\nYxeu5+Vx4mS6wcmszcyP8du/20NG1gUAqrlX4Ymmj7D3/FEa1KjD2ayLN/3umawL1Pf0NiJmEWY+\njoT809uW5sMgLnnG8PHxKfza29ubiIgIIiIiXNFUiVbOSyn8Ov30KZ54uj+pyUsNyVJe/JB5mePp\nJ3jg3vuNjlKixx7pwuoP1tHt4U5cuZrFho83EfPCRKNjAdYZm1bJmfH9OV6cNpn3Fiyjtlct1m3a\nQPNf3kOtml5GR7uJm82NGfGzePC+B/Cv78c7a1fQsmkz6vnUNTraTaySs8AHaVuYPm82b8XGc2+z\nlkbHKaJzu47MnD+XfYcP8KtmLdm1+5+42dxo6OdvdDRLM/Nj/MZvP2fTUXde6fQUOddz2fjtP+jU\n8H7qeXpzt1c9Nvznc3o2bc8nJ/5FJZtb4SZyI5n9OBIstxncJYXGnDlzXHG3TmEz64VObpPLjHmP\npZ/At05dKlWqZHSUW7uhz8J7h3HidDp9RkaQa7cT/kQYD973gIHhbs+M/9e3YtacD973AM8OHkbk\nC8/g7u6Ob526JEybbXSsIpo1aUrUqHGMnDSavDwH9ev6muqUsQUskfOGsfja24kARL32JxwOBzab\njTb3tmbKc+OMSncTH+86JEybzctzY7l67SoeHlWIj5mFR+XKRkf7kVWeh0z9GP9jtgntBxG9/S16\nr5qAzeZGQOO2DPl1/slxXnvsD0xOW8C8r9ZSxd2DNwKeNyqwpY4jwXIX7LM5zLLL61ZOlv2ejv9J\ngxr5n4+ba2lGEXf/9x3d7y4bm6MkDWuavy8hvz/NPjYhf3wqp3MUHOvK6RwFOY+Z/Hhv5AXpJu9L\nAP8a1nnstErOWbuMTlG8sQ/lf7bCMQScPWvu1x9169Y0OsKd+3cply3/2qfk33EBl8xoREZGkpt7\n8wa3gsp4+fLlrmhSRERERKR8s9aEhmsKjbFjxxIVFUVCQoJ5l9eIiIiIiFiJxZZOuaTQaN26NX36\n9OHgwYMEBga6ogkRERERETExl52ncPjw4a66axERERGRisdaExquuY7GTyUlJZVFMyIiIiIi5Zcu\n2FfUjh07yqIZEREREZHyy2IX7CuTQqPgDLo5OTll0ZyIiIiIiBjMJYXG1q1b6datG4GBgWzYsIHk\n5GRA+zZERERERErNYjMaLtkMPn/+fNauXUteXh6jR48mOzubkJAQzHxtQBERERERU7PYZnCXFBqV\nK1emVq1aACQmJjJ06FD8/PywWezcvyIiIiIipmGxl9IuWTrl7+9PbGwsWVlZ1KhRg/j4eGJiYvj2\n229d0ZyIiIiISPlnsaVTLik0ZsyYQYsWLQpnMPz8/EhJSaFHjx6uaE5EREREREzGJUun3N3dCQ0N\nvek2Hx8fJk+e7IrmRERERETKP4ttQ3DZlcFFRERERMSJrFVnqNAQEREREbEEixUaZXLBPhERERER\nqVg0oyEiIiIiYgXaoyEiIiIiIk5nrTpDS6dERERERMT5NKMhIiIiImIFWjolIiIiIiJOZ606Q4WG\niIiIiIglaEZDRERERESczlp1BjaHw+EwOoSIiIiIiJTgZGbp/l2DGs7NcYdUaIiIiIiIiNPp9LYi\nIiIiIuJ0KjRERERERMTpVGiIiIiIiIjTqdAQERERERGnU6EhIiIiIiJOp0JDREREREScrsIUGrt3\n7yYyMtLoGCIilmC32xk/fjyDBg2if//+bN261ehIIoXy8vKYNGkS4eHhDBo0iG+++cboSJZ3/vx5\nunbtypEjR4yOIuVIhSg0FixYQFRUFLm5uUZHKeJ2BdD7779P//79iYiI4OWXX7ZMrrNnz/Lkk08y\nePBgnnvuObKysiyT6erVqwQHB7N9+3anZf45uQvs3LmTrl27OjXTnbhd7g8//JC+ffvSv39/UlJS\nLJfLbP1ZYOrUqcyZM6cMExVv3bp13HXXXbzzzju8+eabTJs2zehIQMkFkFHj01m5ynp83mlBabbx\nuXXrVmw2G8uWLWP06NGmyXanBZDZ+tNutxMdHU3VqlWNjiLlTIUoNBo1akRCQoLRMYq4XQGUnZ3N\nG2+8wZIlS1i6dCmXL19m27Ztlsj15ptvEhoaypIlS2jVqhUrV660TKaYmBjc3Jx7SPzc/+PTp0+z\ncOFC7Ha7U3OV5Ha58/LymDNnDosWLWL58uUsXbqUixcvWiaX2fqzwPLlyzl06FCZZipJjx49GD16\nNJDfv+7u7gYnyldcAWTk+HRGLiPG550UlGYcnwEBAYVZ09PTqVWrlsGJ8t1JAWTG/pw5cybh4eH4\n+voaHQWA1NRUXnjhBUaOHEmvXr1ITU1l165dDB06lCFDhtC3b1+OHTtGeno6AwcOZMyYMYSGhhry\nxqwUr0IUGoGBgVSqVMnoGEXcrgDy8PBg+fLleHh4APnvNFSpUsUSuSZNmkRQUBB5eXmcOnUKLy8v\nS2R66623aNOmDS1atHBKXmfkzsnJ4eWXXzbkgfN2ud3c3Ni4cSOenp5cuHABh8NB5cqVLZHLjP0J\n8NVXX7Fnzx4GDhxYxqmKV61aNapXr05mZiajR49mzJgxRkcCii+AjByfPzeXUeOzpILSrOMT8vt1\nwoQJTJ8+nd69exsdByi5ADJjf65Zs4Y6derQsWNHHA6H0XEKZWZmMn/+fBITE0lOTuabb75h1qxZ\npKSkEBgYyAcffADA0aNHmTFjBqtWrSItLY3z588bnFxuVCEKDbO6XQFks9nw9vYGYPHixVy9epUO\nHTpYJpfdbqd3797s3LmT9u3bmz7TZ599xrFjx+jXr59Tsjord0xMDE8//bQh7zAVV5y7ubmxadMm\n+vTpQ7t27ahevbolcpmxP8+ePUt8fDxTp0411RN8gVOnTjF06FBCQkLo2bOn0XGAkgsgo8bnz81l\n1PgsLrfZxydAXFwcH374IVFRUVy7ds3oOMDtCyCz9ueaNWvYsWMHkZGRHDhwgJdeeskUL9ZbtWoF\ngJ+fH9nZ2fj6+jJt2jQmTpzIP/7xj8IZ4kaNGlGtWjXc3Nzw9fUlOzvbyNjyExWq0DDTgV0Sh8PB\nzJkz+eyzz4iPjzc6TqE7yeXu7s769euJiYlh/Pjxps+0atUqDh8+TGRkJJ988gmvvvoqBw4cMDR3\nRkYGX375JfHx8URGRnLx4kVefPFFl2e6U4GBgWzfvp2cnBzWrl1rdJxCt8tl1v784IMPuHjxIs88\n8wzJycm8//77punPc+fOMWzYMMaNG0dISIjRcW5SUgFk1PgsbS6jx+ftcpt5fL733nskJycDUKVK\nFdzc3Jy+9PXnuFUBZNb+XLJkCYsXL2bx4sW0bNmSmTNnUqdOHaNjYbPZbvp+6tSpxMXFERsbe9uC\n3Eqv8yoKcyy6LSM/HbRmcasDY8qUKVStWpXExEQDEuUrTa5XXnmF7t278/DDD1O9enWnP/C7ItPs\n2bMLv544cSK9evWiZcuWhub29fVl48aNhd936tTpppxl5ae5MzMzefbZZ/nrX/+Kh4cH1apVM+S4\n+l9zmbU/IyMjCzeIp6amcuTIEYKDg8s8160kJSXxww8/kJiYSEJCAjabjQULFhQu9zNKQQE0derU\nIrOTRo7Pn5PLyPFZXG4zj8/f/e53TJw4kcGDB2O325k8ebLhYxPyC6AzZ84wYsSIIgWQmfuzgFlf\nJ9lsNoKCgoiIiKB69er4+PiQkZFR+LMbf0/MpcIUGv7+/ixfvtzoGLdUcGCkpqZis9lo3rw5a9as\noW3btkRGRmKz2RgyZAgBAQGmzPWb3/yGGTNmMGfOHCIjI4mOjiYxMRE3Nzeio6NNl6lgDfSMGTMI\nDQ11elHhrNxm8NPcwcHBBAUFMXjwYCpXrkyLFi3o06ePaXOdO3fOtP0JmG6m4EaTJ09m8uTJRsco\n4lYFUM+ePalSpQohISGGjc/S5DLD+Cwpt1lVq1aNuXPnGh2jiJ8WQJMmTWL9+vWAuY/3AkacSfBW\nbuwrDw8PtmzZctvfvfG1nVlf51VoDjGNAwcOOFavXm10jCJKymW32x1xcXFlmMg5mZYsWeI4fvy4\ns6MVy4x9eSc0Np3LrP1pVWbtT41PcTjUn1Kx2RwOLWgzizNnzlCvXj2jYxRRUi673c6lS5fKdE2n\nMzKdPn2a+vXruyLebZmxL++ExqZzmbU/rcqs/anxKaD+lIpNhYaIiIiIiDideU7RICIiIiIi5YYK\nDRERERERcToVGiIiIiIi4nQqNERETObQoUO0bNmSTZs2GR1FRESk1FRoiIiYTGpqKt27d9c54UVE\nxNJUaIiImMj169dZt24dY8aMYe/evZw4cQKA3/72t7z++uv069eP3r17s2/fPgDefvtt+vTpQ2ho\nKNHR0eTl5dGhQweysrIACA8PZ8GCBQBs2LCBmJgY8vLyiIuLIzQ0lODgYBYtWgTAzp076devH2Fh\nYUycONGAv15ERMoTFRoiIiaybds2/P39adSoEYGBgbz77ruFP/P29mblypUMGDCA+fPnc/36dZKT\nk1mzZg2rV6/Gzc2Nc+fO8cgjj7Br1y6ysrJIT09n165dAKSlpdG1a1dWrFiBzWZjzZo1rFixgs2b\nN/Pll18CcOzYMVJSUoiNjTXk7xcRkfJDhYaIiImkpqbSq1cvALp3705qaiq5ubkAdOrUCYBmzZpx\n6dIlKlWqRJs2bQgLCyM+Pp5Bgwbh6+tL586d+fTTT9m1axdBQUEcPnwYu93OF198Qfv27fn000/Z\nunUrwcHB9O/fn4yMDA4dOgRAkyZN8PT0NOaPFxGRcsXd6AAiIpLv+++/5+9//zt79+4lJSUFh8PB\npUuX+Oijj7DZbFSpUgUAm81GwbVWExIS2L17N2lpaQwbNozZs2fz6KOP8vbbb+Pu7s4jjzzCkSNH\nWLVqFc2bN8fDw4O8vDzGjRtHQEAAABcuXMDT05Ovv/66sA0REZGfSzMaIiIm8d5779GhQwc+/vhj\ntmzZwtatWxk5cuRtN4V///339OjRg+bNmzNq1Cg6duzIwYMH8fb2pmrVqmzbto22bdvy8MMPk5iY\nSLdu3QBo37497777Lna7nStXrhAREcHu3bvL8k8VEZEKQIWGiIhJpKamMmjQoJtui4iIYM+ePeTk\n5BT5fW9vbwYOHEhYWBhhYWFcvnyZkJAQADp37oyXlxfVqlWjffv2nD17li5dugAwcOBAGjduTEhI\nCP369aNv37489NBDrv8DRUSkQrE5CubfRUREREREnEQzGiIiIiIi4nQqNERERERExOlUaIiIiIiI\niNOp0BAREREREadToSEiIiIiIk6nQkNERERERJxOhYaIiIiIiDidCg0REREREXG6/wcmHD1TUbBI\nogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x222587273c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qnresp_qgroup=qnresp.groupby(['quiz_question','learner_id']).size().reset_index().groupby('quiz_question')\n",
    "correct_ans=sorted(qnresp[qnresp['correct']]['qidr'].unique().tolist())\n",
    "\n",
    "#Define a function to find the co-rodinates in the grid of the correct answer\n",
    "def calcCoords(ans):\n",
    "    x=int(ans.split('.')[-1])-1\n",
    "    y=len(correct_ans)-correct_ans.index(ans)-1\n",
    "    return (x,y)\n",
    "\n",
    "\n",
    "def quizHeatmap(df,percent=True,title='Heatmap showing percentage of answer option selection by question (correct answer highlighted)'):\n",
    "    dp = ',.0f'\n",
    "    if percent:\n",
    "        dp = '.1f'\n",
    "        df.apply(lambda x : 100*(x / x.sum()), axis=1)\n",
    "    plt.rc(\"figure\", figsize=(15, 5))\n",
    "    cmap=sns.light_palette(THEME_COL, reverse=False,as_cmap=True)\n",
    "    #The fmt option suppresses default label display using scientific notation\n",
    "    ax=sns.heatmap(df, annot=True, linewidths=.5,cmap=cmap,fmt=dp)\n",
    "    ax.set_xlabel('Answer')\n",
    "    ax.set_ylabel('Question')\n",
    "    ax.set_title(title)\n",
    "    #Highlight correct answer cells\n",
    "    #http://stackoverflow.com/a/31291200/454773\n",
    "    for ans in correct_ans:\n",
    "        ax.add_patch(Rectangle(calcCoords(ans), 1, 1, fill=False, edgecolor='black', lw=3))\n",
    "    for item in ([ax.xaxis.label, ax.yaxis.label] +\n",
    "             ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "        item.set_fontsize(13)\n",
    "        \n",
    "    ax.get_xaxis().set_major_formatter(\n",
    "        tkr.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "    ax.title.set_fontsize(15)\n",
    "\n",
    "def quiz_pivot(df, percent=False):\n",
    "    tmp = df.pivot_table(index=['week_number','step_number','question_number'],\n",
    "                         columns='response',\n",
    "                         values='correct',\n",
    "                         aggfunc='count')\n",
    "    if percent:\n",
    "        return tmp.apply(lambda x : 100*(x / x.sum()), axis=1)\n",
    "    return tmp\n",
    "\n",
    "qnresp_pivot=quiz_pivot(qnresp, percent=False)\n",
    "\n",
    "quizHeatmap(qnresp_pivot, percent=False,\n",
    "            title='{} Heatmap showing count of answer option selection by question (correct answer highlighted)'.format(COURSE_SHORTNAME))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tmp = qnresp_qgroup[0].aggregate([np.mean,np.median])\n",
    "ax = tmp.plot(kind='bar', title='{} mean and media quiz question attempts'.format(COURSE_SHORTNAME),\n",
    "              color=[THEME_COL,'black'])\n",
    "for item in ([ax.xaxis.label, ax.yaxis.label] +\n",
    "         ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(13)\n",
    "    ax.title.set_fontsize(15)\n",
    "ax.legend(loc=2,prop={'size':13})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "This notebook contains a variety of sketches and doodles showing how we can start to have a conversation with data from FutureLearn courses. The aim has been not to model learner behaviour or provide learner level diagnostics, preferring instead to review the mass action behaviour of learners in order to better help us understand how the course materials themselves are performing.\n",
    "\n",
    "The notebook was put together as a recereational data activity, largely in my own time. In part, I've also used it to teach myself about how to use Jupyter notebooks' interactive widgets (I've never really used them before...)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}