Flight_Level_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from bokeh import mpl\n",
    "\n",
    "#https://joernhees.de/blog/2015/08/26/scipy-hierarchical-clustering-and-dendrogram-tutorial/#Perform-the-Hierarchical-Clustering\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from scipy.cluster.hierarchy import cophenet\n",
    "from scipy.spatial.distance import *\n",
    "import pydendroheatmap as pdh\n",
    "\n",
    "import pickle\n",
    "\n",
    "matplotlib.rcParams['savefig.dpi'] = 1.5 * matplotlib.rcParams['savefig.dpi']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#cartodb links\n",
    "general link\n",
    "https://jzangh.cartodb.com/viz/79c6a53c-06b8-11e6-824c-0e3ff518bd15/public_map\n",
    "\n",
    "for embedding\n",
    "<iframe width=\"100%\" height=\"520\" frameborder=\"0\" src=\"https://jzangh.cartodb.com/viz/79c6a53c-06b8-11e6-824c-0e3ff518bd15/embed_map\" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>\n",
    "\n",
    "js for apps\n",
    "https://jzangh.cartodb.com/api/v2/viz/79c6a53c-06b8-11e6-824c-0e3ff518bd15/viz.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#example with JFK-SFO\n",
    "#carrier breakdown\n",
    "airlineBreakdownDict={\"AA\":1714,\"B6\":1765,\"DL\":2275,\"UA\":1920,\"VX\":1606}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1714"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlineBreakdownDict[\"AA\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import the data\n",
    "JFK_SFO_stats=pd.read_csv('/home/vagrant/capstone/2016-03-28_downloads/OTP_detailed/analysis_2016-04-18/JFK_SFO_route_analysis/abridged_JFK-SFO.txt',sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>FlightDate</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>AirlineID</th>\n",
       "      <th>FlightNum</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>DepDelay</th>\n",
       "      <th>...</th>\n",
       "      <th>Cancelled</th>\n",
       "      <th>CancellationCode</th>\n",
       "      <th>Diverted</th>\n",
       "      <th>AirTime</th>\n",
       "      <th>Distance</th>\n",
       "      <th>CarrierDelay</th>\n",
       "      <th>WeatherDelay</th>\n",
       "      <th>NASDelay</th>\n",
       "      <th>SecurityDelay</th>\n",
       "      <th>LateAircraftDelay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>15</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SFO</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>2586.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>15</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SFO</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>2586.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>15</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SFO</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>2586.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>15</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SFO</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>2586.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>15</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SFO</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>2586.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  DayofMonth  DayOfWeek  FlightDate UniqueCarrier  AirlineID  \\\n",
       "0      1           1          4  2015-01-01            AA      19805   \n",
       "1      1           2          5  2015-01-02            AA      19805   \n",
       "2      1           3          6  2015-01-03            AA      19805   \n",
       "3      1           4          7  2015-01-04            AA      19805   \n",
       "4      1           5          1  2015-01-05            AA      19805   \n",
       "\n",
       "   FlightNum Origin Dest  DepDelay        ...         Cancelled  \\\n",
       "0         15    JFK  SFO      -5.0        ...               0.0   \n",
       "1         15    JFK  SFO      -3.0        ...               0.0   \n",
       "2         15    JFK  SFO      -1.0        ...               0.0   \n",
       "3         15    JFK  SFO      -5.0        ...               0.0   \n",
       "4         15    JFK  SFO      -5.0        ...               0.0   \n",
       "\n",
       "   CancellationCode  Diverted  AirTime  Distance  CarrierDelay  WeatherDelay  \\\n",
       "0               NaN       0.0    388.0    2586.0           0.0           0.0   \n",
       "1               NaN       0.0    369.0    2586.0           NaN           NaN   \n",
       "2               NaN       0.0    352.0    2586.0           NaN           NaN   \n",
       "3               NaN       0.0    375.0    2586.0           NaN           NaN   \n",
       "4               NaN       0.0    364.0    2586.0           NaN           NaN   \n",
       "\n",
       "   NASDelay  SecurityDelay LateAircraftDelay  \n",
       "0      48.0            0.0               0.0  \n",
       "1       NaN            NaN               NaN  \n",
       "2       NaN            NaN               NaN  \n",
       "3       NaN            NaN               NaN  \n",
       "4       NaN            NaN               NaN  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JFK_SFO_stats['CarrierDelay'].head()\n",
    "JFK_SFO_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AA' 'B6' 'DL' 'UA' 'VX']\n",
      "['AA' 'B6' 'DL' 'UA' 'VX']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['American', 'Jetblue', 'Delta', 'United', 'Virgin']"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print AirlineMarketShare.index.values\n",
    "[colorDict[x] for x in AirlineMarketShare.index.values]\n",
    "fullNames={'AA':'American','B6':'Jetblue','DL':'Delta','UA':'United','VX':'Virgin','WN':'Southwest'}\n",
    "print AirlineMarketShare.index.values\n",
    "[fullNames[x] for x in AirlineMarketShare.index.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               AirlineID\n",
      "UniqueCarrier           \n",
      "AA                  1714\n",
      "B6                  1765\n",
      "DL                  2275\n",
      "UA                  1920\n",
      "VX                  1606\n"
     ]
    },
    {
     "data": {
      "image/png": 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ioaEBWq0W4eHhmDZtGmbNmgUPD4+rnpednY0ZMywXBq1cuRIzZszAkSNHsH79\neiQlJaGkpAQeHh4YOnQoHnroIdx0000tZmgJ56xRp2RkpOH4D//A/ROKe+R4v85h80DcWWWPHNNe\nlJersHZtHxQVaRAUVI/HHstGZGT7ikpsrM5W6qZOLcXvflcCnc5ShpVKS7GaM+cyNBoRDQ0Cdu7s\n+Omh6mol9u+3fAgMGlSDu+4quOJ0Z2BgA2bNykWfPpY7XWzf3gtm85X72LvXFw0NAgQBuPPOfEye\nXA6t1pLT1dWMqKga3HtvPqwXiB054t3hnFKprRVRmu8HF42r1FEIgLuLO1yrPPDuiv9KHcXGegp0\n7NixCAsLg7+/P2688UYAwHfffYfGxuZ/gLKWTUEQYDabsW7dOsyfPx8nTpxAbW0tGhsbkZWVhc8+\n+wyzZ8+GwWDAjz/+iFmzZmH37t2oqKiA2WxGcXExtmzZgvvuuw8lJSXNHmv79u34/e9/jw8//BAJ\nCQmorq6GQqFASUkJYmJi8Morr2DmzJlISUlp8fsUBAF5eXl45JFHcOrUKSgUCqhUV48ptVSi9+zZ\ng5kzZ+K9995DYmIiamtrbfvcvHkz7r33Xrz33nvNPvf555/HnDlzsHXrVmRmZqK+vh5qtRqVlZU4\ne/YsVqxYgdtuu63ZJUN++zqvWrXKVowrKysBABUVFTh69CgWLFiAL7/8ssXXoCUsa9Rh1dXV+PDl\n+/HPGRk9elxrYatP98C5OBY2K6PRUmKmTCnDI4/k/DLy1b4RgfPnLet5ububMWlS84sY+/g0YsIE\ny2nFlBR3lJd3bEA+Ls4TjY2WjDff3PwbveX0ZSkAy2nZixd//enVZAIuXnSHIAB9+9YhMrK22X0E\nBdUjJMRS+PLzXWA0ymNUpC2HD3lhRODvpY5BTfTy7I2zB+Nw5JD0E/ijo6NtBefee++1ff3+++8H\nAJSUlLR5Vagoijh//jzeeustLFu2DCdPnsTZs2dx4MABTJ06FQCQkpKCt99+G88//zzuueceHDly\nBGfPnkV0dDTuuece27HWrl171f6PHz+OpUuXwmAwYMiQIfj0009x9uxZxMfH48CBA1i2bBm0Wi1y\nc3MxZ84cW4FpLufatWsREBCAzZs3IzY2FnFxcdBq2153MD4+HkuWLIHBYMDgwYPx1Vdf4fz584iP\nj8cPP/yAcePGQRRFvPfee9i0adMVz/3+++/xzTffAACGDh2KjRs3IiEhAbGxsTh27BiWLl0KlUqF\ny5cvY9mzPIKzAAAgAElEQVSyZa2+znv37sX777+PRYsW2V7Ds2fPYuXKlbZRuTfffLPD96dlWaMO\nMZvNePWZ+/Gv6XFQdtGVnx0hCALe+qsBdWksbFaurmbMnZuDqVNL0dGlhwoKLKNqffrUQdnKyxkV\n9ettw5KTrz4N0JqkJMsbba9eDfD1bfkUaliYAS4uliG1xMRf35wVCmDBgmz89a+XMXNmUavHanoR\nRF2d/N/ezGYRaSme8PcOkDoK/UaEzwD897X3WhxJ6inWURgfHx/ccssttq/fcMMN6NOnD4BfR95a\ns2vXLixZsgRz5861lQZ/f3+8/PLLtjXLPv/8c9xwww144YUX4OvrCwDQ6XR47rnn4OXlBQBXXJEK\nWD4TXnrpJZjNZgwYMABffvklJk6cCFdXV9sx5s6di9WrVwMAiouL8cEHH7SYc+/evVi9ejUGDhwI\nAFC29sbUxCuvvAKj0Qi9Xo9PPvkEo0aNAmD5zBg0aBBWr16NgADLv7N33333iluNffvtt7ZtV65c\niZEjR9pGy7y9vfHoo49i1qxZAIDTp08jIyOjxRxbt27FsmXL8Pjjj9teQ7VajRkzZmDu3LkAAIPB\ngJiYmHZ9X1byfzcjWVn91jOYPXw/vDykG7WwjrAZUrUsbAC0WhOCg+s79dz6estbgKtr6/MAm85T\nKyjQtHv/ZjNsp1mDglrPKAhAYKBlm8uXXa74uq9vI0JD6+Dv3/qVnqWlllE/pVKEu7v85zaeOeOO\nYLdJUsegZgiCgIHeg/GPRf+E+bfn5XtIfn4+9u7dCwC44447oFarr3jcOtJ25swZJCcnt7ovT09P\nPPjgg1d93cfHB6GhoQAs3/O8efOu2katVmPYsGEAgJycnCsei46ORnp6OgBg0aJFtpL2W+PGjcP4\n8eMBAD/++GOLOadNm9bsXLTWpKenIzY2FoDlddLr9Vdto9VqMWfOHEybNg3XX389ysp+PZPwySef\nYM+ePdi8eXOLF2tcd53lSm1RFHHp0qUWswQEBGD27NnNPjZx4kTbr7Oystr+xppgWaN2O3zgZ/Sq\nXI/IQOk/BC0jbLUsbNfI1dXyIVRT0/7XsLS0/WWtslJlm3+m17d9YYJeb7lqtbxc3caWV8vLc0F+\nvqXkRUbWdHiUUQpxZz0xJGSk1DGoBRqVBj7GXnj53y9LcvyvvvoKZrMZCoUC991331WP33nnnbYC\n19bo2rBhw6DRNP9v11puXFxcMHTo0Fa3qa29chrCiRMnAFjek1t6rpW1rBUXFyMvL6/ZbUaPHt3q\nPppz8uRJ26/HjGl5rcI5c+Zg1apVePnll20XUwCAu7s7+vTpYxvNa07TU7GtncKcOHFii3Pqmh7z\nt69jW3g1KLVLSUkJ9m98Cs/9sVTqKDbWwvbkZ1qcQzWGjZC+RNqbgIB6pKW5ISfHFY2NAtTq5lfy\nycr69adl62hcezQtge0Z6bKO8IkiYDAo4ObWvhGNhgYBmzf3BmBZduSmm+Tz97Ql2dlKqOoGSB2D\n2uCj9UVGbBq2//Qzbv1Dz12x29DQYDs9N3HixGavwrSeGt22bRu2bt2Kp556qsX5XdZTcs2xTuLX\n6/UtFg3rNr9d7cs6QiSKIm6//fZWr6C1XgghiiJycnKaHcVqLWdLrCN7giB0eFTOqqGhAdu3b8fB\ngweRk5ODkpISVFVV2b5fk6l9ny/+/v4tPtb0Yon27s/23A5tTU7JbDbjzedm4bkZKWjvxPWeIggC\n3v5rLZ5c54FzqGFh66AhQ6qRluaG+noFDh70wfTpV8/Pqa1V4Kefett+39DQ/r8DTSf5q1RtL+nY\ntCy29wKB+noBGzcGorBQA0EAbr21qM3FdeXg0AE9JkT8TuoY1A79fMLx+aovMGLkcAQHB/fIMX/+\n+WeUllp+6Jg0aRJSU5u/u8LEiROxbds2GAwGbN68udlTnQCavaryt9o7P6ypphcLtGe0yFrmmttW\nEIRmT2G2pbr61zm1nVkfLz09HQsXLrxiLlpnl21pz+vcqf12y17JoXz4zv9i9oijcHORV1GzEgQB\nb88xsLB1wogRlTh5Uof8fBccPaqHwaDAdddVwMenATU1KmRmuuLgQR/U1Sng49OI0lJ1i6Nvzeno\nqcimP7S3572yvFyFDRsCUVRkKWrTp5dg1Kiqjh1UApWVIipLekPlz7dgezHQZzCeenwZPvt63VVz\nx7pD0+Ud3njjDbzxxhttPuerr75qsax1l6bLVsTGxja7jlpHdKYwNi1W9fUdm79bX1+PBQsWIDMz\nEwAwdepUzJ49G5GRkdDpdLY/65iYmBbnovUEO5jVQVI6fmQv9KWfIypY3gXIWthqUniVaEcolcD9\n9+fZJu6fOaPD6tUhePnlCLzzTig2bfJHfb0C99yTbytp1is226Ppto2Nbb/dNN2mreNkZrpizRrL\n+nJKpYjbby/EpEn2ceeCgwf0GNf3D1LHoA5QKVUIVvXFc08/1+3Hsi45AVje29rzHwCkpqba5pD1\nFOtVogCumLTfk3Q6HQDL6VXraGR77d6921bUZs6cidWrV2PixInw9fW9opQ3NEh7Gzv+WEctKi0t\nxc4vnsQLf5L20vX2EgQB78wxYAlH2DpEpzNh3rxsxMbqkJioRUmJGqJomewfGVmD0aMr4eZmtt3m\nqa37jDbV9CpTg6HtslZbazmGUim2OoJ36pQOP/9sWTxXqzXhrrvyERpa1+5cUjIaReRkeGFwlP0s\n3EsWOncvZKdk4euvvsG999/TbcexjqopFArs2rXLtkRHS0pKSnDjjTfCaDRiw4YNtisXe0JERAQA\nS1G6cOGCbXmMnhQWFmb7dUsXLrQkISHB9mvr8hzNae0K0J7AkTVqliiKePP5B7F0xkWpo3SItbDV\npHggniNs7aZUAmPGVOKhh3KxZEkmnnwyEw8/fBnXX18ONzczqquVtiLl79/+0ww6ncl2xWlZWdun\njkpLLdu0djP2Xbt88dNPlqIWHGy5Y4O9FDUAOHFCi3DdTVLHoE4K8e6LLeu3IuVSyyvxX4uysjL8\n9NNPACzz0doqaoBlUv60adMAWNYpKyws7JZszRk3bpzt1z///HOr2x49ehQxMTEdnlzflrFjx15x\njJZ8+umnuO666zBhwgRbSbPOd2ttvpzRaLRd7CEVljVq1hcfv4m7Bx6Bh6s856m1xlrYalM8cD6e\nha0rpKRYJu0KAtCvX8fuBWtdOy07u/XbKTU0CLalN6zP+a2ff/az3WN08OBqzJmTA09P+xlBFUUR\nCfGe6B/U8hIBJH8DfQfj38/8u1vWX/v2229tp9zuvvvudj/Puq3RaLStxt8Txo0bZxvZ2rZtm+30\n7W/l5eVh6dKlmD17Np566qkuzRAaGmorbDt37mx2FKyurg7fffcdKioqoNFobPdMtV69KYoiEhMT\nm93/8uXLrzi92tIdGLoTyxpdJTMzA2UJH2JkmPyvqGuJ5SpRA6ousrC1xnITdFecOeMJsZXrBk6f\ntsxL0esbO7wA7+DBlp9cy8rUyM9veY22Cxc8YDJZCuHQodVXPX7smB4xMZYco0ZV4u67C9BNF151\nm5QUDbTmIVLHoGukUCjQC4F4c/mKLt2v2WzGxo0bAVhGy6ZPn97u506ePNm2bMU333zTowv5vvDC\nCxAEASaTCfPmzcOmTZtsV3tWVlZiy5YtmDVrFsrKyqDRaPDYY491eYZnnnkGarUaRqMRjz76KPbv\n3w+TyQSTyYTExEQsWLAAqampEAQBTz75pO2uDdbbbQHA22+/jVOnTsFsNsNkMuH06dOYN28eNm7c\neMU9RXft2oW6up4dzWdZoyuIoojVrz6ChTfltL2xzCkUlhE2FraWJSd74LPPgvHjj70RH+/Z7Dan\nTumQk2MZ8brhho5PIB42rAru7maIIrB7t1+zpbC+XsDBg5YFI3v3brhq9O7yZRfs3WtZf2no0Grc\ndlvrt52Sq2NHdBgXcZPUMagL+Gh9EX/0HBITmh+N6Yx9+/bZbhT+5z//uUPLQAiCgL/85S8AgMLC\nQuzevbvLcrVlwoQJWLFiBVxdXVFRUYFnnnkGo0ePxvDhwzF+/Hj861//Ql5eHrRaLd566y1ERUU1\nu5/fruHWkua2GzJkCN599114eHigoKAACxcuxNChQzFixAj85S9/QXR0NBQKBRYvXow77rjjiudZ\n7wSRn5+PBx98EMOHD8fQoUPxwAMPICYmBitWrMDYsWNto3enTp3CmDFjbPdM7Qksa3SFdR8ux4Mj\nYqBW2d/pz+Y4Q2ErKlIjJ8fliv8qKy3fqyhaVvb/7ePWKSNDh1bB29sygrptWy+cOqVDfb3lz76q\nSok9e3yxfXsvAEBERG2Ly2K8+GIEXnwxAu++G3rVYy4uIn73u2IAQFqaG77+OgCFhRqIoiVfdrYr\nPv88GKWlaiiVwJ/+dPV8mx07LHPUPD1NzT5uD0pKgLqKYNtP9GT/BvhF4T/PvdJlc7C++uorAJbi\n1ZFToFZ33XUXFAoFBEGw7cu6v7a0tU1bj996663YvXs35s+fj2HDhsHLywtmsxk6nQ7Dhw/H4sWL\nsXPnzmZHC5su/9GeDC1tN23aNGzfvh2PPPIIBg4cCE9PTygUCgQFBeH222/H119/jUWLFl31vBdf\nfBEvvvgihg0bBnd3dwiCgMDAQNx///3YsmWL7Z6sr7zyCsaPHw93d3dotVoMGXLlKHl712brzBpu\ngtjeKksOLy0tBdtX3oJFN+dKHaXLmc0ilqxzg2dkDYYOt585Tu2xbl0wMjNbnw/2W0uWZNqu6szP\n12D9+qAr7jagUom2RWkFwXL7pjvvLGjxCs0XX7RcEabXG/HEE5nNbnPsmB579/rabj+lVFrKmvX3\nGo2IO+/MR2TklYtlXr7sgo8/7mN7jkrV9ukd6+K4w4ZdfTpVKt9/540BmgXwcG1+hXmyT+U1ZfAb\n7o1nnnta6ijkwOxsxgd1F7PZjDWvP4aXZl6G3O5S0BWsI2xL1nngvFCDocMcp7AJgtiuBWRbEhDQ\ngEWLsnD2rGXpjooKFerqFNDpjAgOrsfIkZVXFajmc7T++KRJ5ejfvxYnTnghM9MNlZUqKBQivL2N\niIioxYQJ5dBqr/5zaWz8tTSazUBDQ/tGpsxm+fw9rq8XUXBZj5GDWNQcjd7DG0knkhEfF4/hI4ZL\nHYccFEfWCACwZuWLuMHlDQwMcpwS0xyzWcQT69zgNbAGQxyosJG87dunhSJ/FkL8rj5NTPZPFEVc\nrE7EZ9+s69QK/ERt4eQJwqWLF2DM+MzhixpgGWF7d44B5Rc8kHCOb6rU/URRxKULOhY1ByYIAnoJ\ngfjv2yuljkIOimXNyYmiiE/fWYx5UxxvnlpLFAoB/33YgIpkFjbqfgkJrvBVjpE6BnUzH60PTu0/\njazMLKmjkANiWXNyP2z8GH8eeApKpXzm9/QEjrBRTzl5QocRoeOljkE9YIBvFF545t/tXoKCqL1Y\n1pxYdXU1kg6/h3ERHVvk1FFYR9jKL3gg8TwLG3W9/HwFTNWhXK7DSSgVSnjU6fDFZ+uljkIOhu8g\nTmzVG//AwhuSpY4hKWthK0vyQOJ5/nOgrnVgvx6TImZKHYN6kL8uAD9/t+OK2xMRXSt+Ojmp8/Fn\nEdCwHT6eznX6szm/FjYtCxt1mdpaEWUFPnDRdGwNPLJ/EV6R+M/zL0sdgxwIP5mckCiK2LD6H3hg\nYrHUUWSDhY262qFDXhgReKvUMUgCLmoXFKeXdumtqMi58VPJCX39xfu4e/BpKBQcVWuKhY26itks\nIv2SJ/y9/aWOQhKJ8B2At5a/LXUMchD8RHIyFRUVSI/5ECPDGqWOIkssbNQVzpxxRx/366WOQRJS\nKBRQVKqwe2fP3VCdHBc/jZzMh28txcIbLkkdQ9asha00UYukBF4lSh0Xd8YTg0NGSB2DJBbiHYrP\nPvwcZnPb97Mlag3LmhPJysqEV81ueHnw9GdbFAoBK+caUJLowcJGHZKZqYSqPlLqGCQTPuiFdWs/\nkzoG2TmWNSey7r9L8dfr86WOYTcUCgErHzagJIGFjdrv8CE9JkTMkDoGyUQvXW/s+XEv6urqpI5C\ndoxlzUnEx55GpOtRuKg5qtYRthE2FjZqh4oKEdUl/lCpVFJHIRnp49YXK157S+oYZMdY1pzEt2uf\nxd3juUhjZ7CwUXsdPKDHuL5/kDoGyYynmw4JMYkoKiqSOgrZKZY1J7B/zzZMCeJSHdfCWtiKz7Ow\nUfOMRhGXM72g1+qljkIyFKGPxKsvvip1DLJTLGsOThRF7PvhNUwfViN1FLunUAh47xEDis574EIS\nCxtd6cQJLSK8pkodg2RKo9agOL2MC+VSp7CsObjvN67BHYNipY7hMBQKAe8/YkBRPAsb/UoURSTE\neyIikFeBUssifAdgxaucu0Ydx7LmwIxGI84fWoNRYUapozgU6whbIQsb/SIlRQMdhkkdg2TOslCu\nGrt3cKFc6hiWNQf27Zercd9IDrl3B+sIW2G8B5IS+c/I2R074oUxYVOkjkF2oK9PKL76bKPUMcjO\n8FPGQZnNZiSf+BIDg7lydnexnRI9p2Vhc2IlJUB9RRAUCv4doPYRqpSIPh4tdQyyI3x3cVCbv12H\n2wclSB3D4bGw0f79ekwI53Id1H59ffvhkw8+lToG2RF+ujggURQRd2gtRnKuWo9gYXNedXUiCi97\nw8NVK3UUsiMKQYG64npcvHhR6ihkJ/jJ4oB2bPsWt0SclzqGU2Fhc07HjnlicC/eWoo6LsynP95/\na5XUMchO8FPFwYiiiOM7VmFiZIPUUZwOC5tzEUURF5M8EeIXKnUUskMqpQrFWSXIz+f9mqlt/ERx\nMAf3bceNIfFSx3BaCoWA9+aysDmDc+fc0Es1VuoYZMfCvPvj3Tf/K3UMsgP8NHEw+ze/g6lDaqWO\n4dSUSkthK4xnYXNkp096YnjoeKljkB1zUbsg80IWKisrpY5CMsdPEgdy5tQJjPKNhSDwHqBSUyot\np0QL4rVIvsB/Zo4mL08Bsbofl+ugaxbi0Q/vvfO+1DFI5vhO40B++vpN/HFUldQx6BdKpYBVjxiQ\nF8vC5mgOHtBjQsTvpY5BDkDrpkXCqQTU19dLHYVkjJ8gDqKkpATextNQKjmqJicsbI6npkZEaYEv\nXDSuUkchB+GvCcLaD9dKHYNkjJ8eDmLD2uW4f3yu1DGoGSxsjuXQIS+MDpopdQxyIN5aHxzddxxm\nM+84Q83jJ4cDMJlMqMjcCx9PjqrJla2wndXiYjL/2dkrs1lERooOvfT+UkchB+MNX3yz8RupY5BM\n8VPDAfy4aT3+OIgrYcudUilg1aMG5J5hYbNXp0+5o6/HZKljkAPy1wdg97Y9UscgmVJJHYCuXfzh\n9bh9hknqGNQO1sK28GMtgGpEDuRpD3sSF6vD1H7DpI5hU1JdjOMph1FuKAcA3DH6bni4tH7rK6PJ\niPTiVGSXZqKsphT1xnoIggAXlSt8PXzR17cfQv3CoBC6/geK2KzTOH/Zsg5keK/+mNT/hisez6/I\nw57EHR3e78SIyYjoPaBLMkqpodyI9PR0hIWFSR2FZIZlzc4lJpzDIC8ugmtPlEoBqx814G8fawFU\nIXKgKHUkaofMTBXU9fIoBCazCfHZZ5GYex4iLH9/BLQ9DaK8tgwHLuxFdX2V7TlKhRIiRBgaapHd\nUIPssiwk5p7HTVE3t1n8OiK/IhcJl8/Zft9cXoWggEapadf+RIhoNDXanucI+nmHY+0Hn+A/r/2f\n1FFIZljW7Nzm9cux9LoKoB1v1CQf1hG2v33sCRY2+3D4kBcmREh/H9DiqiIcTz2CCkM5lAoVvNy8\nUFpT0ubzGowN2Je0C7UNtVAKSgwJHobIgCi4qt0AADX11bhUkIyEy+dQVluK/Rf24Nbht3VJEapr\nrMPRS4chQoQgCBDF5v++99b5457xD7Rrn6cyTuBCXiI8XLTo49P3mjPKgUatQXLyJZjNZq7hR1fg\n3wY7VltbC5faM1CrWNTskbWwXT7jiYvJ/DOUs4oKEdUl/lCppP35trquCjvP/4QKQzl8Pfxw6/Db\n0Mc7pF3PTS9ORW2D5e4mw0NGYnjIKFtRAwAPFy1G9h2DAf4DAVhG4fLKu+YK8+OpR2BorEWwdwjc\nNR7XvL+iqkIk5yVBgIDrwidBrVR3QUp50Jq9sHvXbqljkMywrNmxLd99ij8Py5A6Bl0D6ylRFjZ5\nO3hAj3F9/yB1DJhEEyAAw/qMxC3D/gAvNy+0d0y2vLYMgOX0Y4BXUIvb+XsFXPWca3EhLxGXy7Kh\nVmowrt9117w/k9mE46lHIEJEP79wBOmDr3mfchLkHYyt3/0odQySGZY1O5Ya+yPCA9rejuStaWG7\ndJGFTW4aG0XkZHpBr9VLHQUapQtuGfoHjAgZ1eHTk03ngpnF9l3YolG1b/5YS8pqSnEm8xQECBgf\nPhFaV89r2h8AxGefRaWhAi4qF4zp53j3ZhUEAeX5FbxfKF2BZc1O5ebmIlCVIHUM6iLWwpZzmoVN\nbqKjtRjgNU3qGAAAN40b/LS9OvXcUL8wCBAgQkRmSXqL210uywEAqBSqdp9ibY7RZMThSwdgFk0I\n79UfYX7hnd6XVXltGRJzzwMARvYdA1e1Y95FIsitDz775DOpY5CMsKzZqe/Xv407xxRJHYO6kLWw\nZZ9iYZMLURSRlOCJ8EB5XAV6LXw8fDG0zwgAllOTJ9OjUV5bDpPZBJPZiLKaUpxIO4a0ohQIEDA6\ndBzcNO6dPt7JjGhUGiqgc/XCuLAJXfI9nM6IgQgRendv9O8d2SX7lCOdhxdiT8RJHYNkhFeD2iFR\nFFGRfRReg/mB7miUSgEfzDNgwRrLVaIDInmVqJQuXXKBTpTPumrXakTIKOjdvZGcl4jk/CQk5ydd\n8bggCAj2DkFU4GAEtjKvrS2ZxelILbwEpaDE5MgboVJe+0dNbvll5FXkWopk37EQBMd+/zNVikhK\nSsKgQYOkjkIywJE1O3T2zEmM6p0sdQzqJtbCxhE26R07qsOYsClSx+gyZtGMukYDGkwNAKzrrKmg\nEJSWdc9EoMFYj+q6qhaX12hLdX01otOOAQBGho6Bj4dvl2Q/lxMLAPD28EWQd58u2aec9fXph88+\n/lzqGCQTHFmzQ7t+eA//HF8Drq3muDjCJr3iYqChog8UwY7xM60oijiUvA85ZdlQCkqMDBmN8N4D\n4P7Lqc6quiqkFl5EQu45FFUVIrcsB1MGTuvQCJZZNOPopYNoNDUgWN8HgwKHdEn2/Io8FFUVAgCG\nBjvOSGdr1Eo1UlNyYDKZoFQqpY5DEnOMdyEn0tjYCFSchUrJoubomo6wpVzin3dPO7DfGxPDpV+u\no6tczE9CTlk2BAiYNOAGDO0zwlbUAMDT1RMj+47B+LCJAIDssixcKuzYCH589lkUVRXCTe1+1a2k\nri37BQCAm9oNIT6hXbZfudMLPvhx6zapY5AMsKzZmb27tuLm/mlSx6AeYi1sWSdZ2HpSXZ2Iwlxv\nuLt2foK93FjvyenhokWob8v3nuzfO9K2yGxq4aV27z+/Ig8Jl89BIShxQ+RNcGnjSk2xnSvE1TXW\nIacsCwAQ1ivC4eeqNeXvFYCdW3dKHYNkgKdB7czZI9/hX5NM4ClQ56FUCljd5JRo/wE8JdrdjhzR\nYYjf76SO0WXqGutgaDQAAPTu3q1uKwgCtC6eKKstRUVtebuPkVaUAhEiRNGEXQnb27V9WlEKAGB4\nH8sdFZqTVZoBs2iGAMGpRtUAy59FZVEl6uvr4eLiInUckhBH1uyI2WyGUJ3kVD9ZkoXqlxG2zBiO\nsHU3s1nEpQue6NOr82uMyc+vBd9kNrW5tdHc2OkjCW38r7ltW/vh83JpNgDLHK7OrjFnz7yUPrz9\nFHFkzZ5EHzuE8cEtL2ZJjk2lFPDBYwYs+IgjbN3p/Hk3+GvGSR2jS7moXKFWatBoakCFofXRMpPZ\niJr6GgCAp6uu3ccYHzYRY9txO6ltcZtR21CDfn7htvlxSkXzE+hFUUR+ZT4AwM+zl1P+oBqgD8S+\nnfvxxz/9UeooJCGOrNmRwzs/ww2D6qWOQRKyFrYMjrB1m1MxnhjhYLcxEgTBdjeC2oYaZBS3PO81\npeCi7XZUfXz6tvsYKqUKGpWmzf+shUspKG1fa6msVdZVwGQ2AgC83KS/3ZcUBEFAUW5xp5dSIcfA\nkTU7IYoiTBWJvAqUoFIK+PAxA+Z/5AmFogrhEXwT7yq5uUqItdd+W6TuUl5bjsZf1kizqm2wjIKJ\nEFFaU4LahlrbYwIEeHv4QKlQYnjISOSUZaPR1IDjKUdQXV+N8F79bVeE1tRXI6XwEs5ftqyc76Hx\nwKDAwVdlWH/8U8vjLlrcMfrubvk+rcqbzJnTuXl167HkTN2gQcL5BAwdNlTqKCQRljU7kZBwDkN8\nU6WOQTLRtLCJqEREhNSJHMPBA16Y1P/3UsdoUUz6cRT+clqwOQeT9131tTtG3w0PFy08XXWYNmgG\nDl88gNqGGsRmnUZs1mkoBMuolln8dS6b3k2PKQOnQaNqeVJ7T/zYWN9YZ/v1td5U3p4F60Pw/Tc/\nsKw5MZY1O7Fr8xosGFoNXgVKVr8WNh3AwnbNampElBX6QeMn31JgmYrf+feAXp69cdvIvyCtKAU5\nZVkory1DvbEegAAPtQd8tL4I8emHfn5hUAgtz5K5lgwdeX5jkzstqBXqazqmPdOoNMhM45JNzkwQ\neSLcLryyZAqemX5S6hgkQ0aTiPkfuiJsYhUL2zXYvl0Hn5q56OXlfFcckvwlFyXhnc9WwM/PT+oo\nJAFeYGAHLl++jGDXFKljkEyplAI+nF+H9OOeSOWZ8k4xmURkpupY1Ei2AtwD8f0330sdgyTCsmYH\n9mzfiJsHlUodg2SMhe3anDntgb4ek6WOQdQiLw89zp05L3UMkgjLmh3ITTmOYF/OVaPWsbB1Xlys\nJ5dYsuIAACAASURBVKL6OMcNwsl+leaXWe4PTU6HZU3mRFEEavnJS+1jLWxpLGztlpmpgro+UuoY\nRG3yUnhj/779UscgCbCsydyFCxcQ5ZstdQyyIyqlgI9Y2Nrt8CEvTOg/XeoYRG0K8A7Erp946yln\nxLImcwd2fImboqqljkF2xlbYjrGwtaaiQkR1cQBUSq5iRPKnEBQoulwkdQySAMuazJXnxkKv5Xw1\n6jiVUsBHC1jYWnNgvx7j+/1B6hhE7WYyiCgqYmFzNixrMmYymaAw8Mbt1HlNC1s6/ypdobFRxOVM\nPbw8nPc2RmR/fDS+OLj/oNQxqIexrMnYqZMnMCY4R+oYZOeshe3SERa2pqKjtYjU3yx1DKIO8fXy\nw8njXCDd2bCsydixfV9jUmS91DHIAVjnsF1kYQNguco68bwnwgJ5yweyLwpBgfLiCqljUA9jWZOx\n+vJUuLlwvhp1DbVKwBoWNgDAxYsu8MJwqWMQdUpVWTWMRqPUMagHsazJlCiKgIGnQKlrWQubs58S\nPX5MhzFhN0gdg6hT3ER3nIs/J3UM6kEsazKVk5ODvp75UscgB6RWOfcp0aIioKGyDxQKvv2RffL3\nCsSenXuljkE9iO9WMhV9eCfGh5VJHYMclDOfEj14wBsTw7hcB9kvN40bcjJ45sWZsKzJVFrSEYQH\ncL4adZ+mhS0jQ+o0PaOuTkRRrjfcXd2ljkJ0TSpKeJGBM2FZkytDDgSBZY26l7WwJR9yjsJ25IgO\nQ3r/XuoYRNfMWGtGSUmJ1DGoh7CsyZDRaISi/rLUMchJqFUC1ixw/MJmNotISfZEsG+w1FGIrpm3\nxgcHDxySOgb1EJY1GUpMTMSQ3ry4gHpO08KWni5KHadbnDvnht6a8VLHIOoSfl69EHMsRuoY1ENY\n1mQo5vCPGB9hkDoGORlrYbt4WOeQhe30SR1GhI6TOgZRl1AICpQVlUsdg3oIy5oMleYlwVfH+WrU\n8xy1sOXmKiHW9JM6BlGXqiqtgslkkjoG9QCWNTlqKJQ6ATkx2ynRwzpkZDhGYTt4wAuTBsyUOgZR\nl9KILsjJ4RIezoBlTY7qWdZIWmqVgI8X1OHCIfsvbDU1IsqL/KBRaaSOQtSl3JUeSDifIHUM6gEs\nazJTWloKb02p1DGIHKawHTzohTFBt0odg6jL+Xj64txZ3nbKGbCsyUxiwjkM9ufaOSQP1sKWZKeF\nzWQSkZmqg69XL6mjEHU5F7ULCguKpI5BPYBlTWaS4g5hUB9OGCX5UKsEfDy/DkkH7a+wnTrlgVAP\n3rCdHJehiisHOAOWNZkpK0iBt5ZXgpK8aNT2eUr0XKwnBvYZKnUMom5TW82y5gxY1uSGFxeQTGnU\nlltT2csIW3q6CprGgVLHIOpW9bX1aGxslDoGdTOWNbnhsh0kY9YRtqSDOmRmyruwHTnkhev63yx1\nDKJu5QI3pKenSx2DuhnLmozU1NTAQ+CVoCRv1sKWeEC+ha28XERtWQBUCpXUUYi6lU6jw7n481LH\noG7GsiYjmZmZCPXm7UNI/uRe2A4c8Mb4fn+UOgZRt/P29EFCPNdac3QsazKSlXERId61Uscgahe5\nFrbGRhG5mV7QueukjkLU7VRKFcpL+EO+o2NZk5HstHMI6cUrQcl+WAtbgowK2/HjWkR6c64aOY/a\nKv6Q7+hY1mSkvDgHeg+pUxB1jEYtYK1MCpsoikg6r0NYQISkOYh6EpfvcHwsa3LSWA5B4Mga2R9b\nYduvQ3a2dIUtOdkVemGEZMcnkkKjoREGAwubI2NZk5PGCqkTEHWaRi1g7cI6nNsnXWGLPuaJ0WHX\nS3JsIqm4KtyRlZUldQzqRixrcmJkWSP7Zh1hk6KwFRUJaKwKgULBtzVyLipRhZJi3lPakfFdTSZE\nUQQaeUUP2T9rYYvf27OF7cB+PSZG3NpjxyOSCzcXN+Tl5kkdg7oRy5pMlJWVwdu1WuoYRF1Coxbw\nycI6nOuhwlZXJ6Io1xtuGvduPxaR3Lhp3JGfly91DOpGLGsykZub+//s3Xd4VHW+x/H3mZ7e6b0G\npIMKKohIs4usomvbxbaWVa+67lVU7Kura1nXftUVbEgVQRRBqQpI7z0BUklCyiSZyZRz7h9DQpAk\nJGEmZ2byfT3P7hMzZ875JExmvudXaRNr1zuGEH5TOYatKVrYVq6MpW/L8QG9hhDBKsIaQf7RfL1j\niAAK+WLt5ptvJjU1lVGjRukd5YwcO5ZPYqRT7xhC+FVTFGyqqnFgbyxtktoG5PxCBDujwYijXD4/\nwlnIF2uVQn3Ji8K8DOKjgmNRUSH8yVpVsMWRkeH/1/jWrRG0spzj9/MKEUrcFW69I4gAatAux2+9\n9RZvv/12jY8ZjUaio6OJj4+nZ8+e9O/fn/Hjx9O2rb53ux988AFWq5Vbb71V1xynU3Qsh4To0C44\nhaiNr2BzcNu7sTC6hHbt/Pda3/BbLCM7DvHb+YQIRW6XFGvhrNEta5GRkcTGxlb9z2q1UlJSwuHD\nh1m8eDGvvPIKF198MXfddRdHjhzxZ+Z6y8vL47XXXmPatGm6XL8hCgtyZPcCEdYqW9g2/xjrtxa2\nzEwjiqOzX84lRCiTYi28Nahlrbq3336bYcOGnfQ9VVU5dOgQmzdvZu7cuaxbt47ly5ezbt06Xn75\nZcaOHXvGgRtiy5YtTXq9M+F2lmIxh27LWsYxIzN+iWb1vgiO5JtwuBSibCpdW7gZnurkuqGlJMWo\nDTrn69/F8cFPvs24rx5Sxj+uP9bgXGv3W/nTey3qfXxqazdzH655VlWpU2H2umh+2hHB3mwzdocB\nq1mjbaKHs7tUMGlYKT1ayxtmXaxmhY/vcTL5nVgYc+YtbMuXxTGsm0wsEEK6QcNbo4u1mhgMBjp3\n7kznzp2ZMGECy5Yt47HHHqOwsJCHHnqI999/n/PPb7rVxTdv3txk1zpjauhuxPvVL9H845t4XF7f\nB68CmI0adoeBTYesbDpk5b8rYvjXTQVc0LN+g2DX7LPy4fFCrfKcZyrSomEy1N2iE2WruaDcmGbh\nwWnJ5NmNVXnMRg2HS2Ffjpl9OWZm/BrN/eOLufPiEj+kDV8nF2zFtGvXuAb+0lKN4qPJWFIsfk4o\nROhxuzx6RxAB5Ndi7fdGjhzJhx9+yM0334zD4eCJJ57g+++/x2q1nnLsmjVrmDVrFps3byYvLw+D\nwUBiYiKDBw9mwoQJp7Ti1eV///d/mTdvXtV/Z2ZmkpqaCsDZZ5/N9OnTTzp+x44dzJgxg02bNpGZ\nmYnT6SQyMpKOHTsyYsQIbr75ZhITExv5W6gnb2jO5PluUyTPzEkAoH2ihwcvKWZELwfRNo2jJQa+\n3xzFvxbGUeIwcP9/k1n4aDatE7x1nrOw1MCjXyShAUYFvH7oMVOA927L4+yuFQ1+7qF8E7d/2AKH\nSyHSovGX0cVcPaSMlFgVu0Nh3QEbz89NIKfYyOuL4uiY4mZcP9mnry4nCra4Rhdsy5fHM7idLIIr\nBIBHukHDWsBng/bp04fbbrsNgOzsbL788suTHne5XDzyyCP86U9/YsGCBWRmZqKqKi6Xi6ysLObP\nn8+f//xnHn74YTye+t05REREEBsbi9F4vBVEUarG1kVFnTww7N1332XixIl8/fXX7Nu3D4fDgdVq\npaysjB07dvDuu+9y2WWXsWPHDj/8NurgDb0Pd7cX/jE/HoCEKJX/3n2USweWE23zVVctYlVuGWHn\noct822g53ArTVsac9ryPzUgiz25kZC8HLePrLuyawmsL43G4FAwK/OumfO4YZScl1tcCFxOhcXEf\nB//+Uz6m439NHyyNreNsolJlwbb5xzgyMhrWRe71ahw+GEtSXEqA0gkRWlSvRkVFw29GRWhokqU7\nbr31VkwmXyPeDz/8cNJjU6dOZcGCBSiKws0338x3333H1q1bq8a9jRs3DoCFCxfy7LPP1ut6U6dO\nZd26dQwaNAiANm3asG7dOtatW8d7771XddyaNWt48803AejQoQOffPIJ27ZtY9OmTfz2228899xz\nREVFUVhYyIMPPljvYrFRQrBY25RuJd9uRAFuOK+UNrW0mN1wnr2qkNmYdmqranXTVkazfJeNaKvG\nlAmFaDqvZuLywLKdNhRgUOcKRvauuQW0b3sXAzr53ih3ZVqQ4SP1U71gy8ysf8G2fn0UnaKHBzCZ\nEKHFbLBw7FjDx/WK0NAkxVpMTAz9+vUDYOvWrTidvg+8jRs3MnfuXADuu+8+pkyZQufOnVEUBbPZ\nTGpqKm+++SbXXHMNADNnzmTv3r1+yzVz5kzA1/L24osvMmzYsKqiMjo6mmuvvZb7778fgCNHjrB2\n7Vq/XfsUmitw5w6Qfh1cfPNwDh/deZRrz619qyyLCaKPjwWzO2t/ye3OMvPqggQUYOrEY7RL1L9V\nzWyEeY/kMO2eozxxdWGdxyZGnSg26vo5xckqC7ZNi+PrXbBt3RxDj7ZnBTiZEKHDpJkpKJDN3MNV\nk32idOrUCfDNGM3LywPgiy++ACAuLo677rqr1ufee++9gG+z8wULFvgt04svvsjPP//MnDlzqlrh\nfu/cc8+t+nrfvn1+u/YpNP0Lk4aymTV6tHYzrEcFrerorrQ7FIrKfS+1lnE1H+dwKTw0PRm31zfz\n8/JBwTHhQlGgc4qHIV0q6Nmm7uayIwW+Qt9i0oiPbFi3XnPnK9gc9SrY0tJM2NypTZRMiNBgQJFu\n0DAW0AkG1cXH+8Y2aZpGYWEh7du3Z926dQD06NGjqkWrJm3btqVNmzZkZWWxdetWv2WyWq20bt2a\n1q1b13pMdHR01ddlZWV+u/bvKSFYrNXXrHUnfodj+tZchL0wN4G0PBOdkj08eU3dLViNVVqh8PGy\nGJZuj2BfjgWHSyEmQqV3WxeXDCjnqsFlmIyNO/eODDO7sswAjOztaPR5mrPKgm3yO/Ewtoi2bWu+\nl1y1Io5zuoX29nJCBILXG76fI81dkxVrLpevm09RFCwWCxUVFRw9ehSATZs2MWRI3SuQOxy+MV3+\nXmBXVVWWLFnC0qVLSU9Pp6CggJKSElRVrXq8khbIAVRhWqztzzHx1vdxAHRI8vCHGrpLF22OZPZv\nUViMGq/dnE+Exf+/Zw148NNkXF4FBV/rl6ZBUZmB1XttrN5r4/PVMbw7Oa/W1r/alFUoPPZVEgAW\no8Z9Y4v9nr+5OLHTQRyMLT6lYCss1CgvbIWpdZO9dQkRIpTAjqsWumqyd7zqAx8TExMpLj7xgebx\neOrVaqUoCuXl/usey8/P59577z1p8Vy99hjVwrBYO5hr4rYPWuBwK8TYNN68NR/L715xmceMPDXT\ntyzKQ5cV06tt4Ebmx0Wq3D2mhDF9y0mOUdE02JRu4d0lcazaY2NXppk7P0xh5oM5p+SsTalT4d5P\nUtiXY0YBnrqmkG6t5A3zTNgsvp0OairYli9L4JxOl+uYTojgZFAMuN0ysylcNVmxVlkQxcbGkpKS\nctJAyEsuuYTXX3+9qaJUefDBB6tyDR48mNtvv52zzjqL+Ph4LBbfQpuZmZlcfPHFAc+iEF7F2uo9\nNh6clkxphUKERePtP+eR+rsxXx4vPPK575gRqU5uHWH3e47OLdxMnViIQdEY08dBQvSJllJFgUGd\nXXx4Rx7/My2J77dGsvf44rY3D699wkSljGNG7v4ohf25vkLtkcuLmHhu4LrKm5PKgm3yO3Eo44tp\n08aAy6WRdSSevqmyNIoQv2dQDNKyFsaapFjbv38/mZmZAJx33nlV655VKiwMzBilumzatIn169cD\nMHDgQKZPn47BcOoYmcru20ALaBdrE/tsVTQvfZOAV4MWsV7enZxH73an3vH9Z3Ecmw9ZSInx8tIN\ngZnF1CJW5fphpy+8/veqIhZvi0TVYOGmqNMWa+sPWrn/02QKywyYjfDstce4eogUav5ksxxfOPfd\neJTxRezdG0PP+MDfOAkRigyKAY9birVw1STF2gcffFD19ZVXXgmAxWKhffv2HDlyxK/LcdRX9UVu\nJ02aVGOhBgGeAVpdGBRrLg88NyeRWet8Cw/3be/irT/l1zgGbO1+Kx8sjcVshDduySchqu4ZgIH+\n7bSM89KlhZv9uWb255rrPParX6J5YV4CHhWSo1VevyWfIV1kFlYg2CwKH9/t4Jb/xLAj28iwTgbS\nc9P1jiVE0Cm051NaevobUxGaAl6s/fjjj8yfPx+Afv36cdFFF1U9NmTIEI4cOcKxY8dYs2YNQ4cO\nrfU8M2fOZOjQobRv394vuaq/qCtnqtbkq6++8sv1Ti+0i7VSp8I9H6fw20ErCr7lN57+w7Fax37N\nWx+Fhm8XhBvfbnna889bH8W89b4i8N4xxdw3zv/7b1buvFDhrn3c4svz4/nvCt8uDHUVo8J/TAaN\n7DwNoxZBt0G5escRIijlHC3B6ZTW/XAV0GJt/vz5PPnkk4BvrNrLL7980uPXXXdd1aK4r776Kl98\n8UXVWLHqvvrqK55++mkAPvzwQ4YPr9/K5Wazr4XEbj91LFSrVq2qvt65cycjR4485ZhPPvmEnTt3\nVv13TecRvjXSbv+gBVsOWzAo8NClRdx2Uf1+V6ebzlG9hK08NlBzQHKLfettJEbXXHy9MC+Bz1b5\nliEZ16+cf/6xoN4TEUTjOF0qFz9po6O1DANWunRIoFvnJL1jCRF09hw4SnKKbL8Wrhr1UVPbjElN\n08jPz2fdunV8+eWXVWPC4uPjeeutt+jcufNJxw8cOJAJEyYwd+5ctm/fzuTJk3n00Ufp06cP4Bvc\n/8UXX/Df//4XgAEDBtS7UAOq1k8rKSnhs88+Y8yYMRQXF9O1a1fOP/98TCYTHo+Hjz/+mNTUVEaM\nGIHBYGDPnj18/PHHfPvtt7zzzjtMmTKFwsJCli9fzj333ENcXFxDf2Wnp4TmiveaBg9NT6oq1F6Y\nVL+xW1MnFjLlNDsCaBpc+WprcoqNXD6wnKkTfTOKLab6tULaHQp/+SiF7CITw7o7eWFS7Vux7M02\nk13kK9YGdTq1S/PjZTFVhdofzinjuetkW5dAKylXGfOkle5RdqIsGj1MBXw2ayNP/22M3tGECDqq\nqmI01T2EQ4SuRhVrmqZxzz33VG2UXsnhcJy0KJ+iKFx44YVMnTq11oVnn3nmGVwuFwsXLmT9+vVc\nd911GI1GFOXkNWP69evHf/7znwblHDduHLNnzwbg+eef5/nnnwdg/fr1pKSkcP/99/Paa69RWlp6\n0s/j9XoxGAxMmTKFUaNGMWLECL755hvS09MZOnQoSUlJrFq1qkFZTksJzSaaaStjWLYrAgV4ckJh\nvQfZ28wa1ON9pfK+wGzUqrop6ysmQsPlUcguMrJgYyQ3nGenT/tTJzp4VXjp+Ib0Cpwyo3PbYQuv\nf+d7/NIB5VKoNYGcQpXLn7XSO9ZOxPHXicEA9uxDbN2VQ79ereo+gRDNjNerYTLKatzhqkEVQmWL\nmqIoVft7Vmc2m0lKSqJ9+/acc845XHLJJfTo0aPOc1osFv71r39x3XXXMXv2bDZt2kR+fj5er5cW\nLVrQu3dvLr/8ci677LJaW/Rq+/6IESN48sknmTZtGllZWURFRdG5c+eq3RLuvPNOOnTowOeff87u\n3btxOBwkJiYydOhQbr31Vs46y7f34KOPPordbmf9+vWoqlrV8udXhtC7IzpWauDN4wveXtTbwfXn\n6TO4tdcjvnGMbRK8LJ2SddJjj15RxJ/ea4HL6+uqvW9cMVcNLiMmQsPjhY3pVv79fRwbjm8wP75/\nORf0PPm1/fzxyQQtYr08d60UaoGWlqNy7UtW+sbbT+lm7hZZxNfzNtM3dZxuayIKEYxUVZOWtTCm\naOG0ZkQIe+Xh83nkoo16x2iQj36O4dWFvhYnm1nDVI+eXEWBbx7OpnVC/Qblj3q+DdlFRiYMKePF\n62sulCqLtbYJXpb8rlgD+H5LBE98nURZxYkP9wizhtOtVI2Jq21SxLbDFq77t28ChMlwvEWwHj/j\nU9ccC5r9TUPJtjSVP79ppX+CvdYtuw7ao5lw0+UMHdyuacMJEcQ2b8/krPPur3Oinghdodn3Fo5C\nsBvU4Tre0opvBmV9F69oyN2Bopx+EkJlhtqM7+9gSJcsZq6JZuVuG2l5ZkqdBqKsGq3iPQzqXMEf\nzi2jb/tT19RzuE/8jF6Vkwq+unhUafVpqNU7NR78wMqARDvGOgr/LjGlzFu0hXMHtZXWNSGO82ra\nKUOTRPiQlrUg8crfRvPIhav1jiGELhauh+e+sNA3oQRDPeqvw2URjLr6Ei4e3iXw4YQIAb9uyOCi\nK5+oGr4jwktoTkEMRyE4Zk0If/h8uYEXv7TQr56FGkCHKAffL92B11v3YspCNBel5V4SExP1jiEC\nRIq1YBGC3aBCnKm3F5r4cKGJs+JLGrx+XkTxQRYubfrdT4QIRvZyLwkJCXrHEAEixVqwMEixJpqX\nF742M2+lgZ6xDS/UANrGuFm+ajdut+wgIYTHAzabTe8YIkCkWAsWyqk7NwgRrv72XwsrN0OX6DPb\nNiyubD9zFu08/YFChDmDUT5DwpkUa0FCMUXqHUGIJvGXd63s2qfSMfLMt29rGQVr1u7F4Tx1Jq8Q\nzYnBKOOew5kUa0FCMUejqjIxV4S3G1+zkp3ppm2E/xZQTqlIY8Y32/12PiFCkSKT1MKaFGtBIj6x\nNUX126lJiJCjqipXvWDFWeiilc2/iwUnRcKWzQewl566q4oQzYVRWtbCmhRrQSKlVUfyiqVlTYQf\nVVUZ90wEtgonyVZHQK7RxnuIz2ZvCci5hQgFMmYtvEmxFiSSW3Um3y7/HCK8uFwqF02xkayVE2+p\n7x4XDRdrgz070igolOZp0TzJmLXwJtVBkGjRogV5ZdF6xxDCb8qdKiOn2GhvKSPW4g749ToaMpg+\nc3PAryNEMDIYpGUtnEmxFiRSUlLIK5MZoSI8HCtRufBxK10jS4gyN806aJEWSNuXTnbumS0HIkSo\ncVa4iYqK0TuGCCAp1oJEbGwsJU6r3jGEOGMZ+Spjp1rpHWsnsol7ZrqZs5k2c1PTXlQInZXYnSQm\nt9A7hgggKdaChKIoYIrSO4YQZ2RPhsrVz1vpG2/HpsOmHBYT5KSnk364sOkvLoROikucJKe00juG\nCCAp1oKIZpRmbBG6NuxTufFVK/0T7ViM+uXoZstj+qyN+gUQoonl5Dvp2rW73jFEAEmxFkzMiXon\nEKJRftqqcfc7VgYm2jHp/K5iMkBh5iF27svTN4gQTeToMTedOnXSO4YIICnWgoklQe8EQjTYnF8N\nPPGphf6JdgxB8o7SPeIYX82VsWuimVBsWK0y5jmcBclbqwCIimtLqUMWxhWh4+Mfjbw+20SfeDsG\nRe80JxgM4MhJZ/3WLL2jCBFwJousJBDupFgLIp17DiItV4o1ERr+Nc/EZ0uM9I4vQQmiQq1Sl8gS\n5ny7GU2TvykR3owmKdbCnRRrQaRr9z6k5cvCuCL4PfWFmR/WKnSPCd41zQwG0ArSWbH2kN5RhAgY\nj1fFapPJaeFOirUg0rFjR9IL4/SOIUSdHvjQwvrt0DnKrneU0+oUXc7CxdtQVWldE+EpN6+ETl16\n6B1DBJgUa0HEarXiUuL1jiFErSb/20raIS/tI4O/UKtkKUpj8fL9escQIiCyckrpmdpX7xgiwKRY\nCzayfIcIUtf900ZRnpvWttDaLL19tIsly3bi8TTNtldCNKXsfDddu3bVO4YIMCnWgoxmluU7RHBR\nVZXLnrWiljpIsZbrHadRou0Hmb94j94xhPC7CreR2NhYvWOIAJNiLciYIlvh9sj4GhEcPB6V0U/Z\niPGUk2Sp0DtOo7WO9rLq1z1UuDx6RxHCr0xm2aawOZBiLcik9r+QPZlSrAn9OV0qF06x0cpQRpzF\nrXecM5ZUfoCZC3boHUMIvzJbpFhrDqRYCzL9Bw1ja6Z0hQp9lZSrXPiYlc62UmIs4THWKzkKNqzf\nR1m5S+8oQviFpmkYZUHcZkGKtSDTpk0bMu0pescQzdjRIpVRU6ykxtiJMqt6x/GrVu50vpi7Ve8Y\nQvhFbp6dTp1k2Y7mQIq1IKMoCtha6R1DNFOHclUufdpKnzg7NpPeafwvPgJ2bDtAUYlD7yhCnLHd\nB4oYdv5IvWOIJiDFWjCSYk3oYMchlYn/sNIvwY41DAu1Sh04wmezt+gdQ4gzdrRQo0uXLnrHEE1A\nirUglNCyBwUlMslANJ1fdmn86XUrAxLtmI16pwmsKAvs25XG0fzQWdhXiJpYbHG+3hgR9qRYC0L9\nzh7NtsNh3LQhgsqiDfDQBxYGJNkxNpN3hC6mTKbN3KR3DCEaTVU1rDaZjNZcNJO35tDSp08/tufI\nJAMReF+uNPD8Fxb6JdoxNKMbdJsJMg4cIiOrSO8oQjTK4cxCevcZrHcM0USkWAtCUVFRlNNS7xgi\nzL3znYn35ps4K76E5tiT0t2Sy6fSuiZC1N40O0PPG653DNFEpFgLUlpEB70jiDD24iwLc1YYSI1r\nnoUagMkE+YfT2Z9WoHcUIRqsqNRAmzZt9I4hmogUa0Eqpf0A8oplkoHwv79/amHFBo2u0SV6R9Fd\nj4gCPpu9Ue8YQjSYxRandwTRhKRYC1Lnj7qGX/bJNiLCv+5+z8r2vSodo2QmJIDBAPasQ2zdlaN3\nFCHqzePxEhmdpHcM0YSkWAtSPXv2ZE+BNHEL/7npNRtZGR7aRZTqHSWodIssYsa8TWiatGSL0HDg\nUAH9Bw3TO4ZoQlKsBSlFUdBsMm5NnDlVVZnwDyuOwgpa2cr0jhN0DAbwHD3E2o2ZekcRol72HSrj\n3HPP0zuGaEJSrAUxa0I3Ktxyty8aT1VVxj8TgdnhJNkqWyzVpktMKfMWbZHWNRESHBUWkpKkG7Q5\nkWItiA0+7wo2HAjz5eRFwLhcKhdNsZFIOQmWCr3jBD1jYTo/rUrTO4YQp2W2xesdQTQxKdaCyPFq\nzgAAIABJREFU2Nnnns9vR1rrHUOEoHKnysgpNtpZyogzu/WOExI6RDn4ful2vF5V7yhC1Cq/oJQO\nnXrpHUM0MSnWglhERAQOQzu9Y4gQc6xEZeTjNrpG2Yk2e/WOE1IiitNYuHSv3jGEqNX6bflcdsVE\nvWOIJibFWpAzxfXA7ZFxNKJ+sgpUxk610iumhEiTvG4aqm2Mm+WrduN2S5ErgpPdaZXFcJsh2S08\nyA0bdT1r1nzJ8N4evaOIILcvU+WGV6z0jbdjkb/sRost28+cRTuZdGVfvaM0yMHDRbz/2SYycnxr\n6L359GiSEyNrPPb+qT9SUNiwCSe9uiXxxP3nNziXx6Oyan0G67dkk55RTEmpC0WBmCgLHdvGMbhv\nKy44ux0Wy+nH5xaVOPnoq61s3O5bF++uGwcw4tzmM2ve61WJiJatCJsjeUsPckPPG8FrMzswvPdB\nvaOIILZxv8qd/7HSP9GOSdrLz0irKFi7bh9Xju1JhM2id5zTcru9zF60hwVLD9R7NmukzYwjon43\ngA6nB03TMBgavi9ZRrad1z5cR27+iSVjTEYDqlejsNhJYbGTzTtz+XbJPh6561zatoqp9VyrfjvC\ntFnbKXNUH4PZvPZK23Mgn2EXXK93DKEDKdaCnMlkwhvRFZBiTdRs2TaNRz+2MjDRjkEKNb9Idh5k\nxjfb+dOkQXpHqdP+9ELe/3wzWbl2LGYjrVtGcyij+LTPe+mxkfU6f9qRIp56dSUaMHZE5wZls5e5\neOmdXyksdmIyGbhydHdGDutAUkIEXq9KVm4p3/y4j183ZHK0oJxX3l/LK49fhNl8cgtbYbGTj77a\nwqYduQD06JzI3rRjDcoSLnbsL+O6O0bpHUPoQN7aQ0CLTkM5WiTjj8Sp5q5ReOwTC/2lUPOrpEjY\nsvkA9lKn3lFqlVdQztOvryIr107n9vG88OiFDO7Tym/n93hV3v9sM6qmMXRgW4b0a9jM9EU/H6Sw\n2Pf7u+OGAUy8tCdJCREAGI0G2reJ5b5bBzPoeOa8gvKqgqy6Z95YxaYducTFWHnkznO57vLUM/zJ\nQpfRkoDVatU7htCBvL2HgHFX3sLiHQl6xxBB5r9LTbw2y0zfBDuN6KESp9HGe4jPZm/RO0atXG4v\nCjBhXA+efXg4bVpGo+G/m7pvftjHkewSoiIt3HptnwY/3+3x0rVjAl07JnDB2bXPah82uG3V11m5\np26F5nJ5GdKvNS8/fhED+7T0408YWuylTlq06qJ3DKET6QYNAe3atSOjvDOwSe8oIki8Nt/Et6sM\n9IorQZFCLSBibbBzRxoFhX1ISojSO84poiPNPP3QBXTt6P8bucNZJcxb7FvC5I9X9SY2uuGtOTde\nfVa9jjNXG2QZGWE+5fHbru/P4L7+azEMVRu2HeWKG/6qdwyhE2lZCxFKTHdUtbneU4rqpn5pZtEv\nBnrESqEWaJ0MGUyftVnvGDWKi7UFpFDTNI3/+3ILqqrRvXMiI4cFbralqmosXZ0OgMlkYFCfU2c6\nSqHmk1tooHv37nrHEDqRYi1EDB4+UbaeEjz4kYXftkGX6BK9ozQLkRZI25tOdm7z+X3/siGTA4cK\nAbhpQv1axxrKWeFhw7YcXn53Ddt252EyGrjzjwNqXWqkudM0DVtkCorcnTVb0g0aIi68aDz/XtiR\ns7vL3oXN1W1vWcnL8dA+suz0Bwu/6WbOZtqsTfz93gv1jhJwHo/KjG93AXDuwDZ06+S/lrvdBwp4\n9f21eDwabo9v0eHkxEhGX9CJ8SO70LpFtN+uFW7SjxQyYNBYvWMIHUnLWoiwWCxU2HrXex0lEV4m\nvWLlWK6b1hFSqDU1iwmy09JJP1yod5SAW7H2CAWFDhRFYcK4Hn49t6ZqOJyeqkINwOFwk3/MQW6e\nvK7rsnlXMaPHXqJ3DKEjaVkLIf2GXcO2Q9/Rr5MUbM2Fqqpc+UIENlcFLWzBu4xEuOtuy2P6rI08\n+dDFekcJGFXVmL9kHwADeregfZtYv56/V/dkPv/3lXi8KkXFTtKOFPPD8jQ278xl885cRp3fkdsm\n9ffrNcOFV4knNta//x4itEjLWggZe+lEFu/tpHcM0UQ8HpUxT9mIcjtIskqhpieTAQozDrFrX57e\nUQJmy86j5BWUA3Dx+Z0Cdh2T0UByYiRn92/NE/efx5jhvsV2f1p9iGVrDgfsuqHqUEYhfQdcoHcM\noTMp1kKI1WrFZeslXaHNgNOlMnKKjZbGcuItLr3jCKB75DG+nBu+y+f89MshAOJirPTv3aLJrnvD\nVb2wWX2dPItXyJjc3/ttaxETJk7SO4bQmRRrIabfsIlsOyT/bOGspFxl5ONWOtlKiTHXb/9GEXgG\nAzhy0lm/NUvvKH7ncHrYsusoAIP6tmrUPqCNZbWYaNPSN7kgM9veZNcNBZqmoVhSiIiI0DuK0Jl8\n6oeYsZdO5EfpCg1bR4tULp5ipUeUnSizqncc8TtdIkuY8+3msGvd3rrrKF6v7/XWLzXljM9XbK/g\nXx+s48lXVzB9zvbTHu89voZkUxaJoWD3/nyGj7xU7xgiCEixFmKsVisV0hUalg7lqlz6tJWz4uzU\nsJC7CAIGA6gF6axcG15jq7bvOTEWr2fXpDM+X0yUhV378zl4uIi1m7LqXNDb5fKSlePbZqplcvDt\nFKGnLXvKGCOzQAVSrIWkfsMmsv2w/NOFkx2HVCb+w0q/BDtWmaMd1DpHl7Pgh61htaPIoUzfor/R\nURbiYs58o3CDQana87Ow2Mm3S/bXeuyCn/ZXLecxpL/sVlDJ61WJjGmDySRvCEKW7ghJYy+dyJuP\nvEjfjgf0jiL8YM1ulb++Z2VAoh2j1OAhwVKcxuLlBxh/UTfdMmRk23E43Sd971jRiVnDaUeKKSw+\n8d+KotCpXRwm06kvsiNZvmKtVUrDWrZuvH8+AMkJkbz5zOiTHpt4SU82bM2h2F7BzIW7KXe4GX1B\nJ1KSfLsU5OaV8cOKNH5YfhCApPgILr2o6+9+HgcFhY6Tvld9XFtufhn70wtP6mlomRLVqL1Mg83G\n7TmMv+wevWOIICHFWgiyWq2osYNxe/ZjNskYj1D2/UaYOt3KgCQ7MlwndLSPdrFk2Q5Gj+iCSacK\n+5Ovt7L7QEGtj7/x0W+nfO/Np0efsqWTy+XF5fa1bEU1tv+9htdufKyNx+8bxhsfrSf7aCkLlu5n\nwdL9Vb8vj/fEmMw2LaP5n9vPOWUj959+OcTc7/fWetl5P+xl3g8nP37XjQMYcW7g9jNtKvuPeLlr\n2Hl6xxBBQoq1EHXpdQ/w3dzvuGpIqd5RRCPNWKnw5jwz/RNlQ/ZQFG0/yPwfdnPNpb11ub6/XjPl\n1VrnbDb/fiS0ax3LS4+NZPVvGazfmkN6RjGlZS40DRLjI+jQNpaz+7fmgiHtamzxU2qqAk8r9P+Y\nKlwe4pM6yl6gooqiyUj1kPX8AxcyZcw6vWOIRnh3kYkvf1JIjbVLoRbC9qhd+cdTV2C1yH2v8J9l\nvx7hkmufpHfvs/SOIoKEjJAJYa16jCX7mNTaoeal2WZmLTPQK04KtVCX5DjAzAU79I4hwkxuoUkK\nNXESKdZC2LU33cfMje31jiEa4O/TLPy8HrrFlOgdRfhBchRs2LCfsnLZZUL4R1FxOW076NO1LoKX\nFGshLC4uDru5n6y5FiLuec/K9j0qnaJklfZw0sqVxpfztuodQ4SJn9bkcsuf79Y7hggyUqyFuBGX\n3cmKXTa9Y4jTuPl1K5kZHtpFyISQcBMfAdu3HKCoxHH6g4Wog8fjRbG0JiEhQe8oIshIsRbihl84\nllWHe+gdQ9Thmn9YKTvmopWtTO8oIkDaK0eYPmuz3jFEiFuxLpM/3nSX3jFEEJJiLcQpikJil9Hk\nFUtXaLBRVZWxUy0YHU5SrNLqEs6iLbB/dzpH86WLWzTe0eIIep/VR+8YIghJsRYGbrzt73y+rqPe\nMUQ1Ho/KRVNsJGoOEi0VescRTaCLKZNpM6V1TTTOrn15DL/wCr1jiCAlxVoYiIuLoyJmKBVuaV0L\nBuVOlRGP2WhnKSPO4j79E0RYsJkg40A6GVlFekcRIWjjbgeXXzlB7xgiSEmxFiaum/wUX69L0jtG\ns1dUqjLycStdI+1Em716xxFNrLsll2mzNukdQ4SY/GNldO42CINBPpJFzeSVESa6dOlKmnOgLOOh\no6wCldFPWukVYyfSLP8OzZHJBEcPpbM/rfY9O4X4vWVrj3KrLNch6iDFWhi5+OoH+GlH5OkPFH53\nIFvlimet9I2zY2vkXtgiPPSMKOCz2Rv1jiFChMvlwRLVjtjYWL2jiCAmxVoYOX/EaFZnyEyiprbp\ngMqkl60MSLIjW0QKgwHsWYfYuitH7ygiBCxfm8VNt96jdwwR5KRYCyOKotDz3EnszpB/1qayYrvG\nXW9ZGZBoxyS/dnFct8givp4nM0NF3TRN41hZJN27y1qZom7y8RJmJl5/J3O299Q7RrMwf63C3z+2\n0j/JjlH+kkQ1BgO4j6azZkOG3lFEENu++yijxkzUO4YIAfIRE2bMZjPJ3a8gI1/vJOFt2s9GXplp\nom9CCQZF7zQiGHWJKWXeoi0y6UfUasteN+PGX6Z3DBECpFgLQ7fe9RjT10uzeqC88a2ZTxYZ6RVn\nR5FCTdTBWJjOz6vT9I4hgtC23XlcPO46FHkTEfUgxVoYstlsJPe4hiN5ckfvb898ZWHhaoUesSVS\nqInT6hDl4PulO/B6Vb2jiCCzea+Ly664Wu8YIkRIsRambrnz73y2Qcau+dNDH1tZs1WjS3SJ3lFE\nCLEVHeS7n/bpHUMEkQ3bcrj8qlukVU3UmxRrYcpms5HSU1rX/OWO/1jZd9BLh0jZqFs0TNsYN8tW\n7sLtlh0thG8G6K50jdFjxusdRYQQKdbC2M13/J3p61P1jhHyJr1iJT/XQxtbqd5RRIiKLdvPnEU7\n9Y4hgsC6zTlcc+3tescQIUaKtTBms9lo0esajuTpnSQ0qarK5c9Z8dpdtLCW6R1HhLBWUbB23T6c\nTrfeUYSONE1jf6aB4SNG6h1FhBgp1sLcLXf8nenrZexaQ3m9KmOmRhDldpBkcegdR4SBZOdBZszf\nrncMoaNVv2Vxw0336h1DhCAp1sKc1WqldZ/rOJgrA1nry+VSufBxGy2VMuItLr3jiDCRFAmbN+3H\nXurUO4rQgapqZORbOfucc/WOIkKQFGvNwC13PMq09bJnaH3Yy1VGPG6lo62UGItH7zgizLTxHuKz\n2Vv0jiF0sGJtJjf/+X69Y4gQJcVaM2AymThn/IOs3mPTO0pQyy9WGTXFSo8oO9FmWRdL+F+sDXbv\nTKOgUMZANicer0puSTT9+w/UO4oIUVKsNROXXnkD3x8cjKrKUh41OZynMn6qlbPi7ESY9U4jwlkn\nJYPps2ST9+bk518ymHz7/+gdQ4QwKdaaCUVRuO6Ol5m5LlHvKEFn1xGVa16w0i/BjtWkdxoR7iIt\nkLY3nexcWVy5OXA4XZS4Ukjt1VvvKCKESbHWjPQbMJj97gtxVEjrWqV1e1Ru+ZeV/ol2zEa904jm\nops5m2mzNukdQzSBhT9l8j+PTNU7hghxUqw1M3c89Ab/t6q93jGCwuJN8Nf3rAxIsmOSvwTRhCwm\nyE5LJ/1wod5RRABl5JTQrutQUlJS9I4iQpx8RDUzLVu2hFZXkdPMPyO+XmXg6ekW+ifaMciqJkIH\n3W15TJ+9Ue8YIoCW/lrIXXc/qHeMKm+99RapqamkpqaSlZWlS4Y5c+ZUZVi3bp0uGUKRFGvN0B33\nP8eHvzTfbag++MHIf74x0SehBNlHWejFZIDCI4fYtU+2GAlHazdlc/UfbsdkCp6BsFarlZiYGGJj\nYzEY9Pn4t1gsVRmC6XcT7BRN02QAUzM0b+YnpBx5mPN6NK/V+V+abebHdQrdYmRwt9CfqkJ2bD+e\nfXSs3lGEH7ndXmYtKeHV1//vjM7z8MMPs3DhQgA+/PBDhg8f3qDn//LLL0yePBmA8ePH88Ybb5xR\nHqEfaVlrpq6+9s8sShuKy918avXHp1v4eT1SqImgYTCAIyedDdv06ZISgbHgp8M88NBTZ3yeG2+8\nserrr776qsHPr3yOoijcdNNNZ5xH6EeKtWbszr+9ywcr2ukdo0nc976VLbtVOkXZ9Y4ixEm6RJYw\n+9stSCdHeMjILqZVx3No377DGZ9r0KBB9O7tW/Jj2bJl5Obm1vu5eXl5LF26FICePXsyZMiQM84j\n9CPFWjPWoUNHrJ1vZH92eL8MbnnDypEjXtpFlOodRYhTGAyg5qexcu1hvaOIM6RpGkt+LeIv9zzk\nt3P+8Y9/BMDr9TJz5sx6P2/WrFl4vV7g5BY6EZrC+1NanNZt9z7FJ+sHhO1d/TUvWSktcNHKJoWa\nCF6do8tZuHib7DAS4pavzeTGWx/068D5K664gri4OABmzpyJqp5+KzxVVasKu7i4OK688kqg7tmg\no0aNIjU1leeeew6v18ubb77JBRdcQGpqKi+++OJJx7rdbj799FOuu+46zj77bAYOHMjll1/Ou+++\nS2mp77125MiRpKam8uijj5703Lpmg950002kpqZy9913A5Cdnc2LL77I+PHjGTBgAAMHDuSqq67i\n7bffxul01vdXGBZkKkYzZzQa+cMdrzBj3rVcP/SY3nH8RlVVLnnWRqzXSaK1Qu84Tc7pNZFVEU+h\nJxKHakbVDBgVlUiDi0RzGa2txVgM3lqfX+KxkVURR7EnApfqe5uwGjwkmMtpYy0iyujyS848VzS5\nrljsHituzYhJUbEZPCSYy2hnLcRsqP2DyaUaya6I45g7inLVglczYFa8RBkraGEpoaXFHlKzfc1F\nB1m8/ADjL+qmdxTRCKVlFRRXtGTosPP8el6r1crEiRP5+OOPyc3N5aeffmL06NF1PmflypVVxdiE\nCROwWq0nPa6c5g/j1Vdf5ZNPPsFgMBAREXHSzNHy8nImT57M5s2bq85lsVhIS0vjzTffZOHChXz0\n0UdVxZTRWPNq44qinJKj8r81TWPnzp3cdtttFBYWYjabMRgMVFRUsGfPHvbs2cPKlSv57LPPms2M\nUmlZEwwach6HDZdSUBIed/Uej8qoJ2wkqI5mWahlVcTxW0knjlQkUOq14tUMKGh4NAMlXhvpziR+\nK+nEMXdkjc8/UJ7MJnt7cl2xOFUzGqABDtVMVkUc60s6kumMP6OMHk1hq70tO8taU+COwqWZUAC3\nZsTutXLYmchvJZ0o8dhqfH6+K4p1JZ1IdyZR4rXhOf4zujQjhZ5I9pS3YktpOzxa6LzFtY92sWTZ\nDjze07eciOAzf2kWDz/6TEDOfcMNN1QVTPWZaFB5jMFgaHAXaH5+Pl988QUPPfQQmzZtYtOmTTz8\n8MNVj7/00ktVhdqwYcP49ttv2bJlCxs2bOC5554jJyeHKVOmnFFvjd1u569//SvdunVj1qxZbNu2\njS1btrBgwYKqsXebN29m3rx5jb5GqAmddzIRUPf9/Q3eXhH6a6+VO1VGPGajjbmcOItb7zhN7qgr\nmn3lLVBRsBnc9IrK5vz4AwxP2M/QuIN0jcjDcLxw21HaBqd68l3pEWc8GRUJAKSY7QyJPcSIhP2M\nSNjPoJjDJJjKAdjvSCHfFdWojJoGO0rbUuiJxKSodIs4yvlxvowXxO8nNTIHs+LFrRnZUdoGj3by\n3XeJx8bOstZ4NQMWxUOvqGyGx++v+hnbWX0rPhd7IthR2rpRGfUSbT/I/B926x1DNNCGbblcOOb6\nqu5Kf2vfvj0jRowAYPXq1WRkZNR6bE5ODsuXLwfgggsuoH37hu1Y8+OPPzJhwgTuvPPOqhY5s9kM\nQEFBAbNnz67K9N5779Gtm68l2Gazce211/LOO++wevVqiouLG/ZDVrNhwwYSExP55JNP6NOnT9X3\nu3btyj//+c+qFrgVK1Y0+hqhRoo1AUBUVBTnXfUki7bE6h2l0YpKVUZOsdI10k6M2aN3nCananCg\nvAUAZsVL/5gMWlhKMSm+lhqrwUs7WxGdI/J9x6Oc1ELmUo2kO5IBSDaX0js656TuzhhTBX2jM4k1\n+ro39pW3oDE3z9muOIo8ERjQ6BedQVtbMabj3Z1GRaOl1U7vqGxijU4SzGVUqOaTnn+gPAUNBSMq\nA47/jAZFq/oZu0bmV/2MRZ5IjrqiGx5SJ62jvaz+dQ8Vrub3+g1VxSUODufHc+VVfwjodSpbyDRN\nY8aMGbUeV31cW2MmFmiaxq233lrjYytWrKiatHDDDTec0r0KcM455zB+/PgGX/f3HnjggRq7ONu0\naUOHDr6ZtocPN59JOVKsiSpjLpnI5vJLyC8Ove7Q7GMqo5+0khptJ9Icevn9ocQTgUvzjQ9pYy3G\nZqj5A7+NtQgF3++o2BNR9f1cVywqvjvWymLn9xQFOkUUAODSTBS4G9a6pmlw2JlYlSPGVHM3dbzZ\nwcDYI6RG5Z5UMDpVEyVeX9doK2sJEcaaW0/bWwurfv7sijPrsm1qieUHmLlgh94xRD1omsbcJdk8\n9sQ/An6t4cOH07FjR8A3SN/tPvW1X33GaIcOHbjwwgsbfJ24uDg6d+5c42O7d/tafRVF4eyzz671\nHJdddlmDr1ud2Wzm3HPPrfXxhARf6395efkZXSeUSLEmTvI/T7zLG8v7htTs0APZKlc8a6VvnJ0I\n8+mPD1cxJidDYg/RLzqD1tbauyAMClWtbd5qY7ryjrdARRlcRNZSBAHEm8ox4nt+njumQRlLvDYq\njne9trI2fHHiMs+JO/k4U+27bygKJJl9s9KKPRF41NB5q0uOgg3r91FW7p9JHCJwfv41k5v+9DBR\nUY0bEtBQlct4FBQUsHjx4lPz/PwzR48eBXwtX42RnJxc62PVZ5C2a1f7Gp3Vuy4bIzExsarrtSaV\nj1W28jUHofMOJppEREQE19z+Bl+sSdE7Sr1sOagy6WUr/RPsWJrHpKBaGRWNKKOLBLMDay2tagAe\nzYD7eAuc5fhxmgalx1usYkx1T4lXFKpaxOy1TACoTdHxSQ0mRSXK6KJCNZHmSGJjSQd+LerC2uLO\nbCttQ25FTI1drNUnDJiUut+oIwy+YkcDylRLg3LqrZU7nS/nbdU7hqhDVq4dJTKVc4f6d/ZnXa65\n5hoiInyt4TVNNKj8XkREBH/4Q+O6ZePja2+Jrt6SVVeBWtny1VjNZYZnQ0ixJk4x+JzzKYy7nrTc\n4H55rNyhcce/rQxItGOqeXa4qEF2xYlxicnHW58qVBOVtZHNcPqJGZXHONWGNWWWea1Vzz/mjmR9\nSUcOOxOxe624NCNO1cQxdxS7j8/mdP+uRcxUbSkPt1b/N3SHN7SKtfgI2L7lAEUlzWvv3lDh9aos\nXl3Igw9NadLrxsTEcMUVVwDw22+/ceDAgarHMjIyWL16NQCXX345MTENa/WuVNcG79XXeKtr+Y/T\nLQ0iGi64P42Fbu55+CU+WDsYrzc4u0O/Xafwt4+s9E+yY5RXcb2VeS1VkwgiDG5aH++KrBzrBqdv\nsap+jAanFFR1qewC9WgGdpW1xmrw0Dsqm6FxBxkev49BMYdJMfu2BCv2RLCz7OTZnNHGE2PcCmtZ\neqRSiffEeLxQWsKjUnvlCJ/P3qJ3DFGDb5ce5sFHntelBaj6Hp/VW9e+/vprNE1DUZSA7Vhgs51o\nSXc4ar+RKCwsDMj1m7PQewcTTcJoNHLHo//H+0G4d+i0n428NMNEv4QSDHIDV2/lXjNb7W1RUTAp\nKr2jsqpmUarVipnK79Wl+jGVkxLqo7JocqpmLIqHgTGHSbGUYjV4MRzvXu0dnUMri6+ILPJEnrRE\niNXgqZqNmuuKodRTc4tZniuao64TLQveECzWoi2wd1caeQWy+0Yw2XPwGF16j6Jrt+66XL9Hjx5V\ng/u//fZbVFVFVVXmzp0L+PYTTU0NzDJMLVu2rPo6Ozu71uN27twZkOs3Z6H3DiaaTNduPYjt9Rc2\npQdPF9K/F5j4eJGRs+JDa3V6vR1zR7LR3gGXZsKIxllRWUSbTgxgV+pRoNWmIf8M1Qu7ThEFGGu5\nbpeIvKoZq7muk5eT6RKZhwJoKGwpbU9WRRwu1YhXUyj1WDjoSGJXWStijCfG3hmV0Fxotospk0+/\n3qh3DHGc0+lm/U6VP/35L7rmqGw5Ky4uZtWqVaxdu5a8vLyTHguEyjXVNE1jy5baW30XLlwYsAzN\nlRRrok433fYwc/ePorhM/+7QZ2eYmb9KoWdsiRRqDZDpjGd7aduqRWT7xxwh3nxyF4apWjGjaqf/\n5VZvqWpIIVS9OIs31T7t3mxQiTne5Wn3njyJIc7kJDUqu2px333lLfi1uAurirqxwd6RI85E4k0O\nulRbfiRUizWbCTIOHCIjq0jvKAKY+2MGjz/1iu5jssaMGUOLFi3QNI3vvvuORYsWAZCSksK4ceMC\ndt0LLrig6usvv/yyxn1Kf/vtt6o8wn+kWBN1UhSFvz8/nX8uOUvX5Twe/sTCr1uga7RdtwyhRtVg\nT1lL9jtS0IAYo5NBsUdqXNuserHm1k4/W6PyGANara1jNak+Hu50BZT1+CQGt3pqnhaWUs6OS6ed\ntZBoYwUWxUuEwU2yuZSzorLpF5OJt9rbW12zY4Ndd0su02Zt0jtGs7ducw6jxt9c59IWTcVoNDJp\n0iQAlixZwg8//ADApEmTat2L0x86derE+eefD8CuXbt4+OGHyczMBMDpdDJr1izuvvturr766oBl\naK6kWBOnFR0dzU0PfMx7y9rqcv073rayZ79Kh0gp1OrLoylsLW1HzvEuxFaWEgbEHKm1aLEaPFUF\nW31meDq8vmMauqF7RLWZpqefzakc//+ai0GbwUPXyHwGxx5mWPxBzolL56zobJItvjFeZdVmgFaf\nmBBqTCY4eiid/WkFekdptrJy7eSXt+HSy67SO0qVSZMmYTKZKC0tpbi4GLPZXFXAnc6t3Ce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iNHTWLEo497pE2R1kphTVqlpP37ePPVGfgXHSDYrD8BkZxyMwOfeIyRj/a6qXbsDifbd2cSHH43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\nEREDU1gTERERMTCFNRERERED+39y1UVRDMeOhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6adb634ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#AirlineMarketShare=JFK_SFO_stats[['UniqueCarrier','AirlineID']].groupby(['UniqueCarrier']).aggregate(np.count_nonzero)\n",
    "print AirlineMarketShare\n",
    "\n",
    "#http://stackoverflow.com/questions/6170246/how-do-i-use-matplotlib-autopct\n",
    "def make_autopct(values):\n",
    "    def my_autopct(pct):\n",
    "        total = sum(values)\n",
    "        val = int(round(pct*total/100.0))\n",
    "        return '{p:.2f}%  ({v:d})'.format(p=pct,v=val)\n",
    "    return my_autopct\n",
    "\n",
    "colorDict={'AA':'#825F87','DL':'#FEB308','UA':'#653700','B6':'#A5A502','WN':'#2100D4','VX':'#CCB974'}\n",
    "fullNames={'AA':'American','B6':'Jetblue','DL':'Delta','UA':'United','VX':'Virgin','WN':'Southwest'}\n",
    "#http://pandas.pydata.org/pandas-docs/stable/visualization.html#colormaps\n",
    "AirlineMarketShare.plot.pie(subplots=True,figsize=(6,6),legend=False,fontsize=20,\n",
    "                            labels=[fullNames[x] for x in AirlineMarketShare.index.values],\n",
    "                           #labels=['American','JetBlue','Delta','United','Virgin'],\n",
    "                            colors=[colorDict[x] for x in AirlineMarketShare.index.values],\n",
    "                            autopct='%.2f'\n",
    "                           )\n",
    "#print AirlineMarketShare.index.values\n",
    "plt.ylabel('')#no ylabel\n",
    "plt.savefig(\"AirlineBreakdown_JFK-SFO_delayTimes.svg\",dpi=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#byDayOfWeek=JFK_SFO_stats.groupby(['DayOfWeek']).aggregate(,'Cancelled','CarrierDelay','WeatherDelay','NASDelay','SecurityDelay','LateAircraftDelay']).aggregate(np.count_nonzero)\n",
    "CancellationbyDayOfWeek=JFK_SFO_stats.groupby(['DayOfWeek']).aggregate({'Cancelled':np.count_nonzero})\n",
    "#byDayOfWeek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Average Flights per Day': 25,\n",
       " 'Dest': 'SFO',\n",
       " 'DestCityName': 'San Francisco, CA',\n",
       " 'Distance': 2586,\n",
       " 'Flights in 2015': 9280,\n",
       " 'Origin': 'JFK',\n",
       " 'OriginCityName': 'New York, NY'}"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#general info\n",
    "{'Flights in 2015': 9280,'Average Flights per Day': 25,'Origin': 'JFK','OriginCityName':'New York, NY','Dest':'SFO','DestCityName':'San Francisco, CA','Distance': 2586}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Cancelled</th>\n",
       "      <th>FlightNum</th>\n",
       "      <th colspan=\"3\" halign=\"left\">CarrierDelay</th>\n",
       "      <th colspan=\"3\" halign=\"left\">WeatherDelay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count_nonzero</th>\n",
       "      <th>count</th>\n",
       "      <th>count</th>\n",
       "      <th>count_nonzero</th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>count_nonzero</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FlightDate</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>2015-01-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>16.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>7</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>11.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>19</td>\n",
       "      <td>14.0</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>19.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>9</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-26</th>\n",
       "      <td>6.0</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>19.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-27</th>\n",
       "      <td>23.0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-28</th>\n",
       "      <td>7.0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>19.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>7</td>\n",
       "      <td>20.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-02</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>11.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>13</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-06</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-07</th>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-09</th>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>14.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>14</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>18.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>4</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>10.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>15</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>15.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>10</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>11.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>13</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>8.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>16.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>9</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>10</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>11.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>12</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-25</th>\n",
       "      <td>1.0</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>13.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>11</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Cancelled FlightNum CarrierDelay                       \\\n",
       "           count_nonzero     count        count count_nonzero    sum   \n",
       "FlightDate                                                             \n",
       "2015-01-01           0.0        19            2          18.0    7.0   \n",
       "2015-01-02           0.0        25            6          24.0   77.0   \n",
       "2015-01-03           0.0        21           10          16.0  125.0   \n",
       "2015-01-04           0.0        23            7          21.0  123.0   \n",
       "2015-01-05           0.0        25            3          25.0   36.0   \n",
       "2015-01-06           0.0        25           19          11.0  290.0   \n",
       "2015-01-07           0.0        23            3          20.0    0.0   \n",
       "2015-01-08           0.0        26            4          24.0   55.0   \n",
       "2015-01-09           0.0        24            8          19.0   54.0   \n",
       "2015-01-10           0.0        19            3          19.0  119.0   \n",
       "2015-01-11           0.0        25            1          25.0   20.0   \n",
       "2015-01-12           0.0        26            3          26.0  181.0   \n",
       "2015-01-13           0.0        25            0          25.0    NaN   \n",
       "2015-01-14           0.0        25            1          25.0   49.0   \n",
       "2015-01-15           0.0        26            1          26.0   38.0   \n",
       "2015-01-16           0.0        25            5          22.0   42.0   \n",
       "2015-01-17           0.0        17            1          17.0   20.0   \n",
       "2015-01-18           0.0        22            9          15.0   85.0   \n",
       "2015-01-19           1.0        26            2          25.0    6.0   \n",
       "2015-01-20           0.0        25            2          24.0   35.0   \n",
       "2015-01-21           0.0        25            1          25.0  103.0   \n",
       "2015-01-22           0.0        26            1          26.0  138.0   \n",
       "2015-01-23           0.0        25            2          24.0   78.0   \n",
       "2015-01-24           0.0        19            3          18.0   41.0   \n",
       "2015-01-25           0.0        25            1          25.0    4.0   \n",
       "2015-01-26           6.0        26            8          19.0   63.0   \n",
       "2015-01-27          23.0        25            0          25.0    NaN   \n",
       "2015-01-28           7.0        25            0          25.0    NaN   \n",
       "2015-01-29           0.0        26            1          26.0   64.0   \n",
       "2015-01-30           0.0        25            7          19.0   61.0   \n",
       "...                  ...       ...          ...           ...    ...   \n",
       "2015-12-02           0.0        22            3          19.0    0.0   \n",
       "2015-12-03           0.0        23           13          11.0  138.0   \n",
       "2015-12-04           0.0        22            0          22.0    NaN   \n",
       "2015-12-05           0.0        16            1          16.0   92.0   \n",
       "2015-12-06           0.0        22            0          22.0    NaN   \n",
       "2015-12-07           0.0        24            2          22.0    0.0   \n",
       "2015-12-08           0.0        22            2          21.0   13.0   \n",
       "2015-12-09           0.0        23            4          20.0   10.0   \n",
       "2015-12-10           0.0        24           14          14.0  201.0   \n",
       "2015-12-11           0.0        23            6          17.0    0.0   \n",
       "2015-12-12           0.0        16            0          16.0    NaN   \n",
       "2015-12-13           0.0        23           12          12.0   11.0   \n",
       "2015-12-14           0.0        24            3          23.0   41.0   \n",
       "2015-12-15           0.0        22            1          22.0   34.0   \n",
       "2015-12-16           0.0        22            2          21.0   12.0   \n",
       "2015-12-17           0.0        21            4          21.0  149.0   \n",
       "2015-12-18           0.0        23           15          10.0   51.0   \n",
       "2015-12-19           0.0        18           10          15.0  157.0   \n",
       "2015-12-20           0.0        22           13          11.0   38.0   \n",
       "2015-12-21           0.0        20           17           8.0  108.0   \n",
       "2015-12-22           0.0        19            9          16.0  108.0   \n",
       "2015-12-23           0.0        21           10          20.0  368.0   \n",
       "2015-12-24           0.0        17           12          11.0  321.0   \n",
       "2015-12-25           1.0        17            6          15.0   60.0   \n",
       "2015-12-26           0.0        16            5          15.0  261.0   \n",
       "2015-12-27           0.0        21           11          13.0   31.0   \n",
       "2015-12-28           0.0        20            1          19.0    0.0   \n",
       "2015-12-29           1.0        19            2          19.0   16.0   \n",
       "2015-12-30           0.0        19            5          19.0   98.0   \n",
       "2015-12-31           0.0        16            0          16.0    NaN   \n",
       "\n",
       "           WeatherDelay                       \n",
       "                  count count_nonzero    sum  \n",
       "FlightDate                                    \n",
       "2015-01-01            2          17.0    0.0  \n",
       "2015-01-02            6          19.0    0.0  \n",
       "2015-01-03           10          11.0    0.0  \n",
       "2015-01-04            7          16.0    0.0  \n",
       "2015-01-05            3          22.0    0.0  \n",
       "2015-01-06           19          14.0  255.0  \n",
       "2015-01-07            3          21.0   13.0  \n",
       "2015-01-08            4          24.0   39.0  \n",
       "2015-01-09            8          20.0  160.0  \n",
       "2015-01-10            3          16.0    0.0  \n",
       "2015-01-11            1          24.0    0.0  \n",
       "2015-01-12            3          23.0    0.0  \n",
       "2015-01-13            0          25.0    NaN  \n",
       "2015-01-14            1          24.0    0.0  \n",
       "2015-01-15            1          25.0    0.0  \n",
       "2015-01-16            5          20.0    0.0  \n",
       "2015-01-17            1          16.0    0.0  \n",
       "2015-01-18            9          13.0    0.0  \n",
       "2015-01-19            2          24.0    0.0  \n",
       "2015-01-20            2          23.0    0.0  \n",
       "2015-01-21            1          24.0    0.0  \n",
       "2015-01-22            1          25.0    0.0  \n",
       "2015-01-23            2          23.0    0.0  \n",
       "2015-01-24            3          17.0   10.0  \n",
       "2015-01-25            1          24.0    0.0  \n",
       "2015-01-26            8          25.0  264.0  \n",
       "2015-01-27            0          25.0    NaN  \n",
       "2015-01-28            0          25.0    NaN  \n",
       "2015-01-29            1          25.0    0.0  \n",
       "2015-01-30            7          20.0   78.0  \n",
       "...                 ...           ...    ...  \n",
       "2015-12-02            3          19.0    0.0  \n",
       "2015-12-03           13          11.0   13.0  \n",
       "2015-12-04            0          22.0    NaN  \n",
       "2015-12-05            1          15.0    0.0  \n",
       "2015-12-06            0          22.0    NaN  \n",
       "2015-12-07            2          22.0    0.0  \n",
       "2015-12-08            2          20.0    0.0  \n",
       "2015-12-09            4          19.0    0.0  \n",
       "2015-12-10           14          10.0    0.0  \n",
       "2015-12-11            6          18.0   30.0  \n",
       "2015-12-12            0          16.0    NaN  \n",
       "2015-12-13           12          11.0    0.0  \n",
       "2015-12-14            3          21.0    0.0  \n",
       "2015-12-15            1          21.0    0.0  \n",
       "2015-12-16            2          20.0    0.0  \n",
       "2015-12-17            4          17.0    0.0  \n",
       "2015-12-18           15           8.0    0.0  \n",
       "2015-12-19           10          10.0   30.0  \n",
       "2015-12-20           13           9.0    0.0  \n",
       "2015-12-21           17           3.0    0.0  \n",
       "2015-12-22            9          11.0   22.0  \n",
       "2015-12-23           10          13.0   15.0  \n",
       "2015-12-24           12           6.0   30.0  \n",
       "2015-12-25            6          12.0   17.0  \n",
       "2015-12-26            5          11.0    0.0  \n",
       "2015-12-27           11          10.0    0.0  \n",
       "2015-12-28            1          19.0    0.0  \n",
       "2015-12-29            2          17.0    0.0  \n",
       "2015-12-30            5          14.0    0.0  \n",
       "2015-12-31            0          16.0    NaN  \n",
       "\n",
       "[365 rows x 8 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def grt0(x):\n",
    "    return 1 if x > 0 else 0\n",
    "\n",
    "\n",
    "DelaybyDayOfYear=JFK_SFO_stats.groupby(['FlightDate']).aggregate({'FlightCount':'count',\n",
    "                                                                  'Cancelled':np.count_nonzero,'CarrierDelay':['count',np.count_nonzero,np.sum],\n",
    "                                                                  'WeatherDelay':['count',np.count_nonzero,np.sum]})\n",
    "DelaybyDayOfYear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OuUsCgQB69eoFy7LonjWbCME5N998MyzLwsKFC70uJe4CgQAGDRpEOTcB/vAV5mAu5vMm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\nKOQAAACAQf8PO8kF9x3k7hcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6adc3c4f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "JFK_SFO_stats.head()\n",
    "axes=plt.gca()\n",
    "axes.set_ylim(0,50)\n",
    "\n",
    "#sns.violinplot(x=\"Month\", y=\"DepDelay\", data=JFK_SFO_stats,inner=\"quart\")\n",
    "\n",
    "sns.boxplot(x=\"Month\", y=\"DepDelay\", data=JFK_SFO_stats)\n",
    "#sns.boxplot(x=\"DayOfWeek\", y=\"DepDelay\", data=JFK_SFO_stats)\n",
    "\n",
    "\n",
    "#unit='DayOfWeek'\n",
    "\n",
    "#sns.boxplot(x='DayOfWeek', y=\"DepDelay\", data=JFK_SFO_stats.sort_values(by='DayOfWeek'))\n",
    "plt.savefig(\"byMonth_JFK-SFO_delayTimes.svg\",dpi=150)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CancellationbyDayOfYear=JFK_SFO_stats.groupby(['DayOfWeek']).aggregate({'Cancelled':np.count_nonzero})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### delay type analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1063439\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CarrierDelay</th>\n",
       "      <th>WeatherDelay</th>\n",
       "      <th>NASDelay</th>\n",
       "      <th>SecurityDelay</th>\n",
       "      <th>LateAircraftDelay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.0</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",
       "</div>"
      ],
      "text/plain": [
       "   CarrierDelay  WeatherDelay  NASDelay  SecurityDelay  LateAircraftDelay\n",
       "0           0.0           0.0      25.0            0.0                0.0\n",
       "1           0.0          79.0       0.0            0.0                0.0\n",
       "2          22.0           0.0       2.0            0.0                0.0\n",
       "3           0.0           0.0      45.0            0.0              105.0\n",
       "4          28.0           0.0       0.0            0.0                0.0"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import the data\n",
    "#DelayInfoOnly_stats=pd.read_csv('/home/vagrant/capstone/2016-03-28_downloads/OTP_detailed/analysis_2016-04-18/2015_delayCategoryCounts.txt',sep=\"\\t\")\n",
    "print len(DelayInfoOnly_stats)#1063439\n",
    "DelayInfoOnly_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'function' object has no attribute '__getitem__'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-190-1ab5e1ddf3dd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mviolinplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mminiDelayInfo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCarrierDelay\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mminiDelayInfo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLateAircraftDelay\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylim\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'function' object has no attribute '__getitem__'"
     ]
    },
    {
     "data": {
      "image/png": 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pieo0AACA2wjTOaStNaYTEj3TEn3TAAAAbiNM55C21phOCBCmAQAAsoYwnUOOtpV4Mto8\nAAAAsocwnUPa1+bRHKapTAMAALiLMJ1DmjdsyZMVCLV6DJVpAACA7CFM55BEm8fRqtKSZFlBWcF8\nSVSmAQAA3EaYziHH2rAlIVGdJkwDAAC4izCdI2pra1VXVyfp6Bu2JCSWx6PNAwAAwF2E6RyRumFL\nYRtHNlemDx06pGg06uq4AAAAujLCdI5IXhavrZ5pKXWt6crKStfGBAAA0NURpnNEezZsaX6dFT0A\nAACygTCdI9qzxnQCW4oDAABkB2E6R6TsfkhlGgAAwBcI0zni4MGDkiQrmC8rEGzz2ACVaQAAgKwg\nTOeI9q4xLaW2eVCZBgAAcA9hOgcYY5p3PzxGi4ek+FbjTduNs5oHAACAewjTOaC6ulqNjY2S2leZ\nlti4BQAAIBsI0zmgIyt5OMexpTgAAIDrCNM5oCNrTDcfF69MV1RUyLZtV8YFAADQ1RGmc0DKsnjt\nbvOIV6ZjsZiqq6tdGRcAAEBXR5jOAem0ebCiBwAAgPsI0zmgOUxbKSG5LYGkjVvomwYAAHAHYToH\nJNo8rFCBLKt9f2RsKQ4AAOA+wnQO6MiGLQm0eQAAALiPMO1zxhinshxoZ4tH/FjaPAAAANxGmPa5\nhoaG5g1bggXHODpJIE9qagmhMg0AAOAOwrTPHTp0yHlsBfPafZ5lWU74pjINAADgDsK0z6WG6fwO\nnZu8cQsAAAAyjzDtc8kbrlih9lempea+6fLychljMjouAAAAEKZ9LyVMp1mZbmhoUF1dXUbHBQAA\nAMK073WuzYMVPQAAANxEmPa51Mp0x9o8WGsaAADAXYRpn+tMZZq1pgEAANxFmPa55jBtSYFwh86l\nMg0AAOAuwrTPJdo8rGCeLMvq0LnJYZrKNAAAQOYRpn2uOUx3rMVDkqxQ8zlUpgEAADKPMO1ziTaP\njt58KEmWFXB2QaysrMzouAAAAECY9r3OVKal5uXxqEwDAABkHmHaxyKRiLPZSvphmi3FAQAA3EKY\n9rGUZfE6uJV483nxynR1dbUikUhGxgUAAIA4wrSPdWYr8YQAK3oAAAC4hjDtY53ZsMU5L2njFvqm\nAQAAMosw7WPJlWmlsZqHxFrTAAAAbiJM+1gmKtMBdkEEAABwDWHaxzLd5kFlGgAAILMI0z6WegNi\n51bzkAjTAAAAmUaY9rGMhOlASAqEJdHmAQAAkGmEaR9z2jwCebKs9P+oAmzcAgAA4ArCtI85W4mH\n0uuXTki0ehCmAQAAMosw7WOJynS6LR4JieXxKisrFYvFOj0uAAAAxBGmfaw5THeuMh0Id5Mk2bZN\n3zQAAEAGEaZ9KhaLqba2VlImKtPdnMcHDx7s1HsBAACgGWHap2pqapzHna1MW+HmjVsOHDjQqfcC\nAABAM8K0T6Vu2NK5ynQgqTJNmAYAAMgcwrRPZWL3Q+f8MG0eAAAAbiBM+1Tqhi2dDNPBfKlpnWoq\n0wAAAJlDmPapTLZ5WJblLI9HmAYAAMgcwrRPZbIyLTX3TROmAQAAMocw7VOZDtOJFT3Ky8vZuAUA\nACBDCNM+ldLmEepcm0f8PZo3bqmsrOz0+wEAAIAw7VtOmA6EZFnBTr9fgBU9AAAAMo4w7VOJNo9M\ntHhIqbsg0jcNAACQGYRpn0pUpju7kkdCgF0QAQAAMo4w7VNUpgEAAPyPMO1Dtm27EKbzJVmS6JkG\nAADIFMK0Dx0+fFi2bUvKXJuHZQXYuAUAACDDCNM+lLr7YWYq05Jkhdm4BQAAIJMI0z7kVphOLI9X\nXl7uVL4BAACQPsK0D6XufpiZNg9JTptHNBpVVVVVxt4XAACgqyJM+1BKmA5lvjIt0eoBAACQCYRp\nH3KtZ5rl8QAAADKKMO1DqWE6c20eyRu3sDweAABA5xGmfSi1Z5rKNAAAgF8Rpn3IqUxbQVmBUMbe\n1woVKLFxC2EaAACg8wjTPtS8+2HmWjykxMYtBZII0wAAAJlAmPahTG8lniyxcQs90wAAAJ1HmPah\nRJtHpivTkhQINe+CaIzJ+PsDAAB0JYRpnzHGJIVpNyrTzRu3JK8aAgAAgI4jTPtMfX29otGoJHfC\ndIAVPQAAADKGMO0zbq0x7bwnuyACAABkDGHaZ9zaSjyByjQAAEDmEKZ9xq2txJ33ZBdEAACAjCFM\n+4zrbR6h5jBNZRoAAKBzCNM+49ZW4s57Jm3cQmUaAACgcwjTPuN2mJYkK2mtaQAAAKSPMO0zbrd5\nSFIgzMYtAAAAmUCY9pnmyrQlBcKuXCNRmW5sbEyphAMAAKBjCNM+k7z7oWVZrlwjwIoeAAAAGUGY\n9pnmMO1Oi4fUXJmW6JsGAADoDMK0zyTaLtzYsCUhea1pwjQAAED6CNM+k9zm4ZbkXRBp8wAAAEgf\nYdpHGhsb1dDQIMntNo/myvRnn33m2nUAAACOd4RpH8nGGtOSZAWCsoJs3AIAANBZhGkfSV1j2r0w\nLTX3TdMzDQAAkD7CtI9kY8OWhETf9MGDB9m4BQAAIE2EaR/JVpuHJFlNuyDW19ertrbW1WsBAAAc\nrwjTPpLcv2yFCly9VvJNiPRNAwAApIcw7SP79+93Hlvh7q5eKxBuXh6PFT0AAADSQ5j2ESfUWoEs\ntHk0h/Xdu3e7ei0AAIDjFWHaRxJh2gp3l2VZrl4rmN/LeVxWVubqtQAAAI5XhGkfSYTp5BYMt1jB\nPKc6TZgGAABID2HaJ+rr61VVVSUpdbtvNwULekuSPv30U0UikaxcEwAA4HhCmPaJ5M1T3L75MCHQ\nFKZjsZi2b9+elWsCAAAcTwjTPpG8kkc22jyk5sq0RKsHAABAOgjTPpG8PF3WKtP5hGkAAIDOIEz7\nRHKYzlZlOhAulBWMbw5DmAYAAOg4wrRPpFSms3QDotTcN71161bFYrGsXRcAAOB4QJj2iUTPtBUq\nkBUIZu26wYJiSfHVRPbs2ZO16wIAABwPCNM+kbxhSzYFuAkRAAAgbYRpH4jFYs7SeNlaYzqBFT0A\nAADSR5j2gcrKSqdfOduVaStcJAVCkqQtW7Zk9doAAAC5jjDtA6lrTGc5TFuWgk1L5JWVlckYk9Xr\nAwAA5DLCtA94sSxeskTf9KFDh3Tw4MGsXx8AACBXEaZ9ILkyne02D6l5RQ+JvmkAAICOIEz7gF8q\n0xJ90wAAAB1BmPYBpzIdCMsK5mX9+oH8XpIV/6tAZRoAAKD9CNM+kKhMe1GVliTLCsQDtQjTAAAA\nHUGY9gGvNmxJlljRY//+/aqurvZsHAAAALmEMO2xmpoaHT58WFL2N2xJxk6IAAAAHUeY9ljyzYee\nVqYJ0wAAAB1GmPaY1yt5ONcu6OU8JkwDAAC0D2HaY6lh2rvKtBUIK5DXQxLL4wEAALQXYdpjqRu2\neFeZlpr7pnft2qWGhgZPxwIAAJALCNMea65MW7JCBZ6OJdC0oodt29q+fbunYwEAAMgFhGmPJSrT\nVribLMvbP47kbcVp9QAAADg2wrTHvN6wJRnL4wEAAHQMYdpDkUhEFRUVkrxdFi8hECqQFSqUJG3a\ntEnGGI9HBAAA4G+EaQ8dPHjQCaxebtiSLFR0kqT4TYgffPCBx6MBAADwN8K0h1JX8vC+Mi1J4eJS\n5/Hq1as9HAkAAID/EaY95JcNW5IFCnorkB+/EfGNN95wtjoHAABAS4RpD/mxMm1ZllOdbmho0Btv\nvOHxiAAAAPyLMO0hP1amJSnca4jUtEzfn/70J28HAwAA4GOEaQ8lwrQVzJcVCHk8mmZWME+hHoMl\nSR9//LG2bdvm7YAAAAB8ijDtISdM+6gqncCNiAAAAMdGmPaIMSZpwxZ/9EsnC3br7/Rx/+///q8i\nkYjHIwIAAPAfwrRHKisr1djYKMmflenkGxGrq6v11ltveTwiAAAA/yFMeyTl5sOQ/yrTkhTuVSrJ\nksSNiAAAAK0hTHskOUz7sTItSYFwoYJFJ0qSNmzYoL1793o8IgAAAH8hTHskdVk8f1ampdQbEV99\n9VUPRwIAAOA/hGmPpG7Y4s/KtCSFigbKChZIiofpWCzm8YgAAAD8gzDtEacybQVlBfO9HUwbLCug\ncPGpkqSDBw/qiSee8HhEAAAA/kGY9khFRYUkyQoVyrIsj0fTtnCfM5zAv2LFCr355psejwgAAMAf\nCNMe83uQlqRAqFAFJ01RYmWPX/3qV9qxY4e3gwIAAPABwjTaJdS9v/L7ny1Jqqur049//GMdPnzY\n41EBAAB4izCNdgv3GapQz5MlSbt27dK9994rY4zHowIAAPAOYRrtZlmWCk4cr0B+L0nS2rVr9eyz\nz3o8KgAAAO8QptEhViCswpOmSIGwJOl3v/ud/vSnP1GhBgAAXRJhGh0WyO+pgoETJEm2beu+++7T\nD3/4Q3ZIBAAAXQ5hGmkJ9xik/AFjJSsoKb7d+PXXX68VK1awsQsAAOgyCNNIW17v09W99IsKdusv\nSWpsbNRjjz2mm266SZs3b6b1AwAAHPdCXg8AuS2Q10OFJ5+nSFWZGvZtkOyItmzZoltuuUWDBg3S\nzJkzdd5556lPnz5eDxUAACDjCNPoNMuylFf8OYWKBqph718VrY5v6LJz50799re/1RNPPKGxY8dq\n5syZGj9+vMLhsMcjBgAAyAzCNDImECpU4aApitWVK1JZpsih7ZIdkW3bevvtt/X222+rqKhIU6dO\n1fTp03XmmWcqEKDTCAAA5C7CNDIuWNhHwcI+yj/hHEWrdypStVWx2n2SpJqaGq1atUqrVq1S//79\nNX36dE2dOlVDhgzJia3VAQAAkhGm4RorEFK41xCFew2RHalVpGqbolXbZTcekiTt379fy5Yt07Jl\ny3TCCSdo4sSJmjRpkoYNG6ZgMOjx6AEAAI6NMI2sCIS7K79kuPL6niW7vkKRQ9sUrfpUJlYvSdq3\nb59WrlyplStXqmfPnjr77LM1cOBADRw4UCeddJIGDhyoHj16ePwpAAAAUhGmkVWWZTltIKb/OYod\n3q9o9S5Fq3fKROskSYcOHdIbb7zR4txu3bopFAo5fdaBQMBpDUksw5f4NRAIqFu3burWrZu6d++u\nwsJCFRUVqbi4WCUlJerbt6/za48ePWgxAQAAaSFM5xgTa1TDZ5sUO7xfduTwMY8PhLsp2K2/8vuN\nlBXMy8II28+yAgp1H6BQ9wEyJ4yRXV+haPVORWt2yW44JCl1nerDh4/9eZMdPHiwXcfl5eWpb9++\n6tevnxOyS0pK1KtXL/Xs2dP5tUePHrSfAACAFITpHGJH63V426sykRr17t1bpaWjJEllZWWqqKho\n/ZyGKtkNVYrW7FG3ITMVCBVkc8jtllyxzu8/SsbEZBprZTdWy26sif8aPSw5G8GY+Jcxkpqqyk5x\n2ZKMLWNHZGIRyY7I2BHJ2K1eu7GxUXv27NGePXuOOc7CwkLnq6CgQAUFBcrPz1coFFIwGFQwGEyp\nnkvxavnRNrCxLCv+2ZvOC4fDCofDzuO8vLyjfoXDYeXn5zvHJc7Ny8trMQa32LatWCwm2259bo/8\nrFJ8PhLnxGIx5ysajSoajaY8Tsxb8r8cWJaVMj+J+Uo8DgaDWfuXBmOMbNt2vhL/WpL4NZvjiMVi\nLeYr8Wvyv+IAyIzk72WtSXwPSP6ekHxu4tfEY9u2nd8n/ytr4nz+O/YvwrTPRU1QtrEUtqJqPPih\nE6RvvvlmDR8+XJK0efNm3XXXXUcN1JJkIjWKlH+o/P7npDWORBY0TYnVyIp/maZfW5xx9P/g27cv\noiWFu0nhflL3+DOBdp8ryVgtRmBkJBOTsWPxYJ302JiYZMck08pW6JYVH48s1VmW6poeq9GSGpvG\nqqSQb3TE740ku+nXpvCf9J7NjyUp1vTVEUeZ61af7uw34g6c3xW/5xtJNdukio1ejwRAF3fuuedq\n0aJF6tatm9dDOe4Rpn3MGKnOLpBkKaaIYjW7JUmlpaVOkJak4cOHq7S0VOvXr2/z/RrLP1a0Zm/L\n68iSQt2lvF5SuKdMuGf8caibZCXaGo6TZGSFJTo14KaCflLVB5Id8XokALqwN954Q+eee64mTZrk\n9VCOe4Rpn7NkZGQpasKyenxOOvhuh99BhSdI+X2lvGLZoW5SsEAKhONBORCSAvnxX3NSu2vVTYcn\nqsNGKS09owLFAAAgAElEQVQjieeTukaOfo5JfU464qREtTn5cSCpAt3W+7T3M7T74A4c28nzWz20\ns9fPQTXbCNIAPDdu3DiNHDnS62F0CbmaoLoEy5IKA/U6bBdKsmT6jJYOfaKysjJt3rw5pc2jrKws\n9eT8vlLP06Qen4tXmDvC2FK0RoFASIFgnmQ1N3dIieaEpkYPy2n4SOMTmqPWu40xMrG6pn7peM+0\niR52+p6d67UWLC1LxtjxXulYU790on+6k+EuEAg4vdLJfdOJfunEV6K3rbW+uPgQU3taQ6GQ85Xc\nM52fn5/SH91a33Ry73Dyc4n3yWSPXXKP8JF9z61p7dqJ90icZ9t2Sq90LBZTJBJJ6UU8cr6SP+OR\nnznx5UbPeGufP9HneLSe6eS/E5mUPI4j/wyOnPfknn52HQXSl/z9KxqNOo9bc+T3gsTXkatPJR4n\n3rut7yeBQID/ln2IMO1zQctWvtWgBlMgWQGp6FRVVGzUXXfdpdLSUklJNyDm95WKz5K6DZTCR1uT\nOTX8Wk2BNmDZCsh2frVCAUm2pPpsfExJUqzhUPNqHvWVrfcvu8CyLPXu3TtlJY++ffuquLg4ZTWP\nnj17qrCwsEvfAJIcDsPhsNfDyTo/ff7EDUmhEN/GgWxJ/h6Ql+evFbLgHb4L54CQFVND0//AhvuN\nlFSnqsP7tf7dpJucCvpLg/5fy3aNaK0CDQeU3723goGA/JQDjTGy68vjAbp6p+zG6jaPTwSHI/8v\nv7X/00+sM51YY7p79+7q1q2bevfu7YTlRHju3bu358EIAADkJsJ0DrCcnlpLtsLqNmBsyuvGSLV2\nNxkFmo63FbKiCllRBfOMrPy+2R90G2INhxSt2qbIoe0ykdoWrxcWFmr48OHOzocnnXSSTjzxRJWU\nlPDPWgAAwFcI0znAsqSAbNkKylbLMFlv8p0gHbIiKrAafFWBluJrZEcPbVekarvs+vIWrxcXF2vC\nhAmaOHGizj77bCrFAAAgJxCmc0TAsmWboIwCMqZ5sQhjpKiJ/zEGZPsqSBtjK1qzW9HKrYrW7NaR\nN/8VFxfrC1/4gs4991ydccYZVJ0BAEDOIUzniKBsRZse2wooqPgqByax8YekkBX1RZCONVQpUrlV\n0aqtMrGGlNfy8/M1adIkTZ8+Xeeccw7bcwMAgJxGmM4RAct2Cru2CShoxcN0LKntI6Bjb+nsJhNr\nUP2+DYpWbW3x2siRIzVz5kxNmjRJhYWFHowOAAAg8wjTOSI5KCf3TdsmKUxb3oRpY4yi1TvUsHd9\nSiW6pKRE559/vmbOnKkBAwZ4MjYAAAA3EaZzRPKKHrGkAN0crI0nlWk7Uqv6veudrc4ladCgQbr6\n6qs1evRo2jgAAMBxjTCdI462okeiMh2QyXq/dKRyq+r3rZfseDd3KBTSvHnzNG/ePFbjAAAAXQJh\nOoc0r+hhObtoJ4J1wMrOboEJ0Zo9qt+zzvn9sGHDtGjRIp1yyilZHQcAAICXCNM5pLmNw0qqTltH\nvOY+u7FGdbvXOr//xje+oYsvvpil7QAAQJdDmM4hKSt6yJKMlfpaFhg7qrqdb0qxRknSFVdcoUsu\nuSQr1wYAAPAbwnQOCSRtehLvlW4O08EsVKaNMarfu152Q4UkaezYsbryyitdvy4AAIBfEaZzSLyV\nI76ih61AUmXaNK324a5I5RZnDekTTjhBN910E60dAACgSyNM5xDLii+RZ2TJNoGm3Q/jIdvtlTxi\ndQfUsPevkqS8vDzddttt6tGjh7sXBQAA8DnKih4zpmMV5cSNhrYCMk1/fEGXV/Kwow3xPumma19/\n/fUqLS119ZoAAAC5gDDtkeLiYkmSidZ1KFA332iYvX7pSPlHMtE6SdJFF12kGTNmuHo9AACAXEGY\n9ki/fv3iD0zMWRmjPQKt9Ea7uca0MbYiTX3SvXv31tVXX+3atQAAAHINYdojTphWfEvu9mq5BJ5p\nNWBnSqx2r1OVPv/889nZEAAAIAlh2iP9+/d3HtuRw+0+78jNWYKKuXrzYaSyzHl8wQUXuHchAACA\nHESY9khyZdpE21+Zji+B11yJDrq4WYsdrVe0epckacSIERo4cKBr1wIAAMhFhGmPpFuZtqzU6rSb\n/dLxNaXjwf3CCy907ToAAAC5ijDtkV69eikUii/zbTrQMy2l3oTo1koexhg1NrV4dO/eXZMnT3bl\nOgAAALmMMO2RQCDgtHp0pDItSeFARJJRyIooYLlz82Gs7oBMY7Ukadq0acrPz3flOgAAALmMMO2h\nRKtHRyvTISumokCtCgMNbgxLUnzr8ARaPAAAAFpHmPZQojJtYg0ydrRD57q5goeJNSp6aIckqbS0\nVJ/73OfcuxgAAEAOI0x7KPkmRNPBVg83RQ59Gt9MRlSlAQAA2kKY9lDqxi0+CtNNNx7m5eVp2rRp\nHo8GAADAvwjTHkoJ0x1Ya9pNsfoK2fXlkqTJkyerqKjI4xEBAAD4F2HaQykbt/ikMp284yEtHgAA\nAG0jTHsotc3DH5XpaE18x8MTTzxRI0aM8Hg0AAAA/kaY9lA4HFbv3r0l+aMybaINzjhGjhwpy80l\nQwAAAI4DhGmPJVb08ENlOtZQ4TwuLS31cCQAAAC5gTDtsZKSEkmSidTJGHd2M2wvu77Secza0gAA\nAMdGmPZY81rTtky03tOxxOrjlWnLsjRkyBBPxwIAAJALCNMeS13Rw9tWD7spTJ900kkqKCjwdCwA\nAAC5gDDtseRdEO2odzchGjsqu7FaEv3SAAAA7UWY9phflsezGyolxXu26ZcGAABoH8K0x5Ir014u\nj5fol5aoTAMAALQXYdpj3bt3V2FhoSSPK9OEaQAAgA4jTHvMsiyn1cPbynR8WbySkhL17NnTs3EA\nAADkEsK0DyTCtFeVaWPspp5p+qUBAAA6gjDtA07ftB2RiTVm/fp2wyHJ2JJo8QAAAOgIwrQPpK7o\nkf1WD/qlAQAA0kOY9gGvN26JNRCmAQAA0kGY9oGUjVs8rEz36NEjJdgDAACgbYRpH0ipTEezW5k2\nxjgreZSWlsqyrKxeHwAAIJcRpn2gT58+CgTifxTZrkybSK1kRyTR4gEAANBRhGkfCAaDKikpkZT9\n5fHY+RAAACB9hGmf8GrjluSVPFhjGgAAoGMI0z7hhOlonYyJZe26icp0fn6+Bg4cmLXrAgAAHA8I\n0z6RvKKHidRl7bp207J4Q4YMUTAYzNp1AQAAjgeEaZ9I3bglO33TdrReJloviX5pAACAdBCmfSK1\nMp2dvml2PgQAAOgcwrRPeFGZZiUPAACAziFM+0Tqxi3ZrUwHAgENGTIkK9cEAAA4nhCmfaKgoEA9\nevSQlMXKdEN858PBgwcrLy8vK9cEAAA4nhCmfSTRN52NnmkTi8g0VkuixQMAACBdhGkfSbR62JHD\nMsa4ei27ocp5TJgGAABID2HaR5wVPUxMJtbg6rXsSI3zeNCgQa5eCwAA4HhFmPaRlJsQXW71sKPN\nG8P07dvX1WsBAAAcrwjTPpIcak3U3V0Qk8N6SUmJq9cCAAA4XhGmfaRnz57OY7fbPBJhOi8vT0VF\nRa5eCwAA4HhFmPaRxNJ4kmRija5eK9Hm0a9fP1mW5eq1AAAAjleEaR/xojJNvzQAAED6CNM+klqZ\ndi9MGxOTidVLol8aAACgMwjTPpKfn6/8/HxJ7rZ5mGi985jKNAAAQPoI0z6TqE67WZm2WckDAAAg\nIwjTPpPom3a1Mk2YBgAAyAjCtM84YTrqYs900hrWhGkAAID0EaZ9prnNo1HGGFeukdzmQc80AABA\n+gjTPtO8ooct2VFXrmGi8TAdDodTluMDAABAxxCmfSYba03bkXibR0lJCRu2AAAAdAJh2meysQti\nojJNiwcAAEDnEKZ9xu3KtDG2s840Nx8CAAB0DmHaZ1wP09E6SfEbGwnTAAAAnUOY9hm3txQ3EZbF\nAwAAyBTCtM+43TNtR1kWDwAAIFMI0z7jepsHlWkAAICMIUz7TGFhoUKhkCR3dkGkMg0AAJA5hGmf\nsSwrZRfETDNNux+GQiH16tUr4+8PAADQlRCmfSjR6uFGm4edtMZ0IMAfPwAAQGeQpnyouTLtXs80\n/dIAAACdR5j2IbfaPOIbtsTDNP3SAAAAnUeY9iFnRQ8Tk7GjGXvf+A2NbNgCAACQKYRpH3JreTzD\nSh4AAAAZRZj2Ibc2brEjzWG6X79+GXtfAACAroow7UNUpgEAAHIDYdqH3KtMs/shAABAJhGmfSil\nMp3BXRATlelgMMiGLQAAABlAmPYh19o8mnqm+/Tpo2AwmLH3BQAA6KoI0z7kWptHlA1bAAAAMokw\n7UPdu3d3tvrOVGXaGMPuhwAAABlGmPahQCCgoqIiSZmrTJtYvSRbEit5AAAAZAph2qeatxTPUGWa\nlTwAAAAyjjDtU4mbEDMVpu2kNaYJ0wAAAJlBmPap5jCdoTaPCBu2AAAAZBph2qecFT3siIyJdfr9\nTLS5zYOtxAEAADKDMO1TqWtNd746bTdVpgOBgIqLizv9fgAAACBM+1am15pO7H7Ihi0AAACZQ5j2\nqZQwnYEtxROVafqlAQAAMocw7VOZ3FLcGOP0TLOSBwAAQOYQpn0qo2E61iAZNmwBAADINMK0T2Wy\nZ5qVPAAAANxBmPapjFamWWMaAADAFYRpn0qtTHcuTNsRdj8EAABwA2Hap4LBoLp37y4pE20eVKYB\nAADcQJj2seYtxTtZmW7qmbYsS3369On0uAAAABBHmPaxRKtHpyvTTW0excXFCoVCnR4XAAAA4gjT\nPtYcpjt5A2K0XpLUu3fvTo8JAAAAzQjTPuas6BFrlGlaJzoddlOYpsUDAAAgswjTPpa6okckrfcw\ndkyy420iVKYBAAAyizDtY8lrTSvNVo/kDVsI0wAAAJlFmPax1I1b0rsJMdEvLdHmAQAAkGmEaR/L\nxMYtNpVpAAAA1xCmfSwTW4pTmQYAAHAPYdrHUivT6bZ5UJkGAABwC2HaxzJdmSZMAwAAZBZh2scy\n2TNdVFSkvLy8jIwLAAAAcYRpH8vLy1NBQYGkzq/mQVUaAAAg8wjTPudsKR7t3DrThGkAAIDMI0z7\nXKJvOp02D2Ns5zxW8gAAAMg8wrTPOZXpNNo84tVsI4nKNAAAgBsI0z6XXJk2xnToXBNjjWkAAAA3\nEaZ9rnl5PCPZkQ6dyxrTAAAA7iJM+1xnNm5J3kqcyjQAAEDmEaZ9rjNrTSdv2FJcXJyxMQEAACCO\nMO1zndkF0VCZBgAAcBVh2uc60+aRqEzn5eWpW7duGR0XAAAACNO+15nKdKJnuk+fPrIsK6PjAgAA\nAGHa91LCdAd3QWT3QwAAAHcRpn0u3TYPY4zT5kG/NAAAgDsI0z5XUFCgcDgsqYNtHnZEMrYkKtMA\nAABuIUz7nGVZThi2o4fbfZ7Nhi0AAACuI0zngJKSEkmSibQ/TLMsHgAAgPsI0znACdPRepmm1o1j\nSd6whco0AACAOwjTOaBfv35Nj0xKxbktVKYBAADcR5jOAYnKtCSZSPvCtE1lGgAAwHWE6RyQHKbb\nexNiojIdDAZT1qoGAABA5hCmc0BqZbq9YTpemS4uLlYgwB8zAACAG0hZOaC5Z1qy2x2m2f0QAADA\nbYTpHNCzZ0/l5eVJkkw72zwSPdOEaQAAAPcQpnOAZVnq27evpPZVpo0dje+AKFbyAAAAcBNhOkck\nWj3a0zNt2P0QAAAgKwjTOcLZuCVWL2NibR6bvCwelWkAAAD3EKZzREfWmmb3QwAAgOwgTOeIjqw1\nze6HAAAA2UGYzhHJy+Mdq286OUwXFxe7NiYAAICujjCdI1Iq08cI02wlDgAAkB2E6RyR0jPdzjaP\nHj16KBwOuzouAACArowwnSO6d++uwsJCSceuTCduQKRfGgAAwF2E6RxhWVbz8njt7JmmxQMAAMBd\nhOkc4oTpNto8jLFlYg2SqEwDAAC4jTCdQ5o3bmmMbxneCtaYBgAAyB7CdA5pz/J4ht0PAQAAsoYw\nnUPas3FL8hrTVKYBAADcRZjOIalbircepm3CNAAAQNYQpnNIezZuoc0DAAAgewjTOSSlZ5o2DwAA\nAM8RpnNIQUGBioqKJEl2pK7VYxKV6cLCQmeTFwAAALiDMJ1jjrXWtM2GLQAAAFlDmM4xiTB9rJ5p\nwjQAAID7CNM5xumbtiMysUjKa8YYwjQAAEAWEaZzTN++fZ3HR641HQ/StiRW8gAAAMgGwnSOaWsX\nxFjtHufxqaeemrUxAQAAdFWE6RzT1lrTkeqdkqRAIKDPf/7zWR0XAABAV0SYzjEpuyAmtXmYWESx\n2r2SpFGjRqlnz55ZHxsAAEBXQ5jOMUerTEdrdksm3i89adKkrI8LAACgKyJM55i8vDz16tVLUmrP\ndLR6hyTJsixNnDjRk7EBAAB0NYTpHOSsNd3U5mHsqKI18ZsPhw0bxkoeAAAAWUKYzkHOLoiRwzLG\nKFqzVzIxSdLkyZO9HBoAAECXQpjOQc7yeCYmxRqdFg+JfmkAAIBsIkznoNSbEGviNx9KOu2003TC\nCSd4NSwAAIAuhzCdg5LDdKRqq2THtxWnKg0AAJBdhOkclBKmK7c5j+mXBgAAyC7CdA5K3lJcJipJ\nGjx4sAYNGuTRiAAAALomwnQO6tOnjyzLSnmOqjQAAED2EaZzUCgUUu/evVOeI0wDAABkH2E6RyW3\negwYMECnnnqqh6MBAADomgjTOSr5JsRJkya1aPsAAACA+wjTOSq5Mk2LBwAAgDcI0zlq5syZOvHE\nEzV9+nSdccYZXg8HAACgSwp5PQCk55RTTtFvfvMbr4cBAADQpVGZBgAAANJEmAYAAADSRJgGAAAA\n0kSYBgAAANJEmAYAAADSRJgGAAAA0kSYBgAAANJEmAYAAADSRJgGAAAA0kSYBgAAANJEmAYAAADS\nRJgGAAAA0kSYBgAAANJEmAYAAADSRJgGAAAA0kSYBgAAANJEmAYAAADSRJgGAAAA0kSYBgAAANJE\nmAYAAADSRJgGAAAA0kSYBgAAANJEmAYAAADSRJgGAAAA0kSYBgAAANJEmAYAAADSRJgGAAAA0kSY\nBgAAANJEmAYAAADSRJgGAAAA0kSYBgAAANJEmAYAAADSRJgGAAAA0kSYBgAAANJEmAYAAADSRJgG\nAAAA0kSYBgAAANJEmAYAAADSRJgGAAAA0kSYBgAAANJEmAYAAADSRJgGAAAA0kSYBgAAANIUytaF\nampqZIzRuHHjsnVJAAAAdEHV1dWyLCsr18paZToQCGTtQwEAAKDrsixLgUB2Yq5ljDFZuRIAAABw\nnKFnGgAAAEgTYRoAAABIE2EaAAAASBNhGgAAAEgTYRoAAABIE2EaAAAASBNhGgAAAEgTYRoAAABI\nE2EaAAAASBNhGgAAAEhTxsP0+++/r4ULF2rChAmaNGmSrr/+eu3YscN5PRaL6Ze//KVmzZqlMWPG\naO7cuXrppZdS3uPNN9/UFVdcofHjx2vGjBn693//d9XX12d6qDmprq5Ot99+u84//3yNGzdOV1xx\nhdasWeP1sHzr4MGDuvXWW3Xuuedq7Nixuvzyy7V27Vrn9Zdeeklz587VmDFjNGvWLP3yl7+UbdvO\n6zt27NC1116rKVOmaPLkybr22mtT/j53VevXr9eZZ56p+++/33mOuey4FStW6Itf/KJGjRqlmTNn\n6re//a3zGvPZMWVlZbruuus0efJkjR8/Xpdffrn+7//+z3md+Ty6HTt2aMGCBRo2bJh2796d8lpn\n5609P/OPN23N5+9//3tddNFFGj16tM4//3zdd999MsaknMt8pmprPhMaGxs1Z84czZgxo8W5WZlP\nk0H79u0z48ePN7/4xS9MfX29OXjwoFm4cKG59NJLnWPuu+8+M23aNPP++++bSCRiXn31VTN8+HCz\nbt06Y4wxW7duNSNGjDBPPvmkqa+vNzt27DBz5841t9xySyaHmrNuueUWc+mll5pt27aZhoYG88wz\nz5iRI0easrIyr4fmS/PnzzdXX321OXDggGloaDC/+MUvzDnnnGP27dtn1q1bZ4YPH25WrVplGhsb\nzUcffWTOO+88c9999xljjGlsbDSzZs0yN998s6moqDCHDh0yt956q5k1a5aJRCIefzLv1NXVmQsv\nvNCMHz/emSvmsuNeeuklM2HCBLNmzRrT2Nho1q1bZ2bPnm02b97MfHZQLBYz5513nvnOd75jqqqq\nTENDg3nsscfM8OHDTVlZGfPZhlWrVpkpU6aY2267zQwdOtTs2rXLeS0T83asn/nHm7bm8+mnnzbj\nxo0zb7/9trFt26xfv96MGTPGPP7448YY5rM1bc1nsnvuuceMGzfOzJgxw3kum/OZ0TC9adMms3jx\nYmPbtvPc6tWrzdChQ82hQ4eMbdtm4sSJzl+chOuvv95cf/31xhhjfvazn6WEb2OM8+EqKioyOdyc\nU1lZaYYPH25effXVlOcvvfRS85Of/MSjUflXdXW1Wbx4sdmyZYvz3KFDh8zQoUPN6tWrzQ033OD8\nvUt4/PHHzYQJE4xt2+a1114zZ555pqmsrHRer6ysNGeddVaLP4Ou5Mc//rG57rrrzFVXXeX8UGUu\nO2727Nnm4YcfbvU15rNjPvvsMzN06FDz+uuvO8/V19eboUOHmpdffpn5bMPKlSvNzp07zZtvvtki\nrHRm3v785z+362f+8aat+XziiSfMsmXLUo7/l3/5F3PdddcZYwzz2Yq25jNh06ZNZvz48eb+++83\n5513nvN8Nuczo20eI0aM0J133inLspznduzYoR49eqioqEiffvqpKioqNGrUqJTzRo4cqQ0bNkiS\nNmzYoJEjR7Z4PRqNavPmzZkcbs7ZvHmzotFoq/P33nvveTQq/yoqKtKdd96p0tJS57lPP/1UkjRg\nwAC99957rc5lZWWltm3bpg0bNujkk09Wr169nNd79eqlwYMHd9n5fuedd/TCCy/ojjvuSHl+w4YN\nzGUH7N+/X2VlZSosLNSVV16psWPHas6cOc4/LzKfHVNSUqLx48dr+fLlKi8vVyQS0dNPP60+ffpo\n4sSJzGcbLrnkEp100kmtvtbZ75Ht+Zl/vGlrPhcsWKB58+Y5vzfGaOfOnRo4cKAkMZ+taGs+pXh7\nx6233qqbbrrJmceEbM5nqCMHx2Ix1dbWHvX1nj17pvz+o48+0v33368bb7xRlmWpvLxcklI+mCT1\n7t1bFRUVkqTy8nIVFxe3eF2K9792ZW3NX1efm/aoqanRrbfeqpkzZ2rEiBEqLy9vdS6l+N+1ioqK\nFq8njkn8WXQldXV1uu2227R48WKVlJSkvNbaXDGXR7d3715J0rJly7RkyRINHjxYy5cv17/9279p\nwIABzGca7r33Xn3jG9/Q5MmTZVmWevfurV/96lfq06cP85mmznyPTLwutf0zvytbunSp9u7dq4UL\nF0pq/fuoxHy2ZenSperfv78uv/xyrVixIuW1bM5nh8L0unXrnD/0I1mWpffee095eXmSpLVr1+rG\nG2/UVVddpX/6p3865nsnqtnJVe2jHYOWmJu27dq1S9dee6369++vu++++5jHtzWfxpguOd/33HOP\nTj/9dM2ZM6dD5zGXLZmmG44WLFig008/3Xm8cuVKPffcc22ey3y21NjYqK9//es67bTT9NBDD6mw\nsFDPP/+8vvnNb2r58uVtnst8pudY89LZ149nsVhMP/vZz/Tiiy/qN7/5TYuKamuYz5Y2bdqkZ555\nRs8//3yHz830fHYoTE+ePFkffvjhMY9bvny5fvKTn+gHP/iBLrvsMuf5RDWrsrIy5fiKigrntZKS\nkhb/R5D4/ZHVsK6mb9++kuLz179/f+f5iooK9evXz6th+d7GjRt17bXX6otf/KIWL16sYDAoKf73\nqbW/i5LUv39/9e3bt8XriWO62t/Fd955R6+88opWrlzZ6uvMZcck/vs98l/hTj75ZO3du7fN74PM\nZ0tvvfWWPvjgAz3yyCPq06ePJOkrX/mKnnnmGT377LPMZ5rS/e+6vLxcJSUlKT+zjnyPrjqv9fX1\n+ta3vqXdu3dr2bJlOvnkk53XmM/2S7R3fPe739WJJ57Y6jHZnM+ML423cuVK/fznP9cjjzySEqQl\nadCgQSopKWnRi7J+/XqNHTtWkjR69OhWX8/Ly2vRS93VjBgxQnl5eS3m569//avGjRvn0aj87eOP\nP9Y3vvENXXfddfrhD3/oBGkp/nft3XffTTl+/fr16t+/vwYPHqzRo0drx44dKf/Me+DAAe3YsaPL\nzfezzz6r6upqXXzxxZo4caImTpyod999Vw8//LAuu+wy5rKD+vfvr+LiYm3cuDHl+W3btjnz1dr3\nQeazdYml2mKxWMrz0WhUxhjmM02d/e968ODBx/yZ35XEYjEtWrRI9fX1euaZZ1KCtCSNGTOG+Wyn\nDRs26JNPPtHdd9/t/Ey68847tXfvXufnU1bns0O3Kx7D7t27zTnnnJNyR/WRHnroITN16lTzt7/9\nzTQ0NJgXX3zRDB8+3GzcuNEYY8zOnTvNOeecYx577DFTV1dntmzZYmbPnm1+9KMfZXKoOev22283\nF198sdm6das5fPiwefjhh83o0aPN7t27vR6a70SjUTN37lxz9913t/r6hg0bzPDhw83LL79sGhoa\nzMaNG82UKVPMo48+aoyJL7c1Z84c853vfMdUVFSYgwcPmhtvvNFceumlKSvWdAVVVVVm7969KV+X\nX365+dnPfmYOHDjAXKbhwQcfNGPHjjVr1qwxDQ0N5sknnzRnnXWW+eCDD5jPDjp06JCZMmWK+d73\nvmcqKipMfX29+e///m9z5plnmvfee4/5bMP+/fvNnj17zEsvvWSGDh1qNmzYYPbs2WOqqqoyMm/H\n+pl/vDnafFZWVprHHnvMXHDBBaa2trbVc5nPlo42nwcOHGjxM+mxxx4z06ZNM3v37jUNDQ1ZnU/L\nmAX4mH4AAAGuSURBVKTVwjvpgQce0K9//WunbzrZnXfeqUsuuURSvGF82bJlKi8vV2lpqb797W/r\nvPPOc45955139POf/1wffvihevbsqTlz5uimm25SKNShrpTjUmNjo5YsWaKXX35ZtbW1OvPMM3Xz\nzTdr9OjRXg/Nd9555x1dddVVCofDLfqfLr30Ut1xxx1avXq1fv3rX2vbtm0qKSnRlVdeqWuuucY5\nbu/evbrjjjv01ltvybIsTZkyRd///vdT2my6qgULFmjChAlatGiRJDGXaVi6dKmWL1+ugwcPqrS0\nVDfffLMmT54sifnsqI8++kj33HOP/va3v6m6ulqf+9zn9K1vfcv52cJ8tm7GjBnORhiWZTn9/HPn\nztVPf/rTjMzbsX7mH0/ams/169dr9+7dCgRSmwIS95xJzOeRjvX3M9lzzz2n+++/X3/+85+d57I1\nnxkN0wAAAEBXkvGeaQAAAKCrIEwDAAAAaSJMAwAAAGkiTAMAAABpIkwDAAAAaSJMAwAAAGkiTAMA\nAABpIkwDAAAAaSJMAwAAAGkiTAMAAABpIkwDAAAAaSJMAwAAAGn6/+wJ1pxjYL4HAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6adba6f910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "miniDelayInfo=DelayInfoOnly_stats.head(1000)\n",
    "sns.violinplot([miniDelayInfo.CarrierDelay])\n",
    "sns.kdeplot(miniDelayInfo.LateAircraftDelay)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f6ae24b1e50>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WAeOsX28Rieg0NBGRxWxGgf7d7353+v4//MM/0NLSwm9/+1t+8Ytf8Ad/8Ae8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\n0Gmexrav5jcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ae256bd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(DelayInfoOnly_stats['CarrierDelay'],DelayInfoOnly_stats['WeatherDelay'],alpha=0.2)\n",
    "#sns.pairplot(DelayInfoOnly_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ae256bb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig('test.png',dpi=160)\n",
    "#plt.savefig('zoomed_ArrivalDelayBreakdown_19busiestAirports_2014.svg',dpi=160)\n"
   ]
  },
  {
   "cell_type": "code",
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