From 2c58dcf702f552d3b26a37975b12b8662aa77ddb Mon Sep 17 00:00:00 2001
From: Neil Smith <neil.git@njae.me.uk>
Date: Fri, 19 Jan 2018 10:25:02 +0000
Subject: [PATCH] Updated day 6

---
 06-tour-shapes/tour-shapes-solution.ipynb |    2 +-
 data-analysis/task-analysis.ipynb         | 6556 ---------------------
 2 files changed, 1 insertion(+), 6557 deletions(-)
 delete mode 100644 data-analysis/task-analysis.ipynb

diff --git a/06-tour-shapes/tour-shapes-solution.ipynb b/06-tour-shapes/tour-shapes-solution.ipynb
index e5efe3d..c2a2a55 100644
--- a/06-tour-shapes/tour-shapes-solution.ipynb
+++ b/06-tour-shapes/tour-shapes-solution.ipynb
@@ -1221,7 +1221,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2+"
+   "version": "3.5.3"
   }
  },
  "nbformat": 4,
diff --git a/data-analysis/task-analysis.ipynb b/data-analysis/task-analysis.ipynb
deleted file mode 100644
index 95e842f..0000000
--- a/data-analysis/task-analysis.ipynb
+++ /dev/null
@@ -1,6556 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Import the required libraries and open the connection to Mongo\n",
-    "\n",
-    "import collections\n",
-    "from datetime import datetime\n",
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "import random\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import scipy.stats\n",
-    "\n",
-    "import pymongo\n",
-    "from bson.objectid import ObjectId"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "client = pymongo.MongoClient('mongodb://localhost:27017')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Connect to the database\n",
-    "summerofcode_db = client.summerofcode\n",
-    "imp = summerofcode_db.imp\n",
-    "icmas = summerofcode_db.icmas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "icmas.find_one();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Attempts per task"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'_id': 0, 'count': 293},\n",
-       " {'_id': 1, 'count': 157},\n",
-       " {'_id': 2, 'count': 104},\n",
-       " {'_id': 3, 'count': 88},\n",
-       " {'_id': 4, 'count': 96},\n",
-       " {'_id': 5, 'count': 76},\n",
-       " {'_id': 6, 'count': 100},\n",
-       " {'_id': 7, 'count': 53},\n",
-       " {'_id': 8, 'count': 60},\n",
-       " {'_id': 9, 'count': 41},\n",
-       " {'_id': 10, 'count': 63}]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pipeline = [{\"$group\": {\"_id\": \"$icma_number\", \"count\": {\"$sum\": 1}}},\n",
-    "            {'$sort': {'_id': 1}}]\n",
-    "list(icmas.aggregate(pipeline))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Submissions per person per task\n",
-    "\n",
-    "Includes non-submitted attempts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "pipeline = [{\"$group\": {\"_id\": {'icma_number': \"$icma_number\",\n",
-    "                                'pi': '$Personal Identifier'},\n",
-    "                        \"scores\": {\"$push\": '$grade'}}}]\n",
-    "list(icmas.aggregate(pipeline));"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "pipeline = [{\"$group\": {\"_id\": {'icma_number': \"$icma_number\",\n",
-    "                                'pi': '$Personal Identifier'},\n",
-    "                        \"scores\": {\"$push\": '$grade'}}},\n",
-    "            {'$project': {'scores': True,\n",
-    "                          'n_attempts': {'$size': '$scores'},\n",
-    "                          'highest': {'$max': '$scores'}}}]\n",
-    "attempts_per_person = pd.DataFrame([\n",
-    "    {'icma_number': r['_id']['icma_number'],\n",
-    "     'pi': r['_id']['pi'],\n",
-    "     'n_attempts': r['n_attempts']}\n",
-    "    for r in icmas.aggregate(pipeline)\n",
-    "])\n",
-    "attempts_per_person;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>icma_number</th>\n",
-       "      <th>pi</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>n_attempts</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>787</td>\n",
-       "      <td>787</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>92</td>\n",
-       "      <td>92</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>20</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>10</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            icma_number   pi\n",
-       "n_attempts                  \n",
-       "1                   787  787\n",
-       "2                    92   92\n",
-       "3                    20   20\n",
-       "4                    10   10\n",
-       "5                     4    4\n",
-       "6                     3    3\n",
-       "7                     2    2\n",
-       "8                     1    1"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "attempts_per_person.groupby(['n_attempts']).count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>icma_number</th>\n",
-       "      <th>pi</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>n_attempts</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>532</td>\n",
-       "      <td>532</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>78</td>\n",
-       "      <td>78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>9</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            icma_number   pi\n",
-       "n_attempts                  \n",
-       "1                   532  532\n",
-       "2                    78   78\n",
-       "3                    18   18\n",
-       "4                     9    9\n",
-       "5                     4    4\n",
-       "6                     3    3\n",
-       "7                     2    2\n",
-       "8                     1    1"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "attempts_per_person[attempts_per_person.icma_number > 0].groupby(['n_attempts']).count()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Number of submissions per person"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "pipeline = [{'$match': {'grade': {'$ne': '-'},\n",
-    "                        'icma_number': {'$gt': 0}}},\n",
-    "            {\"$group\": {\"_id\": {'icma_number': \"$icma_number\",\n",
-    "                                'pi': '$Personal Identifier'},\n",
-    "                        \"scores\": {\"$push\": '$grade'}}},\n",
-    "            {'$project': {'scores': True,\n",
-    "                          'n_attempts': {'$size': '$scores'},\n",
-    "                          'highest': {'$max': '$scores'}}}]\n",
-    "submissions_per_person = pd.DataFrame([\n",
-    "    {'icma_number': r['_id']['icma_number'],\n",
-    "     'pi': r['_id']['pi'],\n",
-    "     'n_attempts': r['n_attempts']}\n",
-    "    for r in icmas.aggregate(pipeline)\n",
-    "])\n",
-    "submissions_per_person;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>icma_number</th>\n",
-       "      <th>pi</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>n_attempts</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>351</td>\n",
-       "      <td>351</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>55</td>\n",
-       "      <td>55</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>16</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>8</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            icma_number   pi\n",
-       "n_attempts                  \n",
-       "1                   351  351\n",
-       "2                    55   55\n",
-       "3                    16   16\n",
-       "4                     8    8\n",
-       "5                     4    4\n",
-       "6                     2    2\n",
-       "7                     2    2\n",
-       "8                     1    1"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "submissions_per_person.groupby(['n_attempts']).count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f640bf668>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = submissions_per_person.groupby(['n_attempts'])['icma_number'].count().plot(kind='bar')\n",
-    "ax.set_xlabel('Number of attempts')\n",
-    "plt.savefig('attempts-per-day.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"8\" halign=\"left\">pi</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>n_attempts</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>icma_number</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>60</td>\n",
-       "      <td>13</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>62</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>56</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>31</td>\n",
-       "      <td>10</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>34</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>18</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>31</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>16</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>27</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>16</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             pi                      \n",
-       "n_attempts    1   2  3  4  5  6  7  8\n",
-       "icma_number                          \n",
-       "1            60  13  3  0  0  0  0  0\n",
-       "2            62   5  2  1  0  0  0  0\n",
-       "3            56   2  1  0  0  0  0  0\n",
-       "4            31  10  4  0  1  0  0  0\n",
-       "5            34   4  1  1  0  0  1  0\n",
-       "6            18   8  2  2  2  2  1  0\n",
-       "7            31   1  0  0  1  0  0  0\n",
-       "8            16   3  1  3  0  0  0  0\n",
-       "9            27   2  0  0  0  0  0  0\n",
-       "10           16   7  2  1  0  0  0  1"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "submissions_per_person.groupby(['icma_number', 'n_attempts']).count().unstack().fillna(value=0).astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'<table border=\"1\" class=\"dataframe\">\\n  <thead>\\n    <tr>\\n      <th></th>\\n      <th colspan=\"8\" halign=\"left\">pi</th>\\n    </tr>\\n    <tr>\\n      <th>n_attempts</th>\\n      <th>1</th>\\n      <th>2</th>\\n      <th>3</th>\\n      <th>4</th>\\n      <th>5</th>\\n      <th>6</th>\\n      <th>7</th>\\n      <th>8</th>\\n    </tr>\\n    <tr>\\n      <th>icma_number</th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>1</th>\\n      <td>60</td>\\n      <td>13</td>\\n      <td>3</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n    </tr>\\n    <tr>\\n      <th>2</th>\\n      <td>62</td>\\n      <td>5</td>\\n      <td>2</td>\\n      <td>1</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n    </tr>\\n    <tr>\\n      <th>3</th>\\n      <td>56</td>\\n      <td>2</td>\\n      <td>1</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n    </tr>\\n    <tr>\\n      <th>4</th>\\n      <td>31</td>\\n      <td>10</td>\\n      <td>4</td>\\n      <td>0</td>\\n      <td>1</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n    </tr>\\n    <tr>\\n      <th>5</th>\\n      <td>34</td>\\n      <td>4</td>\\n      <td>1</td>\\n      <td>1</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>1</td>\\n      <td>0</td>\\n    </tr>\\n    <tr>\\n      <th>6</th>\\n      <td>18</td>\\n      <td>8</td>\\n      <td>2</td>\\n      <td>2</td>\\n      <td>2</td>\\n      <td>2</td>\\n      <td>1</td>\\n      <td>0</td>\\n    </tr>\\n    <tr>\\n      <th>7</th>\\n      <td>31</td>\\n      <td>1</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>1</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n    </tr>\\n    <tr>\\n      <th>8</th>\\n      <td>16</td>\\n      <td>3</td>\\n      <td>1</td>\\n      <td>3</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n    </tr>\\n    <tr>\\n      <th>9</th>\\n      <td>27</td>\\n      <td>2</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n    </tr>\\n    <tr>\\n      <th>10</th>\\n      <td>16</td>\\n      <td>7</td>\\n      <td>2</td>\\n      <td>1</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>1</td>\\n    </tr>\\n  </tbody>\\n</table>'"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "submissions_per_person.groupby(['icma_number', 'n_attempts']).count().unstack().fillna(value=0).astype(int).to_html()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Grade per person per task, keeping only best score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>grade</th>\n",
-       "      <th>icma_number</th>\n",
-       "      <th>num</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10</td>\n",
-       "      <td>5</td>\n",
-       "      <td>40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>10</td>\n",
-       "      <td>6</td>\n",
-       "      <td>28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-</td>\n",
-       "      <td>6</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>10</td>\n",
-       "      <td>7</td>\n",
-       "      <td>31</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>-</td>\n",
-       "      <td>8</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>10</td>\n",
-       "      <td>10</td>\n",
-       "      <td>24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>10</td>\n",
-       "      <td>8</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>-</td>\n",
-       "      <td>10</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>-</td>\n",
-       "      <td>1</td>\n",
-       "      <td>62</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>-</td>\n",
-       "      <td>0</td>\n",
-       "      <td>180</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>-</td>\n",
-       "      <td>5</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>10</td>\n",
-       "      <td>2</td>\n",
-       "      <td>64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>-</td>\n",
-       "      <td>2</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>-</td>\n",
-       "      <td>3</td>\n",
-       "      <td>25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>10</td>\n",
-       "      <td>3</td>\n",
-       "      <td>53</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>10</td>\n",
-       "      <td>1</td>\n",
-       "      <td>69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>87</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>10</td>\n",
-       "      <td>4</td>\n",
-       "      <td>40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>-</td>\n",
-       "      <td>7</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>-</td>\n",
-       "      <td>4</td>\n",
-       "      <td>28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>-</td>\n",
-       "      <td>9</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>10</td>\n",
-       "      <td>9</td>\n",
-       "      <td>27</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   grade  icma_number  num\n",
-       "0      0            6    1\n",
-       "1     10            5   40\n",
-       "2     10            6   28\n",
-       "3      -            6   23\n",
-       "4     10            7   31\n",
-       "5      -            8   23\n",
-       "6      5            6    1\n",
-       "7      5            1    1\n",
-       "8      5            0    3\n",
-       "9     10           10   24\n",
-       "10    10            8   20\n",
-       "11     -           10   15\n",
-       "12     -            1   62\n",
-       "13     5            2    2\n",
-       "14     -            0  180\n",
-       "15     -            5   20\n",
-       "16     5            4    2\n",
-       "17    10            2   64\n",
-       "18     -            2   22\n",
-       "19     0            0    2\n",
-       "20     -            3   25\n",
-       "21    10            3   53\n",
-       "22    10            1   69\n",
-       "23    10            0   87\n",
-       "24    10            4   40\n",
-       "25     -            7   15\n",
-       "26     -            4   28\n",
-       "27     -            9   10\n",
-       "28     0            1    1\n",
-       "29    10            9   27"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pipeline = [{\"$group\": {\"_id\": {'icma_number': \"$icma_number\",\n",
-    "                                'pi': '$Personal Identifier'},\n",
-    "                        \"scores\": {\"$push\": '$grade'}}},\n",
-    "            {'$project': {'scores': True,\n",
-    "                          'n_attempts': {'$size': '$scores'},\n",
-    "                          'highest': {'$max': '$scores'}}},\n",
-    "            {'$group': {'_id': {'icma_number': '$_id.icma_number',\n",
-    "                                'grade': '$highest'},\n",
-    "                        \"count\": {\"$sum\": 1}}}]\n",
-    "icma_score_long = pd.DataFrame([\n",
-    "    {'icma_number': r['_id']['icma_number'],\n",
-    "     'grade': r['_id']['grade'],\n",
-    "     'num': r['count']}\n",
-    "    for r in icmas.aggregate(pipeline)\n",
-    "])\n",
-    "icma_score_long"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Old version, counting each attempt separately\n",
-    "#\n",
-    "# pipeline = [{\"$group\": {\"_id\": {'icma_number': \"$icma_number\",\n",
-    "#                                 'grade': '$grade'},\n",
-    "#                         \"count\": {\"$sum\": 1}}}]\n",
-    "# icma_score_long = pd.DataFrame([\n",
-    "#     {'icma_number': r['_id']['icma_number'],\n",
-    "#      'grade': r['_id']['grade'],\n",
-    "#      'num': r['count']}\n",
-    "#     for r in icmas.aggregate(pipeline)\n",
-    "# ])\n",
-    "# icma_score_long"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>grade</th>\n",
-       "      <th>0.0</th>\n",
-       "      <th>5.0</th>\n",
-       "      <th>10.0</th>\n",
-       "      <th>-</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>icma_number</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>87</td>\n",
-       "      <td>180</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>69</td>\n",
-       "      <td>62</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>64</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>53</td>\n",
-       "      <td>25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>40</td>\n",
-       "      <td>28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>28</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>31</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>20</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>27</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>24</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "grade        0.0  5.0  10.0    -\n",
-       "icma_number                     \n",
-       "0              2    3    87  180\n",
-       "1              1    1    69   62\n",
-       "2              0    2    64   22\n",
-       "3              0    0    53   25\n",
-       "4              0    2    40   28\n",
-       "5              0    0    40   20\n",
-       "6              1    1    28   23\n",
-       "7              0    0    31   15\n",
-       "8              0    0    20   23\n",
-       "9              0    0    27   10\n",
-       "10             0    0    24   15"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "icma_score = icma_score_long.pivot(index='icma_number', columns='grade', values='num').fillna(value=0).astype(int)\n",
-    "icma_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def pandas_df_to_markdown_table(df):\n",
-    "    from IPython.display import Markdown, display\n",
-    "    fmt = ['---' for i in range(len(df.columns))]\n",
-    "    df_fmt = pd.DataFrame([fmt], columns=df.columns)\n",
-    "    df_formatted = pd.concat([df_fmt, df])\n",
-    "    display(Markdown(df_formatted.to_csv(sep=\"|\", index=False)))\n",
-    "    return df_formatted.to_csv(sep=\"|\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "0.0|5.0|10.0|-\n",
-       "---|---|---|---\n",
-       "2|3|87|180\n",
-       "1|1|69|62\n",
-       "0|2|64|22\n",
-       "0|0|53|25\n",
-       "0|2|40|28\n",
-       "0|0|40|20\n",
-       "1|1|28|23\n",
-       "0|0|31|15\n",
-       "0|0|20|23\n",
-       "0|0|27|10\n",
-       "0|0|24|15\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0|5.0|10.0|-\n",
-      "---|---|---|---\n",
-      "2|3|87|180\n",
-      "1|1|69|62\n",
-      "0|2|64|22\n",
-      "0|0|53|25\n",
-      "0|2|40|28\n",
-      "0|0|40|20\n",
-      "1|1|28|23\n",
-      "0|0|31|15\n",
-      "0|0|20|23\n",
-      "0|0|27|10\n",
-      "0|0|24|15\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(pandas_df_to_markdown_table(icma_score))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>grade</th>\n",
-       "      <th>0.0</th>\n",
-       "      <th>5.0</th>\n",
-       "      <th>10.0</th>\n",
-       "      <th>-</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>icma_number</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>69</td>\n",
-       "      <td>62</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>64</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>53</td>\n",
-       "      <td>25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>40</td>\n",
-       "      <td>28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>28</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>31</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>20</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>27</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>24</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "grade        0.0  5.0  10.0   -\n",
-       "icma_number                    \n",
-       "1              1    1    69  62\n",
-       "2              0    2    64  22\n",
-       "3              0    0    53  25\n",
-       "4              0    2    40  28\n",
-       "5              0    0    40  20\n",
-       "6              1    1    28  23\n",
-       "7              0    0    31  15\n",
-       "8              0    0    20  23\n",
-       "9              0    0    27  10\n",
-       "10             0    0    24  15"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "icma_score.loc[1:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'<table border=\"1\" class=\"dataframe\">\\n  <thead>\\n    <tr style=\"text-align: right;\">\\n      <th>grade</th>\\n      <th>0.0</th>\\n      <th>5.0</th>\\n      <th>10.0</th>\\n      <th>-</th>\\n    </tr>\\n    <tr>\\n      <th>icma_number</th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>1</th>\\n      <td>1</td>\\n      <td>1</td>\\n      <td>69</td>\\n      <td>62</td>\\n    </tr>\\n    <tr>\\n      <th>2</th>\\n      <td>0</td>\\n      <td>2</td>\\n      <td>64</td>\\n      <td>22</td>\\n    </tr>\\n    <tr>\\n      <th>3</th>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>53</td>\\n      <td>25</td>\\n    </tr>\\n    <tr>\\n      <th>4</th>\\n      <td>0</td>\\n      <td>2</td>\\n      <td>40</td>\\n      <td>28</td>\\n    </tr>\\n    <tr>\\n      <th>5</th>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>40</td>\\n      <td>20</td>\\n    </tr>\\n    <tr>\\n      <th>6</th>\\n      <td>1</td>\\n      <td>1</td>\\n      <td>28</td>\\n      <td>23</td>\\n    </tr>\\n    <tr>\\n      <th>7</th>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>31</td>\\n      <td>15</td>\\n    </tr>\\n    <tr>\\n      <th>8</th>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>20</td>\\n      <td>23</td>\\n    </tr>\\n    <tr>\\n      <th>9</th>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>27</td>\\n      <td>10</td>\\n    </tr>\\n    <tr>\\n      <th>10</th>\\n      <td>0</td>\\n      <td>0</td>\\n      <td>24</td>\\n      <td>15</td>\\n    </tr>\\n  </tbody>\\n</table>'"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "icma_score.loc[1:].to_html()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>grade</th>\n",
-       "      <th>0.0</th>\n",
-       "      <th>5.0</th>\n",
-       "      <th>10.0</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>icma_number</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>53</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>31</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>27</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>24</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "grade        0.0   5.0   10.0\n",
-       "icma_number                  \n",
-       "1               1     1    69\n",
-       "2               0     2    64\n",
-       "3               0     0    53\n",
-       "4               0     2    40\n",
-       "5               0     0    40\n",
-       "6               1     1    28\n",
-       "7               0     0    31\n",
-       "8               0     0    20\n",
-       "9               0     0    27\n",
-       "10              0     0    24"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "icma_score.loc[1:, icma_score.columns != '-']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f36f4a160>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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Dli34unIJpyH9+sGDD8K2bdCrlzNu/tVXw6ZNbkdmjGkBS/TN8IOBAymuqWHh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\nr7dOLX1dSQmJkZF1a+mJiRz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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f640b3b38>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "icma_score.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f36f10be0>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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KfRK4dAv0DcRD6deAl2LPy1Mt7G6CHreaSeEKjV995dgKjSk5KUxYNYG4jDiW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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f36e165f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "icma_score.T.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f34d5f7f0>"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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f6+dWbcTu3VZSv+MOaxHqjIzDFzJzcw+3wMePh+Rk3ypxHDoEl15qJfN33oHw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\n/w+D0AzmxrjpIgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f36f48940>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "icma_score.loc[1:].plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f34d6f780>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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AqAAUAWAF4F8A7QGUIqmRJKkZgOkkO2lpT21jCEdkOGLOsgcS+PhjeTfll1/K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\n81qYzdGpUyeuWbOmRO5dnvbMaDKy5ketOWS2BUGZnk76+JA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+SUFA0NxGQAAUAB5soxDm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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f34cc95c0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "icma_score.loc[1:].T.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.text.Text at 0x7f5f34c1d390>"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f34c0d4a8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = icma_score.loc[1:, icma_score.columns != '-'].plot(kind='bar', xticks=list(range(1, 11)))\n",
-    "ax.set_xlabel('Day')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f36dc9da0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = icma_score.loc[1:, icma_score.columns != '-'].plot(kind='bar', xticks=list(range(1, 11)))\n",
-    "ax.set_xlabel('Day')\n",
-    "plt.savefig('scores-per-day.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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FzPGIz6pqrsPOHU/NOfqeEJEGYAzmVPuMr2c9sFZV222mrdrJtPqHZqxL2zim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\nyFnQG2NMyFnQG2NMyP0Hrzb9+l6nnHIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f34aaf0b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plt.tight_layout()\n",
-    "ax = icma_score.loc[1:, icma_score.columns != '-'].T.plot(kind='pie', subplots=True, legend=False, layout=(3, 4))\n",
-    "# plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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f4wKVw+FwOHyNC1QOh8Ph8DUuUDkcDofD17hA5XA4HA5f4wKVw+FwOHyNC1QO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\nnEzjjspxHMfJNP8P718bwLVQhuIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f34b1f748>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(nrows=3, ncols=4)\n",
-    "fig.tight_layout()\n",
-    "\n",
-    "target_ax = [axes[r][c] for r in range(3) for c in range(4) if not (r == 2 and c >= 2)]\n",
-    "\n",
-    "ax = icma_score.loc[1:, icma_score.columns != '-'].T.plot(kind='pie', subplots=True, legend=False, \n",
-    "                                                          ax=target_ax, sharex=False, sharey=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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SIyKuMJxwlt1ja0Vid0Cgu4hsmes17FLMbvToEadrEYLN3G64/nr5p9stHxT+6h1CgMPM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\nJDiC7eDg4FAk/D83WSoo0/0x/wAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f34cdd1d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = icma_score.T.plot(kind='pie', subplots=True, legend=False, layout=(3, 4))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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vq2H52RB3JTBfbS089liSiy4y2LTpUo3Hr93a1lsbQ1WX1cBeb8P4vSBWv7eb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\nhmEYhuP+H4K7MmOX1DiWAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f349d97b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(nrows=3, ncols=4)\n",
-    "fig.tight_layout()\n",
-    "\n",
-    "target_ax = [axes[r][c] for r in range(3) for c in range(4) if not (r == 2 and c >= 3)]\n",
-    "\n",
-    "ax = icma_score.T.plot(kind='pie', subplots=True, legend=False, \n",
-    "                                                          ax=target_ax, sharex=False, sharey=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.text.Text at 0x7f5f2fe42d68>"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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K4MGDadKkCd/61rfK/N2iRYvo3bs3qampjBgxgk1fMZ1448aNXHDBBaSkpNCnTx8WLVoU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\nkSShQBcRSRIKdBGRJPH/AcOOrbxMtiFRAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f349d31d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = icma_score.loc[1:, icma_score.columns != '-'].plot(xticks=list(range(1, 11)))\n",
-    "ax.set_xlabel('Day')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f2fe22da0>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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7+GKFjDoLquLd8RVNU/o3E2IM3UAdO3aEj48PSKJWrVpYuHAhOnToYJK2zKXP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\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2fe3b0b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "icma_score.loc[1:, icma_score.columns != '-'].T.plot(xticks=[0, 5, 10])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# People participating in each task"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "pipeline = [{\"$group\": {\"_id\": \"$icma_number\",\n",
-    "                        \"participants\": {\"$addToSet\": '$Personal Identifier'}}},\n",
-    "            {\"$project\": {\"participants\": True,\n",
-    "                         'num_participants': {\"$size\": \"$participants\"}}}]\n",
-    "list(icmas.aggregate(pipeline));"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0     272\n",
-       "1     133\n",
-       "2      88\n",
-       "3      78\n",
-       "4      70\n",
-       "5      60\n",
-       "6      53\n",
-       "7      46\n",
-       "8      43\n",
-       "9      37\n",
-       "10     39\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pipeline = [{\"$group\": {\"_id\": \"$icma_number\",\n",
-    "                        \"participants\": {\"$addToSet\": '$Personal Identifier'}}},\n",
-    "            {\"$project\": {'num_participants': {\"$size\": \"$participants\"}}}]\n",
-    "\n",
-    "participants_per_day = pd.Series(\n",
-    "    {r['_id']: r['num_participants']\n",
-    "        for r in icmas.aggregate(pipeline)\n",
-    "    })\n",
-    "participants_per_day\n",
-    "#     sorted(list(icmas.aggregate(pipeline)), key=lambda r: r['_id'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f34851b70>"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f34cc9668>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "participants_per_day.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f2fd70d30>"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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/hF13rbo6dcqgl9RW63bNW28ttmjOmwf77//6r/vsA294Q9XValsMeknjyoRnnoHVq+GJ\nJ4qvzctPPw3vfGf7XwJ77OFJ3n5g0EvqyqZN8OSTrw//5teXXioCvzX8m3/e+taqKx8eBr2kKbNx\nI6xZ8/rwX726OLbTTq8Pf5eCpo5BL2nauRQ0vQx6SX1l0yZ46qmtXwWsXg0vvvjaK4ADDoATT4R3\nv9sJ3/EY9JIGRutS0MMPw/e+V7wyWLAATj8d5s937b+dSoI+ImYA9wFPZ+YZEbE38B1gZ+B+4I8z\n85U29zPoJb3OunXF9s/ly4s3cDv66CL0Tz+9WO9XdUG/EDgCeGsZ9NcDN2bmsoj4JrAqM5e0uZ9B\nL2lML7wAK1cWwX/rrfC2t8FppxWhP8xLPNMe9BExB1gKfAm4pAz6fwNmZ+bmiDgWWJyZp7S5r0Ev\naUI2b4b77is6/eXLX7/Ec/LJMGtW1RVOnyqCfhlFyM8CPgtcAPxLZs4rr58DrMjMQ9rc16CX1JFh\nXuLpJOhndPFk7wc2ZOYqoPmk0XK5yTSX1FNz58Kf/imsWAHr18NFF8FDD8F73gMHHwyf+1zxC+DV\nV6uutD/M7OK+xwFnRMSpwJuBnYCvA7MiYkZmbgbmAOvHeoDFixdvudxoNGg0Gl2UI2kY7bgjnHVW\n8ad1ieeii15b4jnttGIXzyAu8YyMjDAyMgIUf79O9GR7ZUScAHy25WTsP2Tm9eXJ2Acz88o293Hp\nRtKUWrv2tZO5/bzE8+qr8MtfwrPPFq9Qml9bLz/7bHGbV16paB/9qKD/fV7bXvkA8EeZ+XKb+xj0\nkqZNcxfP8uXwj/8Iu+zyWuhP1S6eyQT4LrvAnnsWE8OtX1svz54N22/vwJQkjWvzZvjJT147oTvZ\nJZ6pCPCJvieQk7GS1IHmEk9zF88xx8D731+s/09XgE+UQS9JXWou8dx2W9G5T1eAT5RBL0k1N637\n6CVJg8Ggl6SaM+glqeYMekmqOYNekmrOoJekmjPoJanmDHpJqjmDXpJqzqCXpJoz6CWp5gx6Sao5\ng16Sas6gl6SaM+glqeYMekmqOYNekmrOoJekmjPoJanmDHpJqjmDXpJqzqCXpJoz6CWp5gx6Sao5\ng16Saq7joI+IORFxR0Q8GhE/i4g/K4/vHBHfj4gnIuL2iJjVu3IlSZPVTUf/CnBJZh4EvBv4dEQc\nAHweWJmZ+wN3AJd2X+b0GBkZqbqEtvqxLmuaGGuauH6sqx9r6kTHQZ+Zz2XmqvLyC8BjwBzgTODq\n8mZXA2d1W+R06df/qP1YlzVNjDVNXD/W1Y81daIna/QRsTfwLuDHwOzM3ADFLwNg1148hySpM10H\nfUTsCNwIXFx29tl1VZKknonMznM5ImYCtwK3Zebl5bHHgEZmboiI3YEfZuaBbe7rLwRJ6kBmxmRu\nP7PL5/tfwKPNkC/dAnwM+ApwPnBzuztOtlBJUmc67ugj4jjgTuBnFMs1CXwBuBe4AZgLrAU+nJkb\ne1KtJGnSulq6kST1v2mfjI2Ib0XEhoh4aLqfeyxjDX9VXNMbI+KeiHigrGlR1TU1RcSMiLg/Im6p\nupamiPjXiHiw/HndW3U9ABExKyKWRcRjEfFIRBxTcT3zyp/P/eXX3/TJ/+sLI+LhiHgoIq6JiO37\noKaLy393leZBu7zsZCi1irdAWArMr+B5t2Ws4a/KZObvgPdl5mEUW1cXRMTRVdbU4mLg0aqLGGUz\nxSaAwzKzX35OlwMrys0Ih1LMmlQmM1eXP5/DgSOAF4HvVllTROwJfAY4PDMPoThveHbFNR0M/Alw\nJMW/vdMjYt+KymmXl5MeSp32oM/Mu4Dnp/t5t2WM4a93VFsVZOZL5cU3UvwDqHydLSLmAKcCf191\nLaMEffTeTRGxE/CfM3MpQGa+kpn/r+KyWp0EPJmZ66ouBNgOeEu5i28HYH3F9RwI/Dgzf5eZrwI/\nAj5QRSFj5OWkh1L75h9Gv2gZ/rqn2kq2LJE8ADwH/CAzf1J1TcBlwF/QB790Rkng9oj4SURcWHUx\nwD7AryJiablUclVEvLnqolp8BLiu6iIycz3wVYqNG88AGzNzZbVV8TDw3nKJZAeKxmZuxTW12m2y\nQ6kGfYs2w1+VyszN5dLNHOCYiDioynoi4v3AhvLVT5R/+sV7MvNIin+Un46I4yuuZyZwOPCNcqnk\nJYqX3JWLiDcAZwDL+qCW36PoUPcC9gR2jIhzq6wpMx+n2B6+ElgBrKJY3h1YBn2pfNl4I/C/M7Pt\n3v+qlC/5R4BTKi7lOOCMiHiKoht8X0R8u+KagC2dDZn5bxTrzlWv0z8NrMvM+8rvb6QI/n6wAPhp\n+bOq2knAU5n57+UyyT8A76m4JjJzaWYekZkNiqWTNRWX1GpDRMwGKIdSfzneHaoK+n7rBqH98Fdl\nIuLtzbPp5Uv+k4DHq6wpM7+Qme/MzH0oTpjdkZnnVVkTQETsUL4aIyLeApxM8fK7MuVL63URMa88\ndCL9cwL7HPpg2aa0Fjg2It4UEUHxc6r0pDVAROxafn0nxfp8lT+v0XnZHEqFbQyltup2MnbSIuJa\noAG8LSLWAouaJ6yqUg5/fRT4WbkmnsAXMvN7FZa1B3B1RMyg+IV8fWauqLCefjYb+G75thozgWsy\n8/sV1wTwZ8A15VLJU8AFFdfT2jR8oupaADLz3oi4EXgAeLn8elW1VQFwU0TsQlHTpzLzN1UU0S4v\ngS8DyyLi45RDqeM+jgNTklRvrtFLUs0Z9JJUcwa9JNWcQS9JNWfQS1LNGfSSVHMGvSTVnEEvSTX3\n/wEOl7h52bRVYwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2fd37a20>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "participants_per_day[1:].plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Actual submissions, not unfinished attempts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "pipeline = [{'$match': {'grade': {'$ne': '-'}}},\n",
-    "            {\"$group\": {\"_id\": \"$icma_number\",\n",
-    "                        \"participants\": {\"$addToSet\": '$Personal Identifier'}}},\n",
-    "            {\"$project\": {'participants': True,\n",
-    "                          'num_participants': {\"$size\": \"$participants\"}}}]\n",
-    "list(icmas.aggregate(pipeline));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Those getting full marks on each day"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "pipeline = [{'$match': {'grade': 10}},\n",
-    "            {\"$group\": {\"_id\": \"$icma_number\",\n",
-    "                        \"participants\": {\"$addToSet\": '$Personal Identifier'}}},\n",
-    "            {\"$project\": {'participants': True,\n",
-    "                          'num_participants': {\"$size\": \"$participants\"}}}]\n",
-    "completers = {r['_id']: r for r in icmas.aggregate(pipeline)}\n",
-    "completers;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "completers[5];"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "prize_candidates = {}\n",
-    "for i in icmas.find({}).distinct('icma_number'):\n",
-    "    earliest = icmas.find_one({'icma_number': i}, sort=[(\"started\", 1)])[\"started\"]\n",
-    "    icma_finish = earliest.replace(day=earliest.day+1, hour=0, minute=0, second=0)\n",
-    "    prize_candidates[i] = list(\n",
-    "        icmas.find({'icma_number': i,\n",
-    "            'grade': 10,\n",
-    "            'completed': {'$lt': icma_finish}},\n",
-    "               ['Personal Identifier', 'Email address', 'First name', 'Surname', 'completed']))\n",
-    "prize_candidates;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# for i in prize_candidates:\n",
-    "#     winner = random.choice(prize_candidates[i])\n",
-    "#     print('Day {}: {} {} PI {} email {}'.format(\n",
-    "#         i,\n",
-    "#         winner['First name'],\n",
-    "#         winner['Surname'],\n",
-    "#         winner['Personal Identifier'],\n",
-    "#         winner['Email address']\n",
-    "#         ))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "# Times of starting and finishing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# all_starts = pd.DataFrame([{'icma_number': r['icma_number'], 'start_time': r['started'].time()}\n",
-    "#               for r in icmas.find({}, ['icma_number', 'started'])])\n",
-    "# all_starts[:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>icma_number</th>\n",
-       "      <th>start_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>9</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>9</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>9</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>9</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>9</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>9</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>9</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>9</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>9</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>9</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   icma_number  start_time\n",
-       "0            9           8\n",
-       "1            9           8\n",
-       "2            9          10\n",
-       "3            9          10\n",
-       "4            9          10\n",
-       "5            9          11\n",
-       "6            9           8\n",
-       "7            9           8\n",
-       "8            9          11\n",
-       "9            9          12"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_starts = pd.DataFrame([{'icma_number': r['icma_number'], 'start_time': r['started'].hour}\n",
-    "              for r in icmas.find({}, ['icma_number', 'started'])])\n",
-    "all_starts[:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "start_time\n",
-       "0     99\n",
-       "1     22\n",
-       "2     20\n",
-       "3      7\n",
-       "4      5\n",
-       "5      5\n",
-       "6     33\n",
-       "7     40\n",
-       "8     60\n",
-       "9     68\n",
-       "10    61\n",
-       "11    73\n",
-       "12    58\n",
-       "13    60\n",
-       "14    49\n",
-       "15    63\n",
-       "16    56\n",
-       "17    51\n",
-       "18    53\n",
-       "19    46\n",
-       "20    55\n",
-       "21    51\n",
-       "22    46\n",
-       "23    50\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_starts_x = pd.crosstab(all_starts.start_time, all_starts.icma_number)\n",
-    "all_starts_x.sum(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f5f2faee518>"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2fc08c50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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OyMoCf6pvJk+GXbvMvIDMTPvXJgY3CUzigpxTRVQIJLiBQ/tuQg1unn/eZG1KS+F7MjhE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gtPfwwOPUVAj79hXdV1ERiuXVMKVLJO4lZMLQ0xPjAwPhn5MFF0sZTBnRF3PnzoVQSlHP5Hv3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\nAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2fc08c88>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure()\n",
-    "fig, ax = plt.subplots(1, 2, figsize=(14, 4));\n",
-    "plt.subplots_adjust(wspace=0.5, hspace=0.5);\n",
-    "\n",
-    "ax = fig.add_subplot(1,2,2)\n",
-    "all_starts_x.ix[:, all_starts_x.columns != 0].plot(ax=ax, legend=False, sharex=False, sharey=False)\n",
-    "\n",
-    "ax = fig.add_subplot(1,2,1)\n",
-    "all_starts_x.plot(ax=ax, legend=False, sharex=False, sharey=False)\n",
-    "\n",
-    "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., title=\"Day\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f5f2fc086a0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5f2e1bfcc0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5f2e18b8d0>],\n",
-       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f5f34851ef0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5f346605f8>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5f3423d588>],\n",
-       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f5f34285898>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5f342ddfd0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5f3468cb70>],\n",
-       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f5f343269b0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5f346652e8>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5f3465ca20>]], dtype=object)"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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e87Jytu+OuCLsisupvjBa59kwNDS0SbRpSmmUQUetkZcN1W3nzdjjo2Q1QzpZ\nhdj0cXm2/wx8038+DTg1ma0vqU1/IEPbWZSsZm1wtfLS2PIGEsnIytYXU+c3Bj43pfOurm106tQd\n/f8XXkaN2qru8Y2lKJ2HyUpjAy61zlO286Q2/Sg9NaOrPHWeRRtvvc7TeO8cCLweOFBE7gW2Bu5r\nRtDhhx/D2rWPp6iK0SK2x4Xkj7CS4IrnCVi9egXU8ZjZuK7BINF5fMrrUVMhMmvnRjlI0+n/DrhI\nVY8FEJF/BPYMP/WWSCHr1j3J888/Tr0OwGENvASEKSFCcdE637AhZE1to6xk0M4bJGQ1WkqafPp7\nA4OqOtN/Pg33ijG35rzmbmBkhmaXW9103iaYzjuPuDpP0+mPBh4GDsL57t4FHKaqS5sSaJQe03nn\nYTqvHk2bd1R1vYh8DliAc/2cZz+EamM67zxM59UjVWplwzAMo72wbImGYRgdhHX6hmEYHUTafPqb\nISLTgVk4n27FRW5el9YOKCL74XzCH9AWhf6LyCeBr+FWDHocOEpVf92Ke7cLeenby26ZzkXkWTa6\nnwqwJXCuqp6U533bkaro3N9vGnAeLn30S8BVwEmquiHvexdFpiN9ETkV+Amu0dwFLPTbP/auXklk\n3RXY/gwuFHwCMJBUVjOIyHuB/wCOVNXxwHuAP+Vwn24RmSMiD4nIGl+W+n1bJ5Q1M7DdLSLzRGSJ\niFwqIj051D0zfXt5helcVSeo6kRVnYjLMfQCcHnW92lnffv7VEbnnvOAYZzOZ+AyiR6X5Q1Kp/Nm\nw7PDCi6Q43Uh+7cAfp9Q1r2B7YXAVL89Drg/oaxu3MLeDwFrfFnq920dcc2vgaMjjs2skT0PWIJb\naKInYd1uBk4FegP7ev2+WxLKuiewfRFumbtpwOeBa7LUddb6LoPOA9ceCfzB9F19nQMP1uj3LOD/\nVlnnWdv0NwDbhex/vT+WhFEiMklEpuC8jFYDqOrzwLqEsi4H1uLWdp2iqlOAA/y+K2pP9kmm9gC2\nFZHfi8hyEfm2iIz1pwTXCD0bZ/r5EO5He37CuvWp6lxVXTWyQ1VXqQt+2SmhrCB7qOpXVPVRVf0W\n0JdCVhRZ6hsK1HkNRwCXBD6bvjdSNZ2fAxwmIluKyPbA3wI3UmWdZzwKmAn8AfelXeDLTX7fzISy\nluHMKY/4v71+/3hgcUJZDyc5xsYf8F3AtsBk4A7gjJCn7eKaa5PWbQFwCoHRA+5V81Tg1oSyVgIn\nA1/w35klS2k/AAAffklEQVQEji3JUtdZ67tonQeO7QS8CkwL7DN9V1TnwHRgkdf5euB7Vdd5phO5\nqnqTiOyCm4jZHmfrWwksVNX1CWX1RRzaAHwkYdUeFZFTgItVdRjA27+OYtMEYiO86P/+t6o+4c//\nJvBl4Ku4N4CTcf/fRBER9d86yedJPoHLXHibr5PibIzXAR9PKOtCnD0UXO7zbYDVItILLE4oqyFZ\n6tvL64s41Aqdj3AEcIeqBtfmNH17qqRzcbmVb8aZc/bBPWjmi8hcqqzzPEYDZSvAJGAuztb3lC9L\n/b5JEdcsB/4x8PmjwN1+e6CmjNghe4FLmqjfdOBgYHzN/mZGTtNxIfOpZbVzaUbn/rqHcZP3wX2m\n7zYoSXUOTMGN7icE9s3C2e4rq/PCFVV0IXqydjZuWbip/sf0K1ziqabk1Tn/RN/RXIN71Z0VOHZP\nQlknZCWryqWOzt8NPAuMSyvL9F2uUkfnf8CZXkbj0kZf3ahTb3edF66MoguwPGL/GOBc3CTQY8C3\ngC2alVfn/PtHnti4iZhFOD9hCHg2tFpWlUsdnX8X+H4Wskzf5Sp1dP524Je4t4IngMuAbaqs84Y2\nfRGZB3wQGFbVt/t97/ANpAs3AXKcqi5qJKsoRGRJ1CHchMpmqOo64HhfUsurw2j1a46q6jIR6Qeu\n9EEjSdPjZiIrQudn4bwXXgb+iBvtPJOwfi2jSZ3/U1ay6lA6fUO4zgPHvohzZdxGVZ9KWMeW0aTO\nl+A8fFLLqkO5dB7jybIfLmhhSWDfzcD7/PbfAr8s+kne4H8Y9v/DtJrSBzxWpDzgF8CMmn1jcC6D\n64uQFaHzg4FRfnsO8B9F67WFOqq0vqN07vfvgPPOeQSYXLReTefpZTUc6avqHf4pEmQDLmABnB3s\nz43kFMzPcK9Em81ui8hQwfKOoMYfWd1bxhEiktQfOBNZYTpX1VsDH38D/F3CurWaLHVUaX3768La\nOTiz5r/iPE3Kjuk8BrFSK/sfw/W68VV/Om60L768W1XrucEZbUatzmuOXQf8RFUvbX3NjLwIaecf\nAg5Q1ZNF5BHgXVpi844Rj4b+pt7Wtwh4c2D3P+M6/Rdwo/5f5VI7o3SIyJeBV63DrzYisiUuLmUg\nuLug6hgZEic4az5wJc6VaYRPAXcCb1XVdSISOaEntnZm4Wh266UeCRwCHNjgPNN5wWSg8zfi7Nf3\n+SCmHYC7RWQv9QGLQUznxRNX5w1H+qp6B1Dbqa8HbvAd/kE4v9F6MjIpAwMDpZRV5rqlYMR05z64\n7H6nAIeq6suNL9fI0tW1DU888URbf69l/v00peyaN3pVfQA3Ofgy8DTwCvAeDenwX9N4Cb+LTpCV\nVOdxzDuX4kb5Y33isaNxM9uniMjz/tg5ie5qxKK3tw8RqVt6e/syv6+I/BHnlrlbQOfnAjsDfxaR\nZ0TkosxvbBTJTrincrCdLwB2U9UZONfsz9cVsNNbI8sNN9yQ/39gxCKO987hIRM8J+NG+v8iInvi\nAhp+lG9VO4/h4UfZuK5H1Dm5mFmPBJ7DRSYGJ+/XqOpZ4nKqT8rjxkYxqOp7603e4/LX1PXYWrHi\nJxFHzmXhwoUccsghKWtpZEHc4KxDccmIRlgBXB0I2lgmIlNUdU2YjMHBwde2+/v76e/vb6qy9a7r\n7e3znWQ0PT3TWLVqWUNZjYi61+zZs0PvlZQ0dRsaGmJoaKjp6yHSfW8WboEJcImehnBJpHIlzXfR\nLrLykJcDx+AWT6nDWyP2b5voRmXVU1llJaWhy6a45csmAlep6pZ+37G4pD9vAXYDUNUdI67XZu2M\nSXBzTY3uI03bPIu4Vxb3ERG0iUm9kLe7p1R1cuD4GnX5ysOu1Xr17uqayvLlDzJ16tSk1TJikJXO\nA/u/DOyuqpEjfafzoKNPvy8AAwwOjmJgYGCz64zmqB3czZ49O7bO43jvHIfL6DZWRJbjNPs9nM33\nZdxjvOyBOkbLGQxs97OxA8iOpG93VSWLt7uwN3oRGUk0+CbgdhHpVtWno6UMpqqDEZ9ai0nQytCI\nZm36H8KN/EeCNu5MWmmj7RgWkR5VHfa5uyO9OByD+VeouDmPUpGmAwgwH7co+FWBfefjsszuAHwa\nOJ0WmPSMfEls0/dBG+cDz4rIgbiR/gRclrpQurt7I+VvscUW3H77AqZPn56s5kbebOKyiQvDP0pE\nXsGt+DNKRH6ES7z2ShEVNDIl7I3+wzhPvVtwfcWOtHmn3+jtsBPeDJsJznojLrvmS7jFkLtwARy7\nRPnwPvPMPwY+vdsXx4QJh7Fs2TLr9DMio1f9S3H2mCmBDmAOcC2wN+6V/+9xD/9Psul6skYbEvFG\nf3Zwrk5EQh012olGb4ed8GYYN+HavoHPD+DWjAVARIaBoagO3/GfkUdGjRobecxIThav+qp6eNh+\nEfkk8L/Ax3AunVvh1howDKNNiGPeuZSa1z5VnR84ZTxwa+jFHc1Y730TTbu9SqrqYyJyNm4pyReA\nBbpp9k2jWqSYx+knj8l7w5HmjT6OeedFXOTuy6q6E7w2q38Z8E7c8nKXN3X3SvMyVZtkFJGtcf76\n03Ch+VeKyOFqydeqQsp5nMEWVNGAnL13CE+4dhpuGcHfAf9Dw1n9wcB2PzYCyI8sbPp1OBj4k/r0\nuiJyNW6CJqTTHwxs92M6zw+bxzESETOZz77Ai4HPK3Arzk/BrQ7/UJ1rFTSydHe/X2+88UZNS6P7\nuELq+yS5V9r6ZCVDs0vqtBdujc4u3Ijw+8DxIefVrXNX1zb6xBNPNPXdZ/39VJGMdb4d8Cgu7cYY\n4Hrg4JDz6ujgazo4ONjCbyCaxr+Z9L+Xnp5p/j7RpadnWvp/JkASnTdl0/c/hJU4Vy5wKViNiqOq\nd4nI9Tjb7hY4D64Liq2VkSdq8ziJKXv8SJzUyofjRngPqOpO6iZxn1bVaaq6u6ruDjyfd0WN0rAd\ncJKqdgHbAA8UXB8jR2rmcbYDxotIqHeX0R7EsemHMSwiPcDhwGeBcfEnePox+25+5GnTF5EJwN+o\n6lEA6tbmjFxAx6gEB+PMuefjMqpNwK3RWrp5nDhpOapC3t47ED6rfwKu078Il5DtDURO8Aw2VTkj\nORmF5EexM/CkiMwH3oFbdOMkVX0xy5sYpWI5bqR/CvBx3DzOkvBTB1tUpXDimFWqsuJjmnYedxGV\nO4FdAosrzAHegwvLfi/wDSxQpxMYA+wOnOvNei/Q5mH5RkOW4nrSk4H7/L7/Lq46RlpiJVyLOPQe\nETkR+HfgQdpigqd6AVMtZiWwQlUX+c9X4vy3QxgMbPdTnEkvG523KqNnM/fJ2U13Z+D3uDb+DlwA\nyhjcSlpGG9KsTX9kguejwM+BXYFZIvJVVT0jq8plT/UCplqJusjMFSKyK/Bj3NvdteFnD7auYnXJ\nRuet8sho5j45m/RG3u6OV9VFInIO7u3OkuO3KU13+rgJnu2Ai1V1vogcSTCT2iYMBrb7sYnc/Mh5\n1AdwIs5VdzyuR/16njczCqcN3+7agfpvoI3eGlsxkRvGkzg3rh+Lq/0BwMLwUwdT3MZIQs6jPoA1\nwMM4s97JWndRDaPdac+3u3ag/htoo7fGvNMwRLEWWA2sAsbi3LpOSCHPaA++Bfwr0F10RYyWYW93\nFaKh904dxgA9uJDsLYEbgC9mUiujlIjIB4BhVV3M5m68RnUZebv7KLCwqLe73t4+RCSyGPFIM9Jf\niRvd3yMi9+Dc99aGnzoY2O6n3La+xt4eZb5Xzjb9fYFDReQQYEtggohcoqpHbH7qYGC7n3LrPCvS\n2WmbpQXzOKV4u2s8yW0dfyziJukJK8BtuFe9H+ISsM0NOadUCdfa65xyJVyr0ev+wHURx+rWudUJ\n19J+h9ndK7v7NJKh2en5A8B3/HY/bmWthDrPJuFaVu0lrY7S1zP7eiTReZqRPsCZuNn81cA4zNZX\naURkB1zEdS8u06blXKo+9nZXQory3gE4Fjfi6wa+oObJUXXW4Tx2FovIeOBuEZmuqg9lfaPW5lFp\npUmv7TgPl0+/139+MrzDB/PeaR2FeO+IyBHAPrjES104m34Eg4HtfmwEkB952ndVdRXOWwtVfU5E\nlgLb40x7mdLaPCqNA7g62F4cfNDPxK2WlsuD3mgNaUb67wJG4zr8rYA+EblGVT+8+amDKW5jJKEF\nfvoAiEgfMAP4bS43MEpBzYP+JhG5lRwe9J2UIbNomnbZVNWTVPX1qroz8AlgGPh2ZjUzSos37VyJ\ny7D5XNH1MVpDng/6jW929YqRBWlt+iP04uz6NuqrOCIyBtfh/0BVIyIzwUx6YeQzd9ACl0170FeI\nVJ2+t/H9Fy4dw2X2Y+gIbsIl4NpZRLpUdW74aYMtrFK7kM/cQd4mPe+5czlu3m46sdIw9NOeD/rG\nD+ZRo7Ziw4Y6U5gtoBDvHREZBXwHF6T1XeDI6AmewcB2P838GLK3+Q01VY/WyGteVs4rZ+2HWy/5\nQeAVYEBE1qjqRbncECjL95qvrDzkZYNv5z/B5d05DlgoItc2budpGKI4PdV7MDtZGzZInXOgFZP+\nqR70cR36awvOjevPwDf959OAU0POqxuEEDc4y8kZyDAwI0pWs4EVtfLSBGgMJJKRVdBGTJ3fGPjc\nlM67urbRqVN3bGTAbaCnonUeJitN8FBpdX6s18di4F7f5ucl0/nXdNy4KTH1nUZPWbTLLHRU3eCs\nA4HXAweKyL3A1mxcWScRhx9+DGvXPp6iKkaL2B6XemOElcBezQhavXoFWEh9O7AGuFBVjwUQkX+k\nCZ0///waTN/lIE2n/zvgopofw57hp/4wUsgrrzzGiy8+Tv0fBNiPohSEKSFCcdE6X7/+pWxqY7SC\nDHQesaSuUQjiX82SXyiyNzCoqjP959Nwrxhza85r7gZGZqhqJk9M03n7YDrvPOLqPE2nPxqXbvUg\n4HHgLuAwVV3alECj9JjOOw/TefVo2ryjqutF5HPAAlyQ1zz7IVQb03nnYTqvHk2P9A3DMIz2I83K\nWYZhGEabYZ2+YRhGB5FV7p3XEJHpwCycT7cCj+FWWEplB/TRoHsBD6jqgtQVbXy/6cC5uGyiTwCn\nqOo1ed+33chL3152rjoXkeOBo4C3AZeq6jGBYwfhIs53xOWUOlpVl2ddh3aknXVuZDzSF5FTcSHb\ngpvlX+i3f+xdvZLIuiuw/RlcA5yAC/1PJCsp3mPhWuA6YBLwWeCHIvKmHO7VLSJzROQhEVnjy1K/\nb+uEsmbWyJ0nIktE5FIR6cmh7pnp28trtc7/DJwBzKupxxTgKuDLwGTgbuCyLG7Yzvr292l3nbec\n0uk8q3BtPyH8O+B1Ifu3AH6fUNa9ge2FwFS/PQ64P6GsbmAOLgf4Gl+W+n1bh5y/G/BMzb6bgdl+\ne2aN7Hm4CJRLgZ6EdbsZOBXoDezr9ftuSSjrnsD2RbjlLKcBnweuyVLXWeu7SJ3jOv7vBT5/Brgj\n8HkWLtnYLp2s7yrpPIasyrbxrG36G4DtQva/3h9LwigRmeRHXaKqqwFU9Xncaj5JuBxYC/Sr6hRV\nnQIc4PddEXJ+WJCDAG/128G1gM/G+S9/CPejPT9h3fpUda66xSoAUNVV6oJfdkooK8geqvoVVX1U\nVb8F9KWQFUWW+oZidR5kNzZNKTIA/NHv72R9Q3V1Xktl23jWNv1/AX4uIr9nY46WnYA3AZ9LKKsb\n91otgIpIr6qu8nm9k0Yb9mlNBKFXwFwROSbk/IeAJ0Tki8A5uDxD+wO/CDl3D1Wd4be/JSJHJqzb\noyJyCnCxqg4D+Ne0o9g0z00cthWRk3Hfz0QREfVDAvKZtM9S31CszoOMx83jBHkaZ3boZH1DdXVe\nj0rpPNNOX91yarvgJmK29xVbCSxU1fUJZfVFHNoAfCRh1RJ96aq6TkQ+jLMvngoswtl0X/anZNnY\nPoHLVnmbr5PiViG7Dvh4QlkX4jomgIuBbYDVItKLy5KYKVnq28vriziUu85reA6YGPi8LU6vB9DB\n+oZK67yW6rbxJPakdi24ydi5uBH8U74s9fsmxZTxa+AzfnugpozYIXuBS5qo33TgYGB8zf6ZTco6\nKAtZ7VyS6JzGNv0zcesH/Lfpu7wli3YekFXZNl64ooouOFe8sP1vA8biFn3/Is6mu9kEVlx5dc4/\nEZfb5BpgGTArcOyehLJOyEpWlcuIjoDRQBfOfnuJ1/do3OhpLW6kOdZ3Gneavtu3JNVTlrLKpvPC\nlVF0AZZH7D/LjxSeAf4H2DmNvDrn3z/yxMZNxCzCrUMKAc+GVsuqchnREW4EtwFYHyhf88cOxI0S\nn8fN5exk+m7fklRPWcoqm84b2vRFZB7wQWBYVd/u903C2bin4Z42H1fVpxvJKgoRiUroLUCob6uq\nngKckpW8OoxWv7awqi4TkX7gShGZRvKJrExkhenc7z8BOB54FfgfVS2tH3UcHanqbCB0nTlV/QXw\nVwFZP5PN106thL6rQpbtstJtPMaTZT9gBrAksG8uLkIV3ETnnKKf5A3+h2H/P0yrKX3AY0XKw40i\nZ9TsG4MzN6wvQlaEzvtxmRbH+M/bFK3XFuqo0vquSjGdx5MV90bTajqAh/ABCriJjYeKVniD+s8D\n9os4dmmR8oAdCARt1Bzbt0BZtTq/DDiwaF0WpKPK67sKxXQeT1as1Mr+1eF63WjeeUpVJweOr1EX\nCGFUhBCd34tLTTETeBH4V1VdVGAVDcNogswTrhmVZQwulH1vEdkTF/24c8F1MgwjIc12+sMi0qOq\nwz4ooDZ68TXE1s4sHE24XqqfyD0UF5k6wgrgah+lfBawTESmqOqakOtN5wWTVOdG5xA3skzYdGb4\nOlyUG8CRuNf+OmhEOYPTT/9ybHvWwMBAZva/LGWVuW5NMt/rNcg1wIdxASZ/xsUsbNbhj7BgwYLI\ncvfdd7f991rm349h1COOy+alOM+NKSKyHOfbPAe4wuezWA58LM9KGi3nOFzE39iAzr+HC1B7GZeW\n4O/qCfjYx86KPPb887czPPwYkydPjjzHMIx8aNjpq+rhEYcOFpHPA58CfiUi9+Mi1V7JsoJG61HV\nw0Mmcj8EXKWqJ4vII8Cd9WQ8/fQtkce6uqayfn3iNC2GYWRA05n4RGQ7XEjw7r5jGAN8MquKhdHf\n319KWVnLy7puaRGRLXELigwEd7fi3mX9Xsv8+zGMeqT13hkNjBORDbgcNY+lr1I0ndJoi+4AQiZy\n34hbS2CNn6R9HXCPiOypqhGT+IOB7X5fklPW77VMv5+hoSGGhoYyq4tRbZru9FX1MRE5G2fTfwFY\noKq3ZlYzo0jm45YLvApAVR/wqaZ/oaobROQvwNXRHT5s2ukbedLf37/JQ2P27NDMEoYBpDPvbI1b\nRm4abiWd8SISZf832ovjcB3/WBFZLiJHq+qtqjqyMtJLhK+eZBhGyUlj3jkY+JOqPgUgIlcD78at\nIVnDYGC7n2Zf9fOgt7eP4eFHI4/39Exj1aplratQSrJ41Q+byK3hLhq66RqGUUbSdPrLgb1FpAvn\nxncQbv3IEAZT3CZfXIcf7ds8PNxeMS55v+qLyJeBV1U15OFuGEbZSWPTv0tErgTuxaXavRe4IKuK\nGcURFpHr02n/CrcW6u0i0q1102kPBrb7KdPbXdWwiVwjCbESrkVeLNINXITz7NgAHKOqv605R6NH\n0mdy+ukv8fWvn9l0HdLicqTX+w6kraMcRQRNnoZhP9wasVep6pZ+3+XAe4DdgE/jlp8LzadfX+fO\nT3/58geZOnVqkmoZMWlG50bn0PRErue/gBtU9a+Ad+BWGjLan80mcnEpGF4FbgH+AfhsgfWrLL29\nfYhI3dLb21d0NY02pmnzjohMAP5GVY8CUNV1uKUFjTYnIiL3bFXdceQcEYnMu2M0T6M5JneODeKN\n5kkz0t8ZeFJE5ovIPSJygY/cNAzDMEpKGu+dMcDuwPGqukhEzgFOY9NQfc9gYLufVk7qNXLJrBo5\nTuoNi0gPcDjOtDNORH5EZL6lwcB2PzaRmx82kWskoemJXN8B/K+q7uw/7wecqqofqjmv0IncOBO1\nNpEbel0fzrzzNv95Ls6mfzhu8n4i8AbcAumX1FxrE7lN0vj3Co1+kzaRa9SjafOOqg4DK0RkF7/r\nIODBTGplFIpPp30nsEtgIncOzntnR+C9wDdoQb6letikp2EkJ23CtROBH4nIbsDTwPT0VTKKpk46\n7feIyInAv+Me8IXmW7JJT8NITqpOX1Xv86PCdwET6wfr5EOn2eyLxOdb+ijwc2BXYJaIfFVVz9j8\n7MHAdj9m088Ps+kbSUjV6YvIDsAhuJHfyZnUKCGNR3s20suQg3GJ1i5W1fkiciQu31IIg62rVYdj\nWTaNJKQNzvoW8K80nnkyqsGTuKyqPxY343gAsKTYKhmGkYQ0qZU/AAyr6mI2XzjdqCZrgdXAKtwa\nCu8GLql7hWEYpSKNeWdf4FAROQTYEpggIpeo6hGbnzoY2O6nSvbdsqVmztm+OwboAfYJxGZ8kdDY\nDMMwykiaLJtfAr4EICL7A18I7/ChyvbdsqVmztm+uxJYgVsq8R7caH9t+KmDwVpR5gd9HGeAMq+r\nYBO5RhLSumwaHYSqDovICuBMnMvmHsCvw88ebFm90tLurp82kWskIU3CtR1w9txeYD1wYVaVMkrN\nmcCVONv+OODrxVbHMIwkpPHeWQecrKpvAfYBjheRzIOzGkVdGi3nWGB/4FPAwmZjM3bb7V0tiqQd\n2zBq1zA6iTRpGFZ5zx1U9TlcLv3ts6rYCBtfvaOK0Sqy9NhavXoF9fSaXcDdy3XvY78ho9PIxKbv\nk3PNAH5b/0yjzZkJfFpEjsV1+Bs60WMrikYTwqNGbcWGDS9kfl+byDWSkGq5RAARGQ8MAWeo6rUh\nx1Nl2cwiS2aeWTbLvtxilhkXRaQX6FXVxSIyE2fb30NVH6o5r2GWzZdeepK031vcjJRZnZO+Pq2p\ni2XZNOqRNg3DGFzD/0FYh7+RwcB2P50w6tvI2IZ240YjwCTugnmO+lR1FS4wC+BF4FmcSe+hyIsM\nwygVaRdGvwR4UlUj8+7YSD+L0V3zbwt5jfq8SW8IeKuf0wkes5F+gXWxkb5RjzRpGPbFLZB9oIjc\n65dMnJld1Yyy4k16VwIn1Xb4hmGUmzQRub/23hzn4B4e81T1psxqZpQSn3bjclw07nQgwqw3GNju\nJ7lJr7FZrLWUrT4bsYlcIxGq2lTBdfR/wGVdfB2wGJgecp6CRpQz9PTTv6z12PT6X4bIqCe/3vFf\nxrx+bCN/vzp1iyM/7JxaWdT9jhp9f83qOELnz+AC8ZrUuWpX1zYJ9Bb1vTbz3eapozS/xSS/x8a/\nhyx1bqV6JU1w1l7A71X1UVV9FfgJMCuFvBgMFSCrkZ93kXVrOZ8GJgB7AncBU3GptXNkqANk5SHP\nMMJJ0+lvj0u+NcJKcgjOMkrFGuBCVZ2hqu8ETgWeL7hOhmEkII3LZpiBU0P2AWdHiLgDl7PLaBMy\n0DmsW5d9gJJhGPFo2mVTRPYGBlV1pv98Gs6WOLfmvOZuYGSGZhecZTpvE7LSuVE90nT6o4GHgYOA\nx3E23sNUdWl21TPKhOncMNqfNC6b60Xkc8ACNrpsWuOvMKZzw2h/UufeMQzDMNqHNN47hmEYRpuR\n+XKJfiGVWTj3TQUeA64zM0A1MX0bRnuR6UhfRE7FBWkJbpJvod/+sff0SCN7PxE5WUTel76mRhbk\nqW8v33RuGFmTZXgv8DvgdSH7t8BF7yaRdVdg+zO4kP8B3ELcpxUdypzhd9YNzMGlJ17jy1K/b+uE\nsmbWyJ0HLAEuBXpyqHtm+u4Unbezvq1Uo2Rt098AbBey//X+WBJeF9g+Fnivqs4G3ofL7hkbEekW\nkTki8pCIrPFlqd+3dcJ6Ecwm6mXPE5ElInKpiPQkFHc5sBboV9UpqjoFOMDvuyKhrOAi5Wfj3Co/\nhBuBn59QVhyy1DeUVOemb6NSZPkEwS2n9wfgRuACX27y+2YmlHUfMAmYAiyqOXZvQlk341IG9Ab2\n9fp9tzTxf94T2L4IOBOXeO7zwDUJZT3czLEY9Vpcc2xxElmt1neZdW76tlKlkulErqreJCK74JKx\nbY+z764EFqrq+oTiuoG7vQwVkV5VXeVzuSeNNuzTmqhRdatAzRWRYxLKqmUPVZ3ht78lIkcmvP5R\nETkFuFhVhwH86PEoNs1tFIdtReRk3PczUUREVUd8cjP31MpY39AeOu9YfRvVIHPvHVXdAPwmAzl9\nEYc2AB9JKC7LhgbZNrZPAKcBt/k6KTAMXAd8PKGsC3FZMAEuBrYBVvu1bRcnlBWLrPTtZfVFHCpa\n56ZvozJ0RHCWiEzCNbRZwLZ+90hDm6OqaxPKG6jZdZ6qjjS2s1T1iITypgM7AL/RwEpUIjJTEy5M\n42VtD/w2rax2Jkudm76NSlG0fanoAhxdpDzgRFw+m2uAZcCswLF7Eso6IStZVS5Z6tz0baXdSuEV\nKLoAy4uUB9wPjPfbfcAi3NqzkHzyMjNZVS5Z6tz0baXdSuY2/TIiIkuiDgFJXe6yljda/Wu5qi4T\nkX7gShGZRvLJyyxltTVZ6sj0bVSJjuj0cQ3z/Thf6CAC3FmwvFUiMkNVFwOo6nMi8kHge8DbCpTV\n7mSpI9O3URk6pdP/Ge41eDOPBhEZKljeEcC64A5VXQccISJJA2yylNXuZKkj07dRGTrCe8cwDMNw\nWACHYRhGB2GdvmEYRgdhnb5hGEYHYZ2+YRhGB2GdvmEYRgfx/wH/ofqqx2YtmgAAAABJRU5ErkJg\ngg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2fab3b00>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "all_starts.start_time.hist(by=all_starts.icma_number)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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SFBERCUhJU0REJCAlTRERkYD09Kz0W5GWg1S8NN27joj0X0qa0m+1Jr5Pygy/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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f34a8d588>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(6):\n",
-    "    plt.hist(all_starts[all_starts.icma_number==i]['start_time'].reset_index(drop=True), \n",
-    "             bins=24,\n",
-    "             alpha=0.6, \n",
-    "             label=\"Day {}\".format(i))\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
-    "plt.title(\"All starts\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f34ae85f8>"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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/QtI73P3XJT1L0pvM7GJJeyR9zt0vknS7pHf2jznyox99X5J3eqyt3Ter3QPAIJm7z+aJ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\nQOEo9ABQOAo9ABTu/wHRsAVvz4QMJQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f5af63978>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "all_starts.start_time.hist(bins=24)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f34639860>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "all_starts[all_starts.icma_number > 0].start_time.hist(bins=24)\n",
-    "plt.savefig('all-starts-hours.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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dew/vYtr8+T7rvBr2lUH27t3DtGnZPuv4X7GktuEAhc9N81ep7k1/5X0oKiqi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\niIiIR0qaIiIiHilpioiIePR/8iKsOzsrZ+MAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f345e59e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(1, 6):\n",
-    "    plt.hist(all_starts[all_starts.icma_number==i]['start_time'].reset_index(drop=True), \n",
-    "             alpha=0.6, label=\"Day {}\".format(i), bins=24)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
-    "plt.title(\"All starts\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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kZWXxz//8z3zwwQdBjV0TqlJKqSFh165dOJ1Opk+fDsD8+fOZO3cu1dXVuFwu8vPzubTG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\nE6pSSikVBP8/qjsdXk0vsu0AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2df7f2b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(1, 11):\n",
-    "    plt.hist(all_starts[all_starts.icma_number==i]['start_time'].reset_index(drop=True), \n",
-    "             alpha=0.6, label=\"Day {}\".format(i), bins=24)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
-    "plt.title(\"All starts\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>icma_number</th>\n",
-       "      <th>start_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>9</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>9</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>9</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>9</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>9</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>9</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>9</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>9</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>9</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>9</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   icma_number  start_time\n",
-       "0            9           8\n",
-       "1            9          10\n",
-       "2            9          11\n",
-       "3            9           8\n",
-       "4            9           8\n",
-       "5            9          11\n",
-       "6            9          12\n",
-       "7            9          12\n",
-       "8            9          12\n",
-       "9            9          12"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "completed_starts = pd.DataFrame([{'icma_number': r['icma_number'], 'start_time': r['started'].hour}\n",
-    "              for r in icmas.find({'completed': {'$exists': True}}, ['icma_number', 'started'])])\n",
-    "completed_starts[:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f3450de10>"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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BN7PEjOKjMvwHABy51MrnoY/Lmi4gIVnTBSQka7qAheYFPUF+xt7MpuFruSSonmtspHod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\n8IJuZtYSXtDNzFrCC7qZWUv8Pwll7Mzm9V5sAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2df06630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "completed_starts.start_time.hist(bins=24)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f3475e5c0>"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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iBvnlp4B7gbMZDvU9+dX2AJeVK9fMzKapVIYuqQdsA24HtkbEAgyHPnDmtItrG+eDy2VN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\nS9F1WgQAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2e1290f0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "completed_starts[completed_starts.icma_number > 0].start_time.hist(bins=24)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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MnOitBDnnMLNWfXnV1ZnmY0Cpc+5GYBzgv2yLiEi/Vl9fz+bNm8nMzCQrK4uk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\nEY9UNEVERDxS0RQREfFIRVNERMSj/w+WRNZOPwIN/wAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f34b0db00>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(6):\n",
-    "    plt.hist(completed_starts[completed_starts.icma_number==i]['start_time'].reset_index(drop=True), \n",
-    "             alpha=0.6, label=\"Day {}\".format(i), bins=24)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
-    "plt.title(\"Completed starts\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Dv/E9j6OOPoEE7x32OmKn+/tdug1/JXbBMl99Dq9I9f0BYNe7X2H8Mn/z7Eqb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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f343006a0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(1, 6):\n",
-    "    plt.hist(completed_starts[completed_starts.icma_number==i]['start_time'].reset_index(drop=True), \n",
-    "             alpha=0.6, label=\"Day {}\".format(i), bins=24)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
-    "plt.title(\"Completed starts\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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YBQx4N/j7N9wVckB/7Hpl8EJCmKC+vpmkpFKDdcbHpaWxMpS5YEfyIZiryeLFi1m8eHHv\n60un2RNXhlCGzWwH/gzcpJQ6qZS6G/g+8Bml1DHg0/7XQgghxIQVylO+Xwry1qdNjkUIIYS4YslM\nSUIIIYQJJKEKIYQQJpCEKoQQQphg7Na5EUIIMao2rFlDc339iLU/zW5nc0loQ6TsdjuNjY3YbDbC\nwsJwOBzk5eWxevVqlFKDN2DAm2++yQMPPMCf//xnvF4vKSkpPPHEE9x0002m9iMJVQghJojm+npK\nk4wNezKiwECyVkpRUVFBamoqHo+HqqoqioqKqK6uZuvWrabG5Xa7ycjI4Oc//zlRUVFs2rSJjIwM\njh49amo/cslXCCHEmLiw2ktUVBTp6ens3LmTsrIyamtrAaisrGTOnDlER0eTlJQUMDY3PT2dLVu2\nBLSXnJzM7t27+/STkpLC3XffTUxMDGFhYdx///0cO3aMDz74wNT9kYQqhBBiXEhJSSExMZGDBw8C\nEBkZSXl5OS0tLVRUVFBSUtKbMFetWkV5eXlv3ZqaGlwuF8uWLRu0n6qqKmbMmEFsbKyp8UtCFUII\nMW4kJCRw5swZABYuXMisWbMAmD17Njk5OVRVVQGQkZFBXV0dx48fB8DpdJKdnY3VOvCdzHfffZfC\nwkIef/xx02OXhCqEEGLcaGhoIC4uDoDq6mqWLFlCfHw8MTExlJaW0tTkmxs+PDycFStW4HQ60Vqz\nY8eOQRcZP336NHfccQeFhYWsWLHC9NgloQohhBgXDh8+jMvlYsGCBQCsXLmSzMxMGhoacLvdFBQU\n9N53Bd/i4k6nk3379hEREcHcuXODtu12u7njjjvIzMzkW98ameW7JaEKIYQYUx6Phz179pCbm0te\nXh4OhwOA1tZWYmNjsdlsHDp0iO3btwfUmzdvHhaLhXXr1g14durxeEhLS+P222/nkUceGbH9kGEz\nQggxQUyz2w0NbRlK+0YsX74cq9WKxWLB4XCwfv36gMXCi4uLWbt2LYWFhSxatIjs7GzcbndAG/n5\n+WzcuJFdu4Ivevbb3/6WF198kaNHj7Jt2zbAN2yntraWxMREQzEPRBKqEEJMEKFOujAaTpw4MWiZ\nrKwssrKyBiwzc+ZM5s+fj32AZJ6fn09+fr7REA0blYR6rP2dkMu2dI2vBWOFEEKMT21tbRQXF1NY\nWDjWoQCjlFBXhA24/niAnkln+chzz/GFL3xhBCMS49mR2jc48vu3jVWq7RyZYC5z8uSzrFq1NOTy\nx46dIibmk4b6sNunUVKy2WhoQkwoe/fuJSsri7S0NHJzc8c6HGCUEuqMrz4aclnXrh+ZPnuFuLK0\nt3cxPfIzhuqcbq8coWgCKdXKhg2hT92WlXWE5ORSQ33U1xcMXkiICS4tLY3W1vF1RVOe8hVCCCFM\nIAlVCCGEMIEkVCGEEMIEklCFEEIIE0hCFUIIIUwgCVUIIYQwgcyUJIQQE8SaBx6gvrFxxNq3x8dT\n8thjoZW122lsbMRmsxEWFobD4SAvL4/Vq1ejlDI1rubmZjIyMnj99dfxer3ccsstPPbYY3zqU58y\ntR9JqEIIMUHUNzaStGHDyLW/OfQJSZRSVFRUkJqaisfjoaqqiqKiIqqrq9m6daupcUVGRrJt2zY+\n+tGPArBr1y6WL1/O6dOnsVjMu1Arl3yFEEKMiQtLsUVFRZGens7OnTspKyujtrYWgMrKSubMmUN0\ndDRJSUls2rSpt256ejpbtmwJaC85OZndu3f36WfSpEm9yVRrjcViwe129y5kbhZJqEIIIcaFlJQU\nEhMTOXjwIOA7sywvL6elpYWKigpKSkp6E+aqVasoLy/vrVtTU4PL5WLZsmVB209OTmby5MlkZmby\n1a9+lWuvvdbU+CWhCiGEGDcSEhJ6zxwXLlzIrFmzAJg9ezY5OTlUVVUBkJGRQV1dHcePHwfA6XSS\nnZ2N1Rr8TmZNTQ0ej4ft27czf/5802OXhCqEEGLcaGhoIC4uDoDq6mqWLFlCfHw8MTExlJaW0tTU\nBEB4eDgrVqzA6XSitWbHjh0DLjJ+QXh4ONnZ2Xzve9/j1VdfNTV2SahCCCHGhcOHD+NyuViwYAEA\nK1euJDMzk4aGBtxuNwUFBb33XcG3zqnT6WTfvn1EREQwd+7ckPvq6urirbfeMjX+YSVUpdT9SqnX\nlFKvKKV+oZQKNyswIYQQE4PH42HPnj3k5uaSl5eHw+EAoLW1ldjYWGw2G4cOHWL79u0B9ebNm4fF\nYmHdunUDnp1WV1fzpz/9ia6uLjo6OvjBD35AY2OjoQQciiEPm1FKJQBfB27WWp9XSu0EcoCnzApO\nCCGEeezx8YaGtgylfSOWL1+O1WrFYrHgcDhYv349BQUXly8sLi5m7dq1FBYWsmjRIrKzs3G73QFt\n5Ofns3HjRnbtCr7udmdnJ0VFRZw4cQKbzcbHPvYxKisruf76643t4CCGOw41DIhQSnmBawDX8EMS\nQggxEkKddGE0nDhxYtAyWVlZZGVlDVhm5syZzJ8/H7vdHrTMwoULefnll42GaNiQL/lqrV3AvwMn\ngQbArbX+o1mBCSGEEANpa2ujuLg44Kx2LA3nkm8MkAEkAS3Ar5VSX9Jab7+87Ad/vDhWaPKNtzLl\nxuShdivEVe/IkddYutTYH4iTJ+uYOfNvDNWx26dRUmLs8t+R2jc48vu3DdWhttNY+VGyZs0a6uvr\nDdWx2+2UlJSMSDwHDhzgwIEDI9L21Wjv3r1kZWWRlpZGbm7uWIcDDO+S76eBt7TWZwCUUv8X+BTQ\nJ6HGfnrwR5mFED7t7YqkpFJDdV544XYWLDBWp77e+Lf69vYupkd+xlCd0+2VhvsZDfX19SQlJRmu\nM1IWL17M4sWLe19fOiuQ6CstLY3W1taxDiPAcJ7yPQnMU0pNVr6ZjP8BOGpOWEIIIcSVZTj3UA8B\nvwb+CtQACvhvk+ISQgghrijDespXa70JkOsSQgghJjyZKUkIIYQwgSRUIYQQwgSSUIUQQggTDHem\nJCGEEFeIB9Y8QGN944i1H2+P57GS0GZjstvtNDY2YrPZCAsLw+FwkJeXx+rVq/ENHBkZZWVl3H33\n3fz0pz/lnnvuMbVtSahCCDFBNNY3siFpw4i1v7k+9IlClFJUVFSQmpqKx+OhqqqKoqIiqqur2bp1\n64jE53a7+f73v8/s2bNHpH255CuEEGJMXFiKLSoqivT0dHbu3ElZWRm1tbUAVFZWMmfOHKKjo0lK\nSgqY7CI9PZ0tW7YEtJecnMzu3buD9vftb3+b++67j2nTpo3A3khCFUIIMU6kpKSQmJjIwYMHAYiM\njKS8vJyWlhYqKiooKSnpTZirVq2ivPzitLY1NTW4XC6WLVvWb9uHDh3ixRdfZM2aNSMWvyRUIYQQ\n40ZCQgJnzpwBfKvEzJo1C4DZs2eTk5NDVVUVABkZGdTV1XH8+HEAnE4n2dnZWK1972R6vV6+9rWv\n8ZOf/GREY5eEKoQQYtxoaGggLi4O8C0MvmTJEuLj44mJiaG0tJSmpiYAwsPDWbFiBU6nE601O3bs\nCLrI+JYtW0hOTuaTn/zkiMYuCVUIIcS4cPjwYVwuFwsWLABg5cqVZGZm0tDQgNvtpqCgoPe+K/gW\nF3c6nezbt4+IiAjmzp3bb7vPPPMMv/3tb5kxYwYzZszgz3/+M+vWraOoqMjU+OUpXyGEEGPqwlO+\n3/jGN8jLy8PhcADQ2tpKbGwsNpuNQ4cOsX37du64447eevPmzcNisbBu3bqgZ6fgGyrT0dHR+/oL\nX/gC//RP/8SXv/xlU/dDEqoQQkwQ8fZ4Q0NbhtK+EcuXL8dqtWKxWHA4HKxfvz5gsfDi4mLWrl1L\nYWEhixYtIjs7G7fbHdBGfn4+GzduZNeuXUH7mTp1KlOnTu19PWnSJKZOnUpUVJSheAcjCVUIISaI\nUCddGA0nTpwYtExWVhZZWVkDlpk5cybz58/HbreH3PczzzwTclkjRiWhnmn+66BletQktPVGurs1\nbW1toxCVEEKIK1lbWxvFxcUUFhaOdSjAKCXU5BCefXpNn+M8Gg10dnaOfFBXmaFMKfb6kZOQlBRy\n+V01r/KbWZ8w1Ed8VAS1z1cZqjNajrz1LEfeXWqoTs/ZRiornw25fGvrOaNhXXVaW1tZurRg8IJ+\ndvs0SkpG7rKkuDrs3buXrKws0tLSyM3NHetwgFFKqDY1eEJV2rdCuRiaoUwptvKFgS+lXK7NauWG\n+39hqE7j4ysNlR9N7d5Wpi8L/QsFgHuHl6iohSGX93r/x2hYVx1vDyQllYZcvr4+9OQrJq60tDRa\nW1vHOowAMmxGCCGEMIEkVCGEEMIEklCFEEIIE0hCFUIIIUwgCVUIIYQwgSRUIYQQwgQyU5IQQkwQ\nDzywhsbG+hFrPz7ezmOPlYRU1m6309jYiM1mIywsDIfDQV5eHqtXr0Yp8wdRWiwWIiIiAFBKkZOT\nw3//93+b2ockVCGEmCAaG+vZsMHY2GsjNm+uD7msUoqKigpSU1N7J8cvKiqiurqarVu3mh6bUopX\nXnmFD3/4w6a3fYFc8hVCCDEmLizFFhUVRXp6Ojt37qSsrIza2loAKisrmTNnDtHR0SQlJbFp06be\nuunp6WzZsiWgveTkZHbv3h20L6/XO0J74iMJVQghxLiQkpJCYmIiBw8eBCAyMpLy8nJaWlqoqKig\npKSkN2GuWrWK8vLy3ro1NTW4XC6WLVsWtP1FixaRkJDAF7/4Rd5++23T45eEKoQQYtxISEjgzJkz\nACxcuJBZs2YBMHv2bHJycqiq8s0NnpGRQV1dHcePHwfA6XSSnZ2N1dr/ncxnn32W+vp6Xn/9dWbM\nmEF6errpZ6ySUIUQQowbDQ0NxMXFAVBdXc2SJUuIj48nJiaG0tJSmpqaAAgPD2fFihU4nU601uzY\nsWPARcZvv/12rFYrU6dO5YknnuDEiRMcPXrU1NiHlVCVUtFKqV8ppY4qpY4opeaaFZgQQoiJ5fDh\nw7hcLhYsWADAypUryczMpKGhAbfbTUFBQe99V/AtLu50Otm3bx8RERHMnRtaCtJao5QKaMsMwz1D\nfQKo1FrfAiQD5qZ7IYQQVz2Px8OePXvIzc0lLy8Ph8MB+Jb+i42NxWazcejQIbZv3x5Qb968eVgs\nFtatWzfg2WltbS01NTV4vV5aW1tZt24diYmJ3HLLLabux5CHzSilooAFWuu7ALTW3cBZk+ISQghh\nsvh4u6GhLUNp34jly5djtVqxWCw4HA7Wr19PQcHF5fuKi4tZu3YthYWFLFq0iOzsbNxud0Ab+fn5\nbNy4kV27dgXt59SpU9x77700NDQQERHBpz71Kfbs2UNYWJiheAcznHGoNwJNSqlt+M5OXwDu01q3\nX14wlJNqHWI5IYQQQxPqpAuj4cSJE4OWycrKIitr4HWbZ86cyfz587Hb7UHLpKam8vrrrxsN0bDh\nJFQrMAf4mtb6BaXUj4BvAQ9dXvDEn57u/TnmQx8hdubf9NvghYTq8XiGEZYI1Rst73DtL38ZcvmO\nc12c/cFqQ310uVwsXWpswejW1nNMN1TDd2no8n7eO/Is1vbgCxBHed5H/aoSgM7oSMLTQl84PFTd\nuo3KPy01VOeD7pcM12ntqTNUHuDIkWdZutRgP41nDP9ujDpy5DXDn5mTJ+uYGeTvSvB+3iApaeQm\nOTDqwIEDHDhwYKzDuKK0tbVRXFxMYWHhWIcCDC+hvgu8o7V+wf/618CD/RX88Pw7QmvRP92U2TeK\nRf+6vN3ccFtKyOU9zx0iP3GtoT62vVNEUlKpoTreF//HUHkAbw99+mk9spTN04P/wTx27udMnRoJ\nwKaW1pG5QqK8RC4z9kdbb+syXOfUtmpD5QHa21sNJxTvswcM92NUe7sy/Jl54YXbWbDAaJ0PGSo/\n0hYvXszixYt7X186iYHoa+/evWRlZZGWlkZubu5YhwMMI6FqrU8ppd5RSt2ktX4D+Aeg1rzQhBBC\niP6lpaXR2hr8CtRYGO5cvkXAL5RSNuAt4O7hhySEEEJceYaVULXWNUDo1wyFEEKIq5TMlCSEEEKY\nQBKqEEIIYQJJqEIIIYQJJKEKIYQQJhjuU75CCCGuEGvWrKG+vn7E2rfb7ZSUhDYbk91up7GxEZvN\nRlhYGA7FzEtwAAAgAElEQVSHg7y8PFavXo3yz0lgJq/Xy8aNG9m2bRsej4ePfvSj7N+/n6lTp5rW\nhyRUIYSYIOrr60d0digjyVopRUVFBampqXg8HqqqqigqKqK6upqtW7eaHtvGjRt5/vnnqa6uJjEx\nkdraWiZPnmxqH3LJVwghxJi4MCteVFQU6enp7Ny5k7KyMmprfXMEVVZWMmfOHKKjo0lKSgqYPSo9\nPZ0tW7YEtJecnMzu3bv79ON2u3niiSd48sknSUxMBMDhcBAeHm7q/khCFUIIMS6kpKSQmJjIwYMH\nAYiMjKS8vJyWlhYqKiooKSnpTZirVq2ivLy8t25NTQ0ul4tly5b1affVV1/FZrPxq1/9ihkzZnDz\nzTdTXFxsevySUIUQQowbCQkJnDlzBoCFCxcya9YsAGbPnk1OTg5VVVUAZGRkUFdXx/HjxwFwOp1k\nZ2djtfa9k/nuu+/idrt58803efvtt/nVr37Fww8/zL59+0yNXRKqEEKIcaOhoYG4uDgAqqurWbJk\nCfHx8cTExFBaWkpTUxMA4eHhrFixAqfTidaaHTt2BF1kfMqUKSileOihhwgPD+djH/sYOTk5VFZW\nmhq7JFQhhBDjwuHDh3G5XCxYsACAlStXkpmZSUNDA263m4KCgoDVyPLz83E6nezbt4+IiAjmzp3b\nb7u33nrrqMQvCVUIIcSY8ng87Nmzh9zcXPLy8nA4HIBvnePY2FhsNhuHDh1i+/btAfXmzZuHxWJh\n3bp1Qc9OAW688UYWLFjAI488wvnz5zl69Cg7d+5k+fLlpu6HDJsRQogJwm63j/g4VCOWL1+O1WrF\nYrHgcDhYv349BQUXF5cvLi5m7dq1FBYWsmjRIrKzs3G73QFt5Ofns3HjRnbt2jVgXzt27OCee+5h\n2rRpXHfddTzyyCMB68+aYVwm1O7u7rEOQQghrjqhTrowGk6cODFomaysLLKysgYsM3PmTObPnz9o\nMp8xYwa///3vjYRo2LhMqB0dHWMdwpjasGYNzQa/Rb5xpAkMjtf2Ws7z/rG/hl7Bct5YB6OoS2vK\n/jgrYNuM5rd5o6U6aB3vEPqxdnfjqisLubynuRvPj0MvD6A6enj7x780Ftj5dir/tNRQFY+7if2V\n+w32owcvc4XwnDnFL3/6C0N1ui09IxSNGIq2tjaKi4spLCwc61CAcZpQJ7rm+npKDc5m8okX3jHc\nj8WicXxsUsjlX3r2nOE+Rs2UKUx56P6ATWGP/QjrtdOCVuk5fNhwN2HAJ6KmhFze29PJpyJDLw+w\nv+M8s665zVCdVzqfJ3KZsc/M6aPw8ciPG6pzUhv/nI1XFq1YeM0thuo80/bqCEUjjNq7dy9ZWVmk\npaWRm5s71uEAklCFEEJcgdLS0mhtbR3rMALIU75CCCGECSShCiGEECaQhCqEEEKYQBKqEEIIYQJJ\nqEIIIYQJJKEKIYQQJpBhM0IIMUGsWbOB+vrmEWvfbp9GScnmEMvaaWxsxGazERYWhsPhIC8vj9Wr\nV6OUMjWu5557js9+9rO97WqtOXfuHL/5zW/4whe+YFo/klCFEGKCqK9vJimpdATbLxi8kJ9SioqK\nClJTU/F4PFRVVVFUVER1dTVbt241Na7bb78dj8fT+7qqqorPf/7zLF1qbHaxwcglXyGEEGPiwlJs\nUVFRpKens3PnTsrKyqitrQWgsrKSOXPmEB0dTVJSEps2beqtm56ezpYtWwLaS05OZvfu3YP2+/Of\n/5wvfvGLTJlibBazwUhCFUIIMS6kpKSQmJjIwYMHAYiMjKS8vJyWlhYqKiooKSnpTZirVq2ivLy8\nt25NTQ0ul4tly5YN2Ed7ezu/+c1vuOuuu0yPXxKqEEKIcSMhIYEzZ84AsHDhQmbN8i16MXv2bHJy\ncqiqqgIgIyODuro6jh8/DoDT6SQ7OxurdeA7mb/+9a+ZPn167yLmZhp2QlVKWZRSLymlBj/PFkII\nIQbQ0NBAXFwcANXV1SxZsoT4+HhiYmIoLS2lqakJgPDwcFasWIHT6URrzY4dOwZcZPyCp556ivz8\n/BGJ3Ywz1PuAWhPaEUIIMYEdPnwYl8vVe/a4cuVKMjMzaWhowO12U1BQ0HvfFXyLizudTvbt20dE\nRARz584dsP13332XAwcOjM+EqpRKBJYBPzUnHCGEEBONx+Nhz5495ObmkpeXh8PhAKC1tZXY2Fhs\nNhuHDh1i+/btAfXmzZuHxWJh3bp1IZ+dzp8/nw9/+MMjsh/DHTbzOPAAED1wsVCWctZo3Q1cPQsY\nCyHEeGK3TzM0tGUo7RuxfPlyrFYrFosFh8PB+vXrKSi4GF9xcTFr166lsLCQRYsWkZ2djdvtDmgj\nPz+fjRs3smvXrkH7czqdfPOb3zQUoxFDTqhKqc8Bp7TWLyulFgNBR+Ie/9PF26sxM2cSM7PvQshe\nvHRaatGqnbfeeYelq1YZisceH0/JY4+FXH7NAw9Q39g4on0M1csnj7Cq6YihOo09H3CwstJgT5rz\nH4zseoJaaypfNPa7tHRr1K+M7cvUZi8Rj+0I2BZW/x49b51A09NvHdXTRWuDb8HsjvPdnPp/lYRP\njuT6BQuD9tPcAt/5eejHrLMbXu82doz1EL5UKo3hY6a8Xt5/56ThOkZ1aU3ZH2eFXL5TNRn+zLQq\nY/8vA5xnKtXtnYbqdOoow/2E6sCBAxw4cGDE2gdCnnRhNJw4cWLQMllZWWRlZQ1YZubMmcyfPx+7\n3T5oexeG44yU4Zyhzgc+r5RaBkwBopRST2mt+1yctn9m8eCtdQNxVlBeznu9JG3YYCiY+s3GPij1\njY0j3sdQ9ah2NqyZbqjOwe+cZkGkwf/ZGxUxYXEGKrxvrH0Ai4XINcaOc9N39vLQ1EhDde53tZJ3\nbVHAtucbHmZK9zuE39D/WDN3+/uExdl8YbZowj8XyfmKgZNfdw/Exxi7U/Kxm4yVf+9FQ8UB37dZ\no8fsbhq53hZuvDOjpkxhykP3h1y88zv/Zvgzc2pThtGogEimWB42VEN71w2hn9AsXryYxYsX976+\ndMyl6F9bWxvFxcUUFhaOdSjAMO6haq03aK1naq1vBHKAZ/pLpkIIIYTZ9u7dS3x8PDNmzCA3N3es\nwwFk6kEhhBBXoLS0NFpbR/aWlVGmJFStdRVQZUZbQgghxJVIZkoSQgghTCAJVQghhDCBJFQhhBDC\nBJJQhRBCCBNIQhVCCCFMIMNmhBBiglhz/xrqXfUj1r49wU7J4yWhlbXbaWxsxGazERYWhsPhIC8v\nj9WrV6NU0In3huyZZ57hgQceoK6ujunTp/Pggw/y1a9+1dQ+JKEKIcQEUe+qJ+nOvlO/mta+sz7k\nskopKioqSE1NxePxUFVVRVFREdXV1WzdutXUuLq7u8nKyuKHP/whX/nKV3jhhRdITU1l3rx5fOxj\nHzOtH7nkK4QQYkxcWIotKiqK9PR0du7cSVlZWe+cu5WVlcyZM4fo6GiSkpICpmNMT09ny5YtAe0l\nJyeze3ffpbnPnDmDx+PhzjvvBOATn/gEt9xyi+lz+0pCFUIIMS6kpKSQmJjIwYMHAYiMjKS8vJyW\nlhYqKiooKSnpTZirVq2ivLy8t25NTQ0ul4tly5b1aTc+Pp7c3Fy2bt2K1+vlL3/5CydPnuT22283\nNX5JqEIIIcaNhIQEzpw5A8DChQuZNcu3UtHs2bPJycmhqso3KV9GRgZ1dXUcP34c8C3Nlp2djdXa\n/53MnJwcvvvd7zJp0iQWLVrEI488wg033GBq7JJQhRBCjBsNDQ3ExflWwaqurmbJkiXEx8cTExND\naWkpTU1NAISHh7NixQqcTidaa3bs2BF0kfFjx46RnZ2N0+mkq6uLI0eO8IMf/IDf//73psYuCVUI\nIcS4cPjwYVwuFwsWLABg5cqVZGZm0tDQgNvtpqCgoPe+K/gWF3c6nezbt4+IiAjmzp3bb7uvvfYa\nN998M5/+9KcB+OhHP8rnPvc5SahCCCGuLh6Phz179pCbm0teXh4OhwOA1tZWYmNjsdlsHDp0iO3b\ntwfUmzdvHhaLhXXr1gU9OwX4+Mc/zptvvsn+/fsBOH78OHv27OG2224zdT9k2IwQQkwQ9gS7oaEt\nQ2nfiOXLl2O1WrFYLDgcDtavX09BQUHv+8XFxaxdu5bCwkIWLVpEdnY2brc7oI38/Hw2btzIrl27\ngvZz4403snXrVoqKijh58iTR0dHceeed3HPPPYbiHYy69PR5JCil9MKH/nXQcjXdPXjipuLdWYH1\nlRe4/hOfMNTPe0ePEnPddSGX7+juZmU/j1cP5O3Nm/lDWZmhOkMxPTaBm2+IMVTn+ddP8RHLtKDv\nv287RVd4d8C2NtqImzoJgEnNXVzTc/Gz4O3RWMICB1e/Hx6J95oIupWiJyysTx9KhxFOVO9r3ePB\neq6Fa7v7lr1cq3UKZ6f7vpWef+tVLJbwPmW8k5phUnef7T49fDh2VsCW8PeP803OMzVper81/rOl\nhebwKQC81QMdMQo+AIs3+KBy77lzLL2pc9D9uWDvS+eZ83FbyOUBXnypi/gpUYMXvMTpNg+zI68x\nVOdIawf/GHM7L+g3aLOGtk+N3W6sMdFYYyND7qftbRfKFkFYVP+/h8t1v/021g/NDLl9gG53M5aI\n0GMC8Lafx2JLNlbnXC36rMtQnaFSSqG1HvIMB0opPdJ/38eD8vJynnzySZ599tlR6zPY72ZcnqHq\nnh4iExIM1fHW1TH9/vtDLn/8e98zGtaoOd8VQcK0hwzV8Xof5LrpHw76fqP3DFMXTw3Y1tbexpTp\nvmQX8XQX34m8mPjaT3YzZWZgIvxeRwTxd63hpe4uOmf0fTrO+945rlGLL77uOMCMp37EBu+nBo3/\nu+ddXBP3IwBOvpXJtddv6FPG3f1DLKnX9lu/o+0FomfEB2zrrnwH3dwRtM/m8Clcd5fvMlFDt8Zy\nvYWe9zTTdPAE6Nq6FQg9oQJYY6INlVeqib+7aZKhOn+o8TA9vO+XkIFo2gFos3ZyzeLQ+lOdiu6X\n27nmc9eH3E/baxD+kmJabFxI5V0nThA/LfiXw37rnG8j7l9D//8foGnzj5my8j5Ddc5t/bqh8mJk\ntbW1UVxcTGFh4ViHAsg9VCGEEFegvXv3Eh8fz4wZM8jNzR3rcIBxeoYqhBBCDCQtLY3W1taxDiOA\nnKEKIYQQJpCEKoQQQphAEqoQQghhAkmoQgghhAkkoQohhBAmkIQqhBBCmECGzQghxASxYc0amuvr\nR6z9aXY7m0tKQiprt9tpbGzEZrMRFhaGw+EgLy+P1atXo9SQJ4gK6n//93/ZsGEDb7/9NrfeeitP\nPvkkt9xyi6l9SEIVQogJorm+ntKkpBFrv8BAslZKUVFRQWpqKh6Ph6qqKoqKiqiurmbr1q2mxlVX\nV8edd97JH/7wB+bOncujjz7K5z//eY4dO4bFYt6FWrnkK4QQYkxcmGs4KiqK9PR0du7cSVlZGbW1\ntQBUVlYyZ84coqOjSUpKYtOmTb1109PT2bJlS0B7ycnJ7O5njvann36ahQsX8vd///dYLBYefPBB\nGhoaehcrN4skVCGEEONCSkoKiYmJHDx4EIDIyEjKy8tpaWmhoqKCkpKS3oS5atUqysvLe+vW1NTg\ncrlYtmxZn3a11gHrqHq9XrTWvPbaa6bGP+SEqpRKVEo9o5SqVUq9qpQqMjMwIYQQE09CQgJnzpwB\nYOHChcya5VtFavbs2eTk5PSeVWZkZFBXV8fx48cBcDqdZGdnY7X2vZP5mc98hqqqKp599lm6urrY\nvHkzXV1dtLW1mRr7cM5Qu4G1WmsH8PfA15RSN5sTlhBCiImooaGBuDjfykTV1dUsWbKE+Ph4YmJi\nKC0tpampCYDw8HBWrFiB0+lEa82OHTuCLjL+t3/7t5SVlfG1r32tN2E7HA4SExNNjX3ICVVr/b7W\n+mX/z63AUaDvml5CCCFECA4fPozL5WLBggUArFy5kszMTBoaGnC73RQUFARcus3Pz8fpdLJv3z4i\nIiKYO3du0LazsrJ49dVXOX36NA8//DD19fWkpKSYGr8p91CVUnbgNqDajPaEEEJMHB6Phz179pCb\nm0teXh4OhwOA1tZWYmNjsdlsHDp0iO3btwfUmzdvHhaLhXXr1gU9O73gpZdewuv1cvr0aQoKCsjI\nyOCmm24ydT+GPWxGKRUJ/Bq4z3+mOnwTYJV5IYQYbdPsdkNDW4bSvhHLly/HarVisVhwOBysX7+e\ngoKC3veLi4tZu3YthYWFLFq0iOzsbNxud0Ab+fn5bNy4kV27dg3Y13333UdNTU3vpeJ///d/NxRr\nKIaVUJVSVnzJtFxrHXRv6g9cfDQ5xp5EzCAHveeaKbzx5+eCvm/t8RJ2SdLVgLKE0XDs9T5luxR0\nh4cFxu1VdHV3s7/ywIBx9GnryDFD5Y24dMB1XPu7HP3rvwUtez78WqYm5Qds02jeaDsctM45m4eO\nbs+w4+zR3Xi1l86ec/28eYZm/tD7MlK34qKLzdY/99+YDgOuAeDkZA+cXA6ANc5Nq/e7fYpbLB30\nVLfQMzWib1vecI6Hvxiwafr5s/Tobs52ng6yNzH0aOXfL+jwhqG9Xpq7JgcpD2gb7i5jX/i63f0c\nq0F0Buni1Lut2Dp7+mz/kIb6lsDfb5vFQueU4PvijYrkD5NdnAubguVYaBeres7GAFbcu9yDlr0o\ngp6Oc7S980aIxSNpPOUy0D6gwvCe7zJWZ5w5cOAABw4cGNE+Qp10YTScOHFi0DJZWVlkZWUNWGbm\nzJnMnz8f+yB55cKTwyNpuGeoW4FarfUTAxWyL15krNUpU5j+jbuDvt3V3Mknwi7OpNHZ5eWFX+4k\nJbLv7rzU7aXnhikB27obuunS8PGojxsK67n2UkPljbh0wPVPlZVrpyYELftQ+2lscTEB25SC2TdP\nCVIDXnq/jSkxgV8sGk6fNxynUr5/1rC+M5l0W7xYEy7GoN5ro2vKJK5ddVf/jbX1YNHXAuDSp7hN\nXQfA4Z0/4pO5/9yneA/N/DUc2mdM6/Oet+Q/CA8LTAoWSyugCFPBPuYKmOzfrx4sKg6v6iDMkhyk\nPICVOBU9wPuXayImLNZAeYB21KTwft+xdWm+O8XWZ7urtYtrYgP7+bfz51EDXdI6+Q5TvryGDt1M\nWEJY8HKX6Hr856jsbCbNGOBLx2U6Sp8kIqqDmz/c/z5d7vnXLMR9466Q2wdofOIprEGO2ZVi8eLF\nLF68uPf1pWMuRf/a2tooLi6msLBwrEMBhjdsZj6wEliilPqrUuolpdRS80ITQggh+rd3717i4+OZ\nMWMGubm5Yx0OMIwzVK31n4DQvtYKIYQQJkpLS6O11ZzHdswiMyUJIYQQJpCEKoQQQphAEqoQQghh\nAkmoQgghhAkkoQohhBAmkIQqhBBCmGDYUw8KIYS4Mqx54AHqGxtHrH17fDwljz0WWlm7ncbGRmw2\nG2FhYTgcDvLy8li9ejVK9Z04ZrgKCgqoqqrizTffZNu2beTnB8429/jjj/Poo4/S0dHBP/7jP/Jf\n//Vf2Gx9J1EZiCRUIYSYIOobG0nasGHk2t+8OeSySikqKipITU3F4/FQVVVFUVER1dXVbN261fTY\nbrvtNnJycnjwwQf7vPf000/z6KOPsn//fmbMmEFmZiYPPfQQmw3sD8glXyGEEGPkwlJsUVFRpKen\ns3PnTsrKyqitrQWgsrKSOXPmEB0dTVJSUsB0jOnp6WzZsiWgveTkZHbv3t1vX/feey+pqalMmjSp\nz3tPPfUUX/7yl7n55puJjo7mO9/5Dtu2bTO8P5JQhRBCjAspKSkkJib2TmQfGRlJeXk5LS0tVFRU\nUFJS0pswV61aRXl5eW/dmpoaXC4Xy5YtM9zvkSNHSE6+OId3cnIyjY2NfPDBB4bakYQqhBBi3EhI\nSODMmTMALFy4kFmzZgEwe/ZscnJyqKryrV6WkZFBXV0dx48fB8DpdJKdnY3VavxOZmtrK9HRFxe9\niI6ORmuNx2NshS5JqEIIIcaNhoYG4uLiAKiurmbJkiXEx8cTExNDaWkpTU1NAL3rmjqdTrTW7Nix\nY9BFxoOJjIzk7Nmzva/Pnj2LUoqoqChD7UhCFUIIMS4cPnwYl8vFggULAFi5ciWZmZk0NDTgdrsp\nKCjove8KvsXFnU4n+/btIyIigrlz5w6p31mzZlFTU9P7+uWXX+a6664jNtbY0ouSUIUQQowpj8fD\nnj17yM3NJS8vD4fDAfguxcbGxmKz2Th06BDbt28PqDdv3jwsFgvr1q0b9Oy0q6uLjo4OtNacP3+e\nzs7O3uScn5/Pz372M44ePcoHH3zAI488wt13B1+TO5jxOWxGWzlzOnhok7vP06kufhfwKi+g6PL2\n9Cnb7dW0dQYupK27vaDh/ZMnQwrnje636LR08MGZBlRi8IW/L3ZgQXl9ix2Hn/uApI72Qat4uzv5\n6STfPj0VGUlzT23Qsm+Fazpr/wULF2PxxnRy4N1rgtbptsShDl/2/allCq5o30LRk1QP3++4ZBdi\nvKiOwPIuHca1g+5JXxod9B0vPf6f6P15SM728MFRb8Cmc9YYnojVqJ7+x7S5tIVp/ti01nRrDfoc\nbWE1/ZYHsFo6qH3tzCW7oLDS96lBgHMWK96IabzkvvhZPtfZRU/Q4+HjjZ3KgePui6+7urD4x8Pd\n0N7Fe519j5MzeirN1gurKSrQFk5OsdHxblPwjqw2PujsRNFFmKtrwJguUF0QcV7D+wN/prut0DXN\n//k5Cy09cTx/LMTv7zZF85lOwqxGxiIqGjtOGyjvM/Bv4upjj483NLRlKO0bsXz5cqxWKxaLBYfD\nwfr16ykoKOh9v7i4mLVr11JYWMiiRYvIzs7G7XYHtJGfn8/GjRvZtWvXgH2lpaVRVVWFUoq//OUv\nFBQUsH//fhYuXMgdd9zBN7/5TVJTU+no6OCLX/wiDz/8sKF9gfGaUAnDFvaJoO9aemqwqMm9rzXN\nvu1M6VNW0Y1FTQ/Y5qUdjeL6EAftvqHPE3vDNXwQMRnLurWDltcN7disn/T1/193siUsbtA6X285\nQURiBADNXddw7V3Bvx29093DeR3BJC4+ldb21NcJz//PoHW81GBJmBywrfvx/ySsoAgA23vNTLNe\nXN5Wt7Wjrgk8nvU/Lxt0P/oTdIy2gjD/m6oHwizDGMxtm8TkwpzATe/2MK1DY43sv936rf/TO4Bc\nKYU1zEK3RWNN6Ps5usBqVXznQ9f1vva+18EUndxv2e96m3hviocp91z85nyurZPwGwbez+4fOwm/\n5W97X7cfOEz44hQALM/WYImY3KdOc4+Xa+/6iu9FWw8WNY13dTtM6j82gI6fF2FRKVi9+5hjDR8w\npgue0/BxBdbwgffhpW6w+L8gttkmw525WBOmD1jngu7H/xMvGluisQto1rDQ2u/tBwb4cF6dQp10\nYTScOHFi0DJZWVlkZWUNWGbmzJnMnz8fu90+YLn9+/cP+P43vvENvvGNbwwa00Dkkq8QQogrUltb\nG8XFxQFntWNJEqoQQogrzt69e4mPj2fGjBnk5uaOdTjAuL3kK4QQQgSXlpZGa2vrWIcRQM5QhRBC\nCBNIQhVCCCFMIAlVCCGEMIHcQxVCiKvQ5MmTTymlrhu8pDBq8uTJp/rbLglVCCGuQu3t7dePdQwT\njVzyFUIIIUwgCVUIIYQwgSRUIYQQwgSSUIUQQggTDCuhKqWWKqVeV0q9oZR60KyghBBCiCvNkBOq\nUsoC/AS4A5gF5CqlbjYrsKtRTdfgy7hNFO76+rEOYdzoORbaMoITgfeNurEOQYghG84Z6ieBN7XW\nb2utu4BfAhnmhHV1qumWhHqBu/7tsQ5h3Oh5QxLqBfrN42MdghBDNpyEegPwziWv3/VvE0IIISac\n4Uzs0N/KvLq/grVP7x20sU4U3vAwaDodpGkhhBBi/FJa95sDB6+o1DzgYa31Uv/rbwFaa/2Dy8oN\nrQMhhJjgtNZydnEFGU5CDQOOAf8AvAccAnK11kfNC08IIYS4Mgz5kq/WukcpVQjsxXcv9meSTIUQ\nQkxUQz5DFUIIIcRFIzZTkkz6EEgpVa+UqlFK/VUpdWis4xlNSqmfKaVOKaVeuWRbrFJqr1LqmFLq\naaVU9FjGOFqCHIuHlFLvKqVe8v9bOpYxjgalVKJS6hmlVK1S6lWlVJF/+4T7XPRzLL7u3z7hPhdX\nuhE5Q/VP+vAGvvurLuAwkKO1ft30zq4QSqm3gL/TWn8w1rGMNqXU7UAr8JTW+lb/th8AzVrrR/1f\nuGK11t8ayzhHQ5Bj8RDg0Vr/x5gGN4qUUtcD12utX1ZKRQIv4hvHfjcT7HMxwLHIZoJ9Lq50I3WG\nKpM+9KWYoHMna62fAy7/IpEBlPl/LgMyRzWoMRLkWMAEGyumtX5fa/2y/+dW4CiQyAT8XAQ5FhfG\n9E+oz8WVbqT+wMukD31p4Gml1GGl1FfHOphxIF5rfQp8f1CA6WMcz1j7mlLqZaXUTyfCZc5LKaXs\nwG3A88B1E/lzccmxqPZvmrCfiyvRSCXUkCd9mEA+pbX+BLAM3/8kt491QGLcKAY+orW+DXgfmDCX\n+PyXOH8N3Oc/O5uwfyf6ORYT9nNxpRqphPouMPOS14n47qVOWP5v22itTwO/xXdZfCI7pZS6Dnrv\nITWOcTxjRmt9Wl98mOFJIGUs4xktSikrvgRSrrXe5d88IT8X/R2Lifq5uJKNVEI9DPyNUipJKRUO\n5AC7R6ivcU8pdY3/2ydKqQggDXhtbKMadYrAKxe7gbv8P68Cdl1e4SoWcCz8ieOCLCbOZ2MrUKu1\nfuKSbRP1c9HnWEzgz8UVa8TGofof8X6Ci5M+fH9EOroCKKU+jO+sVOObTOMXE+l4KKW2A4uBacAp\n4CHgd8CvgA8BJ4F/0lq7xyrG0RLkWKTiu2/mBeqBggv3Ea9WSqn5wLPAq/j+v9DABnwzrv0PE+hz\nMc6xvQYAAABbSURBVMCx+BIT7HNxpZOJHYQQQggTTMhhHEIIIYTZJKEKIYQQJpCEKoQQQphAEqoQ\nQghhAkmoQgghhAkkoQohhBAmkIQqhBBCmOD/b7RCHQWjYBSMglEwCqgAABkwaK58ePtSAAAAAElF\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2e0864a8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(11):\n",
-    "    plt.hist(completed_starts[completed_starts.icma_number==i]['start_time'].reset_index(drop=True), \n",
-    "             alpha=0.6, label=\"Day {}\".format(i), bins=24)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
-    "plt.title(\"Completed starts\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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s2EFOTg65ubmkpKQA0NTUREJCAk6nk4qKCjZv3tyt3vTp03E4HKxYsSLg2SnA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\niEgYKKGKiIiEwf8HLpBB2bxnlHUAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2db2e860>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(1, 11):\n",
-    "    plt.hist(completed_starts[completed_starts.icma_number==i]['start_time'].reset_index(drop=True), \n",
-    "             alpha=0.6, label=\"Day {}\".format(i), bins=24)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
-    "plt.title(\"Completed starts\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f5f2def4b38>"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2e066b00>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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zbBOSSLIZDvtwOtU7scezlEAgufmKMRsvFW68Ue28btiQenxWEo1FOTx4eGSI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\nEuIX0nXipZfCwoXkX5bMjqbt/HDvD7icldjtPxISclXnYQn+J6kXDRTaa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Oz2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\nf8c87iKPB4akAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2e0b0c88>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "completed_starts_x = pd.crosstab(completed_starts.start_time, completed_starts.icma_number)\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig, ax = plt.subplots(1, 2, figsize=(14, 4));\n",
-    "plt.subplots_adjust(wspace=0.5, hspace=0.5);\n",
-    "\n",
-    "ax = fig.add_subplot(1,2,2)\n",
-    "completed_starts_x.ix[:, completed_starts_x.columns != 0].plot(ax=ax, legend=False, sharex=False, sharey=False)\n",
-    "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., title=\"Day\")\n",
-    "\n",
-    "ax = fig.add_subplot(1,2,1)\n",
-    "completed_starts_x.plot(ax=ax, legend=False, sharex=False, sharey=False)\n",
-    "\n",
-    "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., title=\"Day\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f5f2dab2ba8>"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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xZw984QvwxBPw3e/CSy9FYJYD8Xrh0CGYNw9Wr+7rHg3FNaoo4HYP3J5sCU+9\n0ZEIoSHGgHG6EUeJLLUmmRz86Ec/YsWKFeTl5TF//nyWL1/OQw89FNYxNCOEWbG51HbU0u5uJ9tk\notLgQR+jR7FlkNAoLUKt0tDZQIurZVDraViLcNcu+OIX4emnVbfoDTfAq68GV5gwU1YGyckQF9cr\nhIqi9lVscbUMCP7pzw9+AF/72sDtRoMRc5SZZmfzqObnrHSe8RohyN6EksmFwWDgqaeeoqmpiVOn\nTvHEE08QHR0d1jE0I4SnTuoDIkfVdUJTrgmiphHX4qSpLrzNTiXhwZ8/qBPBv0rZ8dnUtNfg8ASx\nUD76CC6/HJ59Fq65Rt2WlQX5+bB9ewRnrXLgACxYoP5/1iz13yNHVGswKz5r0M8EqiX5P/+jfoRg\npFhTRu0eHYlFCLI3oURypmhGCEtKetcJ/cW3zTPMWFrjaYmLJq6jA49HJgprjcLThSxJXTLofr1O\nz4zEGRxtPNp3x44dqvj96U+qGAayfj1s3hz+yfYjUAiF6LUKh3OLKgrceSf85CdQVwdNTQOPCUdf\nwtEI4VTuTVhQAB9+ON6ziDxHjx4d/iBJSIQkhEKItUKIw0KIUiHE94Psv1AI8ZkQwiOEuLbfvlu6\nzysRQmwYbIzS0m4hrC3uSao35Zqw1FioSTAwLy5OrhNqkMLqgYn0/RngHt2+Ha6/XhW7L3xh4AnX\nXw/btoHTGebZ9uXAAVi4sPd9HyGMzxn0vJdegsZGuOsutSrNZ0FSBkebQqH4FFwnXRgzzlwIp3rN\n0WeegV/+crxnEXneffdv4z2FScOwQiiE0AFPApcC84GbhBBz+h1WAdwC/LnfuYnAw8BK4GzgESFE\nfLBxSkq6A2bqisk2GnvKrFlOWqiKF+RbLHKdcAxxer281L/uWBCGyiH000cI33gDvvxleOUVVXmC\nkZYGS5eqx0aQQIsQeiNHjw8RMdraqsbz/Pa3YDDAihWDC+FoLEJ3jRtDvAG96cxLvJmyTXg7vbjr\nIr/OqkX27FEdDpO9c9vHH0f272MqEYpFeBZwRFGUCkVRPMBm4KrAAxRFqVQU5QDQP0zuUuBtRVFa\nFEVpBt4G1gYbxO8aPVh3sLfM2gwzxjIj5bFeZhqN0iIcQz5ubeXfDh6kdoiglQ53BxXNFcxLnjfk\ntXoiR7duhVtvVf+98MKhJxBh96jbDceOqcuRfrKyICEB9lUO7hrdtEk1Ys87T32/fDns3j3wuGTL\n6FIoXFXLF2WdAAAgAElEQVSukLtO9EcIQcyiqdmb0ONR01GTkqCwcLxnE1k++2z/eE9h0hCKEE4H\nqgLen+jeFgr9zz052LmlpZCTkENdZx2Jui5OuVxEZUdjPGLkmMVJlkBahGNIUXs7XuD/amoGPWZf\nzT7mJs8lSh815LXy7Hmkv/Ux/Pu/wz/+AeecM/wErr0W3nwT2iPj4isthZwcMPULyly9Gg5XB2/I\ne+AA/PGP8LOf9W6LlEV4JsW2gzFVO1EcPKj+Xi+7bGC1oMmEy+Xi6NHwFHaXhCaEwQrNhZogFfK5\ntbXgcurJt+dzrOEwSVFRNKQKOAXViVGk+WS90bFkb3s7N6Wk8PwQQlhUXTSsWxRg0T8P8O0/HYG3\n3lJNqFBISlLNrr9FZh2kv1vUz5o1UO0c2JDXHyCzaRN0V3oCYPZsqK+H/s6K0XagGElVmUCm6jrh\nnj2qV33NmskthHv37iUz88zd5pLghCKEJ4CsgPcZwKkQrx/yuTExG7n//o10vdvFln9sIcdkotLr\nxphupNUeT5LTIYVwDNnb3s53MjJo8HjYN4hVFsr6IM8/T/yPHuOKr5ppzMs8s0lE0D06mBCuPL8d\nNx3YjCl9tv/5z6px+u//3vd4nQ6WLVNvwIGMNn1ipBGjfqaqRVhYqP4+Lr4YPv54TNJRx5QdO3aw\nceNGHnnkR4CMog8XoQhhATBLCJEthIgG1gNbhzg+0Ap8C/i8ECK+O3Dm893bBnDBBRtZvXojX777\ny3izvT0pFKZcEx0JidjaO6QQjhFun48Sh4NFVis3p6byQnV10OOGjRh95hn44Q8R776Lb95cjjQc\nObOJXH21Gr3SPLrE9GAMJoSdURUYHTkUFvZ+jZub4YEH1AAZfZCH8GDrhKNNn3BVnnnB7UCsC6w4\nShz4PFOr1JrfIkxIgDlz4NNPx3tG4WXVqlVs3LgRm83A9ddnDX+CJCSGFUJFUbzAXaiBLsXAZkVR\nDgkhNgkhLgcQQqwQQlQB64DfCSH2d5/bBPwY2A18CmzqDpoZQH6+GjAzL3leT8BMhdOJaYYJjykJ\na6eDphCiGCWj51BnJ7kmE2a9ng2pqfy5tpYuX98bqsfr4WDdQRalLhpw/nff/i5/+tUt8NhjqpDl\n54+sSW98vBrK+frrwx765K4neXD7gyFfejAhLGsuI82c08et9sgjaqrj2WcHv1awdcLRpk+M1jWq\nt+gxZhvpPNw54mtMNHw+tbfk0u5ns/5l8yYTn31WxLJli8d7GhEnNjaWuLg44uLiiI2NxWAw8J3v\nfCfs44SUR6goypuKouQrijJbUZSfdW97RFGUv3X/f7eiKJmKosQqipKsKMrCgHOf6z4vT1GUFwYb\nI3+2rzeXMDCpPteMxRVHR5wFToXqkZWMhr3t7SyOiQFgjtVKltHIO/2yxg/XHyYzLpOY6JgB53/0\n2at8svtVNYa9u2TLiLvVh+ge3Vmxk//65L+oaqka9tiODvWrNDNIZ6jy5nLmTcvhn/9U3+/dqw7/\n058Ofr3lywcKYZIliQZHAz5lZBbZaF2j0NubcKpw5Ii6tJzY3Ypxsgphe3s7lZW1LF160XhPJeK0\ntbXR2tpKa2srNTU1WCwWbrjhhrCPo5nKMnkn36OkBHITc6nvrCdF71NzCWeYiG2NpTkpFsMgLjpJ\neNnb3s6SmF6BuyUtjef7/ez9pdX6o/h8HGwvp/jc2Wr4XjchFd8OxuWXq4s9w7jFi+uKWZ27msc/\nfHzYSx48qHogDEG6cZY3l3POnFw++QQcDrjjDrWCTFLS4NebNUtNsA8MmInSRxEbHUuTI0jZmWHw\neXx46jxETxtdPcWpVmHGvz7o54IL1AeUzklmFO/Zs4eZM80kJg4fqDaZePnll0lJSeH8888P+7U1\nI4T5L/yA0lIFgY45SXNwd1T1rBFaG6w0JcdgkXmEY0JRezuLrdae9+tTUnijsZHmgBJ3g3WcOPHp\n23h0CsWuvpZZnj2PkvqSM59MTAysXasm4A+C2+vmeNNxnrniGV4qfomK5oohLzmYWxRU1+jctBzm\nzlUjRLu64Pbbh56iP2AmmHt0JOuErpMuolOj0RlG9+c51XoT7tnTVwitVtVNOlg92InKrl27yMtz\nYbUuHP7gScQLL7zAhg2DFicbFZoRQntaFMLjpq5OdY82NR+myunEmGPCUm2hIclMXKvsvB1pFEXp\n4xoFsEVFcUliIi8HFD4fLFCmeNv/x3kiC4/P02eNbLZtNkcaj4zMVTiMe/RIwxGy4rPIiMvgG8u/\nwU8/GMKPydBC6K8zet558OSTaoCMLoS/kqABMyPsSziaZPpAplrkqD9QJpDJ6B799NOdzJtnJjo6\neUzGE0KE5TUaKisr2blzJ7fcckuYPlVfNCOE/ORR8ruKKSnuYl7yPI7WFxNvMNCQoBDbFkt1QhSp\nHg/uyRYPrTFOud3ohSCtX5uTW9LSeKE7p1BRlOA5hIpC8b7tzJ99Xs9ar594Uzyx0bGcahvBOu8X\nv6j6vU6fDrr7QO0BFqSoynb/uffz10N/paypbNDLDSeEuYm5HD8OsbGhpz0OFjAzIoswDOuDAMYM\nI4pLwV0z+f9mFGWgaxRUIfSv904Wdu8uYNmyQb7AEUBRlLC8RsMLL7zABRdcQHZ2dpg+VV80I4Rt\nyyzk2+oofe7jnptojslEpctFUlwS5dFdzIyKkmXWIkxRtzXY/wlurc1GSWcnxxwOyprLiImOIdna\n74l01y4O2L3kpZ/PvMR5HKg90Gf3iANmTCa44gr461+D7i6uK2Z+8nwA7BY731rxLR774LFBLzeY\nELa6WnF1uSg/aOfTT9W6oh0hLrGFM4VitFVl/AghpkxvwspKiI5Wy9QGcs45asusCGTgjAuNjY3U\n1zexYMG54z2VMeWPf/wjt956a8SurxkhrK9/jbwr51Ly6kHmx8+iuLY3cjQ5KZmjRhdZQshcwgjT\n3y3qJ1qn46aUFF6orlatwWD5g5s3U5wbg/V3VtL2pFFcW9xn93BCqCgKnw7m/h7CPRoohAD3nXsf\nrx1+jWONxwYc29gIbW1qXdH++N2id94p+NnPVCsv1HY+M2dCS4valsnPSFMoRps6EchU6U0YzBoE\nMBpVMdy5c+znFAl2797NnDlxxMZO/tQJPx9//DGnTp1i3bp1ERtDM0JYV/cq+Z/PotSymJyXt9Pg\naGBalKDc6SQlLYUSUyfTfD4phBFmb79AmUD87tE9wQJlfD58f3mJQ4Zmkt5KIu2jtD6uURheCLfU\n1XHOnj3UBHN/X3KJmmhaWTlgV3FtMfNTeoXQZrZx58o7+ckHPxl4bDHMn6/2H+xPWVMZ+rZc9Hq4\n5ZYzW18KFjAzYouwMjxrhKCmUEyFyNFg64N+JlO5tYKCAvLzvcTEDMzfnay88MILXHfddVgHuS+F\nA80IodfbTkZGGSXmJegef5wlcXlEe5qocDpJzUyl1NxKnMdDg0yhiChF/VInAlkaE4NVp+OfjXUD\nhfDDD6nMiidWxJN5diaZxzMpri7uszYwlBC6fT6+f/w4uSYTbzc2DjwgOlpt5PuXv/TZ7OpyUdFS\nQZ49r8/2e8+9l20l2wZUsxl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OHFB/XffebeaWmf/BL3Zt7NnX6GjEoDNweO/hQc+fORP0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47OikC3suy5iPS2x9D6VkisaozOm7RQe0ANLDeXoUkV5bd+hlyOzo8fY7i5uVIqo2IRhPwoM+8o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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2da7add8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "completed_starts_x_normalised = completed_starts_x / completed_starts_x.sum()\n",
-    "ax = completed_starts_x_normalised.ix[:, completed_starts_x_normalised.columns != 0].plot(legend=False)\n",
-    "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., title=\"Day\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>completed</th>\n",
-       "      <th>icma_number</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>20</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>15</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>13</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>9</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>19</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>9</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>17</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>14</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>12</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>21</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   completed  icma_number\n",
-       "0         20            9\n",
-       "1         15            9\n",
-       "2         13            9\n",
-       "3          9            9\n",
-       "4         19            9\n",
-       "5          9            9\n",
-       "6         17            9\n",
-       "7         14            9\n",
-       "8         12            9\n",
-       "9         21            9"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "completions = pd.DataFrame([{'icma_number': r['icma_number'], 'completed': r['completed'].hour}\n",
-    "              for r in icmas.find({'completed': {'$exists': True}}, ['icma_number', 'completed'])])\n",
-    "completions[:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f2dbfec88>"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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Kpc6aVHOlvCbVXNmalCuX0v8o2qZsmJfftPG40s8ZAHqJt/63iA69KMQOkJAQO0BCQuwA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\nAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2daad630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "completions.completed.hist(bins=24)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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OHOhmdr2ZLczsC5V9x8zsFjO728xuNrMndtvmONTN0Ntc9DO8F2xT7gYCSbkbCCTlbmDQ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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2da66860>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "completions[completions.icma_number > 0].completed.hist(bins=24)\n",
-    "plt.savefig('completion-hours.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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qVa0fp6WlkZaW1o1lpaesWLiQE1VVnsc/U/Ea0ZFX+lrj6Nn34LMJvmpOn6wn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\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f34552630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(6):\n",
-    "    plt.hist(completions[completions.icma_number==i]['completed'].reset_index(drop=True), \n",
-    "             alpha=0.6, label=\"Day {}\".format(i), bins=24)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
-    "plt.title(\"Completions\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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NHj2aoUOHsn37dqBpD7GgoIATJ05QVFTE6tWrm0Nx3rx5FBQUNNfu3r2bqqoqpk+f3ub8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\nkUJTRETEo/8Pxyh8NisJmNkAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f346ddc88>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(1, 6):\n",
-    "    plt.hist(completions[completions.icma_number==i]['completed'].reset_index(drop=True), \n",
-    "             alpha=0.6, label=\"Day {}\".format(i), bins=24)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
-    "plt.title(\"Completions\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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UNez0p7bp/v3vkDAIJTcr9v+SA+8GXne0zdZqeY5m/xmunRxqqc8ZWnit+R1r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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f348356a0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(11):\n",
-    "    plt.hist(completions[completions.icma_number==i]['completed'].reset_index(drop=True), \n",
-    "             alpha=0.6, label=\"Day {}\".format(i), bins=24)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
-    "plt.title(\"Completions\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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zYa1l/fr1HRYZ/9SnPsXatWu57777WhO21+slKSnJ0di1hyoiIn3C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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2ddf4978>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(1, 11):\n",
-    "    plt.hist(completions[completions.icma_number==i]['completed'].reset_index(drop=True), \n",
-    "             alpha=0.6, label=\"Day {}\".format(i), bins=24)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
-    "plt.title(\"Completions\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f5f2dd94c88>"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2dab2860>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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TxdixMGoU/Nd/6YVYRo/2fnzOQV5PV9b0JkAzilKKc845h1dffZXly5ezaNEi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\nJ5eTOMIcF9tOLON13hd/w1VzA7ZtIzlgEpVa8waWjgsXwnffQVkZ3CpaT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NZqmX71KqOc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\nJBLJE4Sc5EkkEolEIpFIJBLJE8T/AQUKjnbAsYHvAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f345bc2e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "completions_x = pd.crosstab(completions.completed, completions.icma_number)\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig, ax = plt.subplots(1, 2, figsize=(14, 4));\n",
-    "plt.subplots_adjust(wspace=0.5, hspace=0.5);\n",
-    "\n",
-    "ax = fig.add_subplot(1,2,2)\n",
-    "completions_x.ix[:, completions_x.columns != 0].plot(ax=ax, legend=False, sharex=False, sharey=False)\n",
-    "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., title=\"Day\")\n",
-    "\n",
-    "ax = fig.add_subplot(1,2,1)\n",
-    "completions_x.plot(ax=ax, legend=False, sharex=False, sharey=False)\n",
-    "\n",
-    "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., title=\"Day\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f5f2dad8908>"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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/+WXuXZjFnFTXsoWs5Cxyq3MRQqBP1mOr05OS8mUqKjxrcUcydSKn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2Wi7Ozs6N69O2cKjjH44B6CXINIL0lXVWP0htDJ2gkJFab0mWLW78iRagRnhHoLqK3Nwc7Oj6Qk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\nZNUe6PoAG89v5N4j9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1Lb/zzCW4+/nyGsrIT/+z9YsICwj9tTUPwbVlU+uPWapnoNekzq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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2dd9deb8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "completions_x_normalised = completions_x / completions_x.sum()\n",
-    "ax = completions_x_normalised.ix[:, completions_x_normalised.columns != 0].plot(legend=False)\n",
-    "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., title=\"Day\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Delayed days of starting and finishing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: datetime.datetime(2017, 6, 2, 0, 0),\n",
-       " 1: datetime.datetime(2017, 7, 17, 0, 0),\n",
-       " 2: datetime.datetime(2017, 7, 18, 0, 0),\n",
-       " 3: datetime.datetime(2017, 7, 19, 0, 0),\n",
-       " 4: datetime.datetime(2017, 7, 20, 0, 0),\n",
-       " 5: datetime.datetime(2017, 7, 21, 0, 0),\n",
-       " 6: datetime.datetime(2017, 7, 24, 0, 0),\n",
-       " 7: datetime.datetime(2017, 7, 25, 0, 0),\n",
-       " 8: datetime.datetime(2017, 7, 26, 0, 0),\n",
-       " 9: datetime.datetime(2017, 7, 27, 0, 0),\n",
-       " 10: datetime.datetime(2017, 7, 28, 0, 0)}"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start_days = {}\n",
-    "for i in icmas.find({}).distinct('icma_number'):\n",
-    "    earliest = icmas.find_one({'icma_number': i}, sort=[(\"started\", 1)])[\"started\"]\n",
-    "    start_days[i] = earliest.replace(day=earliest.day, hour=0, minute=0, second=0)\n",
-    "start_days"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: Counter({0: 13,\n",
-       "          1: 3,\n",
-       "          2: 5,\n",
-       "          3: 5,\n",
-       "          4: 9,\n",
-       "          5: 8,\n",
-       "          6: 23,\n",
-       "          7: 20,\n",
-       "          8: 10,\n",
-       "          9: 4,\n",
-       "          10: 10,\n",
-       "          11: 12,\n",
-       "          12: 6,\n",
-       "          13: 6,\n",
-       "          14: 3,\n",
-       "          15: 3,\n",
-       "          16: 3,\n",
-       "          17: 10,\n",
-       "          18: 5,\n",
-       "          19: 2,\n",
-       "          20: 7,\n",
-       "          21: 2,\n",
-       "          22: 4,\n",
-       "          23: 2,\n",
-       "          24: 3,\n",
-       "          25: 5,\n",
-       "          26: 5,\n",
-       "          27: 3,\n",
-       "          28: 2,\n",
-       "          29: 2,\n",
-       "          30: 1,\n",
-       "          31: 1,\n",
-       "          32: 5,\n",
-       "          33: 3,\n",
-       "          34: 2,\n",
-       "          35: 3,\n",
-       "          37: 2,\n",
-       "          38: 5,\n",
-       "          39: 2,\n",
-       "          40: 2,\n",
-       "          41: 13,\n",
-       "          42: 9,\n",
-       "          43: 4,\n",
-       "          44: 2,\n",
-       "          45: 18,\n",
-       "          46: 6,\n",
-       "          47: 4,\n",
-       "          48: 4,\n",
-       "          49: 2,\n",
-       "          50: 4,\n",
-       "          51: 1,\n",
-       "          52: 1,\n",
-       "          53: 2,\n",
-       "          59: 1,\n",
-       "          61: 1}),\n",
-       " 1: Counter({0: 86,\n",
-       "          1: 19,\n",
-       "          2: 13,\n",
-       "          3: 9,\n",
-       "          4: 7,\n",
-       "          5: 5,\n",
-       "          6: 2,\n",
-       "          7: 5,\n",
-       "          8: 1,\n",
-       "          9: 3,\n",
-       "          10: 1,\n",
-       "          12: 1,\n",
-       "          15: 1,\n",
-       "          17: 1,\n",
-       "          18: 2,\n",
-       "          19: 1}),\n",
-       " 2: Counter({0: 62,\n",
-       "          1: 11,\n",
-       "          2: 11,\n",
-       "          3: 7,\n",
-       "          4: 2,\n",
-       "          5: 1,\n",
-       "          6: 3,\n",
-       "          7: 2,\n",
-       "          8: 2,\n",
-       "          13: 1,\n",
-       "          16: 1,\n",
-       "          17: 1}),\n",
-       " 3: Counter({0: 58, 1: 12, 2: 5, 3: 5, 5: 1, 6: 3, 7: 2, 8: 1, 17: 1}),\n",
-       " 4: Counter({0: 73, 1: 5, 2: 2, 3: 5, 4: 8, 5: 1, 7: 1, 8: 1}),\n",
-       " 5: Counter({0: 56, 1: 4, 2: 3, 3: 4, 4: 2, 5: 2, 6: 2, 7: 1, 8: 1, 10: 1}),\n",
-       " 6: Counter({0: 79, 1: 9, 2: 2, 3: 2, 4: 2, 5: 3, 6: 1, 7: 1, 9: 1}),\n",
-       " 7: Counter({0: 47, 1: 1, 3: 1, 4: 2, 5: 2}),\n",
-       " 8: Counter({0: 48, 1: 2, 2: 5, 3: 2, 4: 3}),\n",
-       " 9: Counter({0: 34, 1: 3, 2: 2, 3: 2}),\n",
-       " 10: Counter({0: 52, 1: 7, 2: 1, 4: 1, 5: 1, 6: 1})}"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start_delays_dict = {}\n",
-    "for i in icmas.find({}).distinct('icma_number'):\n",
-    "    attempts = [(a['started'] - start_days[i]).days for a in icmas.find({'icma_number': i}, ['started'])]\n",
-    "    start_delays_dict[i] = collections.Counter(attempts)\n",
-    "start_delays_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
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-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
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-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "      <th>...</th>\n",
-       "      <th>46</th>\n",
-       "      <th>47</th>\n",
-       "      <th>48</th>\n",
-       "      <th>49</th>\n",
-       "      <th>50</th>\n",
-       "      <th>51</th>\n",
-       "      <th>52</th>\n",
-       "      <th>53</th>\n",
-       "      <th>59</th>\n",
-       "      <th>61</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>4.0</td>\n",
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-       "      <td>6.0</td>\n",
-       "      <td>4.0</td>\n",
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-       "      <td>4.0</td>\n",
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-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>86.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>7.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>58.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>5.0</td>\n",
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-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>6</th>\n",
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-       "      <td>2.0</td>\n",
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-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>47.0</td>\n",
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-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
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-       "      <td>0.0</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>48.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>34.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>52.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>11 rows × 55 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      0     1     2    3    4    5     6     7     8    9  ...    46   47  \\\n",
-       "0   13.0   3.0   5.0  5.0  9.0  8.0  23.0  20.0  10.0  4.0 ...   6.0  4.0   \n",
-       "1   86.0  19.0  13.0  9.0  7.0  5.0   2.0   5.0   1.0  3.0 ...   0.0  0.0   \n",
-       "2   62.0  11.0  11.0  7.0  2.0  1.0   3.0   2.0   2.0  0.0 ...   0.0  0.0   \n",
-       "3   58.0  12.0   5.0  5.0  0.0  1.0   3.0   2.0   1.0  0.0 ...   0.0  0.0   \n",
-       "4   73.0   5.0   2.0  5.0  8.0  1.0   0.0   1.0   1.0  0.0 ...   0.0  0.0   \n",
-       "5   56.0   4.0   3.0  4.0  2.0  2.0   2.0   1.0   1.0  0.0 ...   0.0  0.0   \n",
-       "6   79.0   9.0   2.0  2.0  2.0  3.0   1.0   1.0   0.0  1.0 ...   0.0  0.0   \n",
-       "7   47.0   1.0   0.0  1.0  2.0  2.0   0.0   0.0   0.0  0.0 ...   0.0  0.0   \n",
-       "8   48.0   2.0   5.0  2.0  3.0  0.0   0.0   0.0   0.0  0.0 ...   0.0  0.0   \n",
-       "9   34.0   3.0   2.0  2.0  0.0  0.0   0.0   0.0   0.0  0.0 ...   0.0  0.0   \n",
-       "10  52.0   7.0   1.0  0.0  1.0  1.0   1.0   0.0   0.0  0.0 ...   0.0  0.0   \n",
-       "\n",
-       "     48   49   50   51   52   53   59   61  \n",
-       "0   4.0  2.0  4.0  1.0  1.0  2.0  1.0  1.0  \n",
-       "1   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
-       "2   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
-       "3   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
-       "4   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
-       "5   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
-       "6   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
-       "7   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
-       "8   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
-       "9   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
-       "10  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
-       "\n",
-       "[11 rows x 55 columns]"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start_delays = pd.DataFrame(start_delays_dict).T.fillna(0)\n",
-    "start_delays"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
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-       " 8: Counter({0: 26, 1: 1, 2: 6, 3: 1, 4: 2, 5: 1}),\n",
-       " 9: Counter({0: 23, 1: 4, 2: 1, 3: 1, 4: 1, 5: 1}),\n",
-       " 10: Counter({0: 35, 1: 9, 2: 2, 5: 1, 6: 1})}"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "completion_delays_dict = {}\n",
-    "for i in icmas.find({}).distinct('icma_number'):\n",
-    "    attempts = [(a['completed'] - start_days[i]).days for a in icmas.find(\n",
-    "        {'icma_number': i, 'completed': {'$exists': True}}, ['completed'])]\n",
-    "    completion_delays_dict[i] = collections.Counter(attempts)\n",
-    "completion_delays_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
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-       "      <td>31</td>\n",
-       "      <td>4</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>1</td>\n",
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-       "      <td>...</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>26</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>23</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>35</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>11 rows × 39 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    0   1   2   3   4   5   6   7   8   9  ...  43  44  45  46  47  48  49  \\\n",
-       "0    1   1   3   1   1   1   9  10   8   4 ...   2   2  10   1   1   2   2   \n",
-       "1   60  12   5   4   7   2   0   2   1   0 ...   0   0   0   0   0   0   0   \n",
-       "2   49  12   9   5   1   1   2   2   0   0 ...   0   0   0   0   0   0   0   \n",
-       "3   44   4   4   2   2   1   3   1   0   0 ...   0   0   0   0   0   0   0   \n",
-       "4   48   6   1   4   3   2   1   2   0   0 ...   0   0   0   0   0   0   0   \n",
-       "5   36   2   1   3   5   2   3   0   2   0 ...   0   0   0   0   0   0   0   \n",
-       "6   59   9   2   0   0   3   2   2   0   0 ...   0   0   0   0   0   0   0   \n",
-       "7   31   4   0   0   1   0   1   0   1   0 ...   0   0   0   0   0   0   0   \n",
-       "8   26   1   6   1   2   1   0   0   0   0 ...   0   0   0   0   0   0   0   \n",
-       "9   23   4   1   1   1   1   0   0   0   0 ...   0   0   0   0   0   0   0   \n",
-       "10  35   9   2   0   0   1   1   0   0   0 ...   0   0   0   0   0   0   0   \n",
-       "\n",
-       "    50  51  52  \n",
-       "0    4   1   2  \n",
-       "1    0   0   0  \n",
-       "2    0   0   0  \n",
-       "3    0   0   0  \n",
-       "4    0   0   0  \n",
-       "5    0   0   0  \n",
-       "6    0   0   0  \n",
-       "7    0   0   0  \n",
-       "8    0   0   0  \n",
-       "9    0   0   0  \n",
-       "10   0   0   0  \n",
-       "\n",
-       "[11 rows x 39 columns]"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "completion_delays = pd.DataFrame(completion_delays_dict).T.fillna(0).astype(int)\n",
-    "completion_delays"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "longest_completion_delay = max(c for c in completion_delays.columns if completion_delays[c][1:].sum() >= 1)\n",
-    "longest_completion_delay"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>31</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>26</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>23</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>35</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    0   1   2   3   4   5   6   7   8   9   10  11  12  13  14  15  17  18  20\n",
-       "1   60  12   5   4   7   2   0   2   1   0   0   0   0   0   1   1   0   0   0\n",
-       "2   49  12   9   5   1   1   2   2   0   0   0   0   0   0   0   0   0   1   0\n",
-       "3   44   4   4   2   2   1   3   1   0   0   1   0   1   0   0   0   0   0   0\n",
-       "4   48   6   1   4   3   2   1   2   0   0   0   1   0   0   0   0   0   0   0\n",
-       "5   36   2   1   3   5   2   3   0   2   0   2   0   0   0   0   0   0   0   0\n",
-       "6   59   9   2   0   0   3   2   2   0   0   0   0   0   0   0   0   0   0   0\n",
-       "7   31   4   0   0   1   0   1   0   1   0   0   0   0   0   0   0   0   0   0\n",
-       "8   26   1   6   1   2   1   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
-       "9   23   4   1   1   1   1   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
-       "10  35   9   2   0   0   1   1   0   0   0   0   0   0   0   0   0   0   0   0"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "completion_delays.iloc[1:, :longest_completion_delay+1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f345b1ef0>"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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ranB4vURYMrDG10qgCyHEAQh6oM/JnENbwvL+qxfZ0myUfl0KEyZg6Omho66O\nuKhsoiMbJdCFEOIABD3QxyePpyvMyZriQAf52PFjKakqQdvt9JrNWLdsITYyh1izl+q6jmCXJ4QQ\nR62gB3p4WDjT0maxvmklAOMnj2ebfxu90ekYPR7St27FYEwmxRxJeWNVsMsTQoijVtADHeD7eXNw\nxy+nuRnGjx9PTUQNXQ3hhIeHM3XbNjrCEkk2G6h2S6ALIcRghSTQT8yeg2nECjZtCox0qfRVBka6\npKYysaKCJuJJsGjqPJWhKE8IIfbp66+/Zu7cucTFxZGXl8ebb74Z6pKAEAX68enH443ZwpqN7Ywa\nNQpHlwNXqQtycxm5bRt1Pisxhm5a/NJCF0IcWXw+H+eeey7z5s2jpaWFv/zlL1x22WVs3Rr62e0h\nCXSLwUKWeQqflhZisVjISMigZEMJDB9Ot8VCa1UrxjAvXcZtB71esRBCHA5ff/01O3bs4LbbbkMp\nxcknn8wJJ5zA888/H+rSQhPoALMy5lDUugKA0SNGs6VkC9jtNKWloTdvQYXHEp9aTus+F+UVQojg\n0ntoZWqt+eqrr0JQzUBBXW1xV/Mmz+bFZY/g88H4SeMpebkEbCfgiY0lorgY0/gU4odVU1cH8fGh\nqlIIcaRS9w3NNej0vQfWDTB69GiSk5N55JFH+NnPfsZnn33GsmXL+N73vjck9RyKkAX6KaNmodPX\n8HVZNxPzJ/Lsn5/Fn34pKiyMxJISIi0ZxEaXUbO9mzFjTKEqUwhxhDrQIB4qBoOBN998k5tvvpmH\nHnqI448/nksuuQSz2RySenYVsi6XOEscMb25vL1mPeMnjqfaUI3XkEZEWxu2sjLM5lRSwuModtSE\nqkQhhNij8ePHs3TpUhoaGnj//fcpLy9n+vTpoS4rdIEOkGeew2dblzNq1Ci2+7bjdkeTVFlJVnU1\nRpVEmjmGkvrKUJYohBC72bRpE16vl87OTh555BHq6uq48sorQ11WaAN9duYcvmpbjsViIS06jeKi\nCmLCwtiRlES4Q5EWEUFliwxdFEIcWZ5//nnS0tJITU1lyZIlfPzxxxiNxlCXFdpAP3/qbOrNK/Fr\nP3kZeWzesBnsdrbl5OAv7SAlMoztnRLoQogjy8MPP0xzczNut5t3332X3NzcUJcEhDjQZ03IQHus\nrK38mrGjxlJcWgx2O3VZWVDSTGKEj8YeCXQhhBiMkAZ6eDgktM/h9TUrmHDcBEp3lILdTltqKoaS\nOqxmDy6PHWz/AAAgAElEQVQlgS6EEIMR0kAHGB05m6XbljNx9kTK3eVgt9NjtRJdUoXF4KbLJIEu\nhBCDEfJAPyl7Dls6ljNu5ji2+7bTaU3DojUxdQ2Ee1oh2kGntzvUZQohxBEv5IF+yuRRdPk6aOxu\nJMWUwldNmvSaGhyZmcQ4Ysh2n8bYx/IpchaFulQhhDiihTzQJ05UUD2b5dUrGJkwkk3VLWSUlbEl\nJwdrVRSvXnY/zR/cxMn/mMv9y+6nx9cT6pKFEOKIFPJAT0yEyMY5vL95OaMyR/FVZTW2LVtYl51N\ndGU4ubn13DTramYVfcnnjs+ZuWimtNaFEGIP9hvoSqlFSimnUqpol23xSqmPlFIlSqkPlVKxh1LE\n+JjZLK9awZgxY9hSWUISsD4ri4iKHrq7ndxzD5SssXNj9HvcNO0m5j4nrXUhhPi2wbTQ/w6c9q1t\ndwCfaK1HAZ8Bdx5KEScMn8IOzzZyjsuhzFmGstlozMnBXOamu9tJRAQ8+yzcfLPigtyr+fJ6aa0L\nIcS37TfQtdYrgJZvbT4XWNx3fzFw3qEUMWWikbj2GbhHuantrKU7IwMSEghr78HXd6Hok06CM8+E\nO+4Ae6yd9+ZLa10IERoLFy5k2rRpWCwWrr766gG/+/TTTxkzZgzR0dHMnTuX6urqoNV1sH3oyVpr\nJ4DWug4YdihFTJwIvm2z2aA3MIxhlEZFk9HRgXtEGqq4rP95Dz0E77wD//0vKKW4eoq01oUQwZeR\nkcHdd9/NNddcM2B7U1MTF1xwAQ8++CDNzc1MnTqVSy65JGh1hfxLUYC8PGjbPIflNSvJNeey0WvC\n1tyMc2QWhi3ffLrFxcFTT8F114HHE9gmrXUhRLCdd955zJs3j4SEhAHb33jjDcaPH8/555+PyWRi\nwYIFbNy4kdLS0qDUdbAXuHAqpVK01k6lVCpQv68nL1iwoP9+QUEBBQUFA35vNMKoqBlscG7gktSL\nKGrqJMPppGLESI4v3TzgueedBy+8APffDw8+GNi2s7V+au6pXP+f65m5aCZ/P/fvTEyZeJAvTwgh\nDtzmzZuZNGlS/+PIyEiGDx/O5s2bycvL2+e+S5cuZenSpYd0/sEGuuq77fQ2cCXwEHAF8Na+dt41\n0Pdmyrho2sLGETE+guKySo6vqqLo+NHM/sC923OfeirQTXPxxbDLe9ffWv/7hr8z97m53Dr9Vu6Y\nfQfG8NAvaymEGGJqaC5BN5RXom9vbyc5OXnAttjYWNra2va777cbu/fdd98Bn38wwxZfAj4H8pRS\n1Uqpq4DfAacqpUqAU/oeH5KJEyHePYeOUR2UNNSQUVbGiqxxRFb0oP2+Ac9NTYX/9//g2muht3e3\negf0rc/46wzpWxfiWKT10NyGUHR0NG73wEao2+0mJiZmSM+zN4MZ5TJfa52utTZrrTO11n/XWrdo\nrU/RWo/SWp+qtW491EImToTu8tlUJ1VT7aol+euv2Rgdi89qpO313T8vrr4arFZ44ok9H29na/3m\n6TdzynOn8E7JO4daohBC7NO4cePYsGFD/+OOjg7Ky8sZN25cUM5/RHwpCoFAry2czXrfepJNybT7\nfNR3d+P9wwJMty2gt7VuwPOVgmeeCbTUKyr2fMydrfV357/L9f+5nr+s/UsQXokQ4ljn8/nweDz4\nfD56e3vxer34fD5+8IMfsHnzZv7973/j9Xr5zW9+w6RJk/bbfz5ktNaH9RY4xeAkJ2s9/JFReop9\niv7n8OE6ZckSXevx6JZzc3XLlVP2uM/DD2t9yila+/37PnZZU5ke/sRwffdnd2v//p4shAipA8mN\nUFiwYIFWSumwsLD+23333ae11vrTTz/Vo0eP1pGRkfrkk0/WVVVV+zzW3l5r3/YDylulh7gP6duU\nUnqw5zj1VAg/71qaXyji9OYO3nvqcf40YwZTOp3o8aPp+eczRH1v4LjP3l6YMQNuuQX2d43W+o56\nzn7pbMYnj+cvZ/9FviwV4gillOJwZ9ORYm+vtW/7AX3ze8R0uUBgxIrVdSKdeZ1s6tJkdHRQ6/Vi\nTB1J54M3oa6/Cb+nfcA+BgMsWgS//CU4nfs+fnJUMkuuWIKzw8m8V+bR3t2+7x2EEOIockQF+sSJ\n4C2ZQ21GLcXuFmwtLTi8XgBir3uc3vRYXL86d7f9Jk8OfEl66637P0eUKYq3Ln0LW4yNgn8U4Gzf\nz6eAEEIcJY64QC//MhuTwUSlsYkUh4PavkBXYWGYFr1N9N+X0LnuP7vte889sH49vP32/s9jCDPw\nzDnPcE7eOcz62yxKm4Izi0sIIQ6nIyrQx4yBinLFNNMMYoZHY9i8ub+FDmAZOYOO/7kQ/zXz0b6B\nA9B3rsh4003gcu3/XEop7i24l1/N/hUn/eMkVjlWDfXL6dfYCDfeCMuWHbZTCCHEkRXoZjPk5sJw\n02xMWWY8W7ZQ2z3weqKxd7wEgOuhH+22/0knwRlnBFZkHKxrjruGv57zV855+ZzDMlb9jTdgwgTo\n6oKLLoKNG4f8FEIIARxhgQ6BbpfYngI60jpprq6mducqXH1UuIGwRS8R9fBreMq+2G3/hx/+ZkXG\nwTor7yzem//ekI5Vb2yEH/4Q7rwTXn8dFi+GhQvhrLOgsnJITiGEEAMckYHurp+CN8LLVmM3Dq93\ntyE9kVPPpv3qk+m+dh7a7x/wuz2tyDiY0U/TMqax/Krl/P7z33PPknsOacjUzlZ5ejps2ACzZgW2\nX3RRYDTOaacFAl8IsWdZWVkopb4Tt6ysrCF7346ocegA770Hjz8OdRkTaf6yFP3EByyaMIHTExMH\nPM/vacczPonu/72euBv+uNtxLrwQRo2CnBz43e9gyRKw2/d//vqOes566SwmJE844LHqjY2B8fBf\nfgl///s3Qf5tv/oVfPYZfPopREUN+vBCiO+Qo34cOgRa6EVFcIL3BOrSu3mxro4ff/01a7+14E2Y\nJRr9zEIi71pIT13Zbsd56qnA7c47A0vunn46tHz7ukt7cLBj1ffWKt+TBx8MfAF88cXQI0u3CyGG\nyBEX6BkZ0N0N48LnEJ4dzrA1a/jrqFHM++ortnZ2Dnhu1PeuofOcSXTcePpux6moAL8fhg0LtNBP\nPx3mzQt8Obk/0aZo3rr0LTJiMvY7Vv3bfeWPPhoYcbMvO9ehAbj++iFf8E0I8R11xAW6UoFWeoTO\npzfRz7qvizg3KYkF2dmcVlRE3S7DGAGi//AeltXVtP3rwf5txcXwgx/AP/8JaWnwm9/Ar38d6HKZ\nP3/3JXf3xBBm4NlznuXsvLP3Olb9QFrl7Zva6W375sRGY6C+LVvgrrv2X48QQuzXgS7+cqA3DmKR\nnVtu0freS1w69cYkfdlJtv7tC7Zt01PWrNHunp4Bz3e/9oDuSjPonpYduqZG68xMrZ97LvC78nKt\nCwq0jorSeswYrdPTtT7pJK03b9ba5xtcPc+ue1an/D5FF9YUaq21bmjQ+tJLtc7L03rlyr3v5+vx\naeerTr12xlq9MnWlLswu1M2fNA94TkND4Dh//OPgahFCfDdwtC/OtdOiRfDpa93URp5MQ9c6it8P\nDFfRWvOT0lLKPR7enTABU9g3f2C0njscb1Qic4tWc8UV8L//O/CYPT2BvvklS+C3vw10cygVWNgr\nPz9wmz4dYmP3XNO7pe9y5VtXcu2wv/GPO89h/nx44IE9d6/0unrZsWgHjiccWDIt2G63kTQvieaP\nmym9vpTEsxLJfTgXQ0zgglGVlTB7NvzhD4GRMEIIcTBfih6Rgb5mDVx/tZ+TTb/gLxMep+Mf3+zv\n05oLN28mIiyMF8aMIazvMlRtlWV4Rubzj3MX8z+vnbXPq1M5nYHukZ/8BEaOhMLCwG3dOsjO/ibg\n8/MDI2XCwgJ95fP/dzWfpZzL7cct4OGLb9jtuF3buqj9Yy11i+tIOC0B289tWKdbBzyn19XL1tu3\n0vJpC6MXjSZ+bjwQmHB06qnw6qtw8skH9HYJIY5Bx0ygd3ZCUpJmcfibXHzr+ayvv4jJ5m+u09cV\nFsb3TzyR6c3NPFpUhM+vuOjDazih93V+wv/DctbVhIWZvjlgUhJcc82AcYtbt8KJJ8Kf/hQYBQPf\ntOJ3BnxhIbS2Qs6PGin1tXOa3cr/XNbIZf8+k/kT5nNfwX0opXAVunA85qDlsxbSrkkj4+YMLJmW\nfb7Gpg+admutL1kCl1wCH3888FqpQojvnmMm0CHQMr7PW8RfCn7M8tRN3B9xNncknIrqa3q3hIUx\nJzeXK5pbqHh6PGX1cfznhrfofvlOfDnpxJ94yzcHKyuD558PzOi5/XaYNg2AtWsDSwX8+9+BLo9v\na+zu5rqvtrK6pY3vWZKojnTzZXs7NpOB5tr/ct6aOC77dArGRj+2n9lIvSq1vxtlMPbUWv/nPwMl\nrlgR+GtBCPHddEwF+kUXwfFlVVz7YDSPlj3KI1sfYerYqbz6o1fJjM0EoMbjYcKt9cQVplG00ojV\nCp6yLwifnk/PJ28TOfXsbw7odgc65594ItBSv/12mDePjz4N58c/Dkz02fWyf280NHBTWRnzk5N5\nICeHiPBwALpautnw52rcC3ewLa6N1y7UbDw5hmnxceRbreRbrUy3Wok1DD7Yd7bWE85MYPjvh/P0\nPww89RSsXBn440II8d1zTEws2mniRNgWHk1XRRe//dlveeHkFyh6p4iJT03k2XXPorXmwxcsWD+x\n0f7gegp7m4F9rMhotcLPfx7oa7n11sCiL3l5fP/rP/LoA12ccQbU1ARa5T8sLubOigpeHzeOR0eM\nICI8nK7KLrbevpV1w1cTW9RN/huTuLbo++Qc928ytt7LJfGRdPh83F9VRcbnnzN+9WquKynhbzt2\nsKWjA/8+PtQST09k2qZp6B7Nmglr+NGYZi64AM4+Gzo6Dvc7LYQ4VhyxLfS33oIn7ujimdNrGfGH\nEQCsWLGC864/j5gfxxBnzqP26WdZ+V4m9ckufvDVV7w3YQLHW61oXy8dUxPoveh04u76595PUlgY\nGFry6ac8MmExTzhOpfupDVw2IrG/Vb6//nGtNb9Z9hueK3qO93/0PnmJefT4/RR1dFDoclHodlPo\ndtPa28sMq5WZfa34GXtpxTe9/01r/fftw9nRauDNN8Htfp/KygVYLNlYrfnExuYTHT1l4HcFe9Db\n1kvb6jZc727D/UYp2tXBiF9GEXX7+WDa975CiNA5prpctm2D2dN8fHBCMRPemtC/fcuWLRTMvZOW\nMZOJmruQh0/7Ldcedy1vNzXxk9JS/jt5MiMiI+lc9x+Mc+fhW1OIZeSMfZ6rsbycmz8v5MOXppJZ\nZOLzF5roaMzG8ZiDbmf3oPrHF325iLs+u4s3L32TmbaZu/2+zutlVV+4F7rdfNnWRrbFQn5sbH9X\nzajISMKUoqe1h/JflNPyaQuvZKUy/rL7GT/+M0aMeBSfrx2XqxC3u5Curq1ER0/uD/iYmJn4q+Jx\nFbpwr3LjLnTTVdpBdOR2rF1riT0tk+6kEVQuVtgs/8H+f5mE/fQGSEg44P8+QojD65gKdL8f4qya\nNzLXc0rxcf3bi4vhpJN8xMTcwrSzmiifUE5iZCLPnvMsH3YY+F11NSunTCHVbKbl9rmEr/2KmKU7\nUGF77l3ata/8nvB4rpjjprnWxKMxn5D9kwSS7j8DZRpcf/jOseqL5i1i3qh5+3zuYFrxmV98SF3n\nz3Cu+x5bev/EgofjBxzD09pC/br/0lyznA7fanqGFUGPBXPLZGIaM0hcVs6wih0YbrsNrriifyUw\nT5WHkkvW0ru1jtE99xN12Wy47TbIyxvU6xRCHH7HVKADzJqpuXRjEbd0TESFKRwOOOGEwISec891\nc/755xMVE8WUW6awcN1Cfvu937I94RTeampi2eTJRPV68IxPoud/riP2xicHHLuxu5tbtm7ly7Y2\n/haRQ8o/3NQtriP6lAR+vm0kebHV/Ln9xyhnXSDszjgjMCB9P4rqirjh3Ru4dfqt/HDCD785X3MY\nMaMzMFvNe913Zyt+bauDhPoFZPSs5k3fXRQ8NY2Ej3uo/VEWF8800Laz9V3eRfSUaGLzY7HmW4kZ\nrfF/8gjuwr/inhqBa4qZLqOzvxUfGX08bn8CqHC01nT+u5P2p9pIzCshs/wpuieMpm3+BXinTmJv\nA/lbuvwkR01ieEryHn8/GL2uXsJjwlFhB/RvdUhpn8bX4cNgHfyX10IE0zEX6D/5CVg/ruHsxko4\nLp5rN+fxw3N6uOv3JozxRrq7u7n66qupqKjg4X88zM+W/ozEiESSxt1BfXgC706YQPeyxZjPu4bO\nWTbC/+8+ok++qr9VflNtLPNe1biXtAb6x2/JwGK30NYGBQWBxbzu/X5fP/uXXw667g6f5rOWYWzW\nJ1HkP4FCzxSafbH06nAmRVcwM6+J/JPM5P8wG9u0tAH7NjW9T2np9SQmno09+3cUe8ModLnY+noD\no55yU5cOreMs2E+NY2J+IjOS4oitqgqM3nnhhcAVNH7+czjuOHa07aCw+jNKt79NW9sqYqklK0qx\n67+QMH841rYYlA7DZ2ol2tuNX0FbRDgdJoUOM6CVEX/fT1QYUXTyoXM2bRvvYW7eDPLzA437/X3e\ntX3ZhuMPDhpeb8BsNwe6si5PJTwqfNDv7aHqbe+l7u91OJ5w0F3XzbALh2H/uZ3oSdFBq0GIwTjm\nAv3Pfw7M3nzk7m5OP0MxJqqTWyK30b6uDbPdjDXfSvSMaB774jHeW/keb7/7Nq/VvsbjXzyObfRN\njM69iBfHjsXfUkvbE7cQ8ew7tCVa+Dh/Hunrf4ql2RAIlSt37x/fOZv0//4Pbth9UugAdXUDJyOt\nXw/2rF6aEt8hb3IzT99wOePGGOmsb2fNS2UUfuBiVVEEhfXDsYR1k59WxfTj27Gd9gFpY95m4sQ/\nEx8/d7fz9PTA397s5pH3XDQOc5M+vYYqazfZdXXMVIqMEcPo1pVs27GMVTWFtHW3MdM2k3xbPvm2\nfKZnTCfGHLPbcbXW7HhmB+V3VdD9k0Q+n1VDYW01q1JTiVYGzNtTqV2axNm5VhZcHo3D8CnLS3/H\nNNNq1naO54N//5Stb/yYmTMHLqNgtYL2a5r+00TNYzV4yj1k3JpB2rVpdGzqoOaxGtwr3aRdn0bG\nTRmY0/f+18uh8jg81D5Zy45FO4griMN+u53IUZFsf2Y7tU/VEjkmEvvtdhJOTwjpXw5C7HTMBfrn\nnwdGGGZmBq43+uKLgVagv9dPx6YO3IXu/i//Xna8zEv6JRZdvoj40+O5te42tmHiwhMe4tlJJ/Lv\n8jref/wrfvrFX8lpeQdzu5euG84h5raFGOLT93j+rVthzhx4+umBs0k3bhwY4C4Xewyzju4OLv7X\nxfj8Pv518b+INg1sBWq/pnxJNR+/uZKPKzr4ev0MqnYMZ1J0Bfl5TeQXmMi/NJuMabvU19ODfu1f\ntP7mIdp21PNQzgTevjgaZ4qByKRp+GNG4wuP4LioSOYmpjIrNnaPI2r8WlPa2dnff1/odtO+rZO7\nHgsnqVOx+aeZbP5nIz/44gnO0W/B/PlY/m9gP/vWljJeKLqbsf5PcPqHUd12MRT+mi8KjWxe5+OS\nuDpOb3dgTjCQfLONibcMI9w0sBnfubWT2idqcb7oJPGcRGw/txEzefcPnYPlXuvG8QcHze83k3J5\nCrZbbUTkDlyAx9/tp/7VehyPOfB7/Nh+biPlxymERwTvLwchvi3oga6UOh14nMB49kVa64f28JyD\nDnS3O7BY1ty58O67gVDfm+6Gbl567CV+8cdf8MCIBxhdOZpX5r7KC2NfIb/lQkZ8FYVldCS2UxKx\nZFmIW7uSiS99wPCNrWw41c6WH12Ix56723GrqwPLA0w9Hhw14HBAYhLkZAeuhpSTA8nJe+1yxuf3\nsXjlYqqqqxiXMY6EtASMlsBVkJTuIda3EZO/HpfheLxhyegOjWcLuLZFUN8QT21nOhHay/f0Jmb0\nVjFl+w7SdAOrcsAzegTJ5hyaynMoW53F2FEWTv4exKf3UunxsK2ri20eDzVeL3F+P4nNzfT6fKyZ\nNYt3J0wg1mLpH2GTb7UyXEXzyvOKdfft4AdNFfgusHP6P+yYWp2BN+Evfwl8cl1+eeCTKyMDgHZv\nOwu/vIf4rjeJ8XvoWX0uWU/9gLBR6ZROsvGpM5bCVQqXy8/48aUkJ69l+vRU5s+fjs0WWOump6WH\nHc/swPGkg8i8SGy320g8M/GgWsvap2l8pxHHYw48lR5st9lIuzYNQ2zfh5rLBV98AQ0NgVnDI0dC\n4H8eWpe14njMgXuVm/Qb0km/KR1z6u7/8BweD6vcbgxKMdNqJXVf/zj3xO8PrJ28bh3YbIFWQHTw\nu31aWgJvxdath36s2NjAnD2bLfBPY3/XBTjSNHU2scqxCmeHk4yYDOyxdmxWG1azdf87HwZBDXSl\nVBhQCswFtgNrgEu11l9/63kHHegQ6HaZPz/Q4h2MFStWcMEFF/DQ7x7igskX8OnSz3m6YTGZE62Y\n9/AFWGztdk54cwVzljXw1fholpw/ne3jxwx4zo4dUFsLKSmB276Gb3d3d1NfX09dXV3g5qzDaDBi\nibXQ2dVJl6sLU4QJa4KRtIROLPHReCJtaPVNazDR1cOkbZ1Mquhk4rZOxtR4qImxsCE2llW6neEJ\nXRh7Eok3pJIem0peeiqxEdEUF8PGIrDGaEaObCXcUIezrw6X201kQgIJ4eGMb2gg1udja2IiHRMm\noGd+n0/c83nm1Uxmzw5Moj3e7qH0uhJ6W3sZ/Y/RRI2LCiyy8/zz8J//BP40iYzs/7OkLX4G1R9G\ns6NyCY1XvURk7hYKu2Zg65hHU0kzhYWFrFr1BSZTLPHx09ixYwdu93osllyys/M54YR8zj9/Jt+f\nO5Km1xupebQGX7sv0CV2RSrhkftvLfe291L3jzocjzswJhqx324n6QeJhG0tgVWrvvmTqrISpk5l\nKVBQVQVtbbv9idW5IxzHEw7qX6on/txEXNfF80VmT/9fMx6/n5lWKz6tWeV2E2swDPhwnBQdjXHX\nLxVaWwPJubOGL74ITAOeOjXQStiwIfDBsuvKcCNG7L2lcBD8/sAosZ0lrFoVOPXxx8Po0YP6zn+v\ntIaSkqX09BRQUxP4/8VqDYT7zpD/9k+bDSz7XvLosPH5fWxu2ExhTSGFjsBtR9sOpmdMJ8OaQa27\nFofbQY27BkOYAZvVht1qH/DTZrUd1tAPdqDPBO7VWp/R9/gOAuv3PvSt5x1SoB+MLVu2cMYZZ3D9\n9ddz55139q//si+9zY6+fvb/0Jscge/WG7Be/iDKsPf01lpTVlZGYWFh/628vJzJkyczc+ZM8vPz\nyc/PJz39my6Tzs4G3n//WgoLV1BdPYWNayvIaGjgIpuN2QYDIxoaMHd3E7br/9jTp0NMoBtiwYIF\n3Hjr//DysjV8sLmQjU2rqDcXonotRLeOw9JgxbW5Cl9tExbDNE47LZ9bbsknP38Spl0+ieo3baLw\nDy/R8M4S8hq3cBxutpvM1OVko/LzSb/gArJPOw3n3xqouKsC+y/s2P/XTpghbOeLR5eW0fT0Rmr+\nqelsCMfLYirsZayONvJpaz2OhgZGjITEsSnMmHMJv7jgl6SmpvbX0NHRzb/+tZF33ilk7dpV1NYW\n4vO5SUqawYTxMzktbyL521II+9K/z372Af3jJ0RjL2jA6voctaovOOPjBwblxIlgNLJgwQIWLFgA\n27cPCHxHTQ2FBQUU5uezMTEH+8dmzntT4R1hJOymZCafm8aIqMj+f1e7dV+5XGzr7OS4ri5mVlSQ\nv3Il+cuXkzp8+Dc1zJwZ+NNuJ683EOq79uV1du7el3cArfidre+dh1u9OnAFr13fivHj4QBWqdin\n/veTwIdHQ0PgA6OmZs8/d4b+rgF/uEJ/Z+u70FHIKscq1mxfQ2p0av/3S/n2fMYNG0d42MCGg9aa\nVk9rf7g73A5qXDU42vp+7if0dwa+3Wrf4/dX+xLsQL8AOE1rfX3f48uA6VrrW7/1vKAHOsD27ds5\n88wzmTVrFk8++STh4YPrD/V3e2h7/i7Cn3wWQ0MXnuvPIfrWpzDEp9PW1sbq1av7w3vVqlVER0f3\nB3d+fj6TJ08eEJy7amp6n22fX0N61QRSK8cQ9sU6WL+ensxMau121hgMvN3QwJvFxWRmZQ047ujR\nowkLC+Pee+9l/vz5Az5EtpaXkzu1AEN2Ns1RLhrMX+GJKiOibRJmZz6erflcPCufB3+ZQXp64ELc\njz0WWLPsllvguusg2tzJ1jfeoP6ttzCsWYO9tpaYvlZ828hxdNZPIi7y+0x5uQBLtoWyP5fx0WMf\nUUwxpcNK2VC9gbjYWGbm5pIfEUF+ayuTtmyhJTOO13+azrDhW6j3JdFjvYifHn/PXi++vXHjDl56\naRVLlhRSUlKI272eKFM2x0WNZ0ZHDlNnz+b0h2YTd3ws7tUuHL8ppnlJJyk5Zdi6XyZi+7pAq3fX\n4ExJ2eO5FixYwJ333MP6trYB3yV4fD7ytSbf4SB/zRqOf/ddIhtaqc+8HIfzJPymKGy/yCLluqxA\nP/seWt8uu53VZ57J/2/vbkLjKOM4jn9/2zapSUyRgJE2vlFBiULaxDStL1AvNQhaEQXbi17EQ8WC\nF18u9eipUBAvvlHEICpoe2qLFBQhoYW0mtLaipLWRBPTJk0IRkmzfw8zMbtrNs1smZ3M7v8DAzvD\nZp8/w7O/febZebJ9HR30rl9PH1x/FF9oeDj/yqJwFL9163/TRYWj797eIDQ7Oxee3tUVBHpccgN9\nOUoJ/cXCvzD0lxp9z4d314YumuqaihcXQZTQzxvdXyf0yx3ozwI7CgK908z2FjwvkUAHmJoK7lWf\nmJjIGyUv18axSzz16wU6Jv7mVO0q/pqZo6FhDY2NwXZz4xpqa5Z5nTp3jZpfJqn5p47MtkcXRl7z\n3z+6x9oAAANYSURBVKDmmJ2dZWBgIC+0x8fHaW1tpb+/n+bm5rywb2tr+9+HyMj4ND3fnuTImV76\nx3q5clMfzK4lc/kBMqymrh7W1gJLdJem6Rk2D12m/fdxOkcnab8yzWB9LRfra8kCGQllRCaTQcos\nPjtgFozms1myGGSi94XwJcLNwMi/6ipxVqJncobd6womepfRV1Wk0Wr/adieqRl2N6Zs4nwFe3L4\natmnXN42s+5wv+iUS0kNOOdclStnoK8CzhN8KfoHcALYZWbnSnpB55xzN6Tkr0PMbE7SK8AxFm5b\n9DB3zrmExL6wyDnnXHnE9gMXkrol/STpgqTX42qnWkgalPSDpFOSTiRdT9pI+lDSqKQfc47dIumY\npPOSjkpal2SNaVLkfO6TNCSpP9y6k6wxLSS1SDou6aykAUmvhscj989YAj1cdPQu8DhwP7BL0n1x\ntFVFssB2M9tsZluSLiaFPiboj7neAL4xs3uB48CbZa8qvRY7nwD7zaw93I6Uu6iUuga8ZmatwDZg\nT5iXkftnXCP0LcDPZnbRzGaBz4CdMbVVLcQK/snAlc7MvgcmCg7vBA6Gjw8CT5e1qBQrcj6h5JtI\nq5eZjZjZ6fDxNHAOaKGE/hlXQGwAfsvZHwqPudIZcFTSSUkvJV1MhbjVzEYheFMBMS67qRp7JJ2W\n9IFPYUUn6S5gE9AHNEftn3EF+qJLTGJqq1o8ZGYPAk8QvGkeSbog5wq8B2w0s03ACLA/4XpSRVID\n8CWwNxypR87MuAJ9CLgjZ7+F4B94uRKFn9CY2RjwFcG0lrsxo5KaASTdBvyZcD2pZmZjOcvC3wc6\nk6wnTSStJgjzT8zsUHg4cv+MK9BPAvdIulNSDfA8cDimtiqepLrw0xtJ9cAO4EyyVaWSyL96PAy8\nGD5+AThU+AduSXnnMwydec/gfTSKj4CzZnYg51jk/hnbfejhLUsHWFh09E4sDVUBSXcTjMqNYDHY\np34+o5HUA2wHmoBRYB/wNfAFcDtwCXjOzK4mVWOaFDmfjxHM/2aBQeDl+TlgV5ykh4HvgAGC97gB\nbxGsvv+cCP3TFxY551yF8NvgnHOuQnigO+dchfBAd865CuGB7pxzFcID3TnnKoQHunPOVQgPdOec\nqxAe6M45VyH+BRuNEQh/XIDuAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2dc97f60>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "completion_delays.iloc[1:, :longest_completion_delay+1].T.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# People attempting per day"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0.0, 5.0, 10.0}"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pipeline = [{'$match': {'grade': {'$ne': '-'}}},\n",
-    "            {'$project': {'grade': True}}]\n",
-    "set(r['grade'] for r in icmas.aggregate(pipeline))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "pipeline = [{\"$group\": {\"_id\": {'icma_number': \"$icma_number\",\n",
-    "                                'grade': '$grade'},\n",
-    "                        \"participants\": {\"$addToSet\": '$Personal Identifier'}}}]\n",
-    "participant_grades = {(r['_id']['icma_number'], r['_id']['grade']): r['participants'] \n",
-    "                      for r in icmas.aggregate(pipeline)}\n",
-    "participant_grades;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 10.0),\n",
-       " (0, '-'),\n",
-       " (0, 0.0),\n",
-       " (0, 5.0),\n",
-       " (1, 5.0),\n",
-       " (1, 10.0),\n",
-       " (1, 0.0),\n",
-       " (1, '-'),\n",
-       " (2, 5.0),\n",
-       " (2, 10.0),\n",
-       " (2, '-'),\n",
-       " (2, 0.0),\n",
-       " (3, 0.0),\n",
-       " (3, 10.0),\n",
-       " (3, '-'),\n",
-       " (3, 5.0),\n",
-       " (4, 0.0),\n",
-       " (4, '-'),\n",
-       " (4, 10.0),\n",
-       " (4, 5.0),\n",
-       " (5, '-'),\n",
-       " (5, 5.0),\n",
-       " (5, 0.0),\n",
-       " (5, 10.0),\n",
-       " (6, '-'),\n",
-       " (6, 10.0),\n",
-       " (6, 0.0),\n",
-       " (6, 5.0),\n",
-       " (7, 10.0),\n",
-       " (7, 5.0),\n",
-       " (7, 0.0),\n",
-       " (7, '-'),\n",
-       " (8, 0.0),\n",
-       " (8, 5.0),\n",
-       " (8, 10.0),\n",
-       " (8, '-'),\n",
-       " (9, 0.0),\n",
-       " (9, '-'),\n",
-       " (9, 10.0),\n",
-       " (9, 5.0),\n",
-       " (10, '-'),\n",
-       " (10, 0.0),\n",
-       " (10, 5.0),\n",
-       " (10, 10.0)]"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted(participant_grades.keys(), key=lambda k: k[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "icma_numbers = sorted(set(k[0] for k in participant_grades))\n",
-    "best_grades_participants = {}\n",
-    "for icma in icma_numbers:\n",
-    "    prps = set()\n",
-    "    for grade in [10, 5, 0, '-']:\n",
-    "        pis = set(participant_grades.get((icma, grade), []))\n",
-    "        best_grades_participants[icma, grade] = pis - prps\n",
-    "        prps |= pis\n",
-    "best_grades_participants;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "pbgs = [{'pi': pi, 'icma': icma, 'grade': grade} \n",
-    "        for ((icma, grade), pis) in best_grades_participants.items() \n",
-    "        for pi in pis]\n",
-    "ps = set(p['pi'] for p in pbgs)\n",
-    "participant_best_grades = {pi: {p['icma']: p['grade'] \n",
-    "                                for p in pbgs\n",
-    "                                if p['pi'] == pi}\n",
-    "                           for pi in ps}\n",
-    "participant_best_grades;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 98, 1: 76, 2: 70, 3: 59, 4: 46, 5: 41, 6: 35, 7: 33, 8: 23, 9: 29, 10: 27}"
-      ]
-     },
-     "execution_count": 175,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "completers_per_day = {day: set.union(*[best_grades_participants[d, s] \n",
-    "                                       for d, s in best_grades_participants\n",
-    "                                       if d == day\n",
-    "                                       if s != '-'])\n",
-    "                      for day in icma_numbers\n",
-    "                     }\n",
-    "{d: len(completers_per_day[d]) for d in completers_per_day}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 210,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 174, 1: 57, 2: 18, 3: 19, 4: 24, 5: 19, 6: 18, 7: 13, 8: 20, 9: 8, 10: 12}"
-      ]
-     },
-     "execution_count": 210,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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rV8LNN2d3jB073trh8Xe/e+vj0Nu9vfDSSzB9OhxwQPLn6KPhwgsd4ruzaRN873vwm9+8\n9TUd/Druv3/ydRz8OPT2/vsnX9fx4/P+G5Tfjh3wzW/CD34A3/8+nHFG9cdt3ZpcyP3734ePfay1\nNbaLelouHRXovb3JVgDPPz/2b+56g3qkoJk+3QHTDDt2JOdFRvr/4eBvnSeegHPOgWnT4Oqrk6/n\n7tx9N3z0o0k//eijW1NjO3Gg12D2bLj2Wpg/P/ncQd0ZHPzZiUhW5F//Olx6KXz+87UPHlx/PVx8\nsSdfqnGg12DJErjzTpgw4a2g3m+/0b+BHdSdod7gP/30ZHU6cWLef4PWGhxH7O1NFkpHHjn21/jG\nN5JzW5582ZUDvQa9vckJNQe1NWJo8D/9dLJC/e1vk1VqpwT70HHEZcvgHe+o73V27oRPfAImT4Z/\n//fijxW3igPdLEfr1iUth7IH+8BA8t6ANWvgRz96a4KlEdu3w4knwic/CV/9auOvVwZZzqGb2ShO\nPBHuuCNZZQ62H665JlnNl8VI44iNmjwZbr0Vrrgi+Wj18QrdLCNlWrHXOo7YKE++vMUrdLMCKcuK\n/YknkpX4vfcmq/KswhyS2fTvfS+ZTX/uueyOU1YOdLOMtWuwR8C//iscfzycey7cdtvos+XNcNZZ\n8Nd/nfzg8O6MY+OWi1mLtUMrpq8v2YelkXHERnjyxS0Xs7ZQ9BX7qlXJic+5c5OToK0Oc/CeL/Xy\nCt0sZ0VZsWcxjtioLVtgwYLO3PPFK3SzNlSEFXtW44iNOvBAuOmm5N2oDz+cdzXF5xW6WcG0csXe\nqnHERl13HVxySWft+eJ3ipqVSNbBPtbdEfPWaXu+uOViViJZtWLyGkds1KWX+mpHo/EK3axNNGPF\nnvc4YqM6ac8Xr9DNSqzRFXsRxhEbNXly8vfwni/VeYVu1qZqXbEXcRyxUZ2w54tX6GYdpJYVe1HH\nERt13HHJKt17vuzKK3SzkhhcsW/alKzYBy+8UeRxxEaVefLFY4tmxrp1yWz5nnsm0yztMMFSrzLv\n+eJAN7OOU9bJl3oCfUJWxZiZtcLg5MuCBcl5hE7b82UonxQ1s7ZXpj1fXnsNbr65vuc60M2sFNp9\n8uWpp2DpUjjooOSqTfVwoJtZaZx9dntd7ei112DlSvjgB+EDH4DXX4c770wmd+rhk6JmVirtMPny\n1FPwwx8m9c2enexPc8YZu45eZvLGIklXSeqX9PCQ+/aRtFrSRkm3S5o6loOamWWlqFc72t1q/Kyz\nmjNHP+oKXdIJwADwHxFxdHrfcuDFiPiWpK8B+0TE0hGe7xW6mbVcUa52VMtqvJpMVugR8UvgpWF3\nnwasSG+vAD4+loOamWUtz8mXVqzGq6l3Dn2/iOgHiIg+SdObWJOZWVMMnXxpxdWO6l2NN4unXMys\n1LKefMlrNV5NvSv0fkldEdEvaQaw26nP7u7uN29XKhUqlUqdhzUzG7tLL4XHHktWzM2afGn2aryn\np4eenp6GaqppbFHSwcBPI+JP0s+XA7+PiOU+KWpm7aAZe7689lqyzcCVV8JDD8GnPgWf/SwccURz\na4WMNueSdB1QAaYB/cAy4BZgJXAg8AxwZkS8PMLzHehmVgj1Tr7k0Rv3botmZqOo9WpHrVyNV+NA\nNzOrwXXXwSWXVJ98yXtSZZC3zzUzq8HZZycnSc84I1mpS29fjd95Z+tW483iFbqZdaTBPV96e5NV\neZ6r8WrccjEzG4Pt2+Hqq+FDHyreatyBbmZWEpns5WJmZu3BgW5mVhIOdDOzknCgm5mVhAPdzKwk\nHOhmZiXhQDczKwkHuplZSTjQzcxKwoFuZlYSDnQzs5JwoJuZlYQD3cysJBzoZmYl4UA3MysJB7qZ\nWUk40M3MSsKBbmZWEg50M7OScKCbmZWEA93MrCQc6GZmJeFANzMriYYCXdKHJT0u6QlJX2tWUWZm\nNnZ1B7qkccA/A38GzAHOknRkswrLUk9PT94lvI1rql0R63JNtXFN2WpkhX4s8JuI2BwRO4AfA6c1\np6xsFfF/oGuqXRHrck21cU3ZaiTQ/xjYMuTzrel9ZmaWg0YCXVXuiwZez8zMGqCI+jJY0gKgOyI+\nnH6+FIiIWD7scQ55M7M6RES1hfOIGgn08cBG4GSgF7gHOCsiHqvrBc3MrCET6n1iRLwh6QJgNUnr\n5iqHuZlZfupeoZuZWbFk9k5RSVdJ6pf0cFbHGCtJMyWtkbRB0iOSvlCAmiZJulvSA2lNy/KuaZCk\ncZLul3Rr3rUASHpa0kPp1+qevOsBkDRV0kpJj0l6VNJxBajpPenX6P7047aC/Fv/kqRfS3pY0rWS\n3lGAmi5Kv+9yy4NqWSlpH0mrJW2UdLukqbW8VpZv/b+G5E1HRfI68OWIOAr4ALAk7zdDRcSrwOKI\nmAccA5wq6dg8axriImBD3kUMsROoRMS8iCjK1+gK4LaImA3MBXJvO0bEE+nXaD7wXmA7cHOeNUk6\nALgQmB8RR5O0ez+Zc01zgPOA95F87/2FpMNyKKVaVi4F7oiII4A1wMW1vFBmgR4RvwReyur16xER\nfRHxYHp7gOSbL/fZ+Yj4Q3pzEsk/9Nz7YJJmAn8O/FvetQwhCrT/kKS9gRMj4hqAiHg9Iv4v57KG\nOwV4KiK2jPrI7I0HJkuaAOwF/C7nemYD6yPi1Yh4A7gTOL3VRYyQlacBK9LbK4CP1/JahfnmaDVJ\nB5P8VL4730rebG08APQBv4iIe/OuCfgu8PcU4IfLEAHcLuleSZ/NuxjgUOAFSdek7Y0rJe2Zd1HD\n/CVwfd5FRMTvgO8AzwDPAi9HxB35VsWvgT9N2xt7kSxgDsy5pkH7RUQ/JAtRYHotT+rIQJc0BbgB\nuChdqecqInamLZeZwHGSjsqzHkkfAfrT32ZE9TeR5eH4iHgfyTfeEkkn5FzPBGA+8C9pe+MPJL8q\nF4KkicDHgJUFqOWdJKvOWcABwBRJZ+dZU0Q8DiwH7gBuAx4kacu2rY4L9PTXvRuAH0XEqrzrGSr9\ndb0H+HDOpSwEPiZpE8nqbrGk/8i5psGVChHxPElPOO8++lZgS0Tcl35+A0nAF8WpwK/Sr1feTgE2\nRcTv0/bGTcDxOddERFwTEe+NiApJ2+M3OZc0qF9SF4CkGcBztTwp60Av0upu0NXAhoi4Iu9CACTt\nO3gGO/11/RTg8TxriohLIuKgiDiU5MTVmoj4VJ41Sdor/c0KSZOBD5H8ypyb9FfiLZLek951MsU6\niXwWBWi3pJ4BFkjaQ5JIvla5n0CWND39eBBJ/zyvr9fwrLwV+HR6+1ygpsVn3W8sGo2k64AKME3S\nM8CywZNHeZG0EPgr4JG0Zx3AJRHx3zmWtT+wIt2OeBzwk4i4Lcd6iqoLuDndSmICcG1ErM65JoAv\nANem7Y1NwN/kXA+wy+Lgc3nXAhAR90i6AXgA2JF+vDLfqgC4UdK7SGr6fERsa3UB1bISuBxYKekz\nJD8Mz6zptfzGIjOzcui4HrqZWVk50M3MSsKBbmZWEg50M7OScKCbmZWEA93MrCQc6GZmJeFANzMr\nif8HuPWaTomqx0sAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2d66d358>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lurkers_per_day = {day: set.union(*[best_grades_participants[d, s] \n",
-    "                                       for d, s in best_grades_participants\n",
-    "                                       if d == day\n",
-    "                                       if s == '-'])\n",
-    "                      for day in icma_numbers\n",
-    "                     }\n",
-    "\n",
-    "pd.Series({d: len(lurkers_per_day[d]) for d in lurkers_per_day if d > 0}).plot()\n",
-    "{d: len(lurkers_per_day[d]) for d in lurkers_per_day}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 233,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def count_tasks(scores, exclude_intro=True, exclude_lurking=False, only_lurking=False):\n",
-    "    if exclude_intro:\n",
-    "        countable_scores = [s for s in scores if s > 0]\n",
-    "    else:\n",
-    "        countable_scores = [s for s in scores]\n",
-    "    if exclude_lurking:\n",
-    "        return sum(1 for i in countable_scores if scores[i] != '-')\n",
-    "    elif only_lurking:\n",
-    "        return sum(1 for i in countable_scores if scores[i] == '-')\n",
-    "    else:\n",
-    "        return sum(1 for i in countable_scores)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 216,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def consecutive_tasks(scores, exclude_intro=True, exclude_lurking=False, only_lurking=False):\n",
-    "    count = 0\n",
-    "    if exclude_intro:\n",
-    "        task = 1\n",
-    "    else:\n",
-    "        task = 0\n",
-    "    if exclude_lurking:\n",
-    "        usable_scores = sorted(s for s in scores if scores[s] != '-')\n",
-    "    elif only_lurking:\n",
-    "        usable_scores = sorted(s for s in scores if scores[s] == '-')        \n",
-    "    else:\n",
-    "        usable_scores = sorted(scores)\n",
-    "    while task in usable_scores:\n",
-    "        count += 1\n",
-    "        task += 1\n",
-    "    return count        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# for pi in participant_best_grades:\n",
-    "#     grades = participant_best_grades[pi]\n",
-    "#     print('{}, {} tasks, {} consecutive: {}'.format(pi, \n",
-    "#                                                     count_tasks(grades),\n",
-    "#                                                     consecutive_tasks(grades),\n",
-    "#                                                     grades))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "participation = {pi: \n",
-    "                 {'tasks': count_tasks(participant_best_grades[pi]),\n",
-    "                  'consecutive': consecutive_tasks(participant_best_grades[pi])}\n",
-    "    for pi in participant_best_grades\n",
-    "    if count_tasks(participant_best_grades[pi]) > 0\n",
-    "}\n",
-    "participation;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 243,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "completion = {pi: \n",
-    "                 {'tasks': count_tasks(participant_best_grades[pi], exclude_lurking=True),\n",
-    "                  'consecutive': consecutive_tasks(participant_best_grades[pi], exclude_lurking=True)}\n",
-    "    for pi in participant_best_grades\n",
-    "    if count_tasks(participant_best_grades[pi], exclude_lurking=True) > 0\n",
-    "}\n",
-    "completion;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 228,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "lurking = {pi: \n",
-    "                 {'tasks': count_tasks(participant_best_grades[pi], only_lurking=True),\n",
-    "                  'consecutive': consecutive_tasks(participant_best_grades[pi], only_lurking=True)}\n",
-    "    for pi in participant_best_grades\n",
-    "    if count_tasks(participant_best_grades[pi], only_lurking=True) > 0\n",
-    "}\n",
-    "lurking;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "{pi: completion[pi]['tasks'] for pi in completion};"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 249,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>lurked</th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>8</th>\n",
-       "      <th>10</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>completed</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>49</td>\n",
-       "      <td>8</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>19</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "lurked     0   1   2   3   4   5   6   8   10\n",
-       "completed                                    \n",
-       "0           0  49   8   5   0   0   0   0   1\n",
-       "1           5   4   1   0   0   0   1   0   0\n",
-       "2           1   3   1   0   1   0   1   2   0\n",
-       "3           3   2   3   0   1   1   0   0   0\n",
-       "4           0   3   3   0   1   1   1   0   0\n",
-       "5           2   1   1   0   1   0   0   0   0\n",
-       "6           1   0   0   1   1   0   0   0   0\n",
-       "7           2   1   1   3   0   0   0   0   0\n",
-       "8           0   0   1   0   0   0   0   0   0\n",
-       "9           0   6   0   0   0   0   0   0   0\n",
-       "10         19   0   0   0   0   0   0   0   0"
-      ]
-     },
-     "execution_count": 249,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "completed_and_lurked = pd.DataFrame.from_dict(\n",
-    "    {'completed': {pi: completion[pi]['tasks'] for pi in completion},\n",
-    "     'lurked': {pi: lurking[pi]['tasks'] for pi in lurking}}).fillna(0).astype(int)\n",
-    "pd.crosstab(completed_and_lurked.completed, completed_and_lurked.lurked)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 223,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "participation_intro = {pi: \n",
-    "                 {'tasks': count_tasks(participant_best_grades[pi], exclude_intro=False),\n",
-    "                  'consecutive': consecutive_tasks(sorted(participant_best_grades[pi]), exclude_intro=False)}\n",
-    "    for pi in participant_best_grades\n",
-    "    if count_tasks(participant_best_grades[pi]) > 0\n",
-    "}\n",
-    "participation_intro;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "completion_intro = {pi: \n",
-    "                 {'tasks': count_tasks(participant_best_grades[pi], \n",
-    "                                       exclude_intro=False, exclude_lurking=True),\n",
-    "                  'consecutive': consecutive_tasks(participant_best_grades[pi], \n",
-    "                                                   exclude_intro=False, exclude_lurking=True)}\n",
-    "    for pi in participant_best_grades\n",
-    "    if count_tasks(participant_best_grades[pi], exclude_lurking=True) > 0\n",
-    "}\n",
-    "completion_intro;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 236,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "lurking_intro = {pi: \n",
-    "                 {'tasks': count_tasks(participant_best_grades[pi], \n",
-    "                                       exclude_intro=False, only_lurking=True),\n",
-    "                  'consecutive': consecutive_tasks(participant_best_grades[pi], \n",
-    "                                                   exclude_intro=False, only_lurking=True)}\n",
-    "    for pi in participant_best_grades\n",
-    "    if count_tasks(participant_best_grades[pi], only_lurking=True) > 0\n",
-    "}\n",
-    "lurking_intro;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({1: 54, 2: 13, 3: 12, 4: 3, 5: 8, 6: 6, 7: 5, 8: 4, 9: 4, 10: 34})"
-      ]
-     },
-     "execution_count": 186,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "collections.Counter(participation[pi]['tasks'] for pi in participation)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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LT6vqhqq6YUoZALYk2ZbkRAZHcfcAVNWPgYenmOPrSS7qlr+W5GUASZ4HPDSlDFVVh6pq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\nSqk1n7MoAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f343e0828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pd.Series(collections.Counter(participation[pi]['tasks'] \n",
-    "                              for pi in participation)).plot(kind='bar');\n",
-    "collections.Counter(participation[pi]['tasks'] \n",
-    "                    for pi in participation)\n",
-    "plt.savefig('participated-in-days.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({0: 63,\n",
-       "         1: 11,\n",
-       "         2: 9,\n",
-       "         3: 10,\n",
-       "         4: 9,\n",
-       "         5: 5,\n",
-       "         6: 3,\n",
-       "         7: 7,\n",
-       "         8: 1,\n",
-       "         9: 6,\n",
-       "         10: 19})"
-      ]
-     },
-     "execution_count": 187,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "collections.Counter(completion[pi]['tasks'] for pi in completion)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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wQ+AbwCXAZVV1b195B+V+iNF760jgfuAo4HJGrzlVdXaP2ecCvwtcC7wKuAn4PqMS/9Oq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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2da192e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pd.Series(collections.Counter(completion[pi]['tasks'] \n",
-    "                              for pi in completion)).plot(kind='bar');\n",
-    "plt.savefig('completed-days.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 219,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({0: 33, 1: 69, 2: 19, 3: 9, 4: 5, 5: 2, 6: 3, 8: 2, 10: 1})"
-      ]
-     },
-     "execution_count": 219,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "collections.Counter(lurking[pi]['tasks'] for pi in lurking)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 221,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({0: 86, 1: 47, 2: 5, 3: 3, 5: 1, 10: 1})"
-      ]
-     },
-     "execution_count": 221,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "collections.Counter(lurking[pi]['consecutive'] for pi in lurking)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 220,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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q6jU9ZLq+qk7plt/M6O/x08AfAJ/p4/s8yTeBE6vqF0k+CPwU+ASjs8ROrKo/6iHTRxh9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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2d694dd8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pd.Series(collections.Counter(lurking[pi]['tasks'] \n",
-    "                              for pi in lurking)).plot(kind='bar');\n",
-    "plt.savefig('lurking-days.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'<table border=\"1\" class=\"dataframe\">\\n  <thead>\\n    <tr style=\"text-align: right;\">\\n      <th></th>\\n      <th>1</th>\\n      <th>2</th>\\n      <th>3</th>\\n      <th>4</th>\\n      <th>5</th>\\n      <th>6</th>\\n      <th>7</th>\\n      <th>8</th>\\n      <th>9</th>\\n      <th>10</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>0</th>\\n      <td>54</td>\\n      <td>13</td>\\n      <td>12</td>\\n      <td>3</td>\\n      <td>8</td>\\n      <td>6</td>\\n      <td>5</td>\\n      <td>4</td>\\n      <td>4</td>\\n      <td>34</td>\\n    </tr>\\n  </tbody>\\n</table>'"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(collections.Counter(participation[pi]['tasks'] \n",
-    "                    for pi in participation)).to_frame().T.to_html()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({1: 28,\n",
-       "         2: 28,\n",
-       "         3: 15,\n",
-       "         4: 8,\n",
-       "         5: 3,\n",
-       "         6: 9,\n",
-       "         7: 5,\n",
-       "         8: 5,\n",
-       "         9: 4,\n",
-       "         10: 5,\n",
-       "         11: 33})"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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CcEj3dXr++dq9X9bhwLBbfuJB+J06EjgPuBX4XnfZ0607qs/sMeP6t2nd1ppP+Zu2JDcv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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f34394a90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pd.Series(collections.Counter(participation_intro[pi]['tasks'] \n",
-    "                              for pi in participation_intro)).plot(kind='bar');\n",
-    "collections.Counter(participation_intro[pi]['tasks'] \n",
-    "                    for pi in participation_intro)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 207,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({0: 10,\n",
-       "         1: 49,\n",
-       "         2: 10,\n",
-       "         3: 10,\n",
-       "         4: 5,\n",
-       "         5: 7,\n",
-       "         6: 7,\n",
-       "         7: 5,\n",
-       "         8: 4,\n",
-       "         9: 2,\n",
-       "         10: 34})"
-      ]
-     },
-     "execution_count": 207,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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f1+A2Rj8wAlSSmao6lOQU+v0BeRHwj0n+Bvgu8D9JvsXoa/yiHnNh0X7VaC75auDqbn69\nL5cxOto8gdEP6KuSfB14PqPpzz69D7gpyQ3AbwF7AJKcDtzXc/ZsLboivKoOAXuSvH5SIRtmThse\n+XOvuxi9MBdGc9s3VdVPe869DHh/VX1uifs+XFWv7Sl3J6Oj3kNL3HdOVf1XT7knVtWPl1j/BOBJ\nVXWgj9wl8l4OnFNVb1uPvGXGcDKwo6q+0XPOqcAvM/ohdXdVzfeZ12U+varu7DtnmewzAKrqniSP\nZ/R6ycGqunEdsp8JPIPRi8y39523IHcfcC2w98jnN8kO4HXA71bViyeSs5FKW5JalWQ7o7ODzgee\n2K2eZ/TbzaVVtfg3+fFyLG1J6tckz0KztCWpZ0kOVtVTJ/FcG+mFSElqVpJblruLCZ6FZmlL0mSs\ny1lolrYkTca/MbpYbv/iO5IMJxXinLYkNWQjXREpSVqBpS1JDbG0JakhlrYkNcTSlqSG/D9aGdop\n4LJ/lwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2d767550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pd.Series(collections.Counter(participation[pi]['consecutive'] \n",
-    "                              for pi in participation)).plot(kind='bar');\n",
-    "collections.Counter(participation[pi]['consecutive'] for pi in participation)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({0: 67, 1: 9, 2: 9, 3: 14, 4: 6, 5: 6, 6: 2, 7: 10, 9: 1, 10: 19})"
-      ]
-     },
-     "execution_count": 208,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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gHd392yb4fHcCRwDHAI8CP9W1Hw3cMaEabgX+EegBZ3b/fqNbPnOCr8Vt85ZvATZ1y08B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\nnBIvfg14XAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2d72c470>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pd.Series(collections.Counter(completion[pi]['consecutive'] \n",
-    "                              for pi in completion)).plot(kind='bar');\n",
-    "collections.Counter(completion[pi]['consecutive'] for pi in completion)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 237,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({0: 53, 1: 47, 2: 5, 3: 3, 5: 1, 10: 1})"
-      ]
-     },
-     "execution_count": 237,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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pAHDxDEeZlkeT/C6wq6pWAJJsBT7Ea79R3sw1e01bkqalf+lnO727097UX7wC3E7vmvbB\n//ud3WxGW5JGl+Tyqrp2bvs32pI0unnfudXsNW1JmpYj+c4toy1Jhzpi79wy2pJ0qCP2zi2vaUtS\nQ5r93iOSdDQy2pLUEKMtSQ0x2pLUEKMtSQ35f+TwsRNOd/vCAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2d516240>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pd.Series(collections.Counter(lurking[pi]['consecutive'] \n",
-    "                              for pi in lurking)).plot(kind='bar');\n",
-    "collections.Counter(lurking[pi]['consecutive'] for pi in lurking)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({0: 36,\n",
-       "         1: 7,\n",
-       "         2: 23,\n",
-       "         3: 8,\n",
-       "         4: 7,\n",
-       "         5: 5,\n",
-       "         6: 7,\n",
-       "         7: 6,\n",
-       "         8: 5,\n",
-       "         9: 4,\n",
-       "         10: 2,\n",
-       "         11: 33})"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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9CzgDuBqgqn5i4HqfqKrT+unX0D237wWeD7x/6NdKkn8Fnl5VX0vyFuBe4DK6UVNPr6qX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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f34490208>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pd.Series(collections.Counter(participation_intro[pi]['consecutive'] \n",
-    "                              for pi in participation_intro)).plot(kind='bar');\n",
-    "collections.Counter(participation_intro[pi]['consecutive'] for pi in participation_intro)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 241,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({0: 16,\n",
-       "         1: 2,\n",
-       "         2: 7,\n",
-       "         3: 6,\n",
-       "         4: 11,\n",
-       "         5: 5,\n",
-       "         6: 6,\n",
-       "         7: 1,\n",
-       "         8: 9,\n",
-       "         10: 1,\n",
-       "         11: 16})"
-      ]
-     },
-     "execution_count": 241,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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XtUf09eFpPdfYH/nrwAPAo4B7lqx/5PCxziTZt9pDwM4OSx9bVT8EqKqvJukB1yU5g5Wv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\n0JakhvwvsgxSDMXYr7AAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2d3a8358>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pd.Series(collections.Counter(completion_intro[pi]['consecutive'] \n",
-    "                              for pi in completion_intro)).plot(kind='bar');\n",
-    "collections.Counter(completion_intro[pi]['consecutive'] for pi in completion_intro)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 240,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({0: 80, 1: 10, 2: 14, 3: 3, 4: 2, 6: 1})"
-      ]
-     },
-     "execution_count": 240,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAD8CAYAAAC8TPVwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnw+tAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2d384470>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pd.Series(collections.Counter(lurking_intro[pi]['consecutive'] \n",
-    "                              for pi in lurking_intro)).plot(kind='bar');\n",
-    "collections.Counter(lurking_intro[pi]['consecutive'] for pi in lurking_intro)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Midnight starters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "pipeline = [\n",
-    "    {'$match': {'completed': {'$exists': True}}},\n",
-    "    {'$project': {'icma_number': True, 'Personal Identifier': True,\n",
-    "                  'hour': {'$hour': '$started'}}},\n",
-    "    {'$match': {'hour': 0}}] \n",
-    "midnight_completed_starts = pd.DataFrame(\n",
-    "   [{'icma_number': r['icma_number'], 'pi': r['Personal Identifier']} \n",
-    "    for r in icmas.aggregate(pipeline)\n",
-    "])\n",
-    "midnight_completed_starts_x = pd.crosstab(midnight_completed_starts.pi, midnight_completed_starts.icma_number)\n",
-    "midnight_completed_starts_x;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(26, 11)"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "midnight_completed_starts_x.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "icma_number\n",
-       "0      2\n",
-       "1      5\n",
-       "2     10\n",
-       "3      6\n",
-       "4     10\n",
-       "5      9\n",
-       "6      7\n",
-       "7      9\n",
-       "8      4\n",
-       "9      7\n",
-       "10     9\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 99,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# How many attempts were started before 1.00am for each task?\n",
-    "midnight_completed_starts_x.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'<table border=\"1\" class=\"dataframe\">\\n  <thead>\\n    <tr style=\"text-align: right;\">\\n      <th>icma_number</th>\\n      <th>0</th>\\n      <th>1</th>\\n      <th>2</th>\\n      <th>3</th>\\n      <th>4</th>\\n      <th>5</th>\\n      <th>6</th>\\n      <th>7</th>\\n      <th>8</th>\\n      <th>9</th>\\n      <th>10</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>0</th>\\n      <td>2</td>\\n      <td>5</td>\\n      <td>10</td>\\n      <td>6</td>\\n      <td>10</td>\\n      <td>9</td>\\n      <td>7</td>\\n      <td>9</td>\\n      <td>4</td>\\n      <td>7</td>\\n      <td>9</td>\\n    </tr>\\n  </tbody>\\n</table>'"
-      ]
-     },
-     "execution_count": 100,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# How many attempts were started before 1.00am for each task?\n",
-    "midnight_completed_starts_x.sum().to_frame().T.to_html()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f2de06278>"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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1Q9dZofZhAFV1e5KH0b1fcktVXbYBtZ8K/BLdm8zXDl1vUd0LgYuAf174/02yFXg18IKq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\nLAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2d856128>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "midnight_completed_starts_x.sum().plot(kind='bar')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "midnight_completed_starts_x.sum(axis=1);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f2d88b198>"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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RRbNJ7lNm58r9RO3XG+buA58kvVfSX/Y4j+dkcXHRd+y40iWv9TQ9fYNL2uI8h8Z4/YN/\nDA4dOjTQ5bb+uw572uq2SnN/G/R2GqemNaW8T9U9L9bdl9XrNPBO3MxeIulfJD3Q+UvKJd3q7p/Y\ncD4f9DrGgZ14TddoplL+rqhHyvtUwvtyz534wOsUd/9XSecMenkAwPCK/LZ7nicel+fzZ9upAzbJ\n8XaiqR/t1AEDK3KIA0BT8LNTasJOvLZrZifeUOzEu+OROABMsCKHODvxuDx3mO3UAZvkeDvR1I92\n6oCBFTnEAaAp2InXhJ14bdfMTryh2Il3xyNxAJhgRQ5xduJxee4w26kDNsnxdqKpH+3UAQMrcogD\nQFOwE68JO/HarpmdeEOxE++OR+IAMMGKHOLsxOPy3GG2UwdskuPtRFM/2qkDBlbkEAeAphjqN/tM\nqqmpXakTumiN8X2fW+0Tm6KVOmCTVL/JaNu2nTp9+nu1XueuXZfosceODnTZtd/Yk59W6oCBFTnE\ny3NC9X9hpkn/afTWGeD1f/Hr9On6v+i2ulrWxzZ3Ra5T2In3o506oIt26oAJ0U4dsAk78dErcogD\nQFMUOcTL24kPo5U6oItW6oAJ0UodsAk78dErcogDQFMUOcTZifejnTqgi3bqgAnRTh2wCTvx0Sty\niANAUxQ5xNmJ96OVOqCLVuqACdFKHbAJO/HRK3KIA0BTFDnE2Yn3o506oIt26oAJ0U4dsAk78dEr\ncogDQFMUOcTZifejlTqgi1bqgAnRSh2wCTvx0StyiANAUww1xM3sajN70Mz+w8xuHlXUuLET70c7\ndUAX7dQBE6KdOmATduKjN/AQN7Ntkv5a0iskPU/Sa83s2aMKG6dTpx5PndDFcuqAs8ixK8emHOV3\nOy0v59fUkWtXb8M8En+BpK+6+9fd/UlJ75f0mtFkjduTqQO6eCJ1wFnk2JVjU47yu52eeCK/po5c\nu3obZog/U9LD644fqd4GAKjJML8UottPhs/+14xv375dp059Teef/+par/cHPzjc4xxH68gYwNHU\nAV0cTR0wIY6mDtjk6NGjqRPO4mjqgIGZ+2Bz18xeJOnP3P3q6vgWSe7u79hwvuwHOwDkyN17/hql\nYYb4OZK+IukqSf8l6V5Jr3X3IwO9QwBA3wZep7j7KTP7Q0l3qbNbv4MBDgD1GviROAAgvbF9x2aO\n3whkZneY2aqZ9foqY23MbLeZ3WNmK2b2gJndmEHTuWb2OTO7v2ram7ppjZltM7P7zOyjqVvWmNlR\nM/tidXvdm7pHkszsAjP7oJkdMbMvm9kLE/dcVt0+91Uvv53Jff3NZvYlMztsZotm9pQMmm6q/t3F\n5oG7j/ykzn8O/ynpEknb1Xkm/bPHcV19dr1U0pykw6lb1jVdJGmuev08db7OkMNttbN6eY6kz0p6\nQeqmqufNkv5B0kdTt6xrekjShak7NjQtSLquen1K0vmpm9a1bZP0qKRnJe54RvWxe0p1/AFJr0/c\n9DxJhyWdW/3b+5SkH9/qMuN6JJ7lNwK5+6clZfXtmu7+mLsvV68fl3REGTzf3t2/V716rjpDIPne\nzcx2S3qlpHenbtnAlNHPITKzp0l6mbsfkCR3P+nu30mctd7LJX3N3R/uec7xO0fSU81sStJOdf5z\nSek5kj7r7ifc/ZSkf5b0a1tdYFx3PL4RaABmNqvOZwqfS1tyZm1xv6THJH3K3T+fuknSOyW9RRn8\nh7KBS/qkmX3ezH4vdYykSyV9y8wOVOuL281sOnXUOr8p6R9TR7j7o5Juk3RM0jckPeHud6et0pck\n/YKZXWhmO9V50PKsrS4wriE+kd8IlJKZnSfpoKSbqkfkSbn7aXf/aUm7Jb3QzJ6bssfMXiVptfqs\nxdT9PpbKi939Z9X5B/dGM3tp4p4pSVdI+ht3v0LS9yTdkjapw8y2S7pG0gczaJlRZ0NwiTqrlfPM\n7HUpm9z9QUnvkHS3pI+rs4o+udVlxjXEH5F08brj3Ur/aUq2qk/lDkp6n7t/JHXPetWn4W1JVydO\neYmka8zsIXUexf2imb03cZOkzkqsevlNSR9WZ52Y0iOSHnb3L1THB9UZ6jn4FUn/Xt1Wqb1c0kPu\n/j/V6uJOSS9O3CR3P+DuP+PuLXXWv1/d6vzjGuKfl/QTZnZJ9dXeayXl8myC3B7FSdLfS1px93el\nDpEkM/sRM7ugen1anTv7gymb3P1Wd7/Y3S9V5/50j7u/PmWTJJnZzuqzKJnZUyX9sjqfEifj7quS\nHjazy6o3XSVpJWHSeq9VBquUyjFJLzKzHWZm6txOyb/XxcyeXr28WJ19+Ja31zA/O+WsPNNvBDKz\nJXV+hccPm9kxSXvXvviTsOklkn5L0gPVDtol3erun0iY9WOS3lP9uOFtkj7g7h9P2JOzXZI+XP14\niSlJi+5+V+ImSbpR0mK1vnhI0nWJe9Y/IPj91C2S5O73mtlBSfer86NN75d0e9oqSdKHzOyH1Gn6\nA3f/9lZn5pt9AGCCZfO0KABA/xjiADDBGOIAMMEY4gAwwRjiADDBGOIAMMEY4gAwwRjiADDB/hc5\n4ozWfedTmgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2d7cc630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# How many people made this many midnight starts? \n",
-    "# e.g. 12 people did one mightnight start, 5 people did two, 2 people did nine\n",
-    "midnight_completed_starts_x.sum(axis=1).hist()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "# Get participant data\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "308"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pis = icmas.distinct('Personal Identifier')\n",
-    "len(pis)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "participant_df = pd.DataFrame([icmas.find_one({'Personal Identifier': pi}, \n",
-    "                                              ['First name', 'Surname', 'Email address', 'Personal Identifier']) \n",
-    "              for pi in icmas.distinct('Personal Identifier')])\n",
-    "participant_df = participant_df[['Email address', 'First name', 'Surname', 'Personal Identifier']]\n",
-    "participant_df;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# participant_df.to_csv('survey-email.csv', index=False, header=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "pi1s = set([r['Personal Identifier'] for r in icmas.find({'icma_number': {'$ne': 0}})])\n",
-    "participant_1_df = pd.DataFrame([icmas.find_one({'Personal Identifier': pi}, \n",
-    "                                              ['First name', 'Surname', 'Email address', 'Personal Identifier']) \n",
-    "              for pi in pi1s])\n",
-    "participant_1_df = participant_1_df[['Email address', 'First name', 'Surname', 'Personal Identifier']]\n",
-    "participant_1_df;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# participant_1_df.to_csv('survey-email_1.csv', index=False, header=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "# Overall performance prizes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "pipeline = [\n",
-    "    {'$match': {'completed': {'$exists': True}, 'icma_number': {'$gt': 0}}},\n",
-    "    {'$match': {'completed': {'$lt': datetime(2017, 7, 31)}}},\n",
-    "    {\"$group\": {\"_id\": {'icma_number': \"$icma_number\",\n",
-    "                                'grade': '$grade'},\n",
-    "                        \"participants\": {\"$addToSet\": '$Personal Identifier'}}}]\n",
-    "july_grades = {(r['_id']['icma_number'], r['_id']['grade']): r['participants'] \n",
-    "                      for r in icmas.aggregate(pipeline)}\n",
-    "july_grades;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "icma_numbers = sorted(set(k[0] for k in july_grades))\n",
-    "best_grades_july = {}\n",
-    "for icma in icma_numbers:\n",
-    "    prps = set()\n",
-    "    for grade in [10, 5, 0, '-']:\n",
-    "        pis = set(july_grades.get((icma, grade), []))\n",
-    "        best_grades_july[icma, grade] = pis - prps\n",
-    "        prps |= pis\n",
-    "best_grades_july;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "pbgs = [{'pi': pi, 'icma': icma, 'grade': grade} \n",
-    "        for ((icma, grade), pis) in best_grades_july.items() \n",
-    "        for pi in pis]\n",
-    "ps = set(p['pi'] for p in pbgs)\n",
-    "july_best_grades = {pi: {p['icma']: p['grade'] \n",
-    "                                for p in pbgs\n",
-    "                                if p['pi'] == pi}\n",
-    "                           for pi in ps}\n",
-    "july_best_grades;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_values([15, 30, 20, 30, 90, 10, 50, 30, 100, 80, 50, 10, 90, 15, 90, 30, 40, 40, 65, 20, 70, 70, 100, 100, 40, 0, 10, 70, 100, 50, 20, 10, 10, 30, 100, 50, 70, 50, 90, 30, 20, 45, 40, 20, 100, 100, 60, 5, 30, 30, 100, 100, 90, 25, 40, 100, 100, 10, 10, 100, 100, 60, 100, 20, 50, 30, 60, 30, 15, 0, 95, 50, 30, 80, 5, 100, 40, 100, 80])"
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "july_scores = {k: sum(scores.values())\n",
-    "              for k, scores in july_best_grades.items()}\n",
-    "july_scores.values()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f2d8c8668>"
-      ]
-     },
-     "execution_count": 113,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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hKXU0oYamsBdFWx65+yamwffiQKuphqbU0YQamsJeFG0Z7l5+QJowf7e7sBf1cbhLUgsZ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\nIYe7JLWQw12SWuj/AaQ3Cbfni8LMAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5f2d8be5f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pd.Series(list(july_scores.values())).hist(bins=20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(1 for pi in july_scores if july_scores[pi] >= 100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": 115,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(1 for pi in july_scores if july_scores[pi] >= 80 if july_scores[pi] < 100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "[(pi, \n",
-    "  icmas.find_one({'Personal Identifier': pi})['First name'],\n",
-    "  icmas.find_one({'Personal Identifier': pi})['Surname']) \n",
-    " for pi in july_scores if july_scores[pi] >= 100];"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "winners = random.sample([pi for pi in july_scores if july_scores[pi] >= 100], 2)\n",
-    "\n",
-    "# for w in winners:\n",
-    "#     winner = icmas.find_one({'Personal Identifier': w})\n",
-    "#     print('{} {} PI {} email {}'.format(\n",
-    "#         winner['First name'],\n",
-    "#         winner['Surname'],\n",
-    "#         winner['Personal Identifier'],\n",
-    "#         winner['Email address']\n",
-    "#         ));"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "[(pi, \n",
-    "  icmas.find_one({'Personal Identifier': pi})['First name'],\n",
-    "  icmas.find_one({'Personal Identifier': pi})['Surname'])\n",
-    " for pi in july_scores if july_scores[pi] >= 80 if july_scores[pi] < 100];"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "winners = random.sample([pi for pi in july_scores if july_scores[pi] >= 80 if july_scores[pi] < 100], 1)\n",
-    "\n",
-    "# for w in winners:\n",
-    "#     winner = icmas.find_one({'Personal Identifier': w})\n",
-    "#     print('{} {} PI {} email {}'.format(\n",
-    "#         winner['First name'],\n",
-    "#         winner['Surname'],\n",
-    "#         winner['Personal Identifier'],\n",
-    "#         winner['Email address']\n",
-    "#         ));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "# Starting but not finishing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "participant_best_grades;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "score_counts = {pi: \n",
-    "                {'complete': sum(1 for g in participant_best_grades[pi].values() if g != '-'),\n",
-    "                 'incomplete': sum(1 for g in participant_best_grades[pi].values() if g == '-')}\n",
-    "               for pi in participant_best_grades}\n",
-    "score_counts;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complete</th>\n",
-       "      <th>count</th>\n",
-       "      <th>incomplete</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>7</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>8</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>10</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>6</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>11</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>10</td>\n",
-       "      <td>8</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>1</td>\n",
-       "      <td>25</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>0</td>\n",
-       "      <td>168</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>9</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>0</td>\n",
-       "      <td>16</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    complete  count  incomplete\n",
-       "0          7      1           3\n",
-       "1          3      1           0\n",
-       "2          8      2           0\n",
-       "3          5      1           4\n",
-       "4          2      6           1\n",
-       "5          6      1           2\n",
-       "6          5      3           1\n",
-       "7          0      7           3\n",
-       "8          2      1           5\n",
-       "9          4      3           0\n",
-       "10         1      1           2\n",
-       "11         7      1           4\n",
-       "12         5      1           5\n",
-       "13         2      1           9\n",
-       "14         8      1           1\n",
-       "15         4      1           4\n",
-       "16         5      1           6\n",
-       "17         3      1           6\n",
-       "18         0      3           4\n",
-       "19         1      1          10\n",
-       "20        10      1           0\n",
-       "21         4      1           1\n",
-       "22         1      9           1\n",
-       "23         6      2           4\n",
-       "24         3      2           2\n",
-       "25         4      1           5\n",
-       "26         2      1           2\n",
-       "27         6      3           0\n",
-       "28        11     16           0\n",
-       "29        10      8           1\n",
-       "30         4      2           2\n",
-       "31         1     25           0\n",
-       "32         3      1           3\n",
-       "33         0    168           1\n",
-       "34         8      3           3\n",
-       "35         9      1           2\n",
-       "36         6      1           1\n",
-       "37         3      1           1\n",
-       "38         3      1           8\n",
-       "39         2      3           0\n",
-       "40         4      1           3\n",
-       "41         1      1           7\n",
-       "42         5      2           2\n",
-       "43         0     16           2"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cic = pd.DataFrame([{'complete': complete, 'incomplete': incomplete, 'count': count} \n",
-    "              for (complete, incomplete), count \n",
-    "              in collections.Counter(\n",
-    "    (score_counts[pi]['complete'], score_counts[pi]['incomplete']) \n",
-    "    for pi in score_counts).items()])\n",
-    "cic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>incomplete</th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "      <th>10</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>complete</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>168</td>\n",
-       "      <td>16</td>\n",
-       "      <td>7</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>25</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>16</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "incomplete  0    1   2   3   4   5   6   7   8   9   10\n",
-       "complete                                               \n",
-       "0            0  168  16   7   3   0   0   0   0   0   0\n",
-       "1           25    9   1   0   0   0   0   1   0   0   1\n",
-       "2            3    6   1   0   0   1   0   0   0   1   0\n",
-       "3            1    1   2   1   0   0   1   0   1   0   0\n",
-       "4            3    1   2   1   1   1   0   0   0   0   0\n",
-       "5            0    3   2   0   1   1   1   0   0   0   0\n",
-       "6            3    1   1   0   2   0   0   0   0   0   0\n",
-       "7            0    0   0   1   1   0   0   0   0   0   0\n",
-       "8            2    1   0   3   0   0   0   0   0   0   0\n",
-       "9            0    0   1   0   0   0   0   0   0   0   0\n",
-       "10           1    8   0   0   0   0   0   0   0   0   0\n",
-       "11          16    0   0   0   0   0   0   0   0   0   0"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cic.pivot(index='complete', columns='incomplete', values='count').fillna(0).astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'_id': {'count': 5, 'grade': 5.0}},\n",
-       " {'_id': {'count': 5, 'grade': 0.0}},\n",
-       " {'_id': {'count': 2, 'grade': 0.0}},\n",
-       " {'_id': {'count': 3, 'grade': 5.0}},\n",
-       " {'_id': {'count': 3, 'grade': 0.0}},\n",
-       " {'_id': {'count': 4, 'grade': 5.0}},\n",
-       " {'_id': {'count': 2, 'grade': 10.0}},\n",
-       " {'_id': {'count': 2, 'grade': 5.0}},\n",
-       " {'_id': {'count': 6, 'grade': 5.0}},\n",
-       " {'_id': {'count': 1, 'grade': 5.0}},\n",
-       " {'_id': {'count': 4, 'grade': 0.0}},\n",
-       " {'_id': {'count': 1, 'grade': 0.0}},\n",
-       " {'_id': {'count': 3, 'grade': 10.0}},\n",
-       " {'_id': {'count': 1, 'grade': 10.0}},\n",
-       " {'_id': {'count': 1, 'grade': '-'}}]"
-      ]
-     },
-     "execution_count": 125,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pipeline = [\n",
-    "    {\"$group\": {\"_id\": {'icma_number': '$icma_number',\n",
-    "                        'pi': '$Personal Identifier',\n",
-    "                        'grade': '$grade'},\n",
-    "                        \"count\": {'$sum': 1}}},\n",
-    "    {'$group': {'_id': {'grade': '$_id.grade', 'count': '$count'}}}]\n",
-    "list(icmas.aggregate(pipeline))\n",
-    "# july_grades = {(r['_id']['icma_number'], r['_id']['grade']): r['participants'] \n",
-    "#                       for r in icmas.aggregate(pipeline)}\n",
-    "# july_grades;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'_id': 0.0, 'counts': [4, 5, 2, 3, 1]},\n",
-       " {'_id': 10.0, 'counts': [3, 2, 1]},\n",
-       " {'_id': 5.0, 'counts': [5, 6, 3, 4, 2, 1]},\n",
-       " {'_id': '-', 'counts': [1]}]"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pipeline = [\n",
-    "    {\"$group\": {\"_id\": {'icma_number': '$icma_number',\n",
-    "                        'pi': '$Personal Identifier',\n",
-    "                        'grade': '$grade'},\n",
-    "                        \"count\": {'$sum': 1}}},\n",
-    "    {'$group': {'_id': '$_id.grade', 'counts': {'$addToSet': '$count'}}}]\n",
-    "list(icmas.aggregate(pipeline))\n",
-    "# july_grades = {(r['_id']['icma_number'], r['_id']['grade']): r['participants'] \n",
-    "#                       for r in icmas.aggregate(pipeline)}\n",
-    "# july_grades;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "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": 2
-}
-- 
2.34.1