Traffik_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from RomeaJam.traffik import db, Jam, Alert, Segment, SegmentStatus, RoadStatus, RoadAverage\n",
    "from datetime import datetime, timedelta\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Data validation ###\n",
    "Let's take some RoadStatus and compare them with traffic segnalations from a popular Facebook group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_status(dt, category=None):\n",
    "    \"\"\"returns road status given specific datetime\"\"\"\n",
    "    if category:\n",
    "        return db.session.query(RoadStatus).filter(RoadStatus.timestamp > dt.strftime('%s')).\\\n",
    "                             filter(RoadStatus.timestamp < (dt+timedelta(0,60)).strftime('%s')).\\\n",
    "                             filter(RoadStatus.category == category).all()\n",
    "    else:\n",
    "        return db.session.query(RoadStatus).filter(RoadStatus.timestamp > dt.strftime('%s')).\\\n",
    "                             filter(RoadStatus.timestamp < (dt+timedelta(0,60)).strftime('%s')).all()\n",
    "\n",
    "def get_segments(dt, category=None):\n",
    "    \"\"\"prints segment statuses given a specific datetime\"\"\"\n",
    "    for status in db.session.query(SegmentStatus).filter(SegmentStatus.timestamp > dt.strftime('%s')).\\\n",
    "                             filter(SegmentStatus.timestamp < (dt+timedelta(0,60)).strftime('%s')):\n",
    "        if status.segment.category == category or category == None:\n",
    "            print status.segment.category, status.packing_index\n",
    "            \n",
    "def printall(dt, category=None):\n",
    "    print get_status(dt, category)\n",
    "    get_segments(dt, category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<RoadStatus(timestamp=1471365635, category=u'Arrive', id=41965, packing_index=0.175325, accident_alerts=1, traffic_alerts=0)>, <RoadStatus(timestamp=1471365635, category=u'Leave', id=41966, packing_index=0.351075, accident_alerts=1, traffic_alerts=2)>]\n",
      "Leave 0.2432\n",
      "Leave 0.7051\n",
      "Leave 0.456\n",
      "Leave 0.0\n",
      "Arrive 0.0\n",
      "Arrive 0.0\n",
      "Arrive 0.7013\n",
      "Arrive 0.0\n"
     ]
    }
   ],
   "source": [
    "printall(datetime(2016,8,16,18,40)) #accident"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<RoadStatus(timestamp=1471337002, category=u'Arrive', id=41025, packing_index=0.339075, accident_alerts=0, traffic_alerts=1)>]\n",
      "Arrive 0.0\n",
      "Arrive 0.6512\n",
      "Arrive 0.7051\n",
      "Arrive 0.0\n"
     ]
    }
   ],
   "source": [
    "printall(datetime(2016,8,16,10,43), category='Arrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<RoadStatus(timestamp=1471282761, category=u'Leave', id=39244, packing_index=0.46325, accident_alerts=0, traffic_alerts=5)>]\n",
      "Leave 0.0\n",
      "Leave 0.7457\n",
      "Leave 0.6999\n",
      "Leave 0.4074\n"
     ]
    }
   ],
   "source": [
    "printall(datetime(2016,8,15,19,39), category='Leave')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<RoadStatus(timestamp=1471022401, category=u'Leave', id=30694, packing_index=0.466725, accident_alerts=0, traffic_alerts=1)>]\n",
      "Leave 0.6652\n",
      "Leave 0.7457\n",
      "Leave 0.456\n",
      "Leave 0.0\n"
     ]
    }
   ],
   "source": [
    "printall(datetime(2016,8,12,19,20), category='Leave')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<RoadStatus(timestamp=1471022401, category=u'Leave', id=30694, packing_index=0.466725, accident_alerts=0, traffic_alerts=1)>]\n",
      "Leave 0.6652\n",
      "Leave 0.7457\n",
      "Leave 0.456\n",
      "Leave 0.0\n"
     ]
    }
   ],
   "source": [
    "printall(datetime(2016,8,12,19,20), 'Leave')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<RoadStatus(timestamp=1471115737, category=u'Leave', id=33756, packing_index=0.0, accident_alerts=0, traffic_alerts=0)>]\n",
      "Leave 0.0\n",
      "Leave 0.0\n",
      "Leave 0.0\n",
      "Leave 0.0\n"
     ]
    }
   ],
   "source": [
    "printall(datetime(2016,8,13,21,15), 'Leave') #no traffic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<RoadStatus(timestamp=1471074448, category=u'Arrive', id=32399, packing_index=0.2549, accident_alerts=0, traffic_alerts=0)>]\n",
      "Arrive 0.0\n",
      "Arrive 0.5288\n",
      "Arrive 0.4908\n",
      "Arrive 0.0\n"
     ]
    }
   ],
   "source": [
    "printall(datetime(2016,8,13,9,47), 'Arrive') #slow down"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<RoadStatus(timestamp=1470911921, category=u'Arrive', id=27067, packing_index=0.0, accident_alerts=0, traffic_alerts=0)>]\n",
      "Arrive 0.0\n",
      "Arrive 0.0\n",
      "Arrive 0.0\n",
      "Arrive 0.0\n"
     ]
    }
   ],
   "source": [
    "printall(datetime(2016,8,11,12,38), 'Arrive') #no traffic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<RoadStatus(timestamp=1470907239, category=u'Arrive', id=26913, packing_index=0.3841, accident_alerts=0, traffic_alerts=5)>]\n",
      "Arrive 0.1548\n",
      "Arrive 0.2545\n",
      "Arrive 0.7051\n",
      "Arrive 0.422\n"
     ]
    }
   ],
   "source": [
    "printall(datetime(2016,8,11,11,20), 'Arrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<RoadStatus(timestamp=1470724538, category=u'Arrive', id=21021, packing_index=0.175325, accident_alerts=0, traffic_alerts=0)>]\n",
      "Arrive 0.0\n",
      "Arrive 0.0\n",
      "Arrive 0.7013\n",
      "Arrive 0.0\n"
     ]
    }
   ],
   "source": [
    "printall(datetime(2016,8,9,8,35), 'Arrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<RoadStatus(timestamp=1470592849, category=u'Arrive', id=16697, packing_index=0.338125, accident_alerts=3, traffic_alerts=4)>, <RoadStatus(timestamp=1470592849, category=u'Leave', id=16698, packing_index=0.017875, accident_alerts=0, traffic_alerts=9)>]\n",
      "Leave 0.0715\n",
      "Leave 0.0\n",
      "Leave 0.0\n",
      "Leave 0.0\n",
      "Arrive 0.0\n",
      "Arrive 0.1308\n",
      "Arrive 1.2217\n",
      "Arrive 0.0\n"
     ]
    }
   ],
   "source": [
    "printall(datetime(2016,8,7,20,0)) #serious accident"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Exploratory Data Analysis ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SegmentStatus dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>segment_id</th>\n",
       "      <th>road_status_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>jams_list</th>\n",
       "      <th>delayInSec</th>\n",
       "      <th>accident_alerts</th>\n",
       "      <th>traffic_alerts</th>\n",
       "      <th>packing_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23329</td>\n",
       "      <td>1</td>\n",
       "      <td>5834</td>\n",
       "      <td>2016-08-03 22:00:25</td>\n",
       "      <td></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>23330</td>\n",
       "      <td>2</td>\n",
       "      <td>5834</td>\n",
       "      <td>2016-08-03 22:00:25</td>\n",
       "      <td></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>23331</td>\n",
       "      <td>3</td>\n",
       "      <td>5834</td>\n",
       "      <td>2016-08-03 22:00:25</td>\n",
       "      <td></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>23332</td>\n",
       "      <td>4</td>\n",
       "      <td>5834</td>\n",
       "      <td>2016-08-03 22:00:25</td>\n",
       "      <td></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>23333</td>\n",
       "      <td>5</td>\n",
       "      <td>5833</td>\n",
       "      <td>2016-08-03 22:00:25</td>\n",
       "      <td></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": [
       "      id  segment_id  road_status_id           timestamp jams_list  \\\n",
       "0  23329           1            5834 2016-08-03 22:00:25             \n",
       "1  23330           2            5834 2016-08-03 22:00:25             \n",
       "2  23331           3            5834 2016-08-03 22:00:25             \n",
       "3  23332           4            5834 2016-08-03 22:00:25             \n",
       "4  23333           5            5833 2016-08-03 22:00:25             \n",
       "\n",
       "   delayInSec  accident_alerts  traffic_alerts  packing_index  \n",
       "0           0                0               0              0  \n",
       "1           0                0               0              0  \n",
       "2           0                0               0              0  \n",
       "3           0                0               0              0  \n",
       "4           0                0               0              0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qs = db.session.query(SegmentStatus).join(Segment).\\\n",
    "                                 filter(SegmentStatus.timestamp > datetime(2016,8,4,0,0).strftime('%s')).\\\n",
    "                                 filter(SegmentStatus.timestamp < datetime(2016,8,16,0,0).strftime('%s'))      \n",
    "ds = pd.read_sql(qs.statement, qs.session.bind)\n",
    "ds.set_index('id', inplace=True)\n",
    "ds['timestamp'] = pd.to_datetime(ds['timestamp'],unit='s')\n",
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>packing_index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>packing_index</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               packing_index\n",
       "packing_index               \n",
       "1                       3985\n",
       "2                       5335\n",
       "3                       1067\n",
       "4                         81"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's check how many segments can have a packing index <> 0 in the same timestamp/category\n",
    "ds = ds[(ds['packing_index'] > 0)]\n",
    "ds1 = pd.DataFrame(ds['packing_index'].groupby([ds.road_status_id]).count())\n",
    "ds1 = pd.DataFrame(ds1['packing_index'].groupby([ds1.packing_index]).count())\n",
    "ds1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's likely that there is a sort of \"snake effect\" in the data, i.e. the traffic flows and there aren't jams at the same timestamp in all the segments of the road. Because of this, I will introduce a \"snake_parameter\" in the RoadStatus class, so the packing_index will be the result of the average of the first *snake_parameter* segments ordered by packing index desc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RoadStatus dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>category</th>\n",
       "      <th>packing_index</th>\n",
       "      <th>accident_alerts</th>\n",
       "      <th>traffic_alerts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</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>15425</th>\n",
       "      <td>2016-08-07 07:15:37</td>\n",
       "      <td>Arrive</td>\n",
       "      <td>0.644475</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15423</th>\n",
       "      <td>2016-08-07 07:14:37</td>\n",
       "      <td>Arrive</td>\n",
       "      <td>0.644475</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15407</th>\n",
       "      <td>2016-08-07 07:06:31</td>\n",
       "      <td>Arrive</td>\n",
       "      <td>0.627150</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35193</th>\n",
       "      <td>2016-08-14 07:24:47</td>\n",
       "      <td>Arrive</td>\n",
       "      <td>0.625700</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35191</th>\n",
       "      <td>2016-08-14 07:23:46</td>\n",
       "      <td>Arrive</td>\n",
       "      <td>0.625700</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                timestamp category  packing_index  accident_alerts  \\\n",
       "id                                                                   \n",
       "15425 2016-08-07 07:15:37   Arrive       0.644475                0   \n",
       "15423 2016-08-07 07:14:37   Arrive       0.644475                0   \n",
       "15407 2016-08-07 07:06:31   Arrive       0.627150                0   \n",
       "35193 2016-08-14 07:24:47   Arrive       0.625700                0   \n",
       "35191 2016-08-14 07:23:46   Arrive       0.625700                0   \n",
       "\n",
       "       traffic_alerts  \n",
       "id                     \n",
       "15425               4  \n",
       "15423               4  \n",
       "15407               4  \n",
       "35193               5  \n",
       "35191               5  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qr = db.session.query(RoadStatus).filter(RoadStatus.timestamp > datetime(2016,8,4,0,0).strftime('%s')).\\\n",
    "                                 filter(RoadStatus.timestamp < datetime(2016,8,16,0,0).strftime('%s'))      \n",
    "dr = pd.read_sql(qr.statement, qr.session.bind)\n",
    "dr.set_index('id', inplace=True)\n",
    "dr['timestamp'] = pd.to_datetime(dr['timestamp'],unit='s')\n",
    "dr.sort([('packing_index')], ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp          datetime64[ns]\n",
       "category                   object\n",
       "packing_index             float64\n",
       "accident_alerts             int64\n",
       "traffic_alerts              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dr.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>packing_index</th>\n",
       "      <th>accident_alerts</th>\n",
       "      <th>traffic_alerts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>33924.000000</td>\n",
       "      <td>33924.000000</td>\n",
       "      <td>33924.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.065725</td>\n",
       "      <td>0.014975</td>\n",
       "      <td>0.419349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.113799</td>\n",
       "      <td>0.156833</td>\n",
       "      <td>1.304126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.123650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.644475</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       packing_index  accident_alerts  traffic_alerts\n",
       "count   33924.000000     33924.000000    33924.000000\n",
       "mean        0.065725         0.014975        0.419349\n",
       "std         0.113799         0.156833        1.304126\n",
       "min         0.000000         0.000000        0.000000\n",
       "25%         0.000000         0.000000        0.000000\n",
       "50%         0.000000         0.000000        0.000000\n",
       "75%         0.123650         0.000000        0.000000\n",
       "max         0.644475         3.000000       16.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dr.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FrdQEjdcrajXYxZakBgakJDUwICWpgQEpSQ0MSLXSDTeMuwKtBz7NR9K65tN8JGkJDEhJ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\nQEpSAwNSkhr8fw4cjwhoZZowAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fa2f750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,5))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.xlabel('packing index')\n",
    "plt.boxplot(dr[dr.packing_index>0].packing_index.reset_index()['packing_index'], showmeans=True, showfliers=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jxo25+OKLtz/2nXfe4YADDuDiiy+mRIkSHHHEEZxzzjnbR/d69OjBgQceSNOmTfnzzz8Z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cea9490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = pd.DatetimeIndex(dr.timestamp)\n",
    "dr_plt = dr.groupby([time.hour]).mean()\n",
    "dr_plt.reset_index(inplace=True)\n",
    "\n",
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = plt.gca()\n",
    "dr_plt.plot(x='index', y='packing_index', ax=ax)\n",
    "plt.title(\"Hourly average packing_index\")\n",
    "plt.ylabel('Packing index')\n",
    "plt.xlabel('Hour')\n",
    "ax.set_xticks(range(23))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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siYiIiIgkCLPoXLJ+XKNnz55UrFiRUqVKcc899zB9+vSDo3VHHHEExYoV4+6772bevHkA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\nkqQmZLInSZIkSU3IZE+SJEmSmtD/AUz3BrNuAgnZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f396110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = pd.DatetimeIndex(dr.timestamp)\n",
    "dr_plt = dr.groupby([time.weekday]).mean()\n",
    "dr_plt.reset_index(inplace=True)\n",
    "dayDict = {0:'Mon', 1:'Tue', 2:'Wed', 3:'Thu', 4:'Fri', 5:'Sat', 6:'Sun'}\n",
    "def f(x):\n",
    "    daylabel = dayDict[x]\n",
    "    return daylabel\n",
    "dr_plt['daylabel'] = dr_plt['index'].apply(f)\n",
    "\n",
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = plt.gca()\n",
    "dr_plt.plot(x='daylabel', y='packing_index', ax=ax)\n",
    "plt.title(\"average packing_index per weekday\")\n",
    "plt.ylabel('Packing index')\n",
    "plt.xlabel('Day')\n",
    "ax.set_xticks(range(6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9ekG7dkr+Ulm9elCuHHzzTdCRiCQ+5+Cpp+CBB2DChORJ/sAXhfn2W5g/H9q0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d7820d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = pd.DatetimeIndex(dr.timestamp)\n",
    "dr_plt = dr.groupby([time.day]).mean()\n",
    "dr_plt.reset_index(inplace=True)\n",
    "\n",
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = plt.gca()\n",
    "dr_plt.plot(x='index', y='packing_index', ax=ax)\n",
    "plt.title(\"average packing_index per day\")\n",
    "plt.ylabel('Packing index')\n",
    "plt.xlabel('Day (August 2016)')\n",
    "ax.set_xticks(range(18))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jam dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>startLongitude</th>\n",
       "      <th>endLongitude</th>\n",
       "      <th>startLatitude</th>\n",
       "      <th>endLatitude</th>\n",
       "      <th>street</th>\n",
       "      <th>severity</th>\n",
       "      <th>color</th>\n",
       "      <th>delayInSec</th>\n",
       "      <th>source</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>direction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</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>1799</th>\n",
       "      <td>12.2699</td>\n",
       "      <td>12.2296</td>\n",
       "      <td>45.1994</td>\n",
       "      <td>45.2132</td>\n",
       "      <td>SS309</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>75</td>\n",
       "      <td>w</td>\n",
       "      <td>2016-08-03 22:36:57</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1800</th>\n",
       "      <td>12.2699</td>\n",
       "      <td>12.2296</td>\n",
       "      <td>45.1994</td>\n",
       "      <td>45.2132</td>\n",
       "      <td>SS309</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>75</td>\n",
       "      <td>w</td>\n",
       "      <td>2016-08-03 22:37:58</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1801</th>\n",
       "      <td>12.2699</td>\n",
       "      <td>12.2296</td>\n",
       "      <td>45.1994</td>\n",
       "      <td>45.2132</td>\n",
       "      <td>SS309</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>75</td>\n",
       "      <td>w</td>\n",
       "      <td>2016-08-03 22:38:58</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802</th>\n",
       "      <td>12.2699</td>\n",
       "      <td>12.2296</td>\n",
       "      <td>45.1994</td>\n",
       "      <td>45.2132</td>\n",
       "      <td>SS309</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>74</td>\n",
       "      <td>w</td>\n",
       "      <td>2016-08-03 22:40:00</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1803</th>\n",
       "      <td>12.2699</td>\n",
       "      <td>12.2296</td>\n",
       "      <td>45.1994</td>\n",
       "      <td>45.2132</td>\n",
       "      <td>SS309</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>74</td>\n",
       "      <td>w</td>\n",
       "      <td>2016-08-03 22:41:00</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      startLongitude  endLongitude  startLatitude  endLatitude street  \\\n",
       "id                                                                      \n",
       "1799         12.2699       12.2296        45.1994      45.2132  SS309   \n",
       "1800         12.2699       12.2296        45.1994      45.2132  SS309   \n",
       "1801         12.2699       12.2296        45.1994      45.2132  SS309   \n",
       "1802         12.2699       12.2296        45.1994      45.2132  SS309   \n",
       "1803         12.2699       12.2296        45.1994      45.2132  SS309   \n",
       "\n",
       "      severity   color  delayInSec source           timestamp direction  \n",
       "id                                                                       \n",
       "1799         2  Medium          75      w 2016-08-03 22:36:57      West  \n",
       "1800         2  Medium          75      w 2016-08-03 22:37:58      West  \n",
       "1801         2  Medium          75      w 2016-08-03 22:38:58      West  \n",
       "1802         2  Medium          74      w 2016-08-03 22:40:00      West  \n",
       "1803         2  Medium          74      w 2016-08-03 22:41:00      West  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qj = db.session.query(Jam).filter(Jam.timestamp > datetime(2016,8,4,0,0).strftime('%s')).\\\n",
    "                           filter(Jam.timestamp < datetime(2016,8,16,0,0).strftime('%s'))      \n",
    "dj = pd.read_sql(qj.statement, qj.session.bind)\n",
    "dj.set_index('id', inplace=True)\n",
    "dj['timestamp'] = pd.to_datetime(dj['timestamp'],unit='s')\n",
    "dj.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average traffic duration: 4.97 min\n"
     ]
    }
   ],
   "source": [
    "time = pd.DatetimeIndex(dj.timestamp)\n",
    "dj['day']=time.day\n",
    "dj['hour']=time.hour\n",
    "\n",
    "dj_time = dj.groupby([dj.day, dj.hour, dj.startLongitude, dj.endLongitude, dj.startLatitude, dj.endLatitude, dj.street, dj.severity, dj.color, dj.source, dj.direction]).count()\n",
    "print 'Average traffic duration: %.2f min' % dj_time['timestamp'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hour</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hour  duration\n",
       "0     0  6.000000\n",
       "1     1  3.000000\n",
       "2     2  2.909091\n",
       "3     3  4.500000\n",
       "4     4  3.800000"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_dur = pd.DataFrame(dj_time['timestamp'])\n",
    "dj_dur.reset_index(inplace=True)\n",
    "dj_dur = dj_dur[['hour', 'timestamp']]\n",
    "dj_dur.columns=['hour', 'duration']\n",
    "dj_dur = dj_dur.groupby([dj_dur.hour]).mean()\n",
    "dj_dur.reset_index(inplace=True)\n",
    "dj_dur.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L1xR4q4u9pk1LblMeAnzwgRdyL7zgf7x69fLsGpUTkVy1fLkXdpMn\n+wv7yZP992zdul7gFS726tRJTaY33/S/KV9+mRkbwmeLK66A2bN9OYVIKuV0UXfooXDOOT41QSRK\n778PJ5zgU2SqVEnNOX/6ad1Cb+ZMv9hRePrmbrv5XlWPPurF3D//rBmV22KL1GQVEck0q1b5NPnV\nRd7qt+rV1xR5qwu9is6cKCoEb9xx3nlw0kmJO66U7u+//W/n0KHeEVMkVcpd1JnZbXEcf1EI4Zry\nhotHRYq6Cy6ABg3g4osTHEqkjI480teinXNOtDn++cev6ha3Vu/oo72Ya9tWi8BFRMojBPj227WL\nvMmTvQlL4SJv990rtp3NCy/4/npTpmgWRRTeesuL6S++8K0kRFKhIkXdd0BfwIDiHmjAFSGEJokI\nWkKOchd1d94JU6f66INIVD7/3Fv9z5njI2LpJgSfwqnpOyIiiRcCLFiwdpE3ZYqv2WvRYu3pm40b\nl94HYNUqn2ExZAh06pSa5yDr6t3bL4DefXfUSSRXlFTUldY+5JYQwkOlHHzTcidLgcaN4amnok4h\nuW7IEO+WlY4FHfgfJRV0IiLJYeZb1NSrt/ZykF9/XTN1c+xYGDDA9y9t1mztUb1dd4UNNljzcY8+\n6vvRdeyY+uciawwZ4uvU33rLZ7iIRCnr19TNnQt77gk//pjgUCJx+vZb2GMP+Oab6LukiYhIevvz\nT59hVHid3qxZ0KTJmiJv2DAv7DJpg/RsNWYMXHqpL2NI1wu3kj0q3CjFzOoAPYFtWTO6F0IIpyUq\nZCnnL3dRFwLUqOFXvjTnWaJw7rm+d9DgwVEnERGRTPT3395Cf3WRV6cO3HBD1KlkteOOgx139M3q\nRZIpEUXd+8DbwCdAQezmEEJ4NmEpSz5/uYs68Ctbd9/tG0aKpNLPP/sU4OnTYautok4jIiIiifbj\nj77G8ZVXfCRVJFkqsqZutQ1DCJcnMFNKNW7smzOrqJNUu/VWOP54FXQiIiLZqm5dn41zxhkwaVLp\njW5EkiHeJrgvm1nG9ldaXdSJpNKff8LIkT7XXkRERLJXjx7evOamm6JOIrkq3qLuAuAlM1tmZotj\nb38mM1giqaiTKNx9t+9Lt8MOUScRERGRZDLzC7lDhsDMmVGnkVwU1wBxCKF6soMkk4o6SbVly+CW\nW2D8+KiTiIiISCpsvz1cdRX06gVvvOGFnkiqlDhSZ2Y7x/7dvbi31ESsuEaNvB1wQUHpjxVJhIce\n8sXSzZtHnURERERSpU8fWLoU7rsv6iSSa0rsfmlm94YQeppZPrDOA0MI+5d6ArNKwMfA3BDC4UXu\naweMAWbHbno2hLBOk96Kdr8E2HpreOcd2HbbCh1GpFQrV/ro8KhRsO++UacRERGRVJo2DQ480Peu\nq1cv6jSSTcrd/TKE0DP2b7sKnL8PMB2osZ773wohHFGB48elcWOYMUNFnSTfM894t0sVdCIiIrln\nt93gzDPhnHPguec0DVNSI65GKWZW2cyONLM+ZnaRmV1sZhfF8XFbAx2B+4D1fUun5Ftd6+okFUKA\nQYPgyiujTiIiIiJRueYa+OoreDYlOzqLxN/98iWgO1AbH3GrzvpH3gq7GbiUNRuWFxWAvc3sUzMb\nZ2a7xJmnzFTUpU4I8M8/UaeIxv/+52s3O3aMOomIiIhEZYMNfF3d+efDwoVRp5FcEO/2iPVDCLuV\n5cBmdhjwcwhhSmztXHEmAw1CCH+Z2aHAC0Cj4h7Yr1+/f//frl072rVb3yGL17gxvPxymT5EyunJ\nJ+GGG+CTT6BatajTpNbAgXDFFZpqISIikuvatIFjjoFLLoEHHog6jWSi/Px88vPz43psiY1S/n2Q\n2TDg1RDCK/GGMLP/AqcAK4FqQE28EUq3Ej5mDrBHCGFhkdsr3Chlzhxo2xa+/75Ch5E4HH885OfD\nGWfAjTdGnSZ13n0XTjnF125WjvdyiYiIiGStxYth113h/vuhffuo00imK6lRSrzTL98Dni/L5uMh\nhKtCCA1CCNsBXYE3ihZ0ZralmY9pmFkrvMhMyiD1NtvAr796m1lJnpUr4dVXfX+2e++FKVOiTpQ6\ngwbBpZeqoBMRERFXowbcdRd06waDB8Ps2aV/jEh5xFvU3QS0BjYKIdSIvdUs47kCgJn1NrPesds6\nA5+Z2VTgFrz4S4pKlWCHHWDmzGSdQQA++AAaNoTdd4chQ+C002DFiqhTJd9nn8HHH0OPHlEnERER\nkXTSsSM88YTPGmvdGvbc018jzZkTdTLJJvFOv3wb2D+EsCr5kYo9f4WnXwJ07gzHHefTAyU5rr7a\nG6X897/+76GHwn77wVVXRZ0suU4+2adXXHFF1ElEREQkXa1cCW+9BaNHw/PP+4Xw447zt+22izqd\npLuSpl/GW9Q9BGwHjAeWx24OIYSbEpay5PMnpKi7+mrvRtS3bwJCSbFatIARI9bs0fbdd7DHHjBx\nIuy8c7TZkmXOHL/qNns2bLJJ1GlEREQkE6xc6T0Inn56TYHXpYsXeNpXWYqTiDV1c4A3gKr4dgbx\nbmmQVrStQXLNn+9F3F57rbmtYUO4/no4/XRYFck4b/INGwa9eqmgExERkfhVruzNU0aO9NdQAwfC\nrFnQsiW0agVDh8K330adUjJFXCN1UUvUSN2kSXDOOb72SRLvgQd8n7bRo9e+vaAA2rWDY4+FPn0i\niZY0P/0ETZr4BqNbbhl1GhEREcl0q0fwVk/R3G47H8Hr3FkjeLmu3CN1ZtYvjoOX+ph00aiRt5vP\ngDo2I40fX/ym23l5vgHngAHZ1/Xp1lvhhBNU0ImIiEhirB7Bu+ceWLDAt4eaMcNH8P7zH58h9N13\nUaeUdFPiSJ2ZzcU7X5a0lXKvEELjRAcrkiMhI3UAderA1KlQr15CDicxK1b45/bLL6Fu3eIfM2QI\nTJjgWx5kw+bcixZ5R9WPPtLiZhEREUmuFSvWXoO3/fZrmqw0bBh1OkmFiqypuw9fO1e9hLd7Ehc1\n+bSuLjnef99/uayvoAO46CL44w/fgDMb3H03HHKICjoRERFJvipV4KCDfARv/ny44QZ/Tbvnnj6C\nN3y4RvByWU6tqQM44wz/5j/zzIQcTmKuvNL3ArzhhpIfN20aHHigj5bWr5+abMnw999exE6YAM2a\nRZ1GREREctWKFfDmmz6C98ILPoto9QjeNttEnU4SKRHdL7OGRuqSY9y44tfTFbXbbnD22XDWWZm9\ntnHUKL84oIJOREREolSlChx8MNx7r4/g9e/vDdx23903O7/pJvj++6hTSrKpqJMKmzcP5s71of94\nXH21N0x58snk5kqWlSu9zbA2GhcREZF0UrjAW7DAC7zp073A22svFXjZTEWdVNj48f4LpFKl+B5f\ntapvf3DhhfDLL8nNlgyjR8PWW0ObNlEnERERESne6gLvvvu8wOvXb+0Cb9KkqBNKIsW1ps7M6gA9\ngW2ByrGbQwjhtORFW+v8CVtTt2IF1KjhnQs32CAhh8x5xx4LRx4J3bqV7eMuucSnCTz+eHJyJUMI\n0Lw5DBoU33RTERERkXSyYoWvv7vwQnjtNS0lySSJWFM3BqgJvAqMLfSWcapU8bavs2ZFnSQ7LF8O\nr7/uXSDLqn9/3w7gpZcSnytZxo3zffcOPTTqJCIiIiJlV6UKnHgi3HKLv56ZMyfqRJIIlUt/CAAb\nhhAuT2qSFFo9BbNp06iTZL733oOddvI96spqo418SsDJJ8O++0KtWonPl2iDBvlaumzYZ09ERERy\n1wknwMKFPkXznXdgyy2jTiQVEe9I3ctm1impSVJI6+oSZ9y4io1atW0Lhx0Gl16auEzJ8s47Pl20\nc+eok4iIiIhU3Dnn+MX1Qw7xpUmSueIt6i4AXjKzZWa2OPb2ZzKDJZOKusQZP77ia8sGD/b93l5/\nPTGZkmXgQLjsMqgc7/i2iIiISJrr2xf22QeOOML34ZX0NG9eyffHVdSFEKqHEPJCCNVCCDVibzUT\nETAKjRvDjBlRp8h8P/zg3ZRatqzYcWrWhLvvhp49YenSxGRLtGnTYMoU6N496iQiIiIiiWMGt94K\n9etD166+dZOknyuvLPn+uLc0MLMjzWy4mQ0zs8MrGixKGqlLjPHjoUOH+LcyKMmhh/pVoquvrvix\nkmHQILjgAqhWLeokIiIiIomVlwejRnkDvJ49vdu3pI/vvoOxpbSojKuoM7NBwPnAF8CXwPlmNrCi\nAaOyxRZQUAC//hp1ksw2fnxiu0DefLPvAffee4k7ZiLMnu3TQ888M+okIiIiIslRtSo884wPfFx6\nqQq7dDJ8uBfbJYl3n7rPgP8LIayKvV8JmBpCSMnOFoncp2611q39E6QNpMtn+XIvjmfN8n8T5Zln\n4NprfapjuoyKnXUW1K4NN94YdRIRERGR5Fq4EPbbD045BS7Pmt73meuXX3yW4RdfQL16Fd+nLgCF\nG87Xit2WsTQFs2LeeQeaNElsQQfeWXKXXWDAgMQet7x+/BGeegr69Ik6iYiIiEjy1a4Nr7zi/Q7u\nuy/qNHLbbdClC2y1VcmPi7eP30Bgspnlx95vC1xR7nRpQEVdxYwbV/Gul+tzxx2w225e4LVokZxz\nxOuWW3yDzvLswyciIiKSierX96Unbdt6kXfMMVEnyk1LlsBdd8H775f+2LiKuhDCE2b2FtASH6G7\nPITwY4VSRqxxY3jkkahTZK7x4+HBB5Nz7Lp1YcgQOO00+PBDqFIlOecpzR9/wL33wiefRHN+ERER\nkajstJM35+jQAWrVggMOiDpR7rn3Xv+877hj6Y8tcfqlme0c+3cPoC4wF5gH1DOz3SseNToaqSu/\n777z+b177pm8c3TvDltuCUOHJu8cpbnrLh+N3Hbb6DKIiIiIRKVFC3j6ad/q4OOPo06TW5Yvh5tu\nin9dY4mNUszs3hBCz9i0y3UeGELYv5w5yyQZjVKWLYNNN4XFi7WZdFndfTe8+27yRzq/+w722AMm\nToSdd07uuYr6+2/YbjvfEL1p09SeW0RERCSdjBnjXcDz831gRJLvwQfhySd9feNqZutvlFJiORNC\nWN0885AQwrLC95lZmvQmLJ9q1XzB4Zw5Prws8Rs3Dk44IfnnadgQrr8eTj/dC7tE7IcXrwcfhP/8\nRwWdiIiIyJFHelfMDh38NVmDBlEnym4FBTB4sM8ai1e83S+L2zkszXYTKztNwSy7f/7xqzQHH5ya\n8511lo+k3n57as4HsHKlT/u8IqNbAYmIiIgkzqmnwnnneWH3229Rp8luY8bAJptAu3bxf0xpa+q2\niq2n28jMdjezPWL/tgM2qkjYdKCiruzefht23RU22yw158vL83a6Awb4JuCp8NRTPkq4116pOZ+I\niIhIJrj4YjjiCO85sGRJ1GmyUwgwaJAPLlixEy2LV9pI3cHAMKA+MDz2/+HARcBV5YuaPlTUld34\n8XDooak9Z6NGcNll0KuXf6MnU0HBmh8kEREREVnbwIHQrJlvc/DPP1GnyT75+bBokU95LYsSi7oQ\nwkOxZiinhhD2L/R2RAjhufLHTQ+NGqmoK6tx41Jf1AFcdJFvMXD//ck9z9ixvoVChw7JPY+IiIhI\nJjLzpnk1akC3brBqVdSJssvgwT6YkRfvIrmYErtfrvVAs8OAXYB/G6SEEPqX7XTlk4zulwBz53pb\n/h8zese91Jk926ckLlhQ9m+0RJg2DQ48EKZO9U0xEy0EaNMGLrgAunRJ/PFFREREssWyZT4Ns1Ej\nb+hRlqmCUrwpU+Dww+Gbb2CDDda9v6Tul3G9NDezkUAX4HzAYv9vWO7EaaJ+fZ8PvGhR1Ekyw/jx\ncMgh0RR0ALvtBmef7c1TkjENc+JE33/v2GMTf2wRERGRbFKtGrzwgu9f17dv1Gmyw+DBPjutuIKu\nNPG+PN87hNANWBhCuB5oDcS1S4WZVTKzKWb20nruH2FmM83sUzNrEWeehDDTFMyyGD/er8hE6eqr\nfcTwyScTf+xBg3y4O5VbJ4iIiIhkqpo1/fXh6NFw661Rp8lss2b5/sg9e5b+2OLEW9T9Hfv3LzOr\nD6wE6sb5sX2A6RSzebmZdQR2DCHsBPQCyrAbQ2KoWUp8li3zzpcHHRRtjqpV4YEH4MILfVQtUaZO\nhU8/9bnhIiIiIhKfLbaAV1+F4cPh0UejTpO5hg3z2Wg1apTv4+Mt6l4ys02BocAnwLfAE6V9kJlt\nDXQE7sOnbRZ1BPAQQAhhElDLzLaMM1NCqKiLz1tv+fTH2rWjTgKtWsHJJ0OfPok75uDBXiiWZ7hb\nREREJJdtsw38739wySXedE7KZsECH+0877zyH6PUos7M8oA3Qgi/hxCeBbYFmoQQro3j+DcDlwIF\n67m/PvBDoffnAlvHcdyEUVEXnyi2MihJ//7w0UfwUrGTestm1iy/wtS7d8WPJSIiIpKLdtnFN83u\n0QPeeSfqNJnl1lvhpJN81LO8Kpf2gBBCgZndAfxf7P1lwLLSPi7WLfPnEMKU2Gbl631o0VMW96B+\n/fr9+/927drRrixbrJdARV18xo3zTbnTxUYb+abkJ58M++4LtWqV/1hDh1ZsuFtERERE4D//gcce\n86Zzr77qs7ykZIsWwb33wiefrHtffn4++fn5cR0nri0NzGwY8AHwbLx7C5jZf4FT8PV31YCasY/v\nVugxdwP5IYQnY+9/BbQNIfxU5FhJ2dIAvPtlnTr+b1RdHdPdrFleOM2fn37tas86C1au9B+G8liw\nAJo29cK+IldHRERERMSNHu3LWiZOhO23jzpNehs0CL74Ah55pPTHlrSlQbxF3RJgI2AVa0bpQgih\nZjxhzawtcEkI4fAit3cEzg0hdDSz1sAtIYTWxXx80oo6gK239mHibbdN2iky2m23+b4ZDzwQdZJ1\n/fknNGvm2Q48sOwff9ll3gRmxIjEZxMRERHJVXff7c0/3nkH6sbbXjHHLFsG220HEyb469nSlFTU\nlTr9EiCEUL1sEYs/TCxM79gxR4YQxplZRzObBSwFTk3AecqscWOYMUNF3fqMHw+nRvKVKV3Nmv5L\no2dP+Owz2Hjj+D/299/h/vth8uTk5RMRERHJRWeeCb/+Ch06eMO9iiyVyVYPPQR77hlfQVeaeEfq\n9ivu9hDC2xWPULpkj9SdfTbsvHPFOs5kq7//9umpP/yQ3j+M3bp5Z85bbon/Y2680Yv5hx5KXi4R\nERGRXBWCT8P85BN45RXviSBu5UofWHr4YWjTJr6PqfBIHXAZaxqYVANa4VsbHBDnx6c1NUtZv/x8\naNEivQs6gJtv9qscXbrA3nuX/vi//vIpl2++mfxsIiIiIrnIDG66Cbp3h+OPh+eegypVok6VHp59\nFmJpT8QAABzCSURBVOrVi7+gK01crUFCCIeFEA6PvR0E7Ar8kZgI0VNRt37jxqXXVgbrs9lmXqSd\nfrrPTy7NAw948bfLLsnPJiIiIpKr8vL8dVcI/jqtYH0bneWQELxByuWXJ+6Y5e33OBfYOXExoqWi\nrngheFHXsWPUSeLTubMXaQMGlPy4FSt84e4VV6Qml4iIiEguq1LFO2LOng0XX+yvMXPZhAk+/TKR\nr7HjXVN3W6F38/A96+aEEE5OXJQSz5/UNXWrVvkeZb/8UrZGG9luxgzYf3+YOzf9tjJYnx9/9D1R\nXnnFp40W55FH4MEH4Y03UptNREREJJf98Qfstx907QpXXRV1mujsv7+PWp5cxkoqEWvqPsHX1Bm+\n79zjIYR3yxYjfVWqBDvsADNnwv/9X9Rp0sf48T71MlMKOvCWuUOGwGmnwYcfrjtvu6DAh7tvvjma\nfCIiIiK5qlYtv/C+zz6+dKZ376gTpd6kSTBnjq8xTKR419SNAsYDY0MIj2ZTQbeapmCua3VRl2m6\nd4ctt4ShQ9e97+WXoVo1OOig1OcSERERyXVbbeXTD/v3h2eeiTpN6g0eDJdckviGMSUWdeb6mdmv\nwNfADDP71cyuM8uk8ZvSqahb219/wbvvQvv2UScpOzMYOdK7LX355ZrbQ4CBA30tXXZ994qIiIhk\njh128L4NZ58Nr74adZrU+fJLf3192mmJP3ZpI3UXAm2AliGETUMIm+LbGbSJ3Zc1VNSt7c03YY89\nYJNNok5SPg0bwvXX+3zlVav8trffhoUL4Zhjos0mIiIikuuaN/e2/ied5EtmcsHQob4vdjL26yut\nqOsGnBhCmLP6hhDCbOCk2H1ZQ0Xd2jKp6+X6nHUWVK4Mt9/u7w8cCJdd5msoRURERCRa++7r2x0c\nccTas6uy0Q8/wJgxcM45yTl+aY1SKocQfil6YwjhFzOLt8lKRmjc2Ls9hqCpeau3MnjxxaiTVExe\nHtx3n+9Ht/XW8Pnn/sMkIiIiIunhsMO8yd2hh3oTkS23jDpRctx8M/ToAZtumpzjl1aYrSjnfRmn\nVi3YcENYsMB3d89lX3/te2fsumvUSSquUSMfnevSxYe8N9gg6kQiIiIiUli3bvDNN75E5vXXvald\nNvntNxg1CqZNS945SivqdjOzxeu5b8NEh4lao0Ze0OR6UZeJWxmU5KKL4M8/oVevqJOIiIiISHGu\nuw6mT/fXaw89lD2vQwHuuAOOPtpnjiVLiWvqQgiVQgg11vOWVdMvQevqVsuG9XSFVa4MN9wA1atH\nnUREREREipOX58Xc9Ok+HTNbLF3q/R0uvTS554lrn7pcoaIOliyBDz6AAw+MOomIiIiI5JKNNvL+\nB7fdlj19EB54wBvCNGmS3PNk3WhbRTRu7K38c9kbb0DLllCjRtRJRERERCTX1K8Pzz8PnTrBttv6\n1geZasUKGDYMnn46+efSSF0hGqnz9XTZNPVSRERERDJLy5Y+ZfHII+Gnn6JOU35PPgk77gitWiX/\nXBZCSP5ZKsjMQipyrljhI1SLFuVml8QQ/IrIuHHQtGnUaUREREQkl/XrBxMm+EyyTOuIWVAAu+0G\nN90EBx+cmGOaGSGEYlvIaKSukCpVoGFDmDUr6iTRWL3p4y67RJtDRERERKRvX2jQAHr29MGHTDJ2\nLFStCgcdlJrzqagrIpenYI4bl11bGYiIiIhI5srLgwcfhK++gkGDok4TvxBg4EC44orUva5WUVdE\nLhd1Wk8nIiIiIulko43ghRfgzju9gUomeOcd+PlnOPbY1J1TRV0RuVrULV4MH34IBxwQdRIRERER\nkTVWd8Ts1QumTo06TekGDYLLLoNKlVJ3ThV1RTRuDDNmRJ0i9V5/HVq31gbdIiIiIpJ+9twT7rjD\nO2L++GPUadZv2jSYMgW6dUvteVXUFZGrI3Xjx/t6OhERERGRdNSlC5x+Ohx1FCxbFnWa4g0ZAhdc\nkPpundrSoIgQoHZtmDkTNt88JaeMXAiwzTbw6qvJ3+1eRERERKS8QoATToDKleGRR9Krwd+cOT6i\nOHs2bLJJ4o+vLQ3KwCz3Ruu++MJ/MBo3jjqJiIiIiMj6mXlHzBkzvMNkOhk+3Nf9JaOgK03l1J8y\n/a0u6tq0iTpJaowb510v0+lKh4iIiIhIcTbc0Dti/uc/PsvsmGOiTuTdLh9/HKZPj+b8GqkrRq6N\n1Gk9nYiIiIhkknr1vLDr3dsbk0RtxAg4/nioWzea86uoK0YuFXV//gkffwz77x91EhERERGR+O2x\nB9x1l3fEXLAguhx//gl33w2XXBJdBhV1xcilou6112DvvWHjjaNOIiIiIiJSNp07Q8//b+/eg+0q\nyzuOf58QIklhBIoGhACKJIggCVCGoVxOqFoEy0WKl15UmLZWW7TakZt2StUqltoy4uhYBdoqWEqK\nYEvCTTjYTmu45AIICPEGJEC4JcpNCefpH2sdsknOLeTs/a51zvczs+esvc9i7x9777xnPet91/v+\nMZxwAjz7bJkM//RP8Ja3wB57lHl9cPbLIT33HGy3XbUg99QJftXhH/0R7LNPNfWqJEmS1DaZ8Hu/\nV80PcfHFvZ0n4pe/hNe9Dq66CubO7e5rFZv9MiK2iojFEbEsIu6KiI3mqImIvohYGxFL69snu5lp\nLLbaCnbaqZqWdCLLrK6nO/ro0kkkSZKklycCLrwQVqyAz362t6/9zW/Cvvt2v6AbTVf7oTLzuYiY\nn5nPRMRU4H8i4tDM/J8Ndr0pM4/tZpZNNTgEc889SyfpnttvrwrYifz/KEmSpIlv+nS48sr1M2Ke\neGL3X/OFF6rFxr/61e6/1mi6fk1dZj5Tb04DtgCeGGK3xk2mPxmuqxvspXMpA0mSJLXdTjtVM2L+\n6Z/CkiXdf70rrqgu2TriiO6/1mi6XtRFxJSIWAY8AtyYmRuu3pDAIRGxPCIWRsTe3c40FpOlqHMp\nA0mSJE0U++9fzUR5/PHdnREzE845B844oxkdJL3oqRvIzLnALsDhEdG3wS5LgFmZuR9wPnBFtzON\nxezZE7uoW7OmOoPR11c6iSRJkjR+TjyxWr/uuOO6NyPmDTfAU0/BsQ25gKxncztm5tqIuAo4EOjv\nePwXHduLIuLLEbF9Zr5kmObZZ5/94nZfXx99Xa5GJnpP3fXXw6GHwowZpZNIkiRJ4+uss+Cuu+CU\nU+CSS8a/N+2cc+D002FKF7vI+vv76e/vH9O+XV3SICJ2ANZl5pqImA5cA/xNZn63Y5+ZwOrMzIg4\nCPj3zNx9g+fp6ZIGUHWpbrMNrFwJr3xlT1+6J045BebNg1NPLZ1EkiRJGn/PPgvz58Mxx8Bf/dX4\nPe+tt1br4v3oRzBt2vg972iKLWkA7ATcUF9Ttxj4z8z8bkR8ICI+UO/zu8Ad9T7nAe/ucqYxiZi4\nQzAHlzLwejpJkiRNVNOnV5OZfO1rsGDB+D3v5z8PH/tYbwu60bj4+Aje855qdsg//MOev3RXLV0K\n73oX3Htv6SSSJElSdy1dCm99K1x9NRxwwOY91333wSGHVOtZb731+OQbq5I9da02Ua+rs5dOkiRJ\nk8W8edVacscfD6tWbd5znXsufOhDvS/oRmNRN4KJWtQtXGhRJ0mSpMnjHe+AD36wKuxe7oyYq1ZV\nwzibOCeFRd0IJmJR9+STcPvtzVgkUZIkSeqVM8+s5sw4+eRqjolNdd551WVZO+ww/tk2l0XdCGbP\nhhUrYGCgdJLxc+21cNhh1YWjkiRJ0mQRAV//OvzsZ/DpT2/af/vkk3DBBdUEKU1kUTeCrbeG7beH\n++8vnWT8LFpUTf4iSZIkTTZbbQXf/nZVoF122dj/u698Bd7+dthtt+5l2xwWdaOYSEMwBwacJEWS\nJEmT2447wpVXVhOe3Hrr6Ps/+yx88Ytw2mndz/ZyWdSNYs6ciTP1/9KlsN128LrXlU4iSZIklTN3\nbrV+3QknwMqVI+970UVw0EHwxjf2JtvLMbV0gKabSD119tJJkiRJleOPh7vvrn7edBPMmLHxPuvW\nVcsYXHxx7/NtCnvqRjGRirqFC72eTpIkSRp0xhmw117Dz4h52WUwa1a14HiTWdSNYqIUdY8/Dnfe\nCYcfXjqJJEmS1AwR1TDMBx6AT33qpb/LhHPOqQq/prOoG8Wuu8Jjj8HTT5dOsnmuvRb6+uAVryid\nRJIkSWqOwRkxL7oILr10/eNXX10Vdm24fMmibhRbbAF77AH33Vc6yebxejpJkiRpaDNnVjNinnoq\n3HJL9dhgL11E2WxjYVE3Bm0fgjkwUJ1psKiTJEmShrbffutnxFywoFqr+p3vLJ1qbJz9cgzaXtTd\ndhvssAPsvnvpJJIkSVJzHXcc3HMPnHQSfOlLMLUl1VJLYpY1Zw5cc03pFC/fwoX20kmSJEljcdpp\nsNNOVWHXFg6/HIO299QtWuRSBpIkSdJYRMB73wvTp5dOMnaRQy3I0DARkSVzrllTzYK5dm07LpTs\n9Oij8PrXw+rVznwpSZIktVVEkJlDViP21I3BtttWlfpDD5VOsumuvRbmz7egkyRJkiYqi7oxausQ\nTK+nkyRJkiY2i7oxmj27fUXdCy9UE7xY1EmSJEkTl0XdGLWxp+6WW2DHHavrASVJkiRNTBZ1Y9TG\nos5ZLyVJkqSJz6JujNpa1Dn0UpIkSZrYXNJgjJ5/HrbZplrWoA0zSa5eXV0HuHo1TJtWOo0kSZKk\nzeGSBuNgyy1ht91gxYrSScbmmmvgyCMt6CRJkqSJzqJuE7RpCObChV5PJ0mSJE0GFnWboC1F3Qsv\nVIuOH3VU6SSSJEmSus2ibhO0pahbvBh23hl22aV0EkmSJEndZlG3CdpS1LmUgSRJkjR5WNRtgjlz\n4N57S6cY3cKFLmUgSZIkTRYWdZvgVa+CgQF47LHSSYb38MPw4x/DIYeUTiJJkiSpF7pW1EXEVhGx\nOCKWRcRdEfG5Yfb7YkTcFxHLI2Jet/KMh4jmD8G8+mr4rd+qlmCQJEmSNPF1rajLzOeA+Zk5F3gT\nMD8iDu3cJyKOBl6fmXsCfwJ8pVt5xkvTizqvp5MkSZIml64Ov8zMZ+rNacAWwBMb7HIs8C/1vouB\nbSNiZjczba4mF3Xr1sF117mUgSRJkjSZdLWoi4gpEbEMeAS4MTPv2mCXnYEHOu4/CDR6Iv4mF3Xf\n/z7sthu85jWlk0iSJEnqlW731A3Uwy93AQ6PiL4hdosN/7NuZtpcc+bAzTfDJZdUxd3AQOlE6y1a\n5KyXkiRJ0mQztRcvkplrI+Iq4ECgv+NXK4FZHfd3qR/byNlnn/3idl9fH319feMdc0z23htOPx0u\nvxw+8Ql4/HGYNw8OOGD9bfZsmFJgXtGFC+H883v/upIkSZLGV39/P/39/WPaNzK70zEWETsA6zJz\nTURMB64B/iYzv9uxz9HAn2fm0RFxMHBeZh48xHNlt3JurscfhyVL4Lbb1t8eewzmzoX9919f6M2Z\nA1ts0b0cq1bBPvvA6tUwtSeluiRJkqReiQgyc8NRjtXvuljU7Us1CcqU+vaNzDw3Ij4AkJlfrff7\nEnAU8DRwcmYuGeK5GlvUDeWJJzYu9Favhv32e2mP3l57jV+hd+GFcM01cOml4/N8kiRJkpqjSFE3\nntpW1A3lySc3LvQefnjoQu/l9LSddBIccwy8//3jHl2SJElSYRZ1DbVmDSxd+tJCb9UqeNObXlro\nveENIxd6zz8Pr3413H037Lhj7/JLkiRJ6g2LuhZZu3bjQm/lSth335cWenvvvb7Q+9734KMfrfaV\nJEmSNPFY1LXcz3++caH3wAPrC73776+GcX7mM6WTSpIkSeoGi7oJ6Be/WF/oLV8OZ5xRXY8nSZIk\naeKxqJMkSZKkFhupqCuwRLYkSZIkabxY1EmSJElSi1nUSZIkSVKLWdRJkiRJUotZ1EmSJElSi1nU\nSZIkSVKLWdRJkiRJUotZ1EmSJElSi1nUSZIkSVKLWdRJkiRJUotZ1EmSJElSi1nUSZIkSVKLWdRJ\nkiRJUotZ1EmSJElSi1nUSZIkSVKLWdRJkiRJUotZ1EmSJElSi1nUSZIkSVKLWdRJkiRJUotZ1EmS\nJElSi1nUSZIkSVKLWdRJkiRJUotZ1EmSJElSi1nUSZIkSVKLWdRJkiRJUotZ1EmSJElSi3W1qIuI\nWRFxY0T8ICLujIgPD7FPX0SsjYil9e2T3cwkSZIkSRNJt3vqngc+mplvBA4G/iwi3jDEfjdl5rz6\n9pkuZ9ps/f39pSNspGmZmpYHmpfJPKNrWqam5YHmZTLP6JqWyTyja1qmpuWB5mUyz+ialqlpeaCZ\nmYbT1aIuMx/OzGX19lPA3cBrhtg1upljvDXxA25apqblgeZlMs/ompapaXmgeZnMM7qmZTLP6JqW\nqWl5oHmZzDO6pmVqWh5oZqbh9OyauojYHZgHLN7gVwkcEhHLI2JhROzdq0ySJEmS1HZTe/EiEbE1\nsAD4SN1j12kJMCszn4mItwFXALN7kUuSJEmS2i4ys7svELEl8F/Aosw8bwz7/wQ4IDOf6HisuyEl\nSZIkqeEyc8jL1rraUxcRAVwA3DVcQRcRM4HVmZkRcRBVoflE5z7DhZckSZKkya7bwy9/E/gD4PaI\nWFo/dhawK0BmfhX4XeCDEbEOeAZ4d5czSZIkSdKE0fXhl5IkSZKk7unZ7JcvV0QcFRH3RMR9EXF6\n4SwXRsQjEXFHyRyDxrK4e4FMW0XE4ohYFhF3RcTnSmcCiIgt6sXt/7N0FoCI+GlE3F5nurkBebaN\niAURcXf9uR1cMMuc+n0ZvK1tyHf7zPrf2h0RcUlEvKJwno/UWe6MiI8UeP2N2sOI2D4irouIeyPi\n2ojYtgGZTqo/txciYv8G5Dm3/ne2PCIuj4hXNiDTp+s8yyLiuxExq2Sejt/9ZUQMRMT2vcozXKaI\nODsiHuxol44qmad+/NT6u3RnRHy+ZJ6I+LeO9+YnHaOzSmY6KCJurjPdEhG/UTjPfhHxf/Xf/u9E\nxDY9zDPk8WLJNnuETEXa7BHyFG2zN0lmNvYGbAGsAHYHtgSWAW8omOcwqmUZ7ij93tR5dgTm1ttb\nAz8s+f505JpR/5wKfB84tAGZPgZcDHyndJY6z0+A7Uvn6MjzL8ApHZ/bK0tnqrNMAR6imiG3ZI7d\ngR8Dr6jvXwq8r2CefYA7gK3qdvI6YI8eZ9ioPQT+Djit3j4dOKcBmfaimlH5RmD/BuR5CzCl3j6n\nIe/RNh3bpwJfL5mnfnwWcHWJtnKY9+ivgY/1MscoeebX/+63rO+/qvRn1vH7vwc+2YD3qB/47Xr7\nbcCNhfPcAhxWb58MfKqHeYY8XizZZo+QqUibPUKeom32ptya3lN3ELAiM3+amc8D/wYcVypMZv43\n8GSp199Qjn1x957KzGfqzWlUB5xPjLB710XELsDRwNdp1kL3jchSn3U6LDMvBMjMdZm5tnCsQW8G\nfpSZDxTO8XPgeWBGREwFZgArC+bZC1icmc9l5gvATcA7ehlgmPbwWKoTBNQ/jy+dKTPvycx7e5lj\nlDzXZeZAfXcxsEsDMv2i4+7WwGMl89T+ATitVzk6jZCpSJs9TJ4PAp+rj43IzEcL5wFenCDvncC3\nepVnhEwPAYO9KtvSwzZ7mDx71o8DXA+c2MM8Qx0v7kzBNnu4Y9hSbfYIeYq22Zui6UXdzkDnwdyD\n9WPaQAy/uHvPRcSUiFgGPEJ1ZuyuwpH+Efg4MDDajj2UwPURcWtE/HHhLK8FHo2IiyJiSUR8LSJm\nFM406N3AJaVDZDUj7xeA+4FVwJrMvL5gpDuBw+qhMzOAY2jGH5qZmflIvf0IMLNkmBY4BVhYOgRA\nRPxtRNwPvI/qbHTJLMcBD2bm7SVzDOHUegjWBb0eWjyEPYHDI+L7EdEfEQcWzjPoMOCRzPxR6SDA\nGcAX6u/1ucCZhfP8oP5uA5xE1RvdcxscLzaizW7SMSyMmKcxbfZQml7UOYvLGMTIi7v3XGYOZOZc\nqoPMwyOir1SWiHg71ZIZS2lIz1jtNzNzHtWQkD+LiMMKZpkK7A98OTP3B56m+mNYVERMA34HuKwB\nWfYA/oJqGOZrgK0j4vdL5cnMe4DPA9cCi4ClNOukBVmNVbENH0ZEfAL4VWYWP2kBkJmfyMxdgX+m\nOhFWRH2S4iyq4Y4vPlwoTqevUJ0Am0vVA/SFsnGYCmyXmQdTnbT898J5Br2HBpyIq10AfLj+Xn8U\nuLBwnlOAD0XErVQ94r/qdYD6ePE/qI4XO3voi7XZTTuGHS5P09rsoTS9qFvJS89kzKLqrVMtqsXd\n/wP4ZmZeUTpPp3oI31VAyTOIhwDHRrWo/beAIyPiXwvmASAzH6p/Pgp8m2qocSkPUp0Vv6W+v4Cq\nyCvtbcBtvRxWNIIDgf/NzMczcx1wOdV3q5jMvDAzD8zMI4A1VOP/S3skInYEiIidgNWF8zRSRLyf\nakh4sRMDI7gE6NmEEkPYg+rkyfK63d4FuC0iXl0wE5m5OmtUQ/lLttlQtduXA9Rt90BE/HrJQPXQ\n9BOorjlugoMy89v19gIKf2aZ+cPM/O3MPJDqcqKe9mZ2HC9+o+N4sWib3bRj2OHyNLzNflHTi7pb\ngT0jYvf6rP27gO8UztQY9dj1ERd377WI2GFwWEpETKe6wLSns2B1ysyzMnNWZr6WaijfDZn53lJ5\noDoTPTjrVUT8GvBWqkkvisjMh4EHImJ2/dCbgR+UytPhPfT4uowR3AMcHBHT6393bwaKDisePMiN\niF2pDqSacPbwO1TD96h/Fv8jvYHiPT5RzZr4ceC4zHyudB6AiNiz4+5xlG2z78jMmZn52rrdfpBq\nsoSiJwjqA95BJ1Cwza5dARwJULfd0zLz8bKReDNwd2auKpxj0IqIOKLePhIocm3toIh4Vf1zCvBJ\nqt7fXr32cMeLxdrsMR7D9qzNHi5PE9vsYW3qzCq9vlGdrf8h1SyYZxbO8i2q62l+SXWt38mF8xxK\nNeRqGdUf4aXAUYUz7QssqTPdDny89HeoI9sRNGD2S6ohPMvq252lv9d1pv2oZuZaTnX2t+jsl8Cv\nUU3WsE3JHBtkOo2q2L2D6oLyLQvn+V6dZxkwv8DrD7aHvxpsD4HtqSYAuJdqaOi2hTOdQnXh/wPA\ns8DDwKLCee4DftbRZn+5Ae/Rgvp7vYzqLPWrC+QZ8u8q1ayzvZ79cqj36F/rv2nLqQ58Z5Z8j6hm\nBP9G/bndBvSV/syAi4A/6eVnNcJndjLVCIvF9ff6/4B5hb9DH6Y6nv0h8Nkevz9DHi+WbLOHyfS2\nUm32CHmKttmbcnPxcUmSJElqsaYPv5QkSZIkjcCiTpIkSZJazKJOkiRJklrMok6SJEmSWsyiTpIk\nSZJazKJOkiRJklrMok6SNClFxFMb3H9/RJxfKo8kSS+XRZ0kabLacKHWcVm4NSKmjsfzSJI0VhZ1\nkiRV4sWNiN0j4oaIWB4R10fErPrxf46IEzv2e6r+2RcR/x0RVwI/6HlySdKk5tlESdJkNT0ilnbc\n3x64st4+H7goM78REScDXwROYOTevXnAGzPzZ90KLEnSUCzqJEmT1bOZOW/wTkS8DziwvnswcHy9\n/U3g78bwfDdb0EmSSnD4pSRJlRjlPsA66r+dETEFmNbxu6e7lEuSpBFZ1EmStLH/Bd5db/8+8L16\n+6fAAfX2scCWvY0lSdLGLOokSZPVUNfHDT52KnByRCynKuo+Uj/+NeCIiFhGNUTzqQ3+e0mSei4y\n/RskSZIkSW1lT50kSZIktZhFnSRJkiS1mEWdJEmSJLWYRZ0kSZIktZhFnSRJkiS1mEWdJEmSJLWY\nRZ0kSZIktZhFnSRJkiS12P8DB2bcmp5k2loAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d970e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = plt.gca()\n",
    "dj_dur.plot(x='hour', y='duration', ax=ax)\n",
    "plt.title(\"Average jam duration per hour\")\n",
    "plt.ylabel('Duration [min]')\n",
    "plt.xlabel('Hour')\n",
    "ax.set_xticks(range(23))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x111799790>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1117adf10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1104a4b50>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x10f982fd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x110865dd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f950290>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x110754910>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10fdfd550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10fe61c50>]], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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hVC8jIwtz5+4HADz0UA2GDx+iVI9Ci9owmwVxRgMRUX2G63+IiKghu3bl4Ny5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AfVMQ4iIiIiIiIici93L52AJEkNoDQSxrl+XJblM+r1QUmSKvz62bNnazkqKgpRUVHuGCYRUY0V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zzlUb72hwzjnnnHPOOedctfGOBuecc84555xzzlWb/w+VAz0dTac3LAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a03510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.scatter_matrix(dr, alpha=0.2, figsize=(18, 18), diagonal='kde')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}