{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# datascientist_mook_vol1\n",
    "## データサイエンティスト養成読本vol.1（Python 機械学習）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:17.179894",
     "start_time": "2016-04-23T23:44:15.022515"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## irisデータ読み込み(from pandas repogitory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:18.865766",
     "start_time": "2016-04-23T23:44:18.836594"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      "SepalLength    150 non-null float64\n",
      "SepalWidth     150 non-null float64\n",
      "PetalLength    150 non-null float64\n",
      "PetalWidth     150 non-null float64\n",
      "Name           150 non-null object\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "iris_pandas_url = \"https://raw.githubusercontent.com/pydata/pandas/master/doc/data/iris.data\"\n",
    "iris_data_file_name = \"iris.data.csv\"\n",
    "if os.path.exists(iris_data_file_name):\n",
    "    iris = pd.read_csv(iris_data_file_name)\n",
    "else:\n",
    "    iris = pd.read_csv(iris_pandas_url)\n",
    "    iris.to_csv(iris_data_file_name, index=False)\n",
    "iris.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:19.995663",
     "start_time": "2016-04-23T23:44:19.979939"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 50, 50)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setosa = iris[iris.Name == \"Iris-setosa\"]\n",
    "versicolor = iris[iris.Name == \"Iris-versicolor\"]\n",
    "virginica = iris[iris.Name == \"Iris-virginica\"]\n",
    "len(setosa), len(versicolor), len(virginica), "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:20.403649",
     "start_time": "2016-04-23T23:44:20.379532"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>1.464</td>\n",
       "      <td>0.244</td>\n",
       "      <td>5.006</td>\n",
       "      <td>3.418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>4.260</td>\n",
       "      <td>1.326</td>\n",
       "      <td>5.936</td>\n",
       "      <td>2.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>5.552</td>\n",
       "      <td>2.026</td>\n",
       "      <td>6.588</td>\n",
       "      <td>2.974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 PetalLength  PetalWidth  SepalLength  SepalWidth\n",
       "Name                                                             \n",
       "Iris-setosa            1.464       0.244        5.006       3.418\n",
       "Iris-versicolor        4.260       1.326        5.936       2.770\n",
       "Iris-virginica         5.552       2.026        6.588       2.974"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.pivot_table(index=\"Name\", aggfunc=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:21.707544",
     "start_time": "2016-04-23T23:44:21.661106"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>var</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Iris-setosaIris-setosaIris-setosaIris-setosaIr...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLength</th>\n",
       "      <td>73.2</td>\n",
       "      <td>1.464</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.030106</td>\n",
       "      <td>0.173511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidth</th>\n",
       "      <td>12.2</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.107210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalLength</th>\n",
       "      <td>250.3</td>\n",
       "      <td>5.006</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.124249</td>\n",
       "      <td>0.352490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidth</th>\n",
       "      <td>170.9</td>\n",
       "      <td>3.418</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.145180</td>\n",
       "      <td>0.381024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                           sum   mean  median  \\\n",
       "Name         Iris-setosaIris-setosaIris-setosaIris-setosaIr...    NaN     NaN   \n",
       "PetalLength                                               73.2  1.464     1.5   \n",
       "PetalWidth                                                12.2  0.244     0.2   \n",
       "SepalLength                                              250.3  5.006     5.0   \n",
       "SepalWidth                                               170.9  3.418     3.4   \n",
       "\n",
       "                     min          max       var       std  \n",
       "Name         Iris-setosa  Iris-setosa       NaN       NaN  \n",
       "PetalLength            1          1.9  0.030106  0.173511  \n",
       "PetalWidth           0.1          0.6  0.011494  0.107210  \n",
       "SepalLength          4.3          5.8  0.124249  0.352490  \n",
       "SepalWidth           2.3          4.4  0.145180  0.381024  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(\n",
    "    [\n",
    "        setosa.sum(), \n",
    "        setosa.mean(), setosa.median(),\n",
    "        setosa.min(), setosa.max(),\n",
    "        setosa.var(), setosa.std(),\n",
    "    ], axis=1,\n",
    "    keys=[\"sum\", \"mean\", \"median\", \"min\", \"max\", \"var\", \"std\"]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:22.741282",
     "start_time": "2016-04-23T23:44:22.683393"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SepalLength</th>\n",
       "      <td>50.0</td>\n",
       "      <td>5.006</td>\n",
       "      <td>0.352490</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.800</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.200</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidth</th>\n",
       "      <td>50.0</td>\n",
       "      <td>3.418</td>\n",
       "      <td>0.381024</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.125</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.675</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLength</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1.464</td>\n",
       "      <td>0.173511</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.575</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidth</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.107210</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count   mean       std  min    25%  50%    75%  max\n",
       "SepalLength   50.0  5.006  0.352490  4.3  4.800  5.0  5.200  5.8\n",
       "SepalWidth    50.0  3.418  0.381024  2.3  3.125  3.4  3.675  4.4\n",
       "PetalLength   50.0  1.464  0.173511  1.0  1.400  1.5  1.575  1.9\n",
       "PetalWidth    50.0  0.244  0.107210  0.1  0.200  0.2  0.300  0.6"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setosa.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:23.531756",
     "start_time": "2016-04-23T23:44:23.523815"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ]),\n",
       " array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ])]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\n",
    "    np.arange(0, 1.1, 0.1),\n",
    "    np.linspace(0, 1, 11)\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:24.315143",
     "start_time": "2016-04-23T23:44:24.210395"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>10%</th>\n",
       "      <th>20%</th>\n",
       "      <th>30.0%</th>\n",
       "      <th>40%</th>\n",
       "      <th>50%</th>\n",
       "      <th>60%</th>\n",
       "      <th>70%</th>\n",
       "      <th>80%</th>\n",
       "      <th>90%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SepalLength</th>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.006</td>\n",
       "      <td>0.35249</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.59</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.96</td>\n",
       "      <td>5</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.32</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidth</th>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.418</td>\n",
       "      <td>0.381024</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.36</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.53</td>\n",
       "      <td>3.72</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLength</th>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.464</td>\n",
       "      <td>0.173511</td>\n",
       "      <td>1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidth</th>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.10721</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count unique          top freq   mean       std  min   10%  20%  \\\n",
       "SepalLength    50    NaN          NaN  NaN  5.006   0.35249  4.3  4.59  4.7   \n",
       "SepalWidth     50    NaN          NaN  NaN  3.418  0.381024  2.3     3  3.1   \n",
       "PetalLength    50    NaN          NaN  NaN  1.464  0.173511    1   1.3  1.3   \n",
       "PetalWidth     50    NaN          NaN  NaN  0.244   0.10721  0.1   0.1  0.2   \n",
       "Name           50      1  Iris-setosa   50    NaN       NaN  NaN   NaN  NaN   \n",
       "\n",
       "            30.0%   40%  50%  60%   70%   80%   90%  max  \n",
       "SepalLength   4.8  4.96    5  5.1   5.1  5.32  5.41  5.8  \n",
       "SepalWidth    3.2  3.36  3.4  3.5  3.53  3.72   3.9  4.4  \n",
       "PetalLength   1.4   1.4  1.5  1.5   1.5   1.6   1.7  1.9  \n",
       "PetalWidth    0.2   0.2  0.2  0.2   0.3   0.3   0.4  0.6  \n",
       "Name          NaN   NaN  NaN  NaN   NaN   NaN   NaN  NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setosa.describe(include=\"all\", percentiles=np.linspace(0.1, 0.9, 9)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:27.682667",
     "start_time": "2016-04-23T23:44:27.668313"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SepalLength</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.746780</td>\n",
       "      <td>0.263874</td>\n",
       "      <td>0.279092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidth</th>\n",
       "      <td>0.746780</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.176695</td>\n",
       "      <td>0.279973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLength</th>\n",
       "      <td>0.263874</td>\n",
       "      <td>0.176695</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.306308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidth</th>\n",
       "      <td>0.279092</td>\n",
       "      <td>0.279973</td>\n",
       "      <td>0.306308</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SepalLength  SepalWidth  PetalLength  PetalWidth\n",
       "SepalLength     1.000000    0.746780     0.263874    0.279092\n",
       "SepalWidth      0.746780    1.000000     0.176695    0.279973\n",
       "PetalLength     0.263874    0.176695     1.000000    0.306308\n",
       "PetalWidth      0.279092    0.279973     0.306308    1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setosa.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:28.132431",
     "start_time": "2016-04-23T23:44:28.044530"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SepalLength</th>\n",
       "      <td>0.124249</td>\n",
       "      <td>0.100298</td>\n",
       "      <td>0.016139</td>\n",
       "      <td>0.010547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidth</th>\n",
       "      <td>0.100298</td>\n",
       "      <td>0.145180</td>\n",
       "      <td>0.011682</td>\n",
       "      <td>0.011437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLength</th>\n",
       "      <td>0.016139</td>\n",
       "      <td>0.011682</td>\n",
       "      <td>0.030106</td>\n",
       "      <td>0.005698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidth</th>\n",
       "      <td>0.010547</td>\n",
       "      <td>0.011437</td>\n",
       "      <td>0.005698</td>\n",
       "      <td>0.011494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SepalLength  SepalWidth  PetalLength  PetalWidth\n",
       "SepalLength     0.124249    0.100298     0.016139    0.010547\n",
       "SepalWidth      0.100298    0.145180     0.011682    0.011437\n",
       "PetalLength     0.016139    0.011682     0.030106    0.005698\n",
       "PetalWidth      0.010547    0.011437     0.005698    0.011494"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setosa.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:29.329582",
     "start_time": "2016-04-23T23:44:28.674325"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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bnOdn5rPb7l8YEbe1vjmlGtU9h8WOrffXNpY0D6w1kmrT6QU6ByLihIk7EfFC4MEyKUnq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\nkiQ1kk2OJElqJJscSZLUSDY5kiSpkf4/vNqBmblluiMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11736a0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n",
    "iris.SepalLength.plot.hist(ax=axes[0])\n",
    "axes[0].set_xlabel(\"SepalLength\")\n",
    "\n",
    "setosa.SepalLength.plot.hist(ax=axes[1])\n",
    "\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:31.906271",
     "start_time": "2016-04-23T23:44:31.517490"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117375e10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119a7b828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.concat(\n",
    "    {\n",
    "        \"setosa\": setosa.SepalLength, \n",
    "        \"versicolor\": versicolor.SepalLength, \n",
    "        \"virginica\": virginica.SepalLength\n",
    "    }, axis=1\n",
    ").plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:33.107806",
     "start_time": "2016-04-23T23:44:32.812693"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dummy'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KfAk4v8DwLuDWzDy2/PIkSf2o0lr5NPBF4CcLjO0C9kfEkxFxb62VSZIq6RrkEXEbcCYz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119adc898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [setosa.SepalLength, versicolor.SepalLength, virginica.SepalLength]\n",
    "plt.boxplot(data)\n",
    "\"dummy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:36.147861",
     "start_time": "2016-04-23T23:44:35.795785"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119eff4e0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6PFNK67kvkZCkKhDuflrWQUQaqZ6ttdXGWppV2l5Ms4ANRPeV/mPgq8DF7v7LzJKJZKhr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\na0M0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119adc240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "setosa.plot.scatter(x=\"SepalLength\",y=\"SepalWidth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:37.262430",
     "start_time": "2016-04-23T23:44:37.253968"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.74678037326392688"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setosa.corr().ix[\"SepalLength\", \"SepalWidth\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:41.803587",
     "start_time": "2016-04-23T23:44:38.412963"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JkiQpgyKnW7wdeP30APmngXum5ycDnATWznruGuDZAmORJEmSUivsTvJs89xJ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\niIiE/P8T4s8iEaE1OQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119e25358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(setosa)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:47.169752",
     "start_time": "2016-04-23T23:44:46.614097"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 0.80723367]]), array([-0.62301173]), 0.55768092589220974)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+OAwdrVqV5uDB6IQ8blw78+aFK4V3vauN8vI+DlREpEiUILrR0AArV4YahbVr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\n/x/30/K+cLrXWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b22c400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "np.random.seed(0)\n",
    "\n",
    "linear_regr = linear_model.LinearRegression()\n",
    "X = setosa[[\"SepalLength\"]]\n",
    "Y = setosa[[\"SepalWidth\"]]\n",
    "linear_regr.fit(X, Y)\n",
    "\n",
    "plt.scatter(X, Y)\n",
    "# http://sucrose.hatenablog.com/entry/2013/03/16/162019\n",
    "px = np.arange(X.min(), X.max(), 0.01)[:, np.newaxis]\n",
    "py = linear_regr.predict(px)\n",
    "# print(px.shape, py.shape)\n",
    "\n",
    "plt.plot(px, py, color=\"blue\", linewidth=3)\n",
    "plt.xlabel(\"SepalLength\")\n",
    "plt.ylabel(\"SepalWidth\")\n",
    "    \n",
    "linear_regr.coef_, linear_regr.intercept_, linear_regr.score(X, Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:44:51.010247",
     "start_time": "2016-04-23T23:44:50.988988"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 0.79303981, -0.09677873,  0.31530122]]),\n",
       " array([-0.48720671]),\n",
       " 0.56492621264573206)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_regr = linear_model.LinearRegression()\n",
    "X = setosa[[\"SepalLength\", \"PetalLength\", \"PetalWidth\"]]\n",
    "Y = setosa[[\"SepalWidth\"]]\n",
    "linear_regr.fit(X, Y)\n",
    "linear_regr.coef_, linear_regr.intercept_, linear_regr.score(X, Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-23T23:45:03.197860",
     "start_time": "2016-04-23T23:45:01.829166"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "try({summary(r_data)})\n",
      "  SepalLength      SepalWidth     PetalLength      PetalWidth   \n",
      " Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  \n",
      " 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  \n",
      " Median :5.800   Median :3.000   Median :4.350   Median :1.300  \n",
      " Mean   :5.843   Mean   :3.054   Mean   :3.759   Mean   :1.199  \n",
      " 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  \n",
      " Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  \n",
      "              Name   \n",
      " Iris-setosa    :50  \n",
      " Iris-versicolor:50  \n",
      " Iris-virginica :50  \n",
      "                     \n",
      "                     \n",
      "                     \n",
      "\n",
      "<pyper.R object at 0x11b7455f8>\n",
      "try({source(file='example.R')})\n",
      "\n",
      " 次のパッケージを付け加えます: ‘dplyr’ \n",
      "\n",
      " 以下のオブジェクトは ‘package:stats’ からマスクされています: \n",
      "\n",
      "     filter, lag \n",
      "\n",
      " 以下のオブジェクトは ‘package:base’ からマスクされています: \n",
      "\n",
      "     intersect, setdiff, setequal, union \n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pe15uTMGvK86Cm4fgHJeKkNd3/MD2Ci4kloNAFkQni0E+8TAS/MXN9IEPfGCr\n1yIKX89RBfCw3qzHFpYD/IGsn7PPPnubNrKAHRMYIs2AH0rhDhCUYgC//OUvcw9Q75WWhwNSWQ46\n6KAUCBZkcPQd73jHFMoQDfnf3glYQT+E7XOf+9y0nizegu/kpBLIxkc/TLk8nJhdS6EJCtcIbqLI\nCkSU2mcjFCiCvXPHJX54gVxyim20opJdIbEXBD1kw/2UtNxIVAXwRurNTfIsgj5ufetbZ2qQVIhu\n7h/4ySIMsrJAgyVVuFHvVbUccJY0InsFl6IBm4RtG+IxQc8UKkqWvjcW5PLqW9/rZ0FWImv32muv\nRnoRAcKqgnwQyNJcjA0pKz/72c82BF+m+RA2q/jWt76VELEKYXgsWAoPu3NvvW2wkxmrmoXNelUU\npRCXgawDucaOgZg3ElUf8EbqzU3yLKoUSSMBTZm0Nk0XHYsk5Sumv9tuu2Vqg3xg+cHSIkCQSszR\npPmCTehKy8UBVi4YkiD1buGWm33wwQdnfwusAlPrW9WMWFAChyhsCrVYwF/ykpekK0LdZ8LFGKo0\nmAOnnHJKKi+CF6XgsUa5c6QNqQx3/PHHD/7hGr4l1BVDkZmgryhKhRTWASvbo5c7obvtaDlnmd+r\nBbzMvbfB2q4Yvgmo8AULZyWy8Bb/nUVB7mAhMBZtWbk7gviss87KXGAau2AclrGKO6Jg5S3KCSWc\npVdUWiwOCJwSdGU8UJpYW4KtjjzyyKwxLD0GmgHV4NOVEqPSFahagBUIWnlC44EvUZUlwoPAVn5S\n8A/LrtLKHJCqJ/hN7AQhCXHQH+YXOL8/sljfUGy4BvjkbR3qd8Ny581hriKK06mnnpqIRWmROI6b\n3exmCXmrbmczB/5/yhQlHBxuxyXzfildShG2vRT0mMc8pg0NaSnauuyNDP9YGz6XVR8jUn7aWCRX\nPW+UE2JBbGPStTFp27Bo27BgV/xZOdf9Qytuw6rpnRs1aPM6UTyjjQCd/Bzl6toIIsnP7uHZQpPO\n/2PBaI855pg2fFxtWE+96yzahxA27SMe8YhVmxXQeuvcZadY3LN/PEukCrWxOLfh923Dom2julIb\nW0+2oYRl/8cuWG1YvG0swq3PUQGtDSu3jYj4HAOxcPfGwsUvfvG8rt8aAyGY182qCE5qQxis+nv9\n9tWvfnXV8xbxhLB8k18B97cRyNRGzm57vetdL/kcu0i1EWfRa3ZkF2R/xHaDbURO5+9CqW5DaLdh\nya7Ig/Ajt6EEt+Gjzz6OyOreNUO5yuv4zlwPpakNt0IbroM2EK08FopVG3Xes98jQK+NiO3e79f6\nIRT2tf5krPMrBF1Urfo+Vw7QfMGLMclSa7ZB+kokopX2zaLh46NBg5pZND6zcPh3+QL5k1hEyIbr\nochlqUppFaxh0Z0IpMmiom1Xmj8HpJEJvNEnrCy+QNYuaJnfFrohWIcf8hKXuERaTyxkn8HOxgOr\nmaXEPywVTYEHLgoWsf/33nvvWnZyha4G5bM2wb/SvKBHIWmS96FQ9PjMJ18IunD00Uc3oeRk30G0\nuIKQanIsYrsO9RN0wo5KUgjdT1nKQuY4S5fV7Rj065BDDknU6m53u1siGGBqL+eJ+VgLCcizcYTf\nQ8VE1WujMTQLqgJ4Flyu91iVAwSflAeTV5BMt4TkoB9LMfECMUtDIUj5jkBmYEgBOIJvkOsSzKJe\nBV+ZyKHFNxYSviyTj6/LvrCVFoMDIEVpR+p5W/jl8O6yyy6pQFk0CWGpZL4DQxIW/LsnnnhiVkYS\neEdwgzUJcYIaOe9yl7tcRkKrPVxpWw6Ai80f/nFKsXkERiaoQNAililE3r0KUWrUYacY2ZrQtoNy\niPmN1WDnRx5E5iCYW6S6NCQpToVU3TJ/pTvpLwqW7yjM+pT7SHphIAyZ6bDWXYwo6tYaO6XZ2IFy\nr04AwU8hmDZVH/C0OVyvPxIHTGhWsAXUBOymIgy6AGvIJGHVCKhiCSsGz2ckOIQGzwfoxTckXclC\nzI8klcE7jVxqhcXctnQEc6XF4ADfrBKDxoKUI+8Crliw+vGII47IxZ1PWICOIDz5vxZjL5YXwQ39\nEKynYAR/P2VLOpI+NwYqbcsBflxKDSHM90voQRJYnwIaCVMKDkWXElvINp+Et93JCGZzSplJqUXm\nav/8ErVu/rI4VdpSoY4wfutb35oIlvkpcJLQp5RDrSjKto3k9zfHoRzeBWeJ7VgPif+wf7hYEusK\nYc4nPQuqFvAsuFzvMRIHaJw04NUIZKXSDQ1bPqcFAvxI0Ib/Kd/lLdJqBXWVzdUFdtHQnW/hdq7J\nVmnxOKBv9bN0ItavRZJF5jNiqSB53RZPW1BagAlqeaUWb0KZhWPDAEKbQODaIDwE8RXXRF6o/ulx\ngLWLZ95ZpYSS3cPMKalfeM8VIMDNfKIgFYJaeCHFMMzP8MunYkywF/JZGVjuAb+nFItOR4Lv1OyG\nYLBoCfTuusC67hLoeb0EXmdRWwfkPGsHCNwaMQuqFvAsuFzvMVEOiLDcsmVLlpIEQ1p0RcBaJEDN\nJpJFgsYt2rUQ7Z3gBmFa2NUUrrSYHAA7EpCsVtYugcn6UQiCb78Q1EQKEuRDKUML5/7775/WcQRq\nZdUslhZhTGBAVlReUiO8OzbK9ep7k4KR8CSY8JMia47hK5gZMkFAg4W7Vau6vKP4QKbsnc2fLz+/\nKE/O499nxcpO0HcQrEIQLHDzZz7zmbRs11Jt68wzz0xUSz93X/p8EBU3BaQMVE7pmER5zUH3GvRd\ntYAHcaV+t3AcAGMJrDGZLcr8NQQq/6/FAeRoMQYr2pABTGYBMOn5h/n+aO3KGIKq+X3A3pUWjwOg\nYxYS2LG4ECyg/PeEbdc64fdXlUl/giiND3AmfzBhDW4GbbLiHBeoh8QDdPNNF48L82sRBdbcAg+D\nYim8XDWIGwBUKxdfP62U+sPlA/oX8MaipRgTxpQkvKccl1KwLGuWMgWJtQyGltsPseAy4IMGiUNE\nKAbD9vx1X0LdPbskXqRrqXeP9X8WnGlMWWemTsHEpaCahjS7bppHGtKwpwtYKNMNpDxFUEimDMXE\naKUaefcKbbkNmDLTGPwvZaEck8YSkHMbC0t+FxM901ICPht224U6FhDqpklDikW7jaCcNvyGbUDR\nbfgIM40lhGwblnEbC+RWfWMchDLV6+8QGm0s4r3/9bvfGQ+uF7BjG0Jlq2us55+NmoYUQrYNK7Q9\n7bTT8t2cKxQoU/Ixop9zPq2UriidB7+lF4XPtg1LM/83l/G/9Efkb+exyB3OdCVpdH7jt2Fx9+Z6\nVDtrjQupT8NI2lMoWsNOGXosrPGhxyd9sFrA0dOVZscBGi6rlBXDf9Ql1ovIRgEVXT8P7ZhFC4KU\nphSTIDVrmjQfr4Iap4fPj2YsmhpMJv1IcIfvRdIimja/k2CSop2DrfmhVENyf7AkGIq1VGk+HABX\nSmcxDlhKYEIwsrHBimLNsGhFwvLZ8TlCPhSHEBXvHH5Dfl9R8dKN+Pz5iwUFsaqNEzWjWcQlVWY+\nT7t4d5VOxNIF0wu0AgeXdCOxFtL8BD45xu+Ot6xO7h19hcxzCJT+E/2sPKzMA+frDygFNxBkQyEN\n85pFzC3ke1WxWNfmpj4Vzc7tJBgMMuL6+hPpa+4G91sruQf3hMhnaUhqxouSZ4GXZ1nrNdd0/qQl\n+rSuN8wCpm1FEEZaCFF5Ja2kgEmm1ZSB141B2gZMksdojiEwtioQMfBHU/4yJkvvDhFZ2AZ80/t/\n2IdpWcCxaLbhV0oLJgZpFtwo7VBMw3cRSZnv3WT8gJbTYo3giCy+oMCGF23Yb7zCL5WFFWjaAUPl\nZcMPlQUaIv+39XJeQGE5VkIYp3YtmV+RAVp3LOJtBHTkebGIl6YtxPtmsoAjSCr7MlwL2Sf6KhSm\ntJR8F77h7CMFOYyDMgYCMszPrLRY8POz8eAcBSScF0FFbSzqretYU1hgq1lVKw2AjWoBR55tzpPY\nwi95puBNIRYqPkKPvIci3Ua0dPKQVRuCOE81B0MhbqER4SbKc52vKIn3EKTlkm0I5TwvMiDaiOlo\nQwCm1RwKQBvbH+YYCH9uyzJ3LehI5IHndRRUYRmHUtZq71ot4BD2WeDpmCjGU9ZvFn8o8L32TfPD\n0gdh0chYOPx8pVxddEiWpusW9Y5OnyqJrKR5IyH32sAPNS+y162ScIVEkdIU50l4xLdEuxYsQXMu\nxDK1HZnACX6crg9HSgotORbS9O/yS7FQ+Yho1gKwfCcgyzPzFSEWEf8RHyDrltYshYEGrzYw68eY\noYWzjrVN9KX74Vel+XCAL1BOtqIKfMEsFFaZPhatyv/IOpGiouQk60e6jMA7gTSxmGbNb+lJAnyM\nAxaU3GK+RL5Nn1lbxhkrWapLpb9yQLyE9CLrl0C4rq9cAKM5xGKFJMgskPYldUgaEjQKmZvmOB+8\nqGLR0/YSdr4iKqE8/fVm8RcipV9YvvyuYjlYzuI1RL6zthXRKWgIC5j1zSoPoZ+WscI8rGryYC0E\nhTPXIXJSqMx9z6/wxyxo6SFoKQegJgtscbKbdDpbJw2rQToLBs/rHiYAmG7eZMEU8SjvUkCFRdRi\nSTB2oyJN0EAOcsEkmLtJ+yaelCHC2USj3IClFO1wfQEehKtFAAnWKuS6XWFqUiNBXRZr9xTAA3py\nX/9bDEqaS7lOfZ8dBwheEKQ+MXa4EkDN+knkrH42x40hxSGcJzDIGCNQjStBeiBLm29YvEXuEtoU\nOQLZuQSLOSKiFwRZ6a8cIAQFO6l+Ze50o4IFLkZMRUaimyeUYjCueY2HKl4hc5ZSSyhScM1P16Mc\nqZDVTStyvohrr0LdSniCKgWByfMWWAca925td39jwby1BWW5f7nOau8CMwVcibS2xlD4FA8pwXqr\n/X7c40svgPn4dLbFuJAF2WAwWbvEAmMF8lfwG4moNClZXPYZ9b+SaHxQjrOoDKxCfmvwsLD4oRQF\nECG4HrJoiLaz0Gg764vvEpX2KLlmIBIytH0D0IKDaJcWFkKJNsmqZA1YdPjL7FJCWBF2fGWFRCHa\nw1N+nRy4XXbZpRyayrsKN6wL/h0D3eIqatnA71od2m3S89FJOZF/WIi/lnYt4plm6t0z6gOaq8ln\n03BW7UpRmeVa5V2qg6Luco/xj4VkkaBB8wFT4CrNhwP77rtvFlMxPr2gFBZXgpIwoMhBOqA8lCZz\n1NgxllloLC4LqjlsTvHx6mPWTkl3sVCbSxQtAqUo7/N54sW6K+WdgsIHb96ar4XMN3OEAgt5MI98\nZ37y25d5Q1mi4Fi7yvpMIFsP8J3RNArpX31qnWcZQ6j4eo0DY0C/Wg/MX2tAQb9GuXY5x/riNQ9a\neggaHCLnzOQjrCzGSCg7C7iQFAZ1gMGVAjAIJQE8CAQF/jCIBPboYJqQhaCQkmwELsEBIqM5GwAE\n6FqJEDJQLRo0cwoDja5YaqU9ggxoj9pD22OxFQK/CEwC39DyDSDPgBcmhEEpMMUCQ6ghwRQCT1zH\neZQHgU3TJPcqJd0snHhnIllQ/d8lAVCCLli/eKuPWCwUHxA1XuCDPFCLK6vIIkA5cV2WDd7pN/2k\nv4ZRFJLPgC6FOtwPf8BdijoI/Ko0Gw6YcxQwcKUgHQu0caMfCVD9axGHchnrJaiHAsVSYQmzgghS\n1zC/Wc3I780NroquImqXHnAo90QJ0pvN0y7mXbjNrI+UZPwn6Kwhgp5A9r7Da8aLucuAwG/rYPhI\n0xDQX4UIX+spo4GQtk4JeCR4KU6jKjwsX32FWN/WK/djfSvcQaGCfuhHwn3paJoO5klee1gQVizc\nuROGgIvogAy84ZgXnIXC0sqgAjtmFAoLKs/l+I/Bkp/DCi2Hc5cf14rBl99FR7dhHfeOu3ZYkW0I\n6vwuBlsrQAEFBJbXk0YxiCIPNQMUYgHoHbbLSwzKNhaSXnsEmxQShKA9oZn22ha+kHK49byOB+ST\n38Vik7uWlBNCiGfgSeFJLHoZeBQWcTml9z7JIKwoopDpCviHX2G59+4z7EMoH63+kHokdcTzF8In\nz+q4dy9pE2G5trFAt9JVQmvOoJ1YSMrPhr5Lc3CdKCyQgR6l34f+aIYHN3IQVgjYDKwK5SfHiHmq\nL4xhQTalj72HktX733gSeGN8SG8pFNZvry/9JoRAG2hY/jasu3La2O8bJQirBEDalUhQUuGx4CsB\nbHgYClG+Sl/omxDKrUCpQSTQTfpXKMhtKNG5/gqmXCtpm/kcMT5tKFK5G9awa6w1CGvYtWZxbOkh\n6BgQPRgJNKKWsGAoxRZ85rBnpQYz871r2YCuHKOdIVZQIdoUaBisQYNmbYJlwCEsONeh5dEI10q0\nfBqgNhYSZMLC8x4LSn5d2uUf/hNE49RmMC2tvhALWhuHEasbDIQUMRfA4H7TJMEcLFW+INBWscZX\nuycrh39fO/mNQF4sJRYvnguwAcOzmFnF+kgaEg0Z8sE3JIBj1DJ1ICxBe3zERx11VF5vtTbW45Ph\ngHnGWuICECDHv88NxLJVDcnONOYbKJqfjlXmM8tY8CULiPvF7jgsZ6iJICFj27muicwP1xGAtRkJ\nX1mOkLHi7sIHc9M8hcYh/LI2QR0EWoHzWb3I2mTd0S+CqVi3qmGZm6xRCAWyVkKpoAvQNsfMzbUW\nt+Aqg1CZ74LvNhpasfQCGOwKkpRjRiiZhF5y0gwyPghQk4HRv/ibvBbeQiZtIQu4zifwkHJ4fLKi\nbw068DW4cj0kutAkcI9C4B7X737XrfjCV12IcBJcVAa778GvqxHh1SXQ97QJfC5oAoQlv45fmm9p\nNSJQ5eURpq7hNyJjwcP6hW+QPxts7zkId/1BKIOrFZIXnWlhH4W4LviVwGQWItGcXQVnlGvUc9bH\nAQoVdwGFU4wFRchY8bK4C7oyLyhbBECpysRlwUepzxGo1P+U5FKu0vjYEvA2BVVAF8G9GQlETxmx\ndokBsT6WUqx4hu/WS0YFf6t11fwBIeM7d5V5Z+0Rn0HREazkeImjofCKwQBVc4OJKAZtW9sExnVj\nPtbSB9ZtrsaNSNNfgafMNT7NQfVIacfI4OE7ENhkYaXZedHenNPVyPg0CnHmC/SJPMEclFIhStAU\n3wNtzGDjc1wr8UNTCoqmqT0CvigD3WCyla5Lg9c+mmUhmu0iEn8PC4X2K0iK33UU0j8EK8sG7/1W\nIQbaMP5ZDPj19SlBaREmlH3P50yAWlAoZqMQDV30NOHNT2UBqjQbDrB29ZOAGn5ECFMhVqtYCcoV\nFIjvkRAO6DlRpILksOKgVpRWgrkQv77/9acxUdaFcnyzvEOJKDRiW6wdhCFBiyjzkEJoEeXH5hbQ\nQLznF8Z/a6Nz8J7QZREzGKAWkAbIRLm+a1qbzElzEc8pxN1+dc40iHKmTeZz9yW1aBFp6S1gDnra\ns4lo8grsMbhYTgQV2JUQJmh1COFJIFvgRTwTDIVESYuUZAnLfzNwLPwsTYsDiJgmSWMvuaxF+y7X\n6L6b+Cy+LmmTNotgNsAJEdczQN1XMJH/hxFt3nPSCmn3BBzh1CXtBQNRIvqDnbrnTfszy9yk3iWC\n5MDIAmRGIVB5CUoTYEG7RpQe/0MRCFswpb4m6AW3UZJYS+A2C0NXodFXtkkrUCZ0pKAMIj1p7HjK\n7bBebX2UZ6vnbM0Bc5RgMC8t4voFJKo62fYRTMeypaSyrAhhSJc+Mh9ZRxQ16Ii5bH4SEIUgTQTI\nZifuNjAxVxwlRmAjBKkQQVnqGPiuX1E2nxBkCgpFmTH3kH4gmPEfbI3Uk4a4mfuEtflMQZ42ua9n\n7Ue+IHCLSEtvAfP3gFQMBj4HE1gkH8jSILEY63ywiQEojcVgodHRjn0upBAEQUEQWowNUuH0fm8x\nYG2DVvhvfW/SDwunlw5BgHdfNHn3IVz4Gg0Mi7+UmlFzEUE+hJCBJsXCZJFChSxSyD2dB15aT6R2\nXmQCfyg8/Db45jktpGslSpHf6ltRrjRxwheB1fS761NMPD/hrB8pZWUrQufyZ+k3ioDP3ahYCz6F\nzMS1wOj7SrPhAB8vVIMLgMLK3+cdIqEkKYTLnBPjISfUOwTJ2EYRQJcv88rc7Xc1zeYpFvsuoGdI\nET5y6VBKzJm1kvWKYmt9tL5ALnzHioZGgaURZZiiC7I2P63DXWNnrfcd9Xx9T6mWBdN9dRXxUa81\ni/OW3gLGJAuvFwuHNUhLNji6xAIm/ExeFjELsZ/4HGmFzunvMPVLvSwMBnHX/+o68nELGXA0zWHE\navcqVlp30SBE+n9vgSrfsQINboqHgY2KslHazXpnAVuoCCuLWj91obr+Y5P6H88IQxAVOH9UAu1T\nUEBHcjZp0yYzeImlRFiygiggLGAQPkVJ2hVrmVUF0egW5aBsaQ+S4tXV8ikzoFDjx3lFkRm1vSud\nJ1hFTraxKY6gf1yu9LtJfm+8gBc9H0Vy0Nif5P3Wci3KIWWU8KQ4EaDSCSE7xryF3Xw03i32ngM6\npf/wUs425QzSQyiI0ei6ZtbSllmfyy9K0WOlG4vTjsmArlFIrQnmZJf0g7Qu84txwm87iPQHi7ZY\numIm9A3fcdfdY74T8GBniB53EN885I+Var3qX0MH3a//O+uCMcDlsG8gnt3iHf3nLsX/sagvBQ1L\nQxr3AUoaUgy8cS81k9+H0M4Um1hMs950RHa2EXTSht9mIvd3vZV2OeneQJqCNKFpkPqy4WdvpYnF\nQpupEOpIh2DMz2HhZwpELF5tt25zSceKxSbPk5JSKKDM/E46WQihFVMoyvnjvkt1syuPcSUNzBhe\nL603DamMbc8sZS6UlzYE2nqbMdHfhS8y+yMEaNbyjQUz01X0cUCdbSjSvbre5cbht8zfhFXchtDu\n1YWWrhSKUx6LRb+cPtX3UADbEFqr3iPQlTZg9a3OC2Mg2yo9KlCcNip3bXV80v+EUpL3C2WwDd9t\nGwK4l6YZinoek7In3SiQozaU3YFNCKQxzzWeAmVsQxgPPM+X4W7LeuthkORv9K8xGMpu3n/FHw45\noB58BJC1gYJlmlN/GtSypSGdcym0hCk3khVJW+taoVO+5ViXB6/bJJsmyhoHm7IKS5nFsS4+xx+z\nEkHrygmCq8CP0Ao+bBYp6JimjQTcQDREarIkwNKsTZYTfyKrzzEuikL8VzRy/QyxAMtNgkpRgn6/\nsWfhcwbBc4EUn9ko9xQ06Ln40MYh8CwEQUU0Uf+iV1mKXiwdPMdvbV1PQOFa2qb/utXp3J81A93g\nH5QWJ1JZnERpIyuJdat9AmxYyCw4LhtxHKBOY0NAo/KGYg4gH4tOxh8EC0zq2QRJ6Qd9rh9kX+CP\n/psEGXuCpqBhXHSQMogIku4HOeBSEyeDt2IpIIL9Y1Z0uYwBcwiyYzytRDITWNWQDLEf5rMx6PfQ\nq/WMO9a2/mXBcwnKsFhqGqJsLNShaVrAC/Wga2xMwDtr/MXqp8/DAg5B24abIC3vmFC544mCAAEZ\n97Rnu6Gwlhz3ikUj31l1AUflziwh8NJyKju5rFQMZXUujHZGLJRpCUT0dLYlFtLeD2MxzaIwUcEp\nCwhsv/32vWPDPijGwiqKYL3cCUYxk/VawKGIJF8jSj7RBHyLtJ60Tlg8Adu3kf7TRlpd8pbFPA2K\nUq9t+O9z96FwveQtWIGsXMhNKFS9ftXGgGPTooVa6F+Wpu+hP4XCXZBIhu+9FHzwHopYOWWq7+NY\nwCw1Y5lFz0IOJTOLyeiH8G23Adm2dnXyPAFTj/0cIbgSPQrhm0WAXLf0dbiisuAGRCEEdN7T8fDt\n5udw44x9f/MjhHaiTgrm+AzR0mfmfcTnjHSPiHXJNaIgIeFa2+p3y2YB8ysuBVUBPLtumocAJhQi\nMCof0mQEQ0bQSMJN4OgInsvFwPcgaZC7BT1St7JaUuEOoR0aff4LbosUpnJoKu9hhbWh2ee1I30j\nYfPujQKVSBja+A0Lrntoxc8WFZA1Cmsot34LKykX6hV/9P8PqMZGWHcJFI5fBG5YllnpC/yL8JMr\nA1FwwqrIz5P8o8oYIVooLK2ekAwrMJWNCJzpCVNKFJgxLKesDlfg22Niy7jIHCiXyUpoUX84t5cM\nFCsX9djHtXd82h/GEcDapq3g2YjazjFchJDvKJQofKlt1DXIz+P8UUnO1otlHID4jVcUyEQbUdAJ\nDYON8TJiK/KYanOg/UmQ+U3AUy4D9egJ3bWMO0oD5cT4iCDabZo1rgCOaO9trjnNLzZEEFZoa5WW\nnAMgLbV+QVpgSoEbgqkEVUnRkiMtuA4sByKLSZHng9FAdoqvgKFBdyLCBZwgUdPTJAFbYDEkgAXM\n2iUFD7zWQoJUvBCIFUQJvlsvgQC7UKJoWAFt+An+LcVmwKCOTZoEx4lgByGDQW2qoDoaXuk/aUWg\n8EAIMhBLzq7+9QJ5+o1gRecUyBPcL3AIDCp7QICPaOpSN3jSzzCN62lraa/qePpB5D7IvfS/oCkV\n/cYl19MHNjeQDSJSWLqWYCYBiNKQ9AnXh3MFbQpyk9ZlbhYy1/QH9w/Xi3YLIhuFSlqhc40zQahg\naP08qjuIu8F8mBQZN6K4ublA7tyR6ogbS7Og6gOeBZfrPVblgAXBZOa7JRj4pETGmqQWJOXwVESy\nGBF0fKN8gXzCXiaytBQTSPS3lAgTe9oRsWEhpFIghUYkNiE2LvHp89WJ7nVdiyYhNSkSRSwiXSqW\nRdnWfSq74Tn/4KRJRD5fvGeysPPTi1LnH6cs8Y9TmPh9LfgWeYs/YSSaVqwD/zClQawDQYU3NkgR\nOSw1STRuWD+TbvrMrieSXxyKfiCMKCtiB0Tt8wuPS9ISKTgEDSUG/8SSyIQgfPFbPInxhqf6oCgB\nlMxCfLgEtXlI6e1mEpRzRnmnfOl3c5yyTQmYB1kfjCcxNeaB9Cx8UMFrFlQt4Flwud6jxwFFFgSf\nmPCKmcgbZM1IU6BZmwxFAxdU43xCWO6iHF4TlhYsfYO2biKrAiYgw+9Ye3KPBWk4N2C+3r3X8oGV\nINWJ5aU61kqpO9oiF5wVbKEq6Rmj3MtzazdFQfBLNw1F+hTFg3XB4seHcUmwDQSBVS04hsIgPY+V\nJdWn5NWOe59Bvw/4OxEK/S1dRREaAlM/UZ4IAsoTi5cwJVRZSdJVtFNwlnEA5SCcKAqC6gjl/fff\nP0sl4tMykT5Wi8DYYokpaFH6wZgg6IynlcbeWp9V9TA8Nm8oNlKNICtqIxC4+0ZaD6tWgJQ+MvYF\na7GSC7GGXUdwHOWTQF4PUQLCpZD3o1hTvOdFLHPBe1IQrT/mmoC0mVB09FJQ9QHPrpum5QPmc4po\nyDYsujYsoPQDhQXWCzaJAd/y/4UlkDuyhDbehpacvl+BG1IfpBfxZfEjxsTJY3xK0hsELcWi0kau\nYitYw/UCzlwz42IxSv9jWCVtwJttLEJtCKk1X2fYD/j2tI+P2HN67mG03iCscs2INm7DEs17um9Y\nV23kzreC32ZF/HOx8GYbQgHLPpZGpj2CcvijfebHF5jlM7+hIB0+Sv+HMMjmxnacOW6iAlwGkhX/\n6ayexX3G8QGXHZv4tEOYZV9Ms+0hWJN/kYfbhhKUAVbSgUrwYAjmNhCFVmphKLJ5rmAwPBd/USgU\nuBw72m+ulXiLcnzUd/c1R93TPUaNj1jt+mv1ARv/YhSsJVKqzLNhqVWr3X+txysEHb1faTYcAD/a\nx5j1Am5mydDyVaRSvAQEChpTsELpO1YBmIulpOAGqBlEBn6mfSvcEMpC84koVYikOvAjs5akQCjk\nwXoalfgjFYEA+YE6pbmASEGlrLQugUu7ftXuMSX9WDdSqFYiaShSPfiHWfDFj7zS+eN+D3XwHCBN\n/CoaP/4r46m94N9pkj5U1B+0ir+nRy3iAnkaExAB0DifnFQkFhnrBDoAafDZGEHgZxY7hIDlAq5e\nJjI2IzI80Q3jm0+1S6xhsLRUukkQpIjlKt1LX7N4pW7htVrr+G1uKmkLiWGh4jlXDoi4kL4DO0v5\n43tf7yYJfKzcECGw0tKeFAQNUpbmZD3pvlaC8SFx4ku4Yzyzwi7WHmNqJrRWiT2v86sFPDvOT8sC\nVjAjFtCM7GSxxkRvY4JnZC6rVbRlVBrL6EYaeSy8aXnS1GMhGokB0i1Ez7KGYwK1UlVGIdHBLJES\nbS1qV+Sx67mO90KsagU2vMJfu5UVKQpZRK9nYd2tpNkHtJqWneelfXveYTSuBRxQYbYnYPx8Hvfz\nXF7aqTAJPgc8PawZYx/DZ7xl2Xq5v7YE3Nzb51p0sPQxFjsLi6VVzpNWtSg0jgUcedn5TAWVMPYK\nGTOs0ICf8xx9Ny6x8swLRUpCGOV1Q8FpFeiQSeBeUXUuo9PxOuD87APfm7OFIv4i0QhpZc4Lpakc\nWoh3FrBIbql83ddaGodXsypWQwNZCqoCeHbdNC0B7AksrhFlmCkxYaUmfBhBD5l2YSP28D21FqfQ\nYvM8UGRYCyM/vIW8QEjSFEqK0GoXsDiBtZG8Wwtj+S78jvm9PxYslYIiIje/C//jVnmaFqWw7PIY\n2HxYRTGpVxY31wBJD6NxBXAE9rQRUZ781cYIyMl2+lxSnigfUkKmTQQAeJPQDx9wVrOSjyzvFCxZ\nSD4vQRRlJ9sozJLjIKy2cnju7+MIYC4AqTiUNTnAqkoV4p6JoLP8N4KCkgfl2DjvctQDYWrDCk7B\nGTXWMw1KVTE52Y4RyuEDTQGsuph2lrHu3lICi1tHPxLEi0RrhaCtM5QKwjqQoDas+5kqFTUIK1ag\nSrPjQDf1wl3BPV0SaFNoPYFHgkfCospLgKNsvTYKCeCRLoNA4yKpvQ8iaQrdKkGg1UICZgr0DNLu\nBlaVc8q7QI9ZBXuA1gSylX1dpfSAF/GqPKdt6Ia1t7R73HfQILi5SyJQ+yl8+wmZg025JgSscTts\nBBJcpa6yyl5hcWVkf3kuwYQCoJC+0VeTIJuadDdE6I/Y7x5T834QiZgWgIW0bVG3+RvU9kHfSWnS\nByB5rhhR6FxP9gQo68Gg303quyqAJ8XJep2F4IDo2bBcMzKW32vUUnV8cnxBfHEEUXdrtu6D8TXy\n/XoRvHxL3TSNUnqPjzMsg222iexea5af8UWb+MRFeoq25XfTRiklZWHt7hA1y/atdC/t5p8U6czv\n6LURSLyCHGYR6SKS+VYLKSkrPYwgFrG83kjjcr1JvvNbS9MRR2AOSBFbdiJ8lag1d80DKUgiv8Uj\nTJuqAJ42h+v1J8IBQSly9QRNCLIqVm7/xQkZ+cGCiwSUSF0aheSZWkyislKmQ0mBEZAlRceezYJ9\nkDQNO28JFCPIpHAUa8VxgTPSYwhxAVZdi5Kwk3LBMlafWR7mrMjCgi/SfaQiKcAh6EqKlRQQVula\nC4ZMou0BAWaQnYAfQUD9RCnSPjyTXrZswVbd5yFMbXVJ4ApsCrda9kWUAs3AoXIuNIZgFuRkXApE\nnCdpi/rL3gUlStEThPiiF71oTWl383yGle4NzTKX9Yn5YW4LWBTkOQuqUdCz4HK9x1gcMPFFa1p8\nRYwSXsNI0QoRlqMKX9diKYvEBQmztlhdoEGR1+EvTYvnKNRZAABAAElEQVTReYoliCC2EImMFhEK\n2usS4Q1KL1BdOUbDFtUKerTIUhJmSXI4RXqyfsH7lBr7M9vYYlB7p902Cx7FAGxvAw154IMIwsAa\nWWbh67kiniEVIEoZpIGCSAlTaKUfZvasopPnLXxZh5RMUcHcOVwZyPwCoy87qRUguh4cHzupZf6z\nNaarOE/zGasAniZ367XXxAEWIp9Sv/8JzKs4g2IOoC8CRDWsSZBiF/yRigKozEMg8AWxdJWxVBjC\nTkLdXYlYMXzNYCqWGUuSFr1SWlJpJwvbPfg0Wdr9Prhy3rTeFbGIwv/JP8oGFIG1DlmQtoUoBV1f\n4Frb4joRhDbSz1gaBBFrg0WltORGJRYWRVJxF4oGwWsvW1YxS5iVLzXP4o9YyD4XHysBaLyZI5Mi\n/nTX74/D6F7fPQldiiYlibIp9W8jkvkA9p8lVQh6ltyu9xrKAUIBNFo00mJZ8jnJlVU5i69GPrHz\nxiW1ppWec82Iek7rFwTl/qxXQUssE0FV3eAw+ckqNDlG2KjKxWIBSUdEaVoug9oGanRtZRlLKb9B\n503rO3yUP+t5KAKoKDLl+VibnonyAWJcC0EmCA61nr0EtAyj0h6KlWAYitZGJWiMBR7SovoYHnvx\nw8sJR6rBOQccz+UhCEheO0uYssbiJMQpod36zOvhGWvWWKdI8kULOhxk0UJ8KGXmiPaoTmceVpoM\nB6oFPBk+1quMyQGLgKhlEC1rQJGKQgSXIgLelZYUJDEuHKnwhQIPrFIWN0sYFMoSUzeXkCqFCEDO\n/KaI1UCAUghA0L63MIkI5bMbFM1bnoPAZ8kL3gL7TnujiHLf8s4CZq33Ez+jhZblSxjyy1r41xLw\nhB9gbQFEhARrSZnIYaQIh402wMusPUJgo5Lxyo+qz5XjxG8wJwu4BPGxNo2jUviC4NNnUAIwNSGM\nV3g8LhnDrgv9oGgRrIOIpc5qj3Sx3LyB4ilquNJkOFAt4MnwcVNfhS9xy5YtCcHG1nZZ3WglhhCw\nFltC1GRmASACjz8G0f77KwNZqL1WI9chBFkafFegYVYF4s+y6LC03JcfS3UogUmsVxtCgJQtin5j\nYQRHg/0IWkLGAgm2JZyRY8VyYNEJUFmJCHhQ5KyJUvGCF7wgF+5BECbBK2hM+4rvy//4NSrhO0gV\nUUjUE2ZdqzVMkAuwAuPjNfi9RJgKqPPaDCQ6n3KJjE0BflCU4v8F84J3QcLGmSpT+g4VhVN0tLkB\nwdBvIv1H3agjypE25qf5yh9dxi1rmtK1EonY9ptKk+dAtYAnz9NNdUXCSoCUHXxo5iyolfJ3afP8\nfqwjGjh/ZKGS0iPoRESuxWI9BAYFl8kZZVHQ3gsJggJ1ikTWbjCwhUzBeQKYVcwq85mg4hu2uFkQ\nnSs3UHAWP5FFiyUDTuSfA4l7JgFci0QWeW1m5fNvryRUBZuJMNd/yvLJuxVMNioJ7sJ3fWcsCO5y\nX+k1BK78b/50ZRD33HPPVHRGvfZGPI9yAo0oipxnxCfxDcYbpIWywgoW2W+MCvzjfpEyx2qlPI46\n3iidUohYutAkyiSFSZqXvFeCudLsOVAt4NnzfEPdsYTul5qwUUknrdsSLdl9WDCv4g9gLVAsGJYA\nZBUSXuBgMC24q+zn2/39KJ8JVr5HkJ0Fh1JAGLNobXMIJgb/EZ4sCMcthFFtKaOrQYSEKitasBWL\n1s49BIzoZd/zVUdlqrRSLGIWTYsYf5rnXyTi6xNEBt73XCwwCgaYmJJBYOKH7R6dWyywYs2O+ix8\n5hQsFhl0Qf/zryOIh76mzEA5+PbdS3+MQpQmiAmrjQW4rETJMb6MFxbu9hFdzyqGnECG7FYFhicc\nzREpMrYP5AMWka8PjTFBa/ggP5jQHoWgELY5ZMlK7YL2uCfBbntKbpZJkCA8Fjq3RpRqncQl13QN\nbg9jxRrSJUhMiSnpfj/3zzEJl4JqKcrZddNaSlGGlt7e4x73aGNRaCPwRohmG5NgYGMj4jLr/obQ\nzZJ2St/FRGnDsswyiX4buZ5Zx1kd6PWQ0ngBo7ax2XjeyzXVtQ2/Ve64E8KhjSCWNhb/NhaxLIMY\n+b7ZbueGApDnhUDJUphhZfSOhSDI+s+xIK6naWP9Zr2lKO0SpNZvIAtZQtMz9r8CMs5nHquBA34c\nAib5rPShe4Z13RZea9coFH7TrJnt3U5a4Xcf5WczOWetpSgDRs7Sk0qxlj4wDsvnEM65U1j5XxnK\nUEzbQAx6zxMoQ2snqYiZyHkSQqV3bNgH48d1QzHKcR8KaRuW9rCfrPlYKLQ554455phcEyLgcM3X\nGPcHoextVbt63OtN+/cVgo5RWWn9HOAzZGHyl4KeWbnFt9R/VX4vGjJ/Kr+SYBPavrKONHtWKH+g\nQKvVUnr6r13+ZymwrkF3NH6wqvQOFgDomAXOOpaDyQJmjbDAQYJ+B/pmffuf1QxW1RbWHfhPCpFI\n52UhvkPtZ22xSsGZSB6yPtN/nrMETLGQ+H8nQfituId7QSNOj6jfwuvi01ztPhALFrA+YcUI1ltG\nMv6Md9aZ8cdl4cU1ABlgneGTQCtjWHqQ6OhQUDNyvjwzFMOYNB5ZsgVlKMdXetevoShnrIV5KMK/\nP91vpd+O+r2549lY6PptWftq1OedxHkVgp4EFzf5NQTdCPIZhSwaAqMQuNIiA1pUTQosCUImMEtq\nxijX7J5D0BCuILUS8MS/LGI5tNlMNeoWN1BdCTlX5SvQHF8veA/M3CWL4TKSFBjPKQgK5Iy61bv4\nHC3koHj+RQu85xclPg6BVvmT9TmBS2islYfqQVvI+T+NMf8vI3FfUHRE8hvj8swRvlACKUlgaS4P\n/4uYLvOk/3nB1uIo1kLcDOYXeJhyKe2Oi2WSZHtIfUW4i66mjFcazoEqgIfzpx6dIgcISr4sgTpy\nIAkAFppgnWFRmcOaxI/G8rJIUQwIGgUeBE2x+AiDQcRaFjQkelpgimCuUgRh0PnL+J3F1wLP4rLI\nI77G2DUqFRP+YX5KvKIUEdgExnqJMIdsCMqi1KxV+Lov3zphwY9M+Pp/GUmEufFNCOIvYey1fSiM\nFENKD+uR0qIaG7SlqyiO+8yizilVAvEE11EuJ+X3LW0TFFZSpfj8KdiVhnOgCuDh/KlHp8gB0dPS\nV7oUvtvuv2v+bAEDVYLBFJcQdS3gCBGwonGldhA8LO1SLhJsXoJGQLWs4Y1Eop9ZuQKkKBgUnPDV\nZaAPS1e0tAW/ROUS1ITxOCQQh7U3DlEWCpIxznXm/VsoA0WEy4Xg1Q/cI/gskBER0kgf+H7SJEDQ\na1pkLgmqrDQ6B6oAHp1X9cwl4ICITsIXvPaJqBikqAaLWvEAFh4LRG4vXzBLg8WMpM2wwqUbsfxe\n//rXL8HTjt5EPliWFkEs37QoHvzgfIGQB9aRqFuIBChevnOlyXBAtDG+UvQohqxgqUaEMTRC7jvB\na+MPKAzEoNLG50ANwtr4fbypnhB0DGoFd4Ke+ZIFd7F+CZXDDz88g4wU5CBoC/GNsk5Y5YLAXKdL\nLEZ+LbmZy0JKZJacZ1ZkbKSefjk5vqwvwW9777135uaCI6WAsYopJqMG9ywLLxahnXae0ifS2KQY\neecD5v4giOXi6hfj1nul6XOgBAZSTm1BysWhHsCsqArgWXG63mdmHBBhDVblhwIri/BViACsp/gH\nQavgxPbhfyvEBydKlCBW3KAbBKY6FqElD5NPlD9y0Yk/jhUPBeBnt8hTRHwGwYPlHbfgCI4qFjGr\nVzGSKgCm08P6QU65aGZwLT7zlYqQ5j8FVeujAktPpxX1qoUDIuzlvlsTIGPWC3NiXNdJuf5q7xWC\nXo1D9fjSckBkKchPSgSysCk/6Xv+Tv7iQiBoxSgUqCeEWcoEEWIZElZKYYqoBhMqqLCoBGq3sEjF\nQnyL4GVpLoLSWF2qfIGd+cJZxwR1pdlxwGKvP8DO3CQnnXRSWmAs4VICVQR5pelzgFuGAq5Snnki\nVUwVvVIudZotqBbwNLlbr70NB2y9xhKz0BQBsc1JE/riQhe6UFqtJdCFr5OFB2ICSXeL/wsYsggi\nFblsRlCIoCoRnSzpRY+OJmApF/zdFhdlC0WEK5mJFyphgdlZ9PqBRcYnWWl2HNhuu+0yFckcKHnz\nqoSpDsYCM3YrTZ8DXAGyALhcpH5xMQncFIg5C6oW8Cy4XO+RHLAVn1QM2j8/l31RSxDUNFhEqMof\nFQAjhcUWeRQA95aXqYBIIdC0YC0bRMiZJIQLKeXIGnbctfiYF5lE2Erx2j4gdpG3CofwedsQwXNZ\nbPgj8Yf1tVk2Q1ikPuPiKJtfFPhfrjplSEAWn2SltXMAdEzJ7MZ3uAolukSZd69K6bEuITznArDd\nKMV0FlQF8Cy4XO+RHGB5gYGRfFxQ8CQInKoKjwpOiv0LPCIk5Tvy9Qo0KsS3Bm5VDchCV4jVuJJl\nyzr0m2UiykJ3EZf3KbdUJTKBZipg4f8k9lVeJr4sSlspSdwg/L4We66R8lnEevUBr6+n+NTxs78K\nl8jzQQK4exe/U0NAMJwAOX76aVMVwNPmcL1+jwOCnECjKvGAe1lh4xL/pQAplh0/DmhZKg2I1QJH\nwJRtDE0sMBMBLdJX5C/rdjMQiwB/CoHe8IclVuHnwpXZvQv0g/5AJqTOgT6NVTnaYhHEHVRaOwek\nGxKe+Loe4rJRknZWVH3As+J0vU/Ct6xg1hlYlKY/Lqk/TZCD7sDItFhCVdqQrQjl9RYS7WhxY/1a\n/JbNqi3PsZZ3qIAKSwKzwO92iqKIiLq14PMLV5odB0Sci34WJAeZsdhTjMwL0fXGprFblaLZ9IkM\nB31SXne84x3zs7Kgs6BqAc+Cy/UePQ6AelnCkyLWrw0SRC4rOC+wSrUr0LP6x09+8pN7t5JiIL+X\nz8fip2btRiZBWCxdvnapWGB6EBsflyATeY8icSvNhgP6gZtDbW4BcLYDlNamchvodCG3y5sNa+Z2\nF1kBD3rQg7JmOYhacRSKOiRNP02bqgCeNofr9afKAVGjrAkpBKyHG93oRo2i8GBuwUVnnnlm7/5y\nYwlpOcL2AyakNzJt2bIlS2raa5lmT0mR9kIJUvUL/Cw6utL0OQCBIHDLDkSgZgjM4x73uEZEdH/Q\n0PRbVO+AA2oGiIXgBhAzIjBRmuGsqArgWXG63mdqHDBpStSyySN/j6VrI3OCuEuE0mYhiohSk4jl\nJcdRBHil2XMA8iA2Qc1teb5SX4477rgsTzn71tQ7djkgs0HMiPrvZVvO7vFpfq4CeJrcrdeeOQds\n5iDFSMqRjR7ArZuVpGCB0tQWll4h/avSfDjA3/70pz894yDEK4h/UBu60uJwQOzIrIMyl0YAi04T\nqACfrzRdDuAzfq9Gi9onIkr52Ph0ZlnXdTV+jXN8vX1SArAUduAf7xYfGac99bdNrkdrmScsLdH/\n3CI+17Vs8qNo1Hky+Tuv74pLI4CVCATbyJ+rNF0OqBN8z3vec9WbCGKS1lP7ZFVWjX2ChWWPPfZY\n9TrmiQ0Vap+syqqxT9Ane+6556rXKX1iM5BK0+WAPpFiuCx0jkjWryVXlqW3ajsrByoHKgcqBzYM\nB2oe8IbpyvoglQOVA5UDlQPLxIEqgJept2pbKwcqByoHKgc2DAeqAN4wXVkfpHKgcqByoHJgmThQ\nBfAy9VZta+VA5UDlQOXAhuFAFcAbpivrg1QOVA5UDlQOLBMHqgBept6qba0cqByoHKgc2DAcqAJ4\nw3RlfZDKgcqByoHKgWXiwNIU4rBdlH1ca4GB6Q8v1X0UD7j97W8/9GYnnHBCbu9XCwwMZdNEDuoT\n+/faRGEYffzjH88aw7VPhnFpMsf0ib1nbXIxjD760Y/m9pe1T4ZxaTLH9Mluu+22NGU+l0YAv/e9\n782N1dVUXQSyX+TZZ5/dXOQiF1mE5ky0DarJqKa0mgAufTKLbbsm+oBLeDF98oY3vGFVAWyvWVst\nrqao2g9YxTPlOiutjwP6xA5Hqwlg82SUPulvhXKqdq7yqjQaB37/+9+nobYsdbaXRgDbL9OissMO\nO4zWE1M8yzZuhx12WO40YxP47p6zU7ztzC5t55zVFnCNWaQ+mRlz5nSjr371qyP3CYXIfr8r0emn\nn97ss88+DSFMAL/vfe+r2xKuxKwh33/lK19pRrFqzROGg72ZRyXrymmnnZZ9893vfre5ylWuMupP\nN/V5X/rSl0bqk0VhUvUBr7EnfvGLXzR2M1Hk3t6y9vT89Kc/vcar1NMrB+bHAZvAP+MZz2hsWmE/\nZZZ1pcXhwOte97rG9oUs58997nMbTsFfHE7PvyVLYwHPn1V/bQHYjn/0whe+cH7BUvRdpcqBZeHA\nTjvt1Fz3utfN5tozuO6QtFg996c//anZbrvtslHXuMY1cgelxWphbc2kOFAF8Bo5aVPtK1/5yrm5\ntgCMH/zgB6v65dZ4i3p65cBUObDvvvs2l7jEJZrDDz+8edazntX85Cc/mer96sXXxgE7LF360pdu\nfvSjHzWCTw899NC1XaCevTQcqAJ4HV31kpe8pNl1113Th/a85z0vfaHruEz9SeXAXDhgq8kvfvGL\nucDzB9sruNLicMBewYI8xZrc4x73aG52s5stTuNqSybKgSqA18lOfuBKlQPLyoEdd9yx8aq0mBwQ\n+Qxhq7SxOVCDsDZ2/9anqxyoHKgcqBxYUA5UAbxKx/z2t79t7n73uzc3utGNMrn7rLPOWuUX9XDl\nwOJy4DWveU1zpzvdqTnHOc7RfPazn13chm7wlrVt2zzykY/MXHt9IaOi0ubjQBXAq/T5rW51q0w7\n4jO7+tWv3hx11FGr/KIerhxYTA6cfPLJzYMf/OBMofvhD3+YAuA3v/nNYjZ2g7fq+c9/fqYY8fMe\nf/zxzROf+MQN/sT18QZxoArgQVzpfEfoshjQ7W53u0ZSfKXKgWXkAGH7hCc8IXNM5f+q5CTYp9Ls\nOfDnP/+5Ofjgg/PGO++8c0UjZt8FC3HHGoT1/7vh/e9/f0aFijq85CUv2eucvfbaq9l+++2bV7zi\nFY3qNKecckrvWP1QObBMHNhll12aAw44oFHi0DhWxem85z3vMj3CUrdVneJjjz224dYSYHWd61wn\nU8CUs3zc4x631M9WG78+DszMAjbhjzjiiOaZz3xm8/rXv7755S9/ub4WT+FXL3rRi5otW7bkgmRi\nfOtb3+rdhf9XNRrl/XwvB7hS5cAycoDAVd6Q1aui20Me8pAUBKeeeuoyPs7StXmPPfZoPvzhDzf8\nvw972MOa73//+81lLnOZRlrjAx/4wKV7ntrg8TkwEwv4M5/5TA4y/ieCzOS/3/3ul9rgImyuQCFQ\nTvKCF7xgWgSs4Wtd61o97ird51WpcmDZOXDuc5+7UVdazVxV3JQ8VAtaxaVK0+OANU/O9Ze//OW8\nya9+9avmG9/4Rm4wM7271isvOgdmYgEr7s/fYdeQG9/4xlnK8Y53vGNzxhlnLAR/bnrTm+Z2YSCi\npz3taRWWW4heqY2YFgdufvObN+985zsbfkgbidTdrKbF6f+77vnPf/7Gi/LD9w5xU3Cj0ubmwEws\nYH7Vgw46qFFknMVL+1N9h091EUg1KyUm+WLue9/7Ng9/+MNHapaarY9+9KOzqL3nAi+d5zznGem3\n9aTKgVlx4FOf+lTzwhe+sDnxxBNzq7bnPve5Od5twgD6PPDAA2fVlE17H2jDK1/5ykTSbJX35je/\nubFPMAWIUP7e9763Ibc23bQdPuKDz0QA0/SOO+64xl6NoBh1aIdp3QSbQJEuEdrTysG1p+/vfve7\n7u1G+myDdEXtP//5zzcPfehD08f9+Mc/fqTf1pMqB2bBASiTVDrv5zrXuTKiX9pLjX6eBfe3voeg\nqz/+8Y/5Jb/vW97yluab3/xm7r0tOl2OdqXNxYGZCODCUlZi8fmyOgU8SfPpJ/tsijru0gc+8IFG\nFOci1UVVMJ31cM5znjMt59e+9rXdJtfPlQNz58Cvf/3r5kEPelBzuctdLttiX1nf1WDC+XaNvZgF\npNorePfdd29e/OIXz7dB9e5z4cBMBXB5Qr7WYZYiH5VXl653vevNtGg8a1ty/F/+8pdG0jzroZ9E\nSD/mMY9p7n3ve2eBg49//OP9p9T/KwfmygFWFzTpsMMOS5hTsOHFLnaxrdpEuZUjTBDUtKStWDO1\nf+5yl7ukYnTmmWc2L33pS5v99ttvq3sJ1qLQS4EctlZu9aP6z9JxYCZBWOAv22qVlwAsnxcZBpMe\nYGECnbPaC3TU7WECmJAGX3/iE59oBHNVqhxYJA6Ien7729+eVu+73/3uXMxZvyq7occ+9rHNy1/+\n8vRDcsX0u34W6Vk2Uluufe1rN+9617syIOvpT39686hHPar3eD/+8Y8T7bNPM2h677337h2rHzYW\nB7Y166bwfPYbBYMJcLroRS+a2vbXvva1xiAc5gueQlNGuiQfGQu45EdKSZKqJHevnwZZ6/3n1P8r\nB+bJAS4SFpVgLHPu+te/fvof1TcnnJWlRATwhz70oZoaM6POutSlLpW52P23s87YMvLoo4/Ooh3c\nBpU2JgdmYgHz2yr8rvjGNa95zXwpRC4YaxFJmT7Q87e//e2M2FZE5LKXvewiNrW2qXJgJA4QtqJu\nkWj/EnQoBkM+qvH+7Gc/OxXkkS5YT5oaB/jr1YhGr371qxO9mNrN6oXnyoGZCGBPKBJaGD5tWxDI\nIhB4R3Ugu5GAgQrJVVYaTnECCxR4CNxc6Dvf+U5aCVKX3vve95avl+KdO0ARlCtd6UqZCqHRnmG3\n3XbLEpw/+MEPluI5aiPXxoGnPvWp6QeWAiPLAHGziMIVyS9NhqL8pje9qbnCFa7QE9Zru8vyny0Y\n6ja3uU3D6pS1oYiQGI/tttuuB9tP6ilV4FNf/qpXvWqmiHENcBnIEz777LMzuJOLqwjjSd13Ea7z\n05/+tLn//e+ffJae2iUZL9LjrL8bPjgtyqItBcUi0YbPZGJtjUIEbXR6G4EObQRBtCGE2xjoq14/\nJmX+LspTtuGraQPSa6PQyKq/W5QTPHNYQm2gES2eBsyVzxPbobUnnHBCe4tb3KKNRaeNXOhVmxw5\n0G1UVFr1vHrC+ByIzIA28uZXvVAgS61zh1EE9rRRla69173ulX0fm420ERSUnyMVpo1ylW0gVW24\nYIZdZsMdC0HQhqHQhgBsI/CyDZdZ8iSQsPbrX/96GwFtbQiO3nMHrN+Gct77f6UP+i1yfbc6HJZt\nG1kUbcTBtJGXnfd50pOe1IZy34YQbnfaaac8FruvtaEwb/XbjfCPdeg973lPG8ZYe4Mb3KCNGJre\nY4Uy2L7xjW/M548KhG3E4vSOrfYhYhta69Ky0Mws4K6Gswif5e7yeYk+VKVLSb5SJm5Y+1jvaroq\nTQmWpsHS5paFBMDd9ra3zUhYaWBg9kMPPTStX9YRWuTguGXh8yK3k3XHD2wcsH7FMahz7juIkMjb\nW97ylks1rifBby4y6Y94oE4261d+/9WudrXcOAEa9vOf/3wSt0rXlntBHqQySkcSqc4aZgmLjXGM\nJWit2mh0+9vfvlFHwRq85557bjXWQvnIeCHPrx9+9KMfbbTH7z3PTIKwendboA8CHZ7znOc0F77w\nhbN6lbw8FbDAH1I1wHMiQpXr23XXXROm1nz+YRHRgiR22WWXTCE4/PDDF+jJhjdF4AdBu88++zTP\netazsji/RVj6CfhZulV3N6jhV6tHl5EDttcU/VygaG4J6UrGvUhp0beKRBx55JHL+HjrbjO+lAC1\nV73qVU0gBMkPqYYE4he+8IVUWNZ9g84P3WuHHXZo7nrXu6YSTBC5B7hb8KeXOBkbwVDyNxpRamSN\nELBgdjtEFbr1rW+dtfif8YxnNPpBrfKNSpvWAma96mg5ySw+Qpbfhf9LUAoLl4Dip2EpOA+JKA3o\nJI8732J1vvOdb2nGh2huOYgUDZola8c7pSKgoA3pb1qazplRQ/kUCV8WBiIIjAl7XRsDrDFCGSq0\nmUitATywSxHBQEFXJpJwlDvNX443k6CS/SHAU0yJLBGKsYwRwXLiUKxDglc3Ynqj3GcKTtmkwkY4\nhdTjZyBZl0SC9+etl/M2wvumtYAN7PATZKCFjrzPfe6TmpjOv/Od75w1c1nHApTA1OAosBwyCQVw\nLSMJOKPRd0ldbs9daXNw4CMf+UgG/VBALfRg1UMOOSQfnjWymQk8/5SnPKXHAsiQes2Tpn333TcD\nH+UCE/IMgOLKUqaSQLZ7HOOAVSyFcyORdUgd/ZWIW3Az0Ka1gIsGKt+XNfC2t70tfbqKorMMDJCT\nTz45reIICOhZC5thUNRn3Ngc4ONUYAZZ/I31SrPlgFQjaw1S8KfrW1YrgTsA8c0r8lNpY3JgLgIY\ndDsNEiAlaAKkduyxx654i5e97GWZZhSRv1nekvb51re+NasEgT0EBWij6wlaEiwBmltEokCwxlnn\nEUW5iE2sbZoDB0CZBxxwQANWZfEWUoHOd6pf2bkLzGy70M1IFG/phoLQBCLOkuT3CoCk/LC47UFe\nCBrH8t1xxx3TVz9KcGj57WZ7twOdoFjFkrpKzLLwYSYQtL1HbVwgb1ZUn0En2GHSwUsCpASOgIzk\n8gmkshNMlzj1+XZs+CDqUJAVZz+tE+lQgln5SYFZYOpyrHudRfnMdyenF5Rl20dbzMmvq7R5OUAp\nk7cqVkEdYRGn5qBYBXty83N+8pOfbGyIEmlkE/NrLhvHxXbwr+ITt5Po41lBvQQ/Jf8d73hHFkCh\nRAvMQlxc+ot/1B7CxVe/bPyddnsjNSx5dvrpp6evXN/NWpEa9xlnYgGLJpZcLrLSDiAgF9G4ky76\nIHydgBdgxRIWTNFP6qwKRFIYRHECkY39AlbbDHzBF/3H+q837/8l7lMgPI93ikWlzc0BEaUUM2ke\nhIrCK+aaYB8LFD/nAx7wgJwnUm82K5nj1gBzZ8uWLY0FfVZECWL5yqZg/RY4unt/lQKr8O1yZOvP\neGifa4GzUEsbW5Azy0QzsYAxxAbUgpoEGmAS35PggkkSGAJkLIIxkt8zQrn/+rRe6UOCIOTXsZJZ\nzSwDEc1RiGKbqGYLFy2Ltly2U+y/7rz+pzUrM8jyla5Qd2SaV08szn1FjVLMjHHjg3DhTjnxxBMz\nsl9NaHMwCm5kpK2dkBbVxTJNrnI9gXlV6GMBi/mYFYkA5iJgjICipeWINbE+Ck7iHliJQOf6kIGg\n/ZuVZKpIpzTe7S2AL4tuMPX31UwsYNo2KEzEJZ8TbY/GMula0KIHDUiwmhB+lmw/geZA4FIxBDg4\nV6nJG97whglFEbBdq0AwhEVMaoBQeb61RSLh+iJXtc+LclFpc3NAqpwYCJadNBfWb1Q5S18Z2JNP\n2LgmlBVjETMB8txsZN6bP4KcKCez3Gu8bLdqnZICqOyltpTiHCvtSsVlpp4+VxnFG6q4WclazjiC\nYgpqK4Fry8SPmVjAfBqsU69CfKsrkUWjG5TgPELxD3/4w0o/ye/dh193GNE8LTrgORoogSsNxyTY\neeeds/YzTbSk6rCmFSdQrELwSpTqS4ti2D1mfQzcXqlyoMsBFrB9rAuxcCmS0un4iI15Aviggw5K\nQS3fUgzBZiNCeB5UfM6METtSWZcEXoGcudK4kxgU/SRYFFoHeuVOAGHPym/d35ZF+J+7cZkLxszE\nAh7UURYH0MsgYmnaNq37Yjmr0jQu6TDaJcFLYLNoQRki6KRn0KIsUIVMBtA04l8F11WqHFg2Dmwf\nBVfAzuYQ36fdkCidLDA+426k9LI92zK2F1RaDAoWHCrbQopKtxYNIoF0dq5C1rHab4O4tDzfjS/R\nRnxWPlST36RX3GL33XfP6N1BP+eX9eoSgblaZR6LCq0QrCyqkD8YFKfIBCHKbxKbLyQ07XMZyDRQ\nBCrnT2ABO8765SfzG5C5a4B+hhGrQgUtG56L8haFzbck7UB5ORC5ijoKH2hfpcqBWXCAhWvsUTpL\nVTf35YrhuoBOgTONVWkvsgWgPvMmkLltEinG5ssoFitfoHWAYLPOUPTNxy3hC2c9LgIpr1jcYdoj\nNkU5UL5fwllAEaUJNM1K5u9FdmWznSR3Hlca6HyexIApWSVS22S3TJo++MEPZp0GMoSrxA5J4ndk\ne+y9996Tvt1srzeLXSOCWW3AW23Avvke8HIb29+1IYxHvn0sBqvuhhQ+5TagmTYEXBsCvI1FJ3cZ\nseORXWJCe2yj0/K7mKD5HoO89QqrO68fUaNtREnnsZiweb1YtNrjjz++Dct41fbaycT97JRitxM7\neUSP5q5BUfyjjZSh3K1G+2KxW/V68zghAhrqbkjzYPyQe9rhaNzdkGIhy7FoPBrT3r3CIm7Dh5af\no/RqG8pnzomwyIa0aDaHrBORF9vG4ps7j4WSsOqNA9HKXYoiSjZ3OQtFOnczMh9DOV/196OeYI6H\nQrDq6YN2Q4p668nvELy9frAbkt2orEUhcNuPfexjbcSm5HP33ySEchtpSv1fz/T/sOCz7XapCwWp\njTTQNgyWibYhgnXzHuGWbK3Z+ILn+tZabX3tUt0NKWZ0P9E+7berAAb/K/+Hd0FQkyT7R4ogpM1L\nL6A5iVxWSpLWRItU3Dy2/0pNWHoGixUsrSg6R77zWLlSNbTZ9UDhND1+tdWIhu1+Alto6wI83E+Q\nl5ecXVY2q5xFXKlyYFYcYN2qNyxIy/xTZpG7R46+GAsWssAtmQB8iyyveRNr9ogjjshUE+tHgWuH\ntQvSBn0yh1mV6gN4h4otypyz7oTin+hDsWLFryhB6XsBVmoZsPwHpWtKL9NX8yRFi2Sy2EtcUJnP\ngqImSbJloIfWdmlb3IOlb1/wghcsfxGZrvYwrc8hvNL6jcmUt2D5xQRvAwIe+ZajWMAh+NoIS899\nbWMQtB/60IfaWGxaGrD7+Y5V691+twHhtAH3tFETN7+LKkH5zlKIxakNRaGNyMT8LgbbSG2lsUcd\n0zaEeP4uak63kX7VBnTSxgLXBsSU99aGlbTFgLrbiIZsY7EZ6Z6TPmkaFjBrKpSbNvK024iIbyOY\npI2Izkk3fcXrRVGVNgJbWv0IkaFN948/+5M6B/+7ZH9ke7aiKFnaQnBWoggIae0VXQhqUsY9vtpn\ndj00CQvYcxt3kCDv5QW1CfdO/h9Cqo0ylfk54h3W09SJ/ga/QplNnpo7kTKU+1UPGztRiKcN4ZWW\nvGc0J8MVlc9kbhfST/i6XhrHAraPuLaFYMl3n0P4ptVb+iUUjzbSk3KNCsGWVuaw517vc6z3d+ZU\nGDhtBPK1IRTHGjPmCLnAsu9SxCgkQmOvdpZvZLZs1bdRUCbHhPUVLZsFrNLTTCj8Tr376Li10igC\n2GK3//77J9xssUHer3CFK7SRntOGJpmbPBOIYB4DHRQEGo4o0RSMBCcCeUXlrjY0u7xGfjnCH88W\nvuTcFNom3sig2muvvXKTbdAfyM9iEj7rba5o8Q8NPtujfW9605u2OWfaX0xaAJskoCMQVfhs2kAJ\n8vkjb7kNH9e0HyevH4FG2d8muRfeclV0KZCThGYt8l2yITqoEFnMKXQrUezn2sZOL3k4gmRygQ0f\nZP4fsQVtoB8r/XTo95MQwG4QJUtz/Hl+Y5CiSQDrm0hvyU3oIz4hx//QBs3oICUpkKhc5MMCynkR\npQfTlQMCHUSEFJeTl/kcllkq4rGfb/a7OWUcRlxHG9HgbfiYB11m1e/GEcDaju/6wSt8wqmcBuKW\n7itQdFjwbRRUSeWBEhL+7Nb4WovrbtWHGPMEfQPa5/4LFHEbpXaUy+svv2W44EXUMtjqZ9Yj/RW+\n5jYCCXt9SyhbV/DJ7xhgVQBvxbrJ/TOKAB7lbrEPahvVuNIXxO9lcUUGAG1rmmSQFEuab4d13U8R\nXJECyvcRHJZKQP850/5/0gL4Dne4Q+vVpXA/5KIY8FL366l97grgCOxpI6ivjUCj3v1i+71ERIwH\nQrZLFLgo29j9asXPXQFM4dPniyKAI2K2jVzXbDseUIQog0WQGY9RGnHFZ5v3AWhVQSdYuUdHjMco\nZK5FOc48FYIh/iJSCvN/hoFjK6FRw64/jgCGtET+fl6efzNSiVa8FWOh+HspR1F0Z8VzZ3mAkUIA\nFuOKonPSSSetuQlHHXVUG3By/k7/RqDZSNcIN2Oii06OFK78DMWixCwLzS0NKRamuZDN5kPzTz8Y\n36+8SNHQfC2iCqdJohv5tFAM3mxD//20r/iapYconbns5FnD+tjqMRQTkL/neQuJ+OS7l6/KP9+t\n6mX7SNHlNhPga4pguoymL7/1LnJUepvi7CHwsxqafW37iY8wEJHs83KM35/fXiF89xV9i/xeeoh6\nykihBFXdCoki5vOXx+lzoRBqvc0xPFME1JRDGaWv/XLR5XrGYtE7Ns0PfL/iHJAxH4tlxiXwq6FQ\nvKY+B/JG6/wjo0AfIxkR3WjuYZc0xvQ5krqjxrJYDFTmfv4zwz/ysEtWR6BmmXWx0u1tG1n6yG+m\nvU6t1I7+77VDbYSwYPOQWJ/1tM0zla0YrY+KxoxCxjM+Itk1y5giutQCWFEMYegWZouYRU9wk4Ut\nrJicZHJ8ywDRUQFR5/mKDgSsl/mQ0hsU3ugWCnHuWogAVxBDmkARsv2/Vw1MyTkBFHIypSKEZZ8V\ncASBWRyE8lvIBY64HmEkaM1OR1IqbDThGSzghIRFKCJXM03qmGOO6b/lQvyvXFxouSmopKARtMjz\n6Suk7wQBKc2HFyaX5y7CTt1XwlV/61NV1AhL20gWInQVKhC8ok8JUkFxFtl+kvYh1awQAez3irFY\n7PQnEiBjAbfQIHmXNjJAfqP9xp4AO2lsReBb8PUtEgBUFA1KlXY7RilRxcymCNMkSpzxrR9sPGDB\nE3h14IEHZuEHJWK1yRadi1aMw/hWdEIaoLkjQEnfG0+UMXNAMJDNDfSFlCoVpXwu48y5AjLl1jpG\nYTL3BaGplie4x3VnSWrWS5UisCh+6tOvRNYNKUvGl5SqcGetdOpUvheAWviL19YZY0kJT8qucWPd\no6SWPdM1hOIqoM/YjyySrdpm/aOYmuuC5Sio6pe7jjHaJXsDG6P6q1ulUL9RXvSrNdV5jKqlomUx\n1fshaP4APjt+PMcELYH/QgC3IbQS+pNSEZVlMpiqPCdnPR8reDesoPQtgYDGISHx0ekZHALiBsWs\nRFI8YpFPuJNP0O+kTWi/IC3+HQEJr3vd6/JYVOFqI7cuIVO+RedHNGjLDxaTIv3N/ETgc5D6JCDE\nSUPQeFH6R/v5wfnWQxj32MQPFxOpBRUXEtgh6CIEaFt41U07EDAUkaB5+q9+9av0kYG2Cwm4cb/Y\n3Sqv6zP/L3Ke/0Fe4FiQdAjbPMbf69pIYAz/U6FY2NuooJb/8imCEgvF5h/5bMUHPAiCds9uKox+\nDGWhXGLF9/X6gEFz7in9jb/X51j0W23n/xRgZrzyL+LzotG+++6b7iHxFHym5oj5IG5DP4HNPZP5\nHMprfjZnBG8JrgyLOecEfzeY0xwxZvyG79LcAVGvx686DgTtntpQ1irPNoy4SIwBY3WWxOeqnaB7\nL26AEHK57lq3BFZysUTxkK2aZQ3zO+mn+G7MdX27Yg64Aq2HYGzpRl7FRVcuxkXlOvz/4ies5/0k\nwC6Usfx62XzAS2sBqxajyAar1bt0H2H5sSCmFUJDozXRrCIQK/rwryQNieXCmqSV2QFFiPs45Jrg\nVClPrDQa40pEIwxhkaH1rBDtlGag6ActjtZI09Mm30kXUf2GRU/7pTVKYZCM75lZXAqFSJViLRbL\nbaX7z+t7zyJFwQ5VNGfP6NmkliA8ZI0ouqBIiZc0NVtDFn6y3EJpyvP9oXHTiFl44H0FVUCUNHSW\nJWQE0ZL7CQSu2At+hVBOK7bUAmYJg2cRC9j//cRiZ5WzsAuBNVlpw8hzdzc+YPWX5xv2u/Uek3bD\n4mNxQIekt0BTWOKsPjyHGrBs1gMfrrddo/7O+A5lNFEE/DUXPIdCIawncKXUHfOZlej5WFyhCGe1\nu1Cwcv5HRkSiAPhtXWBVspZZb/osfMCjNmki5xnbUBWWOTcYS34YcR0YNwVKH3buJI8ZP6x1CIQX\n9MEaa92FPkEh8K+gPeXe1lXpltZY6IuNYrpVuyB81jLroR3ynK+Ihz7sUgTMJeIFLTDXB6GL3D/d\ndaH7+0X/vLQCGFQINlM3GixpQSXAHvzgBye8puMskiBcuY4WSwOA/4fQU4JPLegCDY7TUQbf0yNn\nmJ9SLt9qk1nbwX8Wfu0EoXiP9I+EzQkdigMohlCxSKrOZaE2ECkakUaVvzPwPI8JbUGyAC0SEY5y\nq4sPyyJqwQQP43+pd8t/o0QiIVBeJiNBXASDye+cQqE550fChZAFO/OXWRict1qNXDA0AWxcEKTF\n907ghnWTyo1cWItHP6nbC9ru90OWa/SfX/5X6xe8Xsizhepe/p34uzkBMsdLCyc/nTzKQCFyRzLj\nTD1oc2URiWJJ+VQ9jqLKTRNWcRMoQyrS+E1JAlPrM89njqvORNFWYtY4sVa4xukRj+A7u4dxcRB8\nxgsYdBak2pi8Zgo2I8J4LRszzOL+a72H8WOOMCwoztZba5AcXALZ8UFkPTY35J1zm3Grdd0blF9K\nsrxubgGKEX5QmLpEyWZAWTMoIa5pfSSwB7mWur9dis/95vy0/gf5gStAduDVEtU36v36IWi/C2GX\nqQTgTfAZ6AmsBloCKYJKQG9gw1j0EsoARUXHJCwtt1MI/CRIvi84S3Qm2G8YgVlFNopA1ZYILkk4\nORaGVr5sLMrZfsfkC4aWmOeVZ4hB2YZ1l/Ca9ocvNaO5wyoedtuRj00SgtbP2l3y9LqNULVMqheS\nQgAejAnWO0X6FugZjFggaHB/ITAjSMpv5BVzLYDqCrknHoKC8dznAkE7xzWl5YgMNoa6ZAzJVwZ3\ndqkLQYPHSySrczyrvisQdChiec9hUdDGYPiIu7cY+Hm9ELSLheKWUejaGwpnb9zhR3l14cGBDZjD\nl9wAcvH1ayiWyWvzBhQZaEiOF+uCXFrfyW/mLuDe8L8+LLC7tcDzc3VwU5mr5haXkXm3HlorBB1K\nXroojJH+XOz+8bee9kzrN8Y4fnIR4a/+MO+MneK2GXTvUK7zXH0VNfW3cnGA/Mtcco2wYtOd5prW\n0kLWdVkIZe0DW4tgD/QsXY6hOJZT833ZIGja99TJohfaUvoA5OUqZMDvNSgPdqXGDBLA/efyoxTB\nHpZwCnsCGOlYE9UCHZZULrz9v5/V/4pRlNzDsL56PkX3t2hYJJGUA74ubefzRgZjv1DIAxP8M0kB\nrFkml8lrQvIVEayUsIAC20AOsuXGhYXJ4ih/NqIic9L6LSoC2AJgwTRJQ8vupXLxAfl9KV4Slk4e\nxzvK3yABTDBRgowbQrpLEeSWyhw/e5fKouE7/aM/whWQbZYf7H5FABvf/g/XQN7fM1jIujQLAdy9\nn8/aZP7xq1FUpeUQSItExgOhifg9FdYwhig1AUVmX/MPigGhVCu6QxEL9CJ5LH4gIM7emNJv8vOV\nTZwUrUUAl/Er5gARKJQJxIdpHC0imTuMmqIYUxzCqs2mmrsrKY/6SKwHXzAKBKK11g0i6YBhVech\n/ctvXEhRmED58l9ru74O6zf/V7qzX3FZNgH8f1hYzMppEWhBhBqYTxFx0A+4kH9nkiT6lY8N8ZOK\nfuaXRfyH4A2wRSyCA/2CeeIM/sTC0ksH0S7pH4X4pEII57/aCeoEp/qMPJNNJ5aJwG4inkPYpN8L\n3CeCkR9uy5Yt+SjGBR+2iFXPD6LDJ1BjIa4EUDP/EWjLb8DbCBTPV2UM+D1oLxaIdD+IeB1EYLBY\n+NIXGpb4VqeAoflJQWArEXcDeNrG7u7JRSAaulAIt/Qfi1rl51oUAj/zgxpLXnjajQhfhHaapwWa\n5fcU/2BnMnMhVt+EjJ0TgiF9vT6HUMsYg+Ir9WzGCvcHnz4fou/mQe7rObggkPaWz+a8sbOIpJ3m\nEt4ivOcKQvpC7MAgcp45aqwhsPFKvHdOOWY9LPEXfqd/y5yytmuP+yLxHtbHpaZUJab8h+Yi2pMG\nqjC5qlDKl62FigUsKo926xogzPClZvSjaxXI0XesRJpzLMxp5dDGoqMSuvJZdCtrKzp0YDNE1dLw\nFGYoVrUKSaIXWdK0a20okKcIQRGaEZCQv3PMS6R2f1Q0y0tbWEwiMr1YugVyc6xALuBn1/Ad68k7\nmLrbPlCs5wHBr0QgzAiGyD7gBpD4r7zdIJq0Bdy9R7Fuu9/1fw6/ccLO3e+71qNnXylqFVyNv7Mk\nWj6NfyVS7SsWmJUOj/T9eiFoc4KLgqVtkxHzAYRoHJUXJEIU8byJhRT+3azKFQFiafWKkGV1sdTN\nO21mzfreZ3MnFuSc1yxk57GgHPM7qIjPLGjVpDy/34j6Np9FTquex4LuJ9aXOcp1NojWYgGL8LUm\naYs2lfWozHMIF1TMejNuVsagtq72nTkHEYH8QIsiJiWROSiDPmF54i8+eobSbjyynkCmzDuZDSpa\nsWhdrzyvqPvuWmv+et6IX8nqVSDtEL7JF9e0ppEX0FNICLSAOwEc7pruoQJWsbDL8y2bBTwTCLow\nByTHh7cW6Ln8lgC2mICUCU6pIAayXYp0kMUFGbxKH4bWm+X0dBYBaGIaNHx+vnM+gVmg3XIf7xZx\ni1RYYwmVWgwIN9eQ2uS+7mlnFkJRGTnXNKD8DmTDr+s713dfg6lL/MQRrZvn8HMVn5AB6Xd8Xt49\nN0XD/dSvBpWW9oFfpGIYuBYQQnxQnWJKgmtFIESe45oqxjhf6kw/TVMA999r1P+7AnjU32yk89Yj\ngAlVfazmsXHK9WIcgNyNGZ9B7MbQKDstTZOfYHzt8TJ/SpqOtBftLcci6CePE6KEcBEEBJrP5Rml\n9xDMlHEKhnXDNQhBPHGuBd1ckApDuJgfhcp4wzu/tRb001oEMEjdPQkN1e7K8+C97z2jvpBmpy0R\nfdx/u6n9T0HUHusZRc1ao52evctP8H5RaHz2G21Xs5rA1m/WP6lt/OuO4zVXkc9dHuqH8rye3XW4\nm/ze/9Z3cQklZofBxlVCCbMGEsz9/l8MqgJ4xGFCU1rJUon0gHbnyJXtvgxKgqj4HwhVFh1tidBl\nbfYTgUiQIRNakI9NGWhR/HSssXK97m8JoK7VqrSZusUmM4FFYBpYiDIQEGTPb+F6BiKfDm1S+0wq\n//cTq7X4e2nnFgxBSe6tji2/iWshWnrxb/KjlvbhkcEqkMFLUEk/Cc4ySZD2CQZB+Mn/1E+LKIDx\n0eTbrLQeAcx6K0Fn+txiKv/SOApYMRc7fGWZUBjnSawlATYCpSjoarMHrJ+KAmuIsiwoLqK5c0En\nuMR5EMjmNgsKWmShNicpy44j/ksKrvkPiePXlMPq+iXGwJojgLIQhdlijsx560Y/rUUAE1AUhkIU\noDKeCRQCqpA8f+fPiqwbXUTSWlXQNGtoWeu0h6CMbItsGmPE/4XwVF8h8R7dZ6JIOV6olET1P4FP\nEUIME78r1rL1qqzhjhe/uc+DqArgQVwZ8J3gibVYwjrIYkEQ0qQKFGGgmniD9uVkCdOMOflNaB0L\nvjagCB7BUIJR+olgpsGxbMMfkb8DqbiWIul+TxNkRbomQeaY/wlU3xVL2zGLARg5/CAJHZvQrNgy\nSC0sBK/fEeYGtXbSBv3W4uAYaAhpH36YJIS8YxZV9xYdHf7vrR7JMQsXntNGTX6FO/xukKa9iAJ4\nqwfahP+sRwCrlUyxE5BmPFu89DmLkDXhM8vMWCOYZ0GgR64PCFWXoFusLlYpwWS8ap95QQn1ufzv\n3WJuDhQLiQAulrJ349ycjDSrVDrNKdcGobq+a7Ci8UXgD95YVwQIImuGaGq8Y01TgPtpLQLYOuIe\nBDtkojwPQ6DA6dYKyoFjlOxZEfgZn6EQpXgNK52BgFd4x9ihIGmbtQ90XyxXaylr2TF8xzNuAv8b\nV8Ul0K2nrt6zAh3leZ2rxjXh77MAK32hj92fEaNGu3sPoyqAV+COQa7KiZQMWtIg6HOFn+bXOkIE\nI+FiCyxR1QYuLVaHEW6DiG/W5DTRLEZ8n4QWQU5TJggHkUHkPqzLskmD8PcyyWnULNZiedMUCUtw\nte8JaQOnQH3up52qdYFfaIS0b9/9P/buA1iWq7gb+GDjDyxXkW2TeWQQAoMNEkHAIxZgg00yIkkC\ngcgIhASIKCSQyDmIKIEAi5wpyqQHiGwoMCIngQjGJroK2xjMfP1r+6zmrTbde3fn7t49XXXf7tud\nmd3tOed097//3cdxzrVAWBR8X7+N12+hkcPrlt/4vvLAvp9JoizJgGb0yzUt2F0Br3FURPEml1xT\nNDHoHjJ4Xg3wQBVL82QzBtiXl5/jZHqUy2N8jTl/UBQIisVV5LFokSdXjmIhNS+GHUUGkBEu368Y\nV/8v0Kfn5jyj7NG6YD4x2OYQqF1+l2CpQ87MRzlI88T/nStqNmcZCseLwEVavh+EC3/E+Yyv40bl\n8DdigOWAObs+H6TN8DLI5XeBWxl8udRxbOFF3h9rnM+31pSOYuU+CAo4M9Yp6537Ur43uNk6pIyQ\nPhhZesafgeCVa7g3XQF7i4L9XukzY1TAAQERKFjLfJbOfsau890z694kqQZ4hHZ42bwgg92jPKVJ\nOI5IM+ISOdG6JQSuVWBV+dxR0Ouo62z2NTCuCcnQG1SlbMNkFWkX8R6SkDo1Blt0wdj6ft5j/Aw6\nDoHBJWo26BnsYuhBZ6KXjYhSl9I+kefIIG9WqgHerOYWd95mDXD3GynVEvkSBtciVwiG3eMW9Ryc\nqPyMWOQZva4UcqJFGMzJ+HFGGVoG1AINKnUdC/4sMK05V+p8Oe3d9qfdz2ZgSikaR4XjPk02aoC7\n19PDoKSRGDFrwrIInXHUSHHYBCqcEihbgatFvngpo8Qa5DqltaR7KL+8aFk1A/y/3PLQ1CIllJIl\nIdoOaj8YRisfA37Y9MdGpDnYPEGHFJ1RFilKNcKTzpKNyD9l+zif5/VwJAYf7b0YtI3vF15bNoSP\nhS67vChtCSQg349FJtuqKYWJ3sBJ2Y9BmtcJr37QSH5w4SlPtGiLxSqPiih6Lyr/lFPr22uiAWM1\nHNf8tcZKLJKDDmV9qCDQn8GuWMZ9RDp7faxyoTC62fktkJ+cW+aJcjClXgGH5mvKGrVx1AFtmgTy\nNNgxJ4zsXnO1e65yM/OGRHQ+9rjuOVt5HlFkY3cnYt5Hrn4rl5vruRHpDtbWCB7y2r5vOEBZhmRt\nI+5hOIb5fPgfndUCxct76b3I6a5+ydDwj5zH/xftkbg+z1bEWijjiCFIDSXRPst3KBB0ORZMEb8/\niRO8MrmERYp8NehKLhmMBI4R2YKTQHhlkwB5XVAyqNv3KznhGLR5vNdEvx55haBsZBkRq9fkthFM\nYrHMSBqLWvSs/GqS+H7gN3ltUDvvmnc6XB7jupADrNdxJLgaAU/S9Pa8N48IWAQD9hNZYrEqpetT\nwLDGuNQPQliJOOWDo5d6QsFgxlIR0IVoQdYF9hSVQYyU/NmMQf7RbwIhD49pY91nSu3IRw5DyfKO\n5i+Oh+Mwc83LkqM2f0Rujhmeg1uJgEXlfo85Km86qgyqz3sDCYHW+Z3SU3RR/nw/+pMPBi97HZIC\n2jcux0lhSkunuafy4FAPuV8QNBtgPWUPRK7Go/tA99IReD3DpZIIddH7PtfEUXnyGgHH3RkWnlLA\nx4MmGTHJmoBfBz1+h4+f5f+agWusIboMiDt7Qc9y3maP4W1rOi6C4N2FsUsPLxaRbBSvD3PAW9mj\nOaC+gXfOsyai4phw2XuaZ0hiMcwomBcZOawsOPd7ytZdwQ7Mfs888nJsnjjiH9F2LD65PVgsGtmo\nIGC7jBr8v4ii+shvr4CxvgAAQABJREFU5xZokW8bW0hfjq+PO0cDIhgNN2Jhyw0N+m4OErnaHKPG\nchivJog/qVxzx8YI1oQw0okcBVu5ieqD3IIvDEA2XYiFPjdjCGczo6ngkWSPYQ06NGwIo9yIziIF\nNLhpkYscbEuq8Y+ND4pESik3eoBaxeKfm5qImCEDwfXIzzQvIV/hVGdjEEjTPCQckFxHoIH6vWtM\ntF0CGfF9rEui37L+lHXKJio2uwgOyWBjG4gE5K9spjLqu1ufrX/WHOLR+mYbWZ+nr7T/a7gSsH82\nWgliaW7KoBkHhMCa67gika5LW+JaUBDo4SrLufv68m5sEQZlHgKe6mvgRoSbTevDi85BZ1GISDgN\na5nYUTuYBjpICbmQRI4k4ayIPHK3H5MZXG5ARYSazy00mp1bGIJ0kM3NGVMbO9hMQKNyEh5qnsd5\nGSdgIgOWcxKeZB7mvGBBJ4TnBYtw6R4VbOr8HN+pynpowDxkZLZLOOP+uhLRYO4Fa9em0q0uWNHp\nJFigOQycSLsiEVC1uRdciWz0z7m1MEtvSd9YxIOcmMfqsMRQE0bEzk+uSeykFISnnMMRgeYc9B2K\ncBIiN7uhOVjOneUxosKJndZmucY8jrE+BNk0/1wv2Ni5Y5ZOb5wgO71Fz4H8KE5SoHS5oYcNLhjO\nUSIYCMRwsAOSMRek0uwMJyUZ6MYgBcEpFNBwRqzn1j8pRSkDm2YwxtZbYp20NtKdMRLktbzuqO+w\nCq+dbRVX4dsu8DvybBmrccKzDigq3w4CSxo1+RHRt518GEoDhwS8krmuqOVNj5KHS3jSUdqUHrkd\nWHjskAELyp49exotG8vixFgzzAZrQGzpNTL+04Rn6ftYUOwAFQzCRu6tiIEb5VzZBtTk6r5XjqmP\nVQN9asA8wo0IaDp3CbOolrko6mIkGV+GWtRlhy3PRV/mjvFuWz9jPYiMOb/kjYnxzrgwMiI70XIR\nhtiWldrWamMKceoKx5RxMAct+iK3WeZg9xrL9pwjU3RTvpvfyRmxBtlBSr42anxT71rB+r/Xo144\n2+DaCpJTw/hC9Vyvizq4rugZYmFNs0badSvIt7n+BQyd6yInyX2BeghK3FOfRzz6v/sKySsCPbEO\n+x0cgEgBlrdW83Ecfr9srw/ngOf5/VDbNSJQgoBhKQcxSsKLSzp83Omk5cuLeF7+5EPkMwIeyeJy\nOSx/vrs8Cjq//LAcMtalP+cqvZC7xRzsihyT+mG1g4Uh3X1/3POAmDM/pgwgJtVeh2Fxy50pvRjV\nNcvBNQe8l8qW4j/zyAEvxQ8Z+hKlJM88CGM5KNszb5S04CwolZIrxKfAw/CIES2HqiTGsWUOmpPm\nqf8r7VH6o3RQswf19l1RhaE0TwmMsqBREhBnzsEwNlke0z1mKzng7nX6eq7OuORww6Du9bHqbq1L\n9KbsB8eFfj3K2cv3Wq8cJxccwUbqTidC90devjThKBdW5eGeWvOUR7pXrukzfJdyX+TdtQWVw5fT\n17nPZ6iU0S2wK/LwxgKWvDaZw7JqOWBeykrIIg0w0kUZkOpkDYZhsd1dodybeAgEBlJErHmohcGf\n1wr1nsHt1vR5Lzy9LOpH3tJ8QMs7fU2XSaoBXqa78b/fZScaYAaRISXKcEqNvYY7iECcYQSrYdGI\nxlyKCCkJOZ5b7JUYea5Fa0RIWdeqJHBRskoGONC0NKJITOqvI5IclKAVcpzAQ3madaw4K0ov6ZSz\nohRJoIIASxDevBcRcHYb2x19tsv96t4jxxYD7rlgQ3nZImTVDHCFoGMEgaXCq4pnTeYa5C+GBawM\nFibKIcDD8qlR15yvIZiAppUalePAwV4r4j0bWINnYhuvhHN8LhimStXAumkA9BuLdv5sUCKuhDwu\nmBHkK6Vj3g0L0pBcofklZyxXaC7Kb4NEA+XJnGJsaZllS8Pnr+P/rUNgdjqy/oBwkUkJHXsP8Ux5\nGl3KpRP3BTQPyscxoV8kOOL8iFIzn+u6UmplveveI8e6R0ifJPrdD0iq+cIa/9MbCWuZdRzedtar\nRceb3DZOjqkr8rQBUzcBieWEV7MXsEvmm+ShohFI5qYKq9QAl3eSvzCAAyJrMC2xPbEpI8pOxjRS\nAvZlNPnoftxCnyOlIDZYnKKgPifjQj+wXrxqIDQgdxtwYi72uBHmgZxvtB5MZ7QQpTD0zRmkHXlh\nOV1MV0QsBoCom0VGRMJEXMSA5ggjZkVU1WDtMuTmm2PMT3MaUQvZZ7NizuBuMEJ+wypJoAn5nTGH\n6cjWsLv+r54XCzki3qzQYCijTDKNpN8r3+ocxwa8n04RB4ngj3CC5GixnR3vc0j3HjG47o0tJeXQ\nbWG4EfKscYD8ZczY1nYnSS8RsEmFVOHGIwchPpgkyyJRg5tECwPJzWZAi0THnubYY4/NCc94lai2\nDDzHeU6wnxlUg4VHaGGImtxcTDAzGTxkB3vW+v2iYEaaMe9LLEyYhDxdni2iQ5WqgUVqwDyAEFmg\nkXVEVwyyxVg0heQDVUL8ifRHMpsj55pEwYA9k6DDYIiQi2DoRiesZDS7lqqC6E6XEVjkG3OuIfJ4\nriwwYNU0ML7DZkQZovmCSIlMdMghh2zmMtt2jntgzYH2mftQAnonCKiIngW5C2g9GcrQOkif9ZuR\n9vuhg91SLISs6H2Qa5zAxLpXpNwj5yrXZIAFHUhY7MAsgmDHIYu8f5aJRb54ltNW5pheImAMOJ6u\nQcDgKAdghDEbwR7LIAaGv2HB4sPC5El7jNaR6Qma3Ly4Uv4ALnMsz91iYWA63jV58VF8nnBN19hu\nBwOZwVVSRSxoWJ6Rzx7+2fX/VQNz04COd6Iqc55YBzBfLczmjUXboi+ytB4wliIo8Ga0V81zGDwL\nOKe1iLlWJFqxlqcNh7qIml5GnXCgo21sEy0py9szP1rDgoCU6BUEi7EHoa+K+N0qNRhLQgdQPbXY\nHBnGttTUek7X1gX3CdpgbSMiUPoUbBSZFM1275ESSWjiRsRaBXmEUoqkS1+FjVxjmY89211Z8Lc0\n6YKRmFASIwSqWHT7yHn8JB5jdGVJr89ioGSCNyxvK2JWHydiFvlaQIqIfNXPyYVYeHjyyyDyNBYT\nkYjfU7zeZfhu9TvsTA2ALiFL5rtIKLpTpYEtv1Zu0MIsF2mBF2mJeJSvOJ4Dq8aeE7xRYcgt4kpl\nosvTpse762i0A8lTjuOaYNVVEd/fvBfBWruUIlrLCP3rZRAVE3kf6FwvAQLdU87IieLMSLmVUsk8\nYMH/uOf6KRgbgh+/YUfJIphow9eMiZStGbVYjM40uQUainkY5eFDx/5/qyxo1HYbJ6C32+Eo4Jfc\nCCG8t6THj2vSbquuMFpJ0ccO9BtiACRjE2szBmjS8e0lGh724PtjFLq20ibt3fzfjkTKJND5u3tj\nDk6KJ3SCim//VtePCd99e8vPA07KlpVo/Jqpj5LKgh6lle19bRVY0LE4D0qAlO90JRb9bPGoHEVL\nwiK2uDMXzCntJrWULBKLbZYPhUHOeYsVjYlLMJBtLWguKXMJ5ze3wSvnlkfz15wz95XQKMNRtqQF\n5UYlosBsZ4mxHQ5FfgftEqeJtq+Yxtstti+NXG7uUhTIV+pW6ZE/94EuIy2QurETUSAUWSIZEH5W\ne9jMIox0lkViQysH0wK3iPVPW133SfmR87SjtEYqUaKrsn7ag9i9jgBgr32Yy7W6jzbuUKqJGR+k\nu+5b53i+aizo3suQGD61fR7HiZuqt3P3L+Da3Edz3DnTXjdJGUCiL7U6NJPeZylVUINmUg2Lvs36\nktrDUy1v5DXyPLshRS4kB2mh3g+fO/x/n2fiM7LhfZ5jNxjHc1AsLESJ0qh9SPPNBf5TDfAClbvJ\nSy+7AbagKiniaKpnjyYXey3Oo342p5dTa14EQSvr5BlI+3AXUQdv/hGOs17ryl6cY02IJhJZGhOo\nWtaHlvOGHyP3m+eogw/2bm6hZ5xvRVapDGn4dzK6yiSJutqu42NtK7vLBbKXa1I5XwmRvgRET2c1\nvEXsxlauqa7aPTIWjAn7DQt+iNph7xnT1kJGOVKT5TJbelw1A9wbBB0KTwHTakUZHlHmGcrr3Uf5\nB/Bt90++pdDmu8fO+nz37t2DnqRyRFiROrHIQYFiPIKphgU5JAZno20j9p+OLXJTdmTBypMvmTUX\n5HOQReS8XHcUAYp+wovMrxEDMwlTw9+p/r9qYNk0YH5IacjhmhOgWuSoSYJgI68qr6ftKog6HOGc\na+U8LQjNPyKfaG2wDsjByhWbK46R1ipM6nJu99HcNvdA24hUYG7feV1FWqAwua1nXVIs2JeuCN3L\n+RaxTsrHEvetW0KJQa36g9jNyrHGgjHh88p9tBa7B4h51kI5ZmNhLWVL7kaPJ28VglacHzc4O7LY\nkzeMZsLDomHNN0Aqo0SBv/N07PGoGQdIBfwCduaBB5lp1KnneI137xq8RI+aBQyLRh3eC0Zoeoz2\nIe5bagTct8anf96yR8CRo0tUJ0iJued3OLcZ3WhgY+6FMR78SBFstHXMeWesawoB/pR2iQV5r2PL\n/NMcR4QtoiI+xzx0vg5LzoceTZKoAEjESnQnGuumjCadN+69VYuAdfwLIlNrv3GRLd0V+N++5EV0\nEPMenetwFTXZ5a1stOE9qETklQeoogPCgcrzoBRheBN+9ui+uI7zdDeLsqbcT93ubXZ7c/+642Pw\nYZt4smoRcC8saN6nxulKAOxAwnvFbBPhTdpcIG7Y3ATtXj0brzlgjyx/QJtXMkSwmkeJHqg8O9Er\nry3glKyjRcXn4SMnKFyfRbCilTTRg/NG1SSWyNixmNRlJ5FZrl+PqRrYLg0g9xnXCDOeK3c7JJjL\n5ovoVLSkFM97oiIs2lgss4zIedYHrzlf04cimLhKZBA4zRnnEuWBIuJwTLJMJhzowQYM5dzhR59v\nvvtOavbN63URxNHI/6bOlH5BAMuahnAlQi2iigPShwQXedyMVMt7IttI1eUGCUhREcyUt5Kh7l7a\n2SpSCnlv6dxnY1xjoSOvIeEh4eqLD4XQTKV7zwcXXIMns1mOLSrCTQivM0ty1PeBJMK5yb8tXnrm\n09HobQCumF/5kzo0JUTqBCeJ3VN05QGNafZucGFFb1YY4GkSnn1S7qcdV9+vGlgmDZhbSkWKKGsx\n5wgn3GIOlpQCMqcYVZAluBPLtUCb5fzyaNH3NyygU38bESWQ6yjKj1RtcOyJNCBmuXKkIFblzkKl\nwYb3BQL+RglD7m+UYFt37+Pw/bG7VBGNUtZdeskBK7gPAlR6QLrS6MQk+uxTlBMpoOeFaURhC7NZ\nhMetoJzXbBs/jkOVqoGqgekaEFXJEUKL5Hr935+oVZmeEj7lMLge89qidPq3Ws8jGEylRiJfwYf1\nzL3RjMP2g8olq/SvgV4MMLiB96X2VPTLCIM9wFHzEO3hwNpgDDDVKNEIw/ZVkX9NCAW8y1sDx0wS\n1xbBuzbISierzYoWbLx9kyFyH5u9TD2vamDDGmAENZDQGEbqxwK8aGF0wcqcXZEPKJrhRf7hDEdu\nMJEl5Bwws2Y9ZctP3w3aJIISNevItJMlygMzGhWs6Ag2b9GyU+MTWw9aA0HwtmeU4rI2g4WLgIyl\n63TMskZDJ6osRgO9GGDMXhvPYx4S7GF9l3VG2aqYuAwrqFhkG2U7g4bg3Wtj9mmGwRkAh9lzVD7a\nIjFJsDNPOOGEXLBsplA2BZ90zrj3TC7fD5NTLmTWdmzjrldfrxqYVQMcVAuqqEdrv9IRadbzN3Oc\n+WJOqhLQzEEDC8IYy/3pxOb7WPzlgB2j2QWYFCtX3pijrBuTNq54HDtVdKTSqcraIE8974YTUAfX\ntPYJUuRfdR/TCU9w0s2HW5etle4RBLBsOLNTdb+dv6sXA7zIH2hSys3y1OwsBErhwY0Tk97xBhzj\nOg0Kt1jJW5fjTZDNCtIIggO4jdcPEahSNdCHBnQvkm9V9sEAKqNbtBjjcrc6Rol+x433Mi/kkJ2j\nhMluPVFxkGVD5iC0atz5i/4dfVyfs4JIBmmjj3n/VvoUcNgYRtkWXTPGo8RaKuhwzJFHHpm5+lHH\n1de2roFeSFhb/5rjrwBCkZuVW0LsEGWarONE4p+nCVYBd5Wa23HHg8BAZlGulAtXIZWMO97r2lLy\n+jG9RR0cAoxBLSt58uCdKGGq0M4kJdb35qoB41grRikQ9e9bSaXM+sWQeOxiBFo2F8bV8YOpzUvk\nw5NPPjnnhcoCRkB+0rzm+I6qGvC6KNoGD+pKV1VA89pwgoPBw2Vbv3n9HjqGOgomtKNU0SHqHiUQ\nRc7aniBnYZuLkLsifQDB0wMa273K5jWw8hGw/BIoV2kRyKqUFY1TiYGOEa3w24CUF5skWJ3gMbli\nBemlR+q4cywEr3jFKxLakbuS29FrFaRmoSjNApAh5pUDH/dd6utVA0UDynk0yjDuQcEW5EWLXKb5\nJuWk6YxFf5QgAilTMScgTfLTIkFOKwPr+6pY4Fh3OR5Y1K7PWEG/bPSyqmItQhT1+5XycNznKXSv\nfEjJjxKjSf2cVWq4d+6Zig9RcBF5Y86S9ZBTV7ksRTObfNxErfO2nLLVRhx9fWnNAopEdN4q/j/s\nsMPKS1mAHgvN4P/L+KQ24li+u7LsjTi2ojHNbLQkJBozRA3p4HJhYActYoMMuVffYC0RSw/3MNTZ\n43hwYg9PVqkRh1aPkXIbaCVKwdoIBgb/n/VJIIxtIId5uOYdkR6Y9dRejlu1RhwrHwFv0u9Y2Gny\nXUgkvPhoOp9eu11ElD/x8sE38/ZuF/Zj6oWrBnrQANQIuhQrdELV3f24ReqgUAKexuEoAuJWW0xA\nogVdKu/Xx7M1QKfKkJCrRNh2n5IP3qiAyMuWgnZGmrUN70Y/Z12O7z0HbJJhRe9UAdnIjegyIz/i\n/3LNGOByv2DozQz8naqv+ruqBsCdjKkUkjxot0HD8ccfn05stGTNnKW8cBEkrRvc4AbZox08rWyp\nymgNgLdVooCewfbKuqxRGxVlmXQtveavmxLY6LXq8U3TiwFGvjBZsBsZXwQLhAl1gDtNEBj8dUXu\nzV+VqoGqgXNqQFTFMR8ljMS4mmW55bKJ/Khz62t7a8Aa7G8rIpIWRVeZjwZ6McCnnnpqowWcEoMi\nPFm7n4CfqlQNVA1UDVQNVA2smwZ6yQEr7FYELgeh9kzBvXKeXdERajtEDkR3qzPOOGM7Pr5+ZtXA\n2mlAhIsbIVc7LtpdO6X0/IMhCRqdVKi+Z8VP+LheImDNJ5Qa6HgDhrYnp80ZtqMMB7Vevkkdrqbw\nSpjkbKtUDVQNLE4D0jKagGico/czQ6zkpko/GrD+IrQdeOCBaYSVpamxrrK9GujFAPuJ+s/6K6IV\nGkM8qh5Nl543velN5dB8NHHnUZyuIYCBpyWkReHoo4+uBngvTdf/VA3MVwNSTWp2tZQk9773vbP3\ns97EVfrRgM1wNCmy7ao/RDc9CrYLheznVy//p/RmgIdVocfouHIcLDut2bqib+klL3nJ7kubem4Q\naopB9Ed13SpVA1UDi9OAea48z4YQol7b4B1xxBGL+8B65XNoANqIDAv+1wVQOeRW+tqf4wPqC5vS\nQG8GGO1d7teuHHZEkgvGqENlH5aybVn3df1LRx3bPWaW51qsgWG0sMTILjWGs5xbj6kaqBrYuAbK\nRgOMwO7du3MbwlVuG7lxDWz/Gcq1rHlQRyk4qTetP6tsrwZ6McB2LLJb0X777dfIPbzhDW9oPvax\nj+WmBHYpmkW0mtRTucBYs5wz7hitIuWiGXRNyv1VOVsD9Ezf02Se92TaZ637+6t+TzjQZYefC1zg\nAnOZx9s9JjZyT6TP5rF2beU3g50ZYmWg1r7t/j5b+S3jzp31now7v+/XezHAcrrRjjELwBlhOSCP\nGxHbqSnGHwdbb+Ra9djJGgBVcZSmiQmtxKzek2ma2vr77olSvmlinoB4Qb5VFqsBi73NLaZJvSfT\nNDS/92e9J/P7xK1d6VyRExhdAb+16+51NgIV6Bf7UQ4WDZ4RVgpkg/AqVQNVA1UDVQNVA+umgV4M\nMKWy86jwpfQA7GvHkxo9rduQq7+3aqBqoGqgaoAGejPAVd1VA1UDVQNVA1UDVQNna6CXTlhnf1x9\nVjVQNVA1UDVQNVA1QAPVANdxUDVQNVA1UDVQNbANGqgGeBuUXj+yaqBqoGqgaqBqoBrgOgaqBqoG\nqgaqBqoGtkED1QBvg9LrR1YNVA1UDVQNVA300ohjHmr+4Ac/mN20atnSPLQ5+Ro6XGkeMNyPe/is\nek+GNbK4/7snGp/c8pa3nPghmu7byKQ24pioprm86Z783d/9XXOzm91s4vVsw/jmN7+53pOJWprP\nm7Pek/Jpv/vd73KXLjt1bYesjAHWPPye97xnc/7zn3879LSjPlNbvP/6r/9q9NweJbrJnHzyyVMN\nsB2tDj744Ln06B71PTTv913Gfc9R5+zU1+jh1a9+9VQDbJ4ccsghtb6+h4FgHunON80A13uy9Zuh\ndfA+++wzdVz/6le/ak477bSp96R8I+ucznEer3CFK5SXe3tcGQOsgYfo95rXvGZvytmJH3T66ac3\nj3nMY5qzzjqrucMd7tA8+9nPPsfP1Dp0FqRhkffku9/9bvOQhzyk4aFq2PK+972v+X//7/+d47uu\nywv69s4S1S7ynqyLrmf9nXZTm/We2IiibkAxq2b3Pu5BD3pQ873vfS/3kKbzSTbAngNvfetbm5/+\n9Kd7XeShD31oc8Mb3nCv18p/7n//+zfHHXdcNok6/PDDm2tf+9q5YVB5f5GPK2OAF6mEdbq2Qfij\nH/2oseUjSBNkOQ3W3A792Cv2da97XXPTm960MUFOOeWUxuSoUjVQNbA+Gnjb297WgPC/9a1v5R+n\n3E5O4+Q85zlP7rL3kpe8ZK9DOEDjxK5QomC79UGZTjzxxAxM+oiIqwEed1d26Ou3uMUtmjPOOKP5\n0Ic+1FzwghdswGjLKNe97nWbfffdN7/ala985XQaut/zJz/5SfODH/ygufrVr77tkfF///d/N1/+\n8pcbO3txbKpUDVQNTNfAd77zndyW9i//8i/PcbCI9+c//3m+L6VCLnOZy0zdv10u125PF7nIRc5x\nzVEvXOlKVxoce8UrXrF58pOfPOqwhb1WDfDCVLucF7YZt4jXnswICyeddNJSflGTjjEzIXikIPMi\njJ2dgUBFb3nLW5of/vCH6UyU9/t8ZHw5C9e5znWal73sZc1nP/vZ/F59fof6WVUDq6YBMPFTn/rU\nTHXZJ8AGPQwn8fxRj3pUc/GLX7z5/ve/n462Of6Vr3yleeQjHznXn3rjG994rtfb6MWqAd6oxlb0\neIMaYcTezB7lrh7xiEfkJJD/WDbBLv2nf/qnNK7ywaL1Ine5y10aezrb29RuWgyfCUs4Fc94xjNy\nv+n999+/eeITn1hOW8jjc5/73MbncGQe/vCHN8cee2ySQBbyYfWiVQM7RAN3vetdGyiWvaHtlPfo\nRz+6sWuev0996lPN17/+9eYSl7hEc+SRR+bWtRe60IWau9/97mPzuKuqlmqAV/XObeB78xxFvaBn\nJSqvf/3rk9BgS8hlFsZ11HaVDJ5cD/mP//iPvQgX8sVy3C960YsySr7UpS6VW18u6ndyZHwGAX99\n4hOfWNRH1etWDewYDdzkJjcZRLxf/OIX03G2LnFkv/3tbw9IUCogRMYc8p0o1QDvxLs69JswB1/4\nwhc2V7va1ZpDDz20QVC4/OUvn6w/EduqyRFHHJFMSOxIkb1IucjPfvaz5sUvfnHmi0zml770pQs1\nwErjrnKVq2QOGHsccaxK1UDVwGQNiHoRo+51r3ulA3388cc3crBPe9rTGuWNf/7nf56RsfKjecPO\nk79Zv+9uT/Vxv79x7T9NrhREi/F8vvOdLyM2pUg//vGPM2p7xzvekR4mNiDvc9lFOQeoivH73Oc+\nlxO5fGc1mbe5zW3SKHu80Y1uVN5ayCN9gvIxKH/5y18217jGNRbyOfWiVQM7SQNQrL/9279NhjM+\nx2GHHdZ85CMfaR7wgAek84z1DKFT01v2kN9Jv7/8lhoBF03sgEesYJR9DECDuwgWsdzvCSeckMxi\n+RW1teTzn/98Gl+e5le/+tVsrPGsZz2rnLq0jzxkf0STCtA6WFpU/Otf/zpzxKL+v//7v1/ob1C2\ngExy5plnNi9/+cubo446Kh8X+qH14lUDK6CB3//+9zkvVVpwlkvayFe/5CUv2bzzne/M8sLdu3c3\nz3zmM/M5lA6ChSQKpdvpUg3wDrnD2M13vvOdc6CrnfvGN77RHH300YNfZ2CPgkexC8E/ci2l4xSD\ntiry29/+NqEruV/54A9/+MNJyjrXuc7Vy0/4t3/7t8w3ywWrU8aGrlI1UDXQNHe7290SYbve9a7X\n3OpWt2re/va3DzoZ4qToQ0A4yUiWulGtm1QDvEPu+Bve8IaEce5zn/ukIdDLuWuAx/1MDS8wEhEd\n1N7xUkHRqyJgKz2rC9v5oIMOyoJ9+aQ+xGeDnTkCmNm6i1WpGlh3DUhvQdQQrAhuxp49ewbIHFQO\nDA1yfuxjH5vpm3XUWc0B74C7LrJ9z3vek4QjZTiey/cqixluyfab3/ymef7zn5/vmSQXvvCFs9SH\nAVEQr+XjKojfjJwBdlcXDAFQrsQR+ehHP5rvgePnJa75+Mc/PuGx7jU1AtFQgPOCWAJNqFI1sO4a\ngAjhR6jAgEw96UlPSt7GE57whOwy9bznPS/Lihhm6ZtJPf5dA1nUuWDtrpjz3mPErX2rJtUA93zH\nkIe+9rWvze1TNarQz1nEiw2sdu4e97hH8+AHPzhzKDzNrhFWeM5giNq0WmPADH7G5b73ve+2d5Wa\nRTEms/wuwhUWpe5el7vc5RrsaFG/fJLI9Na3vnU6I91rWgxMaI4IMYHpY5J88pOfbOjtwAMPzHwv\nB6Yrl73sZbO/tui7StVA1UCTxtcctc4gQ3JMpYkgbqow1OqbrzgTOlyNE7XC0mcc3Y9//OMZOJRj\nf/GLX+S817deBYI8s6YeqyTVAPd4t7R/lPfA7jMo5+GxYTAzKvqj8jgZHTkXRoIR1oT8M5/5zOBX\nYhRqHsFwmRSK3ldNRLZ+NyOrDIlwMpA6TMT3vve9aZwZzm5E7xi9sDkt5z3vebPDFsheQw9e9DjR\ntYd+fR7S2qRetOOuUV+vGlg3DWAy6xKHJ2EOIUrKBSuL7M7LSXox161Xd7zjHRvbn37pS18aHG49\nvda1rtWY53acUolgN6SNSiljdB2Eyu5nbPRaGz2+GuCNamwLx1vAGQnNGrRZ00ZxkojOlLj89V//\n9Tl6IWs2AYJlcES9ukFhDoKVeYYGrgHJsHTZhAyPvUm1dnTtPhqOT/qNm3mPwcToBl8hPnVz1gcc\ncEAjElW4j5TGKSmiBlrNLi8ZCQSkZSGgSyxxxnWU7Lfffs1TnvKUNPKM97L2zx713etrVQPboQFz\nU3mR+SVFhFsCJuYEP/3pT88uWN3vxWmWwjFntXctIjpWlQHVkyITBRdBIHVt70tJMaSQLekohK/u\nH6RvlDDidlByrp4B1g/rgnW0Dzl3Hx9SP+N/NXDzm998YBDsdSvvKqfxsY99LCMyN5+oJ5VzVErE\nYPPydI5hJLRuE/EyugaOaBcRya5BvM1LX/rS2U2GgdFFCisYDF3EQFUbCxriNU7a2quc09ejiWeC\n+Y1+8zgRuXNmkMfAWP6KaOCO/AHqNxG7v917uoGZ7M5XisVpISac+zFK9KVWugW9YPw/8IEPjDqs\nvlY1sJYasC7ZX7zbVxm6Z00STXoPTGzjA8ifNauLyqnlh9hpwiE3LDDgWDPiWlAKFnyG+dotr7TW\nWVOl36ydPoNzbR3ByejKVa961e5/B8+tCRx26wII2zor8BFJ97GxSjXAg1ux+CegZ4PEllqiLVtg\nYc3KwYpYeYAPe9jDsvepgUAMQAs/BiFIR1MNjwaLgcMYM8A8P+QrA2nXrl1ZhjTqF9mxR4nSsokJ\ny+iatHJHHAVlDKNEFO83jxIwEp2Y7Bq5QxkOPvjgPBThzPvKHRhb9dLytyavHFNxgEZdV8RdpWqg\namBvDWjoY69qQYMyRiWQWrJCpXS6wptgyKBGoOhRe3pDBKXENNhRS89p5ohDnnTFYpSl0ggHuAhj\nbqMWUbIGQtAp65t53XXKy/GjHm9/+9snGaysKcoIffe+2vRWCHrUXVnQa+AVtHxG16AV+SIZaOYg\nYgODIBGpiROlqWXVjPzUU09tXvnKV6aBlqNgNJz/qle9asC65T0yXODlVRTfW1T+ghe8IA3oKaec\nsqmfwQHRm5lOCzmjXAiUb3KWvDmDyxAz0gx/laqBqoHZNSBSFERYqxhRyBXkjmjsw0nGMbH5iw1U\nCl9j+BNAyzphiaAZcdeV73VtNcLWRO9xoLvQMGOOPMq4I1JupukOToxuW2Bxxt76Yx3tS2oEvEBN\ng18YXPVuIjICUmZ0DVA3vrBxwSbyJXKUIFRMXuxm29sxwiJjg43Xh/ELBi0RtYEKmpETLtdb4M9a\nyKVBwaVTDujY7zOB5Wf95uEyBTlvTojOV/TBqPJi6VkJBIeEZ6wrFgPLmXE/ODcMvclL/zxueWI5\nJq+BsMDyvO8qVQNVA03zu9/9Ltctc0PEKMIlegeYe9Yuc4fRhOIRhs08Y0w5ueaynPAoMd/NOY+u\nZ21kDF1fUyDXtzaIfs1j66LSJdAzdBDSZZ20WxKm9WZE1NvlkmzmGps6J5S6EhJ5gTYixJX4rr5k\nDIg24OM2vCm8+DY8wzZyr20YgDYglnwtDEs+hgHI9wIabQNiztfCAORjDOw2orM2Bmf+9mATtlGG\nM9BDQC95XAy+NnIrbcCvg/c2+ySchjYgn6mnh2PQRv5m6nGzHBCTvL3d7W7XBgSUf64dk7INiCl/\nX0y4wWWidKGNydnG5Mv3AuJqw/loY8K3wQLP1wKGb/fZZ582YK38LcF+bqP9XRuktbxOODhtGOrB\nNQNVaOk62uDl+cF0Hry3DE+COdoGrDb1q4Sj1jq2yuI1EGmkNmrtp36Q+xbO4tTjlvUA60+UKLaB\nILUBKbfmapFAqtowtvle9JsvL+dj8FByLoUj3Eb+to1Ida/3y3/MxWic0waZKo83b62ZUX6Uj+FM\nl0PbiLLzWpH7zffCAR+8FyVOrTXU2rkqUiPgTbkt00/CmsXCU/YC6hRpnX766Q3y1b777ptwTdlI\nAElBblKeUd6EIEphMReSlrxI6bSE+VyEx4gwgBGI7cwTXUXhMYtikZ1EpH4/4oUcuBzSG9/4xkFn\nL1sNgrqQOcBOmIwiX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0D9rlmNL21UqRrYvAYgSxxk6JN5xbHV26DK9mpgbSJg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\nZjyXsRXRFWEIeOImfyFg8dQZHfCyPHH3+HLePB5Fa8o0iPwog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IB5G83ym9NOO63ZFWzN\nLjRavosFhZG2zR5mZxd2Lcd0Hx2ntMbCIupD9lpFkcs1TrQRVPvcLb+SE+Y8cTI4J5PYyxiy8oIc\nGOScSaVuuADGgvtlMXcu3bs3hay1irrcSd+ZwSXuJ9GsBewMblYbzphiNeNXiIClHcYJBMz5oGuG\nHYO6StXAtmogjOLCxQ4jkd/LXYO0Eoyoow2yTBte7MyfraXisrWiHPXlg/zTRnSbb4UhabXLKxIw\nWrbh9P+AzFo7AG1VopxpsPtPRI9twK5bvWQb+ezeW1FO+tKx4LaxB3AeEsa4jUioDVb0yFPsYKP1\nKQkSzl76754QDOlsI1peC6PbRhOP/G84abnTTnlvGR7XtRWleV92t9K+NcrI8nZoZRtIU+5O5Jgi\nsV1oa6erYYkyvzbKBQcv29konNbB/zfzxDipuyFtRnOLO6e2ohzjTvA21bpqOAEu0jWqNFIYc8pK\nvqxkppTD+H3dWmdRXClXEuGp/92quKbIkYgAurv/5Is74B/10jFl85eA2ZG0MM5HCbZzgZAxzyEv\no0RzEnwAUKYIW3mTmm2iQYqxWmX7NaDMrtxP90uagLhXkA5zCKvdfURexHwe1XjGawiLCFzutyh6\nO3b92n6N1m+wTBroBYLWdEJpCBhJTZ4yGV2hlAAsqyBpKVmxAJQez7rqYFnaGQW82RXQuhIYZUhy\nUViY8pgIJGBi79nuD/TNeKhrVfwPUj300EMTYi3EIyQsuUs5YAuLRQMhSzmO/HJXkE/AacorGA21\nkcskFk8lIEqEQH+TRP4VhBhN9AfG0PFyfyBGJUhyvNoFaiVp4QW/q4PWa5wzctBBBzURBaX+6QK0\nrBMZR0g5ii5ZrgGKvMc97pFpAvdAC0tjs5RD2XqySv8a0BgDVGzecaDuda975f1SVmZOWkfcd9Az\npry1hcPEwHpdo5UCW3e/vXmnbzij635LO+iwVqVqYFs1sDgwYPSVQYhhUEa/OeHVPiHoiLKEW23k\nHNuoI2xtYg/OtNtRRLdtGNU2FvzBtwXZOj7IUPkYdbmtjdqDkZsbhnsP5OkxGJttGMt8bgN6r0Wu\nN+H1cEhyx6jI5bZhuBIGtVsSWNUOSl6z+9IwFB/OQBuL0wD6HnyxTT6ZJwQNko+ayjaMa+qjbHg+\n/NVArHQR3IA2ajgHkHP3uOj9m9BzREK5Absdb8LhyfNisW7f9KY3teH4tOHgJDzpev6CdNeG8c/3\npAUiT94GQauNrke5Y1Q0ZciPiQgq7y99LpusAwRN7+6XuQZGjl7N+f9yHz263+HQ5evhWLWRC87n\n4GXzymNsPjL29gXSkfd/7AEbeKNC0BtQVk+HVgg6Zskk4cmKErUXBPuNEt6tGtDuH++3r85FIF1R\nKUKV4n8esyhWlIkshNDFOy8CakbmEfmKehX1i0xFbqIujFxeOXhaNKi0AkNXkw378fptIrrIQ2YD\njTAk2Sieh49cpdEAFqjm8U9/+tPP0ZyDV+8zHbds4jf7PXSoucG4nZ/oEHEtFtAk1RQ0oPt76Af5\nRjoDhI+Ec/DBB2cjEnotzROQtEQ7PhMSAcmARIArIRhaD2r3iVEtsrYJBFHC5P5WaLKr9f6eu+eI\nkpAf0Sk0g9hYQYtIpEbVAyBkiIq5Zm4pXVPbj/Bogw3zd5y43+5/laqBZdDAtq3Ydi/p5ke7ymCc\n/HVFaQmIaVEib8shAH8yAowjCNPizLharOWtGQAQaLdMxmKBuQvm0ocZ05qRxtrVsYnx9HtBz0FA\nSyPAyWAoQMsMhQXGa6BQCxCoFFta9ye/mz4YXxApQ+X7uuayC30xnBjdmijYKGKUgI/BiRZHTot8\nnt8o5+c9xlGO25hheB3LeYmIOg2mmt+SFsA05+C5DtHxiOPnfZ2xOEB0r992le3VgBSLlJRaXikG\nRpizxGE13uV5jXf3L4KoHAd2QXIvdZBjnBllOV3j5JWvfOVgy8ryyzh3jLPUEYesStXAsmhg20aj\nvBwvdhmEUWTYTon2j4wdIyrylLM1yS0K6lRFb6I5kW4pi/D9lbowugyzHBSD87rXvS5/GqNhsbBz\nkkhMm0m5WsaDgWHkLT4ialGs8hc9i+3gIuoW9csv8/r1L9Z20neyWPmOyy46hDGa+u1qE8h4jhI6\nVIIkz+43nnjiiXlPbEbB2eH82K2GsxPpiFyQ3S/i3tC3hVoERdxT4jxGn65EyBZxeXRRkvxwle3T\nACQDcmFclG5Xano5whxgiE9xut03Rtg5CFge8SgQszhYnFH9nO193SU3Gj+6n5lnDDSDv+wCnbEh\nRPnjNOKFFALarN+fY1K6hk07x5rj8+TgOUTKH+m8K3g7jhneX1zPbc6Se+J9DtI4Ecx0S/y631GT\nFYHHWkkoeeESi28bDMTMocqjlr8wcDN/9iJzwAE7Zu7Rl4kB0Ab0O/P3GnegXFVEsvl2wKRtGJFx\nh+brclnyU0cddVQbcFqWKUVEneVaDgiIOkseStmDfLDc6iJknjngzX4/v72MjyDitP+/vXsNta0q\n+wA+X1+oiCL70oeuGmqYWVmBZWInzRLKArOwLDscUSxvqUFoCccsLLpIiZVHy0S0MiG7IHbxUmRl\nlKRd6CYaaZ/Ol+hbBfN9fqN3LOdZZ6295tqXtfdc53lg7znXvI75H5fnOp4Rgkt5VAygbWiwbWi3\n5XfMAW3DSlD2YwAofuZYUar8NiUlgnPKvn8w5vdFfNKhKZf9IfxbRh9wMN5WHaHQUNsQiEZVIVZA\nH0KmoYUmXHy96ltbiPnyxecbqUdH90zaCeFvNPUvEr20IeBNumxVxzbKBxyCeKv9h/BY/kJzb8MS\nV9p2CPm9yho559tQFNoQ1ntdD/9gfK24Cn/26/Sv+gA+d3Etkc+gHipb41tY6Npg4uV87Zt7XPT/\nP/joQ8Aov8bLGEGQM8fJSc/sHhuaD3ghJuiYw1kiiZl3+UW3Gom4rEkxaJck77WS1Hn1maTKWVmq\n+JolmKf9kfRpvPZJl/ZpdMyutHNEC6AhLyvRaqtJGX51agkNxvGKA2m8nmO2JoXXczQpWCLHmSFd\ng9y36GQw5cX5b4QAt0K1gtHUqsvABUzP4geQOlPv+pR99cbvS3ualRmtar20YpoXi8gQyGwArqtK\nvl+ecxHis7K7uUdbX+1UOlP51Is0uCyVyOpRMu6ZzcG1xqVUiXVJudRZ7V/13ErbtZRxpecO6dxC\nGDBA5F8G+FYkZeNj3h6BVxgwM0klDV+AFFOqBib/LAZgMLAvUIo5eZykVBQYIjDK4M/EzVTmPaZN\n2GrUTGieIQ0js7KBxVxhASeCsxBTqTK6hgnWtCj3drNFjb9/q/42FYnJ0XxlA2k3DkBHhhvGyQ9I\n4FAnruVLZoqGJT+56Uf8eQZWfvrKaPnP4cnMzKzG/AhTJk4J+yMxSsknzMTJ5560OARCUyl9ywpF\nzMV88LLDGfD1L4O39mCrnl1f61X8hH7HPItxcweJ1ZjVB9S3DHECugRg6ctDJGMPBux7md7NaSaE\ni2UwDc84Ioe6MQdOYkuQ2AipXcWQ6A/wI4i4lytNHYwrHMasbREES7gRzIgwXXEughi5eQg/6kTm\nvbDGlbFNvXGzyUzm2cgCK2I/9GGZ5ypNKmM9xx2nnMZX32Pcq+2gXrMs24UxYJXaDVzaSgDyKWJ6\ntCI+Wo29koZEOhcRzedIi9f5+WY1qHPPPbd0bJ2jS3y//JqiMgUKYa7m/fIri6bmM+FP5uvl9+D7\n1Ni9xxYjcZ/lCzU+9yNrBWPgfGM05CGR5eOs7oQ5YrD8wQJkEGldpzeY8KXzmUuiYbARNa9eMGfX\nxxSvcoy/3T6hyYDOp17nl5s/6l0EFgISZm3AJsioA/52Qk7S4hCAPa2KL58/HtOQh1v7pnFhCgZm\nvzFfZJ+QxTqkzet/LEuCrbT/WX2A8IpBYACEsCETJiiAEG5w0ua1YeMQpiofAFxga0wTxCmYDbNG\nxhYYG19YiVwrlmWSJQjDNle+kne7XxIlSgnmrL8Smj2fQoEpG7PMwzduuofAY3Uyvn59ss5kwQ/G\ny8jHTUnD5JVR3zdf2/hLeF5K6trPt/L+RvqAV/pu6SOD6bUhpZe5p3wzfFMxoJfbImq3jQa21yOk\n2gxmUebs8jWao8iXw8/FZ2J+bwSItOagRhR0G6ayNiTI8pyQGNto/Hs9c1EHNsoHzN8XzLB8Rmg4\nxZdbv4n/J4Ko6s+CVf3B7xVBWfVnG4NyG1HO5TffenTM0Tn70gwic0ljcCr7Q/+3DD5gfSAG+1IV\nYYEYpfvkVwwXTPltDncIwOUvNNY2GGwbpug2mE0bgUgtHzDf6FagjfQB83GHkFr+Qqss8/+17RDc\ny6frD2IhgmGNoIgpWQWvcFmVvACurz7g3bt3l5iREN5H14uDcE0oHuU59vl/kev8DqZY8guEgDRK\n3cnfa/xC+lcktCn7fMDuCctS+R0KSJl/X37EvxCQi4+4+oDlLnB9LaMxwG9z+itpM93YgHp82nZo\nPuBNi4IOoAdBpEyaGgmd5iRimSmHX5bWyxTMhENT7RJJke+b9E0TI7mSHPm5zDWtC8Wbx0oz8w7m\nVpHAJFOLMiwbkaqtRCUylWbaJVnRmM1iACgZs1gjmImZr0jTrATMla6hFZGsmbpiYCmPod3QDBBt\ngPRvChjzZtLWQIA1iPmSFUPfYdJkamZxYlViYaJF+dNXaES0INovsyQ/IyuH+/W/ZSZxD/qCP/tM\nsVwmTOoIXrR/swVYFvyxjOk3k+bQsx6wuBm74EyzrAu9TIqUjoQ/xXVD01WGYHijGQw0YW4ERAP2\ne5xo6JYYlb2vknGPJWslEs/RzZBo/J30PSs9Y0jnkgHPqK3IylQGAw3ewM9UihnbN/cQadgaOPNw\nJT5K5hTM1yCDUWuM0iDqJHwvmAQmbQ4wX45GzoQjX7aGt2zEVMY/yy9FkOHrrcQcZQoK07zBNaLB\nSzpCW+YtpjTmZgNO9W/BlmmZG8DgI9CN6dqgLRGK5w/V51dxWaYtBqq9a+sRqV7qjVBGGMNw9Sl/\nlfh6MWf9h5BK0BWUqG9g3pMYR7136FtmWznP/clvra1jwpX4RyWMqZjZioXAiLsY1uthxRxPCDLu\nuNc7ViJ1gwHrl8YuPl+E4Zp2yf9LKZkUWMvkrx+ruy7VZ3SPdffFAOjTlXyLcXFZaWE+4KECKBpR\nIIGGyyfCL4mJaNA0LgE+5p/yTwpAqJKhKEHMhN9DXmG+L3MRp5FG5xnLTpJk+JtEtNUq1PAREWwM\n2BiwIB2DDt8V3Pl6CTOEF4N37aSSLfC1E4BYEmjcs3JQTypLHlt/BAymfP+VMAIMlhbFD8nvp5+o\nPwyXFYkfkTUI86CJicSVrKUKsKxG+yKJCTEuiZSuDIt1AEb6R1e4hY9+xfomsLNmAqv5zqdZEzDg\nXbt2FSYq+rkSYVkQqr6lb1Yfcz1vW/3zda1mx8TZsF5UmiQo1HP7yvZxUWNf+eI5vxNTqKkJmW9o\nr6YMafQkcgEhBn+SXnctXgFYtDykoRk4kvojQJqvuGO6TPiIVkT4qVOP7NN4KzFf1fvUyUpJAeo9\nud08BLgOtm3bNjJD6zN1YMackb4m8p+bph4TFT+uXW3eVyz+zQR8pmnCJcGUuZ6yIEoaRhgkEtTI\nMoQhwqv2Fa4xVgikD00iGjdN1zMIQF0yowNztp1GhCMzHghYymj2QhWU3TNexmnPWebjC9eAVUDt\nYJsNLN8C7dUgLeydGXmcmDeZu/hENF4RzPwbog9FZDI7C5NnmuazrMTcFgFbJVUeKVC0Z9KeCNBc\nWRVgyHogmtlvQg3/rk7P9GywoAURfLQd/iuDg7qRJYx/sJK64DMW+SzCk+sgaTEIYJKsDjQdg3M3\nI9V4CTADDESGJwz2gAMOKGZOfkOuChYMsyZoxSLWHaMRYyTST+q7sqvtq0QxkP0tFmkp8SRmCtBY\njWOIm4ypGFPWl7jSduzYUfoNARWTZqHDyMWdsOSNk2PGMNOL1EGXPJulYiUGTDsXo8FSpb7dIxq6\n0ngZu77fes3Sb4MhbjjFgNpG5bfRqUoWrDArtjvSe/YnAAAeyklEQVR37pzrvesdBR0+ihJxF1Nj\n2vCvlCjDCNHfq0yidkVoRsBCiXwOM3KJJhT5F8E+JaozptWUqMPxm0UohkmojQFp/NSW/r1RUdDj\nHy2aNQaAEl0psjw6W8lEJtNRdPg2/H1t+MTbmLpQbvW7ZucJE1vBVj2OE7zDd9WG1D1+arC/t3oU\ntPpQf2EyLtnc9PUa1T8JdDMHYvAu96j7EHJL5rgIwGojJWEbglO5TSS7b68UPsdyLrS2emjTthsV\nBT3vB4UgU7JQTbrPmNatB5HKIbxMunTDjoUFa8UxcLyMaynI0KKgF6IBW8PT3DSLDlSyigl/AH/C\nZpC8o6TruuIKHyM/1HjwkzKao8uHQvMi0cnJTCOrEbb8VZNIJh9ms6TJCAjUMA+aBgRPZiq+PX+S\nJ7Aw+KvU3ZdUwd8kojlt1Tnnk8q7DMfMe9enaTn+aLDcNRYTmUT8lRJHiIbmkxS5zkdZ54fWe8aj\n5eV8HsIiJLX8i9hOw9i7x+e603wnabsbWU7uouoymvSe8TJOumZZjy3EByz0XAg9H6nIYGYm5ipm\np0WSSeCi9kxrkIyfadifheCZwmqyi26ZmL1kkmEWNcBgDKJxBTl4luPMz6ZOVOKP8Y37EsFj2jcz\n3ddzAj7gpi4MrkyLpkUQbJjwJeGQhETCAFO73BcScYHSvvckbT0EmP1lkJPInxla8BQ/rToVyYu4\nA0TVIoI3QVZEvCxxBF39iBA8K21reUD+SwSWAIGFaMBC2A28NYpY1JzpJYuUfAQSSEvI/yFSkNRI\nIFCObREEIjqw5p7t1qvoP1MA3M+nRZDgu+CLkleWH5Nvkr+RX8o0CYER/MQYjDmvW8Xn3f2u9dy3\n9B/hxMApr7YBuH7z3XffXbLY8AEJWoOxYDYRsIJATCsS/W16gpVUMOQa4cmnJaKTQOO5fIssEd5H\nw0raOgjIGcxSpD+xIhG61C0/Ix8izVYAHauHduD6e+65p8RW6Ffymgu2M1boR+IBZs0Z3TpfnyVJ\nBFaHwEIYsKIZTP1VqlpN/d3d6oACA7pEIzJwr5Z0bNI5sxcmgEnSXs0bxWCnkWkwUigyqxlQaM8m\nsGOwgoRsBf54vmdhIrR875CSTZCQIIRlJQMqjRXzJZhItEDjqcEZgqVorQQWCywwN9NyWRGY7k2l\nMI9QNCyBiBkaMZOZakSIEfiG8ZoC5n2Yr2fVKMplxXZo38XdIuK1kqhXgi2Lh/bAhWOrr+hLgqiM\nCTRfgUIsHgR1jFf74LpKSgSWGYGFmKAnAWgyPil3EmG2znX/MF+S9GqJtsoviEnwIWG+MrN0pw5N\nerbIPFNiEKbqesyYJs3sRpBQNhHUzGcGlDplhnbsb5mJ6d0310UVTGmgyVRyDm6IFowBI3jRkqvZ\nH5bqvZL5vLRfRDuuUZiwFTXdNfnXe3K7tRAwpYifXntQ7/pvbQMEV35BDFpdu9bKRc7zU7IkJSUC\ny47AwjTgcSAl0p9Gpvz465KQ98rYusf77gu4woBpwDQ0jILkTRBYiUyPMRWG9C6JvMHf1CXMwcBB\nUxc8EnlMSyCJrDWYM1MqZk07XGaihfLh+WZJRwTe+F2JCd85VgJ+X1O+XCfoTZYqQpBgNkE5soVV\nMj1CjIDMPvAlBNF8DeYSs0+a/F/vze3WQIC2y7zMukFQlcBGalHmaG4HlhBTY5ijTZdBhDXMV9rR\npERg2RHYNAa8aGCtFCLQgxlTNh6dXxYX2uxKxKyGaWAQzGSYuMARZlPmaIEnkmzI7sTMav4vSZ6p\nlM+7anwrvWPo5zBXPlrfbB5i95sxYAMw07L81/yA/H3SbcJfNh4mysqoKxbVNMkdQfihYfMZ2sI3\naesjwM9v5oM+Z+4ukzItWB9hqsactRvEAkJ4YxUhlElkk5QILDsCC2HABlkdcHzglEZwkVMKaLKV\nalLz+nulLU3MX5fqwOFYNVHX83V6Uv29L2zH67b7zd3J947zAVbiY59GBuHuQNy9b9o9eXxrIcDi\nVMkYUIlLqEvjbaR7LvcTgWVFYCEM2CBr2pF5nJMSdy8ruPldiUAikAgkAonANAQWwoC9nC9PdHBS\nIpAIJAKJQCKQCDTNwqKgBV5kdqJscolAIpAIJAKJwH8RWBgDTsATgUQgEUgEEoFE4HEEkgE/jkXu\nJQKJQCKQCCQCC0MgGfDCoM4XJQKJQCKQCCQCjyOQDPhxLHIvEUgEEoFEIBFYGALJgBcGdb4oEUgE\nEoFEIBF4HIFNYcCbmcdXrllpDmVakpXJCjtJicA4ArJ1aSNSbcp2towkI5xMcLJPycmclAgkAotF\nYCEM2KonFuyWas7KQHIAX3LJJYv90v9/m9WK/vrXv5ZlBeWj3axybMrH50t7IUAoO+aYY0q+Yksh\nSqlowfhlIqlYLRloPWzLc1pGMCkRSAQWi8BCGLBlxqw+dPPNN5fl+qydK2/yZkjdpH0LJSDr+BIM\nkhKBLgJWZZK9TRt94hOf2DznOc8ZLZPYvW7I+3Jzy7+MLPloqc6kRCARWCwCC8uEddNNN5Ulx6wN\nixn/+te/bk455ZTFfm287R3veEcZeCwUvmvXrpKX+JprrilMmUk6ad9BwDKShEIrYxHKLHuI5Ce3\nKo/FIiwmgDmxliwTWaXLilIWSrCCl2U3CR6W60xKBBKBxSCwEA3Y8nOWH7RIAY3C6jcWq3/GM56x\nmK/svEVyf4OOdUqtSaoMlskjDGymb7pTxNxdAAIWfreghuUlrWpF4+2uY3z99dcXa80RRxxRVtFa\nNuFMuycIMz9bm5lbiHuImygpEUgEFoPAQjRgg5c1P/1V+s53vtPQhp/5zGfWQ6Ptvffe21x99dWj\n33asrbteuaSt5XvwwQeX9Xwvv/zy8p4zzjijDMYG3KTlR8DiINrepz/96fKx4gKs7XzUUUeNPh5T\nWmayvjZ/9+9///vymczSlgkkHCclAonAxiOwEAY86TOY+aztOole/vKXjwbGev5JT3rSHkvT1eOr\n3cpL/ac//akIAf/617+a6667rqwTvNrn5X3DQoDWRwAUh2Bd2quuuqo599xzh/UR61Ba/m1M13KS\nlui888471+Gp+YhEIBHog8BCTNAKYtF6C9SLQv7yl7/c0EKn+ZswWybi7p/pIE94whP6fNOK1zA9\nfuhDHyo+vz/84Q9lLd8DDzywed/73leCberNH/zgBxvlELT11re+tR4u2wceeKA56aSTyjfccMMN\ne5zLH8NAgPaL6RL21L3AQG4IbZIveCVT7B//+Mfm5JNPLu2TpYYV5bjjjmue/OQnN9/97ndLO7L4\nyOtf//pVg8HnzCSszdPW10rM62eeeWb5Xmvv0n61bQx327Ztzf77799YoxcmT3/605tbbrllra/M\n+xOBRGAGAgthwCKNL7vsslFQi+CP0047rQR9zCjfup/+2Mc+Vgadiy++uKH5vu1tbytBNnx+NSLa\n1AxBWgLHHPvmN7/ZfOELXyhlEdEtYOWKK65oHnrooTJg/fKXv1z3cuYDNx6Bo48+ukQ3q18xCo89\n9lhxc2BIJ5544sQCiBsQPUyIoz1/6lOfKm3j+9//frGiuM+UHu4SU5cInPMSF8y73/3u5tZbby1t\n07x1710LYeYYq7bK1P7Upz61CBzmxbNGmfN89913F8HSd33iE59YF8a/ljLnvYnAsiOwEBP0gw8+\nWMx7r371q0d4CsTi133Ri140OraIHRqwtYm9W0DWn//85zINw4D0yCOPNK961auKtu531XxpwrQe\nJFIU03Yvok3s3r277Oe/4SIgOJBQtt9++5UtC8ckwgjf+MY3NjVWgAlXGxDnILLY/UzZtrRrTH1e\n4pfduXNn0ahf8pKXNP/85z/3CBCb93muV84dO3YUrZd2booVAeSOO+5o2rZtWHUOO+ywov1j1PYz\nSc1qkM57EoH+CCyEARvMzj777OZLX/pSMfFhWExe559/fv+SrnAlpkiLqeZDUc2YJJOgCFeJQDBR\nZkOBNdu3by9aLOarHMzhBlb3IBoHDed5z3teg2Ez39F4kWOmqIiaZar84Q9/uLSZksoHr/M/fleY\nicI94YQT1vnp/33cz3/+8yIw0WTVVx9inr3oooua2267rfnxj3/cXHDBBcUnKlL4yiuvLO3Hc5T7\nkEMOad7//vcXpuZd2vNrXvOa5uGHH24OOOCAIlRiYHfddVexovR5f/ca5eau4XbxfBr3WtfS1l7f\n/va3l1kI//nPfxpCse81FYsW/OxnP7tovPfff38RQJmmuYqSEoFEYOMQ2G/jHv34kzE4PiWDyjnn\nnNN8/vOfXzemxYzMh0ujPf3005v3vve9ZZqTAC9+PBo2HxqTsuhOzNPgY+DBXN2PgfO1+Y0E5Tjm\nuQbUGqjjHL/ZP/7xj5JFCDNxrQEtaTYCNDlCkGhbWuRGpHik0XkHoUnd9U0wwTpjGhqBTTvgkpAt\nChPVlrgeKn3mM58pU5hYSZhtxTdg1H/7299KBi2ChfZEq6RpzkuEQt9BaGTSNld5raQPaPcVD1qv\nto/0CRoygVKbJ7Sw+NCUkxKBRGDjEFhoD6OhTgu8Wu0n3n777UVzvfTSS4sPDsPFhA0qBkom5aq9\nYsB80TfeeGMZMPntpBykIR900EFlwJWoAzEp3nPPPWW/+4+ZWvS0RCJI5KgoUgkbklZGgE+dkFQF\nJcyFQLaexHepXk154ybAvMw/n0Wf/OQnm29/+9slN7IAKFYSlhQkU5QYAFaRSjWJDN+p/a9+9avN\no48+WszOUjyulTBfU+PWi0y30ta1W4xWEJp2TFBgieK31k/0m3e+853r9dp8TiKQCKyAwEI04BXe\nv+ZTppOYx0h7ef7zn99gyLRSkakGGdorrUZOX1o48yHzGomfRiYghSlaQJVjs4jWw19I46ZlCcJR\nhqTZCMCJiZf2ZbED2uN6E78r7RF95StfKYJWn3dg2PU+bUIZaYvMy7/61a+mtg1atqApWrMI4q3q\nNyVg8kdztWi3hAVWINYbiWmYoT/60Y9mW+7TWPKaRGCdEBgsA2byM02Jv+wnP/lJ2a/BUHxnBhmr\nvdx3331lygVmzJdmvi/zoqhQJjgaLaaMaZtKwvxWV8HhFxNMw4zovOswYJq1697ylreUqSb823xs\naYpeuVUyDcONZvrZz362uApWvqPfWVHrAuZE16tD2hxXAY3bu5773OcWi4Z6o/E5569OPVLPhDcu\nEsddp12ZG8t9ckAwWVox07D7xRs4xkSrfWBq9gljmHGXTFNikubDrRp1PW8KG1O8c9wZ60kEz1NP\nPbWYlM8666zSzgmZGC2BlBsA6Se0fxq3JCSEFnjANCkRSAQ2GIGQ9AdBETDSxhSKUVmDEbYxaLQh\nvbcxMLYxgLYBVRuDfHv44YeX/SOPPLJsg3G2obW2MbiWe1wX6THLuRhAy3HHYj5o2Y/BvJwLRtwG\n4y3HwkzdxgDfxqA2KsOb3vSmNoJ32hjEyvEw843ODXknfJfteeedN/MTLrzwwja0w5nXbeQF4dss\ndRXaXWkf6lh7CItHqXP1GhaPNvIdl+v8jgj8UZ2H9aQNZjU659td4y+sKG2Ys8t+uCTaYKBtRA6X\n38Gs2zDhtsF42/ARt8GE2xAq2jAbjz435gS3MZ2onAu3Rxum3dG5cFuU58R0pTaywrUhEI7OTdoJ\nrbwNU/GkU3sci+CwNqbOlWdHMFkbPuyyX79p2hZuzoWA2kZWsDaCyNrwbe/x7PyxJwIRsNZGsN6e\nByf8Um9h+p9wJg+tNwJ4hHFpKDRYDZiP1lKCNJQPf/jDxbxGC7F0HB+XaFVmYmZPJjbSvXuYqpma\nmeMkI2A6rEk+aMOSJ0jQIRiFVsIn9qxnPasEdEnYUNP2xWBV/Nm0LdqDc4JckhaLANeCaWUsHMzP\nNFi+WlYLgVU01GOPPbZMEVIy7cC8V5onOvTQQ0td22emtQBDTaChXR1//PFFuw2hrrQT84URv6l0\nptqG54u459vuasDKJjraOX5ipuxKTMCiq7WvEORKW63n1rrlWjElympfAhG1bzhwnUwi/SIGrPLt\nAs5YDOTJXikZyaTn5LFEIBGYD4GFBmHNV7TpV5vGwmfHxCzSVLSqqU4Sy9dITkyXidhKLwYe55iI\nBYGJVsWImQ4RkxxztOhmA2hIUMVEytxs8DRYClhhosTgKxnkmKW9g7lPfuukxSIgwErQENMwxoOB\nCIyzwpW55si8VkwGaQdcEaJ8CU4WCvHbeddza1TG47z61W4EL5ma5LnaRGWcckj7Y6K+9tprSwR2\neVH8k1kKU8fc+L65Nvi++YmZtz3be001wuDXiwil2i5zPIFDBLd+wec7iZilCS4ESHES2rskIGYr\nJCUCicDGITA4DZi2I6LT4GWwxYAlGMCEaRwSF2C4/LKWmeNfM5DWKRcVSoMSf6TBl+8QY8a0kcHK\nHEgalAHMvGAM2LFuonr7AndMQxH8tS38hkmLRUC9mX+rjvl7Rbmj6lOl+YkX4H91jeQTBDDasqlm\nBDntyHnaqq12QSijKbOQyIZGuFLPkmxg9JgbAU7AFgbMosIKUt+vDO5XDgyXkGBuMItJndvLmqJs\ntGyR+etJnqft8u/qMwROQsMkgsvOnTtLIKJZA9r/I2ENItQkJQKJwAYiMBRbOR9wBIa0YfIbFTny\nNbff+MY3Rr/Hd0JTbmOqSzkcELZhKmxD8yk+4sheNLo8BsriM3QgArTaj3/846Nz++LOkHzA4/UT\nDLkNbbQcrv7aek1orm1MU6o/F74N7boNhlvey4ccGmbvMszjA3Yt2r59exuabNnn6w4BsY1kHMXX\ny+8cZvg23DFtaMzlmvw3HwLpA54Pr0VcnT7gDRQWaLaiVfl2kcxa06R655nhaCtMbKR8SRWqpsuM\nWImm8Jvf/Kb8NJ+TOS5pmAjQOn/wgx+UwosERqwdtGRarQjfzSI+2RpDoA2LQdhIYhGo89WZ3lmI\nYMHs/bnPfa5EgbMGVPP8RpYln50IJAJ7IzAoHzBTYWinxUQoUEYSB+a7acTPJsCFGZpZkd/PFhPm\ni6tk/qPjkmkI3mEmTBomAl//+teLe8IUI0F5TM0C8TAZbWEzk0xYCETglgAwWwuSbCQJFOOSkVqT\nOVl8A78uEzghhaAJF0w4KRFIBBaPwGAYMC2WNkurvScyVAkoMbDWyFKDCD/vuN+KRmTOqYGGT1iW\nIn4//lzRoZUM3DQkcz1lRBonvkHv60P8beZXYvR9qM4jnRalOv4Mczhp/vybfWiesgs4ggO8Z5Fr\n+FFrHcy6flHnzQunYR4Qc3W1B4lWEKa32WVVNm1DYNg8ZeEr7lsnggk9W92I5HYf4ZXQKdhLClBt\niG9aEJslFMdpnjbTvVcf1Y7H+2H3mkn72p1+sJJFa9J9jq22rPP2u/p+eCovYb1vnVgda576ru/K\n7XwIVIvnfHdt3tWDYcAimjHJaWY7QTI6xbTz4xAzTzJn96V5rsfADH46aB8yEBq0+jJsQT9MqX1N\nh/OUnWBjYPpKJGSYRbPqZNb9G31+fMCzFN9QSZuqq3Ot9A21TrhYBI3J5rVSO5wWuT9Pm+mWRwQ5\nIblvP6z3Eo4xUtO65qXVlnXeflfLZZxhyq8LXNTj07bdOpl2TR5fHwSMXd1A2fV56gY+ZRGO8UW8\nIyT9NiT83q8SkDIPRYRs78vDxNcKEOtLkUGpJGPoe32s6NSGBtP38naesscc6Daivns/Oy/cugjo\nD/rFamieNtN9fszBbkPL7x7qtS9ILHK497p2/KLVllXinLB2jT9u5u+wopVgtpkX5gWJwAwE+tkw\nN1AAyEcnAolAIpAIJAL7IgLJgPfFWs9vTgQSgUQgEdh0BJIBb3oVZAESgUQgEUgE9kUE/jcy4Oxc\nhg831UjUa9/gj7oCTt9vn+d6KyfJp7tS8Ev3vSJilb1vEJa8xwLI+gZhzVN2wWByZa8mGKb7Tbm/\n+QjUdijT27w0T5vpPttMgXn6Yb13Le1utWWdt9/Vspre5p36eFIisBYE/oePeC0PyHsTgUQgEUgE\nEoFEYH4E0gQ9P2Z5RyKQCCQCiUAisGYEkgGvGcJ8QCKQCCQCiUAiMD8CyYDnxyzvSAQSgUQgEUgE\n1oxAMuA1Q5gPSAQSgUQgEUgE5kdgMKkoJ32atIly5HZzNEsTJ6UdEgXaN7K4+/yVniHFpFy+SC5p\na6euhnbv3r3HQvH1GXJVI5GW3e+q52dtPVf5kWeIDq+0XmWvz8vt1kfACkixDOJcBRWX6T7R9vOS\n+0QXW31sXpIvWX/qO5PB8/X12t7njfaWN9hMgu7KaLPKLM927f+u9c5cPW0Wanl+GgKDjoI+++yz\nmwMPPHCP1Yu+9a1vNbt27SqLoR955JHNqaeeOu3bpx5f6Rknn3zyaGC68MILy5SLqQ+acuKSSy4p\ng5RFJSx/WIWERx99tOSXtSi6wc9C6vPSFVdcUQZPeW79xZrIo0esR9lHD8udLY/A1772tea6667b\now3MKjQh7V3velfzyle+suSSvvHGG2fdMjqvXRN6rbZ0zTXXzLX0o0UNjjnmmMbKZK997WtHz5y1\no4/7Q1deeWXvqXlweeyxx5rf/va3TaTPbF760pfOelU5f9NNNzX33XdfYfq33XZbIy/7aoXwXi/M\ni5YagcFqwLfffntZ3Wi8dqx/GnmYyzxZc/VWQys9g7RbGf+8K74oyyOPPFIWOzjvvPOaU045ZY/V\nVLz39NNPb4477riycs9qyn7xxReX297znveUpee6z1hr2bvPyv2tjYBVlzAYDHEeosFedtllzRFH\nHFEYsWf0nRNuidDIsV7u/93vfjcXA4586HMtjuKblPUVr3hFc9ZZZzWHHnroXJqoFc8sOEJ7thBE\nXyLQ+7Ms6he/+MVkvn2By+smIjBIHzDNzhJq27dv3+ujnPvRj35UJGlrn66Gpj2D+clqL3fccUdZ\nO7iai+d5x0MPPdT87Gc/aww4sehBMRPX+w12DzzwQEPKxuRXSzQQpudDDjlk9Ij1KPvoYbmz5RE4\n+uijm4suumgupuSjXvCCFxTm+9Of/rSYWvsyX/divpjSrbfeWtZhdqwPWbGJcGjt5nno4Ycfbv7+\n9783hHFrPfdNaaAvxOIPRVA488wzy8pG87zX/dZYfvOb3zzPbXltIrA3AhJxDI1CS2zPOeecdseO\nHW1I3W2sBTz6hJBoy36YtNrXve51o+Pz7Kz0jHouTMfttddeO89jy7VWigmJvezfcMMNredUUuZK\nJ5xwQhvrG9efc23hE2vg7nXPWsu+1wPzwJZH4MQTT5y7jHfeeWcbSx+2sbTbXPeGb7RcH8xprtXA\nwuXSXnrppa02H0J12+0HswpQr/3ABz7Q3n///bMuL+eVM9ZBLvthgm5DUOl1X73o6quvbm+55Zb6\nM7eJwKoRGKQGfPnllzfnn39+Ewy2SOu0PWu/MiWdccYZTXTE5q677mpe+MIX7i1x9Dgy/oxghGVd\nVQFOJ510UmNLC+Ynm5f4mpihrSfKTCil5F/+8peGNn3VVVc1119/ffnNr7Ra31IIJM1BBx1Uirae\nZZ/3W/P64SHw4IMPNjsjO20sY9jQMPuaZ2MEKusVsxD94he/aF784hf3/vibb765Oe2005qXvexl\nxf2y3379hqV77723EYfhnSxHzNB9SICYGAv+W+PEwQcf3Oe20TViN97whjeMfudOIrBaBAaZC9qC\n3yKQn/a0p5UIT4FYsfZpYWb8pxYZF914wQUXNH07cxdA5rvuM/797383OjuGi7EZMJiPDzvssO5t\nvfb5jQkOoT033nPsscc2/GXKe/zxx5d95jHBKKvxMSuEb64DoEjx9Sp7rw/Mi7YcAocffnjvMmmL\n2k+sC13MtO59ylOeMvN+JuSjjjqq+FUxQoJq3763//77l/4s+pnQ7Hcfkos5rDrN9773veYjH/lI\n7/s8m8k6tNgi5OrLyt+XCLWhQfe9PK9LBKYiMOgo6KlflScSgUQgEUgEEoEtjkA/W88W/4gsXiKQ\nCCQCiUAiMDQEkgEPrcayvIlAIpAIJAJLgUAy4KWoxvyIRCARSAQSgaEhkAx4aDWW5U0EEoFEIBFY\nCgSSAS9FNeZHJAKJQCKQCAwNgWTAQ6uxLG8ikAgkAonAUiCQDHgpqjE/IhFIBBKBRGBoCCQDHlqN\nZXkTgUQgEUgElgKBZMBLUY35EYlAIpAIJAJDQyAZ8NBqLMubCCQCiUAisBQIJANeimrMj0gEEoFE\nIBEYGgLJgIdWY1neRCARSAQSgaVAIBnwUlRjfkQikAgkAonA0BBIBjy0GsvyJgKJQCKQCCwFAsmA\nl6Ia8yMSgUQgEUgEhoZAMuCh1ViWNxFIBBKBRGApEEgGvBTVmB+RCCQCiUAiMDQEkgEPrcayvIlA\nIpAIJAJLgUAy4KWoxvyIRCARSAQSgaEhkAx4aDWW5U0EEoFEIBFYCgSSAS9FNeZHJAKJQCKQCAwN\ngWTAQ6uxLG8ikAgkAonAUiCQDHgpqjE/IhFIBBKBRGBoCCQDHlqNZXkTgUQgEUgElgKB/wPJhKQC\nM4ZSUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyper\n",
    "import os\n",
    "\n",
    "image_file_name = \"image.png\"\n",
    "r_source = \"\"\"library(dplyr)\n",
    "png(\"{0}\", width = 480, height = 480, pointsize = 12, bg = \"white\", res = NA)\n",
    "pairs(select(r_data, c(-Name)))\n",
    "dev.off()\n",
    "\"\"\".format(image_file_name)\n",
    "\n",
    "r = pyper.R(use_pandas = \"True\")\n",
    "r_source_file = \"example.R\"\n",
    "r.assign(\"r_data\", iris)\n",
    "print(r(\"summary(r_data)\"))\n",
    "\n",
    "with open(r_source_file, \"w\", encoding=\"sjis\") as f:\n",
    "    f.write(r_source)\n",
    "print(r(\"source(file='{}')\".format(r_source_file)))\n",
    "os.remove(r_source_file)\n",
    "\n",
    "from IPython.core.display import Image\n",
    "Image(image_file_name)\n",
    "#os.remove(image_file_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-24T13:57:18.250304",
     "start_time": "2016-04-24T13:57:18.245037"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# k-means\n",
    "def category2int(x):\n",
    "    category = {\n",
    "        \"Iris-setosa\": 0,\n",
    "        \"Iris-versicolor\": 1,\n",
    "        \"Iris-virginica\": 2,\n",
    "    }\n",
    "    return category[x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-24T14:10:03.084504",
     "start_time": "2016-04-24T14:10:02.441603"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster_centers_ \n",
      " [[ 5.006       3.418     ]\n",
      " [ 6.81276596  3.07446809]\n",
      " [ 5.77358491  2.69245283]]\n",
      "inertia_ \n",
      " 37.1237021277\n",
      "labels_ \n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 1 2 1 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2\n",
      " 1 1 1 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 1 1 1 2 1 1 1 1\n",
      " 1 1 2 2 1 1 1 1 2 1 2 1 2 1 1 2 2 1 1 1 1 1 2 2 1 1 1 2 1 1 1 2 1 1 1 2 1\n",
      " 1 2]\n",
      "n_iter_ \n",
      " 6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11cebd710>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DKZUVw75fPfDG7E/Rbg0O80x2lrNq3/tk9Sr3Zloex6efh2IwYmyr5vTD7zDD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\nOCeJXAgh4tz/B3ge/7rSYTIWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bf91d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "X = iris[[\"SepalLength\", \"SepalWidth\"]]\n",
    "kmeansCls = KMeans(n_clusters=3)\n",
    "kmeansCls.fit(X)\n",
    "\n",
    "[print(a, \"\\n\", getattr(kmeansCls, a)) for a in dir(kmeansCls) if not a.startswith(\"_\") and a.endswith(\"_\")]\n",
    "\n",
    "Y = iris.Name.map(category2int)\n",
    "xMin = X.SepalLength.min()\n",
    "xMax = X.SepalLength.max()\n",
    "yMin = X.SepalWidth.min()\n",
    "yMax = X.SepalWidth.max()\n",
    "xx, yy = np.meshgrid(np.arange(xMin, xMax, 0.01), np.arange(yMin, yMax, 0.01))\n",
    "Z = kmeansCls.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n",
    "\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Reds)\n",
    "plt.scatter(X.SepalLength, X.SepalWidth, c=np.array(Y), cmap=plt.cm.Blues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-24T14:12:40.001032",
     "start_time": "2016-04-24T14:12:39.421511"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11afeb940>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q3LuKddjG5QvwQ3cRzXOuhNoyrjy+hs/mxOctvfSIlaz5KzBZ0ylf/xrvja4n\nzdL73aW+YJAlZVmMWXgNwUCAuo/eYE3p0FxnYCgZKcM1vSZ5VVUNwJ8AFQgB92iatrfD9muBHwN+\n4AlN0x7r6XjDJclDePm3k5WV2NPTycrK6n2HDoLBICcrK8nOysLhOHNcdeeuXZSfrGT5ssswDqHb\nxEdigoe+jc2HdJ2KVg+ZFhNOi7n3Hfpg86kGmrwBlozJ6dP7IhgM8kFlAzajwsWj41S3meJGSpKP\nZbjmWkDXNG0h4WT+i9MbVFU1AQ8By4BLga+qqjrol6T19XVs3LyF2tq6M7aFQiE+2bmLXXv20tdv\nLYqiMKaoqM8JHqChoYH3165n5569Z91uS0unoKCAYDB4xrampiY2bt5CZdXg3Xhyj30st9nHUKEo\n+Oj73+EyQuw0gIcz/9j60alQFJrPsq03LkLsMCjUceZ50tGpbv+/fpaY6wiyw6Dg6sfr9sSgKIx1\npHWb4P9yqIof7qikpu3MsfpmX4B1tW7KzzKOHwqFKPOGKA8oBPUzP891Hh9ra93UtvnO2GY0Glla\nnNevBO8LhthY60ZrOvvV/9HmNjbUunH7z/w3SBRd19lR38qW+lZCZ/nc1raFz0Wd58xzITrrdTBR\n07RXVFV9rf3heKDjLXzTgIOaprkAVFVdDywCXopznN3atWsnTS0NzDnvfDbu3M4oZxrzzpsDhG8s\n+stzL7Hw0mXtS+e9yJ2fuTnhN3Ns37GTDzbv4LobbuLYkSP8+pHHuP/euyLb//6P1xg/ZSY5Yyby\n1xde4dbrropc7e/TDrD3SDlzL7yIXfv3cvDIsU6N0RJlvyWNraPn4neMwlm3n8Wn9pIb4x/FNxx5\nVI8+D8WSwfa6fVxTtZvc9uuHRhRWj5pGU950zO4aZp/cwgxvbEMJu00WNhfNQckoZqu7mmknt3GR\nN3w3sI7O+xkFlI2ZD8DYio0sba6OLLLykdXOvqLzMKQXsK25gnkntzEzkPiEcPlRG7mXfx97dgE3\nrV7Jw62fMCev/d+2xc9Dznlw8XICx/eyQnuNm9uneUKhEDeWZ5BxxdcwW9N45u2/8XJOJY728f51\nTUGeKFyKceECAtombi97l6VZA/8G2OwP8P22YryX30agqYZzNz3Lt/Oi5+kvdQZWz7od8+iJPPnR\nSn7s3sPY9Ph+e+lK13V+XGunfNHXMJjM5Kz5C7/MbozcwPV+Y4i/jl2OSb2A0N4P+WLlGhZmDp1v\nw0NNTBNwGzacAAAgAElEQVSvmqaFVFX9C/Aw8HSHTU6gqcPjZmBQC3QrKsu56uprKCwsZNkVV3G0\nInrr+1vvreamz97O+AkTmKyqLLvqU3yw/sMejhYfb635kHu+dh+jR4/mogULKCweR2NjuD3B4aPH\nKJk0nXNmz6a4uJjP3PFF3lkdnfzbqR1ixXU3UFhYyKJLl1JZF5/b4rtzj30sd9uL2ZMzkUD2BBSz\nnebCuex0jo5pfxchTuVPx2AvRDHb0UfN5YOs4sj2Hc4CXKPnoZjtBDLHsyd3MqEYvynsyJ2AIUdF\nMdsxZpZyIHdSZNtBk4WycUtRbNkotmzKxi3loMkS2X4gdxLGzFIUsx1DzhR25E7o8bUG0s/mtPfL\na8m8+DpGTZqFI3cUs2+6m39tKYhsf44i0i+7jfTsfJyzF/NmzuzIt8tHj7rIvvar5IydREbBGKZ8\n5n6+VxY99j9sE8lYeH143/kreMUxdcDxAjzZZMN0w7dx5BWSNXEWO+bewmFXuEWHJxBk9dgFZM2Y\njz0nn7QVX+UpX+KHgv5Z46FmxTfJLJ5IRmEJ7hu+y/M10YnkVxwqzvkrSM/Ox7HgOlbahmYDv6Ei\n5uoaTdPuBKYAj6mqeno1AxfQsRg3AxjUZiuWLl+Zjabol5NAIIjNFl0nMzMri1Z34hdH7viaAJmZ\nmbiaw5USLS0tZHdofGY0GjF0iNlk6vz7mDokrnjrOP4eMHZ+3aAxtqs1LyEUU/T3VRSFkCEac8jQ\n9bjWmAeDuu6rG6LnyacYwNjh3Bgt4efO8rPhY/V+Hgea6E+0+rBlRIf2FEXBaMuIPA6aOy8CotvS\nI+eiKkinfY1mC15z9J6MgKXzvgFzfNZ/DRgtKB1aHBidObja1xX2BEPo6Z2v2XRz7/eJDJRLN2BO\nj543k8VGm9Lhc93ldw9aZHGVnvSa5FVVvU1V1QfaH3qAIEQGOfcBk1RVzVJV1UJ4qOajhETajVBQ\n58TxcPvZkxUVGILRNqgXzZvLKy+/AIS/Ar7892e4ZBCGPgpyMvjoo/A3BrfbzYZ1H1AyNpxQZ0yb\nyrrVqwgEwlcm7696m+mTo1eZznRrpDFaXW0tPndsZXR91THBKyiMaTyOHgi3LjS2VDGuuaq7XTvJ\nxYC1bn+kkViouYzJzdFSwHEt1Zhc4e6desBLUcNhjDH2nylsOkHIE75m0H2tZLuil7aT/B4yKzeh\n6zq6rpNZuYlJ/mjrxWxXGbqvvdGbp5HCpsS3KP7cpFEc/mBlpJHYie3rucx9OLJ9XutR3Ed2AeBr\naWJC2dbITVTfGJvO4TefipRnHl69ki+nR//tZ9bup60qXHrbVnuSGdVnn+vpq2WmJlo3vgGEG5+l\nr32GWdnhpJllNTNm/xr87c33Wvd8xEJ/ZbfHipers41433os8i3H/e5fWZERnQ+YUb2XttqTALRV\nnWBGbeKXZxzOYqmuSQeeAAoJj+H/EnAAdk3THlNVdQXwE0ABHtc07dGejpeI6pqNH39I0OPC6bCz\ndNGCTttOVlaxcct2dF3n0oUX9ak1wUC89OobHDleTiDg5/577+p0de92u3nrvTUYDEZmTJ3M5Imd\nhxI2bt5KVU0dNquZ5UsvjfscwtkqaHR09ljSaTWnMdrTTEkw9p7hPkK8a8/Bb0pjUmstMwKd9y03\nmqiwOUkPtDHD68bQhyZjH1nSOZWWSZa3mUUeF4YO1yWt6OxNC1/9Tm9rxN5x0XNCrLU5abRmMKqt\niYt8sZcUDqSnTVVLG184mU4o3clVgZN8e3znVtFrGgN8ojvJC7TwuTyl052yx5vd/HtDJrrRzF3W\n2k6NzwBeqw9yWMlgXKiZG3LjNwa9p9nPuwEn5oCHL2YHOjU+C4RCPFWn0Gyyc6HBxfzMwemEUuvx\n80xLGrpi4Ma01jPmAVbWBTluyGCi3sy1Of07FyOlukbq5EeYkVoi2RfSuGxkGClJPuUblL37wToa\nWz3oIZ3C3CwuueiCZIeUNIlK8NUGI9uzivGb0ihoPsm8tqZIlUsInY3pOdQ5CrD63cxrLCc7ThcW\nB8w2DmaGf6fJTWVM6TBc40bnI+do3LYsnO46Lm6p7tSmeKfVzglnMYaQn5lNZZR0+PZxsCXAY8pY\n2tKzKao7zHdy2iKVHbqu83CtmaP5Kua2Jm4PHOPcjNg+Rv5giP9qsFOTNxFbczX3GCsoTXClSiKF\ndJ2Haq2cyJuE1d3IF/XjTHcM398nVaV0kt++Yxc5o8excGZ4tZ6PN37IoSNHmTShNMmRDb5EJfgg\nOhvyp+AaPQ+AOr+btENvMqu9THKbLZMDk65EaZ9EXW9Yz7V1R7o9XqyqFQObx15MwBmu5Gl0jiXr\n6BoK9PA3uvVZxZwctxRFUajTQxgOv8UlLbUAHDFZ2DH+UkJp4UqRj9L3kVf2MentQ0G/Nk7C/Kn7\nUIATHjePrPw//EteeM7hqTqF/dd8H6szGz/wu7f/zP8Gd2Mx9l7D8NsGG1U3/xij2YIHeGjlr/kt\nFQM+F8nyWJ2RI9c9gNnuxAc8/MajPKIfHHCzNhFfKd275nh5BdNnRpdjm3fBfPbsP5DEiJIjkUM0\nbnRczg7HN6fT0GGZPpctM5LgAZrtBQT6cbNVV6fMlkiCBwg4izlljlbQNKflROYyFMVAsy06F1Nn\nSY8keIC2zFJq2qtx/OhUZo6P/jq2dGod0eXzKmy5WJ3RY3knns+ps9z0dDa1ztEYO8Toyis9640+\nw0VVej5me/Tfuq3kHBq8sv7rUJPSSb64qJAD+6Mz79u3bWHq5JFVU5voMfh0FBzN0atRPeAh0xut\nCnF4XZHKGwCHuwZTHFZ3yg/4MLWcjDw2tZwkPxBNto626P0Fuq5j90Yre7N9bhRP9LHNdZy89hhN\ngOfQnsi2gM9Ldkv03ouCtnp8HZqBWY5+QkFabGWu2c1VBDsMC2U0lA3rq9681hr8nuiEtrVsD9lW\nGa4ZalJ+4vWt99bQ4g2g6yHyMh2dGoWlssGcYK00GNmeXYLfZGNUcyUXuhsiY/JBdD6051LvKMTq\na+X8xjLy9PhMku+zpHEwM7yA9ZSmE0z1Re+BaAY2Zo2h1ZpJZlsdF7uqsHb447Ld5qDMORaDHmBG\nw3FKO9wN22S1UrtgKROmTqCw5iAPZLdibh+OCek6v6q1crxgKmaPi897DzHPGduopzcY5P82OqnO\nm0h6Sy136yeY7Bi+I6bBkM5/1duoyFextjXyBf+RmOcnhoKRMvGa8kl+pJIqmviQSpvUNVKS/PD5\nsytikozkHkJnryUNj9HCOE8L+V2u1HebLBxNy8Lud3Npl1r3ADq7rQ4CioEpnmbiuZbRems69ZYM\nRnuamNeh8gbCt2ofsGVg0kPM9LZ0GkLS0dlvttFqsrH+VAsLR3XuIrrylId/+LPI8DXzUIkRiyn6\n+/iDIf5ap9NmtHGZ1c3UOFabvNMQ4ICezkTdzVW5Q/+j6w0G+WudgtdoZbnVzaQ4not/1gU4rKQz\nRXFzRfbQPxfJJGcnhSQjwevorM4ooKx0GYrBzKH6gywu38ToUPgOxU2WNPaULMDgKKIu4OGl8nV8\nujF812QQnXeyxlI97lIUxcDxU5+wrHJnXJofvZaRS93YxSgWB3VtdbjK1nGZO7w4SxOwavQ5tI6a\nja6HOHl8DcsbyyJ34q6353Kk9DIwpfH5fSf5X38N1xSH//w8Wenh1Vm3UjznEvzeNq575le8UdSA\nwWAgpOt8vyEL76e/j8liZfvGN/jGyXc4J2Pgye3JOoX1C+8jvXgSO6uOUb7693wlb+h+ew2GdL7X\nmEfwlu9iNFvYtn4l361ew9Q4nIs/1RrYvORbpBWOZ2f5QarW/4E7pLtyt1J64nUkSdbwTBs6Jwtm\nRSpovDmTOWyPfuKOZI3F4CgCQDHZaM6agK+9K0aVwUD1mAtR2nvOtIyazcG0+FzL12WOR7GEr8AN\nablUZo6LbDuYlknrqPASfooSjqGqvQ4+iE5FzkQwhW/tr7cW8UJl9GPypnU8xXPCQzhmaxrOi1aw\nu6EFCLfkrZ1/MyZLuL+Lff4K3g7FtppYb7bnTCW9ONygLa1wPDvyZ8TluImyq9GNa/FtkWoix8Ib\neDsQn96FO/JnkFY4HoC04slsy4lPs7ZUJUk+BSRz/N2IgiEQHQrRdR2DHu0zonRpj6AE/ZE3nSmk\nQ9DXYd9Qp30HQulyHCUUjcOgB9E7DCkpAQ/mUHiqSIFO/Y8AzEqHY/k6D/v43S1kmMNfiNOMBnBH\nl3LUdR1jID4lhQa/t8fHQ026USHUFq2y0kMhDMH4tHoebuci2STJD3PJnmC1oqBW70ZpPYUe8JJ1\n8mPOab/pCGBew3FCtXvQg15Cracoqj+Eqf1tV4DOxLIN6J5G9ICHvBMfMMvTEpe4JtQdItRcFn7d\nhkPMrD8a2TbL00zeibXoAQ+6p5EJ5R+R3167b0Bhat0hjM0V6EEvjqpttG7ZFdn3e5YKtDeexNvq\novbYfhzb36LUGe4xU2S3ce7OV2g+ruFpacL7+iPcbo9Pg7kV7oM0b3oLn7uFlq3vsqJ5aDflmpKZ\nztSNz+AqP4SnuRHvK7/h9oz4dIC9unk/zdveC5+LTW9xrftgXI6bqqS6ZhhLdoLvqBZoMRoYEwx2\nah8A0ESI/WYLBX4/pV226eicUhR8ioExoWDMHSpjcZIQJ0wWJge85NK5iVUQnQqDEYseYpSuR0o+\nT6tHp8loZHQwiA2lU5VNldvDEye9TLHq3DT2zFXDtje0UeOHhdkW0s3xayR20u1le0uIWXYDJfbE\nt/yNh631bdQFdC7JsXZqfDZQJ1q97GoNMcdhoCi9f+dipFTXSJIfprpL8CF0vOjYUM5IXIkURMcP\nWKHPr9tIADdQ1I86gNNLFVri/LsG0fF1OY+nE72u69R7/TjMRqxDaH1e0TcjJclLdc0w1F2CP2Uw\n8mHeJFodo3E0V7Cw9nDcbjzqiWZJY0feVHzWDPLqD3FZU8UZV/PdeSpzNP6sUhSjlVDTEW6rO4wt\nhreljs7G9GyO5k0DoLR2H/M73IQ1EEdMVrbmq3ht2WQ3HmVJwwlON/1t9Qf4YUsBDVMXoTRU8qlT\nH3PT4HSvFqJfJMkPI70Nz2zLKsFVFF4UpclZwtaAl+UNJxIakx+dTwqm48kLV3tUOUvYdvB1LnT3\nvkDYx+gECs7BZA/3hjHYC3nR28xtLfW97nvMaOLguCXo1vAKQgfthRQeeJXSsyyM3hc6OtvzJuFu\nr76pcZawzf9PFrbPM/yhKY3QLd8nu/0K/rX1Zpa71uAwy0dJDE0y8ZpCfF2Wl/Ob07v5yfjxouO1\nRcsEFcWAzxjbGOkpswWDNVpWpxjNBCyxxdxiNBOyRG9SClkctMS4ZGFPgoC3w3EVRcHXXk553yPr\n8NicGDoM0YQKxtEoTbnEECZJfhi4xz42pknWvJZK9PbyPz3gJb8l8Uu12VHIrdMiS7UZWk8xpi22\nxccv9rcRaIhWRgSbyynuUJnTk3E+D/baaCMxe+0exnUpb+wPEwp5TSciJZaKp4FCdzSmOf4a3EfD\nS+/puk7GrvcossdnvVUhEkEmXoe4vlTQhND5xOak2ZpBpsfFud7mQZl89aCzzZ6L32SluLWOiYHY\n66F3AlvyJoDBzChXOSs8rTHvW6MY2JeRD8C05poz2in0lx+drenZeM3pjHLXM7VLS4SrPrOA7dZC\nzJ5mvmJvJscqQzXD0UiZeJUkP4QNpRJJESVNy1LDSEnyMlwzRA21BL/fbOPd7BLWOAtp6v3HI3R0\ndljtrMoZx1pHPp4uC4bUKQrvZxbxbnYJh02x9WWPRQCdD+3ZrMoZx+Y0J6E4LFRy2n2PrIvbsUTf\neYNBHqy18iNfCQ/VmvEHU+8CL57ke+YQNNQS/GGTlc2lSwml5wHQVLGRa2q0mG5c2mV18MnE5WBx\noOs6rSdWc1VDORCetF0zahYthXMAqG4ux3ZsLWOCgZ4OGZN1GaM4PuEKFMVARdBP8PA/mR9DxY8Y\n+n7Z6KTm5h9hNFto9Hl58KX/5Id58bmbNhXJlfwQM9QSPMCpNGckwQM05EzGFeOVcU1aDrRXqyiK\nQkPGmMjyf1UGI83tde4A/oxiTlodZz1OX9U7RkUanylGM/Xp+XE5rki+U3kTI43PTBYrVbkjb83m\nvpAkP4QMxQQPkOZv67SEn621mvQYJ3Rt/tZOzcBs3qZIg4GsUBBz66noD/vbsPvjc0WW5uvcA8fq\nj09PnNNkyCZ50rpUYKW11CUpkuFBhmuGiKGa4AHO9TTTeOQdqrNKMAW8nFN3uNNSej05v7WO1mPv\nUZ9RhM3Xwtz6w5GKn0wUZldsZm9bPSGjmeLGo0zzeyAOFUHz6o+zUTHQasvC2VrDBU2JLycVg+Or\nHOfhV39LS04Jztqj3G0oB2Rt2e70WF2jqqoJ+DMwHrAAP9c07bUO278F3AWcXun4bk3TemwJJ9U1\nZxrKCb6jEDoKfe9Nc3pfQzf76ejo0O32gejpdeNBKm2SJxjSMRr6/287UqpreruSvw2o1TTtDlVV\ns4FPgNc6bJ8L3K5p2vb+vPhIpes6L776Nr+74+tMCHhjviqGcOOswyYzIRQmBXydlq1LJDdwzGQl\nTQ8yPhjolOh1dE4YTbQqRsYHvGcM5biAMrOVzGCA4tCZf4wT2Uqtvwn+E6ORWrOVGR43o+McU3eC\nIZ1Xazx4dIXrcs1x7WCZigaS4EeS3pL834EX2v/bAHS9f3su8ANVVUcDb2ia9ss4x5dydF3nS//6\nc57/qIHQjFvYf3ITV9Ro2GNIRkF0VmWOpqpkCSgGDpWt5cqGEwlP9C7g3VHTcRWeh+J3U358NZe0\nRu9q/Sg9m4PjFqNbMth3agdLq3aT3T65espgYG3RHFpzp2Nsq2faibWc74lPj/VEeS0jj9oxF6NY\nMylrPMz8ik1MCwy84qcnwZDO9+qduG/8KUazhTWv/54H9WM4LTKiKgamx4lXTdPcmqa1qqqaQTjZ\n/6jLjzwL3AMsARaqqnp1YsJMHbv37ee51WXoBhuKYqCp6EL2pJ/Zk/xsjhjNVJVcimI0oxiM1JYs\nZp8lrfcdB2ivPYfm0eeHq1UsDo4WzqWlfQm/NkIcGXUOWDNRFAMthXPY64hW4uzLKMSdNwNFUQil\n53I4d3Jca9bjzU+I2uxJGGxZKIqCIXsSO7PGdfvz8ZqAfb/OQ/PV/4Il3YHRbMF63Td4rlGu5MXA\n9Vpdo6rqWOB94ElN057vsvlhTdPqNU0LAG8AcxIQY0r5P/OWoRuip11RwqPcsQgpCigdf1YhNAjD\nNXrXhT4MBjoOuuhK57eRrnQcyumyr2IYwim+XQ+/T6IEdB2lw/sCRSGkSPGbGLge30Wqqo4C3ga+\np2nak122OYHdqqqmq6qqAEuBrQmLNAXcYx9LDgpjKz5GD/rQdR1H1VamxniTzqSAj/wT69D1ILqu\nk1O+nqk+d4KjhqmtddhP7QBAD3goqdxGRnvyTsPAuFM7ob30Mb1mN1NbaiL7Tmk5hbU+PBeveF2U\n1h+M6+pP8WbGQFbDIfT2EsxQ03Gm99KuOR5X88tybVjf/D1Bf/h94Xn9UT7tlO6WYuB6q675H+AW\nYD/hy00d+BNg1zTtMVVVPw98E/AA72ma9tPeXnAkVtd0rZ4JoXPAbMWrGJnkc8c0Hn9aAJ39Zhu6\nojDF19anSduBaAaOWNKwhQJMCfjOmHg9ZLLgNpgo9bXh7LJvnaJQbk7DEfQzMTg8EteHFhsuk40p\nHhcTzjJZ3FU8qmx8wRDP1wTxKQZuyNKl8VmCjZTqGmlQNgiGS4mkGBgppxxeRkqSl0uFBOouuR82\nW9mVXUrAaKGo6QQXxWnZukQJEeKlrDG0ZBRjCPqZWneAC72JHyYSQgycJPkE6S7BtxFi0+g5+HKm\nAHAwRyXz4OvM8A3dBkvv2HNpKV6EwRRe8Wmv2caM4x/ikK4YQgx58ikdZE0oeB1F0SfMabQMwjJ9\nA9FqzYgkeADdlkcdQ3d4LFmkn40YiiTJJ0BPY/A5QEZ9tPODwV1Lvndo3xyU765H97oijw3N5YyS\nt44Qw4JMvMZZLJOsNQYDO5xFBIwWSpormT6Eh2pOeyc9i+rMMRgDXi6sOxJTxclIJROww4NMvIo+\n6UsFTX4oxLLG8gRGE39XuBtBFt0QYtgZ0d+5g8Eg/3jjLVa++Q7vr9vQ7+PEu0RSM9tYnzGK7VbH\nkGkBUG40sSGjgI/Ts/AOkZgSJYjOVlsG6zNGcbjDXESsZGxeDCUj+kr+6Rf/wYobb8Vut1N24gRv\nvPMeK664rE/HiHeC32lNZ3vp5WDLRA/6aT7yDotak7soQoXRxNqSBfgzx6HrOrUn1nBVw4mEtvBN\npjUZoygrvRzFYOSYu5rAsTWoPk+ywxKiX0b0lbzdmY3dbgdgbEkJ3mDfrlATcZPTSUch2DKB8LJ1\np5zFcX+Nvjqelo0/M9ykS1EUagpmxbz833ATQKc6qxTFEG4OFkwvoKLD0odCDDcjOsn7/d5Oj30+\nbzc/eaZE3cVqDnaOwRJM/hWkOejttISf2dtE3wcxhgcjYA5Eb/TSdf2Mf5NYyJCNGCpG9HBN6ZhR\nvPnqPxhXOhFt3y7mz54R036JbFMwp+kkzRUf05g9AXtLFbPrjybstWI1u62J2uOrqc6fhdnnYkbV\nTtJS9PpAQeGcuoNsM1rxpueTU6cxp7maeCxJKEQyjPgSypaWFk5V11A8pgirtefr08HqQRNEpwWd\ndBTMQyS5hNBpRceMgm2IxJRIPnQ86NhRBtQ1U8ophy4poRwhHA4HDoej158bzCZjRhQyh1giNaBE\n2gsPFSFCHG2fGyhFwdCHbxchdOoIL/+cdZbfy4KCZYj9vkL0x4hP8rGQLpJDT4gQz2ePpa0gvE7N\npurt3NpQFlOiD6CzKmsMVYXzMAbcTD65mYsSdA/AfY+sk6t5kVSpObAaR5Lgh6YNljQ8YxZgTMvB\nmJaDZ8wCNsS4FOIuq4NT45ZgsGWiO0ZzoPgialO0WkgISfJiWPIYzCjG6ByKYrTiMZhj2jdgMKEo\n0fVTdbOdNoN8FERqknd2N+6xj5Wr+CFsrqcFaneh6zq6rkPtrvBzMZjgcZFeuwcIl0jmVm6mKIG9\neKScUiSTjMmfhST3oS8HuKpiGxtbqgCY33SSnBivWXJ1naUV2zjUcBRjMMAsT9OQXndWiIEY8SWU\nXUmCF4kiE7BDy0gpoZThmnYyPCMSTYZtRDJIkhdCiBQ24sfk5epdCJHKRvSVvCR4IUSqG7FJXhK8\nSAYZlxeDrcfhGlVVTcCfgfGABfi5pmmvddh+LfBjwA88oWnaY4kLNX4kwQshRoreruRvA2o1TVsE\nXAX87vSG9j8ADwHLgEuBr6qqmp+gOONmOCT4CqOJrWlODpliu4NTDC9yNS8GU29J/u+Er9RP/6y/\nw7ZpwEFN01yapvmB9cCi+IcYP8MhwR80W1lTeim71ev5cPIKNqU5kx2SSABJ9GKw9Dhco2maG0BV\n1QzgBeBHHTY7gaYOj5uBzHgHGC/DIcEDHMkoIuAoAkC3OinLGs8FbTuTHJUQYrjqtYRSVdWxwMvA\n7zRNe77DJhfhRH9aBpCYfq0xqGnt+UvJz6orBimSgSn/5n9QtS+63FyhOomf7fhnEiMSidKQ7ADE\niNBjZlRVdRTwNvA9TdOe7LJ5HzBJVdUsVVUthIdqPkpMmCPHVz57NUXpLeghP06jiztvXJLskIQQ\nw1iPvWtUVf0f4BZgP+FFLnXgT4Bd07THVFVdAfykfdvjmqY92tsLJqp3TSqpqqriw483M3P6NKZM\nnpTscIQQQ0B/e9ekTIMyIYRIZdKgTAghxBkkyQshRAqTJC+EEClMkrwQQqQwSfJCCJHCJMkLIUQK\nkyQvhBApTJK8EEKkMEnyQgiRwiTJCyFECpMkL4QQKUySvBBCpDBJ8kIIkcIkyQshRAqTJC+EEClM\nkrwQQqQwSfJCCJHCJMkLIUQKkyQvhBApTJK8EEKkMEnyQgiRwiTJCyFECpMkL4QQKcwUyw+pqnoh\n8EtN05Z0ef5bwF1AdftTd2uadjC+IQohhOivXpO8qqrfBW4HWs6yeS5wu6Zp2+MdmBBCiIGLZbjm\nEHBDN9vmAj9QVXWdqqoPxC8sIYQQ8dBrktc0bSUQ6Gbzs8A9wBJgoaqqV8cxNiGEEAM00InXhzVN\nq9c0LQC8AcyJQ0xCCCHiJKaJ13ZKxweqqjqB3aqqTgXagKXA470dJD8/Q+ntZ4QQQsRHX5K8DqCq\n6mcBu6Zpj6mq+gNgDeAB3tM07a34hyiEEKK/FF3Xkx2DEEKIBJGboYQQIoVJkhdCiBQmSV4IIVKY\nJHkhhEhhfamu6TNVVQuALcAyTdMOdHj+WuDHgB94QtO0xxIZRx/iSkovHlVVtwJN7Q+Papr25Q7b\nknKueokpWefpAeBTgBn4vaZpT3TYlrT3VC9xDfq5UlX1C8CdhCvi0oBzgUJN01zt2wf9XMUQUzLO\nkwl4EhhP+IbPryQ7T8UQU5/PU8KSfHuwjwLuszz/EOGWCG3ABlVVX9E0rSZRscQSV7tB78WjqqoV\nQNO0pWfZlpRz1VNM7ZJxnhYDF2madrGqqnbgXztsS9p7qqe42g36udI07UnCiQJVVX8HPNYhmSbl\nXPUUU7tk9MG6GjBqmrZAVdVlwC+Am9tjTNZ7qtuY2vX5PCVyuOZXwCPAyS7PTwMOaprm0jTND6wH\nFiUwjljjguT04jkXsKuq+raqqu+2d/w8LVnnqqeYIDnnaTnhm+/+AbwKvN5hWzLfUz3FBUns76Sq\n6vnAdE3TOt6kmNTPXzcxQXLO0wHApKqqAmQCvg7bknWeeooJ+nGeEpLkVVW9E6jWNG0VXe6UBZxE\nhwEAmgn/MgnXS1yQnF48buBBTdOWA/cCT6uqevrfJVnnqqeYIDnnKY/wG/zm9pie6bAtae+pXuKC\n5NzK5qsAAAHXSURBVPZ3+gHw0y7PJfNcwdljguScpxagFNgP/AH4TYdtyTpPPcUE/ThPibqS/yJw\nuaqqq4HZwFPt4+AALsIn8LQMoDFBcfQlLkhOL54DwNMA7WNrdcDo9m3JOlc9xQTJOU91wNuapgXa\nxyg9qqrmtW9L5nuqp7ggSf2dVFXNBKZomvZBl01JO1c9xATJOU/3A29pmqYS/vb6lKqqlvZtyTpP\nPcUE/ThPCRmT1zRt8en/bk+od2uadnqiYB8wSVXVLMJXjIuABxMRR1/i6m8vnjj4EjAL+LqqqkWE\n30yV7duSda66jSmJ52k98A3g1+0xpRNOsJDE91RPcSXxXEH4HLx3lueTea7OGlMSz1M94UlVCCdw\nE2Bsf5ys89RtTP09T4NRQhnpeaOq6l3tf4G+DbwDbCA8AVPZ0wEGKS4X4a+Sa4APgN2D1IvncSBT\nVdV1hL+KfQm4NcnnqqeYknKeNE17A9iuquom4BXg68Bnkv2e6iWuZL2nAFTgSOTB0Pj8dRdTss7T\n/wBzVVVdC7wL/BC4PsnnqaeY+nWepHeNEEKkMLkZSgghUpgkeSGESGGS5IUQIoVJkhdCiBQmSV4I\nIVKYJHkhhEhhkuSFECKFSZIXQogU9v8BdMv3ABaA5FAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b758940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Reds)\n",
    "plt.scatter(X.SepalLength, X.SepalWidth, c=kmeansCls.labels_, cmap=plt.cm.Blues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-24T14:08:40.188796",
     "start_time": "2016-04-24T14:08:40.173041"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeansCls.predict(np.c_[xx.ravel(), yy.ravel()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "toc": {
   "toc_cell": false,
   "toc_number_sections": true,
   "toc_threshold": 6,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}