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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "df = pd.read_csv('/Users/danielforsyth/Desktop/salaries.csv')\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Rk</th>\n",
        "      <th>Player</th>\n",
        "      <th>Tm</th>\n",
        "      <th>Cap Hit</th>\n",
        "      <th>2013-14</th>\n",
        "      <th>2014-15</th>\n",
        "      <th>2015-16</th>\n",
        "      <th>2016-17</th>\n",
        "      <th>2017-18</th>\n",
        "      <th>Signed Using</th>\n",
        "      <th>Guaranteed</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td>       Kobe Bryant</td>\n",
        "      <td> LAL</td>\n",
        "      <td> 30453805</td>\n",
        "      <td> 30453805</td>\n",
        "      <td> 23500000</td>\n",
        "      <td> 25000000</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Bird Rights</td>\n",
        "      <td> 78953805</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>     Dirk Nowitzki</td>\n",
        "      <td> DAL</td>\n",
        "      <td> 22721381</td>\n",
        "      <td> 22721381</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Bird Rights</td>\n",
        "      <td> 22721381</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> Amar'e Stoudemire</td>\n",
        "      <td> NYK</td>\n",
        "      <td> 21679893</td>\n",
        "      <td> 21679893</td>\n",
        "      <td> 23410988</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Bird Rights</td>\n",
        "      <td> 45090881</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td>       Joe Johnson</td>\n",
        "      <td> BRK</td>\n",
        "      <td> 21466718</td>\n",
        "      <td> 21466718</td>\n",
        "      <td> 23180790</td>\n",
        "      <td> 24894863</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Bird Rights</td>\n",
        "      <td> 69542371</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td>   Carmelo Anthony</td>\n",
        "      <td> NYK</td>\n",
        "      <td> 21388953</td>\n",
        "      <td> 21388953</td>\n",
        "      <td> 23333405</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Bird Rights</td>\n",
        "      <td> 44722359</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 11 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 311,
       "text": [
        "   Rk             Player   Tm   Cap Hit   2013-14   2014-15   2015-16  \\\n",
        "0   1        Kobe Bryant  LAL  30453805  30453805  23500000  25000000   \n",
        "1   2      Dirk Nowitzki  DAL  22721381  22721381       NaN       NaN   \n",
        "2   3  Amar'e Stoudemire  NYK  21679893  21679893  23410988       NaN   \n",
        "3   4        Joe Johnson  BRK  21466718  21466718  23180790  24894863   \n",
        "4   5    Carmelo Anthony  NYK  21388953  21388953  23333405       NaN   \n",
        "\n",
        "   2016-17  2017-18 Signed Using  Guaranteed  \n",
        "0      NaN      NaN  Bird Rights    78953805  \n",
        "1      NaN      NaN  Bird Rights    22721381  \n",
        "2      NaN      NaN  Bird Rights    45090881  \n",
        "3      NaN      NaN  Bird Rights    69542371  \n",
        "4      NaN      NaN  Bird Rights    44722359  \n",
        "\n",
        "[5 rows x 11 columns]"
       ]
      }
     ],
     "prompt_number": 311
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df[['Player','Cap Hit']]\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Player</th>\n",
        "      <th>Cap Hit</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>       Kobe Bryant</td>\n",
        "      <td> 30453805</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>     Dirk Nowitzki</td>\n",
        "      <td> 22721381</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> Amar'e Stoudemire</td>\n",
        "      <td> 21679893</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>       Joe Johnson</td>\n",
        "      <td> 21466718</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>   Carmelo Anthony</td>\n",
        "      <td> 21388953</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 313,
       "text": [
        "              Player   Cap Hit\n",
        "0        Kobe Bryant  30453805\n",
        "1      Dirk Nowitzki  22721381\n",
        "2  Amar'e Stoudemire  21679893\n",
        "3        Joe Johnson  21466718\n",
        "4    Carmelo Anthony  21388953\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 313
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "per = pd.read_csv('/Users/danielforsyth/Desktop/per.csv')\n",
      "per.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Rk</th>\n",
        "      <th>Player</th>\n",
        "      <th>Pos</th>\n",
        "      <th>Age</th>\n",
        "      <th>Tm</th>\n",
        "      <th>G</th>\n",
        "      <th>MP</th>\n",
        "      <th>PER</th>\n",
        "      <th>TS%</th>\n",
        "      <th>eFG%</th>\n",
        "      <th>FTr</th>\n",
        "      <th>3PAr</th>\n",
        "      <th>ORB%</th>\n",
        "      <th>DRB%</th>\n",
        "      <th>TRB%</th>\n",
        "      <th>AST%</th>\n",
        "      <th>STL%</th>\n",
        "      <th>BLK%</th>\n",
        "      <th>TOV%</th>\n",
        "      <th>USG%</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> DeAndre Liggins</td>\n",
        "      <td> SG</td>\n",
        "      <td> 25</td>\n",
        "      <td> MIA</td>\n",
        "      <td>  1</td>\n",
        "      <td>    1</td>\n",
        "      <td> 128.3</td>\n",
        "      <td> 1.000</td>\n",
        "      <td> 1.000</td>\n",
        "      <td> 0.000</td>\n",
        "      <td> 0.000</td>\n",
        "      <td> 100.0</td>\n",
        "      <td>  0.0</td>\n",
        "      <td> 62.5</td>\n",
        "      <td>  0.0</td>\n",
        "      <td> 0.0</td>\n",
        "      <td> 0.0</td>\n",
        "      <td>  0.0</td>\n",
        "      <td> 47.5</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>   Tony Mitchell</td>\n",
        "      <td> SF</td>\n",
        "      <td> 24</td>\n",
        "      <td> MIL</td>\n",
        "      <td>  3</td>\n",
        "      <td>   10</td>\n",
        "      <td>  31.0</td>\n",
        "      <td> 0.600</td>\n",
        "      <td> 0.600</td>\n",
        "      <td> 0.000</td>\n",
        "      <td> 0.200</td>\n",
        "      <td>  11.0</td>\n",
        "      <td>  0.0</td>\n",
        "      <td>  5.7</td>\n",
        "      <td> 22.7</td>\n",
        "      <td> 5.2</td>\n",
        "      <td> 0.0</td>\n",
        "      <td>  0.0</td>\n",
        "      <td> 22.6</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td>    Kevin Durant</td>\n",
        "      <td> SF</td>\n",
        "      <td> 25</td>\n",
        "      <td> OKC</td>\n",
        "      <td> 69</td>\n",
        "      <td> 2654</td>\n",
        "      <td>  30.4</td>\n",
        "      <td> 0.639</td>\n",
        "      <td> 0.564</td>\n",
        "      <td> 0.488</td>\n",
        "      <td> 0.285</td>\n",
        "      <td>   2.2</td>\n",
        "      <td> 19.4</td>\n",
        "      <td> 11.2</td>\n",
        "      <td> 27.2</td>\n",
        "      <td> 1.7</td>\n",
        "      <td> 1.6</td>\n",
        "      <td> 12.4</td>\n",
        "      <td> 32.9</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td>    LeBron James</td>\n",
        "      <td> PF</td>\n",
        "      <td> 29</td>\n",
        "      <td> MIA</td>\n",
        "      <td> 66</td>\n",
        "      <td> 2489</td>\n",
        "      <td>  29.1</td>\n",
        "      <td> 0.649</td>\n",
        "      <td> 0.612</td>\n",
        "      <td> 0.420</td>\n",
        "      <td> 0.221</td>\n",
        "      <td>   3.6</td>\n",
        "      <td> 18.6</td>\n",
        "      <td> 11.4</td>\n",
        "      <td> 31.7</td>\n",
        "      <td> 2.3</td>\n",
        "      <td> 0.8</td>\n",
        "      <td> 14.4</td>\n",
        "      <td> 30.4</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td>      Kevin Love</td>\n",
        "      <td> PF</td>\n",
        "      <td> 25</td>\n",
        "      <td> MIN</td>\n",
        "      <td> 66</td>\n",
        "      <td> 2408</td>\n",
        "      <td>  27.9</td>\n",
        "      <td> 0.594</td>\n",
        "      <td> 0.528</td>\n",
        "      <td> 0.449</td>\n",
        "      <td> 0.350</td>\n",
        "      <td>   8.6</td>\n",
        "      <td> 29.8</td>\n",
        "      <td> 18.8</td>\n",
        "      <td> 20.9</td>\n",
        "      <td> 1.1</td>\n",
        "      <td> 1.0</td>\n",
        "      <td>  9.4</td>\n",
        "      <td> 28.7</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 26 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 282,
       "text": [
        "   Rk           Player Pos  Age   Tm   G    MP    PER    TS%   eFG%    FTr  \\\n",
        "0   1  DeAndre Liggins  SG   25  MIA   1     1  128.3  1.000  1.000  0.000   \n",
        "1   2    Tony Mitchell  SF   24  MIL   3    10   31.0  0.600  0.600  0.000   \n",
        "2   3     Kevin Durant  SF   25  OKC  69  2654   30.4  0.639  0.564  0.488   \n",
        "3   4     LeBron James  PF   29  MIA  66  2489   29.1  0.649  0.612  0.420   \n",
        "4   5       Kevin Love  PF   25  MIN  66  2408   27.9  0.594  0.528  0.449   \n",
        "\n",
        "    3PAr   ORB%  DRB%  TRB%  AST%  STL%  BLK%  TOV%  USG%      \n",
        "0  0.000  100.0   0.0  62.5   0.0   0.0   0.0   0.0  47.5 ...  \n",
        "1  0.200   11.0   0.0   5.7  22.7   5.2   0.0   0.0  22.6 ...  \n",
        "2  0.285    2.2  19.4  11.2  27.2   1.7   1.6  12.4  32.9 ...  \n",
        "3  0.221    3.6  18.6  11.4  31.7   2.3   0.8  14.4  30.4 ...  \n",
        "4  0.350    8.6  29.8  18.8  20.9   1.1   1.0   9.4  28.7 ...  \n",
        "\n",
        "[5 rows x 26 columns]"
       ]
      }
     ],
     "prompt_number": 282
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "per['MPG'] = per['MP'] / per['G']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 283
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "per = per[['Player','PER','MPG']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 285
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "per.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Player</th>\n",
        "      <th>PER</th>\n",
        "      <th>MPG</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  Kevin Durant</td>\n",
        "      <td> 30.4</td>\n",
        "      <td> 38.463768</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  LeBron James</td>\n",
        "      <td> 29.1</td>\n",
        "      <td> 37.712121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>    Kevin Love</td>\n",
        "      <td> 27.9</td>\n",
        "      <td> 36.484848</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> Anthony Davis</td>\n",
        "      <td> 27.2</td>\n",
        "      <td> 36.245902</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>    Chris Paul</td>\n",
        "      <td> 26.1</td>\n",
        "      <td> 34.826923</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 286,
       "text": [
        "          Player   PER        MPG\n",
        "2   Kevin Durant  30.4  38.463768\n",
        "3   LeBron James  29.1  37.712121\n",
        "4     Kevin Love  27.9  36.484848\n",
        "5  Anthony Davis  27.2  36.245902\n",
        "6     Chris Paul  26.1  34.826923\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 286
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "final = pd.merge(df, per, on='Player', how='outer')\n",
      "final.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Rk</th>\n",
        "      <th>Player</th>\n",
        "      <th>Cap Hit</th>\n",
        "      <th>PER</th>\n",
        "      <th>MPG</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td>       Kobe Bryant</td>\n",
        "      <td> 30453805</td>\n",
        "      <td> 10.9</td>\n",
        "      <td> 29.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>     Dirk Nowitzki</td>\n",
        "      <td> 22721381</td>\n",
        "      <td> 23.2</td>\n",
        "      <td> 32.318841</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> Amar'e Stoudemire</td>\n",
        "      <td> 21679893</td>\n",
        "      <td> 18.6</td>\n",
        "      <td> 21.296296</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td>       Joe Johnson</td>\n",
        "      <td> 21466718</td>\n",
        "      <td> 15.1</td>\n",
        "      <td> 32.895522</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td>   Carmelo Anthony</td>\n",
        "      <td> 21388953</td>\n",
        "      <td> 24.9</td>\n",
        "      <td> 38.820896</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 287,
       "text": [
        "   Rk             Player   Cap Hit   PER        MPG\n",
        "0   1        Kobe Bryant  30453805  10.9  29.500000\n",
        "1   2      Dirk Nowitzki  22721381  23.2  32.318841\n",
        "2   3  Amar'e Stoudemire  21679893  18.6  21.296296\n",
        "3   4        Joe Johnson  21466718  15.1  32.895522\n",
        "4   5    Carmelo Anthony  21388953  24.9  38.820896\n",
        "\n",
        "[5 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 287
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "final = final[final.MPG >6.09]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 288
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "final.dropna()\n",
      "final.head()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Player</th>\n",
        "      <th>Cap Hit</th>\n",
        "      <th>PER</th>\n",
        "      <th>MPG</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>       Kobe Bryant</td>\n",
        "      <td> 30453805</td>\n",
        "      <td> 10.9</td>\n",
        "      <td> 29.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>     Dirk Nowitzki</td>\n",
        "      <td> 22721381</td>\n",
        "      <td> 23.2</td>\n",
        "      <td> 32.318841</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> Amar'e Stoudemire</td>\n",
        "      <td> 21679893</td>\n",
        "      <td> 18.6</td>\n",
        "      <td> 21.296296</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>       Joe Johnson</td>\n",
        "      <td> 21466718</td>\n",
        "      <td> 15.1</td>\n",
        "      <td> 32.895522</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>   Carmelo Anthony</td>\n",
        "      <td> 21388953</td>\n",
        "      <td> 24.9</td>\n",
        "      <td> 38.820896</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 290,
       "text": [
        "              Player   Cap Hit   PER        MPG\n",
        "0        Kobe Bryant  30453805  10.9  29.500000\n",
        "1      Dirk Nowitzki  22721381  23.2  32.318841\n",
        "2  Amar'e Stoudemire  21679893  18.6  21.296296\n",
        "3        Joe Johnson  21466718  15.1  32.895522\n",
        "4    Carmelo Anthony  21388953  24.9  38.820896\n",
        "\n",
        "[5 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 290
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.options.display.mpl_style = 'default'\n",
      "from matplotlib import rcParams\n",
      "rcParams['figure.figsize'] = (10, 6)\n",
      "rcParams['figure.dpi'] = 150"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 314
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(final['PER'],final['Cap Hit'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 315,
       "text": [
        "<matplotlib.collections.PathCollection at 0x112302450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Efezx8UqNt/XG6QQKCkRYLEqVwn7PHhE332yDxyPgq6+iEB3twLRpodtklai+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\n0gUAAOABSxcAAIAHLF0AAAAefAFpkWByIGM7VwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11178f110>"
       ]
      }
     ],
     "prompt_number": 315
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "`smaller_frame=final[['Cap Hit', 'PER', 'MPG']]\n",
      "from pandas.tools.plotting import scatter_matrix\n",
      "axeslist=scatter_matrix(smaller_frame, alpha=0.8, figsize=(12, 12), diagonal=\"kde\")\n",
      "for ax in axeslist.flatten():\n",
      "    ax.grid(False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ztoE82OPFVCSJx1a4YJijH/rdGqx6PL6iCnZDduf7vPf+nd/5HVgslntub2tr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AeUJswAo+ki370yjPveEA1eG7uqHeRVfUpwfKDezd5aJ52xHM/cHOBN2wLULNJ1azXYtNHGegn8\n4jmVV7rj7Ktz0eJf3X07LQq/fbQBRQZLGcVs5ZyzrmiWgmawfRnC+nKo91j5raMNi/5ctqCV7RUt\nEGx2OiLZRfkvT2d7lYNXuuIVPCKBQLCemb4a1Ra084cPt1DtNKPIEm/aFpjy9/Ex2aTIjKaLvNId\n52iTp6yAuSuapagZyyrkWwzrI2rcxOyodvI/3tTG7trKePsahkEkU0BfQGIxTshjpcZVmUI2TTf4\n1vkhvvB6H7Hs+ugINJmrQ2n+9IUuOiLro72nQLDe6YhmaVvGytf2KiHJEAjWM5peihkWkmUuFYsi\n0xqw47TOzM9eH07zp893cmvsGXFPyMV/e7S1rEm6pht849wgX3ijf4YmeqXYtBnm9YJJLs8QvFyu\nDWf4/Ot9vOdQPXvrVrc4T5El3r6vmoKqC6mEQLDBMQyDzmVKMho8VmI5lWRexT3LgCkQCNaW9qE0\nXzrdz28eaVibplyTasnsZgW7ubwVYEWWeGp/DQVt9eIN8QS7y7CbZeo9tjWTHay0FGM57K5x8l8f\nbhGSDIGgDEYyRcyKhM++9MpyRZbYGrBzczTLvfUr1/lLIBAsDbtZKcUM5tUXHOyoXt6YvNrxxl0j\nyRhJF2gfSm96m7EWv53ff6iZLWXqDguqTvtQmkhm/UkoVgIRLAsE5bFc/fI424QsQyBYt2wJlmKG\nxZoO6IbBjZEMA8n8svY/25gcyxa5Mpia4qSxHrhrAuaz/Sk+83IPvbGl+/jlVG3dfUErTVcsx2de\n7uF8v7BnEwgEd+iI5GjzL12OMc72KrsImAWCu4zBZIFPv9TNMzcjFd/2lcE0f/VKLx2RmfFcKq/O\n6fG80qxqwPzMM8/w3HPPcenSJQC+//3v86Mf/YhIZPknvNFj4ed3V+OyLi2DqOkG37k8zJfeCJMu\nVCZoHkkXONkRYzhVmHe/w6nCil4AybzKK11xemaZTNS5LPz6vXVsr1pdSzuBQLC+6YrlKmJ1WfJi\nFoW2q0EsW+SlzhjhxPKyfoLKYBil8f1uXPn22028+756jjV7K7bN3liOV7riNHit/NrBOkLuqU4Z\nPbEcf/J8FxfH+i8UNZ2zfQluDK/OhHzVAuZMJsO+fft49NFHSSaTXLt2jQceeIAnnniCCxcuLHv7\nvfE837s6TDK/tGBXkko2a26rwlK9s1N5ld54bqLatDee5+vnB+iaJ+t9Ppzku1eGaR+8k+HtjeX4\nyfXReQPtxTCUKvDVs2Guz3JRuW0mHmjx0ehbfiZJIBDcPfTGcjR5l1+Q3OyzMZoubLrVu7WgP1Hg\na+cGhBPQOuF2JMsfP9vBpfDdt4JrMyscbvKwJViejrhnPK5Jzx3XXB/J8NWzYVTN4ESrD/9YZ76B\nZJ5otogig9typ8FJIqdOdBbsW4XOzatW9OdwOHA4HKiqSj6fJxqNsnPnTgCs1uU/lO9tcNPos9Hk\nXVrgJ0sSb9lZBcz0ESyXN3qTfPvyEB8Z0wNtD9p5/wONNM9zTB2RLM/cjPJAy51ZWl8iz/++Mkyj\n10b1HF6E2aJGbzxPg8eCwzL/19jktfHB401UO5fXFlIgEGwODMMoPV8qEDArskSTz0ZnNMfutajC\n30S0BWx84IEm6j3rr+nDZsRhVthT68ItXKPoiuX41oVBGr1Wqp2zX58HQi5CHitNkxJ4kUyBT77Y\nzd5aJ//pUD2//1Az5rGeEEGnhXcfCnEhnOLWaJaGJcZ/5bLqGuZnnnmG48ePT3lNqkCL6iqnhd01\nzokGHLmiNq8XcSKnciGcJJlTJ15TZGnJwTJAo9fKm7cF8IzZJzmtJvYucLO8aWuAP/65rVPsXPbV\nOvmDh1rYFpx7ObR9KM2nTnZzbZ6liI5Ilr842U04mWd3jZOqOS7SO+/P8Jcv99Ad3VjZiZFUgYvh\nJNeH0/SuwixTILjbSeQ1JAm8FRrotwbt3BZZzxXHblbYU+tclrOJoHKEPFZ++2jDirg5qLpBXl05\nqUdXtBQ/3B6d/b4dSRW4NJAiN7ZyNJIujcO5SStJ6bzKuf4EkUwRkwyPtPmxKnPHWMFpcRyUJh2P\nbw9yIFRy2bk0mOZzr/Yxmi4ZFdwTcvPIFj8HV8GFZ1UD5hdeeIGjR49isVjw+/0MDQ0BoGmVXaoL\nJ/N88mQP5/qSc77n+kiGz77Wx83RymlftlU5+Pk91RPLCOXgd5hp8dsn2khCKdDeErTP232v3mPl\nF/fXUD+Ph3OmqNETz5EpLnxTXRlM8fen+gk6TOQWuAmzRW3KRGOtuTiQ4i9f7uVUT4LvXRlZ68MR\nCDY8vbEcDR5rRZIZAG0Bu5AJCAQV5MfXRvji6/0Vq7maTk7Vx+KH2bd/cSDF37zaS0+8pJe/NJDm\nb1/rozt2Rz9/ZSjN//z3Tl7viVPvtlLvtS56MmczK7x5e4ADYwFxPKfSGctS1EtxiiJLtAbsq+LF\nvGrrBO3t7fT19aHrOoVCgccff5ynn34aSZI4ceJEZXdmlCxP5iuja/Xb+I37QhUpalkLat1Wat3z\nL5furnHyscda8ZdxgeoGWBSJ+xo8886GDcPg25eG6E8U+N1jDeuiGcGuGifvO1KP0ywvKE8RCAQL\n05vI01gBOcY4bQE7JztjFdueYHMTyRSQpeV5hG90NAMMWLEOfTuqHHzssVZ8ttnP8c4aJ+8+FKJ+\nrDBvR7WDd98XIjRJDlTrsvDUvmqqnBYM4Kc3RvHaTAQXWO2ejweavRwMufHZV3+sX7U97tq1i127\ndk157cknn1z2dgeTeV7tjnPPWLq+zm3BaVF4bIsfr21ux4wqp2WKRKE/kedUT5yjjR7qV1gHUw6X\nB1KEkwUeaPHgXGIQKEsSQUd5F+beWidbAq04FvApliSJGpcVWZImhPcrzXCqQFHXqffM/r2EPNaK\ndksUCDY7vfE8jRV8Drb5bXRESgXRlcpaCzY+RU2nL5Gn2mGetXXybKQLGn/7ah8+u4nfub9xSeNQ\n+1CarmiO463eiiZ9LoSTjGSKHG/2YiuzY91S+bmdQXTDwGpamf1IC8QP9R7rlBXu6b8DNPvtPOWy\nYFFkhtMFQm4bjgUapPTFc1hN8qwS0o5IlgvhJEGHmYDDzJ7a1e1mvOF9mCMZlX+7EaEzmuXPX+ji\nymCaRF7lWxcGuT6SnbBrU3UDdR7rttF0kX+7EWE4vT4aeFwaTPPDayNkCqtjRyNJ0oLB8jhv3h7g\nlw/Ult3Ccrn888VBPvNSD9FV6Bc/3zUiEGwWemO5imaYfXYzVpO0bp6vgvVBRyTLnz/fxYWB8l0k\nLLLE0SYP94TczCOHnZdrIxl+dH2E1BJctQzDmNMG9nRvkh9dG51TxlAJNN3AMAzMijxvsFyOVe1q\njHd2s4IiS9S5rfzeicZ5g9xIpshfvNTDty8Nz/r3cDLPD9pH6YrluDqUXqlDnhPl4x//+MdXfa+L\noKOjg1AoNOfffXYTB+pd1DjNeO1mWvw2qibpgi+Ek2i6wT+cDhPNqCTy2qyV3367iYMhF60B+6pl\nTuej0WPlSJOHujlkF7FskStDaexmecpMtjOSZSBZIOgwl5XJuT6cJpbTCCxCdw2VKdQsF6tJptVv\np9VvX1ZR5kL0xHL83Wt9BB3mBQskNwPhcHjee09w9/KP5wZ4y67gop8L83GmL0mNy1LRzPXdyma5\n9wzAZTWxJeAoe4ldkSW2BB00+2yLGodSeRVFlpAliZDbwuGx1eSeWI7eeI6gwzxRSxRO5rk9msVr\nM004MoxzsjPGj66NsqPaPiNgbfXbONLooca1MiueiVyRL58Ok1X1WeWkumFwbTjN5YE0P7g2wvag\nY87E1o2RDF841U+Tb/G64qWy0PclSxJui4ntVQ5qZnEIC9hNHGnysjVgZ0e1c9FZ/EimyNWhNE6z\ngnWeTHc4HGbLli0zj29Re1uHmBWZZp8dWZbxWE20D6f54hthXuyI8YP2ETQdRjNFmn028ppOVp19\n5mcxyTT77VhNMlcH01waSC1LG3RjOMO5/uSSG5L4HOY5JQgAt0ezfP71fm5NqmA1DIN/uTTEF97o\nJ5pduCgvlVP5ypkw3zg/sKLVtsvlnpCbE62+iSLIm6MZzvYlKm4Gr+oGsZx6V5rMCwTloukG4US+\n4hZNbX77nBX3gs2BphtcCCe5NlzKDlY5LTyxM0hLBTpKzkd/Is+fv9DNmTEjAK/NPHF9/+R6hL9+\nuYdz/UkGx9o8Xwyn+NypPvpnaQCTLmhEskVmGzL9DvOKygPVsXhmrsx4JFPky2cG6E/kSsc4TwxT\n0HTieZWCNvU9umFwMZwqK4ObzKm82hWnL54jU1A51RNflsuW1STzYJuPfXWzZ6GdVhNNPhshr21J\nQf7Nsbjp9hKP8a6pkOqMZPnauQHetrsKl1XhSKMHiyIhAZ94sYd37K3mgRbvvM4TAHlV59uXh8mp\nGn/0SAsui0J/Io/PZipbYwXww2ujdMezfOyx1rJ1xIuhLWDnPYdCbAncmWVKksQ79tWQK2plzdZd\nNhO/fl8IRZKwLnBe1opwIs+lgRT7Q66JbPtPr0e4NpLmvz/WNqdP9VJoC9j56KMtuEThoGATM5Qu\n4LWZsFX4mdAWsPNad7yi2xRsLFJ5la+fGyDosLDtIUdFVgz7EzncFtO89q2KBE6LjHkWDcfj2/3s\nqnHwnctDvHVXFbVuK/vrXFQ7zbO6UL1pa4AHW3241qDgPeAw86ETTViU2e9Nv93Muw7W4bLIPLm7\net5zsqfGyUcfacE7LfBMFTS+eWEAl8XEtqB9RoZ9MoOpAl85G+YX99eyJWDjy6fDPLEjSPNY9juc\nyOOwyHjnKBzsT+S5PJDinpBrQRODSrAtWIqb2gJLm6DdNZHBrhon7z/WSIvfxitdcT7/ej9/9EgL\nfruZ3ztRatpRzgVuNcn8+n216Dq4rSY6o1k++WI379xXw8Nb/GUfzzv3V5NT9RUJlqE0kz3imNmS\ncnIADSU/6uc7YgQdZg43ema8f2d1ZRsJ9MZzKJJUsVl2OJnnO1eGqXFZJgLmt+2tIlMIULUCjVjm\nurEFgs1CX7yyDhnjbAnY+fq5gYpvV7Bx8NrN/PbRBiyKXJFguS+e489e6OKJHVU8sTM45/tq3VY+\nfKIZ8yyTwGa/nSqnGZ/NRKOvdN3PV0huMckLJt5WkvniGEWW2DtHdnY6kiTNCJYBPFYTv3mkAZMs\nzRssAzT5So1yal1mPDYTHzzeRHBMxjWUzPPHz3TyYJuPXz5QO+vnB8bG91q3Zc6A+eZIhrP9SR7b\n6l+2VDLgMBOYJW4ql7smYPbZzRMp+vqxBiIui4LVJBNyW+iJ5bCb5LKyxM2+O0Gnx2riTVv98/od\nz8ZKd5wpl5xq8OLtGFuDtlkD5lujGUyytGh7vVxR4+Zoljr3HbeRZF7ls6/14bWa+PCDTZgWuNnK\nYXeNk4881EzDlGrc9XFuBYK7kUo7ZIzT5LMymCqQV/V1u6IlWHnKbaVcDi6rwpu3BWgtQ9IxW7A8\njsNiKjvQXE+MpAoMpgtsDdgr6srRNi3x1j6YJqNq7KxyTImhrCaZPbV3km6TG7CphsGeWieeedzK\nZhvfpzOUKnCyM8bhRg9Va9wo9K4JmCezt9bF3kmVmNeGM3z5TJjfvb9hwn6uXAIOM7+wt2bOv6fy\nKu1DGRp8VkIrvKRQVHUyRQ3dKBU7ThbQj5uXO6c5XfjsJv7Lg02zLkWlcipfOh3GZpL5w4dbyh7E\nDMPgfDjFrUiGaNbGQ22lgNlhVnj7nmrMilSRYBlKFbZbK/iAFQgE89Mbr6xDxjhmRabRa6UrlmPH\nCnQ+E2w+vDYzT+6uXvX9arpBIqcuqknZSnBhIMU/nh3gAw80ct8sCbHZiGWLOC3KgtljKEkqumI5\nXu2KcWM0y4eON7KzpryJRdBh4Uije4Ztn6YbJPIqfru5rPH93gY3rQE7IffaF+LfNdP87miOk52x\nWTvQtQXs/Ma9dTRNypokckUimcKy99uXyPPF0/1cn9Siujee48WOKNHM4i2UYm9y3/oAACAASURB\nVNnirJ+LZIqcG0jy/zzTwQ/aR2iftL+8qvHVMwN87ezsxXvVLsusAnmnVeFXDtTyzn01i8r4JHIq\n3740zPXhLDsnDXyKLHGo0bPoSYlAIFg/9MbzszoJVQLR8U+wXLqjWV7siJIoo7B9pbgylOb/e66T\nmyOV6xS8FNoCNh7b6ud8OEmxjGL1nliOP3mukwvh8mz8rgyl+cqZMA+0eHnXwboprl2j6QLP347O\nWhgJpezz4SYvO2umpoUvDqT4k+c6uR25c+6Gknmyc1jx2c0K9R4rI+ki6fzU7zydVxlOLT+OK5e7\nJsN8YyTDty8PUf9Qywyhe43LMsWiRDcMvnl+iIFUng8/2Lws4/Jmn43fvb+B+kkXUlc0xzfOD/Kh\n4xZ64zmGyzAyPx9OEskU6YnlGEoV+cDxxgk7mHRB4+9O9SFLcDDkxmaWsZnuZIxlSaLWZUaWJRYj\nC5MkaUomvly8djO/eSSEWZGpWQWhvkAgWD364vkpyYVK0ua3c1sEzIJlcCuS458vDlLvtuJZg25v\nADaTTI3LsubSoraAA1WnLL0xgFmR2F/noj+RxzaYWnD8319XsuzdXuWYEb8MpAp868Ig/+m+0JyS\n1XRB5dWuBHUey8S+7GaZGqdlonCxN5bjEy928Qt7anh06+x1YgPJPH/+Qhdv2hLgLburJl5/9naM\n525H+KOHW1alaPCuCZjvbXDT7LPR5Ltz0iLZIj9uH2V/yDXFpkSWJA43uhlMWTFJpWWHvKrTGlh8\nm2y7WZmRUd1X5+RDJ5po9dv55vkBzodThNwWGj22OatWX+mMc204zXsP15PMa1O8oM2KxKEGNxZF\n5kSrF1mSpsgxzIrM2/fNLRtZCbZXuFhQIBCsPXlVJ5otzuqBWglaAzZOX0ysyLYFlUcfsyWT11F3\nxoMhFw0eK03z6JYHk3mSeY0tQTtDyQI/uj7Kg20+tlVI3re9ysFHHmpeF10rty9C3lTntvIz2wL8\nyfNdZIr6ggHz9GTjZLYE7HzoRNNEslDTDW6NZnGYZfKaRpPXTrqg8fS1Ee5v8k7sa2e1kx1Vjolz\n57Ao3N/ko9Y1t7zFYVY41uSdKMpM5VV64jla/FYeaPauWhO1uyZg9tpMWE3ylFlWrqjxRl8Cv8OM\nZhhsDdhxWU2c7IhxeTDNW3YF+JtX+9he5eDZW1F+60g9bUH7gic/XdCwmmRMskRR1VENY8pnvDbz\nhNvCk7urORBy8bnX+vjVg3UcbZpZoZktahxr9rA1aKcvnuXVniT3NrgZ36RFkXnz9rkrgNcrqm6Q\nV/UZumqBQLA+6U/kqXNbV6xBUEmSkVuRbQsqS17V+e6VYawmmbfuqlrRplFzMT6+pgslq1STLON3\nmBfUDj97K8pLXTH++2NtpIsaZ/qS7K5xsq2Cw2glguVMQeX6SJYGjxVJgp54nh1B+6IsbKEUQ8hl\n2MOmC6UmZR8+0YzdvLzjt5uVKS5bfYkcXzndx84aF+FEgaf2VbG1yskfPtSMY1pMNfncBRxmfuXg\n7C4a43hsJn7xnlq6o1muDqXJqxp/d6qf3zpSzzv3z//ZSnLXaJjP9if5xPNdE3qa0XSBoMPCf3u0\nlaDDxOde66MrWnpQpwoqI+kCRc1gNFPEZzPx+PYA3zg/wLWhNK/3xGfV00QyRZ67FeF7V4Y51RPn\n2lCa525H+ccz4Vm101C6GBq8Np7aV0OLb+aMOJkrOUtcGEhxMZykJeDgnftrsC3Qb32l0Q2DiwMp\n2pfRfvK17jifOtnNUHL1NEYCgWDp9K6Qpdw4VQ4zqm6sSpt7wfLQDYPRdJFYVp3INK82P7g2wmde\n6uEfzw7walf5KxNHmzz8xn0h/HYTWwN23nVvHef6kxWpW6okPbE8f3eqj7P9SV7vSfCXL/Vwtj+1\nqIZn2YLGl98I81JnjMQccQjASLrAp092c6YvwSvdcb5zeWRO3XCuqPFGb4LORTT4CDrMvGNfDUXN\nQDV0PLZx1zIbvjKLI6OZ4pzHBPCdyyN84fU+FFni7XurV9xoYTp3TYbZLEsEHSZkqSRs/9TJbn52\nR5CQ20Kr3857D9cTdJp59maE7VUOTrT4cNtMfPTRFpwWEz+6PorFpKBj8A9vhHnvkRCyLHMg5JpY\njuqO5fj6+UEe2+LHJMk83T5CRyTHz+4IIM8z+65yWiacJKYjSWCSIKfqPNDqY2e1c01m8tNJFTS+\neb5kXv4HC5iXz4VZlrCblEXpqgUCwdqxUg4Z40iSRGvARmckh79BeJ6vZ+xmhf90KIQksaTnfyWw\nmhQcYyuUi5ELbwk62DIpm1zUSqud07PCV4fSRDNF7mtwT2h086rOmb4kPruJ3TUrKz1s9Fp53+F6\ncqpObzzHm7cHuDCQYG+dE3+5newkaPbb+LcbEbpjOd59KDRr9luWSt+pSZbwWGVUXWauoXk0U+RL\nb/TzSJuf1lksZ0dSBc6FU+yqcUxYUDotJu5r9LKn1oVmzHTsWoiRdIFPvdjD/c0efn7P7M4nP7+n\nioKq80Zfgle74+xbQg3WcrgrAuaCpqPpBjuqnVgUGVky2F7loKgZPHc7yvsfaKLaZaE3mqUnnseq\nyBNWJuOzoCd2BHlsi49wosCTe6oYThU5G05hkiQGU3kebPXhtip88IFGGjzWsRaYFgqaTrPPNu8D\npT+R54WOGMebPURzKn3xPI+0+XBaTbisJt65v4ZnbkQIOswrGiyHk3l03SjLI3rcvFyRl/6wPNzk\n4d5697z+lwKBYP3QG8/P2Za2UrT57XREs9zbINx01juONZbTPb49wM9s8zOSLpBTDXTDWJKe+nir\nl/ubPDPGopc7Y1wZSrOz+k5RWyqv8i+XBtkWdOCyyNR7bPOOyx2RLK/3JHhki2/RhWdOq4lDjR5G\nUgUCDjMOk4xqGOUHy5SC4IfafGSLOkGnGUmSKGo63dEcVa478tCAw8IHHmgkq2r0xQscCLmmFPLd\nGElzIZzm0S1+6txWfu9EE945aq6G0gW+fXmId1tD6LrBy90JHm7zUe+xLtoPOq/qvNgRw2WROdjg\nmuLEMZ1xf2i7WWZntROPdXWvz7sikumIZPnhtRGGU4Uxfz8TT+2tpiua5ZEtfhRZ4nRvgu9eHSGS\nLWIa8yQeTRe4PJgiV9RQZAmHxYSmG7QPpfE5zLz7UB2vdsd5oSNGJFPkb17t5ZXuBAGnBUmSaPLZ\n2Bp0LBhQxrNFBhI5otmSZ/NztyJkJ9m/hTw23nWonh0rWEhX1HS+eibMZ1/rIzXPss1k2gL2KU1c\nppMuaHz93AAvdcZm/bssSSJYFgg2ECWHjJVd5hTWcuuT25Esn3mph655luHz6tzL5SuBMub+cKon\nwSdf7JrTwmw+DMOgqOmzjkVv21PNhx9sRtUN/vaVXq4Opgg6LfyXE80cqHPxJ8910ROfX3M/nC7w\nYmeUeH7pNndVLgu7a5y0BOxsDTroima5PVqeZV0qr2Izyzy1v4ZHxroRd8dyfPJkN+f7p9rHmU0y\nmaLO8x3RGbUEA4kiL3RESRZUFFliZ7VzzuB1W7BU9Li9ys5IpsjJjiixJcqs8qrGix1RLoTTvHNf\nDUeapvpJn+yI8s1zA1Ms5YbSRb74Rj+35nmO5FV9UdKWctiwGWZNN7g6lEailDnNFHUafTZSOZV/\n7oqj6QapgoqqlU7YSKZIVzTHO/bX0DymJb44kOafLg7y4QebJypNLWaZWE7DZVao99j4j/eUNDle\nm4mn9tYgSTCULFCzgIl2dqzBiNOiYDHJRLMqsiTR7LNypLFx2S0eF4tZkfnZHUGKmoG9QlmDomZw\nfTgza1MUgUCw8eiJ51bMg3mcNr+NH18fXdF9CBZPpqDRGc2SLc7u59sRyfB6b5Jd1Y5V99rfV+ci\n4LDgnyPjOR+XBlJ8v32U37ivbkYHy+oxB4juaJZbkQz76pykckXcVoV6r5X/uL+WwALZ3v11Lj72\naGvFbM2Kms7Xzg2SKqh89NFWPNMKAK8Pp9GBXdVObo9m+OyrfTyxq4rHJlmyBR1m3rm/lpZZnETq\n3Fb+6yMtMwrx7mt0sbXKNkMXPJjM0xPPs7PaMWHBazHJ2M0Kn32tnyd3BfnYY0v/93tsZj54vNRc\nbTYpSX+iwI3RDOqk4LfBY+Wd+2qoncPBI5Ip8g+nwxxt8nCi1bek45qNDRswR7OlGUaDx8o9IRcF\nzaAnnkORbdQ4zRR1+NntfnyO0gl9pM3H0SbPlKWO7dV23n1fiKDDxOs9cXx2MxZFwjAMxsU9kxt+\n+O0m/vKVXn71QO28AbOmG/zzxSFiOZXfOlw/8XqmqPHVswO86966irYHLZdKP+R89lIL7LshYE7k\nVFRdJ+BY+25CAsFakMippS6iSwhKFkNrwE5XNIemG+uiXmOzMpwq4DDLE44Mu2uc/PfHWud0oMgU\ndb5xboCn9tWsesC8NehYcsdXnVIWc766xWa/nd8+2sBXTveTLer8+60Iv3O0gUfm8AWejN2sYPeW\nl4RK5lTOh5M0++0TibvpmBWZd+ytRtUNXNOSW9mixjfOD6IbBn/0SCu6ATlNR9OnTnJ8dvOUAHqc\nGyMZYtki++tcM6QTTosJp2XmvX99JMM3zg/yweON7J7U5c8wDAqaTtFgXpnnUCqP22qa132seh4b\nyyd3B7k56uBWJMc9IRMmWSLksRKap522YZQ062qFM8zKxz/+8Y9XdIsVpqOjg1AoNPF7LFvkh9dG\nMQyDYy1e6t02ZEniwVYv5/qTpAs6maLOaz1xto0tKeiGwcnOGN3RHA0e64SEwmM10eC1kcip/NUr\nvai6wSNbAhxt8o7ZvEx9mDssMm1+O1uC9nm1XYZR0i1bZJldtQ5qXFaONHqocVloC9jZEnCsuTas\nEhQ0nbP9KRJ5bdWrVSvN184N8G/XI9zX4MJq2vjfTSUIh8NT7j3B3c2t0SzXRzK8ZVfVwm9eBhZF\n5gftoxxr9uBZ4eB8o7LS995AMs+fvdCFLEkTq6uyJHF9JM2PrkVwWiSC01ZBfXYTe2td7Kt1ztlP\nYD1S47JwtMk766putqjxYkeMTLG0qnx+IM2OageRrMqBeveir8/TvQk6I1nq3JZZJ4P9iZIrRrXT\nwpbg3HLHKmfJ/3h6DGJWZJq8VvbXualzWwk4zNzf5GF7VXlmAU9fHebH1yMcbfLgKtO6zmlR2FHl\noC1gn2g2AiWrt8ONHho8VmRJIp1XeaEjRkHTJ851TyzHnz3fhcdqomWW4sFyUGSJr58d4FRvnKNN\n3rIcxBwWhUNjLbWXonkPh8Ns2bJlxusb56ofI55V+db5QY63+vid+xv49Mlu8prBrxyo4Z37ayjq\noGk6NS4zjrHAp6jp/OT6KKmCxoGQm65YKXB220qa5RqXhQ+daMJlUYjnivz4WoRdNTOXnZwWE3vr\nXDx/K4IsSzzc5p/1IlVkibfsqsIwjIkLfvziPFi/uJn5yY4YibzKm7b6Fy2mX4iCqtMZzVLttCzo\nazkbuaLGGz1xvDbTFDeRjciuaseU7kMCwWZjpR0yJtPqt9ERyZVVgCyoPPmixla/g+C05344UeCb\n5wfZUe1gxzSjAosic2CR41eliGSKjKQLtE4L2spBlqQ5HRsSOZWn20c43ODhV++t43/8TCsmWeZ4\nq2/W8Wy+41C1UvFaPK9yT8g1a21To9fKh040LSjJvDyY4kI4xRM7gzMKAKevTs+2Ktobz/HTGxEe\n2+qfEqj+7I4gJ1p9ZTUmGl8BqnJa5jzeyec1kdd4un2Yh9v8tPhtdEVzuK0KRxq9y2qEJEsSv3yw\njoKm41tEZ8dKSU8ns+EC5paAnf/78S04rApWk8x7Dtczki7wiRe6+fk91RxtcqNIJrZXOyloOnlV\nx2pS+N37G8lrGtdHMpzrT3J/s5cmn41vnBvg53YG2T1mTzKQzHOqN47bOrODH5SC73PhFCZZ4niL\nl4IG14czhDzWGRfFeLDcG88Rz6nsGHPuKCe7nC1qXBvOEM0WOR9O8XCrDyrswnR5MM2VoRStPhsn\n2hZeepqOWZEJOMwEHeYNHSwDPNBSOZ2TQLAR6Y3nVy2AbQuUnDIebBP33VqQ0wz6kzlOmO800ipo\nOgdCLv7457awfZ7s51pwtj/Jty8N8fsPNS9ZmjGdq4Mp/v1mhN+5v4HqsYDQJJeC3LnGs/P9Kf75\n0iAfebCZrdM67JkUmXfdV4emG3Nmb82KPKO4fzRdoDuWZ0fVnYYl0azKaLpArqBxJVGSNDTNIeGY\nzHCqQH8ijyzD6b4E901zoql1W3EXVK4Pp3FaTTM03VCyd7sxkuHGcIZjLd6yzQhCHit/8HALTrPC\nzZEMf/taH+8+FOIX99fM6eNtGAaZ4szmZp2RLAVNn9j3XK23V5sNmU7bUeOc+KKrXRbqvVbesquK\noVSBZ2/F+EH7KDdHMnz59ABfPRNmKFWaESbzOh3RLAdDLurcFgqqxkimSFa982XWua189JFWHh57\nkOuGwUi6MFFtaTcr/OaRet5zKITVpNAdzfG5U31cnafBx/O3ovzNK71cGUzzp8930hO7U506ffvj\ndMdy/N2pPhwWmfc/0Iirwktg2aLGdy4Pcb4/RVMZSyWaXjpOY9KFrxsQyaok8tqaGdsLBILK0LsK\nDhnjtAVsouPfGrIt6OCDx5vYNcln+NlbUb5+bpCtAQduW+VrOQqaztm+BLdHpzobaLrBhXCSGyNz\nu0JsD9r51YN11FSwWD6nGvQnC9hMpcRPOWyrsvNrB+vm1NxWOy3z2qJBacy/NKkpWPtwhr9/vY/O\nSXFBLFuKS4q6wV+/0ssPr42UdXxXhtJ87lQfNpPM//Gmthk+0ulCqUPej69HONUdn3UbV4fSfP71\nfkyKRE4tf1yPZ4vYFBmTUtIY//I9paLD710d5ktvhEkXZjqsXB5M86fPdU5xzdF0g3+6MMQX3uif\n4byRLmgkcmvX9GjDZZhnw2szc6TJw+1IlrN9SSyKjFmWkKRSA46+eI4al4WeWI7nbsc49kgL3786\nioEx0baxN56j1mWhoOq0D2cIuS1ss5p4qTNGPKey1W/D6zATclunFAI2eq2893D9vEuZD7b52Ffn\nwqxI2KY18rg+nOFzp/p43+F69tY6uRAudfnZXmWf2G65N/NisJsV3nVvHQbMWXwwmcuDKb50Osx/\nvr9hYtbntCj89tEGZGnuGblAINgYrK4kw85Xzgysyr4EM1FkaYargVmWsJplFvsovzSQIlPUOVjv\nmlcuEcsW+dLpMPfUuafod+M5la+eCVPvtfHhB5tn/Wyz307zEjWwUEoQnelLUu00T4xf94RcbA3Y\n8CzC87jJZysr0zsfqbzK184N4LWa2Ba0s73KwXsP1dM8KdtrNcm4LAqyJPHUvhqi2SLRTHFB6eTO\nagfvPVxPvds6Z3ttl0XBYzNxf3NpdaE3luP6SIYDIRdBp4UdVQ7+87FGmrzWsty8MgWVq0MZvn15\niDq3Fb/dzC/dU8PDW/wYhoFZkUvXFTODb0WSxnpnlJ4/VQ4zNrPCU/uryavGFB9o3TD4pwuDxHMq\n7ztSP+HYsZpsuKK/a0Np/uXiEE6zPDHLG0kX+LcbEZxmhf11TtoCNrZWOdkWsNPgtVLvsXKmL4lJ\nlnh0i4+uWI5mnw2nRWF3rZOrwxk+/VIPWwJ2JIlSZtessL3KzudP9fNKd4JjLV4+83Ive2qdUwJm\ni0mm3mOdV0CfyKkEHGZaA3aONLnxT9IcZQoaQ+kie2pdGIbB2XCKC+Ekh5u8WGSJn9yIkMyXPj/e\nJ/7yYIpTPXFCbuuCvePnIziPNmk6qYJGJFOqrp1cCGFR5Fm1Wppu8EpXnK5olgavTQTUGxBR9Ld5\n0A2Dv3utj/cdqV+Vrm5uq4nPv97PO/fXYBJOGTOY696LZgr878vDxHNqWYmO2Yhmivz42iiKxJTC\nvmafjUP1bhyzOCXMhWEYfPP8EG/0JTja5Jm3zsZuVtgWtLOnzjnFKs1uVmgL2Lkn5FqxItBIpshn\nX+vDZzdNBMyZgsYLHXEKmr4sjW05pPMqP70ZIVfUaPbbafXbOFDvwu+w4LSULOwmj+WtfjtbAjb+\n14vdyLKEy6KwvcqBxSSTK2o8fzvGaLZIg8dKtqjRHc1hN8v47WbqPVYsc8QFmYJGtdOMTimQrXJa\nuDyY5psXBtlb56LaacFlNU3ENPON24mcyk+uj2IY8OztKNVOC40eGwGHmYKm8UZvknqPlT21rlld\nOaCkELi/2cNQqsgnX+ym0Wcj5CklCaunFT1KkkSmoJFXdcyKtGAmf5xwIk8qry0qwJ6r6G/DSTKG\n04VSk5L0nbR8IqfyckeM4XSRcLLAj65FMQyDjmiOFzriFHWDV7vjvN6TQDfgJzciVDnNRDJFbo5k\nCDhMHG/xMpwuEMup/P5DzTzc5scky7z3SD0fe6wFh1nm4TYfyZxKXzzHpYEUXdEst+YwF9f0kln6\ncKrAp17q4en20pLKdAeGZr+dDx5vYkvAzoWBND++NsqbtwdwWhQiWZV/uxGhJ5afspxxazTLM7ei\npOfpuV5ptgYdfOB4E41lPqRV3eC17gQvdcYpqrP7egoEgvXBcKqI2za/9VMlMckSDR4rXTEhy1gM\nybzG99tHOB9OLvjewWSe9qH0DLlfIq/y7O0ovfE8RU2nfSjNYDJfahKyyASMJEn8ysEa3n+scUoi\naTZkSWJHtZMGz8wxZFuVY1Y97ThFTSdbmLsxSLaocXkgRSQzdbl+NF3gymAKr83EB483EU4WONtf\nOnfpgsYztyLcnCYFef52lH++OES2guNrulja1/WxfW0NOuZtClZqpKbw2NYAhxs9vH1fzURSLq/q\nPHc7ytne0r/j6lCa/3Wym+vDCzc6OdOX5M9e6KYnnqd77N7bV+csabIDU4+nK5rlr17pmZBLaLrB\nteE0A2PNY9IFjWdvRQgn8jy5u4rjzV4e3xHgRKuXq0MZ/v1WhExRn2g+MxdWk4LPbuLhNv+MItTp\nbK9ycC6c5PpIeY2PckWNL77Rzxfe6K/I97nhMszVLjNHG71sr7aTU3VOdSe4OJDiYIMHp0XBYZbY\nW+ui2mXlymCa13riHGv2cm+9m5DXgtUk8zNbAygyfPfqCNuCDvbWutAMg2+cH2RntZO9dS66YzkG\nkgVCHguNXjsBhwUJ+OtX+/DaTXzt7ABmReYH10bZU+OYMnvRdIPvXRnmTH+SvbVOqhwWtlc5Fszm\nSkCj18bOGge2sYvocIOHbVV2gg4LlwZSqJrBzmonRxo98/oQLoVsUaOgGVgUecrPS8EkS+yotnNf\ngxvvIpa8BOsHkWHePLQPp+mJ53h8e3DV9nlpMI3DLFesiOtuYq57z2s3cyDk4p6Qa8GM2Y+uRfjW\nhUEOTrNH89pMHKh3sTVgZyhd4JMvduO3mRjNFBlKFcoeV64PZxjJFGjx21fUHjBb1Hj2Vowvnwmz\nvdqBzzZzPLk5muGvXumlzmWZIt14vTfBl06H2V3jwmtX+EH7CLVuC1uDDlzWkrvTjmrnlOzuS50x\nYpkiAbsZv/1OQXumoKHqxqzBXyJXRJHlOTOyTouJe0Iudk3bV0HVyRS1Ka/phkE8p+K2mdle5aDO\nbcUwDNqHMyRyKiGPjX11LvaHXBMT3CpHyaZuIas4SSq1pt9d42RnlQOruWSeEHCYZzh+hZMFfnpj\nlH11LmpcFgaTBT7xYheqbrA/5MZtNXFPyM3WoIPbkRxfPB2m1mXhq2cHeNPWAI9tDWC3KEgSC1re\njaSLVDnNtC1gA+e0KKXzOBYjLYQsS7htJrYH7TR4bbM2RpmNuTLMGy5gHl9GSOQ02ofSXBnKTHgr\nd0SyNPrsFHUDt1Wh1W/jWLOXapeFbFHjj5/ppCOS47GtfmrdtpJPn9/OcLrAjdEsbquJPbUurIrM\np05283JnyS3Dosh4bSXD7HqPlWavja1Vdlr8Npq8Nr5zaYg6t2ViecswSl0E0wWN/XUu4jkVu1lZ\ncPbks5cumPELQZEl/A4zbquJcCLPp1/qwSTL3Nvgrrh+p9RsZZCXOmPsrnbwL5eGON2bZG+dc8lL\ntA6zclf4TW9WRMC8eXi9J4FhMKFrXA36EqWkxOFGz8Jv3mTMd+8FxsaEhXBaSo4MrX4bpknPcEmS\ncFtNWEwyFqUkKWzw2PjzF7rpiuZ4oMW7YKIkV9T43Kk+zvenONq08Ptno6DqnO1Pki5o846NRU3n\ndiTHtaEM9zd7Z9isAchGaSJQ7bJMkVdYFGlM3lBKeh1q8LA1aJ84Hy6raYascVvQweWhND+5PsrB\nOhf9yTy3I1le7opzPpxiX+1Uz+OeWI6/ONkzIYeYi9n29dytKN+6MMjeWicOi0JB02kfTPPVs2FC\n7jv1S4m8yl+/0sut0QzHWnwTjUAuhpPcGM1wuMFNtWvhiY7Pbqao6/z9qT6a/fY5O+VBqVvgoUYP\nzb6SpHL8WtlZfUeW6h77N1kUiRafDY/VxPmBFPc1uLGaZD79Ui8mufQdzMfXzg7w05sRjjR6FowZ\nXFZTWcEylK71kLvU5KTcYBnuIh/mcXriOfqTBU52xth5JITLomCSJfJFjU+/0sv/9TNtuG2miVaa\nAYeZ37gvRKaoIY218XNYFPqTeSKZIs/fjvLL99TSNrYs8ev31XFzOEteMyY62fkd5gn7sXFLmcsD\nKXKaQW6S7ECRJd6+txrDMEjkVZ5uH6bRY5swiF8KNW4L//lYIwHHynxlsgR+uxlJklAUiYDdTM6s\nC+2xQLAJ6I3nafStrnVTm9/Ot/uHV3WfG5lkXiWaLdLgsZXVpKLFb1+wWYTdrHCkyYthGHzwRBNm\nWZq129t0bGaFXzsYQtN1RtMFFMmy6D4BsVyRb50f4L5Gz7xjo8Ni4rGtPo63eCeK3gaSeczyneYq\nBd3ge1eHeaClVGA/Tr3HRv0kCUg5/QYcFoU6twXDAI/dxL9eGebmSJa365w7cwAAIABJREFU7aki\nkddmaSYiEXSYsS1CznJjOE1PPI/dLFPtvNPk5Mpgmr95pYcndlZN6aDrtZl5z6EQZkWeovk/F07y\n3O0ozT4bgTLrkSyKTMBhwTq2/XAij8UkEZzm5yxL0oTdHpQKEQ/NMbmtc1snMuF/9HALbpuJSKZA\ntdNcVtLsyd1VZIr6ihgcVJINGzDvrHJgUySs+2vw2Ex88sUeHt8WwGKWeWJnEFU3+PLpMD+7PcCh\nJi9DqQJNPhvNPhvn+pNcH86wt9bJ50718Uv31HK4wUO+WMpau60KZ/qSPNzmo9lnn7iYC6rOzdEM\ng6kCTrPC0WYvNpOM2yKTzE/Vx4zPJAdSBepcVh5o8fIvFwfZFnQsyfzdoshTHgSVRpIkntgZxKB0\nozy5u2riZ4FAcHfTG89xpGl1m1K0Bex0RsrTIgrgbF+Sf7o4yEceaplI7CyWaKbI8LTGG53RLM/f\njvL4tgD1i/Dh3hq0cyGc5C9e6uF3jjYselyrdlp4+94aeuI5Unl1XjmBw2JiPJ5L5VS+cmaAVr8N\np1nmcKOXdEHj9443T3FVGCc31s2vymnm3obyVjPeuqsK3Sglv96xt4acqtPityHBjIC5zm3lvzzY\nPBEndEWzPHc7ypu3BSYyztM/c30ky09ujPLRR1u4v9k78VmvzcT9zT4ON3pmuHHM5oe8t9ZFTjUW\nXL2eTJPPxkceKh1vLFvkMy/30OKz8bvHGrk0kMKiSDP2VWrYUqQtYMOsyIQTeU71xND0kvFBk9fG\naLqASZEnvNUDjlKSr9zJ3UZgwxX9jeO0mlBkma+eHWAoWeDndwXZXmVHkQCjJEi/MZyZKGL4zqUh\n/uS5TkbSBdqHMzx7K0JB0/mF3VXIEmiGQVcsR6qgkchrvNGbYCBRmFLU1xnN8umXeumJ5bk2Jt5v\nDdj5pQN13BNykVd1curUwDmeVbk4mCKvGbzWHV/XRS6SJE0EyJN/FggEdze98fysxVgrSbXTTF4r\n6TUFC9M41m9gtqCwXM6FU/zFSz30xnKkcio5VSOWVXmjN0E8v/iiqCqnhSd2BKlyLT4zKEkSA6kC\nZ/qSZBdRGG41y/zMVh/hZJ6TnXG6ohk+dbIbs0mixj0zy1rQDVIFjW9fHqY/kWMoVeDKYJIzfQnO\n9yfmPLbxQC/ksdLqt5Er6kiSRF88x5XB1JRiyslB4fj5jOVUvn15mO9dHUGdVnh5otXLHz7cQsht\nnfLZtoCd9x2pL9u6Lq+Wmn6MZhbnTTy+T7tZ4YkdQY41exlK5vnki938/ev95Kd9H2f7knz6ZDcX\nwyl64zkSeZXBZIFXuxNcDKcIJ/NcHc5wYSBFdszJYvp5uRvYcBrm6dS7rai6gSxLdMfy1Lqs/LB9\nhMNNHt6xr2bMKk4intcI2M00+6x4bSaKmgGSQchj4/nbMc6HU/8/e3ce2NhZ3ov/e1Yd7YtlWZa8\nznj2fc+smYQQkgAp5bIlQNkC6QIUuFAo7S2l/f1u4V4otKVQ1lJogUJIwhICIYRJZjKZmcw+4xnP\n5n2RZcva16Nzzv1DtuNFtuXxIsl+Pv8kI2vkZ2ydcx6953mfB6uHNxRsqDJhu9eMF9tDePZmEDtr\nLNAL3HDvShHrXQbs9Fqg43NF/ja9AIYBHrvUj0u+ONa6jKO3TRwGAbu8FjQ69NhRY8GqSiONXyZl\ngWqYl4dMVsV3z/QN91RfvAscwzA42RlGvV0quEXUcpHv2LMbcpvA5tLJhGeBVQ4DLvhiONIWQjAh\nY2etGXfU2kZrVWfDrOOxpnJ8m7jZqLXqsLvOOu7W/0w4lkG1RUImq4JlAZcpV1ebkhUYRW7cz6c9\nmERPOA2rxOFCXww7vRZcDyTRFU7jhxf60dwfx4EGG0SeRSarju43GqGoGpr742gPpvAfZ3pRZ5Nw\n5FYQP7sygB1eS95a8pFrfrVZxEsdYaSyKra4TeOSR0ngYJX4WdXV5v9ZAP911oesqmF7gavnY/Es\ng3q7Hu7hxN2ky22QqzKNL7HhuVzuc6orDH9Mxp0r7Ki1SdjfYEW9TQLLAPvrbdhcbcQTzYO4MZjA\n2kpD2SbMS66GGQDah1L4/lkf3rHdjR+f78fhFTZwLHB4ZW7M80hjdl80jUaHBJvEgwFQb5MQqjbl\nRkgyDO5aace1gQSsEo9oOjt6QN7V5MDeBnX0E71NL+DgmBHS1wfiGIzL2OY1g2eZ4bGaKsa+RUSO\nhXv4tkyhPY+B3K2zC30xrHBIc2rYTggh0+mJpOE2iUW5uDXa9WgbSmLrbZSpLVftwSQ6g6lJ3S8K\nUWvTw2OR0BFOQWBZ8CwgsBzcluLUjpp0PArYq5aXXmCxocqI59tCaA8ksdljgkHkYDcI8EXSuBVI\n4GYgidM9EXzqcAM+fVcDbHoBIs8ikJDhNYvgOXa0FORMTwRPNA/gw/trUWOVMBDPQFZU/Ne5Puyt\nt4FnGYSSWYg8g7dtrUKFMf/PbOw1/x3bq8EAo3e6o+ksouks+iIZSAKLDVVzK7N0mXR49A7vvHQp\nkYb7ZKeyKswTXq/OJuUaGxgE2PU8OPaVPsjH2sN47uYQPnm4AZV6AQLHDA+OK89keTplnTDrBRZ3\nrbRD5Bh8YLcHEs8gmsqi3q5HpVHA45f9WO8y4EcX+rGzxgK3WYfPP9+BPx/uOSjxLCQOaHAYEE0r\nUBQVCpMrgq+26GZsd3ShN4rnW0OoNAlY5TTiDzdWAsC8NP4fTMh47FI/3rqlas4J843BOK70x3Go\n0V7QpgdCyPLRE05P2wN3ITU4JNwKUB3zbNwYSOBnVwZQZ5duK1HiWAYPrq9EVlXBs/kHT82neDqL\ns70x2CUOG6un/mCUHK41rjKL2DLN84DctLxcL+EkXrPKAXZ1ri3rSC1sbzSNH1zox/t3e7Gn3jq6\nggrkulhd7U9gT934OmGLxGOlQw8dxyKckvEvL3ZhpUOPR3Z5kVVUmEQOBoHFYDyLAw36Ge8UD8Yz\nONoWyk0UHM4lXmgN4ZkbAdxRZ8FALIv1LuNoYqmoGjqCKTiMfN7WeeN+RiYRWzy5hbrN1WZEU1k8\ndXUQjQ4J66dJwoOJDCJpZcqa4elyHpFj89ap76u3Yp3LiGqLDjzL4C2bqwBgSQ4kKrvagGZfFM9c\nD6DZF4EvlsEmtxHne6I42xNFVziNjAps9ZgRTCqIpLJo8Sewv96GlQ497HoeBxvssOh4PHNjCH//\nuzb0xWQYBA4X+2I42hGGqmFcPdCR1iCebPaPq00eSsj495d70VhhwANrK/CDcz4EEzKEKabeFUpR\nNTxzPYCnrg7CaxXx0QN1M544CtETzuC5W0FE0lQrSAgZrzuSgneRRmJP1OjQjw5GIIXZWWPBRw/W\noXaaDzmapk07MErgWOgF/rauV62BBLpnsRfHF8vgdzcD8Menr7NNZBT86loAZ7tfGcrSEUziay91\nT3qPXB9M4OlrAWypNuM7p3uhEzisd5tHx0GvrTTgIwfqsKbSgLWVRrQGEjjaFkQ4mcFQUsavrw3i\nRGcYx9qCo6+5ocqER++oQaVJhI7LDSpbX2VCk9OASEbBE5f9YFkGH9pfW1CNcSSVxXO3htAfzYw+\n1uCQcHeTA7trrHjrFte4VdhTXWH8r9/ewrG2EL57ugeD8cyk10zKCp69OYQzPRG0BRJQtVxtdCyj\n4Hc3A6MfPhVVm1Q3fdUfxzdP9eK5m0PwR9Mzxl8ot1mHdWPKUOeaB5WysvtXdYRS+MILnegOZ/DT\nS3482TyI1693Yo0rV9slcAyuDeQm5p3tiSIhK0grKlZWGLCywoA3b6lCpUnEzhoL3rfbC8/waMk3\nbnCi0Z7btb26MvcpS9U0tA8l0eKPQ87m3nzhpIx0VsHVgTii6SwaHHrcu9oJ4xStUzqCSVz2xSZN\nW8pHVTUMxjLoCqcwFJex0mmYl1stu2rM+OSd9bc9SjWf1qEkrvjjowcsIaQ8dYfSqC1Swtxgl9AR\nStF5ZBbsBgErK6avDz3dHcVXjnfnTbpmEkzION0dmTQ1DwBCSRnfPNWDH13oL+iaBuQ6LQUSWRjE\nXLoRTsrQ8vy+LRKP+1c7YDfwo91TErKK64PxcZNuAaDarMObN7lQbdHhdesqUTGhN7NB5MfVe5/p\nieL/+107rg0ksdppwJ/urcGL7SEEk/kXkSSBwz2rKrCrNlcXvMltwv88WI+ErGAwVtjPtMGhxycP\nN2CL55UV3w1VJqx16vHji35klPE/g3Aqi1c3OVBt1qG5Pz5p411SViDxHD52sA5us4gvHeuEfziW\naosOn7izAXeusENRNfzsygD+62wfrvhiyCi514mmsugMprDWZZxUcjGfYqnskp3uW3YlGU0OPT56\nsBaNdgkf2OOFwDLQNOBH5/uxv8EGkQMYMHj9BicSGQUOg4BoKoveSApWSRgtSdjoNmGj24TzvVF8\n7kgHHtnlwRaPCVaJh8jl5rVH0lm8ZrUDwvC0mFuBBL79ci8e2urGpw7XwyBwM/ae/M31IVzoi+I9\nO6pRa5NG66rzYoBoRkE4KaPAc1FBjDp+9JP3fNA0DU9c9qM/lsGnDjeUfO9EQsjUusNp3Lt68Sb8\njWXW8TCKHPqjhU+YIzPLKCrisjLrDyLxdBYtA3H85zkf3rPTM+7cHkzKYAA8tNUNHc9Om7DH0jJu\nBJKwSTxMIofPvKoRFj2P6wNx/PvpXvzRDg/Wuca3LhM4Fk6DgH890QOjyKHBoceaSgM+fVfjpGtM\ntUU3+n5Zk6fd2kTrq4zDtckiBI7F9hoLTDoub8eRzmASFokfN+pbL3CIywq+eaoXb9/mhnOagR8j\nWIbJW+qUVjQks7nfjaJqaPbFwHMMDjbaISsaJJ7Bpw43jPaYBnIrxo9f9iOcUvCeHdVYW2lClVkH\nx5gYR34eWVVDSlYwEJfxq5ZBvE3vRo1VwlaPGSsr9ONed74NxjP42olu7K+34e4mx4J9n2Ipu4Q5\nkMjimyd78IlD9djbkOv3l5QV/PEdXhgEDgKb25znGk5Mn28N4p9f7MQf31GL51t78d6d1WgZSGK7\n1wy3WQfd8JAOHc+O22V6rjeG/zjTi9evc6LSKMJp0kHgGNgkPvd3DIW96e5b7cAmtxGPX/bjDRtd\n0ybMDMPAbRZhlfiS3jXOMAz+x8Zcb0q7vuzeQmSCvkh6dDwqWX66w6mirTADwxv/gklKmOfRnjor\ntnrMU975nMrvW0PoDifxjm1uNFW8UucaTWfxtZe6UWkU8d5dnhk3iF7pT+AfjrTjg/tq8fMruY10\nTlMuWbVJAkQu/993W0T8yR3e0buhLMOgsoDkdKz2oSRaBhLYXWsZPadtrjZj84Tyxnx9ja8PxPGZ\nZ1rx5s1VeOMm17iv1dslPLLLM+c7tRvdJqxw5MZYR9JZ/PhSPyw6Hh89WDf6+8q3EGeTcmO6GRaw\n6XncGEygV59Gw4Se3DzL4I2bXBiIZRBOKaOTD2OygvSEVW1F1dAbScMi8QgnZVSaxDl1YeFYBg6D\nAMMcXqOUlV1buYF4Bqmshg1VRlh0LHrDaeh4Dp2hFL56ogvbPBY0jTkQ0rIKMMCqCj2yKpBRNDx7\ncwirnUZUmUVUmkTsqbPCqhegadpoTVEioyCeUTAYl1Fnl+C1SrBKwuio7Xx6I2n85IIfPPtKhw6r\nXkCFgUe9XY+VDsOk0ZhjsQyD1ZUGrK8yTVsD5IumMZiQx30CXmw2vQCnUVySO2GXk2BCxhePdsAX\nzUya4kRt5Za+SCqLx5sH8K4d1UU7ltuGkohlVGyuXrjBTOWmkGMvmJDRFUrDLHGTEthMVkVXOAWR\nZaET8l9LOoO5vrni8B1UAIjLCpIZFfvqbbBOuL4kZRUeqw6NDv3otbJ9KIlUVp00dCStaNA0bXT1\nd32VESYdD7tewN5667hVTlmWwXEcQsksvnysCwlZxeGVM69OjsSgaRpebA/h2kACdVYJVwcSePyy\nH1s95mkXAQJxGb2RNKwSP9pOL5lVEc+oWFtpQM2ExFjHs6i26KacXNcZSiKeUWccW84wDMThPEDH\ns1hZoccWjxl2vYCOYBK/uDKISqMwrmyCZRg0OfXYUGWCyLHoiaTxvTN9qLfr8ybwPMvCKglwjel+\n89NLfjzR7Md2r3n039A2lMQXj3bAKHL499O9cJt18M5hA7Be4LDDa0GdvTibiOfLkmkrd3MggWBS\nRiyj4J+P92C7x4xKo4zjHWG8fVs1OCb3qWnkTbLGZcRQQsa3TvXh7duqYNML+ND+WlSPWcHlWAYv\ntAbROpTEmza5YNLxWOMyosaqw0BCHndbZbpP1pFUFs/cCEASGHitutFVaIPIY12B7WNYhgFmuG49\ndsmPzmASn8pzq4qQ2dALLO5b4xwdIU+Wl65wCjVWXVE/+DbY9TjZGS7a9y9Xl3wx/PfFfnzkQN2k\n0dJtwRS+crwLb9/mxr56W96/3+xP4EtHu/CJQ3WoHr7GbfOYsW1MJ4RMVsUvWwah43ITdAHgYl8U\n/bEMNA043hGCx5IrjxzLaxHx/t1eCBw7qWXg2GvodX8M/33Jj51eC+5ucuA1qyumHczSH01jIC7D\nZRTw+OUB7Kq1YIvHjIu+GELJLPY12LCl2oQaaz1qrRKSGQXiFOUjp7rDeOrqID5xZ/1o14gaq4QP\n7a+d9NykrEDkpi5DiaSy+MbJHlglAR89WDerDhFjO1YEk1mc6g5jZ60FngnPG5sbNNr1+Pid9bOa\n8LfFY0a1RTfuroNdL+A1qytQY5Vw7+qK6UtGC1SuvZcLsWgrzIqi4MiRI+jo6EBDQwMA4Je//CVa\nW1vhcDig1+dvczJxhTkpK7BIPDwWAT9vHkClSUS1WQdZyW2ay2pAKJWF2yxiKCFDEliAYVBn06He\nJuEnl/zwWnSTdrme642hdSiJHTWW0dshIs8iq2q43B+HnudmnIlu1wvYUGVEhUGA1yYt2BtnpFl8\n7QzfozOYxK2hJOx6HvwS3bVK5obnWDQMN66fiFaYl75zvbmN0Qca8idVi0GDhl9cDeDB9ZVFi6HU\nFHLs5WpkdVhRoYfEj782cQwDl1FEo0M/5cbxtKyg3i7lynEY5B1NnVFUHG0LI60o2Fxtgj+ewRdf\n6EJW1WDV89hba8FKp2HcjIFYOovvnfGhP5aZsb64P5bG0y1DqLbosM1rRoN95N+ijV6zfNE0mvvj\nsOh4nOqO4AfnfdhSbcaLHWF4rLn2r6ucBuyoscAq8dDxLGx6Af5YBl890Q2DwOXtAiMMD+1odOhH\nV3zzCSQy+PrJHqja1COceTZXprmu0jCncsqRoSczDZEZGZg23R3riapMIlY49OPuXoscC1XTIHAs\n9tbbYKMSSwAlsMLMcRzuvvtunDhxAgBw7do17N27FxUVFThy5AgOHz5c0OtsrTbCJvGoMnL42KF6\nqKqKloE4zvfFsKvWjHAyC55j8VJHGB1DCWyryfUIrLNJ6IukwTAMmDxJ5n2rHdjhNUOY8LX2YArf\nP9uH9+3yTNmofPTfyDLY4F7424oTN0sAuZPUud4YaoZvmQHAqa4ojrQO4VOHG1BjW5o1RYSQ29cd\nShWtB/OIWpsEXzSNTFadNnEh49XapCnbmzkMAg6tsOf92oiN1WZwHIOvHO/G27ZU5U309AKHd+/M\nJe65TXkiHr3DC5OYS0Lz1rsOj5XmCrhrsb7KjE/fVQ/9cNnIQCyDfzrWhTtX2vDqVRWQFRWdoRSe\nvjYIi47DZrcJLqOIOruEj99ZB4Fj0RtOwWkUJ713GAA8w2CqMBocenitOpzvi8HAs1Neuxkw4BgW\n061/cSyD7d7bawHri6Rx1R/HBrcJLpM4OvRkMYRTWXz3TB+qTCI+drCOSixnULSPE8FgEGvWrAEA\n6HSFv0F4nkdTZS7szYbcycJuELG60ohYOotGuwE/PO9DVySN3TUWiIyGK/1xrHLqUW3R4aMHaicd\nWGe6IxiMZ3CuL4rdNdZxuztXOw14dI8XjXk+WXaHUjjZFcHeeis8Rd6wEkxm8eOLPty3xjmaMB9o\ntGJDlXFRD8D5dHMwgSv+3OhSKj0hZP51hdO4e+X0idVCEzkW1WYdOkMpNDmnHxZF8nu5K4K4rGBf\nvXXGgRoA0OyLoSucxu4aM/54jxcey9Qfmsbewhf5ySUWE2mqhlc3OeC2FLZZb6XTCFXTcLIzDJYB\nGh0S7MP109cHEvj3l3vx8DY3Ghx66AVutMZWL3C40h/DV1/qxiO7vZPiqjLr8OcTrvc3BxNo7o/h\nYKMdDoOAeEbF45f64TbrsL7KmDdhdBgE/Nk+7/Ak3/nXHUnjsct+OAzC6Aa9QnSFUtAL7KwmCE/k\nMAh4/24vJJ4tKFlWVA2nunLzKu6osy7p8ot8SuLj/Gw+1UTTWTT3xxBNZ+GLpHC8IwRxeGTlQDwL\nWVFxz2oH3rK5CjzLoCeawROX+9ERzDValwQOLMMgms7iR+d9ONERQl80jfZgCntqrPBOSC4j6SxM\nOi5v38LBhIzf3xrK26+yK5Ra1Ib8HosOHz1Yh7111tHH3GYd1lUZy3biTk8kjWdvBGjgCiELpLuI\nU/7GanBIaAvSAJPboagajneE8Jtrg4imZj5XxtNZdIVTuDEYh6xq2FRtHnf3VFVVnOuJ4NpAfPSx\n3kgaNwcTefsnT3ShL4ovHO1AV+iV4RjprIqr/jgCUwwvURQNx9vD+O2NITy01Y2dwxuQrXoeh1bY\n0WDPJcsT47DpeexvsMExoZSgNZDE1090YzAhjytt6I2k8bubQ6PXFJuex5/urcVbt1RNm4f0RzN4\nuTuCaIHXotn0IV43PGRlZP5DIQbjGXz5WCeeahks+O+0DyXRlWfgzCqnoaBBLACQVVUcaw/haFto\ntL/zclK0hNlut8Pv9wPI1TcXqjuUwlAig/5wCi3+OL58tAstgwl84YVOKJqGvmgax9pCuOyLYVO1\nEf9xxoddtdZJ7c9kRcUlXxytQ0kcbQthXZURdzU5sGZMuYOiavjvC/34xskeBJOTD/R1LiM+ebhh\n0htdVlT84JwP33q5B5ECTmDzgWMZrHAYltRK7E6vGX9xuAH18zhwhRCSo6i582WxpvyN1WjXo22o\n8Olx5BUcy2BvnRWrnAYEEjNfb24EkniyeQDbPOa8m7y6wml87vcd+MlF/+hjP28ewFdf6hqdgtsf\nScEXyf/7qrPr8YYNleOS8M5QbhPiJV8s798ReBZ/tMON9+/xjtsrVGOV8NBW92hC91TLIL7yUhcG\nhxNvjyX39boJd4Cj6SyuDkweeLJj+JoytrNErU2ase74bG8U//9zbbg2kJjyOemsinhGQdtQEv/3\nhQ7cGJz6uWMZdTzcZnFW0/FMIoc3bnRh14TORlOJpbL49ule/OdZHzJzGCqi4zm8Z6cH79vtmVP7\nuXK1aCUZqqri2LFjuHnzJhiGwe7du/HUU0+BYRjs37+/4NcZSmbx3dM+vHO7G2AYvG9XNSw6Hh/Y\n40WlkUdPJAOWYfDqJgeMOg7v3ZVrkD4ywORSfwx1Ngk1Vgl/vMcDjssV/o/tOTmCYxm8bp0TqawK\nTVURT2fHDQDR8WzeT2YCx+LBDZWQFQ0m3fJ7U82X+R64Qgh5hS+aGe1BX2yNDj1+cXWg2GGUrRqr\nDooGuM0z356vterwzu3VkztrDCURSGSg41g8utcLp+GVc+89qxzYU5fbVDcYy+A7Z3zgGOCRXR44\nJpQECCwDvcCNa/bkNol4x7Zq1NleSUyj6SyyijY6TKyQgRp3rbBjh9cM6wyb0za4TfiruxrhnLDv\nqJBrSiKTRVJWx8WzttKIP9tbi7ppPlw+cz2AFn8Cb9jgREJWJo2mnkpfJI1/O9GDB9ZWYM+YO8TT\nkQQO+2exUdeg4/CWTbm77nPdJzCXEpByt2jZCMuyOHToEA4dOjT62Ote97pZv46eZ7GzxgKjyOHa\nQAI6noXbzOA31wP4HxtdqDQIgAZ4bRJ0PDvuE3R/LIPvn/HhDRsqoaoqvnysC2/e7J72jbem0ghf\nJIVfXA2g1iYVPL0m38Y8QggpFd3DLeVKQYNDohXmOfBYJXimKK1JyQpe7o7CYeCxocqECqOI/XmS\nnjPdUXSGEkhlNYRSWfzVXQ2jXxtbWy5ygEPPg2GAfIuibcEkfnDehz/bWzuaDJslHnvrrRiMZeCP\npeEy6fDzKwO4OZjERw7UTur5PJVCa9x5dvYDT0YcaQ3hSGsQHz9UB5cpd3ysdRmxdoZrulHkYDfk\nho79xeEGmAscGhNLZ8GxM3aTnROWYbCJ+pzPWfGXFmaJYxlc7IuCYxisqTRgIJZBRlHxoX25cdlt\nwRTWufIPCKmzSXjb1ircGoojnlGxudo8WqqRVTU8d3MIL7QGoUz4ZJhVcwfRrUWsSSaEkIXUVSL1\ny0Cu5VVCVhathG05iWcU/OyKH6e6IuMeV1QNtwIJhIbLDQ812vDgehfq7BL21lmnnNZm0Yt45zY3\n3rHVDat+clK6vsqID+6rRaNj/HtLVlR8/5wP3zjZi3hGQaMjN7Cj1DqjeCw6bHSbcKY7itZA4df8\nu1ba8Z6dHpglHhYdX/DerGAqCx2fmyJMSlvZ/YbWVBrwiTsb4LGIaA+mAAbgOQZrXUY0+2J4+loA\ndj2PKrMIp3H86onIs6ix6hBNZ1Fl1uHdO19pDS4rKk53RyDyLPZM2P1ZY5Pwl3fVQ1ZVpGQl79hK\nQggpJ93hFFY48veVXWwsw6DBLqE9mJw0wpjM7FxvFCc7wnjzFhcqDOOT2AqjiI8cqIM0ITHtCqfw\no/M+1Nr0cBoEvGZNBQwiA3Z4rXO6SleDjkdKVnCpLwq3WTduNdcmCbBJk1eMeZbBgQYb0ooKkWOm\nHKiy0DJZFdcHE6g0CnlruLd6zLBIHL50tBNOo4gVeco182EYBlNM/J7WhiojKo1iSewlINOb9Ue7\nK1euLEQcBZMELteoXeAQTWdh0fEYGt4A4LXq8JbNLmQUFWYx/2dRygv7AAAgAElEQVSBlRUGvG5d\nrkH+2JVkvcDhkd1evGu7G/FMdtIqc0cwha++1IOWgQTO9UQL3iEaTspIyYVvaiSEkMXQHUqXTEkG\nkKtjprKM2xNJ5TpfyEr+ulmzjsetQBL90TRCw9ekCoOA+9Y4MZSU0RtNQ1U1mHQC7ltTgcMr7aN3\naaPp7KTNc0DuDsW/nezB5Sk28k3EMAx21VpwoME2aYNbOCUjlc19j+sDcVzpjxXUkeN2dIfT+NqJ\nbpzrnTruepsen76rEZvcC19aaRT53PAUGi5W8qb8DSmKgmeffRbnz58ffezUqVO4ePHiogRWiKNt\nIZzsiqDWpsMz1wdxYzCBA/U2vG5dJXQCh4F4BuHU5O4WrYEEPvf7Nlzsi457XFE1+OMy/uH37bjq\nj4/72lqXEQ9tdcMfTeM7p3umbI8z1mA8g3882onn20J5v94XSSORoVuQhJDF1x0p/tCSsRrtemot\nd5v21lnxiTvrp+z20BtO4Xtn+9DcH8MXnu/E8c4wzDoeO2oseGSXBw9tdUMYTpAdBgFDCRnH20MY\njKXxfGsQT1zuhzxmkSiYlGESWbx7h2fG2t6ZBOIZfOloF052hHHk1hCa+2P44XkfYsNJuqJq6Iuk\n523hqdoi4j07PdMmwxzLoNqiK7m7yfFMFr5oeuYnkgUxZcL805/+FJs3b0Y8Hse5c+fwwx/+EDab\nDW9729sWM75pvWmzCx/aVwOOZfGD8/3oDqcRzWShFziEkzL+86wPP2uevPNaElh4rdK4huyBeAZf\neKEDpzojWFNpnNQyJRCX8aPzPlSaRPzZ3tqCGowLHIN6uwR7ntqkjmASnz/SjjPdhX06J4SQ+RLP\nKIhn1EldBIqp0SGhnVaYZ5SSFfzu5hAujFnwEYfHQU+l3i7h0T1erHYaUGeTYBvTKcKk48ddCwHg\nfG8MP7rgQ0cohR9f6IfXmhvV3BtOwRdN4b/O+vDd031YX2VE9RwHY/EcgzqbDjzH4ieX/DDpeLxr\npwfm4Rhbh5L4379vQ7MvPsMrFUYvcNhZYxkdgFJOTnVF8Pkj7Xn7KZOFN2UNs06ng8vlgsvlwt/8\nzd/gIx/5CByOwjpELJY6mx6yokID8PZtbhxtDaLeLiGWzuLnVwbRYJewOs+uWo9Fwp8fqBv3mMSz\nONBgQ41NwpZqEybeDbIbeGzzmtEfzWCb1zzlhBtZUUdvN1klAe/d5c37PJPIYV+DDVVmAaqmQdOw\n7KbmEEKKoyechteiGzfUodga7Hq0B5PQNI1G9E4jISv4zfUANlQZsaXAem+DyI/Whn/gjleuSdFU\nFk+1DKLJaRgdFgIAd66wYZvXBFYD7l/rxHqXEdF0Fl95qRvVFh0ONFgRTGYhzLJod+z1ccTIdTKj\nqKgyiag0CbBKAlJyFpLAw6LjsK/eBpFn8I2TPbh3lQMNU9Tep2QFv74+hAoDj4ONM0+wfLE9BH9M\nxn1rHAvSV1jTNCga5m14WLVZh331NhipXW1RTLnCPLZ+aPv27aPJ8unTpxc+qgJ1BJP44gudaB9K\nwqzjsNplhEFgkFZUnOmJ4mYgiZUOacrpPMmMMlo3ZdTxeP36SmzzmPHb60P43tm+cXVb9XY9dnjN\neOZGAAk5d2uqNZBEy5jSjWsDcXzxhU50FHBbscIo4i2bq7C60ojf3hjC98705q0TI4SQ+dYVTqG2\nhOqXAcAi8dALHPpjmWKHUtIcBhEf3FeLB9ZUzPm10oqKc71R9ITH3+Z3GkWscBjQUGHAQ1vdqDLr\nYBA5vH5dJe5stGGb14K7mxyzqrtt8U9/fRQ5Fk1OA6ySgDPdEfz1b1pxyRdFlVmHh7a6wTAMzvVE\ncbYniusD+VebM6qGy74Ybg4m0eKPoTc8/Ups21ASF32F70maraNtIXzrZA8ieUpDb8dalxFv3lwF\nR4Ft+Mj8mnKF+dKlS5Dl3C+5paVl3P/v3LlzcaKbQVbVEE1nISsadBwLHcvCKgmoMIj45OE6aBpw\nPZDEz68M4F07POOGjMQzCv7jTB8ceh73NDngHFNiEZcVhJNZqBM2/q13mfCXdzWi0iRCUTX85GI/\nQikZnzrcAKtegKzk4im0YfmIREZBKKVM+n6EELIQusNp1JTgBM3G4X7MM01eW85CyVx54Ea3CQ+s\nndvPaTAuY3O1CeurDEjJuTKdiinKdESOxd76wgZr5DNyvS7k+iirKoLJ3GCTEetcRnzkQC2ebPZj\nrSt/P2aLjscH99UglMziCy904O4mB944TenFH2yoRFbVYM3T1WM+JGQF4XQWU+zFnCSTVRFOZW+7\nhzRZWFMmzDt37sT9998/6fGnn356QQOajZUVBnzycD3MOh6qlpuJrhc4nOoMI6tq0PMMvvlyH169\nyjGpwTrLAFaJA8sw+NqJLrxjuweNw7d5HlhTAVXDuBGdQK5ObOSNzLEM3ry5ChlFhWW4Rnl9VW5U\ntmWW/RTvX1MBRcOkOjJCCFkIHcEUDjYWp63XdEbKMuaSmC11LMPAKvEwCHPvqsCxDDpDKeyvt+Fk\nVwS/uDqAjx2om3IISjydxenuKKotIlZXTr/ZL51V4Y9lUGUSIfLsrK6Pu2ssaLTrx7V941gGa1xG\n/ImlZrS+OR+bXoBB4PCBPV5UGKZPhKd7nfnwqpUOHGy0F3xtv9AXxQ/O+/Dh/XWj+QgpHVO+W/Il\ny9M9XgwpWYEvKkNWVHSF06g0iuiNpHHFH8f53ijesc2NXbUW7Ku3Tuq3qBc4vG2LG+3BBKLp7LiT\nT6E7Y0f6M94YjONsTwyvarLf1tjIUtuJSwhZ2jpDKdSX6ArzyxMGbJDxLBKP9+32Yj7KYlc5Dfj4\noXoIHItIOot7V1VAx099PUrIKp5o9uNgY+5aF0zKaLDrx+2/ebkrgkAilyh/++VePLqnBpuqTWAZ\npuDFJJbNTentDqfAQkMyq8Em8agwirAUsBos8mxJ9PMWeBazWbu2Sjw2Vpnm5cMQmX9T/lauXLmC\nK1euwO/3jz7285//HI8//viiBFaIm4Ek/unFTpztieFvf9uGsz1RfOV4FwwCh7duqYLTKOA9Oz15\nm5MDuU+sKyuMeN9u75TPKYQ/JuN4R2i0Dc5SMhDP4FJfFEnqJU3IkiArKvqiadTYSq/sIddajjoA\nzIRnmXnZsNkdTqF1KIlUVsGvWgJ4sT0EaYpk7XR3BD+7MoAP76vF3SvteKkjjC8f60RvZHz98+X+\nGF5sD8MiCXhVkwMVxpmT5K5QCtcH4uP2TsUzCr5zqhcnu6L40tFOHG3P3551KVldOfd8hCycKd/J\nx48fx4EDB8Cyrxw8Dz74IJ588slFCawQVWYRb95UBaeBx+vXOeEyiXjLFjdElsH3zvbh0TtqRp/b\nGUwikMhiQ5Vx3kdxbveascKhh9u89OqOmn1x/ORSP/58fx1WV+avGyOElI+eSDp3m7wEByXU2ST0\nRdJ5uymQ+ff0tQAu+2L467sb8MDaXGngyETAjmASwWQW66uMEDkWoaSMjmAKZomH3SBgvcsIq56H\nXT8+jXjjxkpkFA2VRhErC5yS97MrA2gdSuLTdzWM3qWVeBYPbqgEzwIuoxtuy9K7vpLyMmXC7HQ6\nsXbtWqRSKTz22GNwuVw4dOjQYsY2o0qjiMMrRWiaBpPIocIkwKwT4I9l8M7t1agds4JyvDOCY21B\n/OVdDfPef1EvcAvSkmaigXgGEsfCvIgz59dUGvCuHdXw0MmKkCWhM5hCXQmWYwC5W+lVJhFdoXTB\nI4nJ7XtVkwO7ayywG0RUmsavah5rC+FEVxifvqtxuJWcDbtqLLAOd2horNCjMc/v6HY20N2/pgLx\njAL7mO4PHMtgq2fxyipCSRmqpsFhWLxrXTApQ9Nyw2JI6Zsx85IkCTzPl1yyDOSmF7UMxlFt0uHf\nTvbgoS1u3FFvhcskThoscqjRhs1uY9ne6hhKZPDFFzqwpdqMh7a6F+37Vlt0c25MTwgpHR0lWr88\notGRm/hHCfPt6Q6loBe4KbtdjLVimo1ldzXZsb3GApdJxI3BBK4PJLBvgTZjrqwo7t3LrKLiv875\nEEll8eEDdYuyAV/Oqvj+2T4kMio+tL+WNv2XgSkTZr/fjytXruT9/1LRHkzhS0e78OF9tbij1jrt\n1CqPRQdPGSd+Es9if72trP8NhJDi6wimFizxmQ8rK/S4FUjiVU3FjqT8+GNp/OPRDuysseLhbXNb\nWPFYJHiGZ5l0hVJ45kYAG91G2IdXQxVVA8OgpIbf3C6OZbCl2oxUVoE4y2Est/09OQbbPGbIijbr\nATCkOAq6t3/gwAEA44eZlAKPVcQ7t7nhsYhIygqys2g+Hk1nYRS5sjnYDWJusAohhMxFRyiFh7ZW\nFTuMKa1yGvCj8/3FDqMsmXU8/mCDC648i0eyoiKjaKMrmYqqoWUgDrPIoc4+/Wr+rhoLVjsN8AwP\nu0llFTxxeQAmHYcH1jgXbUqtqmmIZ5R5bwfHMAwOLHKbRZZhCppGSErHlLsqHn74YaTTabS3t8Nm\ns2H9+vXYsGEDHn744cWMb1qRlILjHWH0RTP4ZUsA/fHx03RSsoJAfPKEnfZgEv/nSDsu+2KLFSoh\nhBRdVtXQG0mjZp73ccyn1U4DbgYSUEtsgaYc6AUOd66wY12VadLXRrpaDMRzkxQDiQy+cbIHz9wY\nmvF1zRKPGps0usCkqUAomUU0lX+KbqEC8QxSs+jA1OyL4/8caUf70MzTdAmZb1MmzM899xx0Oh0O\nHz6M8+fP4+bNmwAAk2nygVgsDXY93rG9GiscejQ6JDgm7NY92xPF//59G7pC49sUcQwDg8jP23x3\nQggpB73hNCqNAnTz3CloPlkkHmYdP2lcM5kbkWdhEjmM3P2vMIh4/24v7l3lmPVr6UUO79pRjTdu\ndN326nJfJI3PH2nHS53hgv8Ox+buti7WijYhY015X0PTNKxfvx4A8MADD+DJJ59EU1MTWltbsWLF\nikULcDoWicfOmlyR1ccP1kPgWXSHU/jdjSHcUWeF3SBgQ5VxtE3OiFqbhI8frINQwhcNQgiZb7kN\nf6W/mW51pQE3BhOoLeHNiaUso6h4sT00Os6aZRjsqbVgh9c82q6PYxlsdN/+AtjIJNyMoqIzmEKl\nSZhVhwwdz2JDlQnOWXSIWF9lwqoKA127SVFM+a6bql754sWLCxZMIU51hfH3z7biViAx7vGRA2gw\nnsHPrgyguT+O1U4D3rvLm3cue6EHnKyokLOF10YTQkip6gilUGcv/SR0lVOP64OJmZ9I8pIVDSc6\nwjjTE4Gi5q7lDMNM29u6L5LGV4534Zo/PsVrqpDz7BPqDKbwpWOduNj3SonjuZ4I/va3t3C1f+qy\nR4dBwLt2erBplhP5KFkmxTLlCvOlS5cgy6/U/7a0tECWZbS0tOANb3jDogSXTySl4NpgAik5fxK7\nttKI//WqRkg8O+fbNpqm4elrAQzEM3hoSxUM4uL1PyaEkPnWGUxid23pdsgYsdppwH+e8xU7jLJl\nFDl8YI8X7AxJ8liprIqOYBKxPDXFsqLiicsDyKoq3rSpatzwL6dRwFs2V41rVRiXFVwfSCAxxXWa\nkHI0ZQa4a9cu3HfffZMe//Wvf72gAc1kX70Fa12GKTetSAKHbV7LvH2/rKpBUXOtmDKqhk1uU9l0\n1iCEkLE6gim8aXPpdsgYscppQGsgCUXVqF71NlUYRfRH03i+NYh1LuOk2QQTNTr0+NRdDbBM0YEi\nq6pQVGDivWebXsCdK8Z3e9hdY0X9/RJqZ+i+QUg5mTJhzpcsT/f4YjGIfN6V3luBBK4NJLB3uHZ5\nPjAMg9evcyKdVfEvx7uRzipoqjBQg3FCSNlRVA09kTRqraXfy92s42GVchv/yqGEZLH0RdI41RXG\nrhoLPAV0OumJpPHji/14ZLd3xoQZyG0EzEfgWLxpUxU0oKANoyLPUrJMlpwlU2PQFUrj6ZZBeMwi\nVE1DhXF+xlsKHAuBY/HO7W5oGihZJoSUpZ5IGg6DAL1QHuew1U4Drg8mKGEeYyCewTM3htBg18Nj\nlWZcgV/jNODD+2pRa7u9D0nBhAx/LINGh35cGQYhy9GSOAKSsgKTyOH9u734jzN9+PW1wKRWcnNV\nY5VoxzYhpGy1DSWnHYVcalYNd8ogOZmsCk0DPrivFmsqDXi5K4KvvtSNoURm0nOTsgI5q8Ko47HG\nZbzt/TeX+mP45+NdaA/O7/WUkHK0JBLmrlAKp7rC8MczuG+tEzzHIDOLqX+EELLUtZZZwrzaacC1\nAUqYR/THMvjGqR5c88eRzqpQNQ1DSRlZdXxVcSSdxddP9uDp64E5f88VDj0e3upGlXl+yhwJKWdl\nX5IhKyqeuxVEMJnFepcRVWYdYuksrHo6wAkhZETbUBL3rqoodhgFW+00oHUoCVlRC+70sJS5TLlB\nIw49j6+d6EaVSYePHayFWTf+WscAMArcpPkDt6PGKpX0VEhCFlPZJ8wcy6CpQo8aiw5OowiOZShZ\nJoSQCdqGUmgsoxVmg8jBYxFxK5DEWpex2OEUnY5nsdVjRiqrYK3LCJtemJQsA7kNk+/eWU3dnAiZ\nZ2WfMLMMg3vKaNVkLkaaxtNqCyFkNuIZBeFUFtWW+dkMvVjWuYy46o9TwjyGxHN4wwbXpMc1TUNG\nUaHjObpGELIAyvqoUlQNl30xtA0l834tlJTz/K3yNNI4/rFLfmRo8iAhZBZah5JosEtlt+o4kjCT\n6SUyWVz1x/GPR7vQG0mhO5zCuZ4oUnmGkBBCbk/ZJcytgQR+eXUAbYEEgkkZ3zndi1+1DE563iVf\nDJ8/0p43mZ5OICHjaFsQvZH0fIU8b2RVhaxokxrHE0LIdNqGkmVVjjEilzDTxr/pZFUNP77ox69a\nBlFlFKBpwOnuCL71cg/8sQwURcHRtiCebw0io1ACTcjtKruSjN5oGv6YDJZJ4J4mOz6w2wuDODnv\nl3gWlUYROm52Kyq+aAY/utCPd26rhsdSOg3+X2kcrxXUOJ4QQka0DiWxsqL8EuYaqw4JWUEgIaNi\nngZSLTUMAJdRhEHkcO8qB2x6ASzDoKnCALdZh3A6ix9d6IeiAutdBlSaitOHW9M0nOmJQlZU7Kq1\ngqcJjqTMlF3CvKpCj+dbQ7gVSGCT2whJYFFnm3whWOsyYnWlYda3IFc6JHx4fy2qzaWTLI+gRJkQ\ncjvahpJ4VZOj2GHMGsMwuVXm/jgONNqKHU7JuT6QazF3/9rcPh5m+HpXbdGhenjBx8GLeHSPF9CA\nSlPxrmtZVcMLrUHEZQWb3CaYphjBTUipKrt3bE8kA5dRwM41Tjx20Y+BhIxP3lmftzPG7dTrSQKH\nNZW0wYQQsjSomob2YAqNZToxb53LiCt+SpgnUlUV3zvTh85wGp+7fyU8lql/v5urzYsYWX4Cx+Kd\nO6qhqholy6Qsld27tsYqIZZR4LXqcN+aCqQVOvgIIWQqvmgGZh1XtufJ9S4jvn+2r9hhlByWZfEH\nGyqRkFVUGMqj+0mlsTziJCSfsjuDukwiXCZx9P8JIYRMrXUoiUZ7+dUvj1hTacDNAA0wyWdXrbXY\nIRCybNDZhxBClrBbgSRWlOGGvxEGkUO1WUTrLDseEULIfCq7hDmjqLjaH4c/lil2KIQQUvJuDiaw\nymkodhhzsqHKhOZ+6sd8O3yRNFoG4lBUakhKyFwUPWE+duwYnnvuObS0tBT0/I5gEi91hnC1P4ak\nrCCSkkcn4BFCCBnvRiCB1WWeMG+qNuJSX6zYYZSUaDo75RCrW4EErvrj0DQNv28N4l+Pd6E/Nv+z\nBeIZBUkajkKWiaImzIqigOd53H333ejrK2xTRzCRxQutIcQyKh672I9vv9yDsz3RBY6UEELKTyAh\nI6toqDSWdw/jjW4TLvfnEkACDMYz+NLRThxtC036WlbV8JOL/fj+2T5E0lnsrbPi3Ts8874xMJ5R\n8M1TPXiyeQAq/V7IMlDUTX/hcBhOpxMAIEmFtTxa4zLgg/trMRBLw64XIKsaJKHoC+WEEFJyRsox\nmDIbiT1RpVGEXmDRGUqhvow3MM4XjmXgMoow6iYPIeFZBm/d4oasqLBKAqySgIYFmPLIMoDLKMA+\nPCiFkKWu6F0yVDV3S6nQE7pVErCzRoCqaWAAqFru5EEIIWS8G4MJNJV5OcaITW4TLvnilDADsOsF\nvH+Pd8pr32KMQdcLHN621Q26+pLloqhLs1arFYODgwBy5RmzwTIMGIahZJkQQqZwI5DEKufSSDBz\nCTPVMY8ohWvfyHWYkOWgqAkzx3FQVRXPPvssKioqihkKIYQsOTcGE1hVsVRWmHMb/6iOmRBSDEUv\nyThw4ECxQyCEkCUnmJSRklW4zUtjwJPHooOqafDFMqg264odDiFkmaHdcoQQsgTdHEyiyalfMrfM\nGYbJlWVQezlCSBFQwkwIIUvQzUACTUukHGPERrcJl300wIQQsviWVMIcSso43R3BUEIudiiEEFJU\nN5bAhL+JluvGv5Ss4FxPBH2R+R8+QggpzJJKmNuGUvj30724FUgUOxRCCCmqawPlP+FvogaHhEg6\ni8AyWxTxRTP41su9ON9HQ7oIKZYllTCvqJDwyC7PkrsNSQghszEYzyCjaPBYlsaGvxEsw2C9y4jL\ny2yV2W0W8eieGmzzmIsdCiHL1pJKmK2SgG1eC+yG8h4DSwghc9HiT2BtZflP+Mtnc7UJF5fZxj9J\n4LC52gQ3dQchpGiWVMJMCCEEaBmIY63LWOwwFsSWajMuLLOEmRBSfJQwE0LIEnN1eIV5KVpZocdQ\nQqbN3YSQRUUJMyGELCGKquFmILFkV5g5lsFGt5FWmQkhi4oSZkIIWULag0lUGkUYRa7YoSyYrR4z\nLlDHCELIIqKEmRBClpCr/gTWuZZmOcaILdUmXOilFWZCyOKhhJkQQpaQq/6lu+FvRKNDj2g6i4F4\nptihEEKWCUqYCSFkCbnqjy/ZDX8jWIbBZlplJoQsIkqYCSFkiQgmZQSTWTTY9cUOZcHl2stRHTMh\nZHFQwkwIIUvEZV8cG6uM4NilN7Bkoi0eE87TCjMhZJFQwkwIIUvEJV8Mm9ymYoexKOptEtJZFb5o\nutihEEKWAUqYCSFkibjYF8Om6uWRMDMMk+uWQf2YCSGLgBJmQghZAqLpLPqiaTRVLP365RFbPGZc\n6KU6ZkLIwqOEmRBCloDm/lx3DIFbPqf1LdUmnO+LQdO0YodCCFnils+ZlRBClrBcOYa52GEsqhqr\nDpoG9EaoHzMhZGFRwkwIIUvAJV8Mm91Le2DJRK/UMVNZBiFkYVHCTAghZS6SyqIrlFryE/7y2eIx\n08Y/QsiCo4SZEELK3LneKDa5TRCXUf3yiK3VJpzvjVIdMyFkQS2/syshhCwxZ7qj2FFjKXYYReE2\nixA4Bp2hVLFDIYQsYZQwE0JIGdM0Dad7ItjhXV4b/kYwDIPtHgvO9lAdMyFk4VDCTAghZawrlAbH\nMKix6oodStHsrDHjDCXMhJAFRAkzIYSUsdM9EWz3msEwTLFDKZqtHjMu+2LIKGqxQyGELFGUMBNC\nSBl7qSOMO+qsxQ6jqCwSjzqbhGZfvNihEEKWKEqYCSGkTIVTWdwYTGD7Mq1fHmtnjQWnuyPFDoMQ\nskRRwkwIIWXqRGcY270W6Hg6le+gOmZCyAKisywhhJSp4+1h7G9Y3uUYI9ZWGuGPZTCUkIsdCiFk\nCaKEmRBCylA8o+BCXxR7apdn/+WJOJbBVo8JZ3qoLIMQMv8oYSaEkDJ0rD2ELR4zTDq+2KGUjB01\nFpzuprIMQsj8o4SZEELK0LM3hnBPk6PYYZSUXcMb/xSVxmQTQuYXJcyEEFJm/LEMWoeS2FNH5Rhj\nuUwi3GYRl3yxYodCCFliKGEmhJAy8+yNIRxstEHk6BQ+0b56G17qCBc7DELIEkNnW0IIKSNZVcMv\nWwbx+nXOYodSkvbVW3G8IwxNo7IMQsj8oYSZEELKyPH2ENwmESsrDMUOpSQ12CWwDNA6lCx2KISQ\nJWRREuZAIIBnn30W58+fH30sGAziqaeewtNPP00rAYQQUqAnrwzgDzZUFjuMksUwzOgqMyGEzJdF\nSZgrKipwzz33IJVKjT7W3NyM1772tdi+fTtaWloWIwxCCClrl3wxDMRk7G+wFTuUknag0YbnW0O0\nGEMImTdFK8ng+Vzv0KqqKoTDtBJACCEz+d6ZPrx9mxs8yxQ7lJK23mVERlFxK0BlGYSQ+bEgHe9v\n3ryJF154YfTPDz74IJxO2qBCCCG360JvFAPxDO5ZRb2XZ8IwDO5eacfvbg6hyUm13oSQuVuQhLmp\nqQlNTU3TPiebzQIABgYG4HDQBYAQQqaiqBr+7WQP3rWjmlaXC3R3kwN/8asbeGS3Fxz9zAghc7Qo\nJRlDQ0M4cuQIrly5gmvXrgEA1q9fj6eeegqnTp3C6tWrFyMMQggpS7+8OgiTyOHwCnuxQykbdTYJ\nToOIc700KpsQMncLssI8kcPhwOHDh3H48OFxj732ta9djG9PCCFlqz+awX+e8+H/vrYJDEMrpbNx\n35oKPHV1EDtraCIiIWRuqA8zIYSUKEXV8Lkj7XjzZhca7Ppih1N2XtVkx0VfDP5YptihEELKHCXM\nhBBSor53tg8ix+BNm1zFDqUs6QUO9zQ58Murg8UOhRBS5ihhJoSQEvRk8wCebw3hk4cbwFIpxm17\ncL0TT18LIJFRih0KIaSMUcJMCCEl5pnrAfzkYj8+d/9KOAxCscMpa16rhG0eE35Bq8yEkDmghJkQ\nQkrIsbYQvvNyL/7h/ia4zbpih7MkvH2bGz+95EdSplVmQsjtoYSZEEJKxJnuCP7pxS78/WtWos4m\nFTucJaPersdWjwmPXfIXOxRCSJmihJkQQkpAc38MnzvSgc/c04hVNJ1u3r1vlxdPNg/AF00XOxRC\nSBmihJkQQorsViCBz/62DX9xZz02uk3FDmdJqjKL+MONLrpKYoQAACAASURBVHztRA80TSt2OISQ\nMkMJMyGEFFF3OIW/+s0tfHB/DXbV0oCNhfSWTS70RtL47Y2hYodCCCkzlDATQkiRRFJZ/PVvWvFH\n26txqJHGXi80kWfxqcP1+OapXvRGqDSDEFI4SpgJIaQIZEXF3z3bhn31Vjyw1lnscJaNlRUGvHO7\nG595phVx6s1MCCkQJcyEELLINE3DV453wyhyeN8uT7HDWXZev86JTdUm/MPv26GoVM9MCJkZJcyE\nELLIfnp5ANcG4vjk4XpwLE3xW2wMw+BP99ZA04DPH6GkmRAyM0qYCSFkEZ3oDOOxS/347KtXwiBy\nxQ5n2eJZBn9zTyPCKQVffKGDkmZCyLQoYSaEkEXSGkjiiy904jP3rECVWSx2OMuejmfx2XtXIJTK\n4m9/20qTAAkhU6KEmRBCFkEwIeMzv23Fn9zhxTqXsdjhkGESz+Lv7l0Js8Tjk7+6iVBSLnZIhJAS\nRAkzIYQssExWxWefbcM9qxy4u8lR7HDIBDzL4BOH6rDNa8aHfnYdNwYTxQ6JEFJiKGEmhJAFpGka\n/vFoJ5xGAe/c7i52OGQKDMPgPTs9eP9uDz7961t45nqg2CERQkoIX+wACCFkKfvh+X50h9P4wutW\ngWWoI0apO7TCjnq7hM8+24bm/jj++A4v9AJtziRkuaMVZkIIWSAvtAXxVMsgPnvvCkg8nW7LRb1d\nj6/8wRooqoY/eeIarvrjxQ6JEFJkdAYnhJAFcKU/jn95sRufffUKVBiEYodDZskgcvj4nfV4765q\nfOaZVnz/bB9kRS12WISQIqGEmRBC5llPOI2/e7YVn7izDk1OQ7HDIXNwqNGOr/7hGlwbSOBPnriG\ni32xYodECCkCqmEmhJB5FEjI+Kvf3MI7tldjd6212OGQeeA0ivj7e1fgWHsYn/t9O7Z5zXjPzmo4\njdRLm5DlglaYCSFkngQSMj7x1A28ZrUDr1vnLHY4ZB4xDIODjTZ8603rYJN4PPp4C755sgeRVLbY\noRFCFgElzIQQMg/8sQz+4qkbuKfJgYe2Uvu4pcogcnj/Hi++/sa1SMoq3vOTK/jK8S50hVLFDo0Q\nsoAoYSaEkDlq8cfx5z+/jgfWOvHwNkqWlwOnUcSHD9Ti629cC5PI4X/+8gY+8dQN/KplsCxXnePp\nLM72RNAfzRQ7FEJKEtUwE0LIbVI1DT9rHsAPzvfjYwfrsLeeapaXG6dRxLt3evDwVjdOdUVwpDWI\nb5zswSqnAdu9Zmz3mtFUYQDHlnYP7s5wCt9+uRdv3VyFKjPVZhMyESXMhBByGzqDKfzrS91Iygq+\n/PpV8FqlYodEikjkWRxotOFAow1JWcHFvhjO9kTxhec7MZSUsaHKiE1uEza5TWhyGsCXWAJdZ5Xw\nyG4vvBZdsUMhpCRRwkwIIbPQHU7hsUt+vNgexlu3VOENGypLLvkhxaUXOOyps2JPXe6OQyAh47Iv\nhku+GH53rBO+aAZrKo3YVG3CHbWWkmg9aNTx2OYxFzsMQkoWJcyEEDKDSCqLE51hHGkN4sZgEg+s\nqcC33rQOVolOoWRmFQYBd66w484VdgC591NzfxyXfDFcG0yURMJMCJkene0JIQXRNA2v++4FCCwD\nSWCh5znYDTycBgFOowiHQUCFgYddL8ChF2A38DCJHBimfFZfFVXDYFxGXzSNvkgaNwJJXPXH0RtJ\nY7vHjFc1OfCZe2zQ0ZhrMgcWicfeeivVvBNSRihhJoQU7Mk/2ox0VkUqqyIhqxhKyAgkZATiMvyx\nDK754xhKZhFMyhhKyJBVDUaBg45nIfEsJIGFwDFgwYBhAJbJ9bdlgOE/5/5fA6BpgAZt9Hvn/pz7\nL4Yff+XPr3w993juD2O/PvL4uOdpQDqrIp5REMsoyCgqHHoBbouIarMOKxx6vHqVAysr9BA5SpIJ\nIWS5YjRt7OWk9Jw4cQLxeLzYYRCy7BiNRjr2CCkCOvYIKR6O43D48OFJj5f8CnM8Hsf27duLHQYh\ny87Zs2fp2COkCOjYI6R4zp49m/dxusdICCGEEELINChhJoQQQgghZBqUMJNlryuUwr+82IVbgUSx\nQyGElIjm/hi+fqIbA3EaFU0IoYSZECRlBe3BJOIZpdihEEJKRCSl4NZQEumsWuxQCCEloOQ3/ZH8\nNE1DJJWFReLLqs/tbCVlBZf746g2i6hZoNHDq5wG/OVdDbDrhQV5fULI3Iyc76yLeIzu8JqxptIA\nh2HhvueNwQTSWRXrXEZwNC2SkJJGK8xl6tpAAp870o5rA0u7jKA/msF3T/fiUl9swb4HwzBw/j/2\n7js8srM++P73TO8jzUijGXVpd7W9N6+9zbjQko3BdDAmQCgJgWBaSN7nCUnePC+BAAEbMHkSOphq\ngymmmLXXZXddtveq3kbSjKbPnJlzzvuHtNrVqo16uz/XxXVhaTRzVrtzz+/c96/YTeIDSxDmqPMD\n693MtVozGXTTGiyrmsYvz3bxvaPtRDO5aXsdQRCmhthhnqdMeh1ui2HGhylkFRW9TkI3Q7vapS4z\nf72jnKJp/OASBGFuM+ml/vVu6tadrKJinMVhNDpJ4k1rfWQUTZxuCcI8IALmearWa+UTe6pndFc0\nls7xg2Md1BXbeMVSz4y8psmgY3WJY0ZeSxCEuWmJ1zal693xthhPXgpx3yY/JU7zlDznRFQWWmft\ntQVBGJ8FlZKhqBrH22JcCC6OCUkznUKgahpxWSEtimAEQRhBc2+aQ429xNJTm2YwleudrKjEZQV1\nTs+5FQRhLllQO8zRTI5Hjnfgd5ioK7Yt6GK42eC2GvngjnJMcyzXtymc5mooxcYyB26LONoUhNl0\nqTvJz08HeWBXJU7L3PyI2VzmYqXPjtM8M9d3uTtJZ0xmQ5kDu2lu/k4EQRjdgnnnprMKnTGZ924t\nxW7Si2B5mthN+tm+hCHqwyl+dqqTCrdZBMyCMMvWB5yUucxUFAzf1aYpnEKSpBG/PxP0OmnGgmWA\nkx1xnr4SosZTLQJmQZinFsw7tyGc5sGDzbxtg5/biu2zfTnCDFpf6qDcZaaycPY+gAVB6OO1G/Ha\nh79xTcgK33y5DZNex8d2V2I2zL0b8Omwp6aAjaVO/K7Zy5cWBGFyFkzAXOIw8db1JdR4RNC02BRY\njBSInWVBmPOsRh2vX+NDL0mLJlgG8NpNeMU+jiDMawsmYC60GdlZUzjblyEIgiCMQCdJrAs4Z/sy\nBEEQxm1BdckQBEEQBEEQhKkmAuZZFk3naImkZ/syBEEQ5iVV02juTRMT0/IEQZhGImCeRpF0lmOt\nMXpT2REf81xDL/9xoJFWETQLgiCMW3Nvmp+e7OBwY4TuhDzblyMIwgIlAuZpdLUnzX+/1Mql7uSI\nj1nitfKq5UU4zQYUVePAlTB/uhQiq+Q/HKQhlOJIS5R0VpmKy56QpJzjufpejrREOdISJScmAgiC\nME7H2mI8eipIfBy7xS6zgbV+Jz8+2cmFrsFrbVs0w/7LIbriwwfSl7qSnGiPoUzRepXKKhxs6OVi\n1+IYniUIi4kImKdIKJlFvmkCXnWhhXdtLmWJ1zbizy0vtvOq5V4sBh29qSynO2Ica4uNK+A83Bzh\n20faCI2ykw19i3lCnp6gujet8NOTnRxs7OX3F3tIyOJ4VBCE4eVUjUj6+nqlqBrhVJbmcJrDzZFx\nTRMttBm5pdLNB24pZ1nR4LW2NZLh56eDdMQyw/7sExd6+P7R9kHXMhGhpIycU4mlc/zkVCcvNUcn\n9XyCIMw9C6ZLxlQ52xnnXDDJHUsLKbDm16qsJZLmK883c8+qYm6tLhj4eqHNyFZbfs/xYkuU357v\n5n3bSnFbjURSOVoiGZZ4rejGGMKyt7aQjQEnJY6Re3zmVI2fngwSyyi8e2sAq3FqWzoFnCY+ursS\nTdMw6HRjDhBRVI1jrTGQYFOZc8w/oyAIC8ex1hiPnQly/+YAR1tj1BXZ+MnJTt67tZRbq90U2U3D\n/lxO1bjSncRjM1LsuP4Yp8XAlnLXkMev8tn4u52VVLiHXxtfv7aYdFbFYtDx9JUwfqeJFb7x9X9r\n7k3z4PNN3Lu2hO2Vbj66s3JODngSBGFyFt0Oc3dC5mxngswwOxg9iQyRVI7DTb0kx5HeYNbrWOKx\n4prEGFinSU+l24zDbKDQauT3l3p48PmmEY8Sb+R3mlnus6O/aWR1YzjFw4daaAynkACvzYjPYWQ6\nQlNJkqgutFLjseU1wUtWVP5wqYcnL4WG7MwLgjB/KarG+a7EiLu60NePudhuJJ1VONjYS0JW8DtN\n2M2GEYNlgLZoml+d6+Joa5RUVuHRU0Geqw8DfWv7zeul3WxgWZENywgbBOVuC0uLbMQyCr88G+RE\ne3zcf16TXqLGY8Np7nuNqkLrqH8GQRDmp0W3w3yyPc7PTwd5YGclS244vrvSneRrh1vYUenmI7dV\nUurKfwBKscPE+28pn9R16SRY7XcMjGvdWV3AmhIHhXnucg8nllE4351gt1yAXifxmhVeNJjQbm5b\nNM3ZziTrA45BOzsTZTXqeffWUiQY8cNMEIT5pz2W4aHnm9le4Walz866gAOTYfDezBq/g5X9O7kf\nvrWC5kiat6z3ExhjEp5BJ5HOqthMerKKytlgnFTOynZF5ftHO4jLCn+3swLHOMdelzjNPLCrakI7\nwyVOMx/cMbn1XxCEuW/RBczLi228c1MAowEawimqC60AKJoGSJQ4TficM787cKgxypVQkjUlDswG\n3ah5z/la6bPzj7fX4OlPC5EkacK7y62RDI+dCeJ3mqYkYIa+nXFBEBaWIpuRd20ppSsu89OTHdR6\nq/EYhq4Z107E4rLCz0938f7tZnRS33/XeqxIw9zYG3QSSBI6ScJlMfKhWysw6HUYdBI7qtxkFBWz\nYWIHp/mcjC12OVUjls5hMeqwGHTD/h0JwkIlaZo2p9sZ/OlPf2LTpk15PfZcMEEomWVzmXPUXUtF\n1fjyc010JWQ+tbd6IFc5nsmNe2fiRp2xDMFElmVe67h3TbsTMrKijmtnO1+KqtH3OTPxxS2RydER\nkylzm8WO8CJx9OjRvN97gnCzYFwmk1MHAtFQsq/N5rIiK5X9GxUA6axCR1zG7zDxs1NBXmqJ8g+3\nV1PiNKOo2pBUs4SsYDb0BckA7dEMoWSWZcU2TPqFkWU4l957mZzKExd6+NPlEFd6UthNetJZBYtR\nzyqfnVsqXeysKRg4HZ0J3QmZhnCaUDJLJqdiMeoIOM0s8VqnvD5HWHyOHj3KHXfcMeTrC2qH+cXm\nCCfa+gpIRgvq9DqJV68oIp1VB73JJxMsA5xoj/PLs118bFcltePcIZ6unLeE3Ne9otxt5s5l3gk/\nj91sYMlNv590VuFwcxSvzchav2OylyoIwgLiu+kkqjsh89iZIG9aV0J9OE2h1cC6QN/mxrWTvluq\n3NQV23BbDIRTWX54vIPNpU5uqbpeTH1z2sShpgj7L4f41N5qsUs8xc50xPns043Ueq385ZYAa/yO\ngZuSYFzmTGecZ+sjfOOFVjaWOnnlci9by11DbnImK6uoHGuL8Wx9L0dbY8iKRq3HgtdmxGzQkc6p\ntES6ae5Nsy7g4J7VxWwsdYodcGFKzWjA/OSTT2IwGHA4HFRVVbF//36Ki4tZu3YtxcXFk37+164o\n4vYlhXmlDKwcZyV0Ptb47XhtxjHz8GaSomn0JLM4J1GQOJKErPCbc12sLnGIgFkQhFFVe6w8sKsK\nq0HHfz7fxLIiG+sCzkGPWeq1Qf99fVxWCCWzJOTRi4K3lruoLbRSbJ94vYcw1DNXwzx0sIWP7qpk\nR5V7yPd9DhM+h4fbl3hIyAoHroZ55HgH//lcE3ct8/LKOg/l7onfwMiKytHWGM/U9/JCU4TKAgu7\nawp4y/oSSl3mYYPhhKzwbH0vDx1swWsz8tFdlZTOoc9jYX6blZSMw4cPs2TJEmKxGLW1taM+Nt+U\nDFXTyORUcRwzjISsoJemp7iuLZrGYtAP5EkLC8dcOhYWFpb2aAazQcJjG31zI5bOYTHoMOaZl6xp\nGumsinWet3Wb7ffe4aYI//lsE//nVUup9VrH/oEbNIZT/P5iiCcvhSh3m3nlci+7awry+mxOZZWB\nneQXmqJUeyzsrilk5yitBoejqBq/ONPFI8c7+NvbKthTWziuP4OwuM2ZlIzHH3+ckpISzGYzhw4d\n4urVq9TW1o4ZOI/leGuMJy728O4tpSPu8HbGMpzuTLC6xL6oCs6msyfodORcC4KwMLRHM5wNJlhT\nYqfkhjU331O48Z6MnelM8PPTQd61OUBV4fgCPaFPayTNF55p4l/urh13sAx9bfXet72Md28t5YWm\nCL+/2MM3DreyNuBgeZGNgMtMgcWAJPXtIgfjWVoiac4Hk1wNpVhebGNXTQHv3VaGd4IbMXqdxL1r\nfWwodfC//3CVjpjMm9eXTOi5BOGaGQ+Y9+3bx/79+3G5XOzbtw/o23GebMCMxJgdINpjMo+eDlJo\nLZ1TAXMmN/HK7nwNVzwzXzSGU6SyKsuLbQPHcM29aX55totXL/dOSUcRQRCmXms0w6OngxTbywYF\nzBPVEcsQjGepKxpaWH1tvLXE0M+CrKKi10mTHpDUnZB57HQXt1S6WHtTOslCoKgan326kXds9E86\nbdGgk7ituoDbqgsIJbOc6ohzsSvJwYZeetM5NA1MBoliu4lSl5n7twRY6bNjmcLPwiVeG1/Zt5xP\n/PYSkgRvWieCZmHiZqXoz2KxkMvlMBj6Xl5RJj+ueUOpk5U++6jHPsuLbXx0ZyVlI0x9mg31oSQ/\nONbJq+o81Hpt05La0BnL8MPjHdy51DMvF/lfnOmiOZLm07dX4+0/wk1mFepDqWkb9S0IwuSt8o2+\n5rZF0iSzKku8w7eRu9mxthi/PtfNx3dXUeO5vvvZEknzo+Od7Fvp5RN7qgZ9DoRTWb5/tIONpU52\n1hQM97R5kxWVyz1JVvgW5k3642e7MOt17FtVNKXP67EZ2VNbOCupEV67kc+9ZikP/PoSBRYDd9dN\nvPhdWNxmLGCORqMcPnwYk8mEw+GgpaWFixcvArB69epJP79OksbMkbIa9SwtmthCl84qxDLKlPUg\nvianQjSd42J3knROZWfN1C8oWVWjO5klNcmJepqmzUrV8Z+vKiYlK4OGuNQV2fj07dWTGuwiCML0\nSMg50lkVr93E0qKRP2Z+ebabS90JPv2KGorzyFFdH3Dgd5jw39QrP6to9CRlUoo25HNA0zQSsoKs\nqAP/PdF1rNRl4VN7q7EZF0b7uhtF0jl+cKyDL/153YLrLlFkN/Gvd9fy8d9cpqLAMi1F/8LCt6D6\nME+n5+p7efR0Jx/dVTWp1kVN4RTRjMLKG0ZZB+MZmsJpytyWEXP7GsJJ2iIyRXYjdcXjf7NH0lls\nRj3GCfYpvdyd5Fdnu7h3nY/KApEbuBjMduGRML+0RzMcaYmyudzJ8bY4T10J8bHdVcOmYsiKyvlg\nAh0SWU1lrd+JQSfRk5CJy8qE8o97U1mcZsOwqWcJWcGgg6eu9BLL5PjzlUVzup/8bLz3/uuFVtI5\nlQ/fVjGjrzuTDjVGePBgMw+/bgWuaegcJSwMIxX9Lbzb5GnitRvZUOrEOomdBVXTONkR51svtdKd\nkAe+7nOY2VLhHjFYjmdy/PcLbfz8VJC2mDzsY8bithgnHCwDpHMq7XGZTG5m7680TSOcyjIV93WK\n2vdcgiBMva5Elicu9tAZz1LqMrOxzIVBpyOdG5o2FYzL/NcLrZwOJthY6hoYQvKHiyG+9FwTnbHM\nwGMj6SxZZezTsQKrcUiwnJQVkrKC3aTHoNPRGZNpj2ZQ5vQ20czrSWT5/cUe3r7BP9uXMq12VLnZ\nVVPAV55vnpLPFGFxEbdYeVrps0/6GOdUR5zDTVHu21w6rhY5VqOev1jtQ1E1Vs5S7txKn52/31s9\n4+3jznUl+e6RNt61uZQVk/z9nwsm+P6xdt67tWzCqTmCIAxvebGNT+ypwu8w9U2BK7Hzs1NBMjmV\nt2wowWK4vqPrc5h4/y3leKyDP4I2lTmpKLAM7P61RtJ87XAL+1YWs71yaC/g0WRyKj841gHAOzcH\nMBt0vHFdMYo2vZ2D5qOfnw5y5zIP3kXQy/rdW0r50C8usP9KmDuWemb7coR5RATMM0gH2Iw6iuxD\nd0JGo9dJbK1wTd+F5XkNs9Fr2azX4bWapqSLiFmvo8hmwqRfWPl5gjAXmA26gYl91xh0EqpeQrqp\nb4VJrxt22NFyn53lN/28z26a0MmeToKi/gDw2nJrM4mPvJulsgq/v9jDQ/csH/vBC4DZoOOTe6v4\nh99dYVOZU9TBCHkTq8cMWuN3UFdsw2yY3O5GUlawLZIdkiVeKw/srpySlnjLim18dNfUPJcgCKMz\n6nXcs7p44P/nK6uoKJqGxaCnxGnmQ7dWTOg9e+PrL7Qitqn0x0sh1vkdBOZQq9XptqzIxp1LC/mf\nF9v4+J6q2b4cYZ4QOcwzSJKkSQfLl7oSfP5AA5e6ElN0VXPfcB+WoWSWZ66GaYtmhvmJ8T2XIAjT\nw6jXjbt24jfnuvnukY6BlpGTec9KkjQQLF/qTvBScwR5kt2CFhJN0/jlmS5et6Z4ti9lxt23KcDR\n1hinO+KzfSnCPCEC5jnoWhukEfV/AOTyKISZDqGkPKgoZza0RzP8+GQnTeH0rF6HIAhTS6e7OYFj\nqFAyO7AG5RSVlKwMDC4ZyTNXe/nB8Q4i6dwUXen8d74riaoxbHrMQmcz6Xnf9jIeOtiCKgoAhTyI\ngHkOOhdM8B8HGmgIpYZ8b1mxnU/uqcKg1/H5Zxq50jP0MdNJ0zR+dKKThw62EE3PXseJJV4rH76t\nglUlop+mICwkr17u5Z2bAyMW5mmaxo9PdPDQwRaOtUb51blu/v1AA8fbYqM+72tXFvHh2yqmvJf+\nfPbkpRB3LvMs2pSVPbUFWAw69l8Oz/alCPOACJjnKEkaeda31ahHzqnEMsqkd5l7kjLhZP6BryRJ\nbK9wc8cyD9ZJppdMhsWoZ3mxXfTSFIQFxqjXjVrke+MapKiQySnopbF3pTUNSkSwPEBWVA5cDXPH\n0pmfvjdXSJLEe7eV8p0j7SJVRxjTgo02FFXjeFsMg05ifenQcdA5VaMnIVNkN82JvNbWSJpUVmVp\nkY1VJQ4+4bGOPubbZ+fv91bhsky8wjchK/zfF9qwGXXcvyVAdyJLhduCaYyOFJvLZ7djx3ToSsjI\nOZUy98SH0giCcJ2maZxsj6OoGutLnfSmsiSz6oiDn7KKypWeFB6bEd8Yge2mchdtkTSprMKrlhdx\nz2rdqOtWc2+aLzzbyD2rfOxdsngDxBu92BSlxmPFv4iK/Yazxu+g1mPl8bNdvGFdyWxfjjCHLdgd\n5lRW4VfnuvnDxR6yw9w5ngsm+Lf99VwYo3ju2kSqGweNTIfHznTx9cMt9PS/zlhjvoFJBcsARr3E\n9koXm8tdXOpK8sVnm7jSnwbSFk3T3Dv+/ODL3clhU0nmup+c6OShg830isEmgjAh7dEMTb3X3/uy\novLEhR5+fb6bdE7l9xdDfPHZRoLx4esf2qIZHjrYzAtNEQC64jIn22Kc64wPyU9OyQoHGyN8+ndX\naYtmsBj16EZJK7Cb9OysLiDgEjvM1zx1NcwrRB9iAN69NcCPTwZHrx0SFr0Fu8PsMBt43/ZSdJKE\ncZidB7dZz4ZSF07z6L+C5t40Dx5s5nWrfdy5bPoWl1fWeUnIOdwz2BPSpNexpsSBy2KgKyHz+tU+\niu1G5JzK9452kJQVPrGnCscYv6Nrouks33q5DYdZz8d3V01qsuBM211TQCyjYJvD43IFYa5SVI1H\njnfQnczyqb1VuC1GzAY9928OkFM1krLClnIn5W7ziGuuz2Hi/s2BgbSJ88EEL7ZEOR9M8E931VJ+\nw+mPyaCj2mNlT21BXmlZHpuRN6wVu4fXZHIqR1qi/O2t5bN9KXNCVaGVbRUuHjvTxTs2Luxph8LE\nLdiAGaDUNfLxemWhlXdvtY74/Wt8DhPv2Big3D29x1bL+ifPqZqGomoDaSKnO+I0hFPsqSnEOcX5\nug3hFF9+rom3bPCzvcI96APpNcu9KFp+O93XOM0G3r4xgEEnzatgGWBtYGjajiAI+dHrJF5Z5yWd\nU9E0ePxsF0u8VlaXOHi5Jcr3j7Xz4dsq2F07cjqESa9ja8X1aX5Li2xYjHpurXLjvWlokl4nsaXc\nxZYFmB42E462xlhWZKNADO0Y8NYNJXz0V5d43epiMQlSGNaCDphH0hbN0BrJUOk2cyaYYInXSlXh\n8MGz02xgR1XfIp7KKjSEU5Q5LTgsenpTOQqthoEK43PBBJqmsarEQTqnoI0z4MypGr8514WsaPzF\nqmJMBh1XQykOXA2zpdw15QGz1dA3batwmOedSAApSdKC7VqhqBpnOuM4TAZqvWPfaAnCQtISSZNT\ntUGT/K6tdyt9diRJYrXfQSqrEEpmefpqGJ0ksbrEgduiZ22JY8TTm5yq8Xx9mEimbxf62kZHwGUm\n4JrajYqLXUlyqspKn52krHA2mKTcnd/rZBWVeEahcBYmnk615xt6ubVqfKPGF7pyt4XNZU4eP9vF\nWzeIXWZhqPm1DThFLgQTfPtIGy3RNI+eDtI4Si/fnoTMocZeuhMyV3pSPHSwhbPBOOeDSf6/p+o5\n35UE+oLpn53q5CcnO4mlczx2uovvvNxOKquQzipc6k6SyIze/1PTNMKpHOFUDo2+nL29tYV8Yk/V\ntBRmlDjNvHtrGXXFEw9yG0Ip2iIz2ws5GJdpDA/Ok85Oc0/qULIv3eT3F3um9XUEYa7pS7fo5L9e\naB1oJZnOKvz8VCc/PtFJMqsOfO37Rzt46kqYT+6uZKnXyon2GDUeG/dvKSWaztE1TC1IUs7xq3Pd\nHGmJog3TD1fOqVzuThIbZv0cz/s+p6g8ejrI9491sQhQaAAAIABJREFUEMvkaIvJfPtI28AaPpaT\n7XE++3QDDeH5V6NxI0XVONwU4bbqgtm+lDnnbRv8PHq6i6TIZRaGof/MZz7zmdm+iNHU19cTCASm\n9DntJj0VbguFNgM7qwuo9dhGbGN0oSvBt4+0U+OxUllgwe8wUeOxIUnQGZNZXeKgwGrEqNdRXWhh\nfcBJkd1EQziNJMGqEjuXupM8eLAFp1mPTieNeAym10ksL7axzm/Hauzb9TUbdHnnEM+03lSWLz/f\nRH0ozfZK94z18nzkeAe/Od/NljInNpOeU+0xfnCskxqPdcyc9ImyGnXUeqz9O2Zz8+9jqrW3t0/5\ne0+Yf3SShMdqYIXPTrnbgk6SMOh1VBVaWOt30BHPkM6qOMx62qIZSuwmVpY4eKk5QkskQ7nLRE8y\ny5eebcJm0lPXn352jdmgx20xkM6pBFxmiuyDC/Mudif5yvPNBJxmym/osHGxK8E3X2qnosCcV2qB\nTidRUWBmfcBJwGXB3v+ervVY8zqCj6RzdMWzrAs4pm2duWY633un2uOc70rypvUip/tmbquBy91J\nuhJZ1izCYS5Cn/b2dmpra4d8fXF88t/Eadbzh0s9oGl8Yk81tlEWy+pCKx/aUU6Z24LLYmBnTSFn\nO+P89FSQ+zcHBh1R3pjW8doVRUBfEOx3mtm3qpjmSIbOmDzoZ27WnciiahpVprn/V+M0G3jzej9m\ng27UCvWpdlt1AWtKHAM3ErKiEcnkUKZxWpNOklg+iZ14QZjPVpYMDR6qCq10RDP85/NNbCl3cf/m\nUuqKbPzsVJAlRVbOBZNcDaW4c6mHIpuRd2wKEHBeD4ZjmRxmgw6TXkepy0wqq1JsH9rFwmc3cu8a\nH+UFg0/ZsqpGLJMjO8aEv5uv+RqLUT+u1LOVPjsrffN/DXi5Jcr2SpGOMZK3bfTzyd9cZt+qonGl\nVAoL39yPyvIQjMn85kI3t1W5h6QXKKpGWzRDodUwEGBZjXre0n93PVywnMjkqA+nqSgw86fLIVqj\nGd69tXTI40YLEW/s7VzsMHHXMg+XupOj7k5mcgrfO9pOJqfyqb2jB/JzgV4nsWGYHtfT7eYPrQ2l\nTuqKbdO+6yMIQp+2aJonzvewp7aAd24KUNDf4lKSAAl6kzlKXWY2lznx2IzodRK33BCkdSdkvn64\nhZ3VBdy+xENFgWXE/sxuq5GlRdYhhX+rfHY+Ncle9IvRy60x/maH6I4xkupCK2v8dp640MPr1/hm\n+3KEOWRB5DAnswon22OEU0Nz3Jp603zuQAPH2uIDX2vuTZNTNJZ6bUMeD3A5lOLrh1s4H0ziMBtw\nWwzob4iOV/rsfHJ31YiFgnJOJRjLDOTjyYpKSyRNdaFl1OISs0HPX6wq5t61PizGkf9quuIyV3qS\nqFOwoyrnVM4FEwP9n+cjvU7CaTagqNqwOZCCIEytWEbhZEecnkSOi11Jrvbn9a4qcfDxXZVYjTpK\nXSaWFtmGDIYKxjOksgoFFuOQaaGaphGMZ0hnr+eQXu5O8rmnGzndER/0WEmSsN10EhdOZjnbmSCV\nFTmowwkns3TEZFYsgJ3y6fSWDX5+djKIPM21McL8siAC5mqPlX+4vWbIbmcomcVs0PHKOi9lNzSs\n/9PlEA+/0DLiMJIKt4V3bPRT47Fyd52H+zYFBi3MkiRhHWX392RHnH97qoGroRRZReViMME3Drdw\nNjj6kBSA1X4H6wLOUVMcDlzt5T+fa6IjOvwAgPFo7E3z0MFmTrbHx37wGNoiaSLp2Rn8Ecvk+M6R\nNp66Ep6V1xeExWRZkY1/fEU15QVm/nQlNGhYUSSd48sHm+lO5obsGidlhf/7Yhs/PtHJe7cFqPVa\nBgW39eEU/7a/gZM3BMcem5G767z4HIM3Gy52JfjCgcZBBcBngwm+eqiZ+ikentSVkOmKz99NhWuO\ntMbYEHBgmAPTbeeyuiIb1R4Lf7wUmu1LEeaQBXOGXXzTKNVYJsfDh1vw2ky8d1vpoF2O25cUsrnc\nhXeYfDnoW6B3VN1QQTzOtaXAYmBjqRO7Sc/ZzgQPH27h3rW+YfPzJmJTmYNyt2lK2hv5+/tMVxRM\nrgtHezTD559pZE9NIffMwjGWqmn0JHJ4bWJnSRCmm06S8DnMdMUy3LXMS+kNucmFNiNv3+Afdry1\n2aDjFUsK0UsSnfEsX36uiTesLRno2GA36tlU5qTwhiI+n8PEvlXFQ57rWu1CVrl+qlTrsfL2DX5K\np7AdXSan8O2X2sipGn+3s3LUzZK57khrlM2id3Ve3r7Bz78faORVdd4hpyTC4rRgAuabmfQ6NpY6\ncZj1Q/6xj5RKMVWWFtlY2l8JnsqqbK1wD1SYT4Var43aEdJJxstpud5nelLPY9bz6uVFVEzhgJfm\n3jQtkTRr/GNXpbstRv761vJBqTOCIOQvlVU42R6nxGGi2pPfGmkx6jH0p0RdYzXquaVq+JZlep00\nsBnRnZDZXOai6IYb/xKnmfs3D60XGc6qEjuf3FM1qEPGdPRuNup17K4tRNU0TCN0U5oPVE3jSEuM\nd24WnW/ysdrvwGc38dSV8LRO+RXmjwUTMHcnZLoTWWq9Vkx6HWaDjlf3d6rIR6g/t6vWY8EyhZWx\nNR4rNXl++MxnDrOBu+u8U/qcF7qSPHYmyMd2VeZV0CemMwnCxPUksnzvaDt3LvXkFTBH0zmae9O8\nZrkXe//7M5LO0hrJUOOxjtlhoMhu4h2bJh686aSRW3ROJZ0kLYiuEvWhFDaTnsA09PRfqN66oYSv\nHWrhFUsLZ7QTlDA3zd/b5Zsca4vz4MFmWnrzG6Jxc8Hc6Y44Xz3UTMMoQ0yEwWKZHMoNLZ2C8Qzx\nMYazjMfGUicfvrViUO9VQRCmR8Bl5sO3VeY90OJCV5JvvtxG0w2Di851JvjqoRau9kwuh1jTtCld\nS/IRjGeIpWf2NWfSyfY46wOit/B4bOrv9f9cQ+9sX4owByyYgHlFsY37NgaG5DIPpzOW4aHnWzjd\nHhv42tIiG+/YGJjS3LfhpOdQ9bamaZzuiHM+j2LEmzX3pvn8gUZO9RfndMYyfO5AI09fnbqiO6/d\nyHKfHZN+wfwzFYQ5S6+TqCu25bWGAlQVWnj9Gh+/OddDe38BcrXHytvGyCHOZw081RHnsdNdXOga\n/9o0EV1xmc8faOTJywu3yOtUR5x1ImAeF0mSeNsGP48c7xQdmISFEzBXFFi4pcqd19F9VtXoiGcG\nRroClLrM7Khy45rAFDclz8b5jeEU//FMI+c6Z+ZDYCyJjMKPT3by2OkuMrnxtc/R68DWn78IfXmL\nO6sKqBK7wYKwKPgcJqxGHT1JeaD9lt9p5rbqghELkq/2JPn8M41cHGMcdSqr8NsL3YSTM9N1x2rU\nsbOmkCXehZk+p2kapzoSrBXT68Zte6ULVdV4sTk625cizLIZzWF+8sknMRgMOBwOtmzZwq9//WsM\nBgPbtm3D45m5pPpyt4VP7akiq6jIOXVShRzt0QyPHO/gVcu9rBpmGtaNNA1yqjatE+nGw2Ex8O4t\npeikvup1RdUIp7J4bMYx87VKXRYe2FU58LtzWQyz0h1DEITZsy7gpNZjHZRLHEllMeqH9kgGUDXI\nKdqoPeQbwymeutLLA7sqWVUyer/gSCqLQa+bdP2Cw2zgL4bpxLFQNPWmsRh0w3YuEUankyTe2r/L\nvK3ChSRymRetGQ2Y77zzTgAOHz7MhQsX2LFjB16vl6effpq9e/fO5KUQSed48GAzb1xbwrZJFHTI\nikp3Mks6N3YQXO2x9o3iHmUoyUy7sSDxbGecb77cxvu3l7PCZ6cxnEKvk0bs7mEy6AglZQw6SUzb\nEoRFyKCThgTLX3m+meU+O29a1zdNNZTMYtCBy2JkaZGNT+6tHghwU1mFxnCaUqcZl7V/1H1OJZLO\nYjPpsQ8TdF8TTWd58GALNR4Lb98oOj+M5lRHQqRjTMKumgK+e7SdE+3xWZluOxvCqSyHm6K81Byl\nPpSiI5ZB0fqK6ysLzGwpd3HHUs+0p7HOJTPeJePxxx+npKSEcDjM8uXLATCbZ/4XbjboWOKx4TRP\nbmeiqtDKp/ZUDYzdvuZKT5KkrLCqxDGord1c6uSQ7T9GNfbnCNvNBuqK7NhNemKZHP/9YisOs4EH\ndlUOPOZGsUyOrx5qwe8w855tpaKKWBAWOYNOoq7IRml/J4Z4JsevznahqHD/lgB6nTRoDawPpfjq\noRbu2+gfaEW3rNjOp/ZW47Zc77xxPpikutBCyQ0dHox6HSt8NkrErumYTrbH2FQm+i9PlF4n8Zb1\nJfzweMeCD5jPBxM8dqaLl5qjbC53sqPKxbu2BPA7TRh1EtGMwtVQisNNET7y+EW2Vrj4q22lg3qn\nL1QzHjDv27eP/fv3Y7Nd7yM8G0ccJU4zH9hRPiXP5R7mH8pvzvXQFEnx6b3VIw5ImU1ZReXR012o\nmsYb1vgwGnTUeqx8sP93oqga964twaSXhg2WAYw6iS1lLtwWgwiWBUHAbjbw5g3+gf9ujmQ4G0xw\nz+riYYc/+J1m3rK+hMqbeuPfuGvd0pvhu0fbedsG/6CA2WrU84a1JdPwp1hYruUvv2tLfv2theG9\nYqmH7x3t4FwwwcoFOFq8MZzimy+3c7k7yb1rffztreVDNgIB3P2D2TaWOnnnpgCPHO/gA4+e54Fd\nlQui/eJoZqUPs8ViobCwkGAwiM/nQ1Em1jlC0zTOBZMYdFBXPPo/4PpQilgmx6qSoWNBT3fEScgK\nG8ucU9aR4XVrikllFTz9xS+qps2toFKDnKqiajBcuZ9eJ415J20x6sfV61oQhIWjLZqhNZImlMyy\nscw5ZHQ19A1JtRh0w045VTUNj83IrprCUV+n2mPh/dvLpmzw02LTHusb6R1wzr2Nm/nEoJN44zof\nPzzWwb++cslsX86UScoK3z7SzlNXwrx5nY9/vL0677ouu0nPe7eVsaPSzb/ur+dtG/zDTuVcKGYs\nmTYajfKHP/yBp59+GrPZTF1dHS+++CK/+c1vWL169YSeMy4rPHK8nZ+dCiKP0eXhj5dCfPOlNkJJ\nedDXNU3jqSthfnEmSFIeX6eI0VQUWKgrtiNJEo3hFF98ponL3aNXhs8ko0HHG9aW8MZ1PszzeHqV\nMHndCZnoAu4/K0xcIpMjGMsM+722aIarPSm+crCFhtDw/etX+Ox8cm/1wOTTa8KpLP91uJUXmyJj\nXoPdZGBdwDmw+SCMz+mOOGv8dlGsNgVeVeflUk+SKz1z57N8Ml5oivC+R8+RlBX+5w0recO6kgk1\nQVjtd/ClP6vj56eC/Ops1zRc6dwwYzvMLpeLu+++e9DX/uzP/mxSz+k0G7h/cyl6nTTmX/JrlnvZ\nXVNA0U07HZIk8aZ1PrKqRoF16K8jq6gomobFMPHc40xOpTspkx5n67aJ6k1lcVsMYy6QIlAWelM5\nvvxcM0uLrHmPJBYWj4NNUZ640M3Hd1VSetMO7yqfDate4u/3VFFXbBvys6msgtkwfAeLnKrRGZeJ\nZaavL306qwwUKS1m54NJVi3AFILZYDLoeMMaH48c7+T/uaNmti9nwuKZHA8ebOF8MMEDuyqnJL89\n4DLz2Vcv5WO/voTbYmB37egnR/PRvI+YlhbZKHEYOdIa5WJXYsSm+OUFFlb47MOmRZQ4zcMe92ma\nxi/PdvHw4dYRd1nyUVds5+/3Vo/ZImkqXOhK8NmnGzg3Rp9TQQAwGyR2VLoXZE6eMDHRVI6XW6L0\nJGTKXCb+fEUR1mFurm0mA6sDTpYU2WjqTQ/q5d4WzfDFZ5s40R4f9jWK7SY+uquSXTX5TRWciF+d\n6+bhwy0LenpfPs51JVgh3t9T5rUrizjRHqcpz6nCc82ZzjgffOwCDpOeb9y7ckqLQQMuM//6yloe\nPNhCQ3hy0z7nonkfMAO0RGS+erCFA/W9nAsODRQVVSM2wTGrsqLRm8rRmZDHfvAoCqxj9zaeCia9\nDq/VhFkvjt+EsVmNel6zsohtFQu7WEPIX0Nvim+93MbVUIqm3gy/ONtFYpTTsSMtUb7xQisdN2wq\nSP3/G43LYhjX8W9KVkjn8t+RLrAYKHGYWMyDQlNZhZZIZsEOZJkNVqOee1YX86MTnbN9KeOiqBrf\nP9rOP/+xng/uKONvb6vAMg2nzEu8Nt63vZR/ebJ+xk7VZ8qsFP1NtTK3ib/aVkomp1LsGJrndrQt\nxuNnu/jA9jLKxlE4IkkSr67zsNZvp2KeTLCr8Vh5YHflsBXpgiAIY6kptPKeraVUF1pxW2RetdyL\na5QJqpvLXVQWWAa1dwu4zHx0V+WUpX1dK0wqtBp407qSvNa3u+q8c6/YeoZd6k5SU2iZsmJ2oc9f\nrCri/p+cpT2WIeCc+32Ig3GZzz7dgEEn8bXXLR+SmjrV7lrm5eWWGN96uY0P3jI13cjmggURMNtN\nBnZUjXy0Z9ZLuM2GvBbZdFahIZwm4DJh1uvIqhpr/RPvu9gaTZOQVZZ5rcPmFMuKyom2GA6zYdCx\neDqrcCWUJuA0jbvYZa4Ey+3RDJqmDcl9FARh7nJaDAPHtB6bccwORKUuM6UuM9F0jlQ2i9tqoDGc\nxmXWk1U0mnrTVBdaBrWoUlSN+lAKl8UwMBBhVbGN+nAaj804ZCKdToICix6X2cB44t/FHCxDX/6y\nSLeaeg6zgdeuKOInJzr5yM7K2b6cUT1TH+bB51u4d20xb1yb383mVPibHeW879Fz7KouYM0CGcm+\noG47FVXjTEechtDg3Jl1ASd/t6sSfx53gldCaR482MzZzgTH2uL8n6caJpWL89vzPXztYDPdieyw\n34+lc/z4ZCcHroQHfb2pN83XDjVzquN6DmA0naMlkkabI6O1R5PJKXznSDv//VIbCXn6CnsEQZh9\niqrx4xOdfP1wC629aX56soPnGyOc7Yxzoj3G1Z7Ba2hnLMOXn2/i6Sthnr4S5heng7REMzx0sJnn\nG3rpvKlmxGLU8+b1fl69wrvog+DxOBcU+cvT5fVrinm2vpeWyNzMZU5lFb70bBPffKmNf727lres\n98/oZprLYuBvdlTw5eebUdS5H7PkQ/+Zz3zmM7N9EaOpr68nEMhv7Gk4meVLzzXR1JvGadYPanKf\n7yJr0EmUOEzUeGwYdKCqGiuK7cM28Ia+/L3THXHKXGYMwxx72Y163FYDGUUdtrDQZtKzvNjGWr9j\n0GuY9DoCTjM1XsvAcehTV8J850g7a/0OXJa5fTiglyQsBh1VhVYq3JY5s+st5K+9vT3v956wONSH\nkhxs6KXIbsJqvN59QidJA33nzUYdF7tS7Kkp4NHTXTT2prl7mZdUVuWJCz19fZkdJkocZpYV2ajx\nWlndn/YWcJmRcyqPne5iU7lzUHcinSQtmtZoU/He0zSNh19o5b5N/hE/v4SJsxj1IPVtir1iqWe2\nL2eQy91JPv27K3hsRv75rloCszS+urLAzHMNvWRyKsvHOKmaS9rb26mtrR3y9QWzw3y5O8lzDRHe\ns7WUpV4rz9X35nVXE05mOdzYSzDeV9R3rZF+qctMXXHfIv7HSyFSI3TfONEe45mrvaRzw79WidPE\nibY4wfjwO8zQN1675Kbdb6fFwC1Vbspc14PsJR4Lr15eNOeDZejL/26LZvjR8Q4645MrmBQEYXp1\nxjK80BQhkh68Tl3oSvDrc11E0llimRy/OttNR1wmLg8tot5ZU8irVxSRlFV601msJj33bQ7wge1l\n+F1mIpkcB66GCMYzWI16tle6KXObeex0F48c70RRNbZVuAm4zNxZ58E2iVaeAnQlsmgaYnT4NLpn\ndTHNkTQvt0Rn+1KAvmFAPz8V5NO/u8I7Nvr55J4qbLPYVlGSJD54SznfO9qxIHr9z/3IK0+tkQz7\nr4RYX1rJrtpC9JKU165mSyTN9451cN9G/5C8OU3TCMazhJJZRoq9X7/Gh6wM38MZoNBq5MM7KzBN\nQdeKZcV2ls2ju7R1ASc+hwmPbcH8MxOEBelyT4ofHu/gr3eU47Zcr5moD6V58lKIDaVOnGY9qZxK\nqdtMxSh1CRvKnNQV24esiTWFVv5+b/WgmgyzQcefrywip2kDH+wbp7DN1WJ2PphgRbEYWDKdTHod\n79texsOHW/n66xwYZ7G4MpzM8vlnGolnFL6yr27WdpVvVuOxclu1m5+e7OQ928pm+3ImZcFEMlvK\nnSwpslLmMo9rgaj1WPnA9rJhu2BIksS+1UUo6sjN7wusYxfkjVZhvpDVeKzUeEQ7I0GY61b67Hxg\nexk1hYPXwduq3Kzz2wcKdz90awUG3eipESa9DpN1aOCg10nDdilavUAKguaavvzloQNlhKm1o9LN\nE+d7+NGJTu7bNDspbC81R/nCs428ss7LfZsCGOZYCuTbNvj54GPnuXetL6+Yaa5aMCkZdrOBcrdl\n3HfTdrOBtQHniH+JFoN+0U+KEgRhYfPYjKwNOLGZBt/cOy2GQV1u7Ca9mBA6T1zoTrJiHp1IzleS\nJPHhnRU8frab+tDMDuuQFZWHD7fwpeea+PTeav5yS+mcC5YBfA4Te2oL+dmp4GxfyqQsqpVPzqk0\nhVMjTgO8JpzMjvmYfL3UEuWXZ7pITHBwiiAIwmxr7k3znSNtNE6wY1BvKjuoDiSeydHcm14w1fNz\njappXO1JsbRInPDNhGK7ib/cEuALzzSRVWZmWEdTb5qPPH6RjpjMw69bwfrSibe/nQlv3VDCExd6\nCKdGruea6xZUwNwezXDgapjuhExWUXnyUg9PXwkNLMoXupL8+4FGzo8yNjoYz/C5Aw0829A7Jdd0\npTvJC00RMor4YBAEYX6KyzlMeh1t0czYD75JT0Lmi8828ceLoYGvHW2N8bkDDXmPF5ZzKr+/0M1z\n9eF50VZztrVGMrgsBpyLNB1wNrx6uRevzch/v9Q2ra+jaRpPnO/mY7++xGtXFPFPd9bMi0YAxXYT\nu2sK+NXZ7tm+lAlbUAFzc2+an5zspCMmk1M1jrfFOdkeHwiYvXYjdy3zUGwfOYfGpNexwmenaJzD\nQkby2hVFPLC7Eqd5ZtI6GsMpLnQlxIeKIAhTpsxl5lRHnANXe5HHOe5WJ8HyYhslzutF1eVuM69c\n5sWd5wd9byrLcw0RTrTHRyzAFq671J1kWZHIX55JkiTx8T2VHGqM8MzV8Ng/MAGhZJZ/+uNVfnm2\ni/947VL+bGXRvCrqfP0aH78+101mno7Mnvu3JeOw0mfnw7dVUFlgwWrU855tpegAU3/OXanLzD2r\nfaM+R4HVyP2bS6fsmpwWAyfqwxxri3PfJv+0JrxrmsbPTgXpjMtDqtEFQRAmymUx8sFbytFJ19fT\nfFzqTvLTk528Y6OfysLr6QG1Xhu13vwDusbeNBUFZm6pdIue7nm43JNiqVekY8w0p9nA/7qjhn/4\n3RUKbUbWTlFBq6ZpPH01zNcPtfKaFV7+1x01s9qRY6IqCiwsL7bxp8shXrOiaLYvZ9zm3298FE6L\nYaA59tHWKOmsinsOVGRmchrxTG7ad0YkSeJ1q33cvykwqKVTTyLLxa7kjOVWCYKw8FQUWPA7zVzu\nTtKVZ291RdVIZRUyI6w9DeEUpzviY+YyL/Ha2FjqGrWdnXCd2GGePcuKbHxqbxX/8mQ9l7pHTv/M\nV2dM5p+frOeHxzr5f1+5hHdtKZ2XwfI196718ejpLtR5eAo+f3/ro2iNZPifl9o425mY9te62jP2\ngr+rtoAP76wcc8c3q6j0TjIhvtZrZbXfMWiy4ZHWKF95vomWyPjzDwVBEK5p7x9p/dwoNR7yDevY\nCp+dT+6tZlnR8N0afnu+m68ebOZC1+hrtcdmZGuFi0JxajYmVdNEwDzLtpS7+MjOCv7hd1c4MsGh\nJnJO5QfHOvibX5xnaZGNr96znLri+f93uj7gwKCTONYam+1LGbd5HzArqkZ9KDVoQpXZoON928pY\n47cPetw1STnHyfYYoeTg4HQieb+/PtfNt15uGzXQNel1ebWmO9YW43MHGqd8Nv2KYjtv3eCnaJTc\n7fFQNY0znXEujlI8KQjCwuO1GXn7Bj8bAk5601lOtseI3TQd8Hjr4HXs5sKzS90JLvfvvL16eRF3\nLvPwq3NdxPsngYWTWV5qjhBKiAmhE9EelXGY9XnnhwvTY2d1Af/rjho+f6CRb73clvcJr6yoPH62\ni3f99CyXu5M8dM9y3rHRP65UqLlMkiReu8LLby/0zPaljNu8/RtoDKd4+FALZzrjfOGZRo62xAFI\nyQrfPdrOExd6BtISLnUn+eIzTQMtkRrCab7xQitng9d3NbriMg8dbOFk+/jueu5ZXcT7tpeNuHsc\nlxV+fKKDF5oiYz6XzajH7zBPyVTAG1UWWritumDKKqZj6Rw/ONbBY6eDoi2UICwiVqOeW6oKqPFa\nudiV4muHWnjycpj9l0N86ZlGjrRGsZlGXsd6EjJfea6FbxxuIZ7JUeOxsqnMxb1rSnD0B3iNvWku\n96R46FALTRNsY7eYXepOsnQc+eHC9FkXcPC1162gIZTm3T89x6/PdQ87IlpRNS52JfnG4Rbe8cgZ\nXmqO8r/vqOGf7qrF75wbE/um0iuWejjaGiOcnF8t5ubtLWhcVjjfnWBXjZvXr/FR4+nLbTMZdNy5\n1EM8o9AWzaCTJCLpLIlsjmx/a7dyl4V3bgpQfcNUK1lRaY9lSMrX7wKTco6eZI5Sl3nEQpMbC1mG\nk82pnOlMYNBJbMc96mPX+B2sLpn7o0zdViPv3lKKUZ/f+HFBEOa+YFwmGJdZVmQbGE6iaRqt0Qzu\nYVqU1XosvH2jn/pQCkXVOBNMsr7UySuWekZcx5xmA/eu9aGTwGbse42bp4Eu8VpJyArHWqPII7Tj\nVFSNo60xJAk2lTkHpaAtdiIdY27x2Iz88921nO6I8+jpLv77xVb8ThMlTjN6SSKcytLUm6bQamRH\nlZsv/fmyYSdiLiR2k56d1W7+cCnEm9eXzPaCfS4UAAAgAElEQVTl5G3eBswriu384+01eGxGVvuv\nL5Z6nUSJ08QPjjWyb1UxL7dEScgKH7q1gpL+OzWX1cD2ysHBa4HVwGuWewelTpxsj/P9Yx18ZGfl\nhBegQpuRj+6szHvX+MYPmd5Ulis9KWo91jmXu7dULMiCsKAcb4vxy7NdfGxX5UAHi+ZImi8808S+\nVcUs8Vh5sTnK7toC/E4zRXYTu2pMrA84MekltlW4KLIbkRWV88EEHpuR8ps++E0GHXuXFI56HU6z\ngVsq3awusY/YVUhWVP5wqQe9JLGmxI7FKKaxXnO5J8m9a0fvBiXMvDV+B2v8DjI5lcbeNMGYjKpp\nFFj7phQvtq5Wr11RxGefbuCN63zz5oZ33gbMep1EscM07Pcq3BYe2F2Fx2qkosCComp4bEbOBxMU\nWg2UOM2oWt8RiMOsp9xtIZ3tyxvaWOZibaCvFUyJ08ydSz0U9B8V9qaynOlMUOOxUOrK/w5wosHu\n1VCKb77cxl9uKWXLInszCYIws1aX2PHYjPhv6JfsMhu4a5mHSreZ7mSWM50xKgvMaBoEXP0bEP3r\n47XTtpZImv96oZU9NYW8cf3gdbI7IXMumKCuyDawgTEcvU4aEixf6Uli1EtUFlj72oZuLUUCESzf\nQNM0LvekWCZSMuYss0FHXZGNukW+6bS82IbFoOdEW5yNZXN7SuE18zaHeTR6nUR1oRVXf5u5VSUO\nOmIyDx1s5lB/LnF3Qubrh1t44nxf4rnXbuLje6p4zQrvwPPUeKzcs8Y3EJh3xGR+eLyD+tDki/KC\ncZmvHWrmTGd8xMfUeq28Z2spSzyin6YgCJPzYnOUb73UOqhA+kZlbgtbyl3YTNf3UQqsRv5sZTHL\niu2sKekrHv7+0Q6ebxi5JsPnMPH+W8q5tXpoClprJMOPTnTSPM6OPZF0lv95qY1HjnWS66+b8DvN\nowbdi1FHXMas1825E0lBuJkkSdy1zMOfLofGfvAcMW93mMeryG7kXZsD+PqDX4/NxHu2luK6IS/P\n57i++J4PJpAkBvo6A1QXWvjbWysG7cBMlJxTaQyniaWVga9pmsapjr5JVmv9DgosRjaViYVPEITJ\nCyVlrobSI+YFj+Z0R5xMTmWlz8Z7tpUN23EnlMz2F5xZRxzYsLTIyod2lFNeML4cTafZwFs3+DHp\nJQyibmJEl7qTLC0SGyzC/HD7kkJ+cKyDdE7FMg+6gCyagNlq1LOl4vqOh0EnsS4w/DFAUlZ45HgH\nBr3EJ3ZXDRz5WYx6VviG7yc6XuUFFv5+b/WgnGlZUfnt+R4yOZW6Ihu2PFrRCYIg5GNvbSG3VLrH\nPW1UUTV+d6GHcDrLp4qqRjw+bQyn+e7Rdv5ySyle+/CbCnaTgZUl459+ppOkKZuatpBd6UmxRKRj\nCPOEx2Zkhc/GocYIt49R2zAXLJiAORiX6UrILPVer/CeKJtJzzs3B9BJ05sfV2gz0hbN8EJThK3l\nLsoLLLxzsx9VQwTLgiBMKYtRP6H1TK+TePtGPzlVw2UZOdhe4rXyV9vKqO7vWHS8LUZCVrAb9Swp\nsk5ZW0thZA2hNHcu88z2ZQhC3l6xpC8tYz4EzHN/DzxPJ9pifO1Qy4SGfkSuNeDPXO+PuMRro8Yz\n/XfqoWSWP10O0dXfj7DUZRlSWS4IgjCbAi4zFWOkUbgsBjaUOimwGGmPZmiNpGnqTfHwCy1c7hb9\nlGfC1VBqoMWqIMwHt1W7OdOZIDzJKcczYUZv+ffv349Op6OoqIiSkhL2799PcXExa9eupbi4eFLP\nvarEjttiwD9C54zRXOhK8Z0jbfzVtjI2lM5stWZdsY1P7a0eyK0WBEGY756t7+WpKyE+fFsF1YVW\nqgpFEDfdErJCbzpHQBRCCvOI1ajnlkoXT18J87o1c7sd4owFzMlkkjVr1uDz+Th06BAlJSVs3bqV\n2traKXn+MrcFv9PMC80RDJLElgrXmL39msIpGsJpajxW7tvopzKPQpSsovLkpRB6ncQdSz3oJIhl\nlIHWSuNl0uvG3LkRBEGY69I5BU3r+wDcXuliebGVSDpHTtV4tj5MwGVmW4WbtmiGi91J1pTYKRoh\n11kYv4ZwiqoCixgmJcw7ty8p5IfHOud8wDxjKRk2mw2fz0culyOTyWA2mzl9+jRPPvkkV69enZLX\nSOdU9l8Oc+BqmFweleAN4TQ/PtlJKqtwS1VBXo3DFVXjUneSS90pcqrGuWCSfz/QwOXuJADRdBZV\nm9i4aEXV+O35bh4/25X33HlBEIThqJo26XUkIStkc33PEZcVfnCsnWfrw0Mel8kqPFffy7deaich\nK1QVWlnhc/B8Y4SnroTpSmRpj8oANPem+enJTjrj8qSuTRisPpQW6RjCvLSx1ElzJE13Ym6vCTNe\nhbF//3727t2LyWRi3759ABw+fHhKdprtJj13L/NwNZwiks4N9E/WNI10VsV6QyGdomr4nSbeuzXA\n/ishHCY9pWPkDnfGMpzqSLBvVTFemxGzQYdRL+Ew6dHrJK70JPmfl9p42wY/ayZQ0a1qGq2RDGlF\nJadqiH78giBM1NNXwlzsSvL2jX6cEzgBCyWzPHy4ha3lLu6q85JTVBRVI6toKKo2aCdz/5Uwz9X3\nsntJAde+bDboeOfmAJqmYdTpMBv6vrHKZ+cjt1VSWWCmKyFzpiPBEq9VnLRNUn0oNWTMuCDMB0a9\njlsq3TzXEOGe1ZNLz51OM1r098wzz7Bt2zZMJhO53PUCO0VRRvmp8dHo65ihu+FPdqYzweeeaaQx\nfL3wpDGc4ivPN9MR69v5yKc1aWdc5rEzQTriMoeaIlzqTrDUa+Xju6uo8Vgx6CRcZkPeY7BvZtTr\neOtGP+/aFMAqomVBECYhnVOJy8qET7x0Ut/gkmtrkcWgw2k28MNjHTT3Di6utpv0lBeY2V7uHrR2\nFdtN+BxmCm3GgYEoTouBumIbFqOerniWtmiahw42D5zSCRNTH05RUygCZmF+2lVTwLP1vbN9GaOa\nsR3m8+fP09raiqqqyLJMXV0dFy9eBGD16tVT9jpVBRbK3Wa8tuu5cToJbg5hC61G9q0qZqnXys7q\ngjF3YFKywhKPhY/urMSkk3jkeAe3VLg53ZHgFUsKcVokqgqtfHx3JQb9xO9DHJNoJxdL5zAbdZgm\n8fqCICwMdy31sLe2cNQWlQlZIZbOoaJR6urb4Y2kstjNBgqsRt63vYxr9/8JWUFRNfYuKaTAOni9\n3FVTwK3VBcMOFUnKyojX4DTr8DvNnO5IDF2khbxpmiZSMoR5bVOZk8893Ugomc0rPXY2zFjAvGLF\nClasWDHoa9XV1VP6GpmcwnePtpPOqXxsdxV2k55EJkdCVnnXltJBRX2FNiN3110fg52Qc4RTfRXG\nNxdNhFNZ/ufFNtYHHNxV50XTNB7YVcmZjvj/z959h8l114f+f58zvc/s7O6Urepdqy7ZkiVZNs04\nCQkkYAg1MSHkBn6EGHDyu79780ducgmEBAIESG4ghNiQhDgBJ4ELtmXJKpZlFau37W22TK9nzjm/\nP2a10mpnm7RV+309D88Do5nZs+LRdz7zOZ/Cud40iVyRzTUuNoZc9xQs34ueZJ6vHe3gzSv87Fni\nnZNrEARh/jAZZcb72OlO5nn+Yh89SQVF1fn9ffXEskW+fqyDd66vZmutG6MskcwVudiXpsFr5cFG\nDxaDPGr5iSRJGMsEvJf70jx7uof3bQ6xvHL0mM6CqvNGT5L3bQ5MqulaKK8vrWA2SFNeSiMI84XZ\nILOjzs0rLTF+Ye38LMu4L1KR8ZzCoeYokVSBN62o4LFVlcNrFruSBb59sovrE9zuO9OV4n+/1DJc\ntnFjMMt3T3bTncgjwXC9MpQ+HBp8NvYv8/GepgDt8fyM/n6TYZIlqhxm7Ob74v9SQRCm4PpAhuNt\ncXLK5MvbZCCnaOxb6uXtq/3YTAZMBomqof6MmzriOb5zspur/RnCbuuYW/xeaYnxk8v95Iu3Gg0l\nAElirIFFy/x2Hmzw8fWjnVzpEyUZd0vULwv3g4eWeDnUMn/LMu6L1UuRlMKzZ3p598YAe5eO3BZT\n57HwsZ01E86mDLrMPLK8Yng8XCyr8GpHnJ31bkJuB7+1s2ZU5tluNlJvNvL7e+vvebvgvfI7zHz8\ngVoxUkgQFqHjbQmOt8ep9zYSmmT/Q8Bl4cmdtZgNEtJQRBt0WfjEnvoR50hjhY3f2llDjXvsDLCq\n6ZzrSdOXLrC7wTt8Hq6scvCZfQ3j9mSE3GYeXl5Bhf2++DiaE6X6ZZGhFxa2bbVuvvByG7GsMi/v\nltwXJ1SD18ond9eVXf5hNRnYECotI2mP5ehLF1hb7Ri1Inap385S/61bhusDTv7wwBKqhrIp5jIB\ncVssSySpcG0gzZYaNyurHPf8u+i6Tq6o3VXTnwiWBWFxenRFBTvr3QRdU5trXO6L/p3niM1kYGPI\nhVLUUIYm+FyMpAm7LQRdFgbSCs9f6mPvEg8BlwXnUNIhnlO4HMmwpMI27nlW67FSu0EEe/eieTDH\n1prZXbolCNPNYpTZUuPi1fbEiJLZ+eK+uH9vNsqsrHJM+I3kREeC/3Oii7706BWMHfEcA7c9bjaW\nmlHGC0JfvB7lm692UtR14rnimM+binM9Kb74chsdsamv+BYEYXGqdppZ5rcPZ4qnW66o8syZXp47\n30dHPM8Pz0W40Jse/rMz3Ski6ZHNOm2xPN95vZtrA6VSi6Km0xLNkspPz1kp3CJKMoT7xa56N0db\n43N9GWXdU4Y5kUjgdrun61pmRDyn8F+XB1ldZeeBeg+rquwE7sjCRFJ5/vxQG9tq3Lx3c3DS731g\nWQVba9xUO0zTdvtApzSP+e4GQQmCIEw/idK5JAOJXJFlFXZWVJYCtBqPlacfbsRxxySMpT4rH91Z\nQ93QfPsbA1m+/Eob790U5MFG0Zg8XRRVoyuRp0E0TQr3gR11Hr56pINCUSt7Z38uTSlg/va3v82q\nVat44IEHAPj+97+P3++nqqqKhx56aEYucDJyisqNwRxBl3nUOJKconGyI47LYqAp7CLkHl3L7LIY\n+aW1VWVLOsZT57VSd09XPtqGoJPlfvu4o6AEQRBmk8Vo4IlNQWTg/14d5GIkxdvX3LplevuKa1XT\nudafwWsz0hS6VSbgtxt5fE0VNZ7x+0mEqWmP5Qk4zfMuuBCEu+GxGllaYeN0d5IddZ65vpwRpvQv\nzOv1DgfLAFVVVfzKr/wKsdjcdjW2x/J89Wg7b/SkRv1ZwGXhM/sb2b907IyGzWRg31Ifa6rvvQb5\nXkmSJIJlQRDmHYtRxmSU2bfUy2f2N1LtLB/4difzfOVI+6glBH6Hmbeu8tMglmtMqxuDWZaKcgzh\nPrKrwcOxtsRcX8YoUwqYDYaRgVw4HAZKjWpzKegy8/7NIVb4yx8alQ7z8Jap+aao6XQlchRUbeIn\nC4IgzDGHxTgio3ynSruJD24Ns61u9sr14jmFvlRh1n7efNISzdIoAmbhPrKr3sOxtvicx5Z3mlLA\nXCiMPJB27NgBTO9q67vhshrZ1eAh7LHSly5wpCVGf3pqh2d/ukAkOfsH7pW+NP/vT25w8EZsSjNU\nBUEQJlJQNQ5ej3J8Gj98uuI5ErkiqqZzpjvJlb70iD+3mgxsr3PTOIuZ5OcvDvDFQ60MZhZf0Nw8\nmBMZZuG+UuexYDHIXB/IzvWljDClgNnv93Ps2LERj50+fZrq6uppvah70R7N8b3TPbRNYcqEUtT4\n7us9fPPVTtKF2Q1avVYjm8JOYhmFXHF+fZsSBGFhKxQ1Xm6J8mp7AlW79/OlK5Hnz15u5eXmKMl8\nkWdO9/Dji/1oc5wJWl1tZ2+j767GcS50zYNZGsVKbOE+IkkSu+rdHGubX9MyplSnsH//fl5//XW+\n//3vI8syiqKwcuVKdu/ePVPXN6FETsFhNmKQJQYzCo0VVn57Vy0NUxjibjRI7G7wUND0WV9AEvZY\n+cCWEAVVw2ubn2UjgiAsTE6LkY/tqsUgSRgNY59tiZyChITLOv4Z5DIbePNKP40+G16biSd31GCS\nJYqqjrncbuxZsqXGDTVz9uPnTCJXJKOoBKbYsC4I890DDR6+cbyTX98SmutLGTblCG3Lli1s2bJl\nJq5lylqjWb55vJN3bQgQcpv50qE23rTCz6MrKqb0PpIksaN+7rox5+NGG0EQ7g9V49QbA2QKKn/z\nahcui5EPbQthGiewdlmNvG1V5fD/rrCZ+PqxDrbWuHjLbY8Ls6MlmqXRZ0OeofnbgjBX1gWc9CQL\nDGaUUdPP5sqUAuZMJsNPfvITFEUhGAyyd+/embquSTEZJIJOMxajhMUos7baQZXDRCJX5GhrnCUV\nVlZWOdB1nTd6Umg6bAw5xeEiCIIwxChLrK6yYzbKU94WKsuloNkxQVN1Kl/kdFeKGo/lnhZsJHJF\njrTGWTp0ti92zYM5lohyDOE+ZJAlNoVdnOxM8KYV82Pr35TqD/793/+dt7zlLfzar/0agUCAI0eO\nzNR1TYpBkqj3WXFZjPhsJj64LUxT2EUiX+Q/LvcPF4wXVI3nLw3w7+f7yCoLfxrF9YEM53pS01KT\nKAjC4na2J4XbZmLfUt+IZEJPIs+J9gTJcTbzeawmntxZw54l4y8iGcgoPHumh4uR9LjPm0giX+Q/\nL/dzfXB+NQPNlRtiw59wH9tW6+a1juRcX8awKQXMNpsNu90OwKpVq+jv7wfg1Vdfnf4rm4SBjML/\nvTpIb6qAputkh6ZM1LgtPLWvgQcbS2UWFqOBD24N8r4twWlbYT2Xnr84wLdPdhHLjl7xLQiCMFlF\nTeeVlhg/vzZI/o5kwuX+DN8+2UVXIj/ue9zMSvenC/Qmyz+31mPlUw/Vs+u20rd8UZ3yl/6bZ/vu\nBrEpEEolGSJgFu5X22pdnOyYnobl6TClgPnOsUSaVjpgu7q6pu+KpmB5pZ2n9jWwLuDgdFeSLx1q\nozuRR5Ikaj1WPNZbdS9ht5WjrXH+/FDrlEfOzTfvWFfJR3fUzJu6HkEQFiajLPG+zUE+trMG5x0N\nf+uqHXxsZw31k1i5rGo6z5zu5WtHO0mUyUgbZIllfvvwmTWQKfDlV9o52jq1LvibZ7t7gubExUDT\ndVqiORqn0OAuCAtJlaO0vflKf2auLwWYYg3zG2+8gaLcymr29/fzT//0T1y6dIl3vOMd035xEzEb\n5Ftbo3SY6DtIU8hJyG3BscA36dWLTVmCIEyTsZaQVDrNVE5y+oJBLk0aSisq1ilNGpofmaOFqDdZ\nwGk24LKILw/C/atUlpGYF5uYp/Qv7W1vexvbtm0b9fhrr702bRd0tzbVuFgTcIw7h3NDyDWLVzS7\ncsVSOYrVuLC/DAiCsDBtqZ38Zj+/3czvPlhXdiJHvqih6fqinKk8FaJ+WVgMtte6+fbJLt4/D8bL\nTSlgPnbsGOl0elRpxpkzZ8oG0rNJlqRFe8DmixrPnu5FliSeaApgmuVZ0oIgCFNlLXNe67rOjy72\n0ZtU+NC20IK/GziTmqM5ETAL9711QQet0dJ20bkuxZrST5dlmUQiwc6dOzEaSy/VdZ3u7u4Zubip\nuNKX5lRXikeX+/BPMHdU1fSy45PSBRWTLGFeYAGnLHHrg0VMzLtrvck8L9+Ildb6ig8iQRhTTlHp\nSuQ52hpn/zIfNZ7x62jHOnPvJEkSdqMBh1lFuq1co1DUeKU1ht1kYOcczsyfT5oHs+xuEH8Xwv3N\nbJDZEHTyemeS/ct8c3otU4oMP/7xj7N582ZefvllDh8+DJTWZT/yyCMzcnFT0ZtUONoaIzXBauvW\naJYvvtzKlb6RReSxrMJXXmnjZ9cGZ/IyZ4TJIPPL66v5lfXV4y4dmE7xnMJgZmE3T94pli1ysDlK\nX1pMHxGE26maTk8iT3GoW/0/Lw9wuivJ4ZY4sQkmD13oTfGlQ210JXKT+llvWeXnfZuD2G+b7Vwo\nahxuifHaPOqYn2vNoiRDWCS215XqmOfalKOr2tpa3vWud1FdXc03vvENAKqrq6f9wsZTVLVR/31L\njZPP7G+41QQIZBV1VPmIoupEswoFdeQIJVmSqLCbF+wtQKMsTXnpwJ0uRdK80T3xfGdd1/mXNyJ8\n5UgHiftgTN9NyyvtPP1wIxuCc99cIAizLa+M/W/5UiTNH7/YzIXeFDlFxW83YjcZePrhBlYPLRC5\n3JfmbJnzI1/UGcwqKOrkAl2DLI364u+0llZ8P7EpeM/n3P0gX9SIpArUeixzfSmCMONuNv7dGc/N\ntikFzPl8np///Of84Ac/wO128/TTTwNw4cKFGbm4cm4MZPnCy63cGMhytT/DF19uozWa5bXOJN84\n1kX30MzQ9liOPzvYyvnekYPyl1fa+ez+RtYGRgZFbquR39geZu8EA/jvV6qm86OL/TxzpofUOIsK\noHTbdE21g+21bsyG++fDyyBL1HisZWsrBeF+9mpbnP/+02Yu9qaA0vnZFru1HMRtNbKt1o3NKPOV\nI+3YzUYGsgovXI+iaBqqpvNflwZ49nT3qPNjY8jJZ/eNTGbcjZsjpgRojeWocVtm7Y6iIMylsNuC\n1WTgxhwvLJpSDfMf/uEf8uCDD1JdXU0kEiESiQDw+uuvs3bt2hm5wDspqkYir6KoGmlFpSOeI5Yt\nki6oxPMKsaxCyH3rW3e57yNeW/lDdzFnLgyyxHuaAiiqhmeMv5/bPSAWBwjCfaOgaQxkFBSttADq\nOye7UTWN39/XiMNsoM5r5YNbw/SnC+g6GOXS3TqDLCEhEc0q1PusHFjuG3V+GGRpUmeKMHmiHENY\nbLaEXZzqSrHMb5+za5hSwPzYY4+xYcOGEY/puk4mM3tDpVdVO/js/gY8VhOXIinWBJyouk6l3ciB\npRUYhzKedV4rT+1twGoS38Anq24SCwoEQbj/7Kj1sMJvJ+CyoOs6v7yuCk0H+x3nZ6XDzO/uruNK\nX4b2WI7HVldiMcq0Rov8/NogQVdwjn6DxUUEzMJis7nGxX9dHuBdG2a3BPh2UwqYDxw4UPbxxx57\nbFKvf+GFF5BlmcrKStavX8+Pf/xjjEYjO3bsoKKiYtLXcXOD31K/HavJQMBpJpErYjUaqLutW9t2\nRz1ybzJPVyLPyko7jjLD3gczCpIEvjLZkHRB5bnzEWo9VvYtndtOTUEQhHvVlypgNcm4LEbMRpmA\nq3RnTpIk1gWdY75uIK0gy/D2NZU0DpVZLPXbeGpfA5WTLJk435vihWtR3t1UTbVzcnW4uq4jSYv3\nLuDtmgezbNlw/+4VEIQ7NYWcfPHlVhRVm7NSpFn7qZlMhvXr17N//36SySSXL1/mgQce4K1vfStn\nz569q/c0G2QafTZsJgPpgkp3Mk+uqJV9bk5RebU9weW+DJ1Ddc63yyoqf/NqJ9852U2+qHG9P8ON\ngVuZ86Km0RrN0bfA12rfLl/U+OG5CP9xsV90ngvCIhJJ5fn8wRYO3ohO+bWHW2N841gnQeetmmKj\nLNGbKvDSjSid8bGnYSiqRr6oki6oKKrKjcEcvcnR5/Gdbgxm+fNDbTTPcQ3jfNE8KGYwC4uL22qk\n1mPlYmTu1mTP2hRou92O3W6nWCySz+eJRqOsWrUKAItl8p2+sazC9YEsy/y2EbXI53rT/P3Jbmo9\n1uHHr/WXbhtuq3NTVHUO3ohS57VS67HQny7w0yuDbKt1sbLKgckgs73OjUGSKKga3z3VjSxJPLWv\nAZvJgMdq4r/trsN0H9U5a7pOd6KA3Syj6ToGMcRZEBYFm8nAvqU+XBYD/36+j0eW+8redQNoGcyQ\nKqisD5YymnsavawLOEeszVY1nReuRWkezOKzm4jliqwLjMxSl5aS9NOfVnh3UzUOs4GvHe3gl9dV\nD2e3x5IravSmCuTHSIgsJtGMMlSGKOrChcVlc42LU11JNobGvgM2k2Y9r/3CCy/w4IMPjnhsKrfZ\nTncl+Z8/u8HpruSIxxu9Fp5oCnB7FcalvjTPXegjnivis5v49N4G3r+lNN8zVVB5tT1O/9DMXaMs\n8fCyCvYu9eGyGHlPU5D3NAVGbA90W4yT3ibYnczTMU6mZT6wmQx8aFuId2+cvfnNgiDMPZfFyONr\nquhNKhxpi5FXxw5Ef3iujz/6WQvNg6XMTq3Hyoagc0TywCBLfHBriE/uqeNHF/o42hov+15FVS/d\nUpUllvisfGRbeNTEonJWV9n53P5GVlXNXcPPfNEczdLos4nyFGHR2RJ2caozOfETZ8is7hl8+eWX\n2bFjB2azGZ/PRyQSobq6GlUdf9nI7eo8Vj68NTyiVhlKB3Y8X+RYWwK/w0yF3czuBi8bgk7Cbgux\nbOlbeYW9lMlo9Nl4+uHGsqsWEzmFoMs85jSNiSiqxvde7yGeL/LZfQ04x8jcTEWhqHGqO4nXamRV\n1fTNCV6oc6cFQbjlan+GgbRCU9g56S/1AG9b5efAch8V9rG3o+5b6mNdwIn/tvPwxWuDdCXzvGNt\n1XBmOuS2EHJb+J0H67CU+QKu6jpdiRxppUhXPI/PbmZrrXvCa9R0nf5UAa/dJIJESuUYSytEg7aw\n+KwLOGiOZkkX1DmJXWYtYL506RKdnZ1omkahUOBNb3oTzz//PJIksXv37km/z7JKO8sqR2cZUgWN\n8z1pntgUwDm0IcpnN+Ebum310vUoB5ujfGZf4/DYuXK3ARVV47uv95BVNH7ngdpRjYOTYTLIvGVl\nBYqqT+nDazzxXJFnT/ewPuCc1oBZEISF77WOBMfa4jT4Gqd05risRiZqHSsX1A5kFDriedQyiwRq\nx1iTbZRldtV7kCSJb53oYkWlnd/cUTPhNV4byPK1I+18eHuYppBodGsezLJmEll5QbjfmI0yq6vs\nnO1O8cAcrIWftYB59erVrF69esRjjz/++LS9/+YaF6uq7HhtJi5G0gykC9jMBrbWlA77JX4bPrsJ\np2X8DxNZklgbcJRmjN7DUo4N03ywV0tgiGUAACAASURBVDnNfHJPPTajKJ0QBGGkR5dXsKveQ9A1\ndqZ4MtKFIpf7MqVs8Th1xW9fU0lR1XGVuUNXTkHVuN6fZUmFHY/VQFbR8Nkn91qX2cDbVldyrjtF\nld1EeIyAfLFojmZ5+5rKub4MQZgTN+uY5yJgvm+iL7NBxmszkS6ofO/1bn5wtpe2wdzwKsW2aI6O\neI43upNcv236Rb44shzEMFTL/OaVfsx33FZsiWb5yeUBBjPKzP9CZTT6bBM2xwiCsPhUOc0sqShf\n15pTVF6+EeX80Ba/8XQlCvztiS4u3bEh9U42k2HMYFnVdK70pYkkSxOFFFWjK57nq0fbOdEex2oy\nsH+ZjzqPlecv9o84j8sJuS0EnWaOtsUZyI6/hfR+p2o6bdEcDWJmvrBIbQm756yOeVZrmKdDLKvQ\nFssRdllIKSpBp5nrgxlyBY2WWI7ttS4+uDVEQdUJuy1IkkRWUTnRkSCvaOQVFUWFZX47HfEc//B6\nD4+vqWT9OHNHb+qI5/nRxT6W+W1iRasgCAtCqqDybxf62Bh0jppccadaj4Und9QQcpm5HEnzwvUo\nv7y+iuDQF/XTXQmsRpnV1U6aBzNc6suwLuCg3ntrxFl3Ms+XX2ln/9IKHltdwQ/ORqj1WPjg1tDw\n+wAkckX+60o/Tktgwu1da6odfG5/I9X3mEFf6LoSeSrsJuyi90RYpJb5bQxmFQYyCv5ZjsMWXIb5\n9c4kf/iT67zRk+ILB1u51p/hPy8PMpAt8p+XB+hOKqyocrAu6ByuX7aZDLx7Y4D3bg7y5pV+ttSW\nyiV0vTR/uVwd3p3iOYVqp4mn9jfQKOZfCoKwQFQ6zHzqoXoeX1u6jZ8pFLkxkC07os1mMrAp7CLg\nspDIF0vJiKHnXe1L82cvtXKuJ01WUbnan+VrRzpoHhg5DchvN/GBrSG21rrQkcgpGrFcka217hHb\nROt9Vj6zr5EtNROXr5mNMrVeK5m8yrOnezjfM3G2/H7UPJgVnz/ComaQJZpCczMtY8EFzGF3aXxc\ntcvE42uqyKsaR1riaLrOh7aGqPPcykBotwXCiXyRg81R7GYjrqGu7jqvlc/sb2TDJLLLr3ck+fLh\ndjStNIJOEARhoaj1WPEPTcK4EMnwxUOtXO0fXQpx+5nZFHbx9P7G4W1+HquRj+6q5aUbUQ63xGj0\nWvjYrhpqPCOzvjaTgR11HpZU2HCYS6Mrf3FNFfId5SKyJFHntQ6fx5ORKWqc6EjQnbx/FkhNRXM0\nx1IRMAuL3JahOubZtuBKMlZXO1hdXeoQXheAwUyB336ghpDLMlxWoes6P70yyGBW4R3rqrCZDOSL\nGqm8OuIDAW6NVVNUjebBHJUOU9lyi8YKG7+8rhqfbcH9lQmCIAwLu8z80toqqhwjA92BtML3z/Sw\no97Dtlo3ZoOM/7bnVLssmA0yrdEs1Q4zGhJH2+KsvGNqT/NgFptRJjg0jcg6TZOCoJQw+dz+Rhzm\nBZfrmRY3BrM8ssw315chCHNqc9jFP57qQdf1WR01uSCjv6KmYZRldF3HZzPxphX+4T/rSxUoahrR\nbIHelMLNjc8PNnrZXucZc3ZfZzzPC9cGWO638+jKW+/XEc9RKGos9dvFKlJBEBa8sMdadtKEqmv0\nphRS+bHn4jssMh/YGgZKiYnf39swIkM8kC7wV0faWe638eaVfrxW44ig+271JvPoQNBlocq5eOuY\nWwazLNkenuvLEIQ5FXabMcgS7fE89bPYALvgAuaz3Un+8VQPT2wK0BLNoenw+JpKdB1kWeJ7p3qI\nZot8Yk8tVqOBVL4Iuo7DYmS8PoneVIGWWI49jd7hx1RN5/tneulLF/js/kZ8NhOqpnMpksZuNogA\nWhCE+0a108Kn9449uvJYa4x/PdfH+7YE2Bhyk1VUnjvfh8tsYGeDh1qPFZfVyHs3BZGAvzrSzi+s\nqeLA8ooR71PUdCRKtYiarvNKSwxV01lV7cBhMoxaJqUUS7PxMwWV39tbPy2LoBaiTEFlMKMQdotJ\nScLiJkkSTSEnZ7qSsxowL7j7WrmiRleyQK6o05dWhv5T4EuH27jQm+LAch9vW+XHbTESyyr86Ust\nHGlLTOp9+1IKt7fBGGSJx9dU8sSmIO6hQzqWVfjb17p4/lI/PYn8hCORBEEQFgq31YhpjIA5V9To\niOfJF2+WtUnEs0Va4zmOt8WA0njPjSEnK6rsvHtjkJV3rLLOFzX+6Wwvz53vo6jpqKrOqc4kA5ki\nX3y5lf+6MjDq5xoNEgeW+3jLKv+0LYK600Ba4UJvipwy+a2zs601lqPeZ8UgemgEgaawk9Pds9v8\nu+C+qm+rcfGnb1tG0GlmQ9CJDkQzCol8kebBLD6biZ5EjrDHjNtiZE+jj1p36Raequm0xXJU2I14\nrCPrlLfXuFhWYSPsGfnt/c6tej67iSd31GA3ynzvdA8DGYXP7mvAc5drtAVBEOabnmQeXWd4KyrA\nrjoPjRVWGn12uhN5Xrg2yGOr/RR1HdfQdtVLkTTPne/jvZsC7CqzWEBHJ6uUekl0XcdklHn/1hCF\noobFKFPnGZ09lSSJLTUTr9C+F+d6U/zgbC+f3F03qiZ7vrgxmGWJT9zVFASAppCLbx7vQtP1UQ3F\nM2XBBcyyLBN2l1LwQ03fOMwG/tuuWp4928OaaifH2hOsDjip99p454bq4de2xXJ88eVWfmV9NQeW\nV5AuqJgNEiaDjN1ixD6JW32yJLFmqOnwkeUVFIrajN0ijGUVnBajmMohCMJdSReKdMbz1Hos2M3j\nn1OZgopJllB1nW+/1o2q6/zennpsQ7VsVrOBRnMpY9yfLnC0Lc6agGNEMFvUdFKFIuoYkzqtRgPv\n3RQECUxDi6F8Q8mGx9fMXanBiko7798SGne74VxrGcyKMkBBGFLtNOMwG2iN5mbt38WCC5jHEvJY\neXJHLcahGX1+x+iMb4XdyK+sr6bRZyWaVfjW8U6awi4q7Sb60wX2LfVNuqNb1XR+fm2QwYzC6mo7\nbuv0Zphbo1n++lgHv7oxMOPZFUEQ7k9X+jL8zYkufnN7mM3jnCPxnMLfHO9iTcDBW1f5ecvKCnS9\nNP9YUTWMsjTcjd4ey/H8pX4+sj3M+jsWoawNOPjM/sbhErZyyp2x1wcyHG9L8Mhy35xsMw27LfO+\nNvjGYG5O1gELwnzVFHJyuis5awHzgqthHo/TYsRqMhByW2gezHK1f+R6V4/VRL3XyrNneomkCjgt\nRixGmQuRNEfbEhTU0YP8x2KQJd660s87N1TjmCBzczfMBplqhxnrGPWEgiAIEwm7LbxvcxBNL30J\nH4uMhNNiwGqUkSWJzTVuttS6GUgrfPmVdk7fNvPUZJCwmgyYDTLmO84nWZLGDZbHMpBRONYWI1mY\nvzXEc+2jO8OsnqflIoIwFzbNch3zfZNhvl26oPK9Uz2YDRJP7WvAYryV0ShqOhlFRQae3BHGIEsk\n80UUVZ9ylnjdJBae3K2Q28InH6qftdocQRDuPwGXBUmCP36hhXUBJx/dWVP2eS6rkY9sD486bzR0\nUnmVwm01FkGXhU/srpvWs2lj0MkfPLxk3qy+7ksVaI3lWFVpx2WdHx+Td/bTCMJi1xRy8VdHOlA1\nfVaaYefHSTCN+tMF+tMKH9gcAFkeESxDafHJZ/Y1jhhd5LGaSOaLU/5Z0ayCqulUTsOc0XJEsCwI\nwr3y2818dGcNzvHmanKrpvh2QZeF39tbP2o6hSyVEg1OswFJksgpKs3RHH67kaKqE3BZpvQBZjUZ\nCM7QBIy7caU/wz+e7uHju2pnNDEiCMLdq7Cb8NlM3BjMsqLSPvEL7tF9c7+/NZrlUHOUS71pvnKk\nHVk2sNxf/i/wzjmfV/rSfP6lFi71pcs+vxxV03nmdA9fO9pBKjf1YFsQBGE2GGSJdQEnDVOcsJBV\nVI62xulJFkY1Ht8YyPDFgy1ciJTOzJZojr860s5rHUn+14stXF3g4zZXVdn5zR01NPhmb8arIAhT\nd3Me82xY0AFzX6rAT68M0BnPca0/y/fP9OKxGXn3xgA6pbFFY9F1neJQzbJRlrGaDKiqPu4czu5E\nnq+80s6lSBqDXBp1tKfRi0XUGQuCcB/IKqXlGACJXJFnz/Rwon3kHHtF1VA0nXqfnZyi8pVX2rEa\nZd63KciSCit7l/jwTlMZw0C6wI3BDNo4Z/lMqHSY2Rx2LdolKYKwUMzmPOYFHelF0gX+7UIfXYk8\nW2pcfOqhetYEnHQnC3z5lXZ6koUxX/tKa5y/Pt5JLKuw1G/jw1tDfO90N6+2j73kJK9qtMeypIca\nU3bVeziwvGLMQf+CIAgLyYvXo3z+YAuRVIFqp5n/Z089b1pxa1Pfma4kf3m4nVRe5bWOBImcSnci\nCxI82OhlTbWTX2sKEJymSReHmmN86VAbXYn8tLyfIAj3l6aQi3M9KVRt5r9UL9ivz6qmY5YlPvFg\nHbVeCz3JAi0DWfpSBTaHnNR5LHhtY/96eUUjkSsOZy7MRpkVlQ4q7Lca/168NshgrsjbV/mxmgw0\n+mw8/fASnJb5U2snCIIwXQJOMxsCLixGibZYDqtRpsp5q0dD02FdtYNMociHt4WIJPPsX+Ynp0x+\nwtBUbAg5qXKa8Y1zlguCsHh5rEaCLjNX+jPDOzJmyoJNjXYn8nz5SDuX+tIMZhT+8nA7g7ki1way\nGA0yG4JO3uhJ0Zssn5l4aKmXT+6pp2Jo+0mlw8yHt4VZP9Tgoek67fE81/ozKLd1iPvsprLNMYIg\nCAvd1lo379sSRNfh68c6+P7Z3hGlbXlV42fXBrkRzdGXVojmVH54LsLhlti0rZW+PVO0zG9nd6N3\nxOjO1miW013JKY0BFQTh/tUUcnGme+brmBds5OezG3liU5CNQRdGg0zQbcZskNhe66LBZ6UjnuM7\nJ7u50l+++URVdeymsX99WZJ454ZqfntXzaTHCimqRiSVn/V6O0EQFh9F1cgXJw4a41mF5BQbk50W\nI7+6IcBbVlQMLywBcJhk6rxWdta62VXv4bFVft7TFKCoafSmxi6Bm6yuRJ4vHWrjfO/YNYmHmmP8\n7YlOBtLKPf88QRAWvqawk9NdM1/HvGADZofZSJ3Hyo8u9pPOq/zC6kre6EnhtBgxyBIBl5mP76ph\nbZkUfWs0y+dfbuV87/hTMRxmA1f7sxy8EUWZxAfT+d40f/xCC9fGCNIFQRCmg6rpPHe+j2dO94yb\n2U0XVL51vIsXr0dJFcYOmlujWZ47HyEyFPS2RrO80hLHcccoug0hF5/YU8fqgJMKu4kKh5nV1Q4e\nWuIjNA2b8hRVYzCrkC+OnXQ4sNzHxx+oo9o5P2Y2C4IwtzYGnVyKpFFm+K7Tgi4MyxVV2uJZkvki\n64NOGnxW3EMzlf/uRDcht5m1gdEzNKWb/5lgTKim6xxvi9OTKrA57Jywuc9lMbIh6Jxw3qkgCMJ0\nGe8cM8kSuxo9PH+xDx2dX1pXXfZ5vakCP7s6yJoqB9VOM1lFoy2eJVsmUXDnfPigyzLc5Fcoakgw\n6qzUdH1Sc+UbfDY+t79h3O2pYbeV8NhbvgVBWGScFiM1HguX+zLDZbUzYUEHzCsqHXxsZw0/fCNC\nazRH0G1hZ50boywR9liodJhG3E68qd5n46l9DSPWTvckS/XKKyvtVA8d/rIk8Z5NgVFbALsSeVwW\nA647Rg4t89tY5i+/SWsuqJpOUdNGLW8RBGFhM8gS71hXha4zaj11NKPQn1ZoqLBiNso0BZ1c68/g\nsRrpS+Wpco7OBG8IOvns/sbhLPESn5Wn9zeOaIKeSK6o8vcne7AaZZ7YFBju9bgYSfOfl/p5z6Yg\n4aH3vxRJc6wtzttXV5Irqmg6w3Oip7pxVRAEoSnk4nR3akYD5gVbknGTySATzalEs0UGMgqtsRw2\nk4HHV1ewu8FLMlcsu8XPZjKMCKbP96T4i8PtnLxjAHaF3UzgthFJHfEcf3awhaOt8Zn7pabJ8bY4\nf/VKBwPpe68tFARhfjEZ5FHBMsDZnhR/8UobbdEcUFp7/cGtIbJFjf99sJVIanQjtM1koM5rpahq\nXO/P8BeH2+hLK6MSDvGsQmaM0o5sQSOWVYhlFRTtVmY6p2j0pQsjmvQGMwpnupMkckW+dbyLr7zS\nxmBGnFOCINydTeGZX2CyoDPMUMpK/P5D9bQnc2hFna5EHlXV+edzEYIuMzI66YLOR7aHy3643OSz\nm/j1LUHqPeNvdnJZDDy8rIJ67/zfAKUBiqYjWhAFYfEIOM08tso/YvylJEnUuC3sXeIbteb6di/d\niPLzq1G21rq4c6xpLFvkL19pZ13Awbs2BEa91mc38asbqpEkCbvp1kfLxpCTJRWNeG23Msdbalys\nqLTjtRl4aIkXRdWwiTthgiDcpfUBJ3/c30KhqI0b692LBR8wA/idZrx2E62DWf78cBuPLK9gdZUD\nl8WApukU9dItzPFcjKQ52Zlkd4OXfFFD1XTsZWqRPVYTv7i2aqZ+lWn1QL2HrTWucT8gBUG4v/Qk\nC/zH5QFWVjkIuiCWVTBIEhtDLjaGXOO+ttJhZn3IwYHlPqrvKN0wGSRWVtoJjbOUZInfPuoxgyyN\nCJYBrCYDVpOBQlHjdFeSnKqxf1nFqNcKgiBMht1soNFn5UIkzabw+Ofc3Zq1gFlVVQ4ePIgsy+zf\nv5++vj5eeOEFqqqq2LBhA1VV9xaEnupMcqg5ym9sDxN2W0aUUUzG21ZVsm+pjyqnmX89F6EtluMj\n28KTHik3HxlkCZssgmVBWEzWBx1UO+uo81iIZRW+eqQDn83EkzvCEzYub6t1s622fEedw2zgiU3B\nab1Ws1Hmg9tCaDplExSCIAiTVZrHnJqxgHnWapgNBgMHDhzAar1VyrB9+3YOHDhwz8GyqulIkk6V\nw0xoKFhO5YtcjKRIl6lfLqfCbqJ2qBzDbjLgthiQy2Slr/ZnON4Wn7Yh/YIgCNOp0mFmbcCB1WRA\nlsBjNeC0GNCB/nSBgzeidI+zarorkad5MDvisZyi0hLNzsi5V+20TNsqbUEQFq+m0MzWMc9Z+tRi\nsXD06FFu3LjB0qVLWbp06V2/V/Nglm8c7+Kd66u5uTPkbHeKlmgWRdXZGHKRL2pc6ktT5TARdo9f\nf/zoigp0wFgmYD7RHud4e4IGXyNBUeogCMI85raa+K2dteiU7jh1xHIca4tjM8ljzk3+lzcitMWy\nPP1wI7miRl9aQdN0vn6sk/c0BfDZjARdlmmZuywIgjBd1gWdXBvIklXUGSlFnbMpGW63m1/8xV/k\n0UcfJRKJ3NN7aZpOU8iJLMHB5hiqpnO4Ncar7QkqhmrnepN5vnm8k1fbE6NeX6pfTlAc6nIxyFLZ\nYBng0RV+PrmnjoAYmi8IwgJgMpamabTGsnzrRBdba9ysKbPQKVMocrwtxu4GD09sCuKyGDnRnuCb\nxzuxmWQeWe6jqGp89UgHr7TM/ylBgiAsLlajzPJK24RL6e7WnGWYi8UiRmPpx6vqvd3mq3CYqHWb\n8dqMrKqyY5Al3r85RDqv0p8uYDcbCLos/NbOGiodJrKKyo2BLGG3Ba/NyE+vDNCVyLO0wobPNv4M\n0GqnmWoWT7DcPJjlUl+anXWeKc1kFQRh9nXEcpzuTrK91j2qj8NtMbJ3iZcqh3HUDHmAwUyRfzjV\nwyPLK3jH0IKTbXVuGitsLKmwU+O20pMs8LEHaql2THwWJHNFrg5kqPdaqXQsnjNTEIS5s2mojnms\nXox7MWsBs6ZpHD58mGvXriFJEoFAgCtXrgCwbt26Sb9PWzTD9YEcdR4LOVXDajSwpMLG29dWE0nm\nhw/mkNvCma4k3zrRxQe2hNhZ7xnuEL8YSfO1Yx28d1OQ3Y1e3r0xQEHTJgyWF6POeJ7nL/azstIu\nAmZBmOd6UgX+8/IASypsowLmSoeZWLbIP57qoc5rG/XvOeS28Mk99XiHguljbXEsBonNNaUPHotR\nnlITdEssx9+e6OLXNwenLWBO54sk8qooBxEEoaymkJO/PdE1I+89awGzLMvs3buXvXv3Dj/W2Ng4\n5fe5EMnw54fa+dSeOn5wNsLepR7sJplorsjXj3bwmztqhjskq50mPrAlxHK/bcR7hNxm3rc5yJKh\nzVLN0Sx9qQJusxGvCApHaAo7qfM2DDdECoIw9yKpAllFHd6Od9O6gIOn9jUMb9S708PLfOyq9+Ap\nE/gaZInlQ2PhrkTS/MXhNhq8VtZX2zCZyp+L6XyRl27EqPGYMcoSIZcV/1D2udFr5Te2h2mYxpn1\nr7Yn+LcLfXx6bwN1C2AWviAIs2tNtYPWWI50QcUxzZN3Ftymv1qPhd/eVUON28Jv7AjTFHLhs5vw\n2ozsWeLFZ7v1QXAxkuG7r3cTz40s+fBaTTzY4CXktqDrOsfbEvzbxX46x+gczyoqL12PcqE3NaO/\n23zkshhp8NkmnGMtCMLs+dGFPr78Sjv9d2zxtJkMNPpsmA3lj/aVVQ6awq7hf8/ne1IcvBEle8f0\ni6DLxJPba/ildVVjBssAGUXjxRuDvNGd4h9P93I+cuuMdFmNbKlx45/Gcoyw28JDS3zT/kEoCML9\nwWyUWVVl51zP9MdrC27I8Pqgi/XB0TP2akwG3tM0ckbo0gobv7ohQOU49XaSJPHODVXsXeIdMyuT\nzBf594t9bKtxszYwc3vKBUEQJuPBRi/rAs6ytchTcbIzwZnuFOsCjhFd5W6bmUdWTLxIpMpp5qm9\nDeiUgvHpzCaXs6rawaoyDYuCIAg33ZzHvLPeM63vu+AC5rFEUgVaozlWVdpxD2WZGytsNFbYxn1d\nQdWwmwxsqR37edVOC7/3UD12MUZOEIR5oNyUi7vx9tVVPLK8YsIa41RBRdN03GVKOW7WSs/ULOVE\nrsiVvgwNPitVYjqRIAgT2BRy8tWjHdP+vguuJANKtxFPdSVRNZ1kroiu61wfyPDtk11ciKRojWbH\nfG1bLMeh5ijxXGmhyRvdKf7kpZZRg/rvVOuxiqY3QRCmTTpf5JWWGC0TnD33Kqeo5IvaqMebB7Nc\n7k/jnaCRT9V0/ulsL9883km6MPmJRul8kTe6k0QzypSv+XbtsRx/d7KLK/2Ze3ofQRAWh5VVdroS\neRK5yS2um6wFFzAXVY3/uDzAv7zRS/Ngls+/3MqF3jSVdhNPbg9zpjvJP5zqKfsBAXCtP8P3z/Qy\nkCnV/jktBpb6bFgnWBkrCIIwnQYyCs+c7uF8ZGZmhkKp/+K7r/fwr+cjqENz5m8615PimdM9DGaL\nZBWV9lhueBb97WQJ6j1WlvltGKbQynAjmuOvj3dyqe/efr9Gn5Und9Swusp+T+8jCMLiYDLIrA04\nODvNdcwLriTDaJB53+Ygqqaj6zpWo0yuqPHXx7t4T1OQh5b4kCUJi1EmmSuSKowcQbQ57KLOa6Vu\naOrDqioHq6pKtzfjuSLtsRyNPivOe6wNFARBGE+Nx8qnHqqfMMN7LyTAapKxlmkCfLDBw9qAg7Db\nwsnOBH9/spvffbBuVI2wJEnD9cw9yTzRaJYKm2nU2Lo71XktfGBLiEbf+GVxE3FYjMOTjwRBECZj\nU9jF6a4kexq90/aeCzIqvL057/f3NpDOF3nzCj9hl4Ult42QO9wS46dXB3hqXwPVTguapuOzm/CN\nUVpxtT/D373WxW9ur2FzjTigBUGYOQZZYpl/ZrOmVpOB9zQFkCVp1KQbv8M8PMEi4DTz6IqKsmej\nqukUNZ10QeXzB1vx201sqXHztlXjB8xeq2nam24EQRAmY0vYxZ+82DKt77kgA+bbWYwyFqOZX1hb\nNeLxwYzC0gorB5ZV4DSXtvm1x3K8b3NwzOxxvdfKr28OUi/mewqCsIAVihpZRcVjM2EaY8Tc7Rp8\ntlEznW966UaU8z0p3rspwJuXV2AzG1jiE2ekIAjz11K/jXiuSH+6MG2LkxZc4W6hqI2aPXqnq/1p\n/vSlFhQNHltdWbovqesUNRhdoXdLtdPMAw3e4cH70yVdKHKhNzXtBeiCICwu8axCOj/xOXKkNcYX\nXm6jN1l+tvxUGCXw240UVJ23rq5k31If9fdYZiEIgjCTZEmiKeziVFdy+t5z2t5plhxti/Pff3Kd\nS3c0ymQKKtlCkdND0zOCTjNWo8zZnhSff7GFtQEnv7kjdM9zS+/GtYEsXz3acc/NL4IgLF59yTxf\nfLmNf34jMuFz3RYjNW7LpLLL6YKKrpd6Qm42S1/tTw9PpRjMFnm9K0VPcvxEhSAIwnyyOeziVNf0\nNf4tuIDZKEs4LcYR3dod8RxfeLmVM91pvvVqJ63RPJ96qJ7llXZyioqilj4ILMaZmaPcPJDlJ5cH\nxhyfVOu28kRTcMaH+guCcP+SZAmnxTBiwUg56Xyp2fnNKysmHIXZGs3yhYMtnO9Nc7o7xV8ebqMr\nnuOZ073846lu0gUVt7UUfE/3nTdBEISZtDns4lRnEl0fr7Zg8hZcDfPuRi+bQ07st2WKJUqBtNUk\n8eSOGgJOM5JUiqhdZgPL/DbsM7hKtT2e40cX+1hZZS/bNON3mNizZPo6NQVBWHwqHWZ+d3ctBmn8\nPEeqoPKv5yPsbvSydIKmwlIzoIwkQVHTyRU1ZEniiU1B0HUcZgMHlvnYt9SHUZ7CTDlBEIQ5Fnab\nMcjQHs9PS2/agguYgRHBMpTGM/3eQ/VYjPJwoHzTmoCTJX47jhkMmDeHXTT4rNR6RAZZEISZYzNN\nfGQHXBY+/VADDsvEZ16d18qn99ZjMxlQNZ211Q4cZgPB254jSRJGESsLgrDASJI0nGWejoB5wZVk\njMVqMgwHy5FUfngxiUGWZjRYBnBZjTT4bKPGNgmCIEyndL5IRyw3agnJnWq9Vny2yZVQ3CzxmI2z\nUhAEYTZtnsbGv/smYL4pkS/ytSOdPHO6d8IPFVXTGcyMbGRJ54scvBHlar9o0BMEYX451ZXifx9s\noS2Wm+tLGZYvasSyI/s3YlmFDJLY/QAAIABJREFUF64Nzvjab0EQhPFsDrs4252aMB6cjAUZMGu6\nzoXeNNcHMnTHcyMOa6tR5uHlPnbVeybM+J7rTfEnL7ZwbagbHErb/n54LsK5nrsLmNuiWS73paet\nyFwQhIXr+kCGC5HpOw9CbjNvWuHHfQ/TfnKKSms0S0HVxn1ef6rA2e4kWUUd93nH2+J8/mAr3Ylb\nI+wGswo/PBehJTp/AntBEBYfn91EpcPE1dvivLu1IGuY47ki3znZhc9mxGyQCLotpHIqO+s9NIVd\n7Fvqm9T72E0G6jw2rKZb3xtCbgu/91D9XY+fe+58P62xLH/wcOPwFi1BEBYfVdP5lzf6GMwW+Nz+\nRryTLJEYzzK/fXg7oK7rXIykMcoyK6tuNfddiqQ51BzjF9dWll1ffSGS5v+c6OK3H6hlXcA55s86\nH0nzg7O9fGJ3HauqHGM+z2M10uC1UlA1DjVHWVlpp8Fr49N7G6iwL8iPGEEQ7iM3yzJWV499jk3G\ngjvNrvSluRBJ8+6NAawmmc5EHpfZwLneFKnC1BaDrKi0s2LPyC5ySZLKbrzqTuS50p9hXbWDSufY\ngfDjayrJKOq0fDgKgrBwGWSJX9tYTUHVZuQ8SOSL/MOpHjwWI5/e1zA8xSKRL3KpL82bixUjnn+t\nP0N/Rhleg+0fmuhzuDmKounsqHHjsJY+EnRdx2Mx8qEtIWrc46/Abgq7aAq7ON2V5NkzvXx4W5iA\ny8KSCrHcRBCEube5xsUPz0VK03/uwYILmCPJAu2xPD6rkUQe1gUcxLJFjrQmqPeWDujWaBZZkqib\nxrnHHfE8Pzjby8d21owbMC/1iw8JQRBKGmcwaPRYTXxkWxiTQRox8m1TyEWjzzpqLexrHQmOtsX5\n3P5G3rGuGoBUrsAPz/XRl1ZY4rMOB8zdiTznelNous62OjcAfakCrdEcK6vsuK2jPzpWVNr4bw/W\nimlBgiDMKxuCTv7kxZahfRx3X4m84GqYNeBERwJJkjjTneL7Z3pp9Fl5ckeYaoeJaEbh717r4u9P\ndpMvjl97V/b9dZ3eZJ5CcWR935pqO5/YXcfyyvHnmgqCIMyW5ZX2UXfEzEaZdEHjLw+383pnqTu8\nO5FnSYWNT+6uI+i69YXfaTXz/i1Bfnd3LY0Vt862jkSeF69HWV5pH54+dLU/w9+d7KJ9jIZDh9nI\nmmrnuOVsWUXlje7ktKzsFgRBmAyH2cASn43zvfe29W/BBcwbgk4+taeeZX4rAaeZVVUOskWdU11J\nLvVlGMgovHN9Ne9YV3VXm/2uDWT4Xy+28EZviqyi8tKNKKe6EthMBlZVOSbcsiUIgjDddF3nje4k\n53pS4zYQ5hSV5y/205XI88GtIVYP1Taf6U7x9693A9KoWfWba9zsqPOMeKzBa+XD28OsqnTQEs1y\ntjvJ8ko7T+6oodF39xnki5E0f/SzZg41x0YlJQRBEGbK5pp7X5O94AJmn93E5hoXVU4LdV4ruq7T\nGc9xridNpcNEjcfChpCLdUEn7bEcP7rQN6VshstsZHPYhddqpCuR569eaee19gTJ/NTqowVBEKZL\nRtH44bkIz53vI39HoDmQVvjGsU5OdSXJqxonOhJc6c+wvc5D/VD2eVPYyUd31BB2ly8na42Wzsq+\nVGnMZsBlYU+jF7/DxM+vDvK3J7rQdZ1NYReOe5jQEXZb+PUtQWo8FjG3XhCEWbM57OL1zsQ9vceC\nq2G+yWyQqXaaeO58hA9sCfE/Hl2Cx2rEcFv2JJZVeL0zwTK/rWy3+O2uD2RI5lXWBRx8aFsYKGVr\nPruvAZfVeFdNO/mixrmeFH67aUZrGQVBuD8pRQ2TUcZhNvDh7WEkJKx33OVSNI3WWJZVVXY8VhO/\nu7sWkzwyFxJ0WahymGmJZkkVVNYHnCMC1p5knp9cGWBlpZ0qpxlV0znfm8ZmlHnrSj8PNnqHa6Hv\nRdBlGa6fFgRBmC1rAw66EgWiWWXSS53utOAyzLdbUeng6YcbWRtwEnBZGMwqfOlwG+d6UiRzRV66\nEaMp7BpVd9wymOVUZ2LEHNKfXx3kOye7iGVvZZKtJgPb6z13PYpkMKPw7ZNdHGmN39XrBUFYvM73\npvjzw210xEs1w/VeW9lG5qDLwmf2NfJAQ6mswm83l23KO9wS429e7eL1jgTx3Mg7ZhtDTp7a18Cy\nobMykSvyD6e6+fHFPsIeC2uqHTOWEdZ0nXM9Ka70iWVRgiDMDKMssSnk5GTH3W/9W9ABM0CF3Xzb\nQS6RKqgUhza6SBJYDDJmg8y5nhTPnY+QyBU52Bzl/7zWxWDm1sKTt6+p5Ld31VLpmL7xT9VOM7/z\nYB0PL5vcXGhBEISbVE0nVVAntaHKazMykCnwzOkeWqNjb9er91rZ0+ilwj7ynLOZDDT4bMPTNnx2\nEx/dUcO7NgZG1TxPt2SuyPdOdfPc+f5p2cYlCIJQzrY6N6913H1ZxoItyYBSBrctlmOZ30ZnPM9r\nHQk+vDVEyG3BajLw5I4w8tBh3xLNcqg5xq56D29a4WdXvYeq224x1kzzKKTmwSxFTWP1OAP/BUEQ\nxrIu4GRphQ3nJGuG49kir7TEWFPloKHMd/TVVXZaolnKhaRt0SzNgznWBe1UOkrla7ffmdN0nWhG\nwWc3DZ+p08VjM/Eb22swGyRR1ywIwozZVuPm2691o+n6XZ1jsxYwq6rKwYMHkWWZ/fv3A/DjH/8Y\no9HIjh07qKioGP8NbhPPKhSKGsfaE9jNBoyyNDSLOUaVw8RgtsjWWveIKRn7lvrYXuu+rZZ5/Jrm\ne6GoGs+e6SGVV/ns/gbcVrHERBCEqTHI0qSDZYCVVQ7+4EAjlfbytcZZReNUZ5LVVQ7yRY3zPSlU\nXSeeK2IxyHzhUCtPP9w4HDDf7kpfhr850clHttWwNnB3SYD+VAGn2YDVPHrSkBjXKQjCTAu4zHis\nRq71Z0dsR52sWSvJMBgMHDhwAKu1lMm9fPkyDzzwAG9961s5e/bspN+nO5HjT19q5UxPin89H+FK\nX4ZLkTQbQ05+a0ctz5zuJTpUhxzPKsO3+FwW44SNf7e72pfm2dO99KcLU/gtS0wGmV/dEODXt4Tu\nesW2IMw3BVWjI54T48DmUCRVIJZVyv6ZQZYIu62YxxjM31hh4w8ebqQp5CSaUTjYHCvNsj8bQVF1\nPrG7nuVlmpNPdSboSeWpdVuxme7uI+NSJMX//FkzLzVHAUjli+TuYk6+IAjCvdhW6+LEXZZlzFkN\nczQaxe/3A2CxTD6QlZAwGSQKRY1P7q5jbcBOvddCPKeQLBT51J46jDKc60nxRz9r5j8u9dM3FPT2\npQp892T3pJpL+tIKR9tipAp3d6gvr7Szptox4/V/gjBbLvdl+NMXW7gkmrPmRCyr8OXD7fzwXGTc\n57XHchxqjnKiLc73TnWPOMOqXaVytSqnmTet8LEp5ODdG6qp81p404oKqsskFa72Z/np5QE+tC00\n7rrr9liWn17pp2WwVEOt6TrHWuMcbokBYDZIyEgkcgpfP9rJf1wauJu/BkEQhLu2rfbu65jnRdPf\nVILKoNvCZ/Y18OgKP4mcyhcOtjGYKXK5L8szZ3pRdfjnNyLEsgpbwy6KmkZeKWXE0orKya4EA5mJ\nZypvrnHxuf2NNEzjem1BWMgqbEb2LvFRcZcjeYR7YzXK7F/mpSnkGvd5NwazPHuml3he5Vp/FkUd\nfUfAIEusD7rw2sz86FI/8fzYiYG3rvLzqYcaxhytmS6oKEWNa/1ZvnKkg2sDGQAUVedIa5xDN6LU\ne238f48sYf9yLwZZpsppwltmkocgCMJM2hh00jyYvavdGnN2Yvl8PiKRCNXV1ajq1LK4N+v61gYc\nPLWvgTqPFU3X+OCWEFajzJM7ajDK8F99aT60NUTtUNDb6CvdknQPvT5dUJElym7vs5kMox7PKSrt\n8TxBl1mUWgiLTo3Hyq81iS+Qc8X6/7d378FR3VeCx7+3391qtV6t1gsJ8bAAIWQBMuYhy1hx2Rgn\nLDjGHm+NJxns3Wwlk1RqprYyydb+s1XZpMpVu1OVGpLZnbGrZsqOx0n5gddGYIwVDOElnkZCICxA\n6K3WuyX18/b+0ahRo4clAbrd4nyqXAXtvtyj261fn/7d8zs/o56nH8n4xueVZtvJSTaRk2zk0Zwk\nTDqFRvcISSY9uQ4zITXMpU4PNoOe5c7ImDi+L2lIDdMx5MOZZMJs0E3aom5M70iAfzrRwvpFDspy\n7PzdEwVkJ5vwBkJYjHq+tz4bFbCZ9Nhu1y6b9PDquhxZ4CeEmHcmg46SbDvnWoeoXDq7DmbzNsOs\nqipHjhyhvr6ekydPUlRUxKlTp/jkk09YvXr1jP8dj9fP9Z4R1HAYi0GHw2zgVMsAb9W2c/RGP+fa\nh6i+0kProJ+1ecnY7kpsXfbILclhf4g3a9v44FLXjFsZfd07yj8cbaa+U25JCyG0NegNcrF9iMFx\nPZWb+0f5U1MfDosBm8nIwcZeaq7385tjt/ikwR097l/PtPNBXRdBNUxWsjmm7vlq9wi/+uIGN3s8\nNN2eLZ6KToFUqxGrURct9/jNn1to6o30js5IMsV0IxojybIQQivlixxzqmOet2lSnU5HZWUllZWV\n0ce+/e1vz/rfOXpzkLfPdUZqlfUK//d0K99e5eTJpWmk24wM+4P4gyq3+kYpz3eQ65i8PnrUHyIv\n2YTLbmKmY3eW3cTuNVnkpz64Dht3G/IGCYbDc96ZRgixMF11j/BWbRuvPZbLujwHAO7hAIeu9VKU\naSMzyUSqxYiqhvne+hxSrZHhPs1m5IUSF1/3jHCzzzuhQ0WqzUDFkjSaBwO8VdvM31bksz4/JeY5\n4XBkJ0A1HOb1DbnR/s0FqWZ2r8nCZY8dr3pH/Iz4Q/hCYZamW2VthxBCM48tSubfL3QSDodnNRYl\nXF2BzagnP9WMUa+Qn2rh1XU5LE23MugN0DcaIMtuwhcKM+wLEghNPXPc7w3y+bU+XlzjmvEFcyaZ\n2LrszmzJzb5Rjl7vZ+uytPvexxkit0b//WInXR4/P96cT7LU/AkhbitMi4x/hWl3FuIVu5L42dZC\nspIjGzptX5mBGp44o7soxYw3qJJxewOTQEilc8jH+TYPSzKs/MWjWRy93s+iFDPGSbpu+IIhGt2R\nO31L060Yb9/Jy3VYyEk209zvRacLkG410jPs559Pt2E16Pi6d5RfPFU4q45FQghxP+U6zJj0Ctd7\nvSzNmHoh890SLgOrXJrGxoJkTIZI6BsLUrjRN8qva26yqSCF8kUO1uc5uNTpIXeaJLYg1cJPKvIn\n3C4cDYRo7veS5zB/Yw/U/tEgx5sHKMtLJi9l2qfOiV6nsCLTRnayCaNeZmSEEHc4k0w47xq/LEY9\n+al31l4oisL4oaNjyIcvqLI4zcricYn2pQ4Pb9W2s2lxCrf6fazOslOxJJUN+fboWDueoihcbPcQ\nVMM8c1dddfuQj//1ZTNPLU1jZ4kLk0HHsgwrBSkWNi1OIUW++AshNKQoChvyUzh5a2BhJ8zAhAHc\natCxpSCFwts/eLrNSOWS6Yu5TQYdKybZha/RPcI/nWzlr9fnUp7vmPbfWJUV2SjAZX9wsyVPfMPP\nIYQQM/XehS5aBr38fGshaeO2x7abDaxyJVG+KJmccbO/kyXLAGaDntc35OIeDvDPp1p5uSyLXEdk\ngiLFbOCF1S5yUyLJfLLZwItrsh7gTyWEELPzeIGDt8918EpZ9oyPSciEeUz/aICgGqlBUVHw+EL3\nvG1rbrKZl0qzWDSDOmWTXhf9kBBCiPmihsMcud6PLxCiank6Rv3M1m8/W5TOSEDFbIgdJx9x2nhk\nlrvt5aVY6PIEcI8EYsrfkswGnlwmX/SFEPHr0Rw7jziXzuqYuOjDPBvX3MMcvNrD9d5h/vhVF785\ndguPN8hXHUNk2IxsWnxvtRF6XeQWZnhmjTOEEGLObvSN8FFdFxfbh2Z1XEgNU985zIV2D/5p1mrc\nTQX+5XQrDd0j+IIqlzo8dA75Zhn1HaU5dn62dXFMeYcQQsQ7o1436/KwhEuYb/R6+d2JVq65vZTm\n2NlSmEpuiplfVBWyLi8Z0wxnWqbSNuTn9xc6udE3GvP4sD/E8Bx3/RNCPLyu947y9tl22gcnJqZt\nA372Hm+lqWd0kiPv6Lt9N22MUa/jL9dm8Z8fzyPJNLGP/FRSLAa2FKaSYTPSOuDjtydaONfmmfkP\ncxe9TsFhkQ4+QoiFL+FKMvJSzTy/MgOrUceGca2OLJNsPjIXS9Mt/M3mfHIddxbTBEIq717owBcM\ns6c8576dSwix8PWOBDhxa4DyfAc5xJZ6rci08d+/VThl+0uIdOP57YkWXirNiraPA+aUqOY6zNGa\nvdFAiD3luWQnT+yTLIQQIlbCJcyrXHbSrEaSzXeS1m6Pn0+vuNlUkIIzycTV7mGWO23RFeSjgRD9\no0FypvlQGmM16lnlil0MqFMUsu1m/CEVvfQPFULMwppsO794agku+8TENCPJRMWS6RNWk15HTrIZ\nq0GHL6hS1+khw2a85zIIq1HP+kVTL2weGzezk00zbr3Z1DPKket9PFOUMe2XACGESDQJV5LROeTj\ncucwHl+IEX+QP37VyVcdHi62eajvHOZWn5d/O9fBjT5v9JgzLUP8uuYGN/umv+05Fb1O4flVTnaV\nuCbtSSqEEJPp8vg5dqMfRZl6d7tb/V5+d7yF6713xqemnlEa3ZEdRXMcZn6yJZ9VWXZ6hv28ebqN\n4zcHHnjsF9o9/LrmBtd7RwkEVT653M3n13qn3Rl1wBvgXNsQHl9wyucIIUQiSrjsr23Qxx8vddE2\n6GPIFyIYCuMPqby2IQdFCeOw6vkvj+dRlHFnxbfLbmTz4lTss6j1E0KIe9V+e7xqnaR+eYzHH6LB\nPRxNMn1Bld9f6OBfz7Tjub3t9dgMb1aymb/ZnM+TS+90oej2+LncOYw/pN7X2J02I5sKUkg2Gwiq\nYRq6R2jsjmxWMpXV2XZ+8VThhN0DH7SQGqZ90Ic3IOtMhBAPRsKVZKx0JfHTigIWpZhpdI9Q09TH\nX63LodsT4LPGPspyHazJSY45pigziaJJei4nmrGZnalmqoQQ8aUo08ZPKwpi1kTcbUWmLdIX2Rqp\nSTYbdLxUmkVIDWO/axW3Xqew8q6SsdrWQf7fZTf/tXIxhen3r1vFcqctJvH9TxtyUVCmbWFn0us0\n2cXv694RfnPsFn+1LpfHvqF/vhBCzEXCJcxWoz7aLzQ/xcL31uewLMNG/2iAJ5akYTfpCYRU9Drl\nnnsyx5NASOXjy25CapidxZlSGiJEAhg/Xk0lGIr0VDboFL5TnIlBp8yqJ3JJlp0Mm5FM+711qwgE\n1WnHlXjuhuEwGyJrWGzxG6MQIrElXMI83nAgxIU2Dy67CZNeR36KmUAozL+camNJupVks56VriTS\nF8IgGoZBbxA1HCZEmAXwEwkhgCvuYSwGHd5giHA4DMzui35+qoX81JlvoDQaCFF9pQdnkjG6k+jl\nzmFONveTl2phabqVZRnzW1Jxr7KTzfzHtTlahyGEWMASeppy2B+isSeyALCpd5R3znfQ7w0w5Asx\n6Avy8eVuujx+rcO8L4wGHS8/msUrZdlYDFKLLcRCoIbDHLray7GbA2xdmjbjHfvuRSAU2fTkRu+d\nhdEBNYzNpOePF7u41DH3vsxCCLFQJdwMc8egj4sdHkqz7XgDKrtWu1iRacMbVClMt7A41cqPNlvx\nBVXKcpIpSFs4W1dbpf+zEAtCfaeHus5hNhY4+IuybPwhlUz7/NT+OiwGfrR5EYZxyXlJdhKL08xs\nyE/BMa5uOhBS5yWJF0KIeJd4CbPHz0f13biSjBxo7OVazygZNiOrsuykWo009Yzyxdd9bFuZziOZ\niXVbUQixsLQOeAmoYQrv6pl8o8/Lv51tZ0mahfJxGzCN8QZCHP66D7tJT+W4jhj3S6o1tqhLpyik\nWIykjKtTvtTh4dMGN3+5Lptcx8KZeBBCiLlIuIR5RaaNv32igFyHGUWBM60eOjx+/KFBjHodoZDK\nxY4hnlgS+RByD/tpH/Sx3GmTGVohxLwJqWF+f6GTnmE/f7+1kJRxSWp5XjL/45llFDkndrW43jvK\noDdIk3sYszGylfVcO+Pc7BsloIYpSLXQ2D1Cus04ow2cIFKmMeQLcp+71QkhREJKuITZ4w3SPuTD\natThsBiwGBQMCrxV28GyDAvPrnDy36oKo7v81XUO897FTn6yJZ8VC6C1nBAiMeh1Cs+vdOILqtjN\nsUOtK9mMa4r2a9VXerjqHubvnijAYTEy6AtiM+oxz7IzTiCk8s75Tjz+IH+9Poffnmjh6eXp7Cxx\nzej40mw7yzOsJN8Ve+eQjz981UXVsnSKsyaOqb6gyvm2ITJsxnnvxyyEEA9KwiXMFzuG+Ydjzfx0\nSz4FaRbOtw2xNN3Ky6UuvEEVi17BNa4WsMhp43vrc2SbViHEvFvlmv2X9O0rM6jwpuANhsEb5B+P\n32LbCmfMZiUzYdTr+G5JJgE1zKIUM689loczaeb9dfQ6ZUKyDOAPhWkZ8OLx39nNL6SGo7Pg13tH\n+Z9fXGf7CqckzEKIBSPhVnOsyLTxo02LWJph5RFnEn//VCGZdhO/O9lKy0Bk97/Rcbs95TgiC1km\nG/iFEGKuvIEQX/eMxIw398PiNCsBNcz/PtpM25CPJWk2Ui1zG7+KMpNYnWXHYjSwNi95Vu3nppKf\nauFnTxZSlhvZIOps6yB7j7fgHo50JMpNNvHTLQVsKZxYmy2EEIkq4bLIRakWFo0b9NNtJnSKwu5S\nFx5fiNrWQfwhlbV5stuTEOLBudI9wv851crrj+Xe9/Emz2Fm95osCtOsbJhkUaDW0sb1th8JqHR5\n/ARv70TqsBp5anm6VqEJIcQDkXAJ82RSrUaeLcqgc8hHQ/cI2RpszSqEeLhkJZv4D8WZD2S8yUo2\na7LF9FxsyHfwaLad5DnOggshRCJYMCOcUa9jUaqVRakTV50LIcT9lp1sli/ngEmvwyS9moUQC5yM\nckIIIYQQQkxDEmYhhBBCCCGmIQmzEEIIIYQQ01DC4XBY6yCmc+bMGfr7+7UOQ4iHjl6vJxS6vy3T\nhBDfTH73hNBOamoq69evn/B43CfMQgghhBBCaElKMoQQQgghhJiGJMxCCCGEEEJMQxJmIYQQQggh\npiEJsxBCCCGEENOQhFkIIYQQQohpxP3W2OFwmIaGBgYGBgBIT0+nqKhIk1gCgQBGoxGPx0NdXR2K\nolBWVobJZJr3WOS6iNmIp/fLQtXQ0EB7ezsWiwVFUQiFQmRkZLBy5UqtQxNCCHGP4r6t3P79+1m3\nbh1ZWVkAdHd3c+rUKZ5//vl5j+Xw4cNUVVXx6aef8txzzxEIBDh06BDbt2+f91jkukwunhLDePoi\nEU/vl5mIp9dxpsZ+D8Y7dOgQTz/9tEYRfbPe3l4uXryI2WyOJvmrV68mNTVV69CmJDEvnLi6u7vJ\nzMykqamJrq4uQqEQhYWF5OXlzcv5Qa4ByDWYqbifYVZVNfohD5CZmalhNBE2mw1FUTCZTJjNZk1i\nkOsyuerqatatW8eqVauAyC/iJ598okli+OWXX1JVVcWRI0c0/yIRj++X6cTT6zhTJpOJEydO4HQ6\nUVUVt9uNxWLROqxpnTp1im3btsU8Fu/XWWK+f7SO66uvvqKqqoqmpqboF8uDBw/Oa6Ik10CuwUzF\nfcK8efNmjh49isEQCTUYDLJp0yZNYrFYLNEPxLFYtJohkOsyuXhMDOPhi0Q8vV9mIh5fx29SUVFB\nKBSKzoovW7YMvV6vcVTTCwaD9PT0kJGRAUBXVxdxftNRYr6PtI6ru7ubpqYmRkZG5u2cd5NrINdg\npuK+JEOI2ejr66Ouri4mMSwuLiY9PX3eY/nzn/+MTqfDbrdTUlJCMBjkwoULk265KWLF0+u40F25\ncoW+vj4A0tLSKCoqQlEUjaOansR8/2gZV3d3NwBmsxmHw0EwGKSlpYXCwsJ5Of8YuQZyDWZCEmYh\nhBBRJ0+e5PHHH9c6jCldunSJoaEhfD4fmzdvxmQycfDgQZ555hmtQ5tSbW0twWAQRVEYHh7GYrFg\nNpvj8suz1q//fJ9f6/eT1ucH7d+fk53fZDJRXl4+L+efqYRrKzcwMMDevXu1DgOQWKYST7GIySXi\na3TlyhWtQ1hwampqOHHiRMx/dXV1Woc1LbfbzaZNm6ioqKCmpob+/v6478jj8XjYuHEjnZ2dVFVV\nsXnzZoaGhrQOS/PXX+vzg/bvJ63PD9q/Pyc7v8fjmbfzz1Tc1zDfzWazsW7dOq3DAOIvlrVr12od\nBhBfsQwMDPD222/zwx/+UOtQ4iqWeHqNJnP+/Hm8Xm/MY/X19axYsUKjiBYmVVXZuHFjzGN+v1+j\naGZGVVW6urpwuVw888wzHDlyhPb2dq3DmlYwGARgx44d0cfi4Tpr/fprff6xGLR8P2l9ftD+/an1\n+WdKSjLEghYIBKitrY2LBW7xFEu8m6wdW01NDVu3btUmoAUqFApNWJioqio6XXzffBwZGcFms0X/\n3tDQkHD9rgcHB3E4HJrGoPXrr/X5x2j9ftL6/JPR+v2p9fknE/cJ82SbATidTk1mmuIplsloXXs2\nXjzFIhKnRmzM+BXbbrcbp9NJb2+vLPoTQgihibgvyWhra5t0MwAtktR4iqWmpmZCj9f6+npNktR4\niiUeFlDEYywej4etW7eyb9++6G2vmpqaeY9jpsaSZYB9+/axZ88eSZaFEEJoJu4T5njaDCCeYomH\n2q94jMXtdrN161aCwSCHDx9mw4YNmi0IiqdYEqVGTAghhIhHcV+SAcRsBpCSkqLpZgDxEku81H7F\nWyyHDx+mpKQEl8sFwJEjR2htbeWVV155qGOZTDzWiE3mzTffZM+ePVqHIYQQ4iGWEAmzELMRTwso\n4imWRHXt2jWWL1+udRjdrqoVAAAGhklEQVRCiIfY4cOH+fTTT/nlL38Z3bF179690Tus+/fvZ82a\nNSiKwujoKDt27IiO/TU1NfT09GAymQiFQvT39/P9739fw59GzIUkzEIIIYQQ0+ju7ubLL78kJSWF\nb33rW7S0tHDu3DkcDgdPPvkkf/jDH9i9ezcQmSg5cOAAu3bt4uzZs/j9/piyxePHj0u3pAQU372D\nhBD3RFVVQqGQ1mEIIUTCS09Pp6enB4ATJ07w+OOPM9mco81mIxAIANDY2DhhjY8ky4kp7hf9CSHm\nprq6mqamJnbu3Elubu60z33jjTd45JFHgMhixddff30+QhRCiIRSUlLCiRMnMJlMGAyTp1BXrlwh\nOzsbIO57mouZk4RZiAVq27Zt1NfXz+i5P/7xj6MdX6qrqx9kWEIIkbCKi4v51a9+xQ9+8IOYx91u\nNx9++CE6nQ6n00llZSVAdKYZ4NSpU7S1tdHd3c3LL7+cEIuuxR2SMD/kenp6+Oyzz7BarYTDYUZG\nRigvL6eoqOiBnM/v9/PGG29QXl7Os88+S21tLQcOHOD1118nKysr+ry9e/eye/duMjMzY44/duwY\nxcXFpKWlPZD4Fro//elP9Pb2Eg6HWb58OaWlpQDRZHlsi1YRv6qrqzl9+nR0gZGiKFRVVfHOO+8A\nRF8/t9vN5s2bKS4uZv/+/dTW1kaP8fl8fPe739W045AQiWR86cXPf/5zgGh5BoDT6WTnzp0Tjisq\nKuL8+fOUlZWxYcMGAN5//31JlhOQJMwPuQ8++IA9e/ZEbxudPn36gd5CMplMFBUV8eyzzwJQXl5O\nc3NzTLIMsHv37kmT4kAgEO0pLGanpaUFk8nErl27APjss8/w+XzRFd8Q2XDm7no7EV+2bdvG0NBQ\n9MO5vr6exsZGKioqgMgMGEQ6sqiqCsBzzz2Hx+OJHtPV1cUXX3wxYftxIcTkamtraWhowOVyUVxc\nTFdXF59//jldXV3Y7XYaGhr48MMPyc3NjSbGEPmMO3bsGB988AF6vR5FUSZMBInEIAnzQ+zWrVsU\nFxfHJMiPPfZY9M9dXV3U1NRgMpnw+/2sXLmS0tJSPB4P77zzDjabDZ1Oh8ViYWRkhG3btuF0Ou8p\nJr/fT3V1NdeuXePVV1+NGVjq6+s5e/Ysra2tJCUlodPp2L59+5R1ZCJWS0sLzc3NdHZ2AuD1eunr\n64vW2o09ptVmPGJuCgsLqampobCwMPqY1+uNbrA0mcbGxnv+XRXiYbJ9+3a2b98e/bvL5YrpqV9e\nXj7lsVu2bHmgsYn5IZnGQ2xoaIjU1NQp/7/L5eKll16K/v29996jtLQUu91ORUUFx48f57XXXgMi\n3RjefvttXn311Rmd+8MPP4z+ubu7O/pnk8nEjh07Jt22eexbfUlJiXzYz8GyZcvQ6/XRL0XhcDjm\nNqNWm82Ie1NTU0NZWRn9/f0cPXqUq1evEgwGefHFF2OeNzYDdvbsWXbt2kVZWZlGEQshROKRhPkh\nlpKSwrVr16b8/11dXRw9ehSdToder6e/vz/m/69duzb6Z51Oh9VqnfG5x9d6vf/++7OImknb+IiJ\nDh06RFNTExaLhcLCQiorK7l58yYfffQRiqIwMjLCd77zHZKSkgBobm6WDUISxFjyC5FV+7m5ufT3\n91NRUUFxcTFHjhyZcMzKlSvZuXMnlZWVHDhwIOb3VwghxPQkYX6I5eXl8cUXX8TsRtfc3Mzo6Cgr\nVqzg4MGDvPLKK9GFQe+++27M8RcuXGDdunVAZHtsv9//wGM2GAzSV3iGJqtPLS8vn/LW4fhb+iK+\njSW/44XDYRRFAYiu0N+3bx87duyIeV56ejrr16/n1KlTMbWWQgghpiYJ80PuhRdeYN++fVgsFlRV\nxWKxROu0cnJy2LdvH4qiEAgEuHHjBmfOnGH9+vVAZIZ6rJbZ6/Wybdu2bzxfR0cHDQ0NHDhwINol\n4/Lly9TV1bF69WouXbrEtWvXaGpqitYqb9y4MVpn++ijj/Lxxx9jt9sBsNvtVFVVPaCrI0T8qa6u\njs4wl5aWsnTpUjweD8eOHQPg6tWr0eeO1TDv37+fhoYGamtro11w3n33Xex2e3SRoBBCiKnJ1thi\nTurq6lAURT5shRBCCLHgyQyzmLXxs1kej0du6wohhBBiQZMZZiGEEEIIIaYhPaSEEEIIIYSYhiTM\nQgghhBBCTEMSZiGEEEIIIaYhCbMQQgghhBDTkIRZCCGEEEKIafx/XdIUbtTsn/wAAAAASUVORK5C\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110124850>"
       ]
      }
     ],
     "prompt_number": 293
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "final.corr()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Cap Hit</th>\n",
        "      <th>PER</th>\n",
        "      <th>MPG</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Cap Hit</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.502899</td>\n",
        "      <td> 0.574893</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PER</th>\n",
        "      <td> 0.502899</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.654308</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MPG</th>\n",
        "      <td> 0.574893</td>\n",
        "      <td> 0.654308</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 294,
       "text": [
        "          Cap Hit       PER       MPG\n",
        "Cap Hit  1.000000  0.502899  0.574893\n",
        "PER      0.502899  1.000000  0.654308\n",
        "MPG      0.574893  0.654308  1.000000\n",
        "\n",
        "[3 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 294
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "final.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 295,
       "text": [
        "Player      object\n",
        "Cap Hit    float64\n",
        "PER        float64\n",
        "MPG        float64\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 295
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "final = final.dropna()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 296
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cap = final['Cap Hit'][:, np.newaxis]\n",
      "per = final['PER'][:, np.newaxis]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 297
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cross_validation import train_test_split\n",
      "X_train, X_test, y_train, y_test = train_test_split(per, cap)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 298
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf1 = LinearRegression()\n",
      "clf1.fit(X_train, y_train)\n",
      "predicted_train = clf1.predict(X_train)\n",
      "predicted_test = clf1.predict(X_test)\n",
      "trains=X_train.reshape(1,-1).flatten()\n",
      "tests=X_test.reshape(1,-1).flatten()\n",
      "print clf1.coef_, clf1.intercept_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 524954.09866003]] [-2557366.02426694]\n"
       ]
      }
     ],
     "prompt_number": 299
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(per,cap,c='r')\n",
      "plt.plot(trains, predicted_train, c='b', alpha=0.5)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 316,
       "text": [
        "[<matplotlib.lines.Line2D at 0x111a9cf90>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Po7iYiueeg4yMBI9MnPgvuQT3J5/g/uILqm67LbzxdByYmzdjFBZid+jQYj28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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ff19250>"
       ]
      }
     ],
     "prompt_number": 316
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lr = LinearRegression()\n",
      "lr.fit(per,cap)\n",
      " \n",
      "b_0   = lr.intercept_\n",
      "coeff = lr.coef_\n",
      "\n",
      "pred = lr.predict(33.68)\n",
      "pred"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 301,
       "text": [
        "array([[ 14560859.24396323]])"
       ]
      }
     ],
     "prompt_number": 301
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ncaa = pd.read_csv('/Users/danielforsyth/Desktop/ncaa.csv')\n",
      "ncaa = ncaa[ncaa.MPG > 6.09]\n",
      "ncaa.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>RK</th>\n",
        "      <th>PLAYER</th>\n",
        "      <th>GP</th>\n",
        "      <th>MPG</th>\n",
        "      <th>AST</th>\n",
        "      <th>TO</th>\n",
        "      <th>USG</th>\n",
        "      <th>ORR</th>\n",
        "      <th>DRR</th>\n",
        "      <th>REBR</th>\n",
        "      <th>PER</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> Doug McDermott, CREI</td>\n",
        "      <td> 33</td>\n",
        "      <td> 33.6</td>\n",
        "      <td> 6.6</td>\n",
        "      <td>  7.7</td>\n",
        "      <td> 27.8</td>\n",
        "      <td>  5.7</td>\n",
        "      <td> 18.5</td>\n",
        "      <td> 12.1</td>\n",
        "      <td> 33.68</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>  Alan Williams, UCSB</td>\n",
        "      <td> 26</td>\n",
        "      <td> 31.7</td>\n",
        "      <td> 5.4</td>\n",
        "      <td> 10.2</td>\n",
        "      <td> 27.1</td>\n",
        "      <td> 13.2</td>\n",
        "      <td> 28.4</td>\n",
        "      <td> 20.8</td>\n",
        "      <td> 33.37</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td>   De'Mon Brooks, DAV</td>\n",
        "      <td> 26</td>\n",
        "      <td> 26.8</td>\n",
        "      <td> 7.8</td>\n",
        "      <td> 12.9</td>\n",
        "      <td> 24.2</td>\n",
        "      <td> 10.4</td>\n",
        "      <td> 22.6</td>\n",
        "      <td> 16.5</td>\n",
        "      <td> 32.13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td>    T.J. Warren, NCST</td>\n",
        "      <td> 33</td>\n",
        "      <td> 35.3</td>\n",
        "      <td> 4.5</td>\n",
        "      <td>  9.6</td>\n",
        "      <td> 28.0</td>\n",
        "      <td> 10.9</td>\n",
        "      <td> 13.1</td>\n",
        "      <td> 12.0</td>\n",
        "      <td> 32.06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td>   Javonte Green, RAD</td>\n",
        "      <td> 30</td>\n",
        "      <td> 26.1</td>\n",
        "      <td> 6.1</td>\n",
        "      <td> 10.8</td>\n",
        "      <td> 23.0</td>\n",
        "      <td>  9.7</td>\n",
        "      <td> 27.7</td>\n",
        "      <td> 18.7</td>\n",
        "      <td> 30.61</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 11 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 302,
       "text": [
        "   RK                PLAYER  GP   MPG  AST    TO   USG   ORR   DRR  REBR  \\\n",
        "0   1  Doug McDermott, CREI  33  33.6  6.6   7.7  27.8   5.7  18.5  12.1   \n",
        "1   2   Alan Williams, UCSB  26  31.7  5.4  10.2  27.1  13.2  28.4  20.8   \n",
        "2   3    De'Mon Brooks, DAV  26  26.8  7.8  12.9  24.2  10.4  22.6  16.5   \n",
        "3   4     T.J. Warren, NCST  33  35.3  4.5   9.6  28.0  10.9  13.1  12.0   \n",
        "4   5    Javonte Green, RAD  30  26.1  6.1  10.8  23.0   9.7  27.7  18.7   \n",
        "\n",
        "     PER  \n",
        "0  33.68  \n",
        "1  33.37  \n",
        "2  32.13  \n",
        "3  32.06  \n",
        "4  30.61  \n",
        "\n",
        "[5 rows x 11 columns]"
       ]
      }
     ],
     "prompt_number": 302
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ncaa = ncaa[['PLAYER','PER']]\n",
      "ncaa.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>PLAYER</th>\n",
        "      <th>PER</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> Doug McDermott, CREI</td>\n",
        "      <td> 33.68</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  Alan Williams, UCSB</td>\n",
        "      <td> 33.37</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>   De'Mon Brooks, DAV</td>\n",
        "      <td> 32.13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>    T.J. Warren, NCST</td>\n",
        "      <td> 32.06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>   Javonte Green, RAD</td>\n",
        "      <td> 30.61</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 303,
       "text": [
        "                 PLAYER    PER\n",
        "0  Doug McDermott, CREI  33.68\n",
        "1   Alan Williams, UCSB  33.37\n",
        "2    De'Mon Brooks, DAV  32.13\n",
        "3     T.J. Warren, NCST  32.06\n",
        "4    Javonte Green, RAD  30.61\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 303
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ncaa_per = ncaa['PER'][:, np.newaxis]\n",
      "ncaa_player = ncaa['PLAYER'][:, np.newaxis]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 304
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predictions = []\n",
      "for i in ncaa_per:\n",
      "    pred = lr.predict(i)\n",
      "    pred = float(pred)\n",
      "    pred = format(pred, '.2f')\n",
      "    predictions.append(pred)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 305
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ncaa['Predicted Salary'] = predictions\n",
      "ncaa.head(15)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>PLAYER</th>\n",
        "      <th>PER</th>\n",
        "      <th>Predicted Salary</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>  Doug McDermott, CREI</td>\n",
        "      <td> 33.68</td>\n",
        "      <td> 14560859.24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>   Alan Williams, UCSB</td>\n",
        "      <td> 33.37</td>\n",
        "      <td> 14406288.32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>    De'Mon Brooks, DAV</td>\n",
        "      <td> 32.13</td>\n",
        "      <td> 13788004.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>     T.J. Warren, NCST</td>\n",
        "      <td> 32.06</td>\n",
        "      <td> 13753101.51</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>    Javonte Green, RAD</td>\n",
        "      <td> 30.61</td>\n",
        "      <td> 13030108.47</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>        John Brown, HP</td>\n",
        "      <td> 30.56</td>\n",
        "      <td> 13005177.68</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>    Javon McCrea, BUFF</td>\n",
        "      <td> 30.30</td>\n",
        "      <td> 12875537.55</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>   Frank Kaminsky, WIS</td>\n",
        "      <td> 29.73</td>\n",
        "      <td> 12591326.49</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> Cameron Bairstow, UNM</td>\n",
        "      <td> 29.68</td>\n",
        "      <td> 12566395.70</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>   Jabari Parker, DUKE</td>\n",
        "      <td> 29.46</td>\n",
        "      <td> 12456700.20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>        Troy Huff, UND</td>\n",
        "      <td> 29.44</td>\n",
        "      <td> 12446727.89</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>      Billy Baron, CAN</td>\n",
        "      <td> 29.38</td>\n",
        "      <td> 12416810.93</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>    Justin Sears, YALE</td>\n",
        "      <td> 28.98</td>\n",
        "      <td> 12217364.58</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>    Jordan Parks, NCCU</td>\n",
        "      <td> 28.90</td>\n",
        "      <td> 12177475.31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>      Brad Waldow, SMC</td>\n",
        "      <td> 28.83</td>\n",
        "      <td> 12142572.19</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>15 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 320,
       "text": [
        "                   PLAYER    PER Predicted Salary\n",
        "0    Doug McDermott, CREI  33.68      14560859.24\n",
        "1     Alan Williams, UCSB  33.37      14406288.32\n",
        "2      De'Mon Brooks, DAV  32.13      13788004.62\n",
        "3       T.J. Warren, NCST  32.06      13753101.51\n",
        "4      Javonte Green, RAD  30.61      13030108.47\n",
        "5          John Brown, HP  30.56      13005177.68\n",
        "6      Javon McCrea, BUFF  30.30      12875537.55\n",
        "7     Frank Kaminsky, WIS  29.73      12591326.49\n",
        "8   Cameron Bairstow, UNM  29.68      12566395.70\n",
        "9     Jabari Parker, DUKE  29.46      12456700.20\n",
        "10         Troy Huff, UND  29.44      12446727.89\n",
        "11       Billy Baron, CAN  29.38      12416810.93\n",
        "12     Justin Sears, YALE  28.98      12217364.58\n",
        "13     Jordan Parks, NCCU  28.90      12177475.31\n",
        "14       Brad Waldow, SMC  28.83      12142572.19\n",
        "\n",
        "[15 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 320
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}