Descriptive Stats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "from pylab import rcParams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>93</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6</td>\n",
       "      <td>258.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>18.7</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model   mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  \\\n",
       "0          Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4   \n",
       "1      Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4   \n",
       "2         Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
       "3     Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3   \n",
       "4  Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3   \n",
       "\n",
       "   carb  \n",
       "0     4  \n",
       "1     4  \n",
       "2     1  \n",
       "3     1  \n",
       "4     2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars=pd.read_csv('mtcars.csv')\n",
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model    Mazda RX4Mazda RX4 WagDatsun 710Hornet 4 Drive...\n",
       "mpg                                                  642.9\n",
       "cyl                                                    198\n",
       "disp                                                7383.1\n",
       "hp                                                    4694\n",
       "drat                                                115.09\n",
       "wt                                                 102.952\n",
       "qsec                                                571.16\n",
       "vs                                                      14\n",
       "am                                                      13\n",
       "gear                                                   118\n",
       "carb                                                    90\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.sum() # cars.sum(axis=1), does the sum horizontally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg      19.200\n",
       "cyl       6.000\n",
       "disp    196.300\n",
       "hp      123.000\n",
       "drat      3.695\n",
       "wt        3.325\n",
       "qsec     17.710\n",
       "vs        0.000\n",
       "am        0.000\n",
       "gear      4.000\n",
       "carb      2.000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg      20.090625\n",
       "cyl       6.187500\n",
       "disp    230.721875\n",
       "hp      146.687500\n",
       "drat      3.596563\n",
       "wt        3.217250\n",
       "qsec     17.848750\n",
       "vs        0.437500\n",
       "am        0.406250\n",
       "gear      3.687500\n",
       "carb      2.812500\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model    Volvo 142E\n",
       "mpg            33.9\n",
       "cyl               8\n",
       "disp            472\n",
       "hp              335\n",
       "drat           4.93\n",
       "wt            5.424\n",
       "qsec           22.9\n",
       "vs                1\n",
       "am                1\n",
       "gear              5\n",
       "carb              8\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mpg=cars['mpg'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg       6.026948\n",
       "cyl       1.785922\n",
       "disp    123.938694\n",
       "hp       68.562868\n",
       "drat      0.534679\n",
       "wt        0.978457\n",
       "qsec      1.786943\n",
       "vs        0.504016\n",
       "am        0.498991\n",
       "gear      0.737804\n",
       "carb      1.615200\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg        36.324103\n",
       "cyl         3.189516\n",
       "disp    15360.799829\n",
       "hp       4700.866935\n",
       "drat        0.285881\n",
       "wt          0.957379\n",
       "qsec        3.193166\n",
       "vs          0.254032\n",
       "am          0.248992\n",
       "gear        0.544355\n",
       "carb        2.608871\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    15\n",
       "4    12\n",
       "5     5\n",
       "Name: gear, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gear=cars.gear\n",
    "gear.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.090625</td>\n",
       "      <td>6.187500</td>\n",
       "      <td>230.721875</td>\n",
       "      <td>146.687500</td>\n",
       "      <td>3.596563</td>\n",
       "      <td>3.217250</td>\n",
       "      <td>17.848750</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.406250</td>\n",
       "      <td>3.687500</td>\n",
       "      <td>2.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.026948</td>\n",
       "      <td>1.785922</td>\n",
       "      <td>123.938694</td>\n",
       "      <td>68.562868</td>\n",
       "      <td>0.534679</td>\n",
       "      <td>0.978457</td>\n",
       "      <td>1.786943</td>\n",
       "      <td>0.504016</td>\n",
       "      <td>0.498991</td>\n",
       "      <td>0.737804</td>\n",
       "      <td>1.6152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.400000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>71.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2.760000</td>\n",
       "      <td>1.513000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.425000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>120.825000</td>\n",
       "      <td>96.500000</td>\n",
       "      <td>3.080000</td>\n",
       "      <td>2.581250</td>\n",
       "      <td>16.892500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.200000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>196.300000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>3.695000</td>\n",
       "      <td>3.325000</td>\n",
       "      <td>17.710000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.800000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>3.920000</td>\n",
       "      <td>3.610000</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>33.900000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>5.424000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mpg        cyl        disp          hp       drat         wt  \\\n",
       "count  32.000000  32.000000   32.000000   32.000000  32.000000  32.000000   \n",
       "mean   20.090625   6.187500  230.721875  146.687500   3.596563   3.217250   \n",
       "std     6.026948   1.785922  123.938694   68.562868   0.534679   0.978457   \n",
       "min    10.400000   4.000000   71.100000   52.000000   2.760000   1.513000   \n",
       "25%    15.425000   4.000000  120.825000   96.500000   3.080000   2.581250   \n",
       "50%    19.200000   6.000000  196.300000  123.000000   3.695000   3.325000   \n",
       "75%    22.800000   8.000000  326.000000  180.000000   3.920000   3.610000   \n",
       "max    33.900000   8.000000  472.000000  335.000000   4.930000   5.424000   \n",
       "\n",
       "            qsec         vs         am       gear     carb  \n",
       "count  32.000000  32.000000  32.000000  32.000000  32.0000  \n",
       "mean   17.848750   0.437500   0.406250   3.687500   2.8125  \n",
       "std     1.786943   0.504016   0.498991   0.737804   1.6152  \n",
       "min    14.500000   0.000000   0.000000   3.000000   1.0000  \n",
       "25%    16.892500   0.000000   0.000000   3.000000   2.0000  \n",
       "50%    17.710000   0.000000   0.000000   4.000000   2.0000  \n",
       "75%    18.900000   1.000000   1.000000   4.000000   4.0000  \n",
       "max    22.900000   1.000000   1.000000   5.000000   8.0000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    10\n",
       "2    10\n",
       "1     7\n",
       "3     3\n",
       "8     1\n",
       "6     1\n",
       "Name: carb, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.carb.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>cyl</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
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       "      <td>4</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cyl  vs  am  gear  carb\n",
       "0    6   0   1     4     4\n",
       "1    6   0   1     4     4\n",
       "2    4   1   1     4     1\n",
       "3    6   1   0     3     1\n",
       "4    8   0   0     3     2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars_cat=cars[['cyl','vs','am','gear','carb']]\n",
    "cars_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">am</th>\n",
       "      <th colspan=\"2\" halign=\"left\">carb</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">cyl</th>\n",
       "      <th colspan=\"8\" halign=\"left\">vs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gear</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.414039</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        am                                               carb           ...   \\\n",
       "     count      mean       std  min  25%  50%  75%  max count      mean ...    \n",
       "gear                                                                    ...    \n",
       "3     15.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  15.0  2.666667 ...    \n",
       "4     12.0  0.666667  0.492366  0.0  0.0  1.0  1.0  1.0  12.0  2.333333 ...    \n",
       "5      5.0  1.000000  0.000000  1.0  1.0  1.0  1.0  1.0   5.0  4.400000 ...    \n",
       "\n",
       "      cyl         vs                                               \n",
       "      75%  max count      mean       std  min  25%  50%  75%  max  \n",
       "gear                                                               \n",
       "3     8.0  8.0  15.0  0.200000  0.414039  0.0  0.0  0.0  0.0  1.0  \n",
       "4     6.0  6.0  12.0  0.833333  0.389249  0.0  1.0  1.0  1.0  1.0  \n",
       "5     8.0  8.0   5.0  0.200000  0.447214  0.0  0.0  0.0  0.0  1.0  \n",
       "\n",
       "[3 rows x 32 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gears_group=cars_cat.groupby('gear')\n",
    "gears_group.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'raise_on_error'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-be86cb5aceb0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'group'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'gear'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"category\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mcars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'group'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[1;32m    241\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m                     data = data.astype(dtype=dtype, raise_on_error=False,\n\u001b[0;32m--> 243\u001b[0;31m                                        copy=copy)\n\u001b[0m\u001b[1;32m    244\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m                     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[1;32m   3222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3223\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3224\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'astype'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3226\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[1;32m   3089\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3090\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mgr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3091\u001b[0;31m             \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3092\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3093\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, values, **kwargs)\u001b[0m\n\u001b[1;32m    469\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'raise'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    470\u001b[0m         return self._astype(dtype, copy=copy, errors=errors, values=values,\n\u001b[0;32m--> 471\u001b[0;31m                             **kwargs)\n\u001b[0m\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m     def _astype(self, dtype, copy=False, errors='raise', values=None,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m_astype\u001b[0;34m(self, dtype, copy, errors, values, klass, mgr, **kwargs)\u001b[0m\n\u001b[1;32m    488\u001b[0m         \u001b[0;31m# this is only called for non-categoricals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_categorical_astype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 490\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_block\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCategorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    491\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;31m# astype processing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'raise_on_error'"
     ]
    }
   ],
   "source": [
    "cars['group']=pd.Series(cars['gear'],dtype=\"category\")\n",
    "cars['group'].dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pearson Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#applicable on data that is 1. normally distributed 2.has continous numerical values 3.variables are linealy related\n",
    "\n",
    "from scipy.stats.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "rcParams['figure.figsize']=8,4\n",
    "sb.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f3d92259d10>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vgruWz8C4NJPmc1+OKRwp6QBv3LgRn332Ga5fv46HH34YP/zhD/HZZ5/h4sWL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IAAAR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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d948c8bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=cars[['mpg','hp','qsec','wt']]\n",
    "sb.pairplot(x,palette='hls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonR Correlation coefficient -0.776\n"
     ]
    }
   ],
   "source": [
    "mpg=cars.mpg\n",
    "qsec=cars.qsec\n",
    "wt=cars.wt\n",
    "hp=cars.hp\n",
    "pearsonr_coeff,p_value=pearsonr(mpg,hp)\n",
    "print \"PearsonR Correlation coefficient %0.3f\" %(pearsonr_coeff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonR Correlation coefficient 0.419\n"
     ]
    }
   ],
   "source": [
    "pearsonr_coeff,p_value=pearsonr(mpg,qsec)\n",
    "print \"PearsonR Correlation coefficient %0.3f\" %(pearsonr_coeff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonR Correlation coefficient -0.868\n"
     ]
    }
   ],
   "source": [
    "pearsonr_coeff,p_value=pearsonr(mpg,wt)\n",
    "print \"PearsonR Correlation coefficient %0.3f\" %(pearsonr_coeff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>hp</th>\n",
       "      <th>qsec</th>\n",
       "      <th>wt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.776168</td>\n",
       "      <td>0.418684</td>\n",
       "      <td>-0.867659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hp</th>\n",
       "      <td>-0.776168</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.708223</td>\n",
       "      <td>0.658748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qsec</th>\n",
       "      <td>0.418684</td>\n",
       "      <td>-0.708223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.174716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wt</th>\n",
       "      <td>-0.867659</td>\n",
       "      <td>0.658748</td>\n",
       "      <td>-0.174716</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mpg        hp      qsec        wt\n",
       "mpg   1.000000 -0.776168  0.418684 -0.867659\n",
       "hp   -0.776168  1.000000 -0.708223  0.658748\n",
       "qsec  0.418684 -0.708223  1.000000 -0.174716\n",
       "wt   -0.867659  0.658748 -0.174716  1.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr=x.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## seaborn visualisation  of correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3d913d52d0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d914694d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.heatmap(corr,xticklabels=corr.columns.values,yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Non paramatric Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#spearman'r rank correlation\n",
    "#calculates correlation between ordinal variables(ie those that can be categorised)\n",
    "#model assumes that the variables are related non-linealy and data is non-normally distributed\n",
    "#Chi square tables \n",
    "#1.p<0.05: Reject null hypothesis and conclude variables are correlated\n",
    "#2.p>0.05:: Accept null hypothesis and conclude variables are independent\n",
    "# for this model the varibles need to be categorial or binned numeric variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import spearmanr\n",
    "rcParams['figure.figsize']=14,7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f3d8fbbe290>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2PaKevuR+e/qGLij45jz0btu2TV//+tfl9+e8FMBR/cO27mk5po6uHsVHEuro6tE9LcfU\nP2znujRkwUDc1ro9x5Pmu27PcQ3E3Tdfk3pBKtPm67Vrq2nzS6d/eOx+L2S+lgP1Zezll19WaWmp\nrr76ah0+fHjCteFwOKvvne3j5Tu39xuLxdL2UF1dPUXVTGy8Oisrq3Qk0pv03JFIr4oLAq6fTzqZ\nzM/tKqvGnm/JNGvM3vN5v062F5OwV8fuPa/3q8eurV47P7N5bc1p6H3ttdd08OBBtbe3a3BwUOfP\nn9eGDRv02GOPpaxNf8J1Teq98+UEvnBu73dy9RcVFeVhD2Mbr87zg3EtnlOqjq6e0ecWzylV/7Dt\nmt4uVDgcNr7Hc7HhMefbNxjP697Hqs2tvWQDezW/ex+rPq9dW908vwuRzX5zek/B+vXr1d7eroMH\nD+rxxx/XX/3VX40ZeAETFBcE9ERokZbMLZPl92nJ3DI9EVqk4oJArktDFky3AtqxpiZpvjvW1Gi6\n5b75mtQLUpk2X69dW02bXzrFBWP3eyHzzeknvYCX+P0+lZUU6snPX6figoD6h20VFwTk9/tyXRqy\nwLL8Ki0uVPPnrlXJNEt9g3FNtwKyLPd9v4JJvSCVafP12rXVtPmlEwj4VVaS3G9xQUCBwOT7zZu/\noRtuuEE7d+7MdRmAo/x+n2ZMs/Tb357UjGmWsRdlr7Isv2YWFei3J09qZlGBq/8RMqkXpDJtvl67\ntpo2v3QCgeR+LyTwSnkUegEAAACnEHoBAABgPEIvAAAAjEfoBQAAgPEIvQAAADCeL5FI5P0vpz56\n9GiuS4BLXHvttTl9f/YqJoP9Cjdhv8Itxturrgi9AAAAwMXg9gYAAAAYj9ALAAAA4xF6AQAAYDxC\nLwAAAIxH6AUAAIDxCL0AAAAwHqEXAAAAxiP0AgAAwHiEXgAAABiP0AsAAADjEXoBAABgPEIvAAAA\njEfoBQAAgPEIvQAAADAeoRcAAADGI/QCAADAeIReAAAAGI/QCwAAAOO5IvQePXo07ZpIJOJ8IXmE\nfvNTJntVck8/2UK/+Ylrayr6zV/s11T0OzmuCL2ZGBgYyHUJU4p+3c20ftKhX/cyqZdM0K+7mdZP\nOvQ7OcaEXgAAAGA8hF4AAAAYj9ALAAAA4xF6AQAAYDzHQu/999+vJUuW6Kabbhrz9UQioS1btqi+\nvl6NjY06ceKEU6UAAADA4xwLvbfeeqt27do17uvt7e2KRCI6cOCAHnnkEW3atOmC3iceH9G52LAq\nq6p0LjaseHzkAit2B6/1CyA3uNaYzbT5mtZPOl7rd2QkofODcVVWVun8YFwjI4kLOo6V5bpGLV68\nWG+++ea4r7e1tWnlypXy+XyqqanR2bNndebMGc2aNSvj94jHR9TbP6R1e47rSKRXi+eUaseaGpUW\nF8qyzLtzw2v9AsgNrjVmM22+pvWTjtf6HRlJqKdvSPe0HBvt94nQIpWVFMrv903qWDn724lGoyov\nLx99XF5ermg0OqljDMRtrdtzXB1dPYqPJNTR1aN1e45rIG5nu9y84LV+AeQG1xqzmTZf0/pJx2v9\n9g/buqflWFK/97QcU//w5Pt17JPebAuHwynPVVZV6UikN+m5I5FelUyzxlzvdl7r991isVjaHqur\nq6eomollMotM+jEJ/abK5/3KtcbsHi9kvuzX/OG5fivH7re4IDBmvxPt1ZyF3mAwqO7u7tHH3d3d\nCgaD464fq4lzsWEtnlOqjq6e0ecWzylV32A8b07QbPJav+8WDodd02Mmdbqpn2yg3/zFtTWZm2Z3\nodw8X/ar9/o9Pxgfs9/+YXvS/ebs9oa6ujrt27dPiURCx48f18yZMyd1P68kTbcC2rGmRkvmlsny\n+7Rkbpl2rKnRdCvgUNW55bV+AeQG1xqzmTZf0/pJx2v9FhcE9ERoUVK/T4QWqbhg8v069knvvffe\nq1/96ld6++23tWzZMt19992Kx+OSpFAopNraWh06dEj19fWaPn26tm3bNun3sCy/SosL1fy5a1Uy\nzVLfYFzTrYCRN3JL3usXQG5wrTGbafM1rZ90vNav3+9TWUmhnvz8dSouCKh/2FZxQWDS38QmORh6\nH3/88Qlf9/l8euihhy76fSzLr5mW3xP/S0ryXr8AcoNrjdlMm69p/aTjtX79fp9m/O89yxfTr2u+\nkQ0wyaee7pLUlfH6yPYG54oBAMADzPwsHAAAAHgXQi8AAACMR+gFAACA8Qi9AAAAMB6hFwAAAMYj\n9AIAAMB4hF4AAAAYj9ALAAAA4xF6AQAAYDxCLwAAAIxH6AUAAIDxCL0AAAAwHqEXAAAAxiP0AgAA\nwHiEXgAAABiP0AsAAADjEXoBAABgPEdDb3t7u5YvX676+no1NzenvN7b26s777xTN998sxoaGvTT\nn/7UyXIAAADgUZZTB7ZtW5s3b9ZTTz2lYDCo1atXq66uTvPnzx9d86Mf/UhVVVX63ve+p97eXq1Y\nsUKNjY0qLCx0qiwAAAB4kGOf9HZ2dmr27NmqqKhQYWGhGhoa1NbWlrTmsssuU19fnxKJhPr6+nTJ\nJZfIshzL4QAAAPAoxxJmNBpVeXn56ONgMKjOzs6kNbfddpvuuOMOLV26VH19ffr2t78tv3/sHB4O\nhyd8v1gslnaNSeg3VXV19RRVMzEn5uL2WbNfU7llvzI7s2XaL/s1P9Fvqon2ak4/Vt25c6cqKyu1\ne/du/e53v9PatWt13XXXacaMGSlr051w4XA4b07KqUC/+SuzOrscOGb+ctP8ssFN/XJtTUa/+Y39\nmox+J8ex2xuCwaC6u7tHH0ejUQWDwaQ1r732mlasWCGfz6fZs2friiuuUFfX5MIAAAAAkI5joXfh\nwoWKRCI6ffq0hoaG1Nraqrq6uqQ1c+fOVUdHhyTpj3/8o9544w1dccUVTpUEAAAAj3Ls9gbLsrRx\n40Y1NTXJtm2tWrVKCxYsUEtLiyQpFArpS1/6ku6//341NjYqkUhow4YNKi0tdaokAAAAeJSj9/TW\n1taqtrY26blQKDT659LSUu3cudPJEgAAAAB+IxsAAADMR+gFAACA8Qi9AAAAMB6//gwA0phzX+uk\n1v/s83MdqgQAcKH4pBcAAADGI/QCAADAeIReAAAAGI/QCwAAAOMRegEAAGA8Qi8AAACMR+gFAACA\n8Qi9AAAAMB6hFwAAAMYj9AIAAMB4hF4AAAAYj9ALAAAA4xF6AQAAYDxHQ297e7uWL1+u+vp6NTc3\nj7nm8OHDuuWWW9TQ0KDbb7/dyXIAAADgUZZTB7ZtW5s3b9ZTTz2lYDCo1atXq66uTvPnzx9dc/bs\nWT388MPatWuXLr/8cvX09DhVDgAAADzMsU96Ozs7NXv2bFVUVKiwsFANDQ1qa2tLWvPiiy+qvr5e\nl19+uSSprKzMqXIAAADgYY6F3mg0qvLy8tHHwWBQ0Wg0aU0kEtHZs2f12c9+Vrfeeqv27dvnVDkA\nAADwMMdub8iEbds6ceKEfvCDHygWi2nNmjW65pprdOWVV6asDYfDEx4rFoulXWMS+k1VXV09RdVM\nzIm5uH3W7NdUbtmvzM5smfbLfs1P9Jtqor3qWOgNBoPq7u4efRyNRhUMBpPWlJeX69JLL1VxcbGK\ni4t13XXX6eTJk2OG3nQnXDgczpuTcirQb/7KrM4uB46Zv9w0v7FNbl5FRUWu6ZdrazL6zW/s12T0\nOzmO3d6wcOFCRSIRnT59WkNDQ2ptbVVdXV3Smk984hM6evSo4vG4BgYG1NnZqXnz5jlVEgAAADzK\nsU96LcvSxo0b1dTUJNu2tWrVKi1YsEAtLS2SpFAopHnz5mnp0qW6+eab5ff7tXr1al111VVOlQQA\nAACPcvSe3traWtXW1iY9FwqFkh43NTWpqanJyTIAAADgcfxGNgAAABiP0AsAAADjEXoBAABgPEIv\nAAAAjEfoBQAAgPHSht6jR4+qv79fkvT888/r0Ucf1e9//3vHCwMAAACyJW3o3bRpk6ZPn66TJ0/q\nqaee0gc+8AF94xvfmIraAAAAgKxIG3oty5LP59MvfvELfeYzn9FnPvMZ9fX1TUVtAAAAQFakDb0l\nJSXauXOnXnjhBX3sYx/TyMiI4vH4VNQGAAAAZEXa0PvXf/3Xisfj2rZtm973vvepu7tbd95551TU\nBgAAAGRF2l9DPDIyop/97Gf65S9/qb/5m7/RihUrtHLlyqmoDQAAAMiKtJ/0fu1rX1Nra6s2btyo\nP/zhD7r99tt1xx13TEFpAAAAQHZk/HN6y8rKdNlll+nSSy9VT0+PkzUBAAAAWZX29oYf/ehHeuml\nl9Tb26sVK1Zoy5Ytmj9//lTUBgAAAGRF2tDb3d2tf/zHf1R1dfVU1AMAAABkXdrQu379+qmoAwAA\nAHBMxvf0AgAAAG7laOhtb2/X8uXLVV9fr+bm5nHXdXZ26oMf/KBeeuklJ8sBAACARzkWem3b1ubN\nm7Vr1y61trZq//79OnXq1JjrHnvsMX3kIx9xqhQAAAB4nGOht7OzU7Nnz1ZFRYUKCwvV0NCgtra2\nlHXPPPOMli9frrKyMqdKAQAAgMc5Fnqj0ajKy8tHHweDQUWj0ZQ1v/jFLxQKhZwqAwAAAEj/0xuc\ntHXrVm3YsEF+f/rsHQ6HJ3w9FoulXWMS+k2VLz9Wz4m5uH3W7NdUbtmvzM5smfbLfs1P9Jtqor3q\nWOgNBoPq7u4efRyNRhUMBpPW/PrXv9a9994rSXr77bd16NAhWZalT37ykynHS3fChcPhvDkppwL9\n5q/M6uxy4Jj5y03zG9vk5lVUVOSafrm2JqPf/MZ+TUa/k+NY6F24cKEikYhOnz6tYDCo1tZWfetb\n30pac/DgwdE/33ffffrYxz42ZuAFAAAALoZjodeyLG3cuFFNTU2ybVurVq3SggUL1NLSIkncxwsA\nAIAp4+g9vbW1taqtrU16brywu337didLAQAAgIfxG9kAAABgPEIvAAAAjEfoBQAAgPEIvQAAADAe\noRcAAADGI/QCAADAeIReAAAAGI/QCwAAAOMRegEAAGA8Qi8AAACMR+gFAACA8Qi9AAAAMB6hFwAA\nAMYj9AIAAMB4hF4AAAAYj9ALAAAA4xF6AQAAYDxHQ297e7uWL1+u+vp6NTc3p7z+wgsvqLGxUY2N\njVqzZo1OnjzpZDkAAADwKMdCr23b2rx5s3bt2qXW1lbt379fp06dSlpzxRVX6Ic//KFefPFFffnL\nX9aDDz7oVDkAAADwMMdCb2dnp2bPnq2KigoVFhaqoaFBbW1tSWs+/OEP65JLLpEk1dTUqLu726ly\nAAAA4GGOhd5oNKry8vLRx8FgUNFodNz1zz33nJYtW+ZUOQAAAPAwK9cFSNKrr76q5557Tj/+8Y/H\nXRMOhyc8RiwWS7vGJPSbqrq6eoqqmZgTc3H7rNmvqdyyX5md2TLtl/2an+g31UR71bHQGwwGk25X\niEajCgaDKetOnjypBx54QE8++aTe+973jnu8dCdcOBzOm5NyKtBv/sqszi4Hjpm/3DS/sU1uXkVF\nRa7pl2trMvrNb+zXZPQ7OY7d3rBw4UJFIhGdPn1aQ0NDam1tVV1dXdKat956S3fffbe++c1v6sor\nr3SqFAAAAHicY5/0WpaljRs3qqmpSbZta9WqVVqwYIFaWlokSaFQSN/97nf1zjvv6OGHH5YkBQIB\n7d2716mSAAAA4FGO3tNbW1ur2trapOdCodDon7du3aqtW7c6WQIAAADAb2QDAACA+Qi9AAAAMB6h\nFwAAAMYj9AIAAMB4hF4AAAAYj9ALAAAA4xF6AQAAYDxCLwAAAIxH6AUAAIDxCL0AAAAwHqEXAAAA\nxiP0AgAAwHiEXgAAABiP0AsAAADjEXoBAABgPEIvAAAAjEfoBQAAgPEIvQAAADCeo6G3vb1dy5cv\nV319vZqbm1NeTyQS2rJli+rr69XY2KgTJ044WQ4AAAA8yrHQa9u2Nm/erF27dqm1tVX79+/XqVOn\nkta0t7crEonowIEDeuSRR7Rp06ZJv8/ISELnB+OqrKzS+cG4RkYSWeogP8XjIzoXG1ZlVZXOxYYV\nj4/kuiRHea1fIF9w7pnNtPma1k86tp3cr22b3W+25mtlua5RnZ2dmj17tioqKiRJDQ0Namtr0/z5\n80fXtLVgqqPNAAAgAElEQVS1aeXKlfL5fKqpqdHZs2d15swZzZo1K6P3GBlJqKdvSPe0HNORSK8W\nzynVE6FFKisplN/vc6SvXIrHR9TbP6R1e46P9rtjTY1KiwtlWebdqeK1foF8wblnNtPma1o/6dj2\niHr6UvstKylUIGBev9mcr2N/O9FoVOXl5aOPg8GgotHohGvKy8tT1kykf9jWPS3H1NHVo/hIQh1d\nPbqn5Zj6h+2LbyAPDcRtrdtzPKnfdXuOayBOvwCyh3PPbKbN17R+0ukfHrtfsk96jn3Sm23hcDjl\nucrKKh2J9CY9dyTSq+KCwJjr3a6yaux+S6ZZ9Cupurp6qkqbkBOzcPt8Y7GY63uYjEz6zef96rVr\nzbt5Ya9eyHzZr/mDfi88CzgWeoPBoLq7u0cfR6NRBYPBCdd0d3enrPmLsZo4PxjX4jml6ujqGX1u\n8ZxS9Q/beXOCZtO52PCY/fYNxuk3j2RWW5cDx8xf4XDY5T1Mbl5FRUWu6XesOt167mWD+/dqem6e\nL/uVfqUL79ex2xsWLlyoSCSi06dPa2hoSK2traqrq0taU1dXp3379imRSOj48eOaOXNmxvfzSlJx\nQUBPhBZpydwyWX6flswt0xOhRSouCGS7nbww3Qpox5qapH53rKnRdIt+AWQP557ZTJuvaf2kU1ww\ndr9kn/Qc+6TXsixt3LhRTU1Nsm1bq1at0oIFC9TS0iJJCoVCqq2t1aFDh1RfX6/p06dr27Ztk3oP\nv9+nspJCPfn561RcEFD/sK3igoCR38QmSZblV2lxoZo/d61KplnqG4xruhUw8kZ9yXv9AvmCc89s\nps3XtH7SCQT8KitJ7re4IGDkN7FJ2Z2vo/f01tbWqra2Num5UCg0+mefz6eHHnroot7D7/dpxv/e\n12Hix/r/P8vya6blp18AjuLcM5tp8zWtn3QCAb9mBrzTb7bma+Z/FgAAAADvQugFAACA8Qi9AAAA\nMB6hFwAAAMbzJRKJRK6LSOfo0aO5LgEuce211+b0/dmrmAz2K9yE/Qq3GG+vuiL0AgAAABeD2xsA\nAABgPEIvAAAAjEfoBQAAgPEIvQAAADAeoRcAAADGI/QCAADAeIReAAAAGI/QCwAAAOMRegEAAGA8\nQi8AAACMR+gFAACA8Qi9AAAAMB6hFwAAAMYj9AIAAMB4hF4AAAAYj9ALAAAA4xF6AQAAYDxCLwAA\nAIxH6AUAAIDxXBF6jx49mnZNJBJxvpA8Qr/5KZO9Krmnn2yh3/zEtTUV/eYv9msq+p0cV4TeTAwM\nDOS6hClFv+5mWj/p0K97mdRLJujX3UzrJx36nRxjQi8AAAAwHkIvAAAAjEfoBQAAgPEIvQAAADCe\nY6H3/vvv15IlS3TTTTeN+XoikdCWLVtUX1+vxsZGnThxwqlSAAAA4HGWUwe+9dZbdfvtt+sb3/jG\nmK+3t7crEonowIEDev3117Vp0yb95Cc/mfT7xOMjGojbqqyq0rnYsKZbAVmWuR9ge61f2x5R//D/\n9VtcEFAg4N5+vTY/rzFxvp96uktSV8brI9sbnCsGgCQzrzUTyVa/joXexYsX68033xz39ba2Nq1c\nuVI+n081NTU6e/aszpw5o1mzZmX8HvH4iHr7h7Ruz3EdifRq8ZxS7VhTo9LiQiOH77V+bXtEPX2p\n/ZaVFLoy+Hptfl7DfAFMBa9da7LZb87+dqLRqMrLy0cfl5eXKxqNTuoYA3Fb6/YcV0dXj+IjCXV0\n9WjdnuMaiNvZLjcveK3f/uGx++0fdme/Xpuf1zBfAFPBa9eabPbr2Ce92RYOh1Oeq6yq0pFIb9Jz\nRyK9Kplmjbne7eh34n6rq6unqrQJjTcLr83v3WKxmPE9mrZf8+V4U80Le/XdMu3XLfvVC/Pz2r8l\n2by25iz0BoNBdXd3jz7u7u5WMBgcd/1YTZyLDWvxnFJ1dPWMPrd4Tqn6BuN5c4JmE/26o9/xanNr\nP9kQDoeN79Gt801fW+b382Z2vPzmhb36bm7rN12tbuvnQrj1WnOhstlvzm5vqKur0759+5RIJHT8\n+HHNnDlzUvfzStJ0K6Ada2q0ZG6ZLL9PS+aWaceaGk23Ag5VnVte67e4YOx+iwvc2a/X5uc1zBfA\nVPDatSab/Tr2Se+9996rX/3qV3r77be1bNky3X333YrH45KkUCik2tpaHTp0SPX19Zo+fbq2bds2\n6fewLL9KiwvV/LlrVTLNUt9g3OjvYPRav4GAX2Ulyf26+ac3eG1+XsN8AUwFr11rstmvY6H38ccf\nn/B1n8+nhx566KLfx7L8mmn5PfG/NCTv9RsI+DUzYE6/Xpuf1zBfAFPBa9eabPVr5n8WAAAAAO9C\n6AUAAIDxCL0AAAAwHqEXAAAAxiP0AgAAwHiEXgAAABiP0AsAAADjEXoBAABgPEIvAAAAjEfoBQAA\ngPEIvQAAADAeoRcAAADGI/QCAADAeIReAAAAGI/QCwAAAOMRegEAAGA8Qi8AAACMR+gFAACA8RwN\nve3t7Vq+fLnq6+vV3Nyc8npvb6/uvPNO3XzzzWpoaNBPf/pTJ8sBAACAR1lOHdi2bW3evFlPPfWU\ngsGgVq9erbq6Os2fP390zY9+9CNVVVXpe9/7nnp7e7VixQo1NjaqsLDQqbIAAADgQY590tvZ2anZ\ns2eroqJChYWFamhoUFtbW9Kayy67TH19fUokEurr69Mll1wiy3IshwMAAMCjHEuY0WhU5eXlo4+D\nwaA6OzuT1tx222264447tHTpUvX19enb3/62/P6xc3g4HJ7w/WKxWNo1JqHfVNXV1VNUzcQymQvz\nM5tp+zWXx5tq7NWxuWW/Mj+zXey1Nacfq+7cuVOVlZXavXu3fve732nt2rW67rrrNGPGjJS16U64\ncDicNyflVKDf/JVJnW7qJxvoN3+lr7Mry8fLb26aXTa4rV+yQDL6nRzHbm8IBoPq7u4efRyNRhUM\nBpPWvPbaa1qxYoV8Pp9mz56tK664Ql1dk7vAAgAAAOk4FnoXLlyoSCSi06dPa2hoSK2traqrq0ta\nM3fuXHV0dEiS/vjHP+qNN97QFVdc4VRJAAAA8CjHbm+wLEsbN25UU1OTbNvWqlWrtGDBArW0tEiS\nQqGQvvSlL+n+++9XY2OjEomENmzYoNLSUqdKAgAAgEc5ek9vbW2tamtrk54LhUKjfy4tLdXOnTud\nLAEAAADgN7IBAADAfIReAAAAGI/QCwAAAOMRegEAAGA8Qi8AAACMR+gFAACA8Qi9AAAAMB6hFwAA\nAMYj9AIAAMB4hF4AAAAYj9ALAAAA41m5LgAAAGTHnPtaJ7X+Z5+f61AlQP7hk14AAAAYj9ALAAAA\n4xF6AQAAYDxCLwAAAIxH6AUAAIDxHA297e3tWr58uerr69Xc3DzmmsOHD+uWW25RQ0ODbr/9difL\nAQAAgEc59iPLbNvW5s2b9dRTTykYDGr16tWqq6vT/PnzR9ecPXtWDz/8sHbt2qXLL79cPT09TpUD\nAAAAD3Psk97Ozk7Nnj1bFRUVKiwsVENDg9ra2pLWvPjii6qvr9fll18uSSorK3OqHAAAAHiYY6E3\nGo2qvLx89HEwGFQ0Gk1aE4lEdPbsWX32s5/Vrbfeqn379jlVDgAAADwsp7+RzbZtnThxQj/4wQ8U\ni8W0Zs0aXXPNNbryyitT1obD4QmPFYvF0q4xCf2mqq6unqJqJpbJXJif2Uzbr7k83lRjr47NLfuV\n+ZntYq+tjoXeYDCo7u7u0cfRaFTBYDBpTXl5uS699FIVFxeruLhY1113nU6ePDlm6E13woXD4bw5\nKacC/eavTOp0Uz/ZQL/5K32dXVk+Xn5z0+zGNrl5FRUVuapfskAy+p0cx25vWLhwoSKRiE6fPq2h\noSG1traqrq4uac0nPvEJHT16VPF4XAMDA+rs7NS8efOcKgkAAAAe5dgnvZZlaePGjWpqapJt21q1\napUWLFiglpYWSVIoFNK8efO0dOlS3XzzzfL7/Vq9erWuuuoqp0oCAACARzl6T29tba1qa2uTnguF\nQkmPm5qa1NTU5GQZAAAA8Dh+IxsAAACMR+gFAACA8Qi9AAAAMF5G9/S+/PLL2rFjh9566y3F43El\nEgn5fD699tprTtcHAAAAXLSMQu+2bdv0z//8z6qsrJTP53O6JgAAACCrMrq9oby8XFdddRWBFwAA\nAK6U0Se9GzZs0N/93d/p+uuvV2Fh4ejza9eudawwAAAAIFsyCr07duxQcXGxBgcHNTw87HRNAAAA\nQFZlFHrPnDmj/fv3O10LAAAA4IiM7uldtmyZ/v3f/93pWgAAAABHZPRJb0tLi77//e+rsLBQlmXx\nI8sAAADgKhmF3mPHjumdd97R//zP/2hwcNDpmgAAAICsyij0/uQnP9Hu3bvV3d2tqqoqvf7661q0\naJGuv/56p+sDAAAALlpG9/Tu3r1bzz33nC6//HI988wz+td//VfNnDnT6doAAACArMgo9BYWFmra\ntGmSpKGhIc2bN09vvPGGo4UBAAAA2ZLR7Q3l5eU6e/asPvnJT2rt2rV6z3veo8svv9zp2gAAAICs\nyCj0fve735Uk3X333brhhht07tw5LV261NHCAAAAgGzJ6PaGd7v++uv1iU98IunXEY+nvb1dy5cv\nV319vZqbm8dd19nZqQ9+8IN66aWXJlsOAAAAkNakQ2+mbNvW5s2btWvXLrW2tmr//v06derUmOse\ne+wxfeQjH3GqFAAAAHicY6G3s7NTs2fPVkVFhQoLC9XQ0KC2traUdc8884yWL1+usrIyp0oBAACA\nxzkWeqPRqMrLy0cfB4NBRaPRlDW/+MUvFAqFnCoDAAAAyOwb2ZyydetWbdiwQX5/+uwdDocnfD0W\ni6VdYxL6TVVdXT1F1Uwsk7kwP7OZtl9zebypxl4dm1v2K/Mz28VeWx0LvcFgUN3d3aOPo9GogsFg\n0ppf//rXuvfeeyVJb7/9tg4dOiTLsvTJT34y5XjpTrhwOJw3J+VUoN/8lUmdbuonG+g3f6WvsyvL\nx8tvbprd2CY3r6KiIlf1SxZIRr+T41joXbhwoSKRiE6fPq1gMKjW1lZ961vfSlpz8ODB0T/fd999\n+tjHPjZm4AUAAAAuhmOh17Isbdy4UU1NTbJtW6tWrdKCBQvU0tIiSdzHCwAAgCnj6D29tbW1qq2t\nTXpuvLC7fft2J0sBAACAhzn20xsAAACAfEHoBQAAgPEIvQAAADAeoRcAAADGI/QCAADAeIReAAAA\nGI/QCwAAAOMRegEAAGA8Qi8AAACMR+gFAACA8Qi9AAAAMB6hFwAAAMYj9AIAAMB4hF4AAAAYj9AL\nAAAA4xF6AQAAYDxCLwAAAIznaOhtb2/X8uXLVV9fr+bm5pTXX3jhBTU2NqqxsVFr1qzRyZMnnSwH\nAAAAHuVY6LVtW5s3b9auXbvU2tqq/fv369SpU0lrrrjiCv3whz/Uiy++qC9/+ct68MEHnSoHAAAA\nHuZY6O3s7NTs2bNVUVGhwsJCNTQ0qK2tLWnNhz/8YV1yySWSpJqaGnV3dztVDgAAADzMsdAbjUZV\nXl4++jgYDCoajY67/rnnntOyZcucKgcAAAAeZuW6AEl69dVX9dxzz+nHP/7xuGvC4fCEx4jFYmnX\nmIR+U1VXV09RNRPLZC7Mz2ym7ddcHm+qsVfH5pb9yvzMdrHXVsdCbzAYTLpdIRqNKhgMpqw7efKk\nHnjgAT355JN673vfO+7x0p1w4XA4b07KqUC/+SuTOt3UTzbQb/5KX2dXlo+X39w0u7FNbl5FRUWu\n6pcskIx+J8ex2xsWLlyoSCSi06dPa2hoSK2traqrq0ta89Zbb+nuu+/WN7/5TV155ZVOlQIAAACP\nc+yTXsuytHHjRjU1Ncm2ba1atUoLFixQS0uLJCkUCum73/2u3nnnHT388MOSpEAgoL179zpVEgAA\nADzK0Xt6a2trVVtbm/RcKBQa/fPWrVu1detWJ0sAAAAA+I1sAAAAMB+hFwAAAMYj9AIAAMB4hF4A\nAAAYj9ALAAAA4xF6AQAAYDxCLwAAAIxH6AUAAIDxCL0AAAAwHqEXAAAAxiP0AgAAwHiEXgAAABiP\n0AsAAADjEXoBAABgPEIvAAAAjEfoBQAAgPEIvQAAADAeoRcAAADGczT0tre3a/ny5aqvr1dzc3PK\n64lEQlu2bFF9fb0aGxt14sQJJ8sBAACARzkWem3b1ubNm7Vr1y61trZq//79OnXqVNKa9vZ2RSIR\nHThwQI888og2bdo06feJx0d0LjasyqoqnYsNKx4fyVIH+Yl+3d2vaf0gGfMFcsNr557X+s0Wy6kD\nd3Z2avbs2aqoqJAkNTQ0qK2tTfPnzx9d09bWppUrV8rn86mmpkZnz57VmTNnNGvWrIzeIx4fUW//\nkNbtOa4jkV4tnlOqHWtqVFpcKMsy784N+nV3v6b1g2TMF8gNr517Xus3mxz724lGoyovLx99HAwG\nFY1GJ1xTXl6esmYiA3Fb6/YcV0dXj+IjCXV09WjdnuMaiNsX30Aeol9392taP0jGfIHc8Nq557V+\ns8mxT3qzLRwOpzxXWVWlI5HepOeORHpVMs0ac73b0e/E/VZXV09VaRMabxZem9+7xWIx43s0bb/m\ny/Gmmhf26rtl2m8+71evXVu91u+7ZbJfJ9qrjoXeYDCo7u7u0cfRaFTBYHDCNd3d3Slr/mKsJs7F\nhrV4Tqk6unpGn1s8p1R9g/G8OUGziX7d0e94tbm1n2wIh8PG9+jW+aavrSvLx8tv7t+rk5tXUVGR\nq/olC3iv33e72PPTsdsbFi5cqEgkotOnT2toaEitra2qq6tLWlNXV6d9+/YpkUjo+PHjmjlzZsb3\n80rSdCugHWtqtGRumSy/T0vmlmnHmhpNtwLZbicv0K+7+zWtHyRjvkBueO3c81q/2eTYJ72WZWnj\nxo1qamqSbdtatWqVFixYoJaWFklSKBRSbW2tDh06pPr6ek2fPl3btm2b5Hv4VVpcqObPXauSaZb6\nBuOabgWMvZGbft3dr2n9IBnzBXLDa+ee1/rNJkfv6a2trVVtbW3Sc6FQaPTPPp9PDz300EW9h2X5\nNdPyG/C/pDJDv+5mWj9IxnyB3PDauee1frOF/ywAAACA8Qi9AAAAMB6hFwAAAMYj9AIAAMB4vkQi\nkch1EekcPXo01yXAJa699tqcvj97FZPBfoWbsF/hFuPtVVeEXgAAAOBicHsDAAAAjEfoBQAAgPEI\nvQAAADAeoRcAAADGI/QCAADAeIReAAAAGI/QCwAAAOMRegEAAGA8Qi8AAACMR+gFAACA8Qi9AAAA\nMB6hFwAAAMYj9AIAAMB4hF4AAAAYj9ALAAAA4xF6AQAAYDxCLwAAAIxH6AUAAIDxCL0AAAAwnitC\n79GjR9OuiUQizheSR+g3P2WyVyX39JMt9JufuLamot/8xX5NRb+T44rQm4mBgYFclzCl6NfdTOsn\nHfp1L5N6yQT9uptp/aRDv5NjTOgFAAAAxkPoBQAAgPEIvQAAADAeoRcAAADGs5w8eF1dnUpKSuT3\n+xUIBLR3796k1xOJhLZu3apDhw6pqKhI27dv14c+9CEnSwIAAIAHORp6Jenpp59WaWnpmK+1t7cr\nEonowIEDev3117Vp0yb95Cc/mdTxbXtE/cO2KquqdC42rOKCgAIBcz/AHhlJ/LnfyiqdH4yruCAg\nv9+X67IcY9p84/ERDcT/r5/pVkCW5d5+kMxr5yfci2sRvMjx0DuRtrY2rVy5Uj6fTzU1NTp79qzO\nnDmjWbNmZfT1tj2inr4hrdtzXEcivVo8p1Q71tSorKTQ1cFoPCMjCfX0DemelmOj/T4RWqSykkIj\n/2E1bb7x+Ih6+1P7KS0u5B8bA3jt/IR7cS2CVzm+u9euXatbb71Vzz77bMpr0WhU5eXlo4/Ly8sV\njUYzPnb/sK11e46ro6tH8ZGEOrp6tG7PcfUP21mpPd/0D9u6p+VYUr/3tBwzul+T5jsQH7ufgbg7\n+0Eyr52fcC+uRfAqRz/pbWlpUTAYVE9Pj9auXau5c+dq8eLFF3SscDic8lxlVZWORHqTnjsS6VXJ\nNGvM9W5XWTl2v8UFATP7neR8q6urp6q0CY03C6/t13eLxWLG9zjZ8zPf9+tfeGF27+aFfi/kWsR+\nzU/0m2qivepo6A0Gg5KksrIy1dfXq7OzMyn0BoNBdXd3jz7u7u4e/Zr/31hNnIsNa/GcUnV09Yw+\nt3hOqfoG43lzgmbT+cH4mP32D9tG9uvW+Y5Xm1v7yYZwOGx8j249P9PV5oXZvZsX+nXztYj9mox+\nJ8ex2xv6+/t1/vz50T//8pe/1IIFC5LW1NXVad++fUokEjp+/LhmzpyZ8f28klRcENCONTVaMrdM\nlt+nJXPLtGNNjYoLAlntJV8UFwT0RGhRUr9PhBYZ3a9J851ujd3PdMud/SCZ185PuBfXIniVY5/0\n9vT06Ktf/aokybZt3XTTTVq2bJlaWlokSaFQSLW1tTp06JDq6+s1ffp0bdu2bVLvEQj4VVZSqObP\nXauSaZb6/ve7pd34TU6Z8Pt9Kisp1JOfv07FBQH1D9tGf3e4afO1LL9Ki5P74TumzeG18xPuxbUI\nXuVY6K2oqNALL7yQ8nwoFBr9s8/n00MPPXRR7xMI+DUz4PfMR/x+v08z/ve+Ky/0a9p8LcuvmZY5\n/SCZ185PuBfXIngR/1kHAAAA4xF6AQAAYDxCLwAAAIxH6AUAAIDxCL0AAAAwHqEXAAAAxiP0AgAA\nwHiEXgAAABiP0AsAAADjEXoBAABgPEIvAAAAjEfoBQAAgPEIvQAAADAeoRcAAADGI/QCAADAeIRe\nAAAAGI/QCwAAAOMRegEAAGA8Qi8AAACMR+gFAACA8Qi9AAAAMB6hFwAAAMYj9AIAAMB4Vq4LAAAA\nyIZPPd0lqSvj9ZHtDc4Vg7zj+Ce9tm1r5cqV+uIXv5jy2uHDh3Xttdfqlltu0S233KLvfOc7TpcD\nAAAAD3L8k97du3dr3rx5On/+/JivX3fdddq5c6fTZQAAAMDDHP2kt7u7W6+88opWr17t5NsAAAAA\nE3I09G7btk1f//rX5feP/zbHjh1TY2Ojmpqa9N///d9OlgMAAACPcuz2hpdfflmlpaW6+uqrdfjw\n4THXfOhDH9LLL7+skpISHTp0SF/96ld14MCBMdeGw+EJ3y8Wi6VdYxL6TVVdXT1F1Uwsk7kwP7OZ\ntF+Zndky7dct+zXXx5tq7NdUE+1Vx0Lva6+9poMHD6q9vV2Dg4M6f/68NmzYoMcee2x0zYwZM0b/\nXFtbq4cffli9vb0qLS1NOV66Ey4cDufNSTkV6Dd/ZVKnm/rJBvrNX1xbk9Fvfktfa+Y/uSGz4+U3\nt83vYl1sv46F3vXr12v9+vWS/vxTGr7//e8nBV5J+sMf/qDLLrtMPp9PnZ2dGhkZ0Xvf+16nSgIA\nAIBHTfnP6W1paZEkhUIh/fznP1dLS4sCgYCKior0+OOPy+fzTXVJAAAAMNyUhN4bbrhBN9xwg6Q/\nh92/uP3223X77bdPRQkAAADwMH4NMQAAAIxH6AUAAIDxCL0AAAAwHqEXAAAAxiP0AgAAwHiEXgAA\nABiP0AsAAADjEXoBAABgPEIvAAAAjEfoBQAAgPEIvQAAADAeoRcAAADGs3JdAAAAACbvU093SerK\neH1ke4NzxbgAn/QCAADAeIReAAAAGI/QCwAAAOMRegEAAGA8Qi8AAACMlzb02rat9evXT0UtAAAA\ngCPSht5AIKC33npLQ0NDU1EPAAAAkHUZ/ZzeiooKhUIh1dXVqbi4ePT5tWvXOlYYAAAAkC0Zhd4P\nfOAD+sAHPqBEIqG+vj6nawIAAACyKqPQ+7Wvfc3pOgAAAADHZBR6e3t79eSTT+rUqVMaHBwcfX73\n7t2OFQYAAABkS0Y/smzDhg2aO3eu3nzzTX3ta1/T+9//fi1cuDCjN7BtWytXrtQXv/jFlNcSiYS2\nbNmi+vp6NTY26sSJE5OrHgAAAMhARqH3nXfe0d/+7d/Ksixdf/31evTRR/Xqq69m9Aa7d+/WvHnz\nxnytvb1dkUhEBw4c0COPPKJNmzZlXPhfxOMjOhcbVmVVlc7FhhWPj0z6GG7itX5NY9vJ87Nt5mcS\nk85Pk3pBKuYLL8oo9FrWn++CmDVrll555RX95je/0Z/+9Ke0X9fd3a1XXnlFq1evHvP1trY2rVy5\nUj6fTzU1NTp79qzOnDmTcfHx+Ih6+4d01+6juuqffqa7dh9Vb/+QsSev1/o1jW2PqKcveX49fUME\nX0OYdH6a1AtSMV94VUah98tf/rLOnTunb3zjG/re976nBx54QPfff3/ar9u2bZu+/vWvy+8f+22i\n0ajKy8tHH5eXlysajWZYujQQt7Vuz3F1dPUoPpJQR1eP1u05roG4nfEx3MRr/Zqmf3js+fUPMz8T\nmHR+mtQLUjFfeFVG38j28Y9/XJI0c+ZMPfPMMxkd+OWXX1ZpaamuvvpqHT58+MIr/F/hcDjlucqq\nKh2J9CY9dyTSq5Jp1pjr3c5r/b5bLBZL22N1dfUUVTOx8epkfmb3ONn55vN+Za+a3eOFzDef92s+\nHS/fub3fi80CGYXeN954Q5s2bVJPT4/279+vkydP6uDBg/rKV74y7te89tprOnjwoNrb2zU4OKjz\n589rw4YNeuyxx0bXBINBdXd3jz7u7u5WMBjMuIlzsWEtnlOqjq6e0ecWzylV32A8b07QbPJav+8W\nDodd0+N4dTI/s3t063y5tiZjr+Z37+nr68ry8fKdt/q92PMzo9sbHnzwQa1fv3703t6qqir927/9\n24Rfs379+v/X3t0HR1XebRy/9i3khQQIQmIHpipDJCIWCkyiWDIkxvK2SUSqxAjVQmsHFFqgbbRV\nYFDUkTJVbB1RO3WqE7RTm7RjJVpeEqcRBElggumUltKgYxJMYpOQZJPdvZ8/LKl5ArpIlpM9+/38\nlSXB9nYAABP0SURBVOwez14/73vh4nCWqLKyUnv27NG2bduUmZnZr/BKUnZ2tkpLS2WMUU1NjRIT\nEzV27NiQw8e5XXpyyVRdf9VouZ0OXX/VaD25ZKri3K6QzxFJom1eu4n3nHv94j2snx3Y6f1pp1kw\nEOuLaBXSld6uri5dd911/R5zub7cm6OkpESSVFhYqKysLFVUVCg3N1dxcXHasmXLBZ3L7XYqOT5G\nO5ZNV8Iwt874/Ipzu+R2h9TlI060zWs3LpdToxP6r1+8xyWXi/WzAzu9P+00CwZifRGtQiq9o0aN\nUn19vRwOhyRp165dGjNmTMgvkpGRoYyMDEmflt2zHA6HNmzYcCF5B3C7nUp0O6Pir6Sk6JvXblwu\npxJdrJ9d2en9aadZMBDri2gUUundsGGDHnzwQZ04cULf+MY3NG7cuAG3KgAAAABDVUil9y9/+Yuy\nsrKUkZGhYDCo+Ph4VVVV6dprr+VPiAAAABjyQrqBp7a2Vjt37lRbW5va29u1c+dOvf322/rZz36m\n5557LtwZAQAAgIsS0pXehoYGvfbaa0pISJAk3Xfffbrnnnv08ssva9GiRfrud78b1pAAAADAxQjp\nSm9zc7NiYmL6vvd4PPr4448VGxvb73EAAABgKArpSq/X69Vtt92mnJwcSdKePXu0cOFCdXZ2asKE\nCWENCAAAAFyskErvqlWrNHv2bB0+fFiStGnTJk2ZMkWS9POf/zx86QAAAIBBEFLplaQpU6b0FV0A\nAAAgkvDjVwAAAGB7lF4AAADYHqUXAAAAtkfpBQAAgO1RegEAAGB7lF4AAADYHqUXAAAAtkfpBQAA\ngO1RegEAAGB7lF4AAADYHqUXAAAAtkfpBQAAgO1RegEAAGB7lF4AAADYHqUXAAAAtkfpBQAAgO25\nw3Vin8+noqIi9fT0qLe3Vzk5OVq/fn2/Yw4cOKCVK1dq3LhxkqTc3Fzde++94YoEAACAKBW20hsT\nE6MXX3xRCQkJ6u3t1R133KFDhw5pxowZ/Y6bMWOGnn322XDFAAAAAMJ3e4PD4VBCQoIkye/3KxAI\nKCkpKVwvBwAAAJxXWO/pDQQCys/P1w033KCMjAylpaUNOKa6ulper1crVqzQ8ePHwxkHAAAAUcph\njDHhfpG2tjYtX75c69atU2ZmZt/jHR0dfVeEKyoq9Mgjj+jNN98c8N+/9957io+P/9zX6O7uVmxs\n7KBnH6qYd6D09PRLlOb8QtmrEutnd3bar6ydvYU6b6Ts13kvnrigc77x7asuJpLlom3ei/21NWz3\n9H5WUlKSsrKyVFtb26/0Dh8+vO/rrKwsbdq0SS0tLUpOTh5wji96w9XV1Q2JN+WlwrxDVyg5I2me\nwcC8Qxe/tvbHvEPbF2e9sBIYSbOfW3TNe7H7NWy3N7S0tKitrU3Sp828qqpqQNDTp0/r7IXmo0eP\nKhgMatSoUeGKBAAAgCgVtiu9TU1NKi4uVjAYlDFGXq9Xs2bNUklJiSSpsLBQ5eXlKikpkcvlUmxs\nrLZt2yaHwxGuSAAAAIhSYSu9kyZNUmlp6YDHCwsL+76+8847deedd4YrAgAAAIaoK4pfv6DjL/ae\nZH4iGwAAAGyP0gsAAADbo/QCAADA9ii9AAAAsD1KLwAAAGyP0gsAAADbo/QCAADA9ii9AAAAsD1K\nLwAAAGyP0gsAAADbo/QCAADA9ii9AAAAsD1KLwAAAGyP0gsAAADbo/QCAADA9ii9AAAAsD1KLwAA\nAGyP0gsAAADbo/QCAADA9ii9AAAAsD1KLwAAAGyP0gsAAADbo/QCAADA9ii9AAAAsD13uE7s8/lU\nVFSknp4e9fb2KicnR+vXr+93jDFGjzzyiCoqKhQbG6vHHntMkydPvqDXCQaNOnsDuvrqSerw+RXv\nccnpdAzmKENKIBD8dN5Jk9Te3at4j0sul33/7OL3B9Xl/9+8cW6X3O7Inddu86A/O62vnWYBACmM\npTcmJkYvvviiEhIS1NvbqzvuuEOHDh3SjBkz+o6prKzUyZMn9eabb+rIkSPauHGjfve734X8GsGg\nUfOZHq0uqdbBky2aeUWyniqcptEJMbYsvoFAUM1nerRmZ03fvE8umarRCTG2LL5+f1AtnQPnTY6P\nicjffO02D/qz0/raaRYAOCtsv3o5HA4lJCRIkvx+vwKBgJKSkvods3v3bhUUFMjhcGjq1Klqa2tT\nU1NTyK/R2RvQ6pJqvXOiWf6g0TsnmrW6pFqdvYFBnWWo6OwNaM3Omn7zrtlZY9t5u/znnrfLH5nz\n2m0e9Gen9bXTLABwVtiu9EpSIBDQokWLVF9fryVLligtLa3f842NjUpNTe37PjU1VY2NjRo7duyA\nc9XV1Q147OqrJ+ngyZZ+jx082aJ4j+ucx0e6qyede96EYW7mlZSenn6pon2u861FtK3fZ3V3d9t+\nRjvtV/aqvWf8rFDnHcr7dSidb6iL9HlD2a+ft1fDWnpdLpfKysrU1tam5cuXa//+/crMzPxS5zrX\nEB0+v2Zekax3TjT3PTbzimR19gaGzBt0MLV3955z3jM+P/MOIefLFqnzDIa6ujrbzxip63uubJE6\ny2CIhr36WZE27xdnPTHI5xvqIn3eC8sfGxt7UTNckpuzkpKSlJWVpdra2n6Pp6SkqKGhoe/7hoYG\npaSkhHzeeI9LTxVO0/VXjZbb6dD1V43WU4XTFO9xDVr2oSTe49KTS6b2m/fJJVNtO2+c+9zzxrkj\nc167zYP+7LS+dpoFAM4K25XelpYWud1uJSUlqbu7W1VVVVq1alW/Y7Kzs/XSSy9pwYIFOnLkiBIT\nE895a8P5OJ0OjU6I0XPfnqF4j0udvQFb/+sNLpdToxNitGPZdCUMc+vMf/+1Cjt+iE2S3G6nkuP7\nzxvJnyC32zzoz07ra6dZAOCssJXepqYmFRcXKxgMyhgjr9erWbNmqaSkRJJUWFiorKwsVVRUKDc3\nV3FxcdqyZcsFv47T6dDw/95nNvQu2w8+l8upRJczauZ1u51KdNtnXrvNg/7stL52mgUApDCW3kmT\nJqm0tHTA44WFhX1fOxwObdiwIVwRAAAAAEn8RDYAAABEAUovAAAAbI/SCwAAANuj9AIAAMD2HMYY\nY3WIL/Lee+9ZHQERYvr06Za+PnsVF4L9ikjCfkWkON9ejYjSCwAAAFwMbm8AAACA7VF6AQAAYHu2\nKL2BQEAFBQW65557rI5ySbS1tWn16tWaO3eu5s2bp+rqaqsjhc2zzz6r+fPna+HChVq7dq18Pp/V\nkUJWWVmpb37zm8rNzdWOHTsGPG+M0cMPP6zc3Fx5vV4dO3bMgpSD54vmPXDggKZPn678/Hzl5+fr\n6aeftiDl4Ln//vt1/fXXa+HChed8PtLWl/3aH/s1stbX5/Np8eLFysvL07x587R161arI4VdtHWf\n7Oxseb1e5efna9GiRV/uJMYGfv3rX5u1a9ea733ve1ZHuSR+/OMfm1dffdUYY4zP5zP/+c9/LE4U\nHqdOnTJz5swxXV1dxhhjVq9ebX7/+99bnCo0fr/f5OTkmPr6euPz+YzX6zXHjx/vd8y+ffvM8uXL\nTTAYNNXV1Wbx4sUWpb14ocy7f/9+W71H3333XVNbW2sWLFhwzucjaX3Zr+zXSF/fYDBoOjo6jDHG\n9PT0mMWLF5uDBw9anCq8oq37zJkzxzQ3N1/UOSL+Sm9DQ4P27dunxYsXWx3lkmhvb9fBgwf75o2J\niVFSUpLFqcJj+PDh8ng86u7ult/vV3d3t8aOHWt1rJAcPXpUX/3qVzV+/HjFxMRowYIF2r17d79j\ndu/erYKCAjkcDk2dOlVtbW1qamqyKPHFCWVeu5k5c6ZGjBhx3ucjaX3Zr+zXSF9fh8OhhIQESZLf\n71cgELDt741S9HWfwRLxpXfLli360Y9+JKcz4kcJyQcffKDk5GTdf//9Kigo0E9/+lN1dnZaHSss\nRo4cqe985zuaM2eObrzxRg0fPlw33nij1bFC0tjYqNTU1L7vU1JS1NjY+LnHpKamDjgmUoQyryRV\nV1fL6/VqxYoVOn78+KWMeMlF0vqyX9mvdljfQCCg/Px83XDDDcrIyFBaWprVkcIm2rrPWXfffbcW\nLVqkV1555Uv99xH9f2vv3r1KTk7Wtddea3WUS8bv9+v9999XYWGhSktLFRcXd8770eygvr5ev/nN\nb7R79269/fbb6urqUllZmdWx8CVNnjxZe/fu1Z/+9CctXbpUq1atsjoScF7s18jjcrlUVlamiooK\nHTp0SPv377c6UlhEY/eRpJKSEpWVlem5557Tyy+/rIMHD17wOSK69B4+fFh79uxRdna21q5dq/37\n92v9+vVWxwqr1NRUpaam6mtf+5okae7cuXr//fctThUetbW1mjZtmpKTk+XxeHTzzTdHzIf2UlJS\n1NDQ0Pd9Y2OjUlJSPveYhoaGAcdEilDmHT58eN9fP2ZlZcnv96ulpeWS5ryUIml92a/sVzutb1JS\nkrKyslRbW2t1lLCIxu4jqW8/jh49Wrm5uTp69OgFnyOiS++6detUWVmpPXv2aNu2bcrMzLT9JzbH\njBmj1NRUnThxQpL0zjvvaMKECRanCo8rr7xSR44cUVdXl4wxETXrlClTdPLkSZ06dUo9PT16/fXX\nlZ2d3e+Y7OxslZaWyhijmpoaJSYmRsw9y/9fKPOePn1a5r8/C+fo0aMKBoMaNWqUFXEviUhaX/Yr\n+zXS17elpUVtbW2SpO7ublVVVSk9Pd3iVOERjd2ns7NTHR0dfV//9a9/1cSJEy/4PO7BDobwe/DB\nB7V+/Xr19vZq/PjxevTRR62OFBbp6enKz8/XrbfeKqfTqfT0dN1+++1WxwqJ2+3WQw89pBUrVigQ\nCOjWW2/VxIkTVVJSIkkqLCxUVlaWKioqlJubq7i4OG3ZssXi1F9eKPOWl5erpKRELpdLsbGx2rZt\nmxwOh8XJv7y1a9fq3XffVWtrq2bPnq377rtPfr9fUuStL/uV/Rrp69vU1KTi4mIFg0EZY+T1ejVr\n1iyrY2GQNDc3991iFAgEtHDhQs2ePfuCz8OPIQYAAIDtRfTtDQAAAEAoKL0AAACwPUovAAAAbI/S\nCwAAANuj9AIAAMD2KL02UVxcrF27dlkdAwAAYEii9AIAANjY2X+TOdrxwymGuNLSUr3wwgtyOBwa\nP3686urqVF5eLo/Ho46ODuXl5am8vNzqmIAkaevWrbr88stVVFQkSdq+fbucTqeqqqrU0dGhQCCg\njRs3asaMGRYnRbRbuXKlGhoa5PP5tGzZMt1+++2aNm2alixZosrKSo0ZM0Y/+MEPtHXrVn300Ud6\n4IEHlJOTY3VsRIlf/vKX+uMf/6jk5GRdfvnlmjx5snJzc7Vp0ya1trYqNjZWmzdv1oQJE7Rnzx49\n88wz6u3t1ciRI7V161Zddtll2r59u+rr63Xq1Cl95Stf0bZt26wey3oGQ9bf//53c/PNN5vm5mZj\njDGtra2muLjYvPXWW8YYY3bu3GkeffRRY4wxP/nJT8wbb7xhWVbAGGOOHTtmioqK+r6fN2+e2b59\nu/nVr35ljDHG7/eb9vZ2q+IBfVpbW40xxnR1dZkFCxaYlpYWk5aWZvbt22eMMWblypXmrrvuMj09\nPaaurs7k5eVZGRdR5MiRIyYvL890d3eb9vZ2k5uba55//nmzbNky869//csYY0xNTY1ZunSpMcaY\nTz75xASDQWOMMa+++mpfL3jqqafMLbfcYrq6uiyZYyjiSu8Qtn//fs2dO1fJycmSpJEjR2rx4sV6\n/vnnddNNN+m1117T5s2bLU4J/M8111yj5uZmNTY2qrW1VUlJScrIyNADDzwgv9+vm266Senp6VbH\nBPTb3/5Wb731liTpo48+0r///W95PJ6+H22alpammJgYeTwepaWl6cMPP7QyLqLI4cOHlZOTo2HD\nhmnYsGGaM2eOfD6fqqurtWbNmr7jenp6JEkNDQ364Q9/qNOnT6unp0fjxo3rOyY7O1uxsbGXfIah\nitIbYaZPn65NmzbpwIEDCgQCSktLszoS0M/cuXNVXl6ujz/+WPPnz9fMmTP10ksvqaKiQsXFxbr7\n7rtVUFBgdUxEsQMHDqiqqkqvvPKK4uLitHTpUvl8Pnk8HjkcDkmS0+lUTExM39eBQMDKyIhywWBQ\nSUlJKisrG/Dcww8/rLvuuks5OTk6cOCAnn766b7n4uLiLmXMIY8Psg1hmZmZ2rVrl1pbWyVJn3zy\niSSpoKBA69at06JFi6yMB5zT/Pnz9ec//1nl5eWaO3euPvzwQ1122WW67bbb9K1vfUvHjh2zOiKi\nXHt7u0aMGKG4uDj985//VE1NjdWRgD5f//rXtXfvXvl8Pp05c0b79u1TXFycxo0bpzfeeEOSZIzR\n3/72N0mf7ueUlBRJn34OCOfHld4hbOLEifr+97+vpUuXyul06pprrtFjjz0mr9erX/ziF1q4cKHV\nEYEBJk6cqDNnzmjs2LEaO3as/vCHP+iFF16Q2+1WfHy8Hn/8casjIsrNnj1bO3fu1Lx583TllVdq\n6tSpVkcC+lx33XXKzs5WXl6eRo8erbS0NCUmJuqJJ57Qxo0b9cwzz8jv92v+/PmaNGmS7r33Xq1Z\ns0YjRoxQRkaGPvjgA6tHGLIcxhhjdQhcmF27dmn37t164oknrI4CAAAG2ZkzZ5SQkKCuri4VFRVp\n8+bNmjx5stWxIh5XeiPM5s2bVVlZqR07dlgdBQAAhMFDDz2kf/zjH/L5fLrlllsovIOEK70AAACw\nPT7IBgAAANuj9AIAAMD2KL0AAACwPUovAAAAbI/SCwAAANuj9AIAAMD2/g9zfLifMyxuRwAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d913c0b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=cars[['cyl','vs','am','gear']]#ordinal varibles\n",
    "sb.pairplot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spearman Correlation Coefficient -0.814\n"
     ]
    }
   ],
   "source": [
    "cyl=cars['cyl']\n",
    "vs=cars['vs']\n",
    "am=cars['am']\n",
    "gear=cars['gear']\n",
    "spearman_coeff,p_value=spearmanr(cyl,vs)\n",
    "print 'Spearman Correlation Coefficient %0.3f'%(spearman_coeff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spearman Correlation Coefficient -0.522\n"
     ]
    }
   ],
   "source": [
    "spearman_coeff,p_value=spearmanr(cyl,am)\n",
    "print 'Spearman Correlation Coefficient %0.3f'%(spearman_coeff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spearman Correlation Coefficient -0.564\n"
     ]
    }
   ],
   "source": [
    "spearman_coeff,p_value=spearmanr(cyl,gear)\n",
    "print 'Spearman Correlation Coefficient %0.3f'%(spearman_coeff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chi-Square test for independence "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-square Statitics 8.741 p_value 0.013\n"
     ]
    }
   ],
   "source": [
    "table=pd.crosstab(cyl,am)\n",
    "from scipy.stats import chi2_contingency as ch\n",
    "chi2,p,dof,expected=ch(table.values)\n",
    "print 'Chi-square Statitics %0.3f p_value %0.3f'%(chi2,p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-square Statitics 21.340 p_value 0.000\n"
     ]
    }
   ],
   "source": [
    "table=pd.crosstab(cyl,vs)\n",
    "chi2,p,dof,expected=ch(table.values)\n",
    "print 'Chi-square Statitics %0.3f p_value %0.3f'%(chi2,p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-square Statitics 18.036 p_value 0.001\n"
     ]
    }
   ],
   "source": [
    "table=pd.crosstab(cyl,gear)\n",
    "from scipy.stats import chi2_contingency as ch\n",
    "chi2,p,dof,expected=ch(table.values)\n",
    "print 'Chi-square Statitics %0.3f p_value %0.3f'%(chi2,p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#so that u dont compare apples with oranges\n",
    "#normalisation: rescaling data Making values to lie b/w 0-1 (values_of_obs/sum_of_all_obs)\n",
    "#standardisation: rescaling data making mean of the distribution to be zero and variance to be one\n",
    "import sklearn\n",
    "from sklearn import preprocessing \n",
    "from sklearn.preprocessing import scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3d8d7e4550>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MKJucndDveyqFFj0C4Qg6ev0jP5iSgmGFiIjSli8YhtcXgi2bY4tHo9hqgtcf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d92259c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpg.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    32.000000\n",
       "mean     20.090625\n",
       "std       6.026948\n",
       "min      10.400000\n",
       "25%      15.425000\n",
       "50%      19.200000\n",
       "75%      22.800000\n",
       "max      33.900000\n",
       "Name: mpg, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3d8d668650>]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q1rExL793lYjJCxERydZnlRceGzvbwK4XD5v25eBIsw8TLQbkZ8v/e12vVcOW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d8d755450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpg_matrix=mpg.values.reshape(-1,1)\n",
    "scaled=pp.MinMaxScaler()\n",
    "scaled_mpg=scaled.fit_transform(mpg_matrix)\n",
    "plt.plot(scaled_mpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3d8d5fc0d0>]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s3r07WeciIiKaNMfwSl5rFouV8ZRbjOjoG0KYn05nrPqRYkVZnRWuL1YmzVT+\n8M9//nOcc845+MMf/oBAIACfj//xEBGReJze2DUwaw6vgY2n3GJEMBxF94APJWaj2MehBNTb3bBm\n61E0/MO7UoxOsV9QLvJhKG0S7qwMDAxg+/btuPrqqwEAOp0OubnKqvCJiEha4p2VfM6sjIvrizNf\nvb0ftWXK+5nLNlyccX2xsiTcWTly5Ajy8/Pxve99DwcOHMC8efNw9913w2QyHfd1jY2NUz5ksvh8\nPkmdhxLHZykPfI7yIZVn+Um7A9k6FZo/OSj2USQr0B/rPtXtP4TsoZzjfk8qz5HGFwhHcPDoABYW\nqk/5rOT4LMORKNQCsL+5A415yii25fgcJyvhYiUUCqGhoQE/+MEPsHDhQtxzzz146KGH8M1vfvO4\nr6upqZnyIZOlsbFRUuehxGXas9x5uBd/eOsT/PqqBSNtbMq850jjk8qzDO/ehaLcsCTOIlUzgmHg\npSOIGC2oqTnjuN+TynOk8X10pA+RaCs+taAKNTUl436dXJ9lcW4ngtosWf7fNha5Psex7Ny5c8xf\nT/gamM1mg81mw8KFCwEAl1xyCRoaGhJ9OSJZ+2fjUfzrYA+ue+RDdA+wfU2UKk4P0+tPx6BVozCH\nWSuZSqnD9XFMsVeehIuVwsJC2Gw2NDc3AwC2bt2K6urqpB2MSE6auj3Iz9Khq9+HGx7ZBqeHaxeJ\nUsHhCaAgm/Mqp1NhMeJIH9cXZ6J6ez9y9BpUWEyn/2IZKjEbuA1MYaa0uvgHP/gB7rrrLqxZswaN\njY244447knUuIllpdnixdLoFj3xpKQ47B3HDn+rQNxgQ+1hEssPOysSUW0xod7Gzkonq7W7UlOZC\npRLEPoooinNZrCjNlIqVmpoavPDCC3j55Zdx3333wWxWVjgR0USEwhEcdnpRVZiNVdVWPHzTUjR1\ne3DTo3Vw+4JiH49INoLhCHoHg+ysTEC5xQg7s1YyTjgSRWOnG7UKC4MczZZrgDcQxgC/fyoGE+yJ\nUqy9dwjBcBTVhVkAgHNnFeKBG89EY6cbX3q0Dh5/SOQTEslDbzxjhZ2V0yq3mBCKRHn3P8M093jg\nC0YUO68CHMta4X+7ysFihSjFmns8AICqwuyRX7tgTjH+99oz8dGRftyyYTsGAyxYiKaqZ3gWzMrO\nymmVW2JhkByyzywjw/UKzFiJi2etdPIqmGKwWCFKsabhYiXeWYm7pNaG31+zCDsOu3DrYzvgC4bF\nOB6RbDiWh4KtAAAgAElEQVQ97KxM1LFihUP2maTe3g+dRoXqUR9+Kc3oFHtSBhYrRCnW3ONFQZYO\neaaTP+29fEEpfvuFhdja7MTtT+yEP8SChShRjuHOSgGLldMqzWNnJRPV292YY8uBVq3cH9+KmWKv\nOMr9r50oTZp6PKf8FOxzi8vx6ysX4F8He/C1p3YhEIqk8XRE8nGss8JrYKdj0KpRlKNnZyWDRKNR\n1NvdmKfg4Xog9t+uxaRFF4sVxWCxQpRizT1eVJ1wBexEX1hWgZ+tq8U/G7vxjWd3IxRmwUI0WQ6P\nHzqNCtl6jdhHyQgV+SZ2VjLIkd4h9A8FFT1cH8f1xcrCYoUohfoGA3B6A6ctVgDgxrOm44eXz8Xr\n+7vwH3/ey5WiRJPk8ARQmK2HICgzf2Kyyi1GtLOzkjGUnlw/ms1sYGdFQVisEKVQU48XACY8DHnL\n2TPw3Uvn4OW9dvznXz5ChAUL0YQ5PH5mrExCucWIzj4fO7kZosHeD5UAzLGxWLHlGtDV7xf7GJQm\n7JUTpVDTGGuLT+eOT1UjEIrg3jcPQqcR8PN18xWbVEw0GU6vH0U5BrGPkTFGslYG/CgbHrgn6aq3\nu1FdmA2jTi32UURnMxvg8PgRCEWg0/Bzd7njEyZKoeYeL7RqARWWyf0g8PULz8C/nT8Tz9S148cv\n1yMaZYeF6HQcAwEUZLGzMlEj64tdvAqWCertbtSWKXu4Pi6etdI9wKtgSsDOClEKNfV4ML0gC5oE\n1kx+++JZCIQjeOi9ZujUKtz92RrexScaRzQahdPrhzWHa4snqtxiAhAb3F4h8lno1BweP7rcPs6r\nDCselWIf/++Y5IvFClEKNfd4MLMosfAuQRDwvUvnIBCK4JEPWqDTqPCdz8xmwUI0BvdQCMFwlJ2V\nSSjNM0AQmLWSCeLD9XNZrAAASsxMsVcSFitEKRIMR9DmGsTF82wJv4YgCPjRmrkIhCO4790m6DQq\nfPOiWUk8JZE8OLyxYdtCdlYmTK9RozjHwKyVDFBv7wcAzCvhNTDg2DUwri9WBhYrRCnS7hpEMByd\n8Caw8QiCgHvW1iIYiuB3//wEOo0Kd543M0mnJJIHx8Bwen0Wi5XJKLcY2VnJAPV2N8otRphNWrGP\nIglmoxZ6jYop9grBYoUoRZqH1xZPJGPldFQqAb+6agGC4Qj+398/hk6twq3nVE35dYnkwukdTq/P\n4TWwySi3GLHjcK/Yx6DTaLC7Uavw5PrRBEEYzlrh+mIl4DYwohSJry2utk6tsxKnVgn4zecX4rPz\nS3DPq414fGtrUl6XSA4cHnZWElFuMaGzn1krUjbgC6LF4eVw/QliWSvsCioBOytEKdLc44U1W5fU\ntr1GrcLvrlmEQDiCH26qh1atwrXLpyXt9YkylcMTgCAA+Rywn5RyixHhSBRd3KokWY2dAwCAeWUs\nVkazmQ3Y1cauoBKws0KUIk09HlQlqasymlatwv9dtxjnzy7E91/ch7/sPJL09yDKNA6PH/kmHdQM\nUJ2U0euLSZpGhut5Dew4tlwDjvb7mUOmACxWJK7V4cVgICT2MSgBzQ4vqoumPq8yFr1GjftvWILV\n1Vb851/24m977Sl5H6JM4fT4Yc3mFbDJGgmGZLEiWfV2N6zZOhRx091xbGYDAuEIXMPzaiRfLFYk\n7INPHLjo3n/hc3/cgk7ey8wovd4AXN5ASjorcQatGg/ftBRLK/Nx1/N70TPAQUNSLocngIJsXgGb\nrJKRrBWuL5aqersbc0vNzNg6wcj6Ym4Ekz0WKxL1cdcAvvrkTkzLN6GjbwhX3bcFh7oHxD4WTVCz\nY3i4PkWdlTijTo1fXjkfgVAET29rS+l7EUmZg52VhOg1athyDeysSJQ/FMYnRwdQy+H6k4xOsSd5\nY7EiQd1uH27ZuB1GnRpP3roCz91+FoKRKK66fyt2HnaJfTyagKb42uIUdlbiqguzcd7sQjy57TAC\nIW70IWVyegIsVhIUy1phZ0WKDnZ5EIpEOa8yhmPBkLxVIHcsViRmMBDClx/bgd7BAB69eRlK84yY\nV2rGC19dhfwsHa57eBvebDgq9jHpNJp6PNCpVSP3wVNt/eoZ6Bnw49V9nF0h5fEFw/D4Q7wGlqBy\niwntLnZWpOjYcD07KycqzNFDJYDrixWAxYqEhCNRfP2ZPai39+N/r12M2rJjn6RU5JvwlztWYo4t\nB7c/sQPP1vHKj5Q193gxvcAEjTo9f8XOmWlFVWEWNmxu5WYUUpx4xkohOysJKbcY0eVm1ooU1dvd\nyNZrMC2fa6VPpFWrYM3Wc2ZFAVisSMjPXmnAPxuP4sdXzMOFNcUn/X5Bth5Pf+UsnDurEN99YR/+\n8NYn/MFUopp6PKguTP0VsDiVSsD6VZX46Eg/drX1pe19iaTA4YltA2JnJTHxrJXOfv7QJzX19n7M\nLcmFiiu5x8QUe2VgsSIRGza3YOOWVnz57Bm4aWXluF+Xpdfg4ZuW4qozy3Hvmwfx/720H+EICxYp\nCYYjaHMOoqowtcP1J7ryzHLkGDTYsLklre9LJDbncGeFMyuJYdaKNIUjUTR2DjAM8hSYYq8MLFYk\n4I36Lvz0lQZ8Zl4xvn9ZzWm/XqtW4TefX4CvnleNp7a14c6ndsIXDKfhpDQRba5BhCJRVKWxswLE\nCtkvLq3A6/u7uOqaFCV+DYydlcQcy1rhkL2UtDi8GAqGOVx/CjazAV3sCMoeixWRfXSkD994dg8W\nlOfhd19cPOH0ZUEQ8F+XzMGP1szFGw1HcdOf6tA/GEzxaWkimoc3gVWnubMCAF9aVYloNIonPzyc\n9vcmEkv8Ghg7K4kpMRuHs1b4IYeUcLj+9IpzDXD7QhgK8ANbOWOxIqIjvYO4ZeMOFGTr8MhNS2HU\nqSf9GutXz8D/XrsYe9r78PkHGR4pBU09sYyVdHdWgNgihotqivH0tjZ220gxHB4/svUaGLST/zeU\nAJ1GhRJmrUhOvd0NnUaFmUXp/16SKRgMqQwsVkTSPxTELRu3IxAKY+P6ZSjMSfwTwcsXlGLj+mWw\n9/lw1X1b8MlRhkeKqbnHA2u2HmajVpT3v3l1JXoHg/jbHq4xJmWIZazwCthUlFtMaOc1MEmpt/dj\ndnEOtGnaKpmJSoaDIflBrbzxb4AIAqEI7nxqJ1ocXjxw4xLMLMqZ8muummkdCY+8+oGt2NHK8Eix\nNPV40z5cP9rKqgLMseXg0c0t3BZHiuDw+FHAK2BTUm4xooOdFcmIRqOot7tRy+H6U2KKvTKwWEmz\naDSKu1/ch82HnPjllQuwqtqatNceHR55/SMMjxRLc5rXFp9IEASsX12JA10D+LCZRSvJHzsrU1du\nMaKzfwghbpeUBHu/D32DQczlcP0pMcVeGVispNkf3zmE53cewTcuPANXLylP+uuPhEeW5OL2J3bg\nGYZHppXLG0DvYFCU4frR1i4qg8WkxcYtXGNM8sfOytSVW0yIRAGHNyT2UQjA/g4O109Ell6DHIOG\n64tljsVKGm3a04HfvHEQVy4uwzcvOiNl71OQrcczX1mBc2cV4nsv7MPv/8nwyHRpHh6uF7OzAgAG\nrRrXLp+GNxuOot3Fe+gkX+FIFK7BADeBTVF8ffFRD4sVKai3u6ESgBobi5XTseUaOGAvcyxW0mRb\nsxPfef4jrJiRj19eNR+CkNo0WpMuFh559ZJy/M8/D+JuhkemRXxtsZgzK3E3rpwOQRDw+NZWsY9C\nlDIubwDRKHgNbIriwZAsVqShwd6PqsLshLaEKg1T7OWPxUoaNPV4cNsTO1GRb8RDNy6FXpOef3y0\nahX+++oFuPO8ajy9rQ1ffZLhkanW1OOBTq0a+cYvphKzEZfU2vDs9nZ4/fwBhOTJ6WV6fTKU5Bmg\nEoCjXuZ1SUG93Y1aXgGbEFuuAUcZDClrLFZSzOnxY/2G7dCoBGy4eTnMpvSusxUEAf95yRz8eM1c\nvNl4FDf+aRvDI1OoqceLSqtpwuGeqXbL6koM+EJ4YXeH2EchSgnHQCwQsiCLnZWp0KpVKDEb2VmR\nAJc3gM5+H5PrJ8hmNqB7wIdQOCL2UShFWKykkC8Yxlce34Gjbh8e+dJSTCsQ79P2m4fDI/e29+Pz\nD26BvY/DaKnQ3ONBlVU6AV5nTrNgQbkZGze3IMJrgCRDI52VKWRVUUyZxYijAyxWxMbk+skpzjXE\nlkN4AmIfhVKExUqKRCJRfOvPe7C7vQ+/v2YRFk+ziH2kWHjkLcvQ2efDVfdvwUGGRyZVMBxBm2sQ\n1UXiz6vECYKAm1dVoqnHi/cPOcQ+DlHS9QwMFytZLFamqsJiQsdAkAtZRLa/ww0AmMtiZUKYYi9/\nLFZS5Nf/OIDX9nXh+5fW4JLaErGPM2JVtRXP3b4S4UgUV9+/ZeQTHJq6w85BhCJRSXVWAOCzC0pg\nzdZj42auMSb5cXoD0KoF5Bo1Yh8l4y2elofeoTBandwgKKZ6ez/K8ozIM/Fq40TYzPGsFd4YmYy6\nFheezZB4CxYrKfDUtsN48F/NuPGs6bj1nBliH+ckc0tz8devroJBq8b3X9jH60FJMrK2uEhaxYpe\no8YNZ03DOx/3jJyRSC4cA34UZOlTvmFRCVZVFwAANrMLK6oGu5tXwCbhWLHCzspk/P6tg3iaxYoy\nvftxN364qR7nzy7Ej9bMlew30Ip8E7576RzsPdKPFzl8nRRNElpbfKLrVkyDVi3gsS2tYh+FKKmc\n3gCsOfwEOhlmWLNgNamxtckp9lEUy+sPocXpRW0Zh+snKt+kg1YtcH3xJARCEew83IszJTCiMBEs\nVpKowe7G157ahdnFOfi/686ERi3t//euW1SGRRV5+PXfD3C1bRI093hQmKNHriG9G98moijHgDUL\nSvGXnUfg9nEbHMmHwxPrrNDUCYKAhSVGbGlysOMuksZON6JRDtdPhkoloDjXwGtgk7Df3g9fMIIV\nM/LFPsqESPun6QzS1e/DLRu3I9eoxaM3L0OWXvr3p1UqAT9aMxfdA37c9+4hsY+T8ZodXlRZpddV\niVu/ega8gTCe33FE7KMQJY3Tw/T6ZFpUYkTvYBAHuriARQz7O+KbwNhZmQym2E/OtmYXAGAZixXl\n8PhDWL9xOzz+EB69ednI/clMsHiaBVcuLsPD77egjUOVU9LU40FVobTmVUabX27GkukWPLalFWF+\nakoyEI1G0ePxM70+iRbajACALU2cWxFDvd2NgiwdinNZgE9GsdmAo7wGNmF1LU5UF2ZlzAc90v/4\nP0ke39qKpzZ3wPi2K+mv7fT6Ye/z4dGbl6GmJPNat/95yRy8vr8Lv3itEQ/cuETs42QklzeAvsEg\nqiU4rzLa+tWV+Lend+OdA924aG6x2MchmhKPP4RAKJIx33AzQWGWBjOsWdjS5MSt51SJfRzFqbe7\nMbc0V7LzrlJlyzXg7cZuRKNR/v/uNMKRKHa09mLNolKxjzJhiilWjFo1snUqZBmTP09gNmrx3Utq\n8KlZhUl/7XSwmQ342vnV+M0bB7GlyYFV1Vaxj5RxmuKbwCTcWQGAz8yzocRswIYtLSxWKOPFQ+AK\n2FlJqlXVBXhpdweC4Qi0Ep+9lJNAKIJPugfwqdksEierxGzAUDAM91AIZpP05kalpLHTjQF/KGPm\nVQAFFSufX1qB2iwPampqxD6KJN16ThWeqWvHT19uwKtfPwdqFT+ZmIzmDClWtGoVbjhrOv77Hx/j\n464BzLbliH0kooQ5PcOBkOysJNWqaiue2taGj470Y8n0zNgWJAcHjw4gGI5yuD4BxaOCIVmsnNq2\nluF5lcrMKVb4kQkBAAxaNe7+bA0OdA3g2e2ZsXdbSpp6vNBpVCizGMU+ymldt3wa9BoVNnKNMWU4\nx3Cxws5Kcq0czlvZyrmVtGqwx5LrOVw/eSNZKxyyP626Ficq8o0ozZP+zytxLFZoxKW1NqyYkY/f\n/ONj9A9yve1kNPd4MKMgKyM6UpYsHT63uAwv7j6CvsGA2MchSlj8GlghOytJlZ+lQ01JLrYwbyWt\n9tv7ka3XYHq+SeyjZBxbLlPsJyIajaKuxYUVMwrEPsqksFihEYIg4Idr5qJvKIjfv/WJ2MfJKE09\nXkmGQY7n5tWV8AUjeKauXeyjECUs3lmxZLGzkmyrqguw43AvfMGw2EdRjHq7GzUlOVBlwIdeUjNy\nDayfG8FO5VC3B72DQSzPoHkVgMUKnWBeqRnXLJuGx7e24lC3R+zjZIRAKII216Dk51VGm2PLxcqq\nAjyxtRWhcETs4xAlxOkJwGLScgg8BVbPLEAgFMGuw71iH0URwpEoGjvdvAKWIJ1GhYIsHa+BncaH\nw/MqmTRcD7BYoTF8++JZMGrVuOfVBrGPkhHaXIMIR6IZ1VkBYmuM7f0+vNFwVOyjECXE4fGjgFfA\nUmJZZT7UKgGbObeSFq1OLwYDYQ7XT0FxrgFHWaycUl2LC8W5ekzLsKuGLFboJNZsPb5x0Rl49+Me\nvHOgW+zjSF58bbGUAyHHcmFNMSryjdiwuUXsoxAlJJZezytgqZBj0GJhuZlzK2lSz+H6KSsxG9DZ\nz2JlPLF5FSdWzCjIuCwaFis0pptWVqLKmoWfvdqAIK8JnVJzjxcAMq6zolYJ+NLKSmxv7cX+jn6x\nj0M0aeyspNaqais+OtKPAR8XrqRafUc/dGoVzijOrA+9pCSWYs9iZTxtrkEcdfszbl4FYLFC49Bp\nVPj/Lq9Bc48Xj289LPZxJK2px4PCHD1yDZm32/3zSytg0qmxYXOr2EchmjSHx89NYCm0qroA4Uhs\nexClVr3djVm2bM5fTYEt1wCXN8ClEOPY1pyZ8yoAixU6hfNnF+HcWYX43T8PjoSv0cmaezyozrCu\nSpzZqMVVZ5bj5b129AzwGVPm8IfCcPtCKOAmsJQ5c7oFOo2KV8FSLBqNot7ej1peAZuSeNZKt5vf\ny8ayrcWF/CwdZhZlXveOxQqNSxAE/PDyGgwGwvjtmwfFPo4kRaPR4bXFmfeXP+7m1ZUIhCN4po5h\noJQ5XN5Yxoo1h52VVDFo1Vg63YLNhzhkn0qd/T70DgY5XD9FtlwGQ55KXasTyyvzM25eBWCxQqcx\nsygHN62cjmfr2kbSdekYlzeA/qFgRq0tPlF1YTY+NasQT3x4GIEQ55MoMzgGYsUKOyuptXqmFQe6\nBthdT6H4cP1cdlamhCn247P3DaHdNZSR8yoAixWagG9eOAtmoxY/faUe0WhU7ONISlOGDtef6ObV\nlegZ8OO1fZ1iH4VoQhze2A/P7Kyk1srqWNL1h82cW0mV/R39EASgpiRH7KNktHgw5FFuBDtJfO5M\nscVKOBzGunXrcPvttyfjPCRBZpMW37p4Nj5sduEf9V1iH0dSmofXFs/M4M4KAHzqjEJUWbOwYUur\n2EchmhDH8IyVNYvFSiotKDMjW69h3koK1dvdqLJmwaTTiH2UjJZr0MCkU3N98Ri2tbiQY9CgpiQz\nrxpOuVh5/PHHUV1dnYyzkIRdu6wCs4tzcM+rjdy0MUpTjwc6jQqleUaxjzIlKpWAm1dXYm97H3a1\nMbGapM85MrPCa2CppFGrsGJGPrZyyD5lGuz9zFdJAkEQYGMw5JjqWpwjQa+ZaErFSldXF959911c\nffXVyToPSZRGrcIP18zFkd4h/OkDhgjGNfd4MaMgK2P/ARjtyjPLkaPXcI0xZQTHgB9GrZqfRqfB\nyuoCtDi8sPcNiX0U2en1BmDv96G2LDM/8Zaa4lwDZ1ZO4PD40dTjzdgrYMAUi5Vf/OIX+M53vgOV\niqMvSrB6phUXzy3GH985xE8uhjU7vKguyux5lbhsvQZfWFaB1/d1oottdJI4pzfArkqarJ5pBQCu\nME4BJtcnV4nZwO9fJ8j0eRUASPgjqXfeeQf5+fmora3Ftm3bxv26xsbGRN8i6Xw+n6TOk4m+OFuH\ntw+E8f3ntuGus4tEO4cUnmUwHMVhpxcrSrSinyVZVheF8Ggkit+9shNfOjP1/7BJ4TlScqT7WbZ2\nuWBSRfjfT5KN9Rwj0Shy9Sq8vqsJ80wDIp1Mnt7Z3wcA0Hq60NjYk9TXVuK/r5qgF139Q6hvaIAq\nTSt6O9xBvNU0gOsXWlJyy2Kqz/HvOx3QawRoBzrR2JiZc8cJFyu7du3C22+/jffeew9+vx8ejwd3\n3XUXfvOb3xz3dTU1NVM+ZLI0NjZK6jyZqAbArS4dHvhXE/79koVYVJEnyjmk8CwPdQ8gEm3BirnT\nUVNTLupZkqUGwIWNAbzR3Isff+EsGLTqlL6fFJ4jJUe6n+XQP3pQUWjifz9JNt5zPGe2DztbezFn\nzpyMzGmQqu49u1GWZ8TyRbVJf20l/vs6r7cVf97fh6KKahSmaVPgA8/uxqaP+rD2rDk4q6og6a8/\n1ef4yRvvY1llAebPm5vEU6XGzp07x/z1hO9vffvb38Z7772Ht99+G/feey/OOuuskwoVkqd/u2Am\nCnP0+MnLyl5lfKh7eG2xNbM3gZ3oltWVcHkD+Nteu9hHIRqX0xuANZvXwNJlVXUButw+tDi8Yh9F\nVurt/ZjLMMikGVlfnKar6r3eAF7fF+tWvC7B1f/9g0Ec6HJn9BUwgDkrlIBsvQbf+cxs7G7rw6Y9\nyv2BttkRW1uc6RkrJ1pZXYDZxTnYsLlV0cUoSVckEoXLG4A1m2uL02VVdWxuZTPnVpLG6w+hxeFF\nLedVkqZkOBgyXeuL/7rrCALhCObYcvD6/i5EItL6nrm91YVoNLPnVYAkFSsrVqzAgw8+mIyXogxx\n9ZnlmF9mxq9eP4DBQEjs44iiqduLohw9cgxasY+SVIIgYP3qSjR2urGthUFwJD19Q0GEI1EUsLOS\nNpUFJpSaDdjKvJWkOdDlRjQKzGNnJWnSmWIfjUbxdF0bzpyWh6+eV43uAT92HJbW6v+6Vhd0apVo\nV/aThZ0VSohKJeDHV8xFl9uHB95tEvs4omh2eFCd4WGQ41m7qAx5Ji02co0xSZDDMxwIyc5K2giC\ngJXVVmxtckru0+NMNbIJjGuLk8aarYdaJaQlxb6uxYXmHi+uXT4NF9YUQ6dR4TWJXQXb1uLCooq8\nlM+fphqLFUrYkun5uGJhKR58rxntrkGxj5NW0WgUTd0e2V0BizPq1Lh2+TS80dCluGdL0hcvVthZ\nSa/VMwvQOxhEY5db7KPIwv6OfuRn6WAbnrOgqVOrBBTl6NNyDeyZujbkGDS4fEEpsvUanDerEK/v\n75RMMe/xh7C/oz/jr4ABLFZoir576RwIAvCr1w+IfZS0cnoDcPtCqJJpZwUAbjxrOgRBwBMfHhb7\nKETHcXhi6fWF7Kyk1crq2KYjptknR73djXmludyulmTFaUix7/UG8Nr+LnxucRmMuljX4rMLSnDU\n7ceuNmlcBdt1uBfhSJTFClFpnhF3fKoar+7rxLZm5XwDa+6JbcSplmlnBYg920trbdi4uRWvfKTc\nRQokPc6RzgqLlXQqMRtRZc3C5kOcW5mqoUAYB48OMAwyBWxpSLF/YXcHAqEIrl0+beTXLphTBJ1G\nhVclchWsrsUFtUrAkukWsY8yZSxWaMpuP7capWYDfvJyA8ISaX+mWlNPbBOYXGdW4u5ZV4sF5Wb8\n+zO7sXFzi9jHIQIQuwamVgnIM8pruUUmWDWzAHUtLgTDEbGPktG2t7oQDEdHulWUPLYUp9hHo1E8\nU9eGxdPyUFNybN4ox6DFuWcU4vV90tgKVtfiQm2ZGVn6hCMVJYPFCk2ZUafGdy+rQUOnG8/vaBf7\nOGnR3OOBXqNCaZ5R7KOkVJ5JhydvXYGLaorx45cb8P/+foDrjEl0Tk8A+Vk6qFKQFk2ntqraCm8g\njI+O9It9lIy2+ZADWrWAZZWZ/6m31NjMBnj8IXj8qdlUur21F4e6Pcd1VeI+u8CGLrcPu9v7UvLe\nE+ULhrGnvQ8rZHAFDGCxQkmyZkEJlk634L//8THcvqDYx0m5ph4vZlizoFbAD0sGrRr3X38mrl0+\nDfe924S7nv+In6qSqBwePzeBiWTlcEL3Fl4Fm5LNTQ6cOc0Cky7zP/WWmvjCglR1V56pa0OOXoPL\nF5Sc9HsX1hRDpxZ/K9ie9j4EwhEsr2SxQjRCEAT8aM08uAYD+L+3D4l9nJRr7pHv2uKxaNQq/OJz\ntfjmRWfgr7uO4CuP71Bsvg6Jz+Fher1YLFk6zC3JxRYO2Ses1xtAvd2N1TOtYh9FllKZYt83GMCr\n+zqxbnHZmIVmrkGLc2dZ8fo+cbeC1bW4IAjAMhYrRMebX27G55eUY8PmFjQPz3TIkT8URptrULZr\ni8cjCAK+edEs/PxztXjvYA+ufXgbXN6A2MciBWJnRVyrqguws60XvmBY7KNkpK3NTkSjYLGSIqlM\nsX9h18mD9Se6tLYE9n4f9hwR7ypYXYsLc2y5MJvkMdfHYoWS6q7PzIZeo8bPX20U+ygp0+YcRCQq\n/+H68Vy/Yjruv2EJDnS6cfX9W5jDQmnnZGdFVKtnWhEIRbBTYmndmeKDQw5k6zVYWM5NYKkQT7FP\ndmclPli/sCIPc0vHD/K8aG4xtGoBr4t0FSwYjv3dlMu8CsBihZKsKMeAf7tgJt460I1vPLsbW5uc\nshvIjm8CU1pnZbTPzLPhyVtXwOHx48r7t6DBzpA4Sg+vP4ShYJhri0W0bEY+NCoBW5o4t5KILYcc\nOKsqHxo1fwRLBYNWDbNRm/SZlZ2He/FJtwfXn6KrAgBmoxbnnFGI1/Z1ifLzz76OfgwFw7LIV4nj\n3xRKuvWrK3Hzqkq83diNax/+EOf95l388Z1DKV0lmE5NwxkrM6zKLVaA2F3Yv3x1FTQqAV98cCt/\ncKG0cA4HQvIamHiy9RosrMjD5kOcW5msI72DaHUOYlU1r4Clki3XkPRrYE/XtSFbr8HlC08erD/R\nZfNL0NE3hL0ibM2ra3EBAIsVolPRa9T48RXzUHf3Rbj3CwtRYjbgv//xMVb96i2s31CHv+/vRCCU\nub8tJzQAACAASURBVNukmno8KM7VI8cgj7ugUzGrOAd//eoq2MwG3PzodoZHUsr1jARC8hqYmFZV\nF+CjI32K2P6YTFuGC7yzz2Cxkko2c3JT7PsHg3j1o06sW1w6oQ1un66JXQUTYytYXYsL1YVZsvpA\nh8UKpYxRp8aVZ5bj2dtW4t27zsNXz6tGQ6cbdzy5Cyt/+RbueaUBnxwdEPuYk9bc40WVVZnzKmMp\nzTPiL3eswsIKhkdS6sXT6wtl9I04E62sLkAkCmwf/hSXJuaDQw4U5uhxRhG/h6RSslPsX9h9BP7T\nDNaPZjZpsXqmFa9+1JnWq2DhSBTbW1xYPkNeYaMsVigtKq1Z+M5n5mDzf12ADTcvw7LKfGzc0opP\n/897+Nx9m/FMXRsGMuATumg0GltbXKTsK2AnMpu0eOLLK/Dp4fDIXzM8klLEMXwNjJ0VcZ05zQK9\nRsWrYJMQjUaxpcmB1dUFEAT5Z3SJqdhsgMPjT0om2MhgfbkZ80onvhQhfhVsX0f6roI1drox4A/J\nargeYLFCaaZRq3D+nCI8cOMSfPj9C3H3ZTUY8IXwvRf2YfnP38Jdz+/F9laXZH/QdXgCcPtC7KyM\nwaBV4/4bluC6FdNwP8MjKUXinZWCLHZWxGTQqrG00sJZtUn4+OgAHJ4AVxanQYnZgGgU6B7wT/m1\ndrX14uDRsRPrT+XiucXQqAS8msarYHKcVwFYrJCIrNl6fOXcKrz5H+fihTtXYe2iUry+rxOff2Ar\nLvztv3D/u03oHpDWUH48P6aaLfwxqVUCfr6uFv9x0SyGR1JKODx+5Bo00Gn47Utsq6qtONA1AIdn\n6j8QKkG8C8ViJfWSmWL/9LZ2ZOs1WLOwdFJ/Ls+kw6qZVry2L31XwepaXKjIN6I0z5iW90sX/mtP\nohMEAWdOs+BXVy1A3d0X4b+vXoCCbB1+/fcDWPnLt3HrYzvwRn2XJD6lj28Cq1L4JrBTEQQB37jo\nDPzic/Nj4ZEPfTjyaTjRVDm8AVhz2FWRglXVsXvxHzbzKthEbD7kQJU1S3Y/SEpRslLs+weDeOUj\nO9YuKkWW/vSD9Sf67Hwb2l1D2N+R+vX+0WgUda0uLK+U17wKwGKFJCZLr8Hnl1bg+TtW4a1vfwpf\nOacKe4/04bYndmLlL9/GL19vHMk5EUNzjwd6jQpl/GZzWtetmIYHbliCA10DuPqBrQyPpKRwDPhh\n5RUwSZhfZkaOXoMtTSxWTicYjmBbsxOrZsrvB0kpsiUpxf6lPR2TGqw/0cVzbVCrBLy2P/VXwQ51\ne+DyBmQ3rwKwWCEJqy7MxncvnYMt370AD9+0FIun5eGR91tw8f+8h4Zuca6HNfV4MMOaBZWKw5ET\ncfE8G566dQVc3gCuvH8L6u3p3zlP8uL0BmDN4XC9FGjUKqyoyseWQ5xbOZ297X3wBsI4m1fA0sJi\n0kKnUU2psxKNRvH0tjYsKDejtmzig/XHnSNLh1XVBWm5CrZteF5lRRWLFaK006pV+PTcYjx801Js\n/e4FyNZrsKlRnB96mx1ezqtM0tLKfDx/x8rh8MgP+YMNTYnD4+dwvYSsrLai1TmIjr4hsY8iaZsP\nOSEIwFlV7KykgyAIsfXFU+is7Grrw8dHBxLuqsRdNr8Eh52DqLen9ipYXYsLxbl6TMs3pfR9xMBi\nhTJKUa4BVy8px+bD3rQP3/tDYbS7BlHNeZVJm1WcgxfuXIXSPAO+tKEOL+9leCRNXjAcQd9gUFZh\nZ5lu9fC1Jn4IcWqbDzkwv8yMPBO7guky1ayVZ+rakKVTT3qw/kSfmTd8FSyFW8Gi0Si2tTixfIY8\n12KzWKGMc/2KaQhHgT9vb0/r+x52DiISBaoK2VlJRInZiOdvX4VFFXn4+rO7sYHhkTRJLi8zVqRm\nVlEOCrJ02Mq5lXF5/SHsbu/FqmpeAUsnmznxzkr/UGyw/opFZchOYLB+tPwsHVZWpfYqWJtrEEfd\nftmtLI5jsUIZp6owG4tKjHimrh3hSPryWJq6h9cWs1hJ2OjwyJ+83IBHdzolm6lD0hNfkcvOinSo\nVAJWVhdgSxP/Lo+nrtWFYDjKeZU0s5ljnZVE/rvctKcDvmAE16+Y2hWwuMvml6DVOYjGzoGkvN6J\n4vMqZ7FYIZKOy2bloKNvCO8c6E7bezY7YmuLZxTyGthUjA6PfH5/P779/F5JrKUm6Yun11vZWZGU\nVdVWdLl9I/9G0vG2HHJAp1FhaaVF7KMoSnGuAYFQ7OroZMQH6+eXJT5Yf6LPzCuGSkDKroLVtbiQ\nn6XDTJnO1LJYoYy0cloWinL0eGrb4bS9Z1OPB7Zcw5RbwnQsPPLGRRa8sKsDtz62A14/wyPp1Jzs\nrEhSPG+FK4zH9sEhJ5ZOt8CgVYt9FEUpSXB98Z72Phzomvpg/WgF2XqclcKrYNtanFhWaZHlvArA\nYoUylEYl4JplFXj3YE/a8juaeryoYlclaQRBwHULLfjllfPx/ic9uO5hhkfSqcWvgXFmRVqmF5hQ\nlmfkkP0YHB4/GjvdTK0XQaLBkE9va4NJp8YVi6Y2WH+iy+aXoNnhxcdHk3sVzN43hHbXEJbPkO+m\nORYrlLGuWT4NAoCn69pS/l7RaBTNPR7Oq6TAtcun4cEbl46ER7Y5GR5JY3N6AtBrVOxuSowgxOZW\ntjY7EUnjHGEmiC8eYLGSfvFgyMlsBHP7gnh5OLE+2f/OXFJri10F+yi5V8G2tw7nq8h0XgVgsUIZ\nrDTPiAtrivHn7e3wh8Ipfa8ejx8DvhA7Kyny6bnFePorx8Ij93cwPJJO1uPxw5qtl+1Vh0y2emYB\n+gaDaOxKbZZEptl8yIEcgwbzkzT7QBNXlKOHIGBSG8E27Y4N1ifzClicNVuPFTMK8GqSr4J92OxC\njl6DmpLcpL2m1LBYof+/vTsPbOq80gb+XMmLvMmLZEm2sQ22IRjbGLNjkpCYsBhCgJAVmKZ8WTqZ\nJiSladb52mky7XQ6TZpppulMkk6nnRCapYATKCSBJBBMwCw2ZjGLF7xvkm3Zkm1Zlu784aUkYTG2\npKsrPb+/Gtu695Tra+nc95z3yNr6uckwWfuw+1STW89T2TrQOMqVFfeZkRyDvzw6D0FKAfe9cQiF\nLCmhbzBZ+thc76XmpQysHBwsZ9/KpQorjJiXooFSwQTb0wKVCmjDg0ecrIiiiM2Ha5CZoMbUcVFu\niWlZlgEVrVacb7a47JhFVSbMHB/t079jTFZI1m5K0yIpJhSbD7m3FKyideAPC1dW3CtNF4G//EMu\nEqJC8N0/FOFDLxweea6pa3jeB3mW0WKDhs31XskQqUJKbBgOVvAhw5AaUzdq23pYAiah6xkMeaLO\n7PLG+m9akmmA4MJdwYwWGyparT7drwIwWSGZUygErJ2ThKKLbTjv4qa1S1W2WqEKVCA+MsRt56AB\ncZEheO9785CTGI2NW4rx+wPeMTyy0dyDJ/5cjCWv7sfaNw+h1+7e0kP6Nq6seLf5qVoUVbVxK/JB\nhYOJG5MV6ejVqhE32G8Zaqwf48T6q9FFqDB7fIzLkpUjg/NV5qT4br8KwGSFfMDdM8YhSKnA5kPu\n28a4otWCCdpwKHx4mdWbRIYG4k8PzsaSDD1e2nEG/7KrTLLG3V67A7/ZewF5v9qHXaeasGb6OJxt\n6sJLO85IEo+/EkURJitXVrxZbqoG1j4HSus6pA7FKxwoN0KvDkYqV+QlExepGtHWxZ29dnx4ogF3\nZMcjQhXo1piWT43DhRYLLrjgAevhqjaEBCqRGe/bPVFMVkj2NOHBWJZlwNbj9W6b1VHJbYs9ThWo\nxOvrZmD93CT8175KPOXh4ZGiKGJnaSMWvrwPr3x6HrdOjsXeTQvw8j3Z+N6CFGw+XIMdpd5Xpuar\nOnv6YXeInLHixeamaCAI7FsBAKdTxFcVJsxP03JDCAkZIlUw99ivuRJeUNKAHrvDrSVgQ5ZmDJSC\n7XTB6srhqjZMT45CUIBvf5z37f935DfWz01Gl63fLT0OvXYH6tq72VwvAaVCwEsrM/HDRZOwtbge\nD3poeOTpBjPufeMQvv/OcUSoArDl4bl4fd0MJMaEAgCeWnwDcpKi8NxfTqLaxKndntA6PBCSZWDe\nKjosCFPi1MPlT/6srKkTbdY+zE9lCZiUhmatXK3Jfmhi/ZQ4NaaOc/8KhU6twqzksZeCmbvtONvU\nidnjfbtfBWCyQj5iRnI0Jhsi8PahapdPh602dcMpgkv5EhEEAY8vnIhf3JmFAxdacf+bh4aHA7qa\nyWLDc1tP4vbXDuBCcxd+tjoTOzfehHmpX38zCFQq8Jv7ciAIwONbitHXzxp9d+P0ennITdXgeHWH\n3/d0Da0usV9FWgb1tafYl9aZUdbYibVzkjy2CrYsy4DzzRaUt4y+FOxodRtE0ff7VQAmK+QjBEHA\nurnJON3QiZJa19ZLVw7uBMaVFWndNzsJb/zdTJxv7sJdvzvo0uGRff1OvPVlJW751Rd4/2gtNuRO\nwBdP3Yp1c5KvuB1kYkwofnlXNkrrzPjX3WddFgtdntEysAMbp9d7t9w0LfocThy92C51KJI6UG5E\nmi58eDAhSWPo3/9qTfZbimoQEqjEShdPrL+a/Kw4AMBfT45+7MLhqjYEKRWYluiebZa9CZMV8hmr\npsUjNEiJzYddu43x0LbFE7RcWZHabVP02PzQXHT02F02PPLzcy1Y+u/78c87y5CTFI3dT96EH6+Y\ngsjQazdZLs004IF5yfj9gSp8eqZ5zLHQlZmsXFmRg9njYxCgEPx6C+O+fieKqtowP9X3y3O83bWm\n2Hd5sLH+Unq1CjOTo8dUCna4qg3ZiZFQBSpdGJl3YrJCPiNCFYhVOQn46EQDOrpdNwejstWKuEgV\nwoIDXHZMGr0ZydH44O/nIThAgXv/6yscuDC6D0UVrRZs+EMRNvzhCEQR+O/vzsQfN8xCmi7iuo7z\n3LJ0ZMSr8aMPTqCho2dUsdC1GbtsUAhAdChXVrxZWHAApiVG4WCF/zbZF9e0o8fuYAmYFwgPDkB4\ncMAVe1Y+PNGA7j4H7p/j/sb6b1qWFYezTV3DD0Svh9XWj1P1Zsye4PslYACTFfIx6+ckw9bvxAfH\n6lx2zIpWC3cC8zJpugj85dFcJMaEYsP/FKGgpH7ErzX32PHPO85gya/348jFdrywLB0fP3kz8ibr\nR1WvrApU4j/WToe934mNW4rRzxkTbmG09iEmLMinpzT7itxUDUrrOtDZa5c6FEkUVpigEIA5KVxZ\n8QaGSNVlk5Whxvr0ODWyPdBY/035WQYAwK5RrK4cr2mHwylijo8PgxzCZIV8ypR4NaYnReGdwzUu\nabQXRXFg22It+1W8jSFShXe/Nw85SdF44s8leOvLyqv+vMMpYktRDfJ+9QV+X1iFNdPH4fOnbsHD\nN6eMedvHCdow/PzOLBytbsev95wf07Ho8oxdNmjCWAImB/NStXCKQFFlm9ShSKKw3Iip46IQGeK5\nsiK6sitNsT9Zb8bphk6snZ0oyfbScZEhmJEcjZ2j6Fs5XNkGpULA9ORoN0TmfZiskM9ZPzcZlUar\nS8oQWrts6LL1cycwLxUZEog//b/ZyM804J93luHnf7388MjDlSaseO0Antt6EimxYfjosRvxr3dN\nRWyE6z78rpyWgHtnJuL1Lyqw/3yry45LA0zWPmgjWAImB9OToxAcoPDLLYy7eu0oqe3A/DT/eOIt\nB1eaYj/cWJ+TIEFUA/IzDShr7ESV8fq2wC+qakNmvBrhflKezmSFfM6yrDhEhQbibRdMtK9oHfgD\nksKdwLzWUBnW381Nxhv7K/HD908MbyVc196N779zHPe+cQjt3X34zf05eO9785CZ4J4l/3+6IwNp\nseHY9F4JWrquPTWZRs5o4cqKXAQHKDFrfAy+8sO+laKqNjicIvtVvIghMhgtXTY4LnmQZbH1o6Ck\nASuy46D2YGP9Ny0b3hVs5KVgvXYHSmo7/KZfBWCyQj5IFajE3TPG4ZMzzVfdrnAkhhrfUnVMVryZ\nUiHgxZUZeGrxJGwrrseDfzyCVz49j4Uv78OeM814YuFEfPbDW3BHdrxbl/tDgpT47brpsNj68eSf\nS7725khjY+yycScwGclN0+BsU5fbZiJ5q8JyE4IDFJie5B/lOXJgiAyBwyl+7Xfxw5LBxnoPTKy/\nmvioEOQkRV1XsnKitgN9Dqff9KsATFbIR62dkwyHU8S7R2rHdJzKVitUgQrEqblXvrcTBAGP5U3E\nL9dMxcEKE36z9wIWTdHjs6duwQ8WTUJIkGe2d5ykj8BP78jAwQoTXv+83CPn9HU9fQ5Y+xycsSIj\nuYOT2/1tdaWw3IjZE2L8YjtZuTBcZor9lqIaTDZEeMWMkuVZcTjd0Ilq08hKwQ5XtUEQgFnjubJC\nJGsTtGG4aaIWW4pqxrQ7U6XRghRtOBTcgUg27pmViPe+Nw9b/yEX/7F2OhKiQjwfw8xErJwWj1/v\nOY/Dlf71Yc0dhp6IxnJlRTYy49WICA7wqy2MW7p6ca65azhRI+8wnKwMVlqcrDPjZL3ZoxPrr2Zo\nQOTOEa6uFFW14QZ9xIhmgfkKJivks9bNSUajuRefnW0Z9TG4bbE8zUiOlrQMQxAE/Gx1FpJiQvHE\nn0vQZnXd3B9/ZLJyer3cBCgVmJOi8avhkEOrSDeyX8WrDA+GHFxZeaeoBqpABVZOk66x/lIJUSHI\nThxZKZjd4cSx6nbM8aN+FYDJCvmw29J1MKhVeHuUE+177Q7Utfcglc31NArhwQH4j7XT0Wbtw1Pv\nn7jsLmU0MsYuTq+Xo9xUDapN3ahr75Y6FI84cMGIyJBATIlXSx0KXUITFoRApYCmzl5YbP34sKQe\nK6bGe9XW0suzDDhV34ka09XvlVP1ZvTYHX43w4fJCvmsAKUC981OxP7zrSOuBb3URZMVogiurNCo\nZSZE4oXl6fjsbAt+f6BK6nBky2QdSFa4siIvQzti+UPfiiiKKCw3IjdVw8GlXkahEKCLUKHZ3IuP\nTjTAKtHE+qvJzxzcFezU1VdXDlcNzC7yp34VgMkK+bj7ZiVBqRDwTtH1r65UDm5bzJUVGovvzEvG\nkgw9/nX3WRTXtEsdjiwZLQNlYFxZkZdJ+nBowoL8om/loqkbDeZe5LIEzCvp1cFoNPcON9bneEFj\n/aUSY0KRPS7ymqVgRVVtSIkNc+mMMDlgskI+zRCpwm3pOrx/tA62fsd1vbaiZWDb4glarqzQ6AmC\ngF+uyYZercLjW4ph7rFLHZLsGC02hAcHcIclmREEAfNSB/pWRNG3yyALywd6c9iv4p3iIkNQUtuB\n0joz7p/tHY3135SfFYfSOjNq2y5fCuZwijhysc2vtiwewmSFfN76uclos/Zh18mm63pdpdGKuEgV\nwvxkQiy5T2RoIF5bm4Mmcy+e/Uupz39wczWjpQ9aloDJ0vw0LZo7bcMDdn1VYbkR8ZEqjNeESh0K\nXYZerUKP3YHgAAVWSTix/mqWX2NA5NmmTnT19vtdcz3AZIX8wPxULcZrQq97on1Fq4UlYOQy05Oi\n8aMlN2DXqabr/l30dyaLDRqWgMlSburAU+CvfHhXMIdTxFeVJsxP03rlE3samGIPALd7WWP9pRJj\nQpGVEIm/nrr8g9XDlQP9Kv40uX4IkxXyeQqFgHVzknG0uh1nmzpH9BpRFFHZamVzPbnUwzel4JYb\nYvHSzjKcbjBLHY5sGC02rqzIVFJMKBKiQny6b+VMQyc6uu3DGwqQ95mgHXjwuG6udzXWf9OyrDic\nqO247A56RVVtGBcdgngJZodJjckK+YW7ZoxDUIBixE+0W7tssNj6ubJCLqVQCHj57mxEhwbi8XeK\nYbH1Sx2SLJgsfVxZkSlBEJCbqsFXlSaf3b67cHDVKDfN/3oJ5GLhZB0+++ECSedvjcSyLAMAfKts\nXRRFFPlpvwrAZIX8RHRYEG6fGodtx+tH9AGxvHWguZ4rK+RqmvBg/Pt9ObhosuIft51k/8o19Duc\naOvu405gMjY/TYuObjvONI5sZVtuCsuNmKQPhy5CJXUodAUKhYAUGTx8TNaEISNe/a1p9hWtFrRZ\n+/yyXwVgskJ+ZN2cZFj7HCgoqb/mz3LbYnKnuSkaPLFwEraXNOD9Y3VSh+PV2rvtEEWwDEzG5g32\nrfjiNPteuwNHLraxBIxcZllWHEpqO1Df0TP8tUN+3K8CMFkhPzI9KQrpcWq8fajmmk+zK1otCAlU\nwqDmkzJyj8fy0jAvRYOfFJzGheYuqcPxWkYLp9fLnV6tQmpsGP6n8CJe2nEGW4/X4XxzF/odTqlD\nG7PjNe3otTsxP5XJCrnGssFdwXZdsrpSVNUGXUQwkv10tzkmK+Q3BEHA+rlJKGvsxPGajqv+bGWr\nFRO0YVBwEjG5iVIh4N/vm4bQICUee6cYPX3XNwfIX5gGB0JqwriyImdP3DYJOrUKmw9XY9N7J7D4\n1/uR8ZOPsfK3hXhh20m8c7gGJ2o70GuX131wsNwEpULAnBT/fOJNrjdBG4b0OPXwFsaiKKKoqg1z\nUjR+u9scB0iQX1k1LQH/8tez2HyoGjOSr9xoV9FqQY6XN+KR/OnUKrxy7zQ88N9FeHHHafzLnVOl\nDsnrDK+s+NnEZl9zR3Y87siOR7/DiUqjFacbzDhV34nTDWZ8eKIBmw/XABhI4ifqwjElXo2M+Ehk\nxqsxJV6NCJV3bjd7oNyIaYlRXhsfydPyLAN+9cl5NHT0oMnSj6bOXr8tAQPGkKw0Njbi6aefhslk\ngiAIuOeee/DAAw+4MjYilwsLDsDqnAS8e7QW///2KYi+zNPaXrsD9R09WDN9nAQRkr9ZMCkWj96S\nit99UYF5qVrckR0vdUhehWVgviVAqcAkfQQm6SOwOmfga6IooratB6cbzDjdMJDAfHnBiK3H/9Zf\nmKwJRWZ85GASM5DIxEqcwHb22lFa14HHbk2TNA7yPcuy4vCrT85j96kmdLX1AoDfNtcDY0hWlEol\nnn32WWRkZMBisWDNmjWYP38+0tJ405J3Wzc3Cf97qBofHKvDwzenfOv7VUYrRBFI1bG5njxj06JJ\nKKpqw/NbT2JqQiTGa7kL3RCjpQ9BSgXUKhYC+CpBEJCkCUWSJhT5g/X6ANDS1TuQvNQPJDGl9R1f\n2yVJrw5GRnwkMuLVuCM7HhP1ER6N+1CFCU4RbK4nl0uJDcdkQwT+erIR0QF2RIcGIs2PN/wZ9V9/\nnU4HnU4HAAgPD0dKSgqam5uZrJDXm2xQY2ZyNDYfrsaDN074Vl/K0E5gKfzASB4SqFTgN/fnYPEr\n+/Dbz8vxb3dnSx2S1xiYXh/kt7Xa/kwXoYLuBhVuvUE3/DVzjx1nBldfhlZhvjjXgv89VI0Pv38j\nkjzYgHywwoSQQCVLhsktlmfF4eVPzyNSpcDc1Fi/7qF1SYN9XV0dysrKkJ3NN1iSh/Vzk3HR1D08\nzOtSFZyxQhJIiArBoil6fFrWDLsP7JLkKsbBZIUIACJDAjEvVYOHbkrBr++dhk9+sACf/fAWiCLw\n8J+OenTQ6oFyI2ZPiEFQAPcqItcbWmU09zox20+HQQ4Z87q61WrFxo0b8fzzzyM8/NtLVGVlZWM9\nhcv09vZ6VTw0emO9lhMCRaiDFfjdp6eg7Td87XvFFS2IDVOiuuLCWMOka+A9+XWZUQ5s77bj/S+K\nkRMvry0q3XUt64ydiFQp+HviIXK9J5+5UYN/3NOEh9/6Ev94qx4KN6/Embr7Ud5iwYLEIK/995Lr\ntaS/GR8ViIsdduhg9utrOaZkxW63Y+PGjVixYgUWL1582Z9JT08fyylcqqyszKviodFzxbW8v1bA\nW19WITp+AgyRf5unYtprwuT4aP6ueADvya8bn+rAy4Wf4nRnENYulNe/i7uupXV7A6bqNfw98RC5\n3pPp6YBNVYWffnQGu+uU+OHiG9x6vq3H6wDU4M75U5AeH+nWc42WXK8l/c1321R4c98FLMvNhtIP\nysCOHTt22a+Peu1SFEW88MILSElJwYYNG0YdGJFU1s1OhlMUsaWoZvhroiiiosXCfhWSREiQErdO\njsXHp5vhcF59cKk/EEURRmsftBEsA6Nr+27ueNw7MxGvfVaOHaUNbj1XYbkJMWFBSDeo3Xoe8m/f\nnT8Bb61O9ItE5WpGnawcO3YMBQUFOHToEFauXImVK1di3759royNyK2SNKG4eWIs/nykZniSckuX\nDdY+B1L8eNcNktbSzDgYLTYcq26XOhTJddn60dfvhDaM2xbTtQmCgBdXZWBGcjSeev8ETtWb3XIe\nURRRWG7EvFSNXzc9E3nKqJOVmTNn4ty5c/joo49QUFCAgoICLFiwwJWxEbndujlJaO60YU9ZCwCg\nomWguT6VyQpJJG+yDkEBCuw61XjtH/ZxQ9PrubJCIxUcoMR/rp+B6NAgPPKno2jtsrn8HBWtVjR1\n9mJ+KrcsJvIEbmFBfi1vsg5xkSpsPlwNAKgwDm5bzJ3ASCLhwQG4eaIWH59qgij6dylYQ0cPAEDD\nlRW6DrERwXjzOzPR1t2HR98+Blu/w6XHPzi4i+SNnK9C5BFMVsivBSgVuH92Er68YESV0YqKFgtC\ng5QwqFXXfjGRmyzNjEODuRcn6txTxiIXu081IThAgZykKKlDIZnJTIjEr+7OxtHqdvx4+2mXJv4H\nLhgxLjrEozNdiPwZkxXye/fNSkSAQsA7h6tRabRigjaMdcgkqUXpegQoBL8uBevrd+Kj0gYszjAg\nQhUodTgkQ7dPjcdjt6bh3aO1+OPBiy45psMp4qtKE1dViDyIyQr5PZ1ahcUZerx/rA7nmjrZr0KS\niwwdGHy3249Lwb4414KObjtW58RLHQrJ2KZFk7Boih4v7SxDYfm3hwBfr5P1ZnT19iOXyQqRxzBZ\nIQKwfk4yOrrtaO60sV+FvEJ+ZhyqTd0oa+ySOhRJbC+phyYsCDdNjJU6FJIxhULAr++dhtTYZOJV\nqwAAEJZJREFUMPzD5uOoNlnHdLyhhCc31b8nihN5EpMVIgDzUjXDs1W4skLeYHGGHgoB2O2HpWDm\nHjv2lLVgRXY8ApV8m6KxCQ8OwFvfmQVBAB7641F09dpHfazCciMmGyKgDeemD0SewncBIgzsz79+\nbjIA4AZDhMTREAHa8GDMGh+DXaeapA7F4/56shF9/U6szkmQOhTyEUmaULy+djoqjVY8+eeSUQ1d\n7bU7cLS6nf0qRB7GZIVo0AO54/HB38/DJD2TFfIO+ZkGXGixoHxw/o+/2FZcj5TYMEwdFyl1KORD\nctO0+MmKKdh7tgUvf3Luul9/9GI7+vqdmM9khcijmKwQDVIqBMwcHyN1GETDlmbGAfCvUrDatm4U\nVbXhzpwECAJ35SPX+ru5ybh/dhJe/6ICBSX11/XawgojAhQCZk/g+wSRJzFZISLyUoZIFXKSovyq\nFGzoA+TKaSwBI9cTBAE/vSMDs8fH4OkPSnHyOmYZFZYbkZMUhbDgADdGSETfxGSFiMiL5WcacLqh\nEzWmbqlDcTtRFLGtuB6zx8cgMYYD98g9ggIUeH39dGjDg/Hwn46ipav3mq8xd9txst7MEjAiCTBZ\nISLyYvlDpWCnfb8U7GS9GRWtVqyezlUVci9teDDe+M4MmHvs+Pv/PQZbv+OqP/9VpRGiCDbXE0mA\nyQoRkRdLjAlFRrzaL0rBth6vR5BSgWWDCRqRO2XER+Lle7JxvKYDL2w7ddUBrIXlJoQFKZGdGOXB\nCIkIYLJCROT18jMNKK7pQKO5R+pQ3MbucOKjEw1YmK5DZGig1OGQn1iWFYeNCyfig2N1+O/Ci1f8\nucJyI+akaDj3h0gCvOuIiLzc0K5gH/vw6sqBC0aYrH2crUIe9+TCiViSocfPdp7B/vOt3/p+Q0cP\nKo1WTq0nkgiTFSIiL5emC8dEXbhPl4JtLa5HVGggbrlBJ3Uo5GcUCgGv3DMNk/QReOyd46gyWr/2\n/cJyIwDgxonsVyGSApMVIiIZyM804MjFNhgtNqlDcbmuXjs+Od2E26fGISiAb0vkeWHBAXjzOzOh\nVAh46I9H0NlrH/7ewQoTtOFBuIEDg4kkwXcFIiIZWJoZB6cIfHK6WepQXG73qSbY+p1YnTNO6lDI\njyXGhOL1dTNQberGE1uK4XCKEEURB8qNyE3VckgpkUSYrBARyUB6XASSNaHY5YPT7LeX1CNZE4rp\nSdxpiaQ1L1WDf7ojA5+fa8W/fXwO5S0WtHbZMD+N/SpEUuEYViIiGRAEAUszDfj9l1Uwd9t9Zses\nRnMPDlaYsDFvIp9ck1dYPzcZZY2d+M99FSit6wAADoMkkhBXVoiIZCI/Mw79ThGflvlOKVhBSQNE\nEdwFjLzKT1ZkYM6EGBysMCFZE4px0aFSh0Tkt5isEBHJRPa4SMRHqrDbh0rBthfXIycpCuO1YVKH\nQjQsKECB362fgQnaMCzNNEgdDpFfYxkYEZFMCIKAJZkGbD5cA4utH+HB8v4TfqahE2ebuvDSygyp\nQyH6lpiwIOzZtABKBcsTiaTElRUiIhnJz4xDX78Tn51tkTqUMdtWXIcAhYDbp8ZLHQrRZTFRIZIe\nkxUiIhmZkRwNbXiw7EvBHE4RBSUNuOUGHaLDgqQOh4iIvBSTFSIiGVEqBCzJ0OPzs63o6XNIHc6o\nHawwoqXLhjuns7GeiIiujMkKEZHM5GfGocfuwL7zrVKHMmrbjtcjQhWAvMk6qUMhIiIvxmSFiEhm\n5qTEICo0ULalYN19/dh9ugnLs+KgClRKHQ4REXkxJitERDITqFRgUboee8taYOuXXynYJ6eb0d3n\n4GwVIiK6JiYrREQylJ9lQJetHwfLTVKHct22FtcjISoEs8bHSB0KERF5OSYrREQyND9Ni4jgAOyS\nWSlYS1cvDlxoxaqceCi4LSwREV0DkxUiIhkKDlAiL12HT880o9/hlDqcEfuwpAFOEVidM07qUIiI\nSAaYrBARyVR+pgHt3XYcrmqTOpQR21Zcj6njIpGmC5c6FCIikgEmK0REMrVgkg4hgUrZlIKdb+7C\n6YZOrJrGxnoiIhoZJitERDIVEqTELTfE4uPTzXA6RanDuaZtxfVQKgTcMS1e6lCIiEgmmKwQEcnY\n0kwDWrtsOFbTLnUoV+V0iigorsfNE7XQhgdLHQ4REckEkxUiIhnLm6xDkFKBXSebpA7lqg5XtaHB\n3ItVnK1CRETXgckKEZGMRagCcdNELT4+3QRR9N5SsG3FdQgLUmLxFIPUoRARkYwwWSEikrmlmQbU\nd/SgtM4sdSiX1Wt3YNfJJizNjENIkFLqcIiISEaYrBARydyiKXoEKATsOuWdpWB7yprRZevHndNZ\nAkZERNeHyQoRkcxFhQZhXqoGu081emUp2Lbj9TCoVZibopE6FCIikhkmK0REPmBppgEXTd0429Ql\ndShfY7LYsO98K1ZOi4dSIUgdDhERyQyTFSIiH7B4igGCAK8rBdtR2oh+p4jVLAEjIqJRYLJCROQD\nYiOCMWt8DHZ72TT7rcX1SI9TY7JBLXUoREQkQ0xWiIh8RH6mAeebLahotUgdCgCgstWCE7UdWJ3D\nifVERDQ6TFaIiHzE0syBGSa7vaQUbHtxPRQCsHIaS8CIiGh0mKwQEfmIuMgQTEuMwi4vKAUTRRHb\nSuoxP00LvVoldThERCRTTFaIiHxIfqYBp+o7UdvWLWkcx6rbUdvWg1VcVSEiojFgskJE5EPyM+MA\nSF8KtrW4HiGByuHSNCIiotFgskJE5EOSNKGYEqeWtBTM1u/AztJGLMnQIyw4QLI4iIhI/pisEBH5\nmPxMA47XdKDJ3CvJ+T8/2wpzjx2rclgCRkREY8NkhYjIx+RnDZRefXxamlKwbcV10IYH48Y0rSTn\nJyIi38FkhYjIx6TpIpCmC5ekFKyjuw+fnW3BymnxCFDyLYaIiMaG7yRERD4oP9OAoqo2mCw2j553\n58lG2B0iVrMEjIiIXIDJChGRD1qaaYBTBD450+zR8247Xo+JunBkxKs9el4iIvJNTFaIiHzQlDg1\nkmJCPbqFcY2pG0er27F6egIEQfDYeYmIyHcxWSEi8kGCICA/04CDFUaYe+weOef2knoAwEoOgiQi\nIhdhskJE5KOWZhpgd4jYW+b+UjBRFLGtuB5zU2KQEBXi9vMREZF/YLJCROSjssdFIS5ShV0eKAU7\nb7ShymjFnTnj3H4uIiLyH0xWiIh8lEIhYEmGAfvPt8Jq63frufZWWhAcoMDSwRkvRERErjCmZGX/\n/v1YsmQJFi1ahDfeeMNVMRERkYvkZxpg63fi83MtbjuH3eHEvioLbpuih1oV6LbzEBGR/xl1suJw\nOPDiiy/irbfews6dO7Fjxw6Ul5e7MjYiIhqjmeNjoA0Pcmsp2P7zrei0OXEnZ6sQEZGLjTpZKS0t\nRXJyMhITExEUFITly5dj7969royNiIjGSKkQsDjDgM/PtqDX7nDLObYW10MdrMDNk2LdcnwiIvJf\nAaN9YXNzMwyGv9Um6/V6lJaWuiQoIiJynfxMA945XIO7//MrhAUrXX7849UdWDIxHIFKtkESEZFr\njTpZGamysjJ3n2LEent7vSoeGj1eS9/A6+gZUU4Rt0wIg7HbBosb+uwzdMFYkqLitfQBvCd9B6+l\nb+B1HEOyotfr0dT0txro5uZm6PX6b/1cenr6aE/hcmVlZV4VD40er6Vv4HX0nP/JmOLW4/Na+gZe\nR9/Ba+kb/Ok6Hjt27LJfH/WafVZWFi5evIja2lr09fVh586dyMvLG3WARERERERElxr1ykpAQAB+\n/OMf46GHHoLD4cCaNWswceJEV8ZGRERERER+bEw9KwsWLMCCBQtcFQsREREREdEwbt1CRERERERe\nickKERERERF5JSYrRERERETklZisEBERERGRV2KyQkREREREXonJChEREREReSUmK0RERERE5JWY\nrBARERERkVdiskJERERERF6JyQoREREREXklJitEREREROSVmKwQEREREZFXEkRRFN118GPHjrnr\n0ERERERE5ENmzJjxra+5NVkhIiIiIiIaLZaBERERERGRV2KyQkREREREXilA6gA8Yf/+/fjZz34G\np9OJu+++G4888ojUIdEo5eXlISwsDAqFAkqlElu3bpU6JBqh5557Dl988QU0Gg127NgBAOjo6MAP\nfvAD1NfXIyEhAa+++ioiIyMljpSu5nLX8bXXXsN7772HmJgYAMCmTZuwYMECKcOkEWhsbMTTTz8N\nk8kEQRBwzz334IEHHuB9KTNXuo68L+XHZrNh3bp16Ovrg91ux8KFC/HUU0/5/T3p8z0rDocDS5Ys\nwR/+8Afo9XrcddddeOWVV5CWliZ1aDQKeXl5+OCDD4b/+JJ8HDlyBKGhoXjmmWeGP+T+8pe/RFRU\nFB555BG88cYbMJvN+NGPfiRxpHQ1l7uOr732GkJDQ/Hggw9KHB1dj5aWFrS2tiIjIwMWiwVr1qzB\nb3/7W2zdupX3pYxc6Tru2rWL96XMiKKI7u5uhIWFwW63Y+3atXjmmWfw2Wef+fU96fNlYKWlpUhO\nTkZiYiKCgoKwfPly7N27V+qwiPzOrFmzvvUkaO/evVi1ahUAYNWqVdizZ48UodF1uNx1JHnS6XTI\nyMgAAISHhyMlJQXNzc28L2XmSteR5EcQBISFhQEA+vv74XA4oFar/f6e9Plkpbm5GQaDYfi/9Xo9\nb2KZ27BhA+688068++67UodCY2QymaDT6QAAsbGxMJlMEkdEo/X2229jxYoVeO6552A2m6UOh65T\nXV0dysrKkJ2dzftSxi69jgDvSzlyOBxYuXIlcnNzMWfOHEyaNMnv70mfT1bIt2zZsgUFBQV48803\nsXnzZhw5ckTqkMhFBEGAIAhSh0GjcP/992PPnj0oKCiATqfDL37xC6lDoutgtVqxceNGPP/88wgP\nD//a93hfysc3ryPvS3lSKpUoKCjAvn37cPToURw6dOhr3/fHe9LnkxW9Xo+mpqbh/25uboZer5cw\nIhqLoWun0WiwaNEilJaWShwRjYVGo0FLSwuAgbpr9iLJk1arhVKphEKhwN13342TJ09KHRKNkN1u\nx8aNG7FixQosXrwYAO9LObrcdeR9KW9qtRoLFizAqVOn/P6e9PlkJSsrCxcvXkRtbS36+vqwc+dO\n5OXlSR0WjUJ3dzcsFsvw/y4sLMTEiRMljorGIi8vD9u3bwcAbN++HQsXLpQ4IhqNoTdRANizZw/v\nS5kQRREvvPACUlJSsGHDhuGv876UlytdR96X8tPW1obOzk4AQG9vLw4ePIj09HS/vyd9fjcwANi3\nbx9+/vOfw+FwYM2aNXj00UelDolGoba2Ft///vcBDNR03n777byWMrJp0yYUFRWhvb0dGo0Gjz/+\nOG677TY8+eSTaGxsRHx8PF599VVERUVJHSpdxeWuY1FREc6ePQsASEhIwIsvvjhcX03e6+jRo1i3\nbh0mTZoEhWLg2eWmTZswdepU3pcycqXruGPHDt6XMnP27Fk8++yzcDqdEEURK1aswCOPPIL29na/\nvif9IlkhIiIiIiL58fkyMCIiIiIikicmK0RERERE5JWYrBARERERkVdiskJERERERF6JyQoRERER\nEXklJitEREREROSVmKwQEREREZFXYrJCRERERERe6f8AyC0umf/fxSsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d8ea41050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#custom_scaling\n",
    "mpg_matrix=mpg.values.reshape(-1,1)\n",
    "scaled=pp.MinMaxScaler(feature_range=(0,10))\n",
    "scaled_mpg=scaled.fit_transform(mpg_matrix)\n",
    "plt.plot(scaled_mpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3d8f834890>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cl1xyCTZt2gQA2LRpE1atWhXfkxIREU2CyxffQEipWUw6pDNrhZLIa/scEEVg7YICAECe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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d8d5939d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#standardize\n",
    "standardised_mpg=scale(mpg,axis=0,with_mean=False, with_std=False)\n",
    "plt.plot(standardised_mpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3d8d736550>]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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El+T2+pHHys2AYqsRgVAELs66kZ1wRER1czdmJmBL2lCWTc3H5geXw2rS4fZf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\n2zMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d8ebea910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "std_mpg=scale(standardised_mpg)#making mean=0 and var=1\n",
    "plt.plot(std_mpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}